From a7f6ffacac43e68e018ca9f8cecc37453ff9936d Mon Sep 17 00:00:00 2001 From: lakeseafly <21230309@student.uwa.edu.au> Date: Thu, 1 Aug 2019 13:53:37 +0800 Subject: [PATCH 1/8] Add files via upload --- .../02simpleSingleCell_en/intro.Rmd | 237 +++++++++--------- 1 file changed, 116 insertions(+), 121 deletions(-) diff --git a/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd b/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd index 1183028..24e4766 100644 --- a/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd +++ b/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd @@ -1,121 +1,116 @@ ---- -title: Workflows for analyzing single-cell RNA-seq data with R/Bioconductor -author: -- name: Aaron T. L. Lun - affiliation: &CRUK Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom -- name: Davis J. McCarthy - affiliation: - - &EMBL EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom - - St Vincent's Institute of Medical Research, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia -- name: John C. Marioni - affiliation: - - *CRUK - - *EMBL - - Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom -date: "`r Sys.Date()`" -vignette: > - %\VignetteIndexEntry{01. Introduction} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} -output: - BiocStyle::html_document: - toc_float: true -bibliography: ref.bib ---- - -```{r style, echo=FALSE, results='hide', message=FALSE, cache=FALSE} -library(BiocStyle) -library(knitr) -opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE, cache=TRUE) -``` - -# Workflow version information - -**R version**: `r R.version.string` - -**Bioconductor version**: `r BiocManager::version()` - -**Package**: `r packageVersion("simpleSingleCell")` - -# Motivation - -Single-cell RNA sequencing (scRNA-seq) is widely used to measure the genome-wide expression profile of individual cells. -From each cell, mRNA is isolated and reverse transcribed to cDNA for high-throughput sequencing [@stegle2015computational]. -This can be done using microfluidics platforms like the Fluidigm C1 [@pollen2014lowcoverage], protocols based on microtiter plates like Smart-seq2 [@picelli2014fulllength], or droplet-based technologies like inDrop [@klein2015droplet;@macosko2015highly]. -The number of reads mapped to each gene is then used to quantify its expression in each cell. -Alternatively, unique molecular identifiers (UMIs) can be used to directly measure the number of transcript molecules for each gene [@islam2014quantitative]. -Count data are analyzed to detect highly variable genes (HVGs) that drive heterogeneity across cells in a population, to find correlations between genes and cellular phenotypes, or to identify new subpopulations via dimensionality reduction and clustering. -This provides biological insights at a single-cell resolution that cannot be achieved with conventional bulk RNA sequencing of cell populations. - -Strategies for scRNA-seq data analysis differ markedly from those for bulk RNA-seq. -One technical reason is that scRNA-seq data are much noisier than bulk data [@brennecke2013accounting;@marinov2014singlecell]. -Reliable capture (i.e., conversion) of transcripts into cDNA for sequencing is difficult with the low quantity of RNA in a single cell. -This increases the frequency of drop-out events where none of the transcripts for a gene are captured. -Dedicated steps are required to deal with this noise during analysis, especially during quality control. -In addition, scRNA-seq data can be used to study cell-to-cell heterogeneity, e.g., to identify new cell subtypes, to characterize differentiation processes, to assign cells into their cell cycle phases, or to identify HVGs driving variability across the population [@vallejos2015basics;@fan2016characterizing;@trapnell2014dynamics]. -This is simply not possible with bulk data, meaning that custom methods are required to perform these analyses. - -# scRNA-seq data analysis with Bioconductor - -This package contains a set of computational workflows for basic analysis of scRNA-seq data, using software from the open-source Bioconductor project [@huber2015orchestrating]. -The workflows start from a count matrix and describe a number of key steps for scRNA-seq data analysis, including: - -- quality control to remove problematic cells; -- normalization of cell-specific biases, with and without spike-ins; -- correction for batch effects; -- cell cycle phase classification from gene expression data; -- data exploration to identify putative subpopulations; -- and finally, HVG and marker gene identification to prioritize interesting genes. - -The application of these procedures will be demonstrated on several public scRNA-seq datasets involving immortalized myeloid progenitors, brain cells, haematopoietic stem cells, T-helper cells and mouse embryonic stem cells, generated with a range of experimental protocols and platforms [@lun2017assessing;@wilson2015combined;@zeisel2015brain;@islam2011characterization;@buettner2015computational;@zheng2017massively]. -The aim is to provide a variety of modular usage examples that can be applied by readers to construct custom analysis pipelines for their own experiments. - -See the `r Biocpkg("simpleSingleCell")` landing page for links to individual workflows and for instructions on how to install the required packages. -To cite any of these workflows, please refer to http://f1000research.com/articles/5-2122/v2 for instructions. - -# Obtaining a count matrix - -All of these workflows start from a publicly available count matrix. -For simplicity, we forego a description of the read processing steps required to generate the count matrix, i.e., read alignment and counting into features. -For SMART-seq2 data [@picelli2014fulllength], quantification procedures developed for bulk RNA-seq are generally satisfactory [@love2015rnaseq;@chen2016from]. -Users favouring an R-based approach to read alignment and counting might consider using the methods in the `r Biocpkg("Rsubread")` package [@liao2013subread;@liao2014featurecounts]. - -Many other scRNA-seq protocols contain bespoke sequence structures that require careful processing: - -- Unique molecular identifiers (UMIs) [@islam2014quantitative] are widely used to mitigate the effects of amplification biases. -Reads with the same UMI mapping to the same gene represent a single underlying transcript molecule and only increment the count of that gene by one. -Processing of this data requires extraction of the UMI sequence from each read or read pair, and a method to collapse UMI-based duplicates into a single count [@smith2017umitools]. -- Protocols may also use custom cell barcodes to improve multiplexing efficiency beyond that offered by the standard sequencing barcodes (e.g., from Illumina). -This includes data generated from droplet-based experiments [@zheng2017massively] or from very high-throughput plate-based protocols like MARS-seq [@jaitin2014massively]. -Processing of this data usually requires a separate step to extract the barcode sequence from each read and to allocate the read to the correct per-cell sequencing library. - -For these data sets, the `r Biocpkg("scPipe")` package [@lian2018scpipe] provides an R-based processing pipeline for obtaining a count matrix. - -If spike-in RNA was added, the sequences of the spike-in transcripts can be included as additional FASTA files during genome index building prior to alignment. -Similarly, genomic intervals for both spike-in transcripts and endogenous genes can be concatenated into a single GTF file prior to counting. - -# Author information - -## Author contributions - -A.T.L.L. developed and tested workflows on all datasets. -A.T.L.L. and D.J.M. implemented improvements to the software packages required by the workflow. -J.C.M. provided direction to the software and workflow development. -All authors wrote and approved the final manuscript. - -## Competing interests - -No competing interests were disclosed. - -## Grant information - -A.T.L.L. and J.C.M. were supported by core funding from Cancer Research UK (award no. A17197). -D.J.M. was supported by a CJ Martin Fellowship from the National Health and Medical Research Council of Australia. -D.J.M and J.C.M. were also supported by core funding from EMBL. - -## Acknowledgements - -We would like to thank Antonio Scialdone for helpful discussions, as well as Michael Epstein, James R. Smith and John Wilson-Kanamori for testing the workflow on other datasets. - -# References - +--- +title: 使用R / Bioconductor分析单细胞RNA-seq数据的工作流程 +author: +- name: Aaron T. L. Lun + affiliation: &CRUK Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom +- name: Davis J. McCarthy + affiliation: + - &EMBL EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom + - St Vincent's Institute of Medical Research, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia +- name: John C. Marioni + affiliation: + - *CRUK + - *EMBL + - Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom +date: "`r Sys.Date()`" +vignette: > + %\VignetteIndexEntry{01. Introduction} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +output: + BiocStyle::html_document: + toc_float: true +bibliography: ref.bib +--- + +```{r style, echo=FALSE, results='hide', message=FALSE, cache=FALSE} +library(BiocStyle) +library(knitr) +opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE, cache=TRUE) +``` + +# 分析流程版本信息 + +**R 版本**: `r R.version.string` + +**Bioconductor 版本**: `r BiocManager::version()` + +**该R包版本**: `r packageVersion("simpleSingleCell")` + +# 动机 + +单细胞RNA测序（scRNA-seq）b被广泛用于测量单个细胞的全基因组表达状况。 +从每个细胞中分离mRNA并将其逆转录为cDNA以进行高通量测序 [@stegle2015computational]. +测序的方法可以使用微流体平台，如Fluidigm C1 [@pollen2014lowcoverage], 基于微量滴定板的方案，如Smart-seq2 [@picelli2014fulllength], 或基于液滴的技术，如inDrop [@klein2015droplet;@macosko2015highly]。 +然后使用比对到每个基因的read的数量来量化其在每个细胞中的表达量。又或者，独特的分子标识物（UMI）可用于直接测量每个基因的转录物分子数 [@islam2014quantitative]。 +分析单细胞转录组计数数据来检测高度多变的基因（HVG），这些高度多变的基因能驱动群体中细胞的异质性。分析单细胞转录组数据也能发现基因和细胞表型之间的相关性，或通过降维和聚类来鉴定新的亚群。这能让我们从单细胞水平去理解生物学问题，这是细胞群水平的传统的RNA测序无法实现的。 + +scRNA-seq数据分析的策略与传统的RNA-seq的策略明显不同。 +一个技术原因是scRNA-seq数据比传统批量数据噪声更大 [@brennecke2013accounting;@marinov2014singlecell]. +由于单个细胞中RNA的量低，难以将转录物可靠地捕获（即转化）成cDNA用于测序。 +这增加了drop-out事件的频率，并产生没有捕获基因的转录物。在分析过程中需要专门的步骤来处理这种噪音，特别是在质控的时候。此外，scRNA-seq数据可用于研究细胞间异质性，例如，识别新的细胞亚型，表征分化过程，将细胞分配到细胞周期阶段，或识别驱动整个种群的变异性的高度多变基因(HVG) [@vallejos2015basics;@fan2016characterizing;@trapnell2014dynamics]。 +传统的批量RNA数据并不能实现上面提到的应用，这意味着需要新的分析流程来执行单细胞转录组分析。 + +# 使用Bioconductor 进行单细胞数据分析 + +该软件包包含一组用于scRNA-seq基本数据分析的计算工作流程，使用来自开源Bioconductor项目的软件 [@huber2015orchestrating]. +工作流程从计数矩阵开始，描述了一系列scRNA-seq数据分析的关键步骤，包括： + +- 质控，以消除有问题的细胞; +- 细胞特异性偏倚的正常化, 有还是没有标记; +- 批量效应的修正; +- 基因表达数据的细胞周期阶段分类; +- 数据探索，以确定推定的亚群; +- 最后，HVG和标记基因的识别以确认感兴趣的基因。 + +几个公共scRNA-seq数据集将用于展示该工具的分析流程，这些数据集包括永生化骨髓祖细胞，脑细胞，造血干细胞，T辅助细胞和小鼠胚胎干细胞。 这些数据集都是通过一系列实验方案和平台生成。 [@lun2017assessing;@wilson2015combined;@zeisel2015brain;@islam2011characterization;@buettner2015computational;@zheng2017massively]. +z这样做的目的是提供各种模块化使用示例，以便读者可以更好的使用这些示例来为自己的实验构建自定义分析流程。 + +请参阅 `r Biocpkg("simpleSingleCell")` 登录页面，以获取各个工作流程的链接以及有关如何安装所需软件包的说明。要引用任何这些工作流程，请参阅 http://f1000research.com/articles/5-2122/v2 以获取相关说明。 + +# 获取计数矩阵 + +所有这些工作流程都从公开可用的计数矩阵开始。 +为简单起见，我们放弃了生成计数矩阵所需的读取处理步骤的描述，即read的比对和其相关特征的统计。 +对于SMART-seq2数据 [@picelli2014fulllength], 针对大量RNA-seq开发的定量程序通常是令人满意的 [@love2015rnaseq;@chen2016from]. +喜欢使用基于R的方法来进行read的比对和计数的用户可以考虑使用 `r Biocpkg("Rsubread")` 包中的方法 [@liao2013subread;@liao2014featurecounts]。 + +许多其它scRNA-seq实验方法包含需要小心处理的预定的序列结构： + +- 独特的分子标识物 (UMIs) [@islam2014quantitative] 被广泛用于减轻扩增偏差的影响。 +具有相同的UMI比对到相同基因的reads代表单个基础转录物分子并且仅将该基因的计数增加1。 +处理此数据需要从每个read或reads对中提取UMI序列，以及将基于UMI的重复项整合为单个计数的方法 [@smith2017umitools]。 +- 有些方法还可以使用定制细胞条形码来提高多路复用效率，以超过标准测序条形码的效率（例如，来自Illumina）。 +这包括基于液滴的实验， [@zheng2017massively] 或非常高通量的基于板的实验技术（如MARS-seq)生成的数据。 [@jaitin2014massively]。 +处理该数据通常需要单独的步骤，从每次read中提取条形码序列并将read，并准确地分配给每个细胞的测序文库。 + +对于这些数据集,`r Biocpkg("scPipe")` 包 [@lian2018scpipe] p）提供了用于获得计数矩阵的基于R的处理流程。 + +如果添加加标RNA，则在比对之前的基因组索引构建期间可以将包括加标转录物的序列作为额外的FASTA文件。 +类似地，可以在计数之前将加标转录物和内源基因的基因组间隔连接成单个GTF文件。 + +# Author information + +## Author contributions + +A.T.L.L. developed and tested workflows on all datasets. +A.T.L.L. and D.J.M. implemented improvements to the software packages required by the workflow. +J.C.M. provided direction to the software and workflow development. +All authors wrote and approved the final manuscript. + +## Competing interests + +No competing interests were disclosed. + +## Grant information + +A.T.L.L. and J.C.M. were supported by core funding from Cancer Research UK (award no. A17197). +D.J.M. was supported by a CJ Martin Fellowship from the National Health and Medical Research Council of Australia. +D.J.M and J.C.M. were also supported by core funding from EMBL. + +## Acknowledgements + +We would like to thank Antonio Scialdone for helpful discussions, as well as Michael Epstein, James R. Smith and John Wilson-Kanamori for testing the workflow on other datasets. + +# References + From f4e9a210287d9bd97201601c64645a6d6397506f Mon Sep 17 00:00:00 2001 From: lakeseafly <21230309@student.uwa.edu.au> Date: Thu, 1 Aug 2019 13:54:31 +0800 Subject: [PATCH 2/8] Add files via upload --- .../02simpleSingleCell_en/reads.Rmd | 1857 ++++++++--------- .../02simpleSingleCell_en/tenx.Rmd | 881 ++++---- .../02simpleSingleCell_en/umis.Rmd | 1193 ++++++----- 3 files changed, 1961 insertions(+), 1970 deletions(-) diff --git a/BiocWorkflow_todo/02simpleSingleCell_en/reads.Rmd b/BiocWorkflow_todo/02simpleSingleCell_en/reads.Rmd index e0a8e3e..6c42855 100644 --- a/BiocWorkflow_todo/02simpleSingleCell_en/reads.Rmd +++ b/BiocWorkflow_todo/02simpleSingleCell_en/reads.Rmd @@ -1,931 +1,926 @@ ---- -title: Analyzing single-cell RNA-seq data containing read counts -author: -- name: Aaron T. L. Lun - affiliation: &CRUK Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom -- name: Davis J. McCarthy - affiliation: - - &EMBL EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom - - St Vincent's Institute of Medical Research, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia -- name: John C. Marioni - affiliation: - - *CRUK - - *EMBL - - Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom -date: "`r Sys.Date()`" -vignette: > - %\VignetteIndexEntry{02. Read count data} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} -output: - BiocStyle::html_document: - titlecaps: false - toc_float: true -bibliography: ref.bib ---- - -```{r style, echo=FALSE, results='hide', message=FALSE, cache=FALSE} -library(BiocStyle) -library(knitr) -opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE, cache=TRUE) -opts_chunk$set(fig.asp=1) -``` - -# Overview - -In this workflow, we use a relatively simple dataset [@lun2017assessing] to introduce most of the concepts of scRNA-seq data analysis. -This dataset contains two plates of 416B cells (an immortalized mouse myeloid progenitor cell line), processed using the Smart-seq2 protocol [@picelli2014fulllength]. -A constant amount of spike-in RNA from the External RNA Controls Consortium (ERCC) was also added to each cell's lysate prior to library preparation. -High-throughput sequencing was performed and the expression of each gene was quantified by counting the total number of reads mapped to its exonic regions. -Similarly, the quantity of each spike-in transcript was measured by counting the number of reads mapped to the spike-in reference sequences. - -Counts for all genes/transcripts in each cell are available from ArrayExpress using the accession number [E-MTAB-5522](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5522). -We download both the count tables (in the "processed files") as well as the metadata file using the `r Biocpkg("BiocFileCache")` package. -This saves the files to a local cache (`raw_data`) and avoids re-downloading them if they are already present. - -```{r} -library(BiocFileCache) -bfc <- BiocFileCache("raw_data", ask = FALSE) -lun.zip <- bfcrpath(bfc, - file.path("https://www.ebi.ac.uk/arrayexpress/files", - "E-MTAB-5522/E-MTAB-5522.processed.1.zip")) -lun.sdrf <- bfcrpath(bfc, - file.path("https://www.ebi.ac.uk/arrayexpress/files", - "E-MTAB-5522/E-MTAB-5522.sdrf.txt")) -unzip(lun.zip, exdir=tempdir()) -``` - - - -# Setting up the data - -## Loading in the count matrix - -Our first task is to load the count matrices into memory. -One matrix was generated for each plate of cells used in the study. -In each matrix, each row represents an endogenous gene or a spike-in transcript, and each column represents a cell. -Subsequently, the count in each entry of the matrix represents the number of reads mapped to a particular gene/transcript in a particular cell. - -```{r} -plate1 <- read.delim(file.path(tempdir(), "counts_Calero_20160113.tsv"), - header=TRUE, row.names=1, check.names=FALSE) -plate2 <- read.delim(file.path(tempdir(), "counts_Calero_20160325.tsv"), - header=TRUE, row.names=1, check.names=FALSE) - -gene.lengths <- plate1$Length # First column is the gene length. -plate1 <- as.matrix(plate1[,-1]) # Discarding gene length (as it is not a cell). -plate2 <- as.matrix(plate2[,-1]) -rbind(Plate1=dim(plate1), Plate2=dim(plate2)) -``` - -We combine the two matrices into a single object for further processing. -This is done after verifying that the genes are in the same order between the two matrices. - -```{r} -stopifnot(identical(rownames(plate1), rownames(plate2))) -all.counts <- cbind(plate1, plate2) -``` - -For convenience, the count matrix is stored in a `SingleCellExperiment` object from the `r Biocpkg("SingleCellExperiment")` package. -This allows different types of row- and column-level metadata to be stored alongside the counts for synchronized manipulation throughout the workflow. - -```{r} -library(SingleCellExperiment) -sce <- SingleCellExperiment(list(counts=all.counts)) -rowData(sce)$GeneLength <- gene.lengths -sce -``` - -We identify the rows corresponding to ERCC spike-in transcripts from the row names. -We store this information in the `SingleCellExperiment` object for future use. -This is necessary as spike-ins require special treatment in downstream steps such as normalization. - -```{r} -isSpike(sce, "ERCC") <- grepl("^ERCC", rownames(sce)) -summary(isSpike(sce, "ERCC")) -``` - -This dataset is slightly unusual in that it contains information from another set of spike-in transcripts, the Spike-In RNA Variants (SIRV) set. -For simplicity, we will only use the ERCC spike-ins in this analysis. -Thus, we must remove the rows corresponding to the SIRV transcripts prior to further analysis, which can be done simply by subsetting the `SingleCellExperiment` object. - -```{r} -is.sirv <- grepl("^SIRV", rownames(sce)) -sce <- sce[!is.sirv,] -summary(is.sirv) -``` - -**Comments from Aaron:** - -- Some feature-counting tools will report mapping statistics in the count matrix (e.g., the number of unaligned or unassigned reads). -While these values can be useful for quality control, they would be misleading if treated as gene expression values. -Thus, they should be removed (or at least moved to the `colData`) prior to further analyses. -- Be aware of using the `^ERCC` regular expression for human data where the row names of the count matrix are gene symbols. -An ERCC gene family actually exists in human annotation, so this would result in incorrect identification of genes as spike-in transcripts. -This problem can be avoided by publishing count matrices with standard identifiers (e.g., Ensembl, Entrez). - -## Incorporating cell-based annotation - -We load in the metadata for each library/cell from the `sdrf.txt` file. -It is important to check that the rows of the metadata table are in the same order as the columns of the count matrix. -Otherwise, incorrect metadata will be assigned to each cell. - -```{r} -metadata <- read.delim(lun.sdrf, check.names=FALSE, header=TRUE) -m <- match(colnames(sce), metadata[["Source Name"]]) # Enforcing identical order. -stopifnot(all(!is.na(m))) # Checking that nothing's missing. -metadata <- metadata[m,] -head(colnames(metadata)) -``` - -We only retain relevant metadata fields to avoid storing unnecessary information in the `colData` of the `SingleCellExperiment` object. -In particular, we keep the plate of origin (i.e., `block`) and phenotype of each cell. -The second field is relevant as all of the cells contain a _CBFB-MYH11_ oncogene, but the expression of this oncogene is only induced in a subset of the cells. - -```{r} -colData(sce)$Plate <- factor(metadata[["Factor Value[block]"]]) -pheno <- metadata[["Factor Value[phenotype]"]] -levels(pheno) <- c("induced", "control") -colData(sce)$Oncogene <- pheno -table(colData(sce)$Oncogene, colData(sce)$Plate) -``` - -## Incorporating gene-based annotation - -Feature-counting tools typically report genes in terms of standard identifiers from Ensembl or Entrez. -These identifiers are used as they are unambiguous and highly stable. -However, they are difficult to interpret compared to the gene symbols which are more commonly used in the literature. -Given the Ensembl identifiers, we obtain the corresponding gene symbols using annotation packages like `r Biocpkg("org.Mm.eg.db")`. - -```{r} -library(org.Mm.eg.db) -symb <- mapIds(org.Mm.eg.db, keys=rownames(sce), keytype="ENSEMBL", column="SYMBOL") -rowData(sce)$ENSEMBL <- rownames(sce) -rowData(sce)$SYMBOL <- symb -head(rowData(sce)) -``` - -It is often desirable to rename the row names of `sce` to the gene symbols, as these are easier to interpret. -However, this requires some work to account for missing and duplicate symbols. -The code below will replace missing symbols with the Ensembl identifier and concatenate duplicated symbols with the (unique) Ensembl identifiers. - -```{r} -library(scater) -rownames(sce) <- uniquifyFeatureNames(rowData(sce)$ENSEMBL, rowData(sce)$SYMBOL) -head(rownames(sce)) -``` - -We also determine the chromosomal location for each gene using the `r Biocpkg("TxDb.Mmusculus.UCSC.mm10.ensGene")` package. -This will be useful later as several quality control metrics will be computed from rows corresponding to mitochondrial genes. - -```{r} -library(TxDb.Mmusculus.UCSC.mm10.ensGene) -location <- mapIds(TxDb.Mmusculus.UCSC.mm10.ensGene, keys=rowData(sce)$ENSEMBL, - column="CDSCHROM", keytype="GENEID") -rowData(sce)$CHR <- location -summary(location=="chrM") -``` - -Alternatively, annotation from BioMart resources can be directly added to the object using the `getBMFeatureAnnos` function from `r Biocpkg("scater")`. -This may be more convenient than the approach shown above, but depends on an available internet connection to the BioMart databases. - -# Quality control on the cells - -## Defining the quality control metrics - -Low-quality cells need to be removed to ensure that technical effects do not distort downstream analysis results. -We use several quality control (QC) metrics: - -- The library size is defined as the total sum of counts across all features, i.e., genes and spike-in transcripts. -Cells with small library sizes are of low quality as the RNA has not been efficiently captured (i.e., converted into cDNA and amplified) during library preparation. -- The number of expressed features in each cell is defined as the number of features with non-zero counts for that cell. -Any cell with very few expressed genes is likely to be of poor quality as the diverse transcript population has not been successfully captured. -- The proportion of reads mapped to spike-in transcripts is calculated relative to the library size for each cell. -High proportions are indicative of poor-quality cells, where endogenous RNA has been lost during processing (e.g., due to cell lysis or RNA degradation). -The same amount of spike-in RNA to each cell, so an enrichment in spike-in counts is symptomatic of loss of endogenous RNA. -- In the absence of spike-in transcripts, the proportion of reads mapped to genes in the mitochondrial genome can also be used. -High proportions are indicative of poor-quality cells [@islam2014quantitative;@ilicic2016classification], possibly because of loss of cytoplasmic RNA from perforated cells. -The reasoning is that mitochondria are larger than individual transcript molecules and less likely to escape through tears in the cell membrane. - -For each cell, we calculate these quality control metrics using the `calculateQCMetrics` function from the `r Biocpkg("scater")` package [@mccarthy2017scater]. -These are stored in the row- and column-wise metadata of the `SingleCellExperiment` for future reference. - -```{r} -mito <- which(rowData(sce)$CHR=="chrM") -sce <- calculateQCMetrics(sce, feature_controls=list(Mt=mito)) -head(colnames(colData(sce)), 10) -``` - -The distributions of these metrics are shown in Figure \@ref(fig:qcplot416b), stratified by oncogene induction status and plate of origin. -The aim is to remove putative low-quality cells that have low library sizes, low numbers of expressed features, and high spike-in (or mitochondrial) proportions. -Such cells can interfere with downstream analyses, e.g., by forming distinct clusters that complicate interpretation of the results. - -```{r qcplot416b, fig.wide=TRUE, fig.cap="Distributions of various QC metrics for all cells in the 416B dataset. This includes the library sizes, number of expressed genes, and proportion of reads mapped to spike-in transcripts or mitochondrial genes."} -sce$PlateOnco <- paste0(sce$Oncogene, ".", sce$Plate) -multiplot( - plotColData(sce, y="total_counts", x="PlateOnco"), - plotColData(sce, y="total_features_by_counts", x="PlateOnco"), - plotColData(sce, y="pct_counts_ERCC", x="PlateOnco"), - plotColData(sce, y="pct_counts_Mt", x="PlateOnco"), - cols=2) -``` - -It is also valuable to examine how the QC metrics behave with respect to each other (Figure \@ref(fig:qcbiplot416b)). -Generally, they will be in rough agreement, i.e., cells with low total counts will also have low numbers of expressed features and high ERCC/mitochondrial proportions. -Clear discrepancies may correspond to technical differences between batches of cells (see below) or genuine biological differences in RNA content. - -```{r qcbiplot416b, fig.width=10, fig.asp=0.5, fig.cap="Behaviour of each QC metric compared to the total number of expressed features. Each point represents a cell in the 416B dataset."} -par(mfrow=c(1,3)) -plot(sce$total_features_by_counts, sce$total_counts/1e6, xlab="Number of expressed genes", - ylab="Library size (millions)") -plot(sce$total_features_by_counts, sce$pct_counts_ERCC, xlab="Number of expressed genes", - ylab="ERCC proportion (%)") -plot(sce$total_features_by_counts, sce$pct_counts_Mt, xlab="Number of expressed genes", - ylab="Mitochondrial proportion (%)") -``` - -## Identifying outliers for each metric - -Picking a threshold for these metrics is not straightforward as their absolute values depend on the experimental protocol. -For example, sequencing to greater depth will lead to more reads and more expressed features, regardless of the quality of the cells. -Similarly, using more spike-in RNA in the protocol will result in higher spike-in proportions. -To obtain an adaptive threshold, we assume that most of the dataset consists of high-quality cells, and identify cells that are outliers for the various QC metrics. - -Outliers are defined based on the median absolute deviation (MADs) from the median value of each metric across all cells. -We remove cells with log-library sizes that are more than 3 MADs below the median log-library size. -A log-transformation improves resolution at small values, especially when the MAD of the raw values is comparable to or greater than the median. -We also remove cells where the log-transformed number of expressed genes is 3 MADs below the median value. - -```{r} -libsize.drop <- isOutlier(sce$total_counts, nmads=3, type="lower", - log=TRUE, batch=sce$PlateOnco) -feature.drop <- isOutlier(sce$total_features_by_counts, nmads=3, type="lower", - log=TRUE, batch=sce$PlateOnco) -``` - -The `batch=` argument ensures that outliers are identified _within_ each level of the specified plate/oncogene factor. -This allows `isOutlier()` to accommodate systematic differences in the QC metrics across plates (Figure \@ref(fig:qcplot416b)), -which can arise due to technical differences in processing (e.g., differences in sequencing depth) rather than any changes in quality. -The same reasoning applies to the oncogene induction status, where induced cells may have naturally fewer expressed genes for biological reasons. -Failing to account for these systematic differences would inflate the MAD estimate and compromise the removal of low-quality cells. - -We identify outliers for the proportion-based metrics in a similar manner. -Here, no transformation is required as we are identifying large outliers, for which the distinction should be fairly clear on the raw scale. -We do not need to use the mitochondrial proportions as we already have the spike-in proportions (which serve a similar purpose) for this dataset. -This avoids potential issues arising from genuine differences in mitochondrial content between cell types that may confound outlier identification. - -```{r} -spike.drop <- isOutlier(sce$pct_counts_ERCC, nmads=3, type="higher", - batch=sce$PlateOnco) -``` - -Subsetting by column will retain only the high-quality cells that pass each filter described above. -We examine the number of cells removed by each filter as well as the total number of retained cells. -Removal of a substantial proportion of cells (> 10%) may be indicative of an overall issue with data quality. - -```{r} -keep <- !(libsize.drop | feature.drop | spike.drop) -data.frame(ByLibSize=sum(libsize.drop), ByFeature=sum(feature.drop), - BySpike=sum(spike.drop), Remaining=sum(keep)) -``` - -We then subset the `SingleCellExperiment` object to retain only the putative high-quality cells. -We also save the original object to file for later use. - -```{r} -sce$PassQC <- keep -saveRDS(sce, file="416B_preQC.rds") -sce <- sce[,keep] -dim(sce) -``` - -**Comments from Aaron:** - -- See `r simpleSingleCell:::.link("qc", "Assumptions of outlier identification", "this section")` for a more detailed discussion of the assumptions underlying the outlier-based detection of low-quality cells. -- `isOutlier()` will also return the exact filter thresholds for each metric (within each batch, if `batch=` is specified). -These may be useful for checking whether the automatically selected thresholds are appropriate. - - ```{r} - attr(libsize.drop, "thresholds") - attr(spike.drop, "thresholds") - ``` - -# Classification of cell cycle phase - -We use the prediction method described by @scialdone2015computational to classify cells into cell cycle phases based on the gene expression data. -Using a training dataset, the sign of the difference in expression between two genes was computed for each pair of genes. -Pairs with changes in the sign across cell cycle phases were chosen as markers. -Cells in a test dataset can then be classified into the appropriate phase, based on whether the observed sign for each marker pair is consistent with one phase or another. - -This approach is implemented in the `cyclone` function from the `r Biocpkg("scran")` package. -The package contains a pre-trained set of marker pairs for mouse data, which we can load in the the `readRDS` function. -We use the Ensembl identifiers for each gene in our dataset to match up with the names in the pre-trained set of gene pairs. - -```{r} -set.seed(100) -library(scran) -mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds", - package="scran")) -assignments <- cyclone(sce, mm.pairs, gene.names=rowData(sce)$ENSEMBL) -``` - -The `cyclone` result for each cell in the HSC dataset is shown in Figure \@ref(fig:phaseplot416b). -Each cell is assigned a score for each phase, with a higher score corresponding to a higher probability that the cell is in that phase. -We focus on the G1 and G2/M scores as these are the most informative for classification. - -```{r phaseplot416b, message=FALSE, fig.cap="Cell cycle phase scores from applying the pair-based classifier on the 416B dataset. Each point represents a cell, plotted according to its scores for G1 and G2/M phases."} -plot(assignments$score$G1, assignments$score$G2M, - xlab="G1 score", ylab="G2/M score", pch=16) -``` - -Cells are classified as being in G1 phase if the G1 score is above 0.5 and greater than the G2/M score; - in G2/M phase if the G2/M score is above 0.5 and greater than the G1 score; - and in S phase if neither score is above 0.5. -Here, the vast majority of cells are classified as being in G1 phase. -We save these assignments into the `SingleCellExperiment` object for later use. - -```{r} -sce$phases <- assignments$phases -table(sce$phases) -``` - -Pre-trained classifiers are available in `r Biocpkg("scran")` for human and mouse data. -While the mouse classifier used here was trained on data from embryonic stem cells, it is still accurate for other cell types [@scialdone2015computational]. -This may be due to the conservation of the transcriptional program associated with the cell cycle [@bertoli2013control;@conboy2007cell]. -The pair-based method is also a non-parametric procedure that is robust to most technical differences between datasets. - -__Comments from Aaron:__ - -- To remove confounding effects due to cell cycle phase, we can filter the cells to only retain those in a particular phase (usually G1) for downstream analysis. -Alternatively, if a non-negligible number of cells are in other phases, we can use the assigned phase as a blocking factor. -This protects against cell cycle effects without discarding information, and will be discussed later in more detail. -- The classifier may not be accurate for data that are substantially different from those used in the training set, e.g., due to the use of a different protocol. -In such cases, users can construct a custom classifier from their own training data using the `sandbag` function. -This will also be necessary for other model organisms where pre-trained classifiers are not available. -- Do not filter out low-abundance genes before applying `cyclone`. -Even if a gene is not expressed in *any* cell, it may still be useful for classification if it is phase-specific. -Its lack of expression relative to other genes will still yield informative pairs, and filtering them out would reduce power. - -# Examining gene-level expression metrics - -## Inspecting the most highly expressed genes - -We examine the identities of the most highly expressed genes (Figure \@ref(fig:topgene416b)). -This should generally be dominated by constitutively expressed transcripts, such as those for ribosomal or mitochondrial proteins. -The presence of other classes of features may be cause for concern if they are not consistent with expected biology. -For example, a top set containing many spike-in transcripts suggests that too much spike-in RNA was added during library preparation, while the absence of ribosomal proteins and/or the presence of their pseudogenes are indicative of suboptimal alignment. - -```{r topgene416b, fig.asp=1.2, fig.wide=TRUE, fig.cap="Percentage of total counts assigned to the top 50 most highly-abundant features in the 416B dataset. For each feature, each bar represents the percentage assigned to that feature for a single cell, while the circle represents the average across all cells. Bars are coloured by the total number of expressed features in each cell, while circles are coloured according to whether the feature is labelled as a control feature."} -fontsize <- theme(axis.text=element_text(size=12), axis.title=element_text(size=16)) -plotHighestExprs(sce, n=50) + fontsize -``` - -## Filtering out low-abundance genes - -Low-abundance genes are problematic as zero or near-zero counts do not contain much information for reliable statistical inference. -In applications involving hypothesis testing, these genes typically do not provide enough evidence to reject the null hypothesis yet they still increase the severity of the multiple testing correction. -The discreteness of the counts may also interfere with statistical procedures, e.g., by compromising the accuracy of continuous approximations. -Thus, low-abundance genes are often removed in many RNA-seq analysis pipelines before the application of downstream methods. - -The "optimal" choice of filtering strategy depends on the downstream application. -A more aggressive filter is usually required to remove discreteness (e.g., for normalization) compared to that required for removing underpowered tests. -For hypothesis testing, the filter statistic should also be independent of the test statistic under the null hypothesis [@bourgon2010independent]. -Thus, we (or the relevant function) will filter at each step as needed, rather than applying a single filter for the entire analysis. - -Several metrics can be used to define low-abundance genes. -The most obvious is the average count for each gene, computed across all cells in the dataset. -We calculate this using the `calcAverage()` function, which also performs some adjustment for library size differences between cells. -We typically observe a peak of moderately expressed genes following a plateau of lowly expressed genes (Figure \@ref(fig:abhist416b)). - -```{r abhist416b, fig.cap="Histogram of log-average counts for all genes in the 416B dataset."} -ave.counts <- calcAverage(sce, use_size_factors=FALSE) -hist(log10(ave.counts), breaks=100, main="", col="grey80", - xlab=expression(Log[10]~"average count")) -``` - -A minimum threshold can be applied to this value to filter out genes that are lowly expressed. -The example below demonstrates how we _could_ remove genes with average counts less than 1. -The number of `TRUE` values in `demo.keep` corresponds to the number of retained rows/genes after filtering. - -```{r} -demo.keep <- ave.counts >= 1 -filtered.sce <- sce[demo.keep,] -summary(demo.keep) -``` - -We also examine the number of cells that express each gene. -This is closely related to the average count for most genes, as expression in many cells will result in a higher average (Figure \@ref(fig:nexprshist416b)). -Genes expressed in very few cells are often uninteresting as they are driven by amplification artifacts (though they may also also arise from rare populations). -We could then remove genes that are expressed in fewer than _n_ cells. - -```{r nexprshist416b, fig.cap="The number of cells expressing each gene in the 416B dataset, plotted against the log-average count. Intensity of colour corresponds to the number of genes at any given location."} -num.cells <- nexprs(sce, byrow=TRUE) -smoothScatter(log10(ave.counts), num.cells, ylab="Number of cells", - xlab=expression(Log[10]~"average count")) -``` - -As mentioned above, we will apply these filters at each step rather than applying them globally by subsetting `sce`. -This ensures that the most appropriate filter is used in each application. -Nonetheless, we remove genes that are not expressed in any cell to reduce computational work in downstream steps. -Such genes provide no information and would be removed by any filtering strategy. - -```{r} -to.keep <- num.cells > 0 -sce <- sce[to.keep,] -summary(to.keep) -``` - -# Normalization of cell-specific biases - -## Using the deconvolution method to deal with zero counts - -Read counts are subject to differences in capture efficiency and sequencing depth between cells [@stegle2015computational]. -Normalization is required to eliminate these cell-specific biases prior to downstream quantitative analyses. -This is often done by assuming that most genes are not differentially expressed (DE) between cells. -Any systematic difference in count size across the non-DE majority of genes between two cells is assumed to represent bias and is removed by scaling. -More specifically, "size factors" are calculated that represent the extent to which counts should be scaled in each library. - -Size factors can be computed with several different approaches, e.g., using the `estimateSizeFactorsFromMatrix` function in the `r Biocpkg("DESeq2")` package [@anders2010differential;@love2014moderated], or with the `calcNormFactors` function [@robinson2010scaling] in the `r Biocpkg("edgeR")` package. -However, single-cell data can be problematic for these bulk data-based methods due to the dominance of low and zero counts. -To overcome this, we pool counts from many cells to increase the size of the counts for accurate size factor estimation [@lun2016pooling]. -Pool-based size factors are then "deconvolved" into cell-based factors for normalization of each cell's expression profile. - -```{r} -sce <- computeSumFactors(sce) -summary(sizeFactors(sce)) -``` - -The size factors are well-correlated with the library sizes for all cells (Figure \@ref(fig:normplot416b)). -This suggests that most of the systematic differences between cells are driven by differences in capture efficiency or sequencing depth. -Any DE between cells would yield a non-linear trend between the total count and size factor, and/or increased scatter around the trend. -We observe some evidence of this after oncogene induction, where the size factors after induction are systematically lower. -This is consistent with composition biases [@robinson2010scaling] introduced by upregulation of genes after induction. - -```{r normplot416b, fig.cap="Size factors from deconvolution, plotted against library sizes for all cells in the 416B dataset. Axes are shown on a log-scale. Wild-type cells are shown in black and oncogene-induced cells are shown in red."} -plot(sce$total_counts/1e6, sizeFactors(sce), log="xy", - xlab="Library size (millions)", ylab="Size factor", - col=c("red", "black")[sce$Oncogene], pch=16) -legend("bottomright", col=c("red", "black"), pch=16, cex=1.2, - legend=levels(sce$Oncogene)) -``` - -__Comments from Aaron:__ - -- While the deconvolution approach is robust to the high frequency of zeroes in scRNA-seq data, it will eventually fail if too many counts are zero. -This manifests as negative size factors that are obviously nonsensical. -To avoid this, the `computeSumFactors` function will automatically remove low-abundance genes prior to the calculation of size factors. -Genes with a library size-adjusted average count below a specified threshold (`min.mean`) are ignored. -For read count data, the default value of 1 is usually satisfactory. -- Cell-based QC should always be performed prior to normalization, to remove cells with very low numbers of expressed genes. -Otherwise, `computeSumFactors()` may yield negative size factors for low-quality cells. -This is because too many zeroes are present in these cells, reducing the effectiveness of pooling to eliminate zeroes. -See `?computeSumFactors` for more details on how the function resolves negative size factors. -- The `sizes` argument can be used to specify the number of pool sizes to use to compute the size factors. -More `sizes` yields more precise estimates at the cost of some computational time and memory. -In general, all `sizes` should be above 20 cells to ensure that there are sufficient non-zero expression values in each pool. -The total number of cells should also be at least 100 for effective pooling. -- For highly heterogeneous datasets, it is advisable to perform a rough clustering of the cells to weaken the non-DE assumption. -This can be done with the `quickCluster()` function and the results passed to `computeSumFactors()` via the `clusters` argument. -We demonstrate this approach with a larger data set in the next `r simpleSingleCell:::.link("umis", "Normalization of cell-specific biases", "workflow")`. - -## Computing separate size factors for spike-in transcripts - -Size factors computed from the counts for endogenous genes are usually not appropriate for normalizing the counts for spike-in transcripts. -Consider an experiment without library quantification, i.e., the amount of cDNA from each library is _not_ equalized prior to pooling and multiplexed sequencing. -Here, cells containing more RNA have greater counts for endogenous genes and thus larger size factors to scale down those counts. -However, the same amount of spike-in RNA is added to each cell during library preparation. -This means that the counts for spike-in transcripts are not subject to the effects of RNA content. -Attempting to normalize the spike-in counts with the gene-based size factors will lead to over-normalization and incorrect quantification of expression. -Similar reasoning applies in cases where library quantification is performed. -For a constant total amount of cDNA, any increases in endogenous RNA content will suppress the coverage of spike-in transcripts. -As a result, the bias in the spike-in counts will be opposite to that captured by the gene-based size factor. - -To ensure normalization is performed correctly, we compute a separate set of size factors for the spike-in set. -For each cell, the spike-in-specific size factor is defined as the total count across all transcripts in the spike-in set. -This assumes that none of the spike-in transcripts are differentially expressed, which is reasonable given that the same amount and composition of spike-in RNA should have been added to each cell [@lun2017assessing]. -(See below for a more detailed discussion on spike-in normalization.) -These size factors are stored in a separate field of the `SingleCellExperiment` object by setting `general.use=FALSE` in `computeSpikeFactors`. -This ensures that they will only be used with the spike-in transcripts but not the endogenous genes. - -```{r} -sce <- computeSpikeFactors(sce, type="ERCC", general.use=FALSE) -``` - -## Applying the size factors to normalize gene expression - -The count data are used to compute normalized log-expression values for use in downstream analyses. -Each value is defined as the log~2~-ratio of each count to the size factor for the corresponding cell, after adding a prior count of 1 to avoid undefined values at zero counts. -Division by the size factor ensures that any cell-specific biases are removed. -If spike-in-specific size factors are present in `sce`, they will be automatically applied to normalize the spike-in transcripts separately from the endogenous genes. - -```{r} -sce <- normalize(sce) -``` - -The log-transformation is useful as it means that any differences in the values directly represent log~2~-fold changes in expression between cells. -This is usually more relevant than the absolute differences in coverage, which need to be interpreted in the context of the overall abundance. -The log-transformation also provides some measure of variance stabilization [@law2014voom], so that high-abundance genes with large variances do not dominate downstream analyses. -The computed values are stored as an `"logcounts"` matrix in addition to the other assay elements. - -```{r, echo=FALSE, results="hide"} -gc() -``` - -# Modelling the technical noise in gene expression - -Variability in the observed expression values across genes can be driven by genuine biological heterogeneity or uninteresting technical noise. -To distinguish between these two possibiltiies, we need to model the technical component of the variance of the expression values for each gene. -We do so using the set of spike-in transcripts, which were added in the same quantity to each cell. -Thus, the spike-in transcripts should exhibit no biological variability, i.e., any variance in their counts should be technical in origin. - -We use the `trendVar()` function to fit a mean-dependent trend to the variances of the log-expression values for the spike-in transcripts. -We set `block=` to block on the plate of origin for each cell, to ensure that technical differences between plates do not inflate the variances. -Given the mean abundance of a gene, the fitted value of the trend is then used as an estimate of the technical component for that gene. -The biological component of the variance is finally calculated by subtracting the technical component from the total variance of each gene with the `decomposeVar` function. - -```{r} -var.fit <- trendVar(sce, parametric=TRUE, block=sce$Plate, - loess.args=list(span=0.3)) -var.out <- decomposeVar(sce, var.fit) -head(var.out) -``` - -We visually inspect the trend to confirm that it corresponds to the spike-in variances (Figure \@ref(fig:hvgplot416b))). -The wave-like shape is typical of the mean-variance trend for log-expression values. -A linear increase in the variance is observed as the mean increases from zero, as larger variances are possible when the counts increase. -At very high abundances, the effect of sampling noise decreases due to the law of large numbers, resulting in a decrease in the variance. - -```{r hvgplot416b, fig.cap="Variance of normalized log-expression values for each gene in the 416B dataset, plotted against the mean log-expression. The blue line represents the mean-dependent trend fitted to the variances of the spike-in transcripts (red)."} -plot(var.out$mean, var.out$total, pch=16, cex=0.6, xlab="Mean log-expression", - ylab="Variance of log-expression") -curve(var.fit$trend(x), col="dodgerblue", lwd=2, add=TRUE) -cur.spike <- isSpike(sce) -points(var.out$mean[cur.spike], var.out$total[cur.spike], col="red", pch=16) -``` - -We check the distribution of expression values for the genes with the largest biological components. -This ensures that the variance estimate is not driven by one or two outlier cells (Figure \@ref(fig:hvgvioplot416b)). - -```{r hvgvioplot416b, fig.cap="Violin plots of normalized log-expression values for the top 10 genes with the largest biological components in the 416B dataset. Each point represents the log-expression value in a single cell."} -chosen.genes <- order(var.out$bio, decreasing=TRUE)[1:10] -plotExpression(sce, features=rownames(var.out)[chosen.genes]) + fontsize -``` - -**Comments from Aaron:** - -- In practice, trend fitting is complicated by the small number of spike-in transcripts and the uneven distribution of their abundances. -See `r simpleSingleCell:::.link("var", "Details of trend-fitting parameters", "here")` for more details on how to refine the fit. -- In the absence of spike-ins, users can set `use.spikes=FALSE` to fit a trend to the variances of the endogenous genes (see `r simpleSingleCell:::.link("var", "Wwhen spike-ins are unavailable", "here")`). -Alternatively, we can create a trend based on the assumption of Poisson technical noise, as described `r simpleSingleCell:::.link("tenx", "Modelling the mean-variance trend", "here")`. -- Negative biological components are often obtained from `decomposeVar`. -These are intuitively meaningless as it is impossible for a gene to have total variance below technical noise. -Nonetheless, such values occur due to imprecise estimation of the total variance, especially for low numbers of cells. -- `decomposeVar` also yields _p_-values that can be used to define HVGs at a specific threshold for the false discovery rate (FDR). -We will discuss this in more detail later, as formal detection of HVGs is not necessary for feature selection during data exploration. - -# Removing the batch effect - -As previously mentioned, the data were collected on two plates. -Small uncontrollable differences in processing between plates can result in a batch effect, i.e., systematic differences in expression between cells on different plates. -Such differences are not interesting and can be removed by applying the `removeBatchEffect()` function from the `r Biocpkg("limma")` package [@ritchie2015limma]. -This removes the effect of the plate of origin while accounting for the (interesting) effect of oncogene induction. - -```{r} -library(limma) -assay(sce, "corrected") <- removeBatchEffect(logcounts(sce), - design=model.matrix(~sce$Oncogene), batch=sce$Plate) -assayNames(sce) -``` - -Manual batch correction is necessary for downstream procedures that are not model-based, e.g., clustering and most forms of dimensionality reduction. -However, if an analysis method can accept a design matrix, blocking on nuisance factors in the design matrix is preferable to using `removeBatchEffect()`. -This is because the latter does not account for the loss of residual degrees of freedom, nor the uncertainty of estimation of the blocking factor terms. - -**Comments from Aaron:** - -- `removeBatchEffect()` performs a linear regression and sets the coefficients corresponding to the blocking factors to zero. -This is effective provided that the population composition within each batch is known (and supplied as `design=`) or identical across batches. -Such an assumption is reasonable for this dataset, involving a homogeneous cell line population on both plates. -However, in most scRNA-seq applications, the factors of variation are not identical across batches and not known in advance. -This motivates the use of more sophisticated batch correction methods such as `mnnCorrect()`. - -# Denoising expression values using PCA - -Once the technical noise is modelled, we can use principal components analysis (PCA) to remove random technical noise. -Consider that each cell represents a point in the high-dimensional expression space, where the spread of points represents the total variance. -PCA identifies axes in this space that capture as much of this variance as possible. -Each axis is a principal component (PC), where any early PC will explain more of the variance than a later PC. - -We assume that biological processes involving co-regulated groups of genes will account for the most variance in the data. -If this is the case, this process should be represented by one or more of the earlier PCs. -In contrast, random technical noise affects each gene independently and will be represented by later PCs. -The `denoisePCA()` function removes later PCs until the total discarded variance is equal to the sum of technical components for all genes used in the PCA. - -```{r} -sce <- denoisePCA(sce, technical=var.out, assay.type="corrected") -dim(reducedDim(sce, "PCA")) -``` - -The function returns a `SingleCellExperiment` object containing the PC scores for each cell in the `reducedDims` slot. -The aim is to eliminate technical noise and enrich for biological signal in the retained PCs. -This improves resolution of the underlying biology during downstream procedures such as clustering. - -__Comments from Aaron:__ - -- `denoisePCA()` will only use genes that have positive biological components, i.e., variances greater than the fitted trend. -This guarantees that the total technical variance to be discarded will not be greater than the total variance in the data. -- For the `technical=` argument, the function will also accept the trend function directly (i.e., `var.fit$trend`) or a vector of technical components per gene. -Here, we supply the `DataFrame` from `decomposeVar()` to allow the function to adjust for the loss of residual degrees of freedom after batch correction. -Specifically, the variance in the batch-corrected matrix is slightly understated, requiring some rescaling of the technical components to compensate. -- No filtering is performed on abundance here, which ensures that PCs corresponding to rare subpopulations can still be detected. -Discreteness is less of an issue as low-abundance genes also have lower variance, thus reducing their contribution to the PCA. -- It is also possible to obtain a low-rank approximation of the original expression matrix, capturing the variance equivalent to the retained PCs. -This is useful for denoising prior to downstream procedures that require gene-wise expression values. - -```{r} -sce2 <- denoisePCA(sce, technical=var.fit$trend, - assay.type="corrected", value="lowrank") -assayNames(sce2) -``` - -```{r, echo=FALSE, results="hide"} -rm(sce2) -gc() -``` - -# Visualizing data in low-dimensional space - -## With PCA - -We visualize the relationships between cells by constructing pairwise PCA plots for the first three components (Figure \@ref(fig:pcaplot416b-onco)). -Cells with similar expression profiles should be located close together in the plot, while dissimilar cells should be far apart. -In this case, we observe a clear separation of cells based on the oncogene induction status, consistent with the expected effects on the transcriptome. - -```{r pcaplot416b-onco, fig.cap="Pairwise PCA plots of the first three PCs in the 416B dataset, constructed from normalized log-expression values of genes with positive biological components. Each point represents a cell, coloured according to oncogene induction status.", fig.width=9} -plotReducedDim(sce, use_dimred="PCA", ncomponents=3, - colour_by="Oncogene") + fontsize -``` - -By comparison, we observe no clear separation of cells by batch (Figure \@ref(fig:pcaplot416b-batch)). -This indicates that our batch correction step using `removeBatchEffect()` was successful. - -```{r pcaplot416b-batch, fig.cap="Pairwise PCA plots of the first three PCs in the 416B dataset, constructed from normalized log-expression values of genes with positive biological components. Each point represents a cell, coloured according to the plate of origin.", fig.width=9} -plotReducedDim(sce, use_dimred="PCA", ncomponents=3, - colour_by="Plate") + fontsize -``` - -Note that `plotReducedDim()` will use the PCA results that were already stored in `sce` by `denoisePCA()`. -This allows us to rapidly generate new plots with different aesthetics, without repeating the entire PCA computation. -Similarly, `plotPCA()` will use existing results if they are available in the `SingleCellExperiment`, and will recalculate them otherwise. -Users should set `rerun=TRUE` to forcibly recalculate the PCs in the presence of existing results. - -__Comments from Aaron:__ - -- For each visualization method, additional cell-specific information can be incorporated into the size or shape of each point. -This is done using the `size_by=` and `shape_by=` arguments in most plotting functions. -- More components can be shown but these are usually less informative as they explain less of the variance. -They are also often more difficult to interpret as they are defined to be orthogonal to earlier PCs (and thus dependent on what is detected in those PCs). - -## With _t_-SNE - -Another widely used approach for dimensionality reduction is the _t_-stochastic neighbour embedding (_t_-SNE) method [@van2008visualizing]. -_t_-SNE tends to work better than PCA for separating cells in more diverse populations. -This is because the former can directly capture non-linear relationships in high-dimensional space, whereas the latter must represent them on linear axes. -However, this improvement comes at the cost of more computational effort and requires the user to consider parameters such as the random seed and perplexity (see comments). - -We demonstrate the generation of _t_-SNE plots in Figure \@ref(fig:tsneplot416b) using the `plotTSNE()` function. -We set `use_dimred="PCA"` to perform the _t_-SNE on the low-rank approximation of the data, allowing the algorithm to take advantage of the previous denoising step. - -```{r tsneplot416b, fig.cap="_t_-SNE plots constructed from the denoised PCs in the 416B dataset, using a range of perplexity values. Each point represents a cell, coloured according to its oncogene induction status. Bars represent the coordinates of the cells on each axis.", fig.width=15, fig.asp=0.5} -set.seed(100) -out5 <- plotTSNE(sce, run_args=list(use_dimred="PCA", perplexity=5), - colour_by="Oncogene") + fontsize + ggtitle("Perplexity = 5") - -set.seed(100) -out10 <- plotTSNE(sce, run_args=list(use_dimred="PCA", perplexity=10), - colour_by="Oncogene") + fontsize + ggtitle("Perplexity = 10") - -set.seed(100) -out20 <- plotTSNE(sce, run_args=list(use_dimred="PCA", perplexity=20), - colour_by="Oncogene") + fontsize + ggtitle("Perplexity = 20") - -multiplot(out5, out10, out20, cols=3) -``` - -_t_-SNE is a stochastic method, so users should run the algorithm several times to ensure that the results are representative. -Scripts should set a seed (via the `set.seed()` command) to ensure that the chosen results are reproducible. -It is also advisable to test different settings of the "perplexity" parameter as this will affect the distribution of points in the low-dimensional space. - -Here, we call `runTSNE()` with a perplexity of 20 to store the _t_-SNE results inside our `SingleCellExperiment` object. -This avoids repeating the calculations whenever we want to create a new plot with `plotTSNE()`, as the stored results will be used instead. -Again, users can set `rerun=TRUE` to force recalculation in the presence of stored results. - -```{r} -set.seed(100) -sce <- runTSNE(sce, use_dimred="PCA", perplexity=20) -reducedDimNames(sce) -``` - -There are many other dimensionality reduction techniques that we do not consider here but could also be used, e.g., multidimensional scaling, diffusion maps. -These have their own advantages and disadvantages -- for example, diffusion maps (see `plotDiffusionMap`) place cells along a continuous trajectory and are suited for visualizing graduated processes like differentiation [@angerer2016destiny]. - -__Comments from Aaron:__ - -- A good guide on how to interpret _t_-SNE plots can be found at http://distill.pub/2016/misread-tsne/. -This demonstrates how distances between clusters in the 2-dimensional embedding have little meaning, as does the apparent "size" (i.e., spread) of the clusters. - -# Clustering cells into putative subpopulations - -## Defining cell clusters from expression data - -The denoised log-expression values are used to cluster cells into putative subpopulations. -Specifically, we perform hierarchical clustering on the Euclidean distances between cells, using Ward's criterion to minimize the total variance within each cluster. -This yields a dendrogram that groups together cells with similar expression patterns across the chosen genes. - -```{r} -pcs <- reducedDim(sce, "PCA") -my.dist <- dist(pcs) -my.tree <- hclust(my.dist, method="ward.D2") -``` - -Clusters are explicitly defined by applying a dynamic tree cut [@langfelder2008defining] to the dendrogram. -This exploits the shape of the branches in the dendrogram to refine the cluster definitions, and is more appropriate than `cutree` for complex dendrograms. -Greater control of the empirical clusters can be obtained by manually specifying `cutHeight` in `cutreeDynamic`. -We also set `minClusterSize` to a lower value than the default of 20, to avoid spurious aggregation of distant small clusters. - -```{r} -library(dynamicTreeCut) -my.clusters <- unname(cutreeDynamic(my.tree, distM=as.matrix(my.dist), - minClusterSize=10, verbose=0)) -``` - -We examine the distribution of cells in each cluster with respect to known factors. -Each cluster is comprised of cells from both batches, indicating that the clustering is not driven by a batch effect. -Differences in the composition of each cluster are observed with respect to `Oncogene`, consistent with a biological effect of oncogene induction. - -```{r} -table(my.clusters, sce$Plate) -table(my.clusters, sce$Oncogene) -``` - -We visualize the cluster assignments for all cells on the _t_-SNE plot in Figure \@ref(fig:tsnecluster416b). -Adjacent cells are generally assigned to the same cluster, indicating that the clustering procedure was applied correctly. - -```{r tsnecluster416b, fig.cap="_t_-SNE plot of the denoised PCs of the 416B dataset. Each point represents a cell and is coloured according to the cluster identity to which it was assigned."} -sce$cluster <- factor(my.clusters) -plotTSNE(sce, colour_by="cluster") + fontsize -``` - -We check the separatedness of the clusters using the silhouette width (Figure \@ref(fig:silhouette416b)). -Cells with large positive silhouette widths are closer to other cells in the _same_ cluster than to cells in _different_ clusters. -Conversely, cells with negative widths are closer to other clusters than to other cells in the cluster to which it was assigned. -Each cluster would ideally contain many cells with large positive widths, indicating that it is well-separated from other clusters. - -```{r silhouette416b, fig.cap="Barplot of silhouette widths for cells in each cluster. Each cluster is assigned a colour and cells with positive widths are coloured according to the colour of its assigned cluster. Any cell with a negative width is coloured according to the colour of the cluster that it is closest to. The average width for all cells in each cluster is shown, along with the average width for all cells in the dataset."} -library(cluster) -clust.col <- scater:::.get_palette("tableau10medium") # hidden scater colours -sil <- silhouette(my.clusters, dist = my.dist) -sil.cols <- clust.col[ifelse(sil[,3] > 0, sil[,1], sil[,2])] -sil.cols <- sil.cols[order(-sil[,1], sil[,3])] -plot(sil, main = paste(length(unique(my.clusters)), "clusters"), - border=sil.cols, col=sil.cols, do.col.sort=FALSE) -``` - -The silhouette width can be used to determine the parameter values that maximize the separation between clusters. -For example, we could vary the cut height or splitting depth in `cutreeDynamic` to maximize the average silhouette width across all cells. -This usually provides a satisfactory initial clustering for further examination. -However, keep in mind that the granularity of clustering is much like the magnification on a microscope. -Different views of the data can be obtained with different granularities, some of which may be suboptimal on measures of separation. -Users should not fixate on the clustering with the greatest separation if it does not provide the desired granularity for a particular biological question. - -Most cells have relatively small silhouette positive widths in Figure \@ref(fig:silhouette416b), indicating that the separation between clusters is weak. -This may be symptomatic of over-clustering where clusters that are clearly defined on oncogene induction status are further split into subsets that are less well separated. -Nonetheless, we will proceed with the current clustering scheme in `my.clusters`, as it provides reasonable partitions for further characterization of heterogeneity. - -```{r echo=FALSE, results='hide'} -gc() -``` - -**Comments from Aaron:** - -- An alternative clustering strategy is to use a matrix of distances derived from correlations (e.g., as in `quickCluster`). -This is more robust to noise and normalization errors, but is also less sensitive to subtle changes in the expression profiles. -- Both Ward's criterion and complete linkage yield spherical, compact clusters. -In particular, complete linkage favours the formation of clusters with the same diameter. -This may be desirable in some cases but is less appropriate when subpopulations differ in their variance. -Thus, we typically use Ward's criterion for our initial clustering. -Of course, it is simple (and recommended) to try other approaches provided that some assessment is performed, e.g., using the silhouette width. - -## Detecting marker genes between clusters - -Once putative subpopulations are identified by clustering, we can identify marker genes for each cluster using the `findMarkers` function. -This performs Welch $t$-tests on the log-expression values for every gene and between every pair of clusters [@soneson2018bias]. -The aim is to test for DE in each cluster compared to the others while blocking on uninteresting factors such as the plate of origin (see `r simpleSingleCell:::.link("de", "Blocking on uninteresting factors of variation", "here")` for details). -The top DE genes are likely to be good candidate markers as they can effectively distinguish between cells in different clusters. - -```{r} -markers <- findMarkers(sce, my.clusters, block=sce$Plate) -``` - -For each cluster, the DE results of the relevant comparisons are consolidated into a single output table. -This allows a set of marker genes to be easily defined by taking the top DE genes from each pairwise comparison between clusters. -For example, to construct a marker set for cluster 1 from the top 10 genes of each comparison, one would filter `marker.set` to retain rows with `Top` less than or equal to 10. -Other statistics are also reported for each gene, including the adjusted $p$-values (see below) and the log-fold changes relative to every other cluster. - -```{r, echo=FALSE, results="hide"} -old.digits <- options()$digits -options(digits=3) -``` - -```{r} -marker.set <- markers[["1"]] -head(marker.set, 10) -``` - -```{r, echo=FALSE, results="hide"} -# Crashing if cluster 1 is not what we think it is; -# thus, avoid mismatch between text and results. -stopifnot(all(sapply(marker.set["Myh11",-(1:3)], sign)==1)) -stopifnot(all(sapply(marker.set["Mcm2",-(1:3)], sign)==-1)) - -options(digits=old.digits) -``` - -We save the list of candidate marker genes for further examination. - -```{r} -write.table(marker.set, file="416B_marker_1.tsv", sep="\t", - quote=FALSE, col.names=NA) -``` - -We visualize the expression profiles of the top candidates to verify that the DE signature is robust (Figure \@ref(fig:heatmapmarker416b)). -Most of the top markers have strong and consistent up- or downregulation in cells of cluster 1 compared to some or all of the other clusters. -A cursory examination of the heatmap indicates that cluster 1 contains oncogene-induced cells with strong downregulation of DNA replication and cell cycle genes. -This is consistent with the potential induction of senescence as an anti-tumorigenic response [@wajapeyee2010senescence]. -A more comprehensive investigation of the function of these markers can be performed with gene set enrichment analyses, e.g., using `kegga` or `goana` from `r Biocpkg("limma")`. - -```{r heatmapmarker416b, fig.width=10, fig.asp=0.8, fig.cap="Heatmap of mean-centred and normalized log-expression values for the top set of markers for cluster 1 in the 416B dataset. Column colours represent the cluster to which each cell is assigned, the plate of origin or the oncogene induction status of each cell, as indicated by the legend."} -top.markers <- rownames(marker.set)[marker.set$Top <= 10] -plotHeatmap(sce, features=top.markers, columns=order(sce$cluster), - colour_columns_by=c("cluster", "Plate", "Oncogene"), - cluster_cols=FALSE, center=TRUE, symmetric=TRUE, zlim=c(-5, 5)) -``` - -Many of the markers in Figure \@ref(fig:heatmapmarker416b) are not uniquely up- or downregulated in the chosen cluster. -Testing for unique DE tends to be too stringent as it overlooks important genes that are expressed in two or more clusters. -For example, in a mixed population of CD4^+^-only, CD8^+^-only, double-positive and double-negative T cells, neither _Cd4_ or _Cd8_ would be detected as subpopulation-specific markers because each gene is expressed in two subpopulations. -With our approach, both of these genes will be picked up as candidate markers as they will be DE between at least one pair of subpopulations. -A combination of markers can then be chosen to characterize a subpopulation, which is more flexible than trying to find uniquely DE genes. - -We strongly recommend selecting some markers for use in validation studies with an independent replicate population of cells. -The aim is to identify a corresponding subset of cells that express the upregulated markers and do not express the downregulated markers. -Ideally, a different technique for quantifying expression would also be used during validation, e.g., fluorescent _in situ_ hybridisation or quantitative PCR. -This confirms that the subpopulation genuinely exists and is not an artifact of scRNA-seq or the computational analysis. - -__Comments from Aaron:__ - -- By setting `direction="up"`, `findMarkers` will only return genes that are upregulated in each cluster compared to the others. -This is convenient in highly heterogeneous populations to focus on genes that can immediately identify each cluster. -While lack of expression may also be informative, it is less useful for positive identification. -- `findMarkers` can also be directed to find genes that are DE between the chosen cluster and _all_ other clusters. -This should be done by setting `pval.type="all"`, which defines the p-value for each gene as the maximum value across all pairwise comparisons involving the chosen cluster. -Combined with `direction="up"`, this can be used to identify unique markers for each cluster. -However, this is sensitive to overclustering, as unique marker genes will no longer exist if a cluster is split into two smaller subclusters. -- It must be stressed that the (adjusted) _p_-values computed here cannot be properly interpreted as measures of significance. -This is because the clusters have been empirically identified from the data, see `r simpleSingleCell:::.link("de", "Misinterpretation of DE $p$-values", "here")`. - -# Concluding remarks - -Once the basic analysis is completed, it is often useful to save the `SingleCellExperiment` object to file with the `saveRDS` function. -The object can then be easily restored into new R sessions using the `readRDS` function. -This allows further work to be conducted without having to repeat all of the processing steps described above. - -```{r} -saveRDS(file="416B_data.rds", sce) -``` - -A variety of methods are available to perform more complex analyses on the processed expression data. -For example, cells can be ordered in pseudotime (e.g., for progress along a differentiation pathway) with `r Biocpkg("monocle")` [@trapnell2014dynamics] or `r Biocpkg("TSCAN")` [@ji2016tscan]; -cell-state hierarchies can be characterized with the `r Biocpkg("sincell")` package [@julia2015sincell]; -and oscillatory behaviour can be identified using `r Biocpkg("Oscope")` [@leng2015oscope]. -HVGs can be used in gene set enrichment analyses to identify biological pathways and processes with heterogeneous activity, using packages designed for bulk data like `r Biocpkg("topGO")` or with dedicated single-cell methods like `r Biocpkg("scde")` [@fan2016characterizing]. -Full descriptions of these analyses are outside the scope of this workflow, so interested users are advised to consult the relevant documentation. - -```{r, echo=FALSE, results='hide'} -gc() -``` - -All software packages used in this workflow are publicly available from the Comprehensive R Archive Network (https://cran.r-project.org) or the Bioconductor project (http://bioconductor.org). -The specific version numbers of the packages used are shown below, along with the version of the R installation. - -```{r} -sessionInfo() -``` - -# References - +--- +title: 分析包含Read计算的单细胞RNA-seq数据 +author: +- name: Aaron T. L. Lun + affiliation: &CRUK Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom +- name: Davis J. McCarthy + affiliation: + - &EMBL EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom + - St Vincent's Institute of Medical Research, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia +- name: John C. Marioni + affiliation: + - *CRUK + - *EMBL + - Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom +date: "`r Sys.Date()`" +vignette: > + %\VignetteIndexEntry{02. Read count data} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +output: + BiocStyle::html_document: + titlecaps: false + toc_float: true +bibliography: ref.bib +--- + +```{r style, echo=FALSE, results='hide', message=FALSE, cache=FALSE} +library(BiocStyle) +library(knitr) +opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE, cache=TRUE) +opts_chunk$set(fig.asp=1) +``` + +# 概述 + +在这个工作流程中，我们使用一个相对简单的数据集 [@lun2017assessing] 来介绍scRNA-seq数据分析的大部分概念。 +该数据集包含两个416B细胞板（永生化小鼠骨髓祖细胞系），使用Smart-seq2s实验方法来处理 [@picelli2014fulllength]。 +在文库制备之前，还将the External RNA Controls协会（ERCC）的z指定的恒定量的加标RNA添加到每个细胞的裂解物中。 +然后进行进行高通量测序，并通过计数能比对到外显子区域的reads的总数来量化每个基因的表达量。 +类似地，通过计算比对到加标参考序列的reads的数量，可以测量每个加标转录物的量。 + +每个细胞中所有基因/转录物的计数l量可以从 accession number为 [E-MTAB-5522](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5522)中的ArrayExpress获得。 +这里将会使用`r Biocpkg("BiocFileCache")`下载两个计数表（在“已处理文件”中）以及metadata数据文件。 +这会将文件保存到本地缓存 (`raw_data`)中 ，并避免在已存在的情况下重新下载它们。 + +```{r} +library(BiocFileCache) +bfc <- BiocFileCache("raw_data", ask = FALSE) +lun.zip <- bfcrpath(bfc, + file.path("https://www.ebi.ac.uk/arrayexpress/files", + "E-MTAB-5522/E-MTAB-5522.processed.1.zip")) +lun.sdrf <- bfcrpath(bfc, + file.path("https://www.ebi.ac.uk/arrayexpress/files", + "E-MTAB-5522/E-MTAB-5522.sdrf.txt")) +unzip(lun.zip, exdir=tempdir()) +``` + + + +# 数据设置 + +## 加载计数矩阵 + +我们首先要将计数矩阵加载到内存中。 +在这个研究中，每一个细胞板会产生一个计数矩阵。 +在每个矩阵中中，每行代表内源基因或加标转录物，每列代表一个细胞。 +l另外，矩阵中的每个条目中的计数表示比对到特定细胞中的特定基因/转录物的reads的数量。 + +```{r} +plate1 <- read.delim(file.path(tempdir(), "counts_Calero_20160113.tsv"), + header=TRUE, row.names=1, check.names=FALSE) +plate2 <- read.delim(file.path(tempdir(), "counts_Calero_20160325.tsv"), + header=TRUE, row.names=1, check.names=FALSE) + +gene.lengths <- plate1$Length # 第一列是基因长度. +plate1 <- as.matrix(plate1[,-1]) # 去除基因长度那一列 (as it is not a cell). +plate2 <- as.matrix(plate2[,-1]) +rbind(Plate1=dim(plate1), Plate2=dim(plate2)) +``` + +我们将两个矩阵组合成一个对象以进行进一步处理。 +但这一步是基于在确认基因排列顺序是在两个计数矩阵上是相同的，才能进行。This is done after verifying that the genes are in the same order between the two matrices. + +```{r} +stopifnot(identical(rownames(plate1), rownames(plate2))) +all.counts <- cbind(plate1, plate2) +``` + +为方便起见，计数矩阵已经存储在`r Biocpkg("SingleCellExperiment")` 包中的 `SingleCellExperiment` 对象中。 +这允许将不同类型的行和列水平的metadata与整个工作流中的同步操作的计数一起存储。 + +```{r} +library(SingleCellExperiment) +sce <- SingleCellExperiment(list(counts=all.counts)) +rowData(sce)$GeneLength <- gene.lengths +sce +``` + +我们从行名中能识别对应着ERCC加标的转录本的行。 +我们将此信息存储在 `SingleCellExperiment` 对象中以供以后使用。 +这一步是必要的，因为加标的数据需要在下游步骤中进行特殊处理，例如标准化。 + +```{r} +isSpike(sce, "ERCC") <- grepl("^ERCC", rownames(sce)) +summary(isSpike(sce, "ERCC")) +``` + +该数据集略微不同寻常，因为它包含来自另一组加标转录物的信息，即Spike-In RNA Variants（SIRV）组。 +为简单起见，我们将仅在此分析中使用ERCC加标。因此，我们必须在进一步分析之前删除对应于SIRV转录本的行，这可以简单地通过对 `SingleCellExperiment` 对象进行子集化来完成。 + +```{r} +is.sirv <- grepl("^SIRV", rownames(sce)) +sce <- sce[!is.sirv,] +summary(is.sirv) +``` + +**来自作者Aaron的评价:** + +- 一些特征计数工具将在计数矩阵中报告比对信息统计数据（例如，比对不上的reads数量）。 +虽然这些值可用于质量控制，但如果将其错误地视为基因表达值，则会对结果产生误导。 +因此，在进一步分析之前，这些值应当被移除或至少移到`colData`中。 +- 在人类数据中，注意使用 `^ERCC` 正则表达式， 其中计数矩阵的行名称是基因符号。 +ERCC基因家族实际上存在于人类注释文件中，如果不小心将导致基因被错误地识别为加标转录本。An +通过使用已发表的具有标准标识符的计数矩阵（例如，Ensembl，Entrez）可以避免该问题。 + +## 合并基于细胞的注释 + +我们从`sdrf.txt` w文件中加载每个库/细胞的metadata。 +这里要记得检查meta数据表的行与计数矩阵的列的顺序是否相同。 +否则，将为每个细胞分配不正确的meta数据。 + +```{r} +metadata <- read.delim(lun.sdrf, check.names=FALSE, header=TRUE) +m <- match(colnames(sce), metadata[["Source Name"]]) # 确保顺序相同 +stopifnot(all(!is.na(m))) # 确保没有丢失信息 +metadata <- metadata[m,] +head(colnames(metadata)) +``` + +我们只保留相关的meta数据信息，以避免`colData`在`SingleCellExperiment`对象中存储不必要的信息。 +我们会特别地保留每个细胞的起源板 (例如 `block`) 和表型。 +第二个癌基因项是相关的，因为所有细胞都含有`CBFB-MYH11`癌基因，但该癌基因的表达仅在一部分细胞中被诱导。 + +```{r} +colData(sce)$Plate <- factor(metadata[["Factor Value[block]"]]) +pheno <- metadata[["Factor Value[phenotype]"]] +levels(pheno) <- c("induced", "control") +colData(sce)$Oncogene <- pheno +table(colData(sce)$Oncogene, colData(sce)$Plate) +``` + +## 整合基于基因的注释 + +特征计数工具通常根据Ensembl或Entrez的标准标识符报告基因。 +使用这些标识符是因为它们已经被明确肯定且高度稳定。 +然而，与文献中更常用的基因符号相比，它们更难被解释。 +根据给定Ensembl标识符，我们使用`r Biocpkg("org.Mm.eg.db")`等注释包来获取相对应的基因符号。 + +```{r} +library(org.Mm.eg.db) +symb <- mapIds(org.Mm.eg.db, keys=rownames(sce), keytype="ENSEMBL", column="SYMBOL") +rowData(sce)$ENSEMBL <- rownames(sce) +rowData(sce)$SYMBOL <- symb +head(rowData(sce)) +``` + +通常需要将行名重命名为`sce`基因符号，因为这更便于解释。 +但是，这需要额外的功夫来解决丢失和重复的符号的问题。 +下面的代码将使用Ensembl标识符替换缺少的符号，并使用（唯一的）Ensembl标识符连接重复的符号。 + +```{r} +library(scater) +rownames(sce) <- uniquifyFeatureNames(rowData(sce)$ENSEMBL, rowData(sce)$SYMBOL) +head(rownames(sce)) +``` + +我们还使用 `r Biocpkg("TxDb.Mmusculus.UCSC.mm10.ensGene")` 包来确定每个基因的染色体位置。 +这一步将在以后的分析有用，因为质控用到的度量将会从对应于线粒体基因的行来计算。 + +```{r} +library(TxDb.Mmusculus.UCSC.mm10.ensGene) +location <- mapIds(TxDb.Mmusculus.UCSC.mm10.ensGene, keys=rowData(sce)$ENSEMBL, + column="CDSCHROM", keytype="GENEID") +rowData(sce)$CHR <- location +summary(location=="chrM") +``` + +或者，可以使用`r Biocpkg("scater")`中的`getBMFeatureAnnos`函数将BioMart资源中的注释直接添加到对象中。 +这可能比上面显示的方法更方便，但取决于与BioMart数据库的可用互联网连接。 + +# 对细胞的质控 + +## 定义质控的指标 + +低质量的细胞需要移除，以确保技术带来的影响不会扭曲下游分析结果 +我们使用多个质控（QC）的指标: + +- 文库大小定义为所有特征（即基因和加标转录本）的总计数。 +细胞具有小文库意味着质量低，因为在文库制备期间RNA未被有效捕获（转化成cDNA并扩增）。 +- 每个细胞中表达的特征量被定义为该单细胞所具有的非零计数的特征量。 +表达基因量低的细胞通常意味着质量很差，因为细胞中含有的多样的转录本群体尚未被成功捕获。 +- 比对到加标转录物的reads的比例是相对于每个细胞的文库大小计算的。该比例越高表示细胞质量差，其中内源RNA在加工过程中已经丢失（例如，由于细胞裂解或RNA降解）。 +每个细胞加入相同量的加标RNA，因此加标计数的富集是内源性RNA丢失所引起的。 +- 在没有加标转录物的情况下，也可以使用比对到线粒体基因组中的基因的reads的比例作为指标。 +该比例高表明细胞质量差[@islam2014quantitative;@ilicic2016classification], 可能是因为穿孔细胞中细胞质RNA的丢失。 +原因是线粒体比单个转录物分子大，不大可能从细胞膜的液体中逃脱。 + +对于每个细胞，我们使用 `r Biocpkg("scater")` 包中的`calculateQCMetrics`函数计算这些质控指标 [@mccarthy2017scater]。 +这些指标存储在meta数据`SingleCellExperiment`中的行和列中，以供以后使用。 +```{r} +mito <- which(rowData(sce)$CHR=="chrM") +sce <- calculateQCMetrics(sce, feature_controls=list(Mt=mito)) +head(colnames(colData(sce)), 10) +``` + +这些指标的分布如图 \@ref(fig:qcplot416b)所示,由癌基因诱导状态和细胞起源板分层。 +这一步目的是去除假定的低质量细胞，这些细胞具有小的文库，低表达特征量和高加标物（或线粒体）比例。 +此类细胞可干扰下游分析，例如，通过形成不同细胞簇使结果复杂化，更难以解析。 + +```{r qcplot416b, fig.wide=TRUE, fig.cap="416B数据集中所有细胞的各种QC指标的分布。这包括文库大小，表达基因的数量和比对到加标转录物或线粒体基因的reads的比例。"} +sce$PlateOnco <- paste0(sce$Oncogene, ".", sce$Plate) +multiplot( + plotColData(sce, y="total_counts", x="PlateOnco"), + plotColData(sce, y="total_features_by_counts", x="PlateOnco"), + plotColData(sce, y="pct_counts_ERCC", x="PlateOnco"), + plotColData(sce, y="pct_counts_Mt", x="PlateOnco"), + cols=2) +``` + +另外明白质控指标是怎样相互影响关联的的也是非常重要的 (Figure \@ref(fig:qcbiplot416b))。 +通常，它们是大概一致的，比如有低总计数的细胞也将具有低数量的表达特征和高ERCC /线粒体比例。 +明显的差异可能是由细胞之间技术差异导致的批次效应产生的（见下文）又或者是由真正生物学差异导致RNA量的不同。 + +```{r qcbiplot416b, fig.width=10, fig.asp=0.5, fig.cap="每个QC指标与表达特征总量的比较。每个点代表416B数据集中的一个细胞。"} +par(mfrow=c(1,3)) +plot(sce$total_features_by_counts, sce$total_counts/1e6, xlab="Number of expressed genes", + ylab="Library size (millions)") +plot(sce$total_features_by_counts, sce$pct_counts_ERCC, xlab="Number of expressed genes", + ylab="ERCC proportion (%)") +plot(sce$total_features_by_counts, sce$pct_counts_Mt, xlab="Number of expressed genes", + ylab="Mitochondrial proportion (%)") +``` + +## 识别每个指标的异常值 + +选择这些指标的阈值并不简单，因为它们的绝对值取决于实验方案。 +例如，无论细胞的质量如何，对更深的测序将导致更多的reads和更多表达的特征量。 +类似地，在方案中使用更多的加标RNA将导致更高的加标比例。 +为了获得合适的阈值，我们假设大多数数据集由高质量的细胞组成，并且识别含有各种异常QC指标的细胞。 + +基于所有细胞中每个指标中值的中值绝对偏差（MAD）来定义异常值。 +我们删除log-库大小值低于log库大小中间值的3 MAD的细胞。 +对数转换可以提高小的数量值的分辨率，特别是当原始值的MAD等于或大于中值时。 +我们还去除了表达基因的对数转换数量低于中值3个MAD的细胞。 + +```{r} +libsize.drop <- isOutlier(sce$total_counts, nmads=3, type="lower", + log=TRUE, batch=sce$PlateOnco) +feature.drop <- isOutlier(sce$total_features_by_counts, nmads=3, type="lower", + log=TRUE, batch=sce$PlateOnco) +``` + +这个`batch=`选项确保能够在指定的不同水平的板/癌基因因子中别出异常值。 +上面的选项将会让`isOutlier()`这功能适应不同细胞板之间的QC指标的系统差异 (Figure \@ref(fig:qcplot416b)), +这些系统之间的差异可能是由于处理技术的差异产生的（例如，测序深度的差异）而不是质量的变化而导致的。 +同样的推理适用于癌基因诱导状态，其中出于生物学原因诱导细胞可能天然地含有较少的表达基因。 +如果不考虑这些系统性差异，则会增加MAD估计值并影响低质量细胞的去除。 + +我们以类似的方式识别基于比例的指标的异常值。 +这里，我们不需要进行任何转换，因为我们正在识别大的异常值，因此在原始衡量比例上已经非常清楚。 +我们不需要使用线粒体比例，因为我们已经具有该数据集的加标比例（其用于类似目的）。 +同时可以避免由细胞类型之间产生的线粒体含量的差异所引起的潜在问题，这可能会进一步影响异常值的识别。 + +```{r} +spike.drop <- isOutlier(sce$pct_counts_ERCC, nmads=3, type="higher", + batch=sce$PlateOnco) +``` + +按列进行子集化且只保留能够通过上面过滤条件的高质量细胞。 +我们检查每次过滤去除的细胞数量以及保留下来的细胞总数。 +如果大部分细胞被过滤掉（> 10％）可能表明数据质量存在整体问题。 + +```{r} +keep <- !(libsize.drop | feature.drop | spike.drop) +data.frame(ByLibSize=sum(libsize.drop), ByFeature=sum(feature.drop), + BySpike=sum(spike.drop), Remaining=sum(keep)) +``` + +然后我们将 `SingleCellExperiment` 象子集化以仅保留假定的高质量细胞。 +另外，我们还将原始对象保存到文件中以供以后使用。 + +```{r} +sce$PassQC <- keep +saveRDS(sce, file="416B_preQC.rds") +sce <- sce[,keep] +dim(sce) +``` + +**Aaron的评论** + +- 有关更详尽的基于异常值来检测低质量细胞的讨论，请参阅 `r simpleSingleCell:::.link("qc", "Assumptions of outlier identification", "本节")`。 +- `isOutlier()` 还将返回精确的过滤器阈值给每个质控指标（如果batch=已指定，则在每个批次中）。 +这些值可用于检查自动选择的阈值是否合适。 + + ```{r} + attr(libsize.drop, "thresholds") + attr(spike.drop, "thresholds") + ``` + +# 细胞周期阶段的分类 + +我们使用Scialdone等人描述的预测方法 @scialdone2015computational基于基因表达数据将细胞分类为不同的细胞周期阶段。 +使用训练数据集，计算每对基因之间两个基因之间表达差异的信号。 +选择具有跨细胞周期阶段的信号变化的基因对作为标记。 +然后，基于每个标记对的观察到的信号是否与一个阶段或另一个阶段一致，可以将测试数据集中的细胞归类到对应的阶段。 + +这种方法是由`r Biocpkg("scran")`包中的`cyclone`函数实现的。 +该包包含一组预先训练好的鼠标数据标记对，我们可以在`readRDS`函数中加载。 +另外我们使用我们数据集中每个基因的Ensembl标识符来匹配上预先训练好的基因对中的名称。 + +```{r} +set.seed(100) +library(scran) +mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds", + package="scran")) +assignments <- cyclone(sce, mm.pairs, gene.names=rowData(sce)$ENSEMBL) +``` + +HSC数据集中每个细胞的 `cyclone` 结果如图\@ref(fig:phaseplot416b)所示。 +每个细胞在每一个周期阶段会有一个分数，如果分数越高意味着那个细胞在那个细胞周期的概率就更大。 +我们重点关注细胞在G1和G2/M周期的分数，因为这几个细胞周期的信息量最大。 + +```{r phaseplot416b, message=FALSE, fig.cap="在416B数据集上使用基于标记对的分类的细胞周期阶段评分。每个点代表一个细胞，根据其G1和G2 / M期的分数绘制。"} +plot(assignments$score$G1, assignments$score$G2M, + xlab="G1 score", ylab="G2/M score", pch=16) +``` + +如果G1评分高于0.5且高于G2 / M评分，则细胞被分类为处于G1期; +如果G2 / M评分高于0.5且高于G1评分，则处于G2 / M期; +如果得分均不高于0.5，则处于S阶段。 +在这里，大部分细胞被归类为处于G1期。我们将这些分配保存到`SingleCellExperiment`对象中供以后使用。 + +```{r} +sce$phases <- assignments$phases +table(sce$phases) +``` + +存储于 `r Biocpkg("scran")`包中的预先训练好的分类方法可适用于人类和小鼠的数据。 +虽然这里使用的小鼠分类方法是根据胚胎干细胞的数据进行训练的，但对于其它细胞类型仍然是准确的 [@scialdone2015computational]。 +这可能是由于与细胞周期相关的转录过程的保守性所造成的 [@bertoli2013control;@conboy2007cell]。 +基于配对的方法也是一种非参数化过程，能很好的处理数据集之间的大多数技术产生的差异。 + +__Aaron的评论:__ + +- 为了消除由细胞周期阶段引起的混杂效应，我们可以过滤细胞以仅保留特定阶段（通常为G1）的细胞以方便下游分析。 +或者，如果不可忽略数量的细胞处于其他阶段，我们可以使用指定的相位作为阻塞因子。 +这可以在不丢弃信息的情况下防止细胞周期效应，这将会在后面进行更详细地讨论。 +- 由于使用不同的实验方法，如果使用与训练数据集中使用的数据明显不同，使用同源法人分类的方法可能会不准确。 +在这种情况下，用户可以使用`sandbag` 功能对自己的训练数据库构建自定义的分类标准。 +对于没有预训练好的分类方法的其他模型生物，这也是必要的。 +- 在应用 `cyclone`之前不要过滤低丰度基因。 +即使基因不在任何细胞中表达，如果它是细胞阶段性具有特异性，它仍可用于分类。 +虽然相对于其它基因，这些基因缺乏表达，但其仍然会产生信息对，直接过滤它们会降低分类的能力。 + +# 检查基因水平表达指标 + +## 检查表达最高的基因 + +我们检查了表达量最高的那些基因 (Figure \@ref(fig:topgene416b)). +表达量最高的基因通常由参与基本构成的转录物组成，例如核糖体或线粒体蛋白质的转录物。 +如果这些表达量高的基因是由其它类型的RNA构成，与生物学预期的结果不同，对这些不同的特征需要引起我们的关注。 +例如在表达量高的基因中含有许多加标转录物，意味着在文库制备过程中加入了过多的加标RNA，而核糖体蛋白的缺失或者其假基因的存在会导致非最优的比对结果。 + +```{r topgene416b, fig.asp=1.2, fig.wide=TRUE, fig.cap="分配给416B数据集中前50个最高丰富特征的总计数百分比。对于每个要素，每个条形表示为m每个细胞分配给该要素的百分比，而圆圈表示所有细胞的平均值。条形图根据每个细胞中表达的特征的总数着色，而圆形根据特征是否标记为控制特征而着色。"} +fontsize <- theme(axis.text=element_text(size=12), axis.title=element_text(size=16)) +plotHighestExprs(sce, n=50) + fontsize +``` + +##过滤掉低丰度基因 + +低丰度基因是存在问题的，为零或接近零的计数不包含太多能进行可靠统计推断的信息。 +在涉及假设检验的应用中，这些基因不能提供足够的证据来拒绝原假设，但它们仍然会增加多重检验校正的严重度。 +计数的离散性也可能干扰统计过程，例如，通过影响连续近似的准确性 +因此，在进行下游分析之前，通常许多RNA-seq分析流程中都会去除低丰度基因。 + +最佳的过滤策略取决于下游的应用。 +与去除underpowered的测试所需相比，通常需要一个更加进击地过滤来消除离散型（例如标准化）。 +对于假设检验，过滤器的统计量也应该独立于零假设下的检验统计量[@bourgon2010independent]。 +因此，我们（或相关函数）将根据需要在每个步骤进行过滤，而不是对整个分析应用单个过滤器。 + +有几个指标可以用来定义低丰度基因。 +最明显的是每个基因的平均计数，在数据集中的所有细胞中计算。 +我们使用`calcAverage()`函数计算，并对单细胞之间的库大小差异进行一些调整。 +我们通常观察到低表达基因平台后中度表达基因的峰值 (Figure \@ref(fig:abhist416b))。 + +```{r abhist416b, fig.cap="416B数据集中所有基因的对数平均计数的直方图。"} +ave.counts <- calcAverage(sce, use_size_factors=FALSE) +hist(log10(ave.counts), breaks=100, main="", col="grey80", + xlab=expression(Log[10]~"average count")) +``` + +最小阈值可用于过滤掉表达量低的基因 +下面的例子展示如何移除平均计数小于1的基因。 +`demo.keep`中的`TRUE`值对应着过滤后保留下来的行/基因的数目。 + +```{r} +demo.keep <- ave.counts >= 1 +filtered.sce <- sce[demo.keep,] +summary(demo.keep) +``` + +我们同时还检查了表达的每个基因的细胞数量。 +这个量在大多数基因中与平均计数密切相关，因为在大多数细胞中，细胞表达将导致更高的计数平均值 (Figure \@ref(fig:nexprshist416b))。 +只在少数细胞表达的基因通常是没什么意义的，因为他们很可能是因为扩增中假象造成的（尽管它们也可能来自罕见的群体）。 +我们可以将这种低于n个细胞表达的基因移除。 + +```{r nexprshist416b, fig.cap="416B数据集中表达每个基因的细胞数和相对于对数平均计数的关系。颜色强度对应给定位置的基因数量。"} +num.cells <- nexprs(sce, byrow=TRUE) +smoothScatter(log10(ave.counts), num.cells, ylab="Number of cells", + xlab=expression(Log[10]~"average count")) +``` + +如上所述，我们将在每个步骤使用这些过滤方法，而不是只应用于`sce`的子集中。 +这可确保在每个应用程序中使用最合适的过滤方法。 +尽管如此，我们去除了在任何细胞中不表达的基因，以减少下游分析的计算量。 +些基因不提供任何有效信息，并且可以通过任何过滤方法去除。 + +```{r} +to.keep <- num.cells > 0 +sce <- sce[to.keep,] +summary(to.keep) +``` + +# 细胞特异性偏差的标准化 + +## 使用反卷积方法处理零计数 + +Read 计数受细胞间捕获效率和测序深度的差异影响[@stegle2015computational]。 +在下游定量分析之前，需要归一化处理来消除这些细胞特异性偏差。 +这通常基于假设大多数基因在细胞之间没有表达差异(DE) 来进行的。 +两个细胞之间的非差异表达基因的任何系统差异代表偏差并通过缩放换算去除。 +更具体地，"size factors"被计算，其表示在每个库应缩放换算的程度。 + +Size factors 可以用不同的方法计算,例如使用`r Biocpkg("DESeq2")`包中的`estimateSizeFactorsFromMatrix` 函数[@anders2010differential;@love2014moderated]，或者使用 `r Biocpkg("edgeR")`包中的`calcNormFactors` 函数 [@robinson2010scaling] 。 +可是，由于低计数和零计数量是占主导的，单细胞数据直接使用传统的基于批量数据的方法可能是有问题的。 +为了克服这个问题，我们将来自许多细胞的计数汇集起来以增加计数的大小，来准确估计size factor[@lun2016pooling]。 +然后。Pool-based size factors“去卷积”到cell-based factors，并用于每个细胞的表达谱的标准化。 + +```{r} +sce <- computeSumFactors(sce) +summary(sizeFactors(sce)) +``` + +Size factors 与所有细胞的文库大小密切相关 (Figure \@ref(fig:normplot416b))。 +这再次表明细胞之间的大多数系统差异是由捕获效率或测序深度的差异导致的。 +细胞之间的任何表达差异(DE)将导致总计数和size factors之间的非线性趋势，和或者增加围绕趋势的散幅。 +我们在癌基因诱导后观察到这方面的现象，其中诱导后szie factors系统性地降低了。 +这与诱导后基因上调引入的构成偏差一致 [@robinson2010scaling]。 + +```{r normplot416b, fig.cap="来自解卷积的尺寸因子，针对416B数据集中所有细胞的文库大小绘制。轴以对数刻度显示。控制组细胞以黑色显示，癌基因诱导的细胞以红色显示。"} +plot(sce$total_counts/1e6, sizeFactors(sce), log="xy", + xlab="Library size (millions)", ylab="Size factor", + col=c("red", "black")[sce$Oncogene], pch=16) +legend("bottomright", col=c("red", "black"), pch=16, cex=1.2, + legend=levels(sce$Oncogene)) +``` + +__Aaron的评论:__ + +- 虽然反卷积方法对于scRNA-seq数据中的高频率零计算非常好用，但是吐过太多计数为零，这方法将会失效。 +这情况下size factors将会是负数，显然是没有道理的。 +为了避免这种情况，`computeSumFactors`函数将在计算size factors之前自动删除低丰度的基因。 +基因库的调整后的大小平均计数低于指定阈值（`min.mean`）将被忽略。 +对于read计数数据，通常取用默认阀值为1。 +- 基于细胞的质控(QC)应在标准化之前进行，以去除只含有少量表达基因的细胞。 +否则，computeSumFactors()可能会在低质量细胞中产生负的size factors。 +这是因为在这些细胞中存在太多的零的计数，进而降低了消零汇集的有效性。 +有关`computeSumFactors`函数如何解决负size factors的讨论可以参与 `?computeSumFactors` 。 +- 该sizes参数可用于指定用于计算szie factors的pool大小的数量。 +越大的size会有更加准确的估算，但是同时要消耗更多的计算时间和内存。 +通常，所有都sizes应该高于20个细胞，以确保每个池中有足够的非零表达量。 +有效汇集的细胞总数也应至少为100。 +- 对于高度不同的数据集，建议对细胞进行粗略聚类以削弱非DE假设。 +这可以通过`quickCluster()`函数完成，其结果通过`clusters`参数传递给在`computeSumFactors()`函数。 +我们在下一个`r simpleSingleCell:::.link("umis", "Normalization of cell-specific biases", "流程")`中使用更大的数据集演示了这种方法。 + +## 为加标转录物计算 separate size factors + +根据内源基因计数计算的size factors通常不适合标准化加标转录本的计数。 +如果一个实验的文库没有定量，即自每个文库的cDNA的量不是在汇集和多重测序之前均衡。 +在这里含有更多的RNA的细胞会有更大的内源基因的计数，因此需要更大的size factors去缩小这个计数量。 +可是，在文库制备时，每个细胞中添加了相同量的加标RNA。 +这意味着加标转录本的计数不受RNA含量的影响。 +试图用基于基因的size factors正常化加标计数，将导致过度标准化和不准确的表达定量。 +类似的原因可应用于当文库量化。 +对于恒定的cDNA总量，内源RNA含量的任何增加都将抑制加标转录物的覆盖度。F +结果会导致加标计数的偏差将与基于基因的size factors所捕获的值相反。 + +为了确保标准化的准确执行，我们单独地为加标组计算了一组size factors。 +在每个细胞中，特定地加标size factors被定义为加标组中所有转录物的总计数。 +这基于所有加标转录物都没有表达差异，这是非常合理的，因为我们应当在每个细胞中加入等量的加标RNA[@lun2017assessing]。 +(有关加标物归一化的更详细讨论，请参见下文。) +这些 size factors 单独储存于 `SingleCellExperiment` 的对象中通过设置 `computeSpikeFactors`中单独的选项`general.use=FALSE`。 +该选项`type="ERCC"`确保它们仅与加标转录物一起使用，而不对内源基因起作用。 + +```{r} +sce <- computeSpikeFactors(sce, type="ERCC", general.use=FALSE) +``` + +## 在基因表达中应用 size factors + +计数数据用于计算归一化的对数化表达量，可用于下游分析。 +预先在计数量加1，以避免零计数产生未定义值后，将每个值定义为每个计数的log 2 -ratio到相应细胞的size factors。 +按size factor划分可确保消除任何由细胞产生的偏差。 +如果特定的加入 size factors存在于`sce`中, 它们将自动应用于将加标转录物标准化，使它与内源基因分开。 + +```{r} +sce <- normalize(sce) +``` + +对数转换非常有用，因为它意味着值的任何差异直接表示细胞之间表达量的log2倍的变化。 +这通常比覆盖率的绝对差异更为重要，需要在总体丰度的背景下进行解释。 +对数变换也提供了方差稳定性的测量 [@law2014voom], 防止具有大方差的高丰度基因影响下游分析。 +除了其它试验因素外，计算值也会储存在`"logcounts"矩阵中。 + +```{r, echo=FALSE, results="hide"} +gc() +``` + +# 在基因表达中模拟技术产生的噪音 + +观察到的基因表达值得多样性，可以由镇长的生物特异性或者没有意义的技术噪声所产生。 +为了区分这两种不同的因素，我们需要对每个基因中由技术噪音所产生的表达量的差值进行建模。 +这里我们会使用加入转录物，并确保它在每个细胞的加入量是相同的。 +因此，加标转录物应该没有生物性的不同，任何它们的差异都是由技术噪音所造成的。 + +我们使用trendVar()函数将mean-dependen趋势拟合到加入转录物对数化的表达量值的差值。 +我们设置`block=`选项，来防止每个细胞的原始平板不同，以确保板之间的技术差异不会扩大差异。 +根据给定的基因平均丰度，趋势的拟合值，然后评估该基因的技术噪音的影响。 +最终通过用`decomposeVar`函数从每个基因的总差异中减去，技由术噪音产生的成分，来计算生物学成分所产生的差异。 + +```{r} +var.fit <- trendVar(sce, parametric=TRUE, block=sce$Plate, + loess.args=list(span=0.3)) +var.out <- decomposeVar(sce, var.fit) +head(var.out) +``` + +我们检查趋势变化，来确定它和加入物的变化相对应(Figure \@ref(fig:hvgplot416b)))。 +波状形的阴影是对数表达量的平均方差趋势的典型例子。 +随着平均值从零增加，观察到方差的线性增加，因为当计数增加时可能有更大的方差。 +在非常高的丰度下，由于大数定律，样本的技术噪声的影响会减小，进而导致方差减小。 + +```{r hvgplot416b, fig.cap="416B数据集中每个基因的归一化对数表达量的方差，相对于平均对数表达量作图。蓝线表示适用于加标转录本（红色）方差的平均相关趋势。"} +plot(var.out$mean, var.out$total, pch=16, cex=0.6, xlab="Mean log-expression", + ylab="Variance of log-expression") +curve(var.fit$trend(x), col="dodgerblue", lwd=2, add=TRUE) +cur.spike <- isSpike(sce) +points(var.out$mean[cur.spike], var.out$total[cur.spike], col="red", pch=16) +``` + +我们检查具有最大生物成分的基因的表达值的分布。 +这确保了方差估计不是由一个或两个h含有异常值的细胞产生的 (Figure \@ref(fig:hvgvioplot416b)). + +```{r hvgvioplot416b, fig.cap="416B数据集中具有最大生物成分的前10个基因的归一化对数表达值的小提琴图。每个点代表单个细胞中的表达量的对数值。"} +chosen.genes <- order(var.out$bio, decreasing=TRUE)[1:10] +plotExpression(sce, features=rownames(var.out)[chosen.genes]) + fontsize +``` + +** Aaron的评价:** + +- 在实践中，趋势拟合因加标转录物的量不足及其丰度的不均匀分布而变得复杂。 +有关如何优化拟合的更多详细信息，请参见 `r simpleSingleCell:::.link("var", "Details of trend-fitting parameters", "这里")` 。 +- 在没有加标的情况下，用户可以设定`use.spikes=FALSE`适应内源基因变异的趋势。(见`r simpleSingleCell:::.link("var", "Wwhen spike-ins are unavailable", "此处")`). +或者，我们可以创建一个基于Poisson的技术噪音趋势, 具体描述见 `r simpleSingleCell:::.link("tenx", "Modelling the mean-variance trend", "这里")`. +- 通常可以从 `decomposeVar`中获得负性的生物噪音。 +在直观上，这些负性的生物噪音毫无意义，因为基因不可能具有除了技术噪音外的差异。 +尽管如此，这些值还是可以由于总方差的估计不精确而导致，特别是对于数量少的细胞。 +- `decomposeVar`还会产生一个p值，可用于定义特定错误发现率(FDR)的阀值所对应的HVGs。 +我们将在后面更详细地讨论这个问题，因为在数据探索期间不需要正式检测HVG来进行特征选择。 + +# 移除批次效应 + +如上面所述，数据收集发生在两个平板上。 +平板之间处理的小得不可控制的差异可导致批次效应，即不同平板上的细胞之间的表达的系统差异。 +这些差异并不紧要，可以通过应用`r Biocpkg("limma")`包中的`removeBatchEffect()`函数来消除[@ritchie2015limma]。 +这会消除初始板的影响，同时考虑了致癌基因诱导的（通常非常有趣的）效应。 + +```{r} +library(limma) +assay(sce, "corrected") <- removeBatchEffect(logcounts(sce), + design=model.matrix(~sce$Oncogene), batch=sce$Plate) +assayNames(sce) +``` + +对于非基于模型的下游分析步骤，例如聚类和大多数形式的降维，手动批量校正是必要的。 +但是，如果分析方法可以接受设计矩阵，则阻断设计矩阵中的nuisance factors优于使用`removeBatchEffect()`。 +这是因为后者不考虑剩余自由度的损失，也不考虑blocking factor的不确定性。 + +**Aaron的评论:** + +- `removeBatchEffect()`执行线性回归并将blocking factors对应的系数设置为零。 +只要每批中的种群组成已知（并提供为design=）或批次之间相同，这方法就是有效的。 +这种假设对于该数据集是合理的，只要涉及两个平板上的细胞系群体是同源的 +然而，在大多数scRNA-seq应用中，变异因子在批次之间并不相同，并且我们无法事先知道。 +这促使使用更复杂的批量校正方法，例如`mnnCorrect()`。 + +# 使用PCA去除表达值的噪音 + +一旦对技术噪声进行建模,我们就可以使用主成分分析（PCA）来消除随机技术噪声 +每个细胞格代表高维表达空间中的一个点，其中点的扩展代表总的差异。 +PCA识别此空间中的轴，尽可能多地捕获这种差异。 +每个轴都是一个主要组分（PC），任何早期的PC都会比后来的PC解析更多的差异。 + +我们假设涉及共同调节的基因组的生物过程将解释数据中大部分的差异。 +如果是这种情况，则此过程应由一个或多个早期PC表示。 +相反，随机技术噪声会独立影响每个基因，并将由后来的PC代表。 +该`denoisePCA()`功能删除后来的PC，直到总丢弃差异等于PCA中所有基因中的技术噪音的总和。 + +```{r} +sce <- denoisePCA(sce, technical=var.out, assay.type="corrected") +dim(reducedDim(sce, "PCA")) +``` + +该函数返回一个 `SingleCellExperiment`对象，其中`reducedDims`slot包含了每个细胞的PC分数。 +目的是消除技术噪音并丰富保留PC中的生物信号。 +这提高了下游分析（例如聚类）潜在生物学问题的分辨率。This improves resolution of the underlying biology during downstream procedures such as clustering. + +__Aarond的评价:__ + +- `denoisePCA()` 只会使用具有正生物成分的基因，例如差异大于拟合趋势。 +这保证了要丢弃的总的技术噪音引起的差异不会大于数据中的总差异。 +- 对于`technical=`参数，该函数还将直接接受趋势函数（即`var.fit$trend`）或每个基因的技术噪音相关的向量。 +这里，我们提供从`decomposeVar()`函数提供`DataFrame`,来允许函数调整批量校正后残余自由度的损失。 +具体而言，批量校正矩阵的差异略微被低估，需要对技术噪音相关成分进行一些重新调整以进行补偿。 +- 这里没有对丰度进行过滤，这确保了仍然可以检测到稀有子群体的PC。 +离散性不是问题，因为低丰度基因也具有较低的差异，因此降低了它们对PCA的影响。 +- 还可以获得原始表达矩阵的低秩近近似值，捕获等同于残留的PC的差异。 +进行去噪是有用的，特别是对于那些下游分析步骤中需要用到基因的表达量。 + +```{r} +sce2 <- denoisePCA(sce, technical=var.fit$trend, + assay.type="corrected", value="lowrank") +assayNames(sce2) +``` + +```{r, echo=FALSE, results="hide"} +rm(sce2) +gc() +``` + +# 在低维空间中可视化数据 + +## 使用PCA + +我们通过构建前三个组成成分的成对PCA图来可视化细胞之间的关系(Figure \@ref(fig:pcaplot416b-onco))。 +具有表达模式相似的细胞应该在图中聚集在一起，而表达模式不同的细胞应该相距比较远。 +在这种情况下，我们观察到基于癌基因诱导状态的细胞有明显地分离，这与对转录组的预期效果是一致的。 + +```{r pcaplot416b-onco, fig.cap="Pairwise PCA plots of the first three PCs in the 416B dataset, constructed from normalized log-expression values of genes with positive biological components. Each point represents a cell, coloured according to oncogene induction status.", fig.width=9} +plotReducedDim(sce, use_dimred="PCA", ncomponents=3, + colour_by="Oncogene") + fontsize +``` + +对比之下，我们发现不同批次间没有明显的细胞分离 (Figure \@ref(fig:pcaplot416b-batch))。 +这表明我们使用`removeBatchEffect()`函数进行批次修正是成功的。 + +```{r pcaplot416b-batch, fig.cap="416B数据集中前三个PC的成对PCA图，由具有阳性生物成分的基因的标准化表达量的对数值来构建。每个点代表一个细胞，根据癌基因诱导状态着色。", fig.width=9} +plotReducedDim(sce, use_dimred="PCA", ncomponents=3, + colour_by="Plate") + fontsize +``` + +注意，`plotReducedDim()` 将会使用已经存储好的PCA结果被`denoisePCA()`存储于`sce` 中。 +这使我们能够快速生成具有不同美学的新图，而无需重复整个PCA的计算。 +同样，`plotPCA()`会使用现有可用的结果如果它存在于`SingleCellExperiment`中, 否则将重新计算它们。 +用户应设置`rerun=TRUE`，使即在存在现有结果的情况下，也强制重新计算PC。 + +__Aaron的评论:__ + +- 对于每种可视化方法，可以将额外的细胞特异性信息结合到每个点的大小或形状中。 +在大多数绘图参数中，使其中的`size_by=`和`shape_by=`参数来完成。T +- 在PCA中，其实更多的成分可用展示，但是这些成分通常信息量较小，它们能解析的差异就越小。 +它们通常也更难以解释，因为它们被定义为正交的早期PCs（因此取决于在那些PCs中检测到的内容）。 + +## 使用 _t_-SNE + +另一种广泛使用的降维方法是t-随机邻域嵌入 (_t_-SNE) 方法[@van2008visualizing]。 +_t_-SNE 往往比PCA更好地分离更多不同群体中的细胞。 +这是因为前者可以直接捕获高维空间中的非线性关系，而后者必须在线性轴上表示它们。 +然而，这种改进是以更多计算量为代价的，并且需要用户考虑诸如随机种子和混乱度之类的参数（参见下面注释）。 + +我们使用`plotTSNE()`函数来演示了_t_-SNE 图的生成 \@ref(fig:tsneplot416b)。 +我们设置了`use_dimred="PCA"` 去计算数据的 _t_-SNE 的低秩近似量, 允许算法利用先前的去噪步骤。 + +```{r tsneplot416b, fig.cap="使用一系列混乱度从416B数据集中的去噪PCs构建_t_-SNE 图。 每个点代表一个细胞，根据其癌基因诱导状态着色。条形表示每个轴上的细胞的坐标。", fig.width=15, fig.asp=0.5} +set.seed(100) +out5 <- plotTSNE(sce, run_args=list(use_dimred="PCA", perplexity=5), + colour_by="Oncogene") + fontsize + ggtitle("Perplexity = 5") + +set.seed(100) +out10 <- plotTSNE(sce, run_args=list(use_dimred="PCA", perplexity=10), + colour_by="Oncogene") + fontsize + ggtitle("Perplexity = 10") + +set.seed(100) +out20 <- plotTSNE(sce, run_args=list(use_dimred="PCA", perplexity=20), + colour_by="Oncogene") + fontsize + ggtitle("Perplexity = 20") + +multiplot(out5, out10, out20, cols=3) +``` + +_t_-SNE 是一种随机方法，因此用户应多次重复运行该算法以确保结果具有代表性。 +脚本应设置种子（通过 `set.seed()` 命令) 去确保所选结果是可重复的。 +还建议测试不同的混乱度参数，因为这将影响低维空间中的点的分布。 + +这里我们使用20的混乱度去调用`runTSNE()` ，并将 _t_-SNE 的结果存储于`SingleCellExperiment` 对象中。 +如果我们想使用`plotTSNE()`创建新图时，这将避免我们重复计算，因为可以直接使用已经存储好的结果。 +同样用户可以设置 `rerun=TRUE` 去强制重新计算即使在有存储结果的情况。 + +```{r} +set.seed(100) +sce <- runTSNE(sce, use_dimred="PCA", perplexity=20) +reducedDimNames(sce) +``` + +还有许多其它降维技术，虽然我们在这里就不深入探究了但也可以使用，例如，多维缩放，扩散图。 +它们各有利弊 - 例如，扩散图(参见`plotDiffusionMap`) 沿着连续的轨迹放置细胞，适合于可视化分化等渐进过程 [@angerer2016destiny]. + +__Aaron的注释:__ + +- 有关如何解析 _t_-SNE 图的指南请访问该网站 http://distill.pub/2016/misread-tsne/。 +这证明了二维嵌入中的聚集的距离，大小（扩展)几乎没有意义。 + +# 将细胞聚类成假定的亚群 + +## 根据表达数据定义细胞集聚 + +去噪的对数表达量用于将细胞聚类成推定的亚群。 +具体来说，我们对细胞之间的欧几里德距离进行层次聚类，使用Ward的标准来最小化每个集群内的总差异。 +这产生了树状图，将所选基因中具有相似表达模式的细胞聚合在一起。 + +```{r} +pcs <- reducedDim(sce, "PCA") +my.dist <- dist(pcs) +my.tree <- hclust(my.dist, method="ward.D2") +``` + +聚类被明确地定义为应用动态的树切割于树状图中 [@langfelder2008defining]。 +这利用了树状图中分支的形状来细化聚类的定义，并且比`cutree`应用于更复杂的树状图更合适。 +通过人工指定`cutreeDynamic`中的`cutHeight`可以获得对聚类的更多控制。 +我们也通过设置`minClusterSize`比默认值20更低的值来避免远端小聚类的假阳性聚合。 + +```{r} +library(dynamicTreeCut) +my.clusters <- unname(cutreeDynamic(my.tree, distM=as.matrix(my.dist), + minClusterSize=10, verbose=0)) +``` + +我们根据已知因素检查每个聚类中细胞的分布。 +每个聚类由来自两个批次的细胞组成，表示聚集不是由批次效应造成的。 +观察到每个聚类的组成的差异，由`Oncogene`对象表示，与癌基因诱导的生物学效应一致 + +```{r} +table(my.clusters, sce$Plate) +table(my.clusters, sce$Oncogene) +``` + +我们可视化所以细胞上的聚类分配使用 _t_-SNE 图 \@ref(fig:tsnecluster416b)。 +通常将相邻细胞分配给相同的聚类，引导正确的聚类步骤。 + +```{r tsnecluster416b, fig.cap="416B数据集的去噪PC的t -SNE图。每个点代表一个细胞，并根据分配给它的聚类标识进行着色。"} +sce$cluster <- factor(my.clusters) +plotTSNE(sce, colour_by="cluster") + fontsize +``` + +我们使用轮廓宽度检查簇的分离度 (Figure \@ref(fig:silhouette416b))。 +具有大的正轮廓宽度的细胞比更靠近同一个聚类相比于不同的聚类。 +相反，具有负轮廓宽度的细胞更靠近其它不同的聚类，而不是分配和它有相同聚类的细胞中。 +理想情况下，每个聚类都包含具有比较大的正宽度的细胞，表明它与其它聚类完全分离。 + +```{r silhouette416b, fig.cap="每个聚类中的细胞的轮廓宽度的条形图。为每个聚类分配一种颜色，并根据其指定聚类的颜色对具有正宽度的单元格进行着色。具有负宽度的任何细胞根据其最接近的聚类的颜色着色。显示每个聚类中所有细胞的平均宽度，以及数据集中所有细胞的平均宽度。"} +library(cluster) +clust.col <- scater:::.get_palette("tableau10medium") # hidden scater colours +sil <- silhouette(my.clusters, dist = my.dist) +sil.cols <- clust.col[ifelse(sil[,3] > 0, sil[,1], sil[,2])] +sil.cols <- sil.cols[order(-sil[,1], sil[,3])] +plot(sil, main = paste(length(unique(my.clusters)), "clusters"), + border=sil.cols, col=sil.cols, do.col.sort=FALSE) +``` + +轮廓宽度可用于确定最大化聚类之间分离的参数值。 +例如，我们可以改变`cutreeDynamic`中的切割高度或分割深度以最大化所有细胞的平均轮廓宽度。 +这可以提供满意的初始聚类以供进一步检查 +但是，请记住，聚类的粒度很像显微镜上的放大倍数。 +可以使用不同的粒度(间隔尺寸)获得数据的不同视图，其中一些粒度可能在分离度量上是次优的。 +如果用户不能为特定的生物问题提供所需的粒度，则不应该执着于使用具有最大分类度的聚类。 + +大多数细胞在图 \@ref(fig:silhouette416b), 中具有相对小的轮廓正宽度，表明簇之间的分离很弱。 +这可能是过度聚类的症状，其中在癌基因诱导状态上明确定义的聚类被进一步分成不能明确分开的子集。 +尽管如此，我们将继续使用当前的聚类方法`my.clusters`, 因为它进一步为表征异质性提供了合理的区分。 + +```{r echo=FALSE, results='hide'} +gc() +``` + +**Aaron的注释:** + +- 另一种聚类策略是使用从相关性导出的距离矩阵（例如，如`quickCluster`）。 +这对于噪声和归一化误差更加有效，但对表达模式的细微变化不太敏感。 +- Ward的标准和完全连锁均都可以产生球形和紧凑的聚类。 +特别地，完全连锁有利于形成具有相同直径的聚类。 +在某些情况下这非常合适，但当子群体的变异是不同时则不太合适。 +当然，尝试其他方法是简单的（并且推荐），如果要执行某些评估（例如，使用轮廓宽度）。 + +## 检测聚类之间的标记基因 + +一旦通过聚类找到推定的亚群，我们就可以使用该`findMarkers`功能鉴定每个聚类的标记基因。 +这一步执行了Welch $t$ 测试在每个基因和每对聚类之间的表达量的对数值 [@soneson2018bias]。 +目的是测试每个聚类中的与其他聚类相比表达量的差异，同时防止引入不感兴趣的因素，例如板的起源(`r simpleSingleCell:::.link("de", "Blocking on uninteresting factors of variation", "此处")` 详情见). +顶端的表达差异基因可能是很好的候选标记，因为它们可以有效地区分不同聚类中的细胞。 + +```{r} +markers <- findMarkers(sce, my.clusters, block=sce$Plate) +``` + +对于每个聚类,相关的表达差异的结果会合并到单个输出列表中。 +这允许通过从聚类之间的每个成对比较中，获取顶部差异表达基因，更简单地定义一组标记基因。 +例如，为了从每个比较的前10个基因构建聚类1的标记基因集，可以过滤`marker set`以保留含有`Top`的小于或者等于10的行。 +每个基因的其它统计数据也会被报道，包括调整后的$p$-values（见下文）和log-fold,相对于每个其它聚类的变化。 + +```{r, echo=FALSE, results="hide"} +old.digits <- options()$digits +options(digits=3) +``` + +```{r} +marker.set <- markers[["1"]] +head(marker.set, 10) +``` + +```{r, echo=FALSE, results="hide"} +# Crashing if cluster 1 is not what we think it is; +# thus, avoid mismatch between text and results. +stopifnot(all(sapply(marker.set["Myh11",-(1:3)], sign)==1)) +stopifnot(all(sapply(marker.set["Mcm2",-(1:3)], sign)==-1)) + +options(digits=old.digits) +``` + +我们保存候选标记基因列表以供进一步检查。 + +```{r} +write.table(marker.set, file="416B_marker_1.tsv", sep="\t", + quote=FALSE, col.names=NA) +``` + +我们可视化顶端候选基因的表达图谱，以验证表达差异信号是否稳健 (Figure \@ref(fig:heatmapmarker416b))。 +与一些或所有其它聚类相比，大多数顶端标记在聚类1的细胞中具都会有强且一致的上调或下调。 +简单查看一下热图，聚类1含有致癌基因诱导的细胞，具有DNA复制和细胞周期基因的强烈下调。 +这与作为抗肿瘤发生反应的潜在诱导衰老现象一致[@wajapeyee2010senescence]。 +可以使用`r Biocpkg("limma")`包中的`kegg`和`goana`来对进行聚类富集分析，对这些标记基因进行更全面的功能研究。 + +```{r heatmapmarker416b, fig.width=10, fig.asp=0.8, fig.cap="416B数据集中聚类1的顶端标记集的平均中心和标准化对数表达量的热图。柱颜色代表每个细胞所分配的聚类，原始平板或每个细胞的癌基因诱导状态，如图例所示。"} +top.markers <- rownames(marker.set)[marker.set$Top <= 10] +plotHeatmap(sce, features=top.markers, columns=order(sce$cluster), + colour_columns_by=c("cluster", "Plate", "Oncogene"), + cluster_cols=FALSE, center=TRUE, symmetric=TRUE, zlim=c(-5, 5)) +``` + +图 \@ref(fig:heatmapmarker416b) 中许多标记在所选择的聚类中都不是唯一的上调或者下调。 +只测试独特表达差异往往是过于严格，因为它忽略了在两个或更多个聚类中表达的重要基因。 +例如，在这样的混合群体中， CD4^+^-only, CD8^+^-only, 双阳性和双阴性T细胞, _Cd4_ 或 _Cd8_ 都不会被检测为亚群特异性标志物，因为每个基因都在两个亚群中有表达。 +通过我们的方法，这两个基因将被选为候选标记，因为它们将是至少一对亚群之间的差异表达基因。 +选择标记组可以被选来表征亚群，这比找到唯一的差异表达基因的方法更加灵活。 + +我们强烈建议选择一些标记用于实验验证，并使用独立的重复细胞群进行验证。 +目的是鉴定表达上调标记物并且不表达下调标记物的相应的细胞子集。 +理想地，在验证期间还将使用不同的用于量化表达的技术，例如荧光原位杂交或定量PCR。 +这证实了亚群确实存在并且不是scRNA-seq或由于计算分析产生的人为产物。 + +__Aaron的注释:__ + +- 通过设置`direction="up"`, `findMarkers`将仅返回在每个聚类中与其它聚类相比上调的基因。 +这在高度异质的群体中很方便，可以专注于识别每个聚类的基因。 +虽然缺乏基因表达也可能提供一定的信息，但这对于正向鉴定则没那么作用。 +- `findMarkers`也可以指导在所选择的聚类和所有其它聚类之间找到有表达差异的基因。 +这应该通过设置 `pval.type="all"`来完成,该设置将每个基因的p值定义为涉及所选聚类的所有成对对比的最大值。 +结合使用 `direction="up"`, 可用于识别每个聚类中唯一的标记。 +然而，这方法对过度聚类很敏感，因为如果将聚类分成两个较小的子聚类，则不再存在独特的标记基因。 +- 必须强调的是，这里计算的（调整的）p值不能被直接地解释为测量significance的度量。 +这是因为聚类已经被根据经验从数据中识别出来了，参见 `r simpleSingleCell:::.link("de", "Misinterpretation of DE $p$-values", "此处")`. + +# 结束语 + +完成基本分析后，, 通常要使用`saveRDS` 函数去保存`SingleCellExperiment` 对象到文件中。 +然后使用`readRDS`函数该对象可以轻松的恢复并加载到新的R窗口中。 +这允许我们进行下一步的工作而不必重复上述所有处理步骤。 + +```{r} +saveRDS(file="416B_data.rds", sce) +``` + +有多种方法可用于对处理过的表达数据进行更复杂的分析。 +例如，细胞可以按照时间进行排序。 (e.g., 沿着分化途径进行排序)，使用到`r Biocpkg("monocle")` [@trapnell2014dynamics] or `r Biocpkg("TSCAN")` [@ji2016tscan]两个包; +细胞状态层次结构可以用 `r Biocpkg("sincell")` 包来进行表征分析。 [@julia2015sincell]; +可以使用 `r Biocpkg("Oscope")`来识别oscillatory behaviour。 [@leng2015oscope]. +HVG可用于基因集富集分析，以识别具有差异活动的生物途径和过程。可以使用专门为传统批量数据设计的`r Biocpkg("topGO")`包或者专门给单细胞设计的`r Biocpkg("scde")`包 [@fan2016characterizing]。 +这些分析的完整描述过程，超出了本工作流程的范围，因此建议感兴趣的用户查阅相关文档。 + +```{r, echo=FALSE, results='hide'} +gc() +``` + +All software packages used in this workflow are publicly available from the Comprehensive R Archive Network (https://cran.r-project.org) or the Bioconductor project (http://bioconductor.org). +The specific version numbers of the packages used are shown below, along with the version of the R installation. + +```{r} +sessionInfo() +``` + +# References + diff --git a/BiocWorkflow_todo/02simpleSingleCell_en/tenx.Rmd b/BiocWorkflow_todo/02simpleSingleCell_en/tenx.Rmd index 73c6838..62e193e 100644 --- a/BiocWorkflow_todo/02simpleSingleCell_en/tenx.Rmd +++ b/BiocWorkflow_todo/02simpleSingleCell_en/tenx.Rmd @@ -1,442 +1,439 @@ ---- -title: Analyzing single-cell RNA sequencing data from droplet-based protocols -author: -- name: Aaron T. L. Lun - affiliation: Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom -date: "`r Sys.Date()`" -vignette: > - %\VignetteIndexEntry{04. Droplet-based data} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} -output: - BiocStyle::html_document: - titlecaps: false - toc_float: true -bibliography: ref.bib ---- - -```{r, echo=FALSE, message=FALSE, results="hide", cache=FALSE} -library(BiocStyle) -library(knitr) -opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE, cache=TRUE) -opts_chunk$set(fig.asp=1) -``` - -# Overview - -Droplet-based scRNA-seq protocols capture cells in droplets for massively multiplexed library prepation [@klein2015droplet;@macosko2015highly]. -This greatly increases the throughput of scRNA-seq studies, allowing tens of thousands of individual cells to be profiled in a routine experiment. -However, it (again) involves some differences from the previous workflows to reflect some unique aspects of droplet-based data. - -Here, we describe a brief analysis of the peripheral blood mononuclear cell (PBMC) dataset from 10X Genomics [@zheng2017massively]. -The data are publicly available from the [10X Genomics website](https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k), -from which we download the raw gene/barcode count matrices, i.e., before cell calling from the _CellRanger_ pipeline. - -```{r} -library(BiocFileCache) -bfc <- BiocFileCache("raw_data", ask = FALSE) -raw.path <- bfcrpath(bfc, file.path("http://cf.10xgenomics.com/samples", - "cell-exp/2.1.0/pbmc4k/pbmc4k_raw_gene_bc_matrices.tar.gz")) -untar(raw.path, exdir=file.path(tempdir(), "pbmc4k")) -``` - -# Setting up the data - -## Reading in a sparse matrix - -We load in the raw count matrix using the `read10xCounts()` function from the `r Biocpkg("DropletUtils")` package. -This will create a `SingleCellExperiment` object where each column corresponds to a cell barcode. - -```{r} -library(DropletUtils) -fname <- file.path(tempdir(), "pbmc4k/raw_gene_bc_matrices/GRCh38") -sce <- read10xCounts(fname, col.names=TRUE) -sce -``` - -Here, each count represents the number of unique molecular identifiers (UMIs) assigned to a gene for a cell barcode. -Note that the counts are loaded as a sparse matrix object - specifically, a `dgCMatrix` instance from the `r CRANpkg("Matrix")` package. -This avoids allocating memory to hold zero counts, which is highly memory-efficient for low-coverage scRNA-seq data. - -```{r} -class(counts(sce)) -``` - -## Annotating the rows - -We relabel the rows with the gene symbols for easier reading. -This is done using the `uniquifyFeatureNames()` function, which ensures uniqueness in the case of duplicated or missing symbols. - -```{r} -library(scater) -rownames(sce) <- uniquifyFeatureNames(rowData(sce)$ID, rowData(sce)$Symbol) -head(rownames(sce)) -``` - -We also identify the chromosomal location for each gene. -The mitochondrial location is particularly useful for later quality control. - -```{r} -library(EnsDb.Hsapiens.v86) -location <- mapIds(EnsDb.Hsapiens.v86, keys=rowData(sce)$ID, - column="SEQNAME", keytype="GENEID") -rowData(sce)$CHR <- location -summary(location=="MT") -``` - -# Calling cells from empty droplets - -## Testing for deviations from ambient expression - -An interesting aspect of droplet-based data is that we have no prior knowledge about which droplets (i.e., cell barcodes) actually contain cells, and which are empty. -Thus, we need to call cells from empty droplets based on the observed expression profiles. -This is not entirely straightforward as empty droplets can contain ambient (i.e., extracellular) RNA that can be captured and sequenced. -The distribution of total counts exhibits a sharp transition between barcodes with large and small total counts (Figure \@ref(fig:rankplot)), -probably corresponding to cell-containing and empty droplets respectively. - -```{r rankplot, fig.cap="Total UMI count for each barcode in the PBMC dataset, plotted against its rank (in decreasing order of total counts). The inferred locations of the inflection and knee points are also shown."} -bcrank <- barcodeRanks(counts(sce)) - -# Only showing unique points for plotting speed. -uniq <- !duplicated(bcrank$rank) -plot(bcrank$rank[uniq], bcrank$total[uniq], log="xy", - xlab="Rank", ylab="Total UMI count", cex.lab=1.2) - -abline(h=metadata(bcrank)$inflection, col="darkgreen", lty=2) -abline(h=metadata(bcrank)$knee, col="dodgerblue", lty=2) - -legend("bottomleft", legend=c("Inflection", "Knee"), - col=c("darkgreen", "dodgerblue"), lty=2, cex=1.2) -``` - -We use the `emptyDrops()` function to test whether the expression profile for each cell barcode is significantly different from the ambient RNA pool [@lun2018distinguishing]. -Any significant deviation indicates that the barcode corresponds to a cell-containing droplet. -We call cells at a false discovery rate (FDR) of 0.1%, meaning that no more than 0.1% of our called barcodes should be empty droplets on average. - -```{r} -set.seed(100) -e.out <- emptyDrops(counts(sce)) -sum(e.out$FDR <= 0.001, na.rm=TRUE) -``` - -We then subset our `SingleCellExperiment` object to retain only the detected cells. - -```{r} -# using which() to automatically remove NAs. -sce <- sce[,which(e.out$FDR <= 0.001)] -``` - -**Comments from Aaron:** - -- `emptyDrops()` computes Monte Carlo $p$-values based on a Dirichlet-multinomial model of sampling molecules into droplets. -These $p$-values are stochastic so it is important to set the random seed to obtain reproducible results. -- The stability of the Monte Carlo $p$-values depends on the number of iterations used to compute them. -This is controlled using the `niters=` argument in `emptyDrops()`, set to a default of 10000 for speed. -Larger values improve stability at the cost of time only, so users should set `niters=` to the largest value they are willing to tolerate. -- The function assumes that cell barcodes with total UMI counts below a certain threshold (`lower=100` by default) correspond to empty droplets, -and uses them to estimate the ambient expression profile. -By definition, these barcodes cannot be cell-containing droplets and are excluded from the hypothesis testing, hence the `NA`s in the output. -- Users wanting to use the cell calling algorithm from the _CellRanger_ pipeline can call `defaultDrops()` instead. -This tends to be quite conservative as it often discards genuine cells with low RNA content (and thus low total counts). -It also requires an estimate of the expected number of cells in the experiment. - -## Examining cell-calling diagnostics - -The number of Monte Carlo iterations (specified by the `niters` argument in `emptyDrops()`) determines the lower bound for the _p_values [@phipson2010permutation]. -The `Limited` field in the output indicates whether or not the computed _p_-value for a particular barcode is bounded by the number of iterations. -If any non-significant barcodes are `TRUE` for `Limited`, we may need to increase the number of iterations. -A larger number of iterations will often result in a lower _p_-value for these barcodes, which may allow them to be detected after correcting for multiple testing. - -```{r} -table(Sig=e.out$FDR <= 0.01, Limited=e.out$Limited) -``` - -As mentioned above, `emptyDrops()` assumes that barcodes with low total UMI counts are empty droplets. -Thus, the null hypothesis should be true for all of these barcodes. -We can check whether the hypothesis test holds its size by examining the distribution of $p$-values for low-total barcodes. -Ideally, the distribution should be close to uniform. - -```{r ambientpvalhist, fig.cap="Distribution of $p$-values for the assumed empty droplets."} -full.data <- read10xCounts(fname, col.names=TRUE) -set.seed(100) -limit <- 100 -all.out <- emptyDrops(counts(full.data), lower=limit, test.ambient=TRUE) -hist(all.out$PValue[all.out$Total <= limit & all.out$Total > 0], - xlab="P-value", main="", col="grey80") -``` - -Large peaks near zero indicate that barcodes with total counts below `lower` are not all ambient in origin. -This can be resolved by decreasing `lower` further to exclude barcodes corresponding to droplets with very small cells. - -# Quality control on the cells - -The previous step only distinguishes cells from empty droplets, but makes no statement about the quality of the cells. -It is entirely possible for droplets to contain damaged or dying cells, which need to be removed prior to downstream analysis. -We compute some QC metrics using `calculateQCMetrics()` [@mccarthy2017scater] and examine their distributions in Figure \@ref(fig:qchist). - -```{r qchist, fig.width=10, fig.asp=0.5, fig.cap="Histograms of QC metric distributions in the PBMC dataset."} -sce <- calculateQCMetrics(sce, feature_controls=list(Mito=which(location=="MT"))) -par(mfrow=c(1,3)) -hist(sce$log10_total_counts, breaks=20, col="grey80", - xlab="Log-total UMI count") -hist(sce$log10_total_features_by_counts, breaks=20, col="grey80", - xlab="Log-total number of expressed features") -hist(sce$pct_counts_Mito, breaks=20, col="grey80", - xlab="Proportion of reads in mitochondrial genes") -``` - -Ideally, we would remove cells with low library sizes or total number of expressed features as described `r simpleSingleCell:::.link("reads", "Quality control on the cells", "previously")`. -However, this would likely remove cell types with low RNA content, especially in a heterogeneous PBMC population with many different cell types. -Thus, we use a more relaxed strategy and only remove cells with large mitochondrial proportions, using it as a proxy for cell damage. -(Keep in mind that droplet-based datasets usually do not have spike-in RNA.) - -```{r} -high.mito <- isOutlier(sce$pct_counts_Mito, nmads=3, type="higher") -sce <- sce[,!high.mito] -summary(high.mito) -``` - -**Comments from Aaron:** - -- The above justification for using a more relaxed filter is largely retrospective. -In practice, we may not know _a priori_ the degree of population heterogeneity and whether it manifests in the QC metrics. -We recommend performing an initial analysis with some QC, and then relaxing the filter (or making it more stringent) based on further diagnostics. -See `r simpleSingleCell:::.link("qc", "Checking for discarded cell types", "here")` for an example of a potential diagnostic approach. -- Removal of cells with high mitochondrial content assumes that the damage to the cell membrane is modest enough that the mitochondria are retained. -It is possible for the cells to be so damaged that all cytoplasmic content is lost and only the stripped nucleus remains for sequencing. -This manifests as mitochondrial proportions of zero, usually accompanied by low library sizes/numbers of expressed genes and possibly low ribosomal protein gene expression. -If a cluster of such cells is observed in the data, they can be removed by using `isOutlier()` on the mitochondrial proportions with `type="lower"` and `log=TRUE` -(to improve resolution around zero). - -# Examining gene expression - -The average expression of each gene is much lower here compared to the previous datasets (Figure \@ref(fig:abhist)). -This is due to the reduced coverage per cell when thousands of cells are multiplexed together for sequencing. - -```{r abhist, fig.cap="Histogram of the log~10~-average counts for each gene in the PBMC dataset."} -ave <- calcAverage(sce) -rowData(sce)$AveCount <- ave -hist(log10(ave), col="grey80") -``` - -The set of most highly expressed genes is dominated by ribosomal protein and mitochondrial genes (Figure \@ref(fig:highexpr)), as expected. - -```{r highexpr, fig.wide=TRUE, fig.asp=1.5, fig.cap="Percentage of total counts assigned to the top 50 most highly-abundant features in the PBMC dataset. For each feature, each bar represents the percentage assigned to that feature for a single cell, while the circle represents the average across all cells. Bars are coloured by the total number of expressed features in each cell."} -plotHighestExprs(sce) -``` - -# Normalizing for cell-specific biases - -We perform some pre-clustering as described `r simpleSingleCell:::.link("umis", "Normalization of cell-specific biases", label="previously")`. -Recall that we need to set the seed when using `BSPARAM=IrlbaParam()` due to the randomness of `r CRANpkg("irlba")`. - -```{r} -library(scran) -library(BiocSingular) -set.seed(1000) -clusters <- quickCluster(sce, use.ranks=FALSE, BSPARAM=IrlbaParam()) -table(clusters) -``` - -We apply the deconvolution method to compute size factors for all cells [@lun2016pooling]. -Again, we use `min.mean=0.1` to account for the fact that UMI counts are lower. -The specification of `cluster=` also ensures that we do not pool cells that are very different. - -```{r} -sce <- computeSumFactors(sce, min.mean=0.1, cluster=clusters) -summary(sizeFactors(sce)) -``` - -The size factors are well correlated against the library sizes (Figure \@ref(fig:sfplot)), indicating that capture efficiency and sequencing depth are the major biases. - -```{r sfplot, fig.cap="Size factors for all cells in the PBMC dataset, plotted against the library size."} -plot(sce$total_counts, sizeFactors(sce), log="xy") -``` - -Finally, we compute normalized log-expression values. -There is no need to call `computeSpikeFactors()` here, as there are no spike-in transcripts available. - -```{r} -sce <- normalize(sce) -``` - -**Comments from Aaron:** - -- Larger droplet-based datasets will often be generated in separate batches or runs. -In such cases, we can set `block=` in `quickCluster()` to cluster cells within each batch or run. -This reduces computational work considerably without compromising performance, provided that there are enough cells within each batch. -- Even in the absence of any known batch structure, we can improve speed by setting an arbitrary factor, e.g., using `block=cut(seq_len(ncol(sce)), 10)` to split the cells into ten "batches" of roughly equal size. -Recall that we are not interpreting the clusters themselves, so it is not a problem to have multiple redundant cluster labels. -Again, this assumes that each cluster is large enough to support deconvolution. -- On a similar note, both `quickCluster()` and `computeSumFactors()` can process blocks or clusters in parallel. -This is achieved using the `r Biocpkg("BiocParallel")` framework, which provides support for a range of parallelization strategies. - -# Modelling the mean-variance trend - -The lack of spike-in transcripts complicates the modelling of the technical noise. -One option is to assume that most genes do not exhibit strong biological variation, and to fit a trend to the variances of endogenous genes -(see `r simpleSingleCell:::.link("var", "When spike-ins are unavailable", "here")` for details). -However, this assumption is generally unreasonable for a heterogeneous population. -Instead, we assume that the technical noise is Poisson and create a fitted trend on that basis using the `makeTechTrend()` function. - -```{r} -new.trend <- makeTechTrend(x=sce) -``` - -We estimate the variances for all genes and compare the trend fits in Figure \@ref(fig:trendplot). -The Poisson-based trend serves as a lower bound for the variances of the endogenous genes. -This results in non-zero biological components for most genes, which is consistent with other UMI-based data sets -(see the `r simpleSingleCell:::.link("umis", "Modelling and removing technical_noise", "corresponding analysis")` of the @zeisel2015brain data set). - -```{r trendplot, fig.cap="Variance of normalized log-expression values for each gene in the PBMC dataset, plotted against the mean log-expression. The blue line represents the mean-dependent trend fitted to the variances, while the red line represents the Poisson noise."} -fit <- trendVar(sce, use.spikes=FALSE, loess.args=list(span=0.05)) -plot(fit$mean, fit$var, pch=16) -curve(fit$trend(x), col="dodgerblue", add=TRUE) -curve(new.trend(x), col="red", add=TRUE) -``` - -We decompose the variance for each gene using the Poisson-based trend, and examine the genes with the highest biological components. - -```{r} -fit$trend <- new.trend # overwrite trend. -dec <- decomposeVar(fit=fit) # use per-gene variance estimates in 'fit'. -top.dec <- dec[order(dec$bio, decreasing=TRUE),] -head(top.dec) -``` - -We can plot the genes with the largest biological components, to verify that they are indeed highly variable (Figure \@ref(fig:hvgplot)). - -```{r hvgplot, fig.wide=TRUE, fig.cap="Distributions of normalized log-expression values for the top 10 genes with the largest biological components in the PBMC dataset. Each point represents the log-expression value in a single cell."} -plotExpression(sce, features=rownames(top.dec)[1:10]) -``` - -**Comments from Aaron:** - -- The Poisson-based trend from `makeTechTrend()` tends to yield large biological components for highly-expressed genes for which Poisson noise is low (in the log-expression space). -This often includes so-called "house-keeping" genes coding for essential cellular components such as ribosomal proteins. -These genes are often considered uninteresting for characterizing cellular heterogeneity, -though this is debatable as they are often differentially expressed in a variety of conditions [@glare2002betaactin;@nazari2015gapdh;@guimaraes2016patterns]. -Indeed, the fact that they have large biological components indicates that there is strong variation in their expression across cells, which warrants some further investigation. -Nonetheless, if they are deemed to be uninteresting, their impact can be reduced by fitting the mean-variance trend to the endogenous genes. - -# Dimensionality reduction - -We use the `denoisePCA()` function with the assumed Poisson technical trend to choose the number of dimensions to retain after PCA. -Recall that this involves a random initialization when `BSPARAM=IrlbaParam()`, which motivates the call to `set.seed()` to obtain reproducible results. - -```{r} -set.seed(1000) -sce <- denoisePCA(sce, technical=new.trend, BSPARAM=IrlbaParam()) -ncol(reducedDim(sce, "PCA")) -``` - -```{r screeplot, fig.cap="Variance explained by each principal component in the PBMC dataset. The red line represents the chosen number of PCs."} -plot(attr(reducedDim(sce), "percentVar"), xlab="PC", - ylab="Proportion of variance explained") -abline(v=ncol(reducedDim(sce, "PCA")), lty=2, col="red") -``` - -Examination of the first few PCs already reveals some strong substructure in the data (Figure \@ref(fig:pcaplot-init)). - -```{r pcaplot-init, fig.cap="Pairwise PCA plots of the first three PCs in the PBMC dataset, constructed from normalized log-expression values of genes with positive biological components. Each point represents a cell, coloured by the log-number of expressed features.", fig.width=9} -plotPCA(sce, ncomponents=3, colour_by="log10_total_features_by_counts") -``` - -This is recapitulated with a _t_-SNE plot (Figure \@ref(fig:tsneplot-init)). -Again, note that we set `use_dimred=` to perform _t_-SNE on the denoised expression matrix. - -```{r tsneplot-init, fig.cap="_t_-SNE plots constructed from the denoised PCs of the PBMC dataset. Each point represents a cell and is coloured according to the log-number of expressed features."} -set.seed(1000) -sce <- runTSNE(sce, use_dimred="PCA", perplexity=30) -plotTSNE(sce, colour_by="log10_total_features_by_counts") -``` - -# Clustering with graph-based methods - -We build a shared nearest neighbour graph [@xu2015identification] and use the Walktrap algorithm to identify clusters. - -```{r} -snn.gr <- buildSNNGraph(sce, use.dimred="PCA") -clusters <- igraph::cluster_walktrap(snn.gr) -sce$Cluster <- factor(clusters$membership) -table(sce$Cluster) -``` - -We look at the ratio of the observed and expected edge weights to confirm that the clusters are modular. -(We don't look at the modularity score itself, as that varies by orders of magnitudes across clusters and is difficult to interpret.) -Figure \@ref(fig:clustermod) indicates that most of the clusters are well seperated, with few strong off-diagonal entries. - -```{r clustermod, fig.cap="Heatmap of the log~10~-ratio of the total weight between nodes in the same cluster or in different clusters, relative to the total weight expected under a null model of random links."} -cluster.mod <- clusterModularity(snn.gr, sce$Cluster, get.values=TRUE) -log.ratio <- log2(cluster.mod$observed/cluster.mod$expected + 1) - -library(pheatmap) -pheatmap(log.ratio, cluster_rows=FALSE, cluster_cols=FALSE, - color=colorRampPalette(c("white", "blue"))(100)) -``` - -We examine the cluster identities on a _t_-SNE plot (Figure \@ref(fig:tsneplot-cluster)) to confirm that different clusters are indeed separated. - -```{r tsneplot-cluster, fig.cap="_t_-SNE plots constructed from the denoised PCs of the PBMC dataset. Each point represents a cell and is coloured according to its cluster identity."} -plotTSNE(sce, colour_by="Cluster") -``` - -# Marker gene detection - -```{r, echo=FALSE, results="hide"} -# Hidden variables for use in text or hidden chunks, -# to avoid the need for manual changes. -chosen.platelets <- 9 -``` - -We detect marker genes for each cluster using `findMarkers()`. -Again, we only look at upregulated genes in each cluster, as these are more useful for positive identification of cell types in a heterogeneous population. - -```{r} -markers <- findMarkers(sce, clusters=sce$Cluster, direction="up") -``` - -We examine the markers for cluster `r chosen.platelets` in more detail. -The upregulation of genes such as _PF4_ and _PPBP_ suggests that this cluster contains platelets or their precursors. - -```{r} -marker.set <- markers[["9"]] -head(marker.set[,1:8], 10) # only first 8 columns, for brevity -``` - -```{r, echo=FALSE, results="hide"} -# Checking 'marker.set' and 'chosen.platelets' are consistent. -stopifnot(identical(marker.set, markers[[chosen.platelets]])) - -# Checking the cluster is what we wanted. -pf4 <- sapply(marker.set["PF4",-(1:3)], sign) -stopifnot(all(pf4==1)) -``` - -This is confirmed in Figure \@ref(fig:heatmap), where the transcriptional profile of cluster `r chosen.platelets` is clearly distinct from the others. - -```{r heatmap, fig.wide=TRUE, fig.cap=sprintf("Heatmap of mean-centred and normalized log-expression values for the top set of markers for cluster %s in the PBMC dataset. Column colours represent the cluster to which each cell is assigned, as indicated by the legend.", chosen.platelets)} -chosen <- rownames(marker.set)[marker.set$Top <= 10] -plotHeatmap(sce, features=chosen, exprs_values="logcounts", - zlim=5, center=TRUE, symmetric=TRUE, cluster_cols=FALSE, - colour_columns_by="Cluster", columns=order(sce$Cluster), - show_colnames=FALSE) -``` - -# Concluding remarks - -Having completed the basic analysis, we save the `SingleCellExperiment` object with its associated data to file. -This avoids having to repeat all of the pre-processing steps described above prior to further analyses. - -```{r} -saveRDS(sce, file="pbmc_data.rds") -``` - -All software packages used in this workflow are publicly available from the Comprehensive R Archive Network (https://cran.r-project.org) or the Bioconductor project (http://bioconductor.org). -The specific version numbers of the packages used are shown below, along with the version of the R installation. - -```{r} -sessionInfo() -``` - -# References +--- +title: 从基于液滴的实验方法来分析单细胞RNA测序数据 +author: +- name: Aaron T. L. Lun + affiliation: Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom +date: "`r Sys.Date()`" +vignette: > + %\VignetteIndexEntry{04. Droplet-based data} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +output: + BiocStyle::html_document: + titlecaps: false + toc_float: true +#bibliography: ref.bib +--- + +```{r, echo=FALSE, message=FALSE, results="hide", cache=FALSE} +library(BiocStyle) +library(knitr) +opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE, cache=TRUE) +opts_chunk$set(fig.asp=1) +``` + +# 概述 + +基于液滴的scRNA-seq实验方法来捕获液滴中的细胞可用于大规模多重文库制备[@klein2015droplet;@macosko2015highly]。 +这大大增加了scRNA-seq研究的通量，允许在常规实验中分析数万个单个细胞。 +但是，它（再次）与先前工作流程存在一些差异，以反映基于液滴的数据的一些独特方面。 + +在这里，我们描述了来自10X Genomics的外周血单核细胞（PBMC）数据集进行简要分析 [@zheng2017massively]。 +这些数据可从[10X Genomics网站](https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k)公开获得, +我们从中下载原始基因/条形码计数矩阵，例如 _CellRanger_ 流程中细胞calling之前。 + +```{r} +library(BiocFileCache) +bfc <- BiocFileCache("raw_data", ask = FALSE) +raw.path <- bfcrpath(bfc, file.path("http://cf.10xgenomics.com/samples", + "cell-exp/2.1.0/pbmc4k/pbmc4k_raw_gene_bc_matrices.tar.gz")) +untar(raw.path, exdir=file.path(tempdir(), "pbmc4k")) +``` + +# 数据准备 + +## 在稀疏的矩阵中读取计数 + +我们使用`r Biocpkg("DropletUtils")` 包中的 `read10xCounts()` 函数加载原始计数矩阵。 +这将创建一个`SingleCellExperiment`其中每列对应一个细胞的条形码。 + +```{r} +library(DropletUtils) +fname <- file.path(tempdir(), "pbmc4k/raw_gene_bc_matrices/GRCh38") +sce <- read10xCounts(fname, col.names=TRUE) +sce +``` + +这里，每个计数表示分配给细胞条形码的基因的独特分子标识符（UMI）的数量。 +请注意这里的计数是以稀疏矩阵的形式加载的，例如`r CRANpkg("Matrix")`中的`dgCMatrix`就是其中一个例子。 +这样避免了需要分配内存去保持零计数，这样对于低覆盖率的scRNA-seq来说是非常节省内存的。 + +```{r} +class(counts(sce)) +``` + +## 注释行 + +我们将行重新标记为基因符以方便计数 +这里用的是 `uniquifyFeatureNames()`函数,该函数确保在重复或缺失符号的情况下的唯一性。 + +```{r} +library(scater) +rownames(sce) <- uniquifyFeatureNames(rowData(sce)$ID, rowData(sce)$Symbol) +head(rownames(sce)) +``` + +我们还确定每个基因的染色体位置。 +线粒体位置对于后期质控特别有用。 + +```{r} +library(EnsDb.Hsapiens.v86) +location <- mapIds(EnsDb.Hsapiens.v86, keys=rowData(sce)$ID, + column="SEQNAME", keytype="GENEID") +rowData(sce)$CHR <- location +summary(location=="MT") +``` + +# 从空水滴中调用细胞 + +## 测试与外界表达的偏差 + +基于液滴的数据的一个有趣方面是我们没有先验知道哪些液滴（即细胞条形码）实际上包含细胞，哪些是空的。 +因此，我们需要根据观察到的表达变化谱从空液滴中调用细胞。 +这不是完全简单的，因为空液滴可以包含可以捕获和测序外界（即细胞外）的RNA。 +总计数的分布表现出具有大和小总计数的条形码之间的急剧转变 (Figure \@ref(fig:rankplot)), +可能分别对应于含细胞和空液滴。 + +```{r rankplot, fig.cap="PBMC数据集中每个条形码的总UMI计数，根据其排名绘制（按总计数的降序排列）。还示出了拐点和拐点的推断位置。"} +bcrank <- barcodeRanks(counts(sce)) + +# Only showing unique points for plotting speed. +uniq <- !duplicated(bcrank$rank) +plot(bcrank$rank[uniq], bcrank$total[uniq], log="xy", + xlab="Rank", ylab="Total UMI count", cex.lab=1.2) + +abline(h=metadata(bcrank)$inflection, col="darkgreen", lty=2) +abline(h=metadata(bcrank)$knee, col="dodgerblue", lty=2) + +legend("bottomleft", legend=c("Inflection", "Knee"), + col=c("darkgreen", "dodgerblue"), lty=2, cex=1.2) +``` + +我们使用该 `emptyDrops()` 函数 来测试每个细胞条形码的表达变化谱是否与外境RNA库显着不同[@lun2018distinguishing]。 +任何显着偏差都表明条形码对应于含有细胞的液滴。 +我们称细胞的错误发现率（FDR）为0.1％，这意味着平均来说不超过0.1%调用的条形码是空液滴。 + +```{r} +set.seed(100) +e.out <- emptyDrops(counts(sce)) +sum(e.out$FDR <= 0.001, na.rm=TRUE) +``` + +然后我们将`SingleCellExperiment` 对象子集化以仅保留检测到的细胞。 + +```{r} +# using which() to automatically remove NAs. +sce <- sce[,which(e.out$FDR <= 0.001)] +``` + +**Aaron的注释:** + +- 使用`emptyDrops()`来计算Monte Carlo $p$-值 基于分子取样成液滴的Dirichlet-多项式模型。 +这些 $p$-值是随机的，所以设置随机种子以获得可重复的结果非常重要。 +- Monte Carlo $p$-值得稳定性取决于用于计算它们的迭代次数。 +这是使用 `emptyDrops()`中的 `niters=` 选项，将默认的速度设置为10000。 +较大的值能够提升稳定性只是要付出时间的代价，所以用户可以根据自己能承受的时间来设置 `niters=`的最大值。 +- 该函数假设具有总UMI计数并低于某个阀值(`lower=100`默认情况下)的细胞条码对应于空液滴。 +并使用它们来估计外界表达变化谱。 +根据定义，这些条形码不能是含有细胞的液滴，而是从假设检验中排除，因此以`NA`s在输出中表示。 +- 用户想使用 _CellRanger_ 流程中细胞calling的算法可以使用`defaultDrops()` 。 +这往往是非常保守的，因为它经常丢弃具有低RNA含量的真正细胞（总计数低） +它还需要估计实验中预期的细胞数。 + +## cell-calling的诊断 + +蒙特卡罗迭代次数 (由`emptyDrops()`中的`niters` 参数指定 ) 决定了 _p_valuesd的下限 [@phipson2010permutation]。 +输出中`Limited`字段表明了特定条形码的计算的p值是否受迭代次数的限制。 +如果任何不重要的条形码在`Limited`显示`TRUE`,我们可能需要增加迭代次数。 +更大数量的迭代通常会导致这些条形码的p值降低，这可能允许在校正多次测试后检测它们。 + +```{r} +table(Sig=e.out$FDR <= 0.01, Limited=e.out$Limited) +``` + +如上所述, `emptyDrops()`假设具有低的总UMI计数的条形码是空的液滴。 +因此，对于所有这些条形码，初步假设应该是正确的。 +我们可以通过检查低的总条形码值得分布来检测假设试验是不是保持其大小一致。 +理想情况下，分布应接近均匀。 + +```{r ambientpvalhist, fig.cap="假定空滴液的$p$-values分布。"} +full.data <- read10xCounts(fname, col.names=TRUE) +set.seed(100) +limit <- 100 +all.out <- emptyDrops(counts(full.data), lower=limit, test.ambient=TRUE) +hist(all.out$PValue[all.out$Total <= limit & all.out$Total > 0], + xlab="P-value", main="", col="grey80") +``` + +接近零的大峰值表明具有低于`lower`的值的条形码的总数并非全部来自外部环境。 +这可以通过进一步减少 `lower`的值去进一步排除条形码对应于具有非常小的细胞的液滴。 + +# 细胞的质控 + +前一步骤仅区从空液滴中区分了细胞，但没有说明细胞的质量。 +液滴完全可能含有受损或垂死的细胞，需要在下游分析之前将其除去。 +我们使用`calculateQCMetrics()`来计算一些质控(QC)的指标 [@mccarthy2017scater]并检查它们在图 \@ref(fig:qchist)中的分布。 + +```{r qchist, fig.width=10, fig.asp=0.5, fig.cap="PBMC数据集中QC指标分布的直方图。"} +sce <- calculateQCMetrics(sce, feature_controls=list(Mito=which(location=="MT"))) +par(mfrow=c(1,3)) +hist(sce$log10_total_counts, breaks=20, col="grey80", + xlab="Log-total UMI count") +hist(sce$log10_total_features_by_counts, breaks=20, col="grey80", + xlab="Log-total number of expressed features") +hist(sce$pct_counts_Mito, breaks=20, col="grey80", + xlab="Proportion of reads in mitochondrial genes") +``` + +理想情况下，我们会删除具有小的文库或低的表达特征总数的细胞，如`r simpleSingleCell:::.link("reads", "Quality control on the cells", "之前所述")`。 +然而，这可能会去除具有低RNA含量的细胞类型，尤其是在含有许多不同细胞类型的异质PBMC群体中。 +因此，我们使用更宽松的策略，只去除具有大线粒体比例的细胞，使用它作为细胞损伤的标识。 +(请记住，基于液滴的数据集通常没有加标RNA。) + +```{r} +high.mito <- isOutlier(sce$pct_counts_Mito, nmads=3, type="higher") +sce <- sce[,!high.mito] +summary(high.mito) +``` + +**Aaron的注释:** + +- 使用更宽松的过滤方法往往很大程度上是可追溯的。 +在实践中，我们可能不会预先地知道群体异质性的程度和它是否出现在QC的指标中。 +我们建议使用某些QC进行初始的分析，然后根据进一步的判断来选择是否放宽过滤的标准(或使其更加严格）。 +请查阅 `r simpleSingleCell:::.link("qc", "Checking for discarded cell types", "此处")` 以获取潜在判断方法的示例。 +- 去除含有大量线粒体的细胞，基于假定对细胞膜的损伤是适度，以保留线粒体。 +细胞可能受到非常大的破坏，导致所有细胞质内容物丢失，只有剥离的细胞核保留用于测序。 +这表现为线粒体比例为零，通常伴随着低文库大小/表达基因数量和可能低核糖体蛋白基因表达。 +如果在数据中观察到这样的细胞聚类，他们可以通过对线粒体比例`type="lower"` 和 `log=TRUE`使用`isOutlier()`去除去它们。（以提高零附近的分辨率） + +# 检查基因的表达 + +与之前的数据相比，这里每个基因的平均表达量要低得多 (Figure \@ref(fig:abhist))。 +这是因为当将数千个细胞多路复用在一起进行测序时每个细胞的覆盖率会降低。 + +```{r abhist, fig.cap="PBMC数据集中每个基因的log 10-平均值的直方图"} +ave <- calcAverage(sce) +rowData(sce)$AveCount <- ave +hist(log10(ave), col="grey80") +``` + +如预期那样，最高表达基因的集合由核糖体蛋白和线粒体基因控制(Figure \@ref(fig:highexpr))。 + +```{r highexpr, fig.wide=TRUE, fig.asp=1.5, fig.cap="分配给PBMC数据集中前50个最丰富的功能的总计数百分比。对于每个要素，每个条形表示为单个细胞分配给该要素的百分比，而圆圈表示所有细胞的平均值。条形由每个细胞中表达的特征的总数着色。"} +plotHighestExprs(sce) +``` + +# 针对细胞特异性差进行标准化 + +像之前描述那样，我们执行了一些预聚类`r simpleSingleCell:::.link("umis", "Normalization of cell-specific biases", label="previously")`。 +我们需要设置种子当我们使用`BSPARAM=IrlbaParam()` 由于`r CRANpkg("irlba")`的随机性。 + +```{r} +library(scran) +library(BiocSingular) +set.seed(1000) +clusters <- quickCluster(sce, use.ranks=FALSE, BSPARAM=IrlbaParam()) +table(clusters) +``` + +我们应用反卷积方法来计算所有细胞的size factors [@lun2016pooling]。 +同样，我们`min.mean=0.1`用来解释UMI计数较低的现象。 +具体化`cluster=` 还确保我们不会汇集非常不同的细胞。 + +```{r} +sce <- computeSumFactors(sce, min.mean=0.1, cluster=clusters) +summary(sizeFactors(sce)) +``` + +Size factors 与文库大小密切相关 (Figure \@ref(fig:sfplot)),表明捕获效率和测序深度是引起偏差的主要因素。 + +```{r sfplot, fig.cap="PBMC数据集中所有细胞的size factors，根据文库大小绘制。"} +plot(sce$total_counts, sizeFactors(sce), log="xy") +``` + +最后，我们计算规一化的对数表达量值。 +这里没有必要调用 `computeSpikeFactors()`, 因为加标准转录物是没有的。 + +```{r} +sce <- normalize(sce) +``` + +**Aaron的注释:** + +- 基于液滴的较大数据集通常会在不同的批次或运行中生成。 +在这种情况下，我们可以设置`quickCluster()`中的 `block=`，在每个批次或者每次运行中去聚类细胞。 +如果每批中有足够的细胞，这可以在不影响性能的情况下明显地减少计算量。 +- 即使在没有任何已知批量结构的情况下，我们也可以通过设置任意因子来提高速度，例如使用 `block=cut(seq_len(ncol(sce)), 10)`将细胞分成大小相等的十个“批次”。 +记住我们不是为了解析聚类，所以使用多个冗余聚类群标签是没问题的。 +同样，这假设每个聚类足够大以支持反卷积。 +- 在一个类似的说明中, `quickCluster()`和`computeSumFactors()` 都可以并行处理块或聚类。 +这是使用`r Biocpkg("BiocParallel")`框架实现的, 该框架为一系列并行化策略提供帮助。 + +# 模拟均值-差异趋势 + +缺少加标转录物使得技术噪声的建模变得复杂。 +一种选择是假设大多数基因没有表现出强烈的生物学变异，并且适应内源基因变异的趋势，详见 +(see `r simpleSingleCell:::.link("var", "When spike-ins are unavailable", "此处")`。 +然而，对于异质群体而言，这种假设通常是不合理的。 +相反，我们假设技术噪声是遵循泊松的，并使用该`makeTechTrend()`z哎此基础上创建拟合趋势。 + +```{r} +new.trend <- makeTechTrend(x=sce) +``` + +我们估计所有基因的差异，并比较图 \@ref(fig:trendplot)的趋势拟合度。 +基于泊松的趋势作为内源基因变异的下限。 +这能让我们测量许多细胞中非零的生物成分, 这是与其它基于UMI-数据集相一致 +(参见 `r simpleSingleCell:::.link("umis", "Modelling and removing technical_noise", "相对应的分析")` 的@zeisel2015brain 数据集)。 + +```{r trendplot, fig.cap="PBMC数据集中每个基因的归一化对数表达值的方差，相对于平均对数表达作图。蓝线表示与方差拟合的平均相关趋势，而红线表示泊松噪声。"} +fit <- trendVar(sce, use.spikes=FALSE, loess.args=list(span=0.05)) +plot(fit$mean, fit$var, pch=16) +curve(fit$trend(x), col="dodgerblue", add=TRUE) +curve(new.trend(x), col="red", add=TRUE) +``` + +我们使用基于Poisson的趋势分解每个基因的差异，并检查具有最高生物成分的基因。 + +```{r} +fit$trend <- new.trend # overwrite trend. +dec <- decomposeVar(fit=fit) # use per-gene variance estimates in 'fit'. +top.dec <- dec[order(dec$bio, decreasing=TRUE),] +head(top.dec) +``` + +我们可以绘制具有最大生物成分的基因，以验证它们确实是高度可变的 (Figure \@ref(fig:hvgplot))。 + +```{r hvgplot, fig.wide=TRUE, fig.cap="PBMC数据集中具有最大生物组分的前10个基因的归一化对数表达量的分布。每个点代表单个细胞中的对数表达量的值。"} +plotExpression(sce, features=rownames(top.dec)[1:10]) +``` + +**Aaron的注释:** + +- 基于泊松的趋势`makeTechTrend()`倾向产生具有高表达量的基因大的生物成分通常其对应的泊松噪声是比较低的（在对数表达空间中）。 +这通常包括编码必需细胞组分（如核糖体蛋白）的所谓“管家”基因。 +这些基因通常被认为对表征细胞异质性不产生影响，尽管这是有争议的，因为它们通常在各种条件下差异表达[@glare2002betaactin;@nazari2015gapdh;@guimaraes2016patterns]。 +事实上，它们具有大量生物成分的事实表明它们在细胞中的表达存在很大差异，这需要进一步研究。 +尽管如此，如果它们被认为是不重要的，那么通过将均值差异的趋势拟合到内源基因，可以减少它们的影响。 + +# 降维 + +我们使用 `denoisePCA()` 功能基于假设的泊松技术趋势来选择PCA后需要保留下来的维度。 +这涉及随机初始化当 `BSPARAM=IrlbaParam()`, 这会促使调用`set.seed()`去获取可重复的结果。 + +```{r} +set.seed(1000) +sce <- denoisePCA(sce, technical=new.trend, BSPARAM=IrlbaParam()) +ncol(reducedDim(sce, "PCA")) +``` + +```{r screeplot, fig.cap="PBMC数据集中每个主成分解释的差异。红线表示所选的PC数量。"} +plot(attr(reducedDim(sce), "percentVar"), xlab="PC", + ylab="Proportion of variance explained") +abline(v=ncol(reducedDim(sce, "PCA")), lty=2, col="red") +``` + +查看前几个PCs已经能够揭示数据中含有强烈的子结构 (Figure \@ref(fig:pcaplot-init))。 + +```{r pcaplot-init, fig.cap="PBMC数据集中前三个PC的成对PCA图，由具有阳性生物成分的基因的标准化对数表达值构建。每个点代表一个细胞，由表达特征的对数值表示。", fig.width=9} +plotPCA(sce, ncomponents=3, colour_by="log10_total_features_by_counts") +``` + +这用 _t_-SNE 图重现 (Figure \@ref(fig:tsneplot-init)). +再次注意，我们设置`use_dimred=`去执行 _t_-SNE 计算在去噪表达式矩阵中. + +```{r tsneplot-init, fig.cap="_从PBMC数据集的去噪PC构建的t- SA图。每个点代表一个细胞，并根据表达的特征的对数值来着色。"} +set.seed(1000) +sce <- runTSNE(sce, use_dimred="PCA", perplexity=30) +plotTSNE(sce, colour_by="log10_total_features_by_counts") +``` + +# 使用基于图形的方法进行聚类 + +我们构建了一个共享的最近邻图[@xu2015identification]并使用Walktrap算法来识别聚类。 + +```{r} +snn.gr <- buildSNNGraph(sce, use.dimred="PCA") +clusters <- igraph::cluster_walktrap(snn.gr) +sce$Cluster <- factor(clusters$membership) +table(sce$Cluster) +``` + +我们查看观察到的和预期的边缘权重的比率，以确认聚类是模块化的。 +（我们不会看模块性其本身的得分，因为它会因聚类之间的数量级而变化，并且难以解释。） +图 \@ref(fig:clustermod) 表明大多数聚类分开得很好，几乎没有强烈的非对角线聚类。 +```{r clustermod, fig.cap="相对于在随机链接的空模型下预期的总权重，相同聚类或不同聚类中的节点之间的总权重的log 10的比率。"} +cluster.mod <- clusterModularity(snn.gr, sce$Cluster, get.values=TRUE) +log.ratio <- log2(cluster.mod$observed/cluster.mod$expected + 1) + +library(pheatmap) +pheatmap(log.ratio, cluster_rows=FALSE, cluster_cols=FALSE, + color=colorRampPalette(c("white", "blue"))(100)) +``` + +我们对 _t_-SNE 图上的聚类标识进行了检验， (Figure \@ref(fig:tsneplot-cluster))以确认不同的聚类确实是分开的。 + +```{r tsneplot-cluster, fig.cap="从PBMC数据集的去噪PC构建的t- SEL图。每个点代表一个细胞，并根据其聚类标识进行着色。"} +plotTSNE(sce, colour_by="Cluster") +``` + +# 标记基因的检测 + +```{r, echo=FALSE, results="hide"} +# Hidden variables for use in text or hidden chunks, +# to avoid the need for manual changes. +chosen.platelets <- 9 +``` + +我们使用`findMarkers()`来检测每个聚类中标记基因的数量。 +同样，我们只关注每个聚类中的上调基因，因为这些基因对于异质群体中细胞类型的阳性鉴定会起到更好的作用。 + +```{r} +markers <- findMarkers(sce, clusters=sce$Cluster, direction="up") +``` + +我们更详细地检查了聚类 `r chosen.platelets`中的标记基因。 +_PF4_ 和 _PPBP_等基因的上调表明该聚类含有血小板或其前体。 + +```{r} +marker.set <- markers[["9"]] +head(marker.set[,1:8], 10) # only first 8 columns, for brevity +``` + +```{r, echo=FALSE, results="hide"} +# Checking 'marker.set' and 'chosen.platelets' are consistent. +stopifnot(identical(marker.set, markers[[chosen.platelets]])) + +# Checking the cluster is what we wanted. +pf4 <- sapply(marker.set["PF4",-(1:3)], sign) +stopifnot(all(pf4==1)) +``` + +这在图 \@ref(fig:heatmap), 得到证实，其中聚类 `r chosen.platelets`的转录变化谱明显不同于其他聚类。 + +```{r heatmap, fig.wide=TRUE, fig.cap=sprintf("PBMC数据集中聚类`r chosen.platelets`的顶部标记集的平均中心和标准化对数表达值的热图。列颜色表示每个细胞分配到的聚类，如图例所示。", chosen.platelets)} +chosen <- rownames(marker.set)[marker.set$Top <= 10] +plotHeatmap(sce, features=chosen, exprs_values="logcounts", + zlim=5, center=TRUE, symmetric=TRUE, cluster_cols=FALSE, + colour_columns_by="Cluster", columns=order(sce$Cluster), + show_colnames=FALSE) +``` + +# 结束语 + +完成基本分析后，我们将`SingleCellExperiment` 对象及其关联数据保存到文件中。 +这避免了在进一步分析之前必须重复上述所有预处理步骤。 + +```{r} +saveRDS(sce, file="pbmc_data.rds") +``` + +All software packages used in this workflow are publicly available from the Comprehensive R Archive Network (https://cran.r-project.org) or the Bioconductor project (http://bioconductor.org). +The specific version numbers of the packages used are shown below, along with the version of the R installation. + +```{r} +sessionInfo() +``` + +# References diff --git a/BiocWorkflow_todo/02simpleSingleCell_en/umis.Rmd b/BiocWorkflow_todo/02simpleSingleCell_en/umis.Rmd index cb7d4dc..b65c726 100644 --- a/BiocWorkflow_todo/02simpleSingleCell_en/umis.Rmd +++ b/BiocWorkflow_todo/02simpleSingleCell_en/umis.Rmd @@ -1,597 +1,596 @@ ---- -title: Analyzing single-cell RNA-seq data containing UMI counts -author: -- name: Aaron T. L. Lun - affiliation: &CRUK Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom -- name: Davis J. McCarthy - affiliation: - - &EMBL EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom - - St Vincent's Institute of Medical Research, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia -- name: John C. Marioni - affiliation: - - *CRUK - - *EMBL - - Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom -date: "`r Sys.Date()`" -vignette: > - %\VignetteIndexEntry{03. UMI count data} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} -output: - BiocStyle::html_document: - titlecaps: false - toc_float: true -bibliography: ref.bib ---- - -```{r style, echo=FALSE, results='hide', message=FALSE, cache=FALSE} -library(BiocStyle) -library(knitr) -opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE, cache=TRUE) -opts_chunk$set(fig.asp=1) -``` - -# Overview - -In this workflow, we examine a heterogeneous dataset from a study of cell types in the mouse brain [@zeisel2015brain]. -This contains approximately 3000 cells of varying types such as oligodendrocytes, microglia and neurons. -Individual cells were isolated using the Fluidigm C1 microfluidics system [@pollen2014lowcoverage] and library preparation was performed on each cell using a UMI-based protocol. -After sequencing, expression was quantified by counting the number of UMIs mapped to each gene. -Count data for all endogenous genes, mitochondrial genes and spike-in transcripts are available from http://linnarssonlab.org/cortex. - -```{r} -library(BiocFileCache) -bfc <- BiocFileCache("raw_data", ask = FALSE) -base.url <- file.path("https://storage.googleapis.com", - "linnarsson-lab-www-blobs/blobs/cortex") -mRNA.path <- bfcrpath(bfc, file.path(base.url, - "expression_mRNA_17-Aug-2014.txt")) -mito.path <- bfcrpath(bfc, file.path(base.url, - "expression_mito_17-Aug-2014.txt")) -spike.path <- bfcrpath(bfc, file.path(base.url, - "expression_spikes_17-Aug-2014.txt")) -``` - -# Setting up the data - -The count data are distributed across several files, so some work is necessary to consolidate them into a single matrix. -We define a simple utility function for loading data in from each file. -(We stress that this function is only relevant to the current dataset, and should not be used for other datasets. -This kind of effort is generally not required if all of the counts are in a single file and separated from the metadata.) - -```{r} -readFormat <- function(infile) { - # First column is empty. - metadata <- read.delim(infile, stringsAsFactors=FALSE, header=FALSE, nrow=10)[,-1] - rownames(metadata) <- metadata[,1] - metadata <- metadata[,-1] - metadata <- as.data.frame(t(metadata)) - - # First column after row names is some useless filler. - counts <- read.delim(infile, stringsAsFactors=FALSE, - header=FALSE, row.names=1, skip=11)[,-1] - counts <- as.matrix(counts) - return(list(metadata=metadata, counts=counts)) -} -``` - -Using this function, we read in the counts for the endogenous genes, ERCC spike-in transcripts and mitochondrial genes. - -```{r} -endo.data <- readFormat(mRNA.path) -spike.data <- readFormat(spike.path) -mito.data <- readFormat(mito.path) -``` - -We also need to rearrange the columns for the mitochondrial data, as the order is not consistent with the other files. - -```{r} -m <- match(endo.data$metadata$cell_id, mito.data$metadata$cell_id) -mito.data$metadata <- mito.data$metadata[m,] -mito.data$counts <- mito.data$counts[,m] -``` - -```{r, echo=FALSE} -stopifnot(identical(endo.data$metadata$cell_id, spike.data$metadata$cell_id)) # should be the same. -stopifnot(all(endo.data$metadata$cell_id==mito.data$metadata$cell_id)) # should now be the same. -``` - -In this particular dataset, some genes are represented by multiple rows corresponding to alternative genomic locations. -We sum the counts for all rows corresponding to a single gene for ease of interpretation. - -```{r} -raw.names <- sub("_loc[0-9]+$", "", rownames(endo.data$counts)) -new.counts <- rowsum(endo.data$counts, group=raw.names, reorder=FALSE) -endo.data$counts <- new.counts -``` - -The counts are then combined into a single matrix for constructing a `SingleCellExperiment` object. -For convenience, metadata for all cells are stored in the same object for later access. - -```{r} -library(SingleCellExperiment) -all.counts <- rbind(endo.data$counts, mito.data$counts, spike.data$counts) -sce <- SingleCellExperiment(list(counts=all.counts), colData=endo.data$metadata) -dim(sce) -``` - -We add gene-based annotation identifying rows that correspond to each class of features. -We also determine the Ensembl identifier for each row. - -```{r} -# Specifying the nature of each row. -nrows <- c(nrow(endo.data$counts), nrow(mito.data$counts), nrow(spike.data$counts)) -is.spike <- rep(c(FALSE, FALSE, TRUE), nrows) -is.mito <- rep(c(FALSE, TRUE, FALSE), nrows) -isSpike(sce, "Spike") <- is.spike - -# Adding Ensembl IDs. -library(org.Mm.eg.db) -ensembl <- mapIds(org.Mm.eg.db, keys=rownames(sce), keytype="SYMBOL", column="ENSEMBL") -rowData(sce)$ENSEMBL <- ensembl - -sce -``` - -```{r, echo=FALSE, results='hide'} -# Save some memory. -rm(mito.data, endo.data, spike.data, new.counts) -gc() -``` - -# Quality control on the cells - -The original authors of the study have already removed low-quality cells prior to data publication. -Nonetheless, we compute some quality control metrics with `r Biocpkg("scater")` [@mccarthy2017scater] to check whether the remaining cells are satisfactory. - -```{r} -library(scater) -sce <- calculateQCMetrics(sce, feature_controls=list(Mt=is.mito)) -``` - -We examine the distribution of the QC metrics across all cells (Figure \@ref(fig:libplotbrain)). -The library sizes here are at least one order of magnitude lower than observed in the 416B dataset. -This is consistent with the use of UMI counts rather than read counts, as each transcript molecule can only produce one UMI count but can yield many reads after fragmentation. -In addition, the spike-in proportions are more variable than observed in the 416B dataset. -This may reflect a greater variability in the total amount of endogenous RNA per cell when many cell types are present. - -```{r libplotbrain, fig.wide=TRUE, fig.cap="Histograms of QC metrics including the library sizes, number of expressed genes and proportion of UMIs assigned to spike-in transcripts or mitochondrial genes for all cells in the brain dataset."} -par(mfrow=c(2,2), mar=c(5.1, 4.1, 0.1, 0.1)) -hist(sce$total_counts/1e3, xlab="Library sizes (thousands)", main="", - breaks=20, col="grey80", ylab="Number of cells") -hist(sce$total_features_by_counts, xlab="Number of expressed genes", main="", - breaks=20, col="grey80", ylab="Number of cells") -hist(sce$pct_counts_Spike, xlab="ERCC proportion (%)", - ylab="Number of cells", breaks=20, main="", col="grey80") -hist(sce$pct_counts_Mt, xlab="Mitochondrial proportion (%)", - ylab="Number of cells", breaks=20, main="", col="grey80") -``` - -We remove small outliers for the library size and the number of expressed features, and large outliers for the spike-in proportions. -Again, the presence of spike-in transcripts means that we do not have to use the mitochondrial proportions. - -```{r} -libsize.drop <- isOutlier(sce$total_counts, nmads=3, type="lower", log=TRUE) -feature.drop <- isOutlier(sce$total_features_by_counts, nmads=3, type="lower", log=TRUE) -spike.drop <- isOutlier(sce$pct_counts_Spike, nmads=3, type="higher") -``` - -Removal of low-quality cells is then performed by combining the filters for all of the metrics. -The majority of cells are retained, which suggests that the original quality control procedures were generally adequate. - -```{r} -sce <- sce[,!(libsize.drop | feature.drop | spike.drop)] -data.frame(ByLibSize=sum(libsize.drop), ByFeature=sum(feature.drop), - BySpike=sum(spike.drop), Remaining=ncol(sce)) -``` - -We could improve our cell filtering procedure further by setting `batch` in `isOutlier` to one or more known factors, e.g., mouse/plate of origin. -As previously mentioned, this would avoid inflation of the MAD and improve power to remove low-quality cells. -However, for simplicity, we will not do this as sufficient quality control has already been performed. - -```{r echo=FALSE, results='hide'} -gc() -``` - -# Cell cycle classification - -Application of `cyclone` [@scialdone2015computational] to the brain dataset suggests that most of the cells are in G1 phase (Figure \@ref(fig:phaseplotbrain)). -This requires the use of the Ensembl identifiers to match up with the pre-defined classifier. - -```{r phaseplotbrain, message=FALSE, fig.cap="Cell cycle phase scores from applying the pair-based classifier on the brain dataset, where each point represents a cell."} -library(scran) -mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds", package="scran")) -assignments <- cyclone(sce, mm.pairs, gene.names=rowData(sce)$ENSEMBL) -table(assignments$phase) -plot(assignments$score$G1, assignments$score$G2M, xlab="G1 score", ylab="G2/M score", pch=16) -``` - -However, the intepretation of this result requires some caution due to differences between the training and test datasets. -The classifier was trained on C1 SMARTer data and accounts for the biases in that protocol. -The brain dataset uses UMI counts, which has a different set of biases, e.g., 3'-end coverage only, no length bias, no amplification noise. -Furthermore, many neuronal cell types are expected to lie in the G0 resting phase, which is distinct from the other phases of the cell cycle [@coller2006new]. -`cyclone` will generally assign such cells to the closest known phase in the training set, which would be G1. - -```{r echo=FALSE, results='hide'} -gc() -``` - -# Examining gene-level metrics - -Figure \@ref(fig:topgenebrain) shows the most highly expressed genes across the cell population in the brain dataset. -This is mostly occupied by spike-in transcripts, reflecting the use of spike-in concentrations that span the entire range of expression. -There are also a number of constitutively expressed genes, as expected. - -```{r topgenebrain, fig.asp=1.2, fig.wide=TRUE, fig.cap="Percentage of total counts assigned to the top 50 most highly-abundant features in the brain dataset. For each feature, each bar represents the percentage assigned to that feature for a single cell, while the circle represents the average across all cells. Bars are coloured by the total number of expressed features in each cell, while circles are coloured according to whether the feature is labelled as a control feature."} -fontsize <- theme(axis.text=element_text(size=12), axis.title=element_text(size=16)) -plotHighestExprs(sce, n=50) + fontsize -``` - -Gene abundance is quantified by computing the average count across all cells (Figure \@ref(fig:abhistbrain)). -As previously mentioned, the UMI count is generally lower than the read count. - -```{r abhistbrain, fig.cap="Histogram of log-average counts for all genes in the brain dataset."} -ave.counts <- calcAverage(sce, use_size_factors=FALSE) -hist(log10(ave.counts), breaks=100, main="", col="grey", - xlab=expression(Log[10]~"average count")) -``` - -We save the average counts into the `SingleCellExperiment` object for later use. -We also remove genes that have average counts of zero, as this means that they are not expressed in any cell. - -```{r} -rowData(sce)$ave.count <- ave.counts -to.keep <- ave.counts > 0 -sce <- sce[to.keep,] -summary(to.keep) -``` - -```{r echo=FALSE, results='hide'} -gc() -``` - -# Normalization of cell-specific biases - -We normalize endogenous genes using the `computeSumFactors()` function with an additional pre-clustering step [@lun2016pooling]. -Cells in each cluster are normalized separately, and the size factors are rescaled to be comparable across clusters. -This avoids the need to assume that most genes are non-DE across the entire population - only a non-DE majority is required between pairs of clusters. -Scaling is then performed to ensure that size factors of cells in different clusters are comparable. - -- We use a average count threshold of 0.1 to define high-abundance genes to use during normalization. -This is lower than the default threshold of `min.mean=1`, reflecting the fact that UMI counts are generally smaller than read counts. -- We speed up clustering by performing fast dimensionality reduction and then clustering cells on the PCs. -This is the purpose of the `BSPARAM=` argument, which instructs `quickCluster()` to use a approximate algorithm for PCA^[Using methods from the `r CRANpkg("irlba")` package.]. -The approximation relies on stochastic initialization so we need to set the random seed for reproducibility - see below for more detail. - -```{r} -library(BiocSingular) -set.seed(1000) -clusters <- quickCluster(sce, use.ranks=FALSE, BSPARAM=IrlbaParam()) -sce <- computeSumFactors(sce, cluster=clusters, min.mean=0.1) -summary(sizeFactors(sce)) -``` - -Compared to the 416B analysis, more scatter is observed around the trend between the total count and size factor for each cell (Figure \@ref(fig:normplotbrain)). -This is consistent with an increased amount of DE between cells of different types, which compromises the accuracy of library size normalization [@robinson2010scaling]. -In contrast, the size factors are estimated based on median ratios and are more robust to the presence of DE between cells. - -```{r normplotbrain, fig.cap="Size factors from pooling, plotted against library sizes for all cells in the brain dataset. Axes are shown on a log-scale."} -plot(sizeFactors(sce), sce$total_counts/1e3, log="xy", - ylab="Library size (thousands)", xlab="Size factor") -``` - -We also compute size factors specific to the spike-in set, as previously described. - -```{r} -sce <- computeSpikeFactors(sce, type="Spike", general.use=FALSE) -``` - -Finally, normalized log-expression values are computed for each endogenous gene or spike-in transcript using the appropriate size factors. - -```{r} -sce <- normalize(sce) -``` - -__Comments from Aaron:__ - -- Only a rough clustering is required to avoid pooling together very different cell types in `computeSumFactors()`. -This reduces the chance of violating the non-DE assumption that is made during any gene-based scaling normalization. -There is no need for precise clustering at this step, as we will not be interpreting the `clusters` at all, -`computeSumFactors()` is robust to a moderate level of differential expression between cells in the same cluster, so careful definition of subclusters is not required. -That said, there does need to be sufficient cells in each cluster for pooling, which can be guaranteed with the `min.size=` argument in `quickCluster()`. -- Older versions of `quickCluster()` performed clustering based on rank correlations between cells. -The current version (with `use.ranks=FALSE`) uses graph-based clustering on principal components obtained from log-expression values. -This is faster and yields higher resolution clusters than before. -Nonetheless, the previous behaviour can be recovered by setting the arguments appropriately, see `?quickCluster` for more details. - -```{r echo=FALSE, results='hide'} -gc() -``` - -# Modelling and removing technical noise - -We model the technical noise by fitting a mean-variance trend to the spike-in transcripts with the `trendVar()` function. - -```{r} -var.fit <- trendVar(sce, parametric=TRUE, loess.args=list(span=0.4)) -var.out <- decomposeVar(sce, var.fit) -``` - -Figure \@ref(fig:hvgplotbrain) indicates that the trend is fitted accurately to the technical variances. -The technical and total variances are also much smaller than those in the 416B dataset. -This is due to the use of UMIs, which reduces the noise caused by variable PCR amplification [@islam2014quantitative]. -Furthermore, the spike-in trend is consistently lower than the variances of the endogenous genes. -This reflects the heterogeneity in gene expression across cells of different types. - -```{r hvgplotbrain, fig.cap="Variance of normalized log-expression values against the mean for each gene, calculated across all cells in the brain dataset after blocking on the sex effect. The blue line represents the mean-dependent trend in the technical variance of the spike-in transcripts (also highlighted as red points)."} -plot(var.out$mean, var.out$total, pch=16, cex=0.6, xlab="Mean log-expression", - ylab="Variance of log-expression") -points(var.out$mean[isSpike(sce)], var.out$total[isSpike(sce)], col="red", pch=16) -curve(var.fit$trend(x), col="dodgerblue", add=TRUE, lwd=2) -``` - -We check the distribution of expression values for the genes with the largest biological components to ensure that they are not driven by outliers (Figure \@ref(fig:hvgvioplotbrain)). -Some tweaking of the `plotExpression` parameters is necessary to visualize a large number of cells. - -```{r hvgvioplotbrain, fig.cap="Violin plots of normalized log-expression values for the top 10 HVGs in the brain dataset. For each gene, each point represents the log-expression value for an individual cell."} -chosen.genes <- order(var.out$bio, decreasing=TRUE)[1:10] -plotExpression(sce, rownames(var.out)[chosen.genes], - point_alpha=0.05, jitter_type="jitter") + fontsize -``` - -Finally, we use PCA to denoise the expression values, yielding a set of coordinates for each cell where the technical noise has been removed. -Setting `BSPARAM=IrlbaParam()` in `denoisePCA()` will perform an approximate singular value decomposition (SVD) using methods from the `r CRANpkg("irlba")` package. -This is much faster than the exact algorithm on large datasets without much loss of accuracy. -The approximate algorithm involves a random initialization so we set the seed to guarantee reproducibility. - -```{r} -set.seed(1000) -sce <- denoisePCA(sce, technical=var.fit$trend, BSPARAM=IrlbaParam()) -ncol(reducedDim(sce, "PCA")) -``` - -**Comments from Aaron:** - -- In theory, we should block on the plate of origin for each cell. -However, only 20-40 cells are available on each plate, and the population is also highly heterogeneous. -This means that we cannot assume that the distribution of sampled cell types on each plate is the same. -Thus, to avoid regressing out potential biology, we will not block on any factors in this analysis. -- The upper limit of PCs to retain in `denoisePCA()` is specified by the `max.rank=` argument. -This is set to 100 by default to ensure that the approximate SVD runs quickly. -A higher `max.rank` may be more appropriate for extremely heterogeneous populations, though the default setting is generally satisfactory for dimensionality reduction. - -```{r, echo=FALSE, results='hide', message=FALSE} -gc() -``` - -# Data exploration with dimensionality reduction - -We perform dimensionality reduction on the denoised PCs to check if there is any substructure. -Cells separate into clear clusters in the _t_-SNE plot [@van2008visualizing] in Figure \@ref(fig:tsneplotbrain), corresponding to distinct subpopulations. -This is consistent with the presence of multiple cell types in the diverse brain population. -We increase the perplexity to favour visualization of the overall structure at the expense of local scale. - -```{r tsneplotbrain, fig.cap="_t_-SNE plots constructed from the denoised PCs of the brain dataset. Each point represents a cell and is coloured according to its expression of _Neurod6_ (left) or _Mog_ (right).", fig.width=12, fig.asp=0.5} -set.seed(1000) -sce <- runTSNE(sce, use_dimred="PCA", perplexity=50) -tsne1 <- plotTSNE(sce, colour_by="Neurod6") + fontsize -tsne2 <- plotTSNE(sce, colour_by="Mog") + fontsize -multiplot(tsne1, tsne2, cols=2) -``` - -The PCA plot is less effective at separating cells into many different clusters (Figure \@ref(fig:pcaplotbrain)). -This is because the first two PCs are driven by strong differences between specific subpopulations, which reduces the resolution of more subtle differences between some of the other subpopulations. -Nonetheless, some substructure is still visible. - -```{r pcaplotbrain, fig.cap="PCA plots constructed from the denoised PCs of the brain dataset. Each point represents a cell and is coloured according to its expression of the _Neurod6_ (left) or _Mog_ (right).", fig.width=12, fig.asp=0.5} -pca1 <- plotReducedDim(sce, use_dimred="PCA", colour_by="Neurod6") + fontsize -pca2 <- plotReducedDim(sce, use_dimred="PCA", colour_by="Mog") + fontsize -multiplot(pca1, pca2, cols=2) -``` - -For both methods, we colour each cell based on the expression of a particular gene. -This is a useful strategy for visualizing changes in expression across the lower-dimensional space. -It can also be used to characterise each cluster if the selected genes are known markers for particular cell types. -For example, _Mog_ can be used to identify clusters corresponding to oligodendrocytes. - -```{r echo=FALSE, results='hide'} -gc() -``` - -# Clustering cells into putative subpopulations - -## Using graph-based clustering - -The reduced dimension coordinates are used to cluster cells into putative subpopulations. -We do so by constructing a shared-nearest-neighbour graph [@xu2015identification], in which cells are the nodes and edges are formed between cells that share nearest neighbours. -Clusters are then defined as highly connected communities of cells within this graph, using methods from the `r CRANpkg("igraph")` package. -This is more efficient than forming a pairwise distance matrix for hierarchical clustering of large numbers of cells. - -```{r} -snn.gr <- buildSNNGraph(sce, use.dimred="PCA") -cluster.out <- igraph::cluster_walktrap(snn.gr) -my.clusters <- cluster.out$membership -table(my.clusters) -``` - -We visualize the cluster assignments for all cells on the _t_-SNE plot in Figure \@ref(fig:tsneclusterbrain). -Adjacent cells are generally assigned to the same cluster, indicating that the clustering procedure was applied correctly. - -```{r tsneclusterbrain, message=FALSE, fig.cap="_t_-SNE plot of the denoised PCs of the brain dataset. Each point represents a cell and is coloured according to its assigned cluster identity."} -sce$cluster <- factor(my.clusters) -plotTSNE(sce, colour_by="cluster") + fontsize -``` - -```{r echo=FALSE, results='hide'} -gc() -``` - -An alternative approach is to use graph-based visualizations such as force-directed layouts (Figure \@ref(fig:fdlbrain)). -These are appealing as they directly represent the relationships used during clustering. -However, convergence tends to be slow for large graphs, so some tinkering with `niter=` may be required to ensure that the results are stable. - -```{r fdlbrain, message=FALSE, fig.cap="Force-directed layout for the shared nearest-neighbour graph of the brain dataset. Each point represents a cell and is coloured according to its assigned cluster identity."} -set.seed(2000) -reducedDim(sce, "force") <- igraph::layout_with_fr(snn.gr, niter=5000) -plotReducedDim(sce, colour_by="cluster", use_dimred="force") -``` - -Very heterogeneous datasets may yield a few large clusters on the first round of clustering. -It can be useful to repeat the variance modelling, denoising and clustering using only the cells within each of the initial clusters. -This can be achieved by subsetting `sce` according to a particular level of `my.clusters`, and re-applying the relevant functions on the subset. -Doing so may focus on a different set of genes that define heterogeneity _within_ an initial cluster, as opposed to those that define differences _between_ the initial clusters. -This would allow fine-scale structure within each cluster to be explored at greater resolution. -For simplicity, though, we will only use the broad clusters corresponding to clear subpopulations in this workflow. - -**Comments from Aaron:** - -- Many different clustering methods are available in the `r CRANpkg("igraph")` package. -We find that the Walktrap algorithm is usually a good default choice [@yang2016comparative], though users are encouraged to experiment with different algorithms. -- Decreasing the number of neighbours `k` in `buildSNNGraph` will reduce the connectivity of the graph. -This will generally result in the formation of smaller clusters [@xu2015identification], which may be desirable if greater resolution is required. -- Notice that we do not run `library(igraph)`, but instead use `igraph::` to extract methods from the package. -This is because `r CRANpkg("igraph")` contains a `normalize` method that will override its counterpart from `r Biocpkg("scater")`, resulting in some unusual bugs. - -## Evaluating graph-based clusters - -The modularity score provides a global measure of clustering performance for community detection methods. -Briefly, it compares the number of within-cluster edges to the expected number under a null model of random edges. -A high modularity score (approaching the maximum of 1) indicates that the detected clusters are enriched for internal edges, with relatively few edges between clusters. - -```{r} -igraph::modularity(cluster.out) -``` - -We further investigate the clusters by examining the total weight of edges for each pair of clusters. -For each pair, the observed total weight is compared to what is expected under a null model, similar to the modularity calculation. -Most clusters contain more internal links than expected (Figure \@ref(fig:heatmodbrain)), while links between clusters are fewer than expected. -This indicates that we successfully clustered cells into highly-connected communities. - -```{r heatmodbrain, fig.cap="Heatmap of the log~10~-ratio of the total weight between nodes in the same cluster or in different clusters, relative to the total weight expected under a null model of random links."} -mod.out <- clusterModularity(snn.gr, my.clusters, get.values=TRUE) -ratio <- mod.out$observed/mod.out$expected -lratio <- log10(ratio + 1) - -library(pheatmap) -pheatmap(lratio, cluster_rows=FALSE, cluster_cols=FALSE, - color=colorRampPalette(c("white", "blue"))(100)) -``` - -To summarize the relationships between clusters, we use the ratio of the observed and expected total weights to build a graph across clusters. -The cluster-based graph can be visualized using a force-directed layout to identify "clusters of clusters" that are highly interconnected. -This is similar to the "graph abstraction" strategy proposed by @wolf2017graph. - -```{r graphbrain, fig.cap="Force-directed layout showing the relationships between clusters based on the ratio of observed to expected total weights between nodes in different clusters. The thickness of the edge between a pair of clusters is proportional to the corresponding ratio."} -cluster.gr <- igraph::graph_from_adjacency_matrix(ratio, - mode="undirected", weighted=TRUE, diag=FALSE) -plot(cluster.gr, edge.width=igraph::E(cluster.gr)$weight*10) -``` - -**Comments from Aaron:** - -- We do not use the silhouette coefficient to assess clustering for large datasets. -This is because `cluster::silhouette` requires the construction of a distance matrix, which may not be feasible when many cells are involved. -- Technically, the modularity score is obtained by subtracting the observed from expected total weights. -We use the ratio instead as this is guaranteed to be positive and does not exhibit differences in magnitude due to differences in the number of cells in each cluster. - -# Detecting marker genes between subpopulations - -```{r, echo=FALSE, results="hide"} -# Hidden variables for use in text or hidden chunks, -# to avoid the need for manual changes. -chosen.inter.cluster <- 4 -chosen.inter.other <- 11 -``` - -We use the `findMarkers` function with `direction="up"` to identify upregulated marker genes for each cluster. -As previously mentioned, we focus on upregulated genes as these can quickly provide positive identification of cell type in a heterogeneous population. -We examine the table for cluster `r chosen.inter.cluster`, in which log-fold changes are reported between cluster `r chosen.inter.cluster` and every other cluster. -The same output is provided for each cluster in order to identify genes that discriminate between clusters. - -```{r, echo=FALSE, results="hide"} -old.digits <- options()$digits -options(digits=3) -``` - -```{r} -markers <- findMarkers(sce, my.clusters, direction="up") -marker.set <- markers[["4"]] -head(marker.set[,1:8], 10) # only first 8 columns, for brevity -``` - -```{r, echo=FALSE, results="hide"} -# Checking the cluster is what we wanted, along with its related cluster. -other.inter <- chosen.inter.other - 1 -gad1 <- sapply(marker.set["Gad1",-(1:3)], sign) -stopifnot(gad1[other.inter]==-1) -stopifnot(all(gad1[-other.inter]==1)) - -gad2 <- sapply(marker.set["Gad2",-(1:3)], sign) -stopifnot(gad2[other.inter]==-1) -stopifnot(all(gad2[-other.inter]==1)) - -stopifnot(all(sapply(marker.set["Synpr",-(1:3)], sign)==1)) - -options(digits=old.digits) -``` - -Figure \@ref(fig:heatmapmarkerbrain) indicates that most of the top markers are strongly DE in cells of cluster `r chosen.inter.cluster` compared to some or all of the other clusters. -We can use these markers to identify cells from cluster `r chosen.inter.cluster` in validation studies with an independent population of cells. -A quick look at the markers suggest that cluster `r chosen.inter.cluster` represents interneurons based on expression of *Gad1* and *Slc6a1* [@zeng2012largescale], -differing from closely related cells in cluster `r chosen.inter.other` by virtue of high *Synpr* expression. - -```{r heatmapmarkerbrain, fig.wide=TRUE, fig.cap=sprintf("Heatmap of mean-centred and normalized log-expression values for the top set of markers for cluster %i in the brain dataset. Column colours represent the cluster to which each cell is assigned, as indicated by the legend.", chosen.inter.cluster)} -top.markers <- rownames(marker.set)[marker.set$Top <= 10] -plotHeatmap(sce, features=top.markers, columns=order(my.clusters), - colour_columns_by="cluster", cluster_cols=FALSE, - center=TRUE, symmetric=TRUE, zlim=c(-5, 5)) -``` - -An alternative visualization approach is to plot the log-fold changes to all other clusters directly (Figure \@ref(fig:heatmaplfcbrain)). -This is more concise and is useful in situations involving many clusters that contain different numbers of cells. - -```{r heatmaplfcbrain, fig.asp=1.5, fig.cap=sprintf("Heatmap of log-fold changes in expression for the top set of markers for cluster %i, compared to every other cluster in the brain data set.", chosen.inter.cluster)} -logFCs <- as.matrix(marker.set[1:50,-(1:3)]) -colnames(logFCs) <- sub("logFC.", "", colnames(logFCs)) - -library(pheatmap) -max.lfc <- max(abs(range(logFCs))) -pheatmap(logFCs, breaks=seq(-5, 5, length.out=101)) -``` - -We save the list of candidate marker genes for further examination, using compression to reduce the file size. - -```{r} -gzout <- gzfile("brain_marker_1.tsv.gz", open="wb") -write.table(marker.set, file=gzout, sep="\t", quote=FALSE, col.names=NA) -close(gzout) -``` - -**Comments from Aaron:** - -- The `overlapExprs()` function may also be useful for summarizing differences between clusters. -This is discussed in more detail `r simpleSingleCell:::.link("de", "Using the Wilcoxon rank sum test", "here")`. - -# Concluding remarks - -Having completed the basic analysis, we save the `SingleCellExperiment` object with its associated data to file. -This is especially important here as the brain dataset is quite large. -If further analyses are to be performed, it would be inconvenient to have to repeat all of the pre-processing steps described above. - -```{r} -saveRDS(file="brain_data.rds", sce) -``` - -```{r, echo=FALSE, results='hide'} -gc() -``` - -All software packages used in this workflow are publicly available from the Comprehensive R Archive Network (https://cran.r-project.org) or the Bioconductor project (http://bioconductor.org). -The specific version numbers of the packages used are shown below, along with the version of the R installation. - -```{r} -sessionInfo() -``` - -# References - +--- +title: 分析含有UMI计数的单细胞RNA-seq数据 +author: +- name: Aaron T. L. Lun + affiliation: &CRUK Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom +- name: Davis J. McCarthy + affiliation: + - &EMBL EMBL European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom + - St Vincent's Institute of Medical Research, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia +- name: John C. Marioni + affiliation: + - *CRUK + - *EMBL + - Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom +date: "`r Sys.Date()`" +vignette: > + %\VignetteIndexEntry{03. UMI count data} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +output: + BiocStyle::html_document: + titlecaps: false + toc_float: true +bibliography: ref.bib +--- + +```{r style, echo=FALSE, results='hide', message=FALSE, cache=FALSE} +library(BiocStyle) +library(knitr) +opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE, cache=TRUE) +opts_chunk$set(fig.asp=1) +``` + +# 概述 + +在这个工作流程中，我们研究了小鼠脑细胞异质数据集 [@zeisel2015brain]。 +这包含大约3000种不同类型的细胞，例如少突胶质细胞，小胶质细胞和神经元。 +使用 Fluidigm C1 microfluidics 系统 [@pollen2014lowcoverage] 分离单个细胞，并使用基于UMI的方法进行细胞文库制备。 +测序后，通过计数比对到每个基因的UMI的数量来定量表达。 +所有内源基因，线粒体基因和加标转录物的计数数据可从下面网址中获得 http://linnarssonlab.org/cortex。 + +```{r} +library(BiocFileCache) +bfc <- BiocFileCache("raw_data", ask = FALSE) +base.url <- file.path("https://storage.googleapis.com", + "linnarsson-lab-www-blobs/blobs/cortex") +mRNA.path <- bfcrpath(bfc, file.path(base.url, + "expression_mRNA_17-Aug-2014.txt")) +mito.path <- bfcrpath(bfc, file.path(base.url, + "expression_mito_17-Aug-2014.txt")) +spike.path <- bfcrpath(bfc, file.path(base.url, + "expression_spikes_17-Aug-2014.txt")) +``` + +# 准备好数据 + +计数数据分布在多个文件中，因此要将它们合并为单个矩阵 +我们定义了一个简单实用的函数，用于从每个文件加载数据。 +（我们强调此函数仅与当前数据集相关，不能应用于其它数据集。 +如果所有计数都在单个文件中并与meta数据分开，则通常不需要这样的步骤。 + +```{r} +readFormat <- function(infile) { + # First column is empty. + metadata <- read.delim(infile, stringsAsFactors=FALSE, header=FALSE, nrow=10)[,-1] + rownames(metadata) <- metadata[,1] + metadata <- metadata[,-1] + metadata <- as.data.frame(t(metadata)) + + # First column after row names is some useless filler. + counts <- read.delim(infile, stringsAsFactors=FALSE, + header=FALSE, row.names=1, skip=11)[,-1] + counts <- as.matrix(counts) + return(list(metadata=metadata, counts=counts)) +} +``` + +使用此功能，我们读入了内源基因，ERCC加标转录本和线粒体基因的计数。 + +```{r} +endo.data <- readFormat(mRNA.path) +spike.data <- readFormat(spike.path) +mito.data <- readFormat(mito.path) +``` + +我们还需要重新排列线粒体数据的列，因为它的顺序与其他文件不一致。 + +```{r} +m <- match(endo.data$metadata$cell_id, mito.data$metadata$cell_id) +mito.data$metadata <- mito.data$metadata[m,] +mito.data$counts <- mito.data$counts[,m] +``` + +```{r, echo=FALSE} +stopifnot(identical(endo.data$metadata$cell_id, spike.data$metadata$cell_id)) # should be the same. +stopifnot(all(endo.data$metadata$cell_id==mito.data$metadata$cell_id)) # should now be the same. +``` + +在该特定数据集中，一些基因由对应于可供选择的基因位置的多行来表示。 +为了便于解释，我们将对应于单个基因的所有行的计数相加。 + +```{r} +raw.names <- sub("_loc[0-9]+$", "", rownames(endo.data$counts)) +new.counts <- rowsum(endo.data$counts, group=raw.names, reorder=FALSE) +endo.data$counts <- new.counts +``` + +然后将计数组合成单个矩阵以构造 `SingleCellExperiment` 的对象。 +为方便起见，所有细胞的meta数据都存储在同一对象中以供以后使用。 + +```{r} +library(SingleCellExperiment) +all.counts <- rbind(endo.data$counts, mito.data$counts, spike.data$counts) +sce <- SingleCellExperiment(list(counts=all.counts), colData=endo.data$metadata) +dim(sce) +``` + +我们添加基于基因的注释，标识与每类特征对应的行。 +我们还确定每行的Ensembl标识符。 + +```{r} +# Specifying the nature of each row. +nrows <- c(nrow(endo.data$counts), nrow(mito.data$counts), nrow(spike.data$counts)) +is.spike <- rep(c(FALSE, FALSE, TRUE), nrows) +is.mito <- rep(c(FALSE, TRUE, FALSE), nrows) +isSpike(sce, "Spike") <- is.spike + +# Adding Ensembl IDs. +library(org.Mm.eg.db) +ensembl <- mapIds(org.Mm.eg.db, keys=rownames(sce), keytype="SYMBOL", column="ENSEMBL") +rowData(sce)$ENSEMBL <- ensembl + +sce +``` + +```{r, echo=FALSE, results='hide'} +# Save some memory. +rm(mito.data, endo.data, spike.data, new.counts) +gc() +``` + +# 对细胞的质控 + +虽然该研究的原始作者在数据发布之前已经删除了低质量的细胞。 +但是,我们还是可以使用`r Biocpkg("scater")`包来进行一些质控 [@mccarthy2017scater]，来检查剩下的细胞是否令人满意达到过滤后的要求。 + +```{r} +library(scater) +sce <- calculateQCMetrics(sce, feature_controls=list(Mt=is.mito)) +``` + +我们检查了所有细胞的质控(QC)指标分布 (Figure \@ref(fig:libplotbrain))。 +此处的库大小至少比416B数据集中观察到的数量级低一个数量级。 +这与使用UMI计数而不是read计数一致，因为每个转录物分子只能产生一个UMI计数，但在片段化后可以产生许多reads。 +此外，加标比例比416B数据集中观察到的更多变化。 +当存在许多细胞类型时，这可以反映每个细胞的内源RNA总量的更大变化。 + +```{r libplotbrain, fig.wide=TRUE, fig.cap="QC指标的直方图，包括大脑数据集中所有细胞的文库大小，表达基因数和分配给加标转录本或线粒体基因的UMI比例。"} +par(mfrow=c(2,2), mar=c(5.1, 4.1, 0.1, 0.1)) +hist(sce$total_counts/1e3, xlab="Library sizes (thousands)", main="", + breaks=20, col="grey80", ylab="Number of cells") +hist(sce$total_features_by_counts, xlab="Number of expressed genes", main="", + breaks=20, col="grey80", ylab="Number of cells") +hist(sce$pct_counts_Spike, xlab="ERCC proportion (%)", + ylab="Number of cells", breaks=20, main="", col="grey80") +hist(sce$pct_counts_Mt, xlab="Mitochondrial proportion (%)", + ylab="Number of cells", breaks=20, main="", col="grey80") +``` + +我们删除了库大小和表达特征数量的低异常值，以及加入物比例的大异常值。 +同样，加标转录本的存在意味着我们不必使用线粒体比例。 + +```{r} +libsize.drop <- isOutlier(sce$total_counts, nmads=3, type="lower", log=TRUE) +feature.drop <- isOutlier(sce$total_features_by_counts, nmads=3, type="lower", log=TRUE) +spike.drop <- isOutlier(sce$pct_counts_Spike, nmads=3, type="higher") +``` + +然后通过组合不同的质控指标来过滤这些低质量的细胞。 +大部分细胞被保留下来，这表明原始的质控步骤已经是足够的。 + +```{r} +sce <- sce[,!(libsize.drop | feature.drop | spike.drop)] +data.frame(ByLibSize=sum(libsize.drop), ByFeature=sum(feature.drop), + BySpike=sum(spike.drop), Remaining=ncol(sce)) +``` + +我们可以进一步提高细胞过滤的质量，通过设置`isOutlier`中的`batch`的一个或多个已知因素，例如小鼠或者板的原产地。 +如之前所述，这将避免MAD的膨胀并提高去除低质量细胞的能力。 +但是，为简单起见，我们不会这样做，因为前面的质控已经足够了。 + +```{r echo=FALSE, results='hide'} +gc() +``` + +# 细胞周期分类 + +将 `cyclone` [@scialdone2015computational] 应用于大脑数据集建议大多数细胞处于G1期 (Figure \@ref(fig:phaseplotbrain))。 +这需要使用Ensembl标识符来匹配预定义的分类器。 + +```{r phaseplotbrain, message=FALSE, fig.cap="在大脑数据集上应用基于配对分类器的细胞周期阶段评分，其中每个点代表一个细胞。"} +library(scran) +mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds", package="scran")) +assignments <- cyclone(sce, mm.pairs, gene.names=rowData(sce)$ENSEMBL) +table(assignments$phase) +plot(assignments$score$G1, assignments$score$G2M, xlab="G1 score", ylab="G2/M score", pch=16) +``` + +但是，由于训练和测试数据集之间存在差异，因此对此结果的解释需要谨慎。 +分类器使用C1 SMARTer数据的进行训练，并解释了该实验方法中的偏差。 +大脑数据集使用UMI计数，其具有不同的偏差集合，例如，仅3'端覆盖，没有长度偏差，没有放大噪声。 +此外，预计许多神经细胞类型位于G0休眠期，这与细胞周期的其他阶段不同[@coller2006new]。 +`cyclone` 通常会将这些细胞分配给训练集中最接近的已知阶段，即G1。 + +```{r echo=FALSE, results='hide'} +gc() +``` + +# 检查基因水平的指标 + +图 \@ref(fig:topgenebrain)显示了大脑数据集中细胞群中表达最高的基因。 +主要呗加标转录本占据，反映了跨越整个表达范围的加标浓度的使用。 +正如预期的那样，还有许多与基本构成相关的表达的基因。 + +```{r topgenebrain, fig.asp=1.2, fig.wide=TRUE, fig.cap="分配给大脑数据集中前50个最丰富的特征的总计数百分比。对于每个要素，每个条形表示为单个细胞分配给该要素的百分比，而圆圈表示所有细胞的平均值。条形图由每个细胞中表达的特征的总数着色，而圆形根据特征是否标记为控制特征而着色。"} +fontsize <- theme(axis.text=element_text(size=12), axis.title=element_text(size=16)) +plotHighestExprs(sce, n=50) + fontsize +``` + +通过计算所有细胞的平均计数来量化基因丰度 (Figure \@ref(fig:abhistbrain))。 +如前所述，UMI计数通常低于reads的计数。 + +```{r abhistbrain, fig.cap="大脑数据集中所有基因的对数平均计数的直方图。"} +ave.counts <- calcAverage(sce, use_size_factors=FALSE) +hist(log10(ave.counts), breaks=100, main="", col="grey", + xlab=expression(Log[10]~"average count")) +``` + +我们将平均计数保存到 `SingleCellExperiment`对象中供以后使用。 +我们还删除了平均计数为零的基因，因为这意味着它们不会在任何细胞中表达。 + +```{r} +rowData(sce)$ave.count <- ave.counts +to.keep <- ave.counts > 0 +sce <- sce[to.keep,] +summary(to.keep) +``` + +```{r echo=FALSE, results='hide'} +gc() +``` + +# 细胞特异性偏差的正常化 + +我们使用 `computeSumFactors()` 的功能将内源基因标准化，并进行额外的预聚类步骤 [@lun2016pooling]。 +每个聚类中的细胞分别进行标准化，并且size factors被重新调整，以便在聚类之间进行比较。 +这避免了假设大多数基因在整个群体中都没有表达差异，只要大多数群集对是没有表达差异就可以了。 +然后进行缩放，以确保不同聚类中的细胞的size factors是可比较的。 + +- 我们使用0.1的平均计数阈值来定义在标准化期间使用的高丰度基因。 +这低于默认阈值 `min.mean=1`, 反映了UMI计数通常小于read计数的事实。 +- 我们加速聚类的速度通过快速降维并集聚细胞在PCs上。 +这是 `BSPARAM=` 参数的使用目的,它指示`quickCluster()`使用PCA^[使用 `r CRANpkg("irlba")`包中的方法。]的近似值算法。 +近似值依赖于随机初始化，因此我们需要将随机种子设置为可重复性的-有关更多详细信息，请参见下文。 + +```{r} +library(BiocSingular) +set.seed(1000) +clusters <- quickCluster(sce, use.ranks=FALSE, BSPARAM=IrlbaParam()) +sce <- computeSumFactors(sce, cluster=clusters, min.mean=0.1) +summary(sizeFactors(sce)) +``` + +与416B分析相比，更多的散射可以在每个细胞的总计数和 size factor的趋势间观察到 (Figure \@ref(fig:normplotbrain))。 +这与不同类型的细胞之间DE的增加量一致，这折中了文库大小标准化的准确性[@robinson2010scaling]。 +相反，the size factors 基于中值比估算并且对于细胞之间的DE的存在是更稳健的。 + +```{r normplotbrain, fig.cap="来自汇集的size factors，针对大脑数据集中所有细胞的文库大小绘制。轴以对数刻度显示。"} +plot(sizeFactors(sce), sce$total_counts/1e3, log="xy", + ylab="Library size (thousands)", xlab="Size factor") +``` + +我们还计算特定的在顶端聚类的size factors子，如前所述。 + +```{r} +sce <- computeSpikeFactors(sce, type="Spike", general.use=FALSE) +``` + +最后，使用适当的size factors计算每个内源基因或加标转录物的标准化对数表达值。 + +```{r} +sce <- normalize(sce) +``` + +__Aaron的注释:__ + +- 只需要粗略的聚类就可以避免将非常不同的细胞类型汇集在一起`computeSumFactors()`之中。 +这降低了违反在任何基于基因的缩放归一化期间进行的非DE假设可能性。T +在这一步骤中不需要精确的聚类，因为我们根本不会解释所提及的聚类。 +`computeSumFactors()`对于同一聚类中的细胞之间的中等水平的差异表达是很好用的的，因此不需要仔细定义子聚类。 +也就是说，每个集群中都需要有足够的细胞用于pooling，这可以通过设置`quickCluster()`中的`min.size=`参数。 +- 更旧版本`quickCluster()`执行聚类基于细胞之间的秩相关性。 +当前版本 (`use.ranks=FALSE`)使用基于图像化的聚类根据从表达量的对数值获得的主成分。 +这比以前更快并且产生更高分辨率的聚类。 +尽管如此，可以通过适当设置参数来恢复以前的行为分析，请参考 `?quickCluster` 来获取更多相关信息。 + +```{r echo=FALSE, results='hide'} +gc() +``` + +# 建模并消除技术噪音 + +我们对技术噪音建模通过使用`trendVar()` 函数拟合加标转录物的差异的均值。 + +```{r} +var.fit <- trendVar(sce, parametric=TRUE, loess.args=list(span=0.4)) +var.out <- decomposeVar(sce, var.fit) +``` + +图 \@ref(fig:hvgplotbrain) 表明趋势与技术产生的差异准确拟合。 +技术和总差异也远小于416B数据集中的差异。 +这是由于使用了UMI，它降低了由可变PCR扩增引起的噪音 [@islam2014quantitative]。 +此外，加入物的趋势始终低于内源基因的差异。 +这反映了不同类型细胞中基因表达的异质性。 + +```{r hvgplotbrain, fig.cap="标准化的对数表达值相对于每个基因的平均值的变化，在除去性别影响后在大脑数据集中的所有细胞上计算。蓝线表示加标转录本的技术差异的平均趋势（也突出显示为红点）。"} +plot(var.out$mean, var.out$total, pch=16, cex=0.6, xlab="Mean log-expression", + ylab="Variance of log-expression") +points(var.out$mean[isSpike(sce)], var.out$total[isSpike(sce)], col="red", pch=16) +curve(var.fit$trend(x), col="dodgerblue", add=TRUE, lwd=2) +``` + +我们检查具有最大生物成分的基因的表达值的分布，以确保它们不是由异常值引起的 (Figure \@ref(fig:hvgvioplotbrain)). +为了可视化大量的细胞，需要对参数进行一些调整。 + +```{r hvgvioplotbrain, fig.cap="大脑数据集中前10个HVG的归一化对数表达值的小提琴图。对于每个基因，每个点代表单个细胞的对数表达值。"} +chosen.genes <- order(var.out$bio, decreasing=TRUE)[1:10] +plotExpression(sce, rownames(var.out)[chosen.genes], + point_alpha=0.05, jitter_type="jitter") + fontsize +``` + +最后，我们使用PCA对表达l量值进行去噪，为每个已去除技术噪声的细胞生成一组坐标。 +设置`denoisePCA()`中的`BSPARAM=IrlbaParam()`，会执行近似奇异值分离法(SVD)，通过使用到`r CRANpkg("irlba")`包中的方法。 +这比大型数据集上的精确算法快得多，而且没有损失太多的准确度。 +近似算法涉及随机初始化，因此我们设置种子值以保证再现性。 + +```{r} +set.seed(1000) +sce <- denoisePCA(sce, technical=var.fit$trend, BSPARAM=IrlbaParam()) +ncol(reducedDim(sce, "PCA")) +``` + +**Aaron的注释:** + +- 理论上，我们应该暂时保留每个细胞的板的起源。 +然而，每个平板上只有20-40个细胞，并且种群也是高度异质的。 +这意味着我们不能假设每个板上的采样细胞类型的分布是相同的。 +因此，为避免消除潜在的生物学问题，我们不会暂且保留分析中的任何因素。 +- 要保留`denoisePCA()`中的的PC的上限，由`max.rank=`参数设定。 +较高的`max.rank`可能更适合含有极度差异的群体，尽管降低维度默认设置t已经可以令人满意了。 + +```{r, echo=FALSE, results='hide', message=FALSE} +gc() +``` + +# 降低维数的数据探索 + +我们对去噪的PC进行降维，以检查是否存在子结构。 +细胞在 _t_-SNE 图 [@van2008visualizing] in Figure \@ref(fig:tsneplotbrain)中分类成明显的不同聚类，这些聚类对应于不同的亚群。 +这与脑细胞群体中存在多种细胞类型一致。 +我们增加了混乱度，以牺牲局部比例尺为代价，以便维持整体结构的可视化。 + +```{r tsneplotbrain, fig.cap="从大脑数据集的去噪PC构建的_t_-SNE 图。 每个点代表一个细胞，并根据其表达着色 _Neurod6_ (左) or _Mog_ (右)。", fig.width=12, fig.asp=0.5} +set.seed(1000) +sce <- runTSNE(sce, use_dimred="PCA", perplexity=50) +tsne1 <- plotTSNE(sce, colour_by="Neurod6") + fontsize +tsne2 <- plotTSNE(sce, colour_by="Mog") + fontsize +multiplot(tsne1, tsne2, cols=2) +``` + +PCA图在将细胞分离成许多不同的聚类时效果较差 (Figure \@ref(fig:pcaplotbrain))。 +这是因为前两个PC是由特定子群体之间的强烈差异导致的，这降低了一些其它子群体之间更微妙差异的识别度。 +尽管如此，仍然可以看到一些子结构的差异。 + +```{r pcaplotbrain, fig.cap="PCA plots constructed from the denoised PCs of the brain dataset. Each point represents a cell and is coloured according to its expression of the _Neurod6_ (left) or _Mog_ (right).", fig.width=12, fig.asp=0.5} +pca1 <- plotReducedDim(sce, use_dimred="PCA", colour_by="Neurod6") + fontsize +pca2 <- plotReducedDim(sce, use_dimred="PCA", colour_by="Mog") + fontsize +multiplot(pca1, pca2, cols=2) +``` + +对于这两种方法，我们根据特定基因的表达对每个细胞着色。 +这是一种有用的策略，用于可视化整个低维空间中表达量的变化。 +如果所选基因是特定细胞类型的已知标记，它还可用于表征每个聚类。 +例如， _Mog_ 可用于识别对应于少突胶质细胞的聚类。 + +```{r echo=FALSE, results='hide'} +gc() +``` + +# 将细胞聚类成假定的亚群 + +## 使用基于图形的聚类 + +减小的维度坐标用于将细胞聚类成推定的亚群。 +我们通过构建共享最近邻图 [@xu2015identification]来实现,其中细胞是节点，并且在共享最近相邻的细胞之间形成边。 +然后使用`r CRANpkg("igraph")`包中的方法将聚类定义为此图中高度相连的细胞社群。 +这比使用成对距离矩阵形成大量细胞的分层聚类的方法更有效。 + +```{r} +snn.gr <- buildSNNGraph(sce, use.dimred="PCA") +cluster.out <- igraph::cluster_walktrap(snn.gr) +my.clusters <- cluster.out$membership +table(my.clusters) +``` + +我们通过 _t_-SNE图可视化所有细胞聚类的分布情况 \@ref(fig:tsneclusterbrain)。 +通常将相邻细胞分配给相同的聚类，同时也证明了整个聚类的过程是正确的。 + +```{r tsneclusterbrain, message=FALSE, fig.cap="大脑数据集的去噪PC的_t_-SNE 图。每个点代表一个单元格，并根据其指定的聚类标识进行着色。"} +sce$cluster <- factor(my.clusters) +plotTSNE(sce, colour_by="cluster") + fontsize +``` + +```{r echo=FALSE, results='hide'} +gc() +``` + +另一种方法是使用基于图形的可视化，例如力导向布局 (Figure \@ref(fig:fdlbrain))。 +这方法吸引关联性很强，因为它们直接表示聚类期间的相互关系。 +但是，对于大型图形，收敛趋于缓慢，因此需要使用`niter=`参数以确保结果的稳定性。 + +```{r fdlbrain, message=FALSE, fig.cap="大脑数据集的共享最近邻图的强制定向布局。每个点代表一个细胞，并根据其指定的聚类标识进行着色。"} +set.seed(2000) +reducedDim(sce, "force") <- igraph::layout_with_fr(snn.gr, niter=5000) +plotReducedDim(sce, colour_by="cluster", use_dimred="force") +``` + +非常异构的数据集可能会在第一轮聚类中产生一些大的聚类。 +这是有用的仅使用每个初始聚类的细胞重复构建差异模型，去燥和聚类。 +这可以通过构建子集化`sce`基于特定水平的`my.clusters`，并重新应用相关的函数在子集上。 +这样做可能集中于在初始聚类内，已被定义为异质性的不同基因集，而不是那些被定义为初始聚类之间差异基因。 +这将允许以更高的识别率去探索每个聚类内的精结构。 +但是，为简单起见，我们将仅使用与此工作流程中的已明确子群体相对应的广泛聚类。 + +**Aaron的注释:** + +- `r CRANpkg("igraph")`包中提供了许多不同的聚类方法。. +我们发现Walktrap算法通常是一个很好的默认选择[@yang2016comparative]，但鼓励用户尝试不同的算法。 +- 减少近邻数量`buildSNNGraph`中的 `k`，将降低图形的连通性。 +这通常会导致形成较小的聚类[@xu2015identification], 如果需要更高的识别度，这可能是理想的做法。 +- 请注意，我们不运行`library(igraph)`, 而是使用`igraph::` 从包中提取方法。 +这是因为 `r CRANpkg("igraph")`包含了 `normalize`的方法，他将会覆盖`r Biocpkg("scater")`的对应物, 导致一些不寻常的错误。 + +## 评估基于图的聚类232323232323 + +模块化得分制为community检查方法提供了全局测量聚类情况的表现。 +简而言之，它将聚类内边缘的数量与随机边缘的空模型下的预期数量进行比较。 +高模块性分数（接近最大值1）表示检测到的聚类富集内部边缘，聚类之间的边缘相对较少。 + +```{r} +igraph::modularity(cluster.out) +``` + +我们通过检查每对聚类的边缘的总比重来进一步研究聚类。 +对于每对边缘，将观察到的总比重与空模型下的预期总比重进行比较，类似于模块化计算。 +大多数聚类包含的内部链接多于预期 (Figure \@ref(fig:heatmodbrain)),而聚类之间的链接少于预期。 +这表明我们成功地将细胞聚类成高度连接的社区群。 + +```{r heatmodbrain, fig.cap="相对于在随机链接的空模型下预期的总权重，相同聚类或不同聚类中的节点之间的总权重的log 10的比率的热图。"} +mod.out <- clusterModularity(snn.gr, my.clusters, get.values=TRUE) +ratio <- mod.out$observed/mod.out$expected +lratio <- log10(ratio + 1) + +library(pheatmap) +pheatmap(lratio, cluster_rows=FALSE, cluster_cols=FALSE, + color=colorRampPalette(c("white", "blue"))(100)) +``` + +为了总结聚类之间的关系，我们使用观察到的和预期的总权重的比率来构建跨聚类的图。 +可以使用力导向布局来可视化基于群聚类的图，以识别高度互连的“聚类群”。 +这类似于@wolf2017graph 提出的“图形抽象”策略. + +```{r graphbrain, fig.cap="力导向布局，显示基于不同聚类中节点之间观察到的预期总重量的比率的聚类之间的关系。一对聚类之间的边缘的厚度与相应的比例成比例"} +cluster.gr <- igraph::graph_from_adjacency_matrix(ratio, + mode="undirected", weighted=TRUE, diag=FALSE) +plot(cluster.gr, edge.width=igraph::E(cluster.gr)$weight*10) +``` + +**Aaron的注释:** + +- 我们不使用轮廓系数来评估大型数据集的聚类。 +这是因为 `cluster::silhouette` 需要构建距离矩阵，当涉及许多细胞时这可能是不可行的。 +- 从技术上讲，模块性分数制是通过从预期的总权重中减去观察到的。 +我们使用该比率，因为它的值保证是正的，并且没有因每个聚类中的细胞数量的差异而，表现出幅度差异。. + +# 检测亚群之间的标记基因 + +```{r, echo=FALSE, results="hide"} +# Hidden variables for use in text or hidden chunks, +# to avoid the need for manual changes. +chosen.inter.cluster <- 4 +chosen.inter.other <- 11 +``` + +我们使用该 `findMarkers` 函数和`direction="up"`选项去识别每个聚类的上调标记基因。 +如上所述，我们重点关注于上调基因，因为这些基因可以快速提供异质群体中细胞类型的有效鉴定。 +我们检查了这个 `r chosen.inter.cluster`聚类的表, 在这个表中报告了聚类`r chosen.inter.cluster`和其它聚类对数倍的变化。 +每个聚类具有相同的输出，以便识别区分聚类中的基因。 + +```{r, echo=FALSE, results="hide"} +old.digits <- options()$digits +options(digits=3) +``` + +```{r} +markers <- findMarkers(sce, my.clusters, direction="up") +marker.set <- markers[["4"]] +head(marker.set[,1:8], 10) # only first 8 columns, for brevity +``` + +```{r, echo=FALSE, results="hide"} +# Checking the cluster is what we wanted, along with its related cluster. +other.inter <- chosen.inter.other - 1 +gad1 <- sapply(marker.set["Gad1",-(1:3)], sign) +stopifnot(gad1[other.inter]==-1) +stopifnot(all(gad1[-other.inter]==1)) + +gad2 <- sapply(marker.set["Gad2",-(1:3)], sign) +stopifnot(gad2[other.inter]==-1) +stopifnot(all(gad2[-other.inter]==1)) + +stopifnot(all(sapply(marker.set["Synpr",-(1:3)], sign)==1)) + +options(digits=old.digits) +``` + +图 \@ref(fig:heatmapmarkerbrain) 表明与一些其它聚类相比，聚类 `r chosen.inter.cluster`含有与其它聚类对比的大多数具有比较大表达差异的顶端标记。 +我们可以使用这些标记在具有独立细胞群的验证研究中鉴定聚类`r chosen.inter.cluster` 中的细胞。 +快速观察聚类表明聚类 `r chosen.inter.cluster`代表基于 *Gad1* 和 *Slc6a1*的中间神经元 [@zeng2012largescale], +与在聚类`r chosen.inter.other`中密切相关的细胞不同，因为*Synpr* 高度表达。 + +```{r heatmapmarkerbrain, fig.wide=TRUE, fig.cap=sprintf("大脑数据集中聚类`chosen.inter.cluster`的顶部标记集的平均中心和标准化对数表达值的热图。列颜色表示每个细胞分配到的聚类，如图例所示。", chosen.inter.cluster)} +top.markers <- rownames(marker.set)[marker.set$Top <= 10] +plotHeatmap(sce, features=top.markers, columns=order(my.clusters), + colour_columns_by="cluster", cluster_cols=FALSE, + center=TRUE, symmetric=TRUE, zlim=c(-5, 5)) +``` + +另一种可视化方法是直接将对数倍变化绘制到所有其它聚类(Figure \@ref(fig:heatmaplfcbrain))。 +这更简洁，在涉及包含不同数量细胞的多个聚类的情况下非常有用。. + +```{r heatmaplfcbrain, fig.asp=1.5, fig.cap=sprintf("与大脑数据集中的每个其它聚类相比，聚类`chosen.inter.cluster`的顶部标记集的表达式中对数倍变化的热图。" )} +logFCs <- as.matrix(marker.set[1:50,-(1:3)]) +colnames(logFCs) <- sub("logFC.", "", colnames(logFCs)) + +library(pheatmap) +max.lfc <- max(abs(range(logFCs))) +pheatmap(logFCs, breaks=seq(-5, 5, length.out=101)) +``` + +我们保存候选标记基因列表以供进一步检查，使用压缩的方法来减小文件大小。 + +```{r} +gzout <- gzfile("brain_marker_1.tsv.gz", open="wb") +write.table(marker.set, file=gzout, sep="\t", quote=FALSE, col.names=NA) +close(gzout) +``` + +**Aaron的注释:** + +- 该 `overlapExprs()`函数也可用于总结集群之间的差异。 +更详细的的讨论 `r simpleSingleCell:::.link("de", "Using the Wilcoxon rank sum test", "在这里")`. + +# 结束语 + +完成基本分析后，我们将`SingleCellExperiment` 对象及其关联数据保存到文件中。 +这非常重要，因为大脑数据集非常大。 +如果要进行进一步的分析，则必须重复上述所有的预处理步骤是不方便的。 + +```{r} +saveRDS(file="brain_data.rds", sce) +``` + +```{r, echo=FALSE, results='hide'} +gc() +``` + +All software packages used in this workflow are publicly available from the Comprehensive R Archive Network (https://cran.r-project.org) or the Bioconductor project (http://bioconductor.org). +The specific version numbers of the packages used are shown below, along with the version of the R installation. + +```{r} +sessionInfo() +``` + +# References + From 9d36852efb53b554726950afa82ea5ff47f5eb5a Mon Sep 17 00:00:00 2001 From: lakeseafly <21230309@student.uwa.edu.au> Date: Thu, 1 Aug 2019 14:02:36 +0800 Subject: [PATCH 3/8] =?UTF-8?q?=E4=BD=BF=E7=94=A8R=20/=20Bioconductor?= =?UTF-8?q?=E5=88=86=E6=9E=90=E5=8D=95=E7=BB=86=E8=83=9ERNA-seq=E6=95=B0?= =?UTF-8?q?=E6=8D=AE=E7=9A=84=E5=B7=A5=E4=BD=9C=E6=B5=81=E7=A8=8B=EF=BC=9A?= =?UTF-8?q?=E4=BB=8B=E7=BB=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 使用R / Bioconductor分析单细胞RNA-seq数据的工作流程：介绍 --- BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd b/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd index 24e4766..7a83dc6 100644 --- a/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd +++ b/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd @@ -75,7 +75,7 @@ z这样做的目的是提供各种模块化使用示例，以便读者可以更 对于SMART-seq2数据 [@picelli2014fulllength], 针对大量RNA-seq开发的定量程序通常是令人满意的 [@love2015rnaseq;@chen2016from]. 喜欢使用基于R的方法来进行read的比对和计数的用户可以考虑使用 `r Biocpkg("Rsubread")` 包中的方法 [@liao2013subread;@liao2014featurecounts]。 -许多其它scRNA-seq实验方法包含需要小心处理的预定的序列结构： +许多其它scRNA-seq实验方法都包含需要小心处理的预定的序列结构： - 独特的分子标识物 (UMIs) [@islam2014quantitative] 被广泛用于减轻扩增偏差的影响。 具有相同的UMI比对到相同基因的reads代表单个基础转录物分子并且仅将该基因的计数增加1。 From 61fa65a0e1e9171f70f2d5a923e1ce6ab7ec5288 Mon Sep 17 00:00:00 2001 From: lakeseafly <21230309@student.uwa.edu.au> Date: Thu, 1 Aug 2019 14:04:51 +0800 Subject: [PATCH 4/8] =?UTF-8?q?=E5=88=86=E6=9E=90=E5=8C=85=E5=90=ABRead?= =?UTF-8?q?=E8=AE=A1=E7=AE=97=E7=9A=84=E5=8D=95=E7=BB=86=E8=83=9ERNA-seq?= =?UTF-8?q?=E6=95=B0=E6=8D=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 分析包含Read计算的单细胞RNA-seq数据 --- BiocWorkflow_todo/02simpleSingleCell_en/reads.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/BiocWorkflow_todo/02simpleSingleCell_en/reads.Rmd b/BiocWorkflow_todo/02simpleSingleCell_en/reads.Rmd index 6c42855..a399c42 100644 --- a/BiocWorkflow_todo/02simpleSingleCell_en/reads.Rmd +++ b/BiocWorkflow_todo/02simpleSingleCell_en/reads.Rmd @@ -33,7 +33,7 @@ opts_chunk$set(fig.asp=1) # 概述 -在这个工作流程中，我们使用一个相对简单的数据集 [@lun2017assessing] 来介绍scRNA-seq数据分析的大部分概念。 +在这个工作流程中，我们使用一个相对比较简单的数据集 [@lun2017assessing] 来介绍scRNA-seq数据分析的大部分概念。 该数据集包含两个416B细胞板（永生化小鼠骨髓祖细胞系），使用Smart-seq2s实验方法来处理 [@picelli2014fulllength]。 在文库制备之前，还将the External RNA Controls协会（ERCC）的z指定的恒定量的加标RNA添加到每个细胞的裂解物中。 然后进行进行高通量测序，并通过计数能比对到外显子区域的reads的总数来量化每个基因的表达量。 From 56850b1fdda343802331eacb681079f32a91bd35 Mon Sep 17 00:00:00 2001 From: lakeseafly <21230309@student.uwa.edu.au> Date: Thu, 1 Aug 2019 14:06:19 +0800 Subject: [PATCH 5/8] =?UTF-8?q?=E4=BD=BF=E7=94=A8R=20/=20Bioconductor?= =?UTF-8?q?=E5=88=86=E6=9E=90=E5=8D=95=E7=BB=86=E8=83=9ERNA-seq=E6=95=B0?= =?UTF-8?q?=E6=8D=AE=E7=9A=84=E5=B7=A5=E4=BD=9C=E6=B5=81=E7=A8=8B=E4=BB=8B?= =?UTF-8?q?=E7=BB=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 使用R / Bioconductor分析单细胞RNA-seq数据的工作流程介绍 --- BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd b/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd index 7a83dc6..9a25791 100644 --- a/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd +++ b/BiocWorkflow_todo/02simpleSingleCell_en/intro.Rmd @@ -46,7 +46,7 @@ opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE, cache=TRUE) 分析单细胞转录组计数数据来检测高度多变的基因（HVG），这些高度多变的基因能驱动群体中细胞的异质性。分析单细胞转录组数据也能发现基因和细胞表型之间的相关性，或通过降维和聚类来鉴定新的亚群。这能让我们从单细胞水平去理解生物学问题，这是细胞群水平的传统的RNA测序无法实现的。 scRNA-seq数据分析的策略与传统的RNA-seq的策略明显不同。 -一个技术原因是scRNA-seq数据比传统批量数据噪声更大 [@brennecke2013accounting;@marinov2014singlecell]. +其一个技术原因是scRNA-seq数据比传统批量数据噪声更大 [@brennecke2013accounting;@marinov2014singlecell]。 由于单个细胞中RNA的量低，难以将转录物可靠地捕获（即转化）成cDNA用于测序。 这增加了drop-out事件的频率，并产生没有捕获基因的转录物。在分析过程中需要专门的步骤来处理这种噪音，特别是在质控的时候。此外，scRNA-seq数据可用于研究细胞间异质性，例如，识别新的细胞亚型，表征分化过程，将细胞分配到细胞周期阶段，或识别驱动整个种群的变异性的高度多变基因(HVG) [@vallejos2015basics;@fan2016characterizing;@trapnell2014dynamics]。 传统的批量RNA数据并不能实现上面提到的应用，这意味着需要新的分析流程来执行单细胞转录组分析。 From de5bd12330a71f7e7e74d938466299b5c2b67a88 Mon Sep 17 00:00:00 2001 From: lakeseafly <21230309@student.uwa.edu.au> Date: Thu, 1 Aug 2019 14:07:52 +0800 Subject: [PATCH 6/8] =?UTF-8?q?=E4=BB=8E=E5=9F=BA=E4=BA=8E=E6=B6=B2?= =?UTF-8?q?=E6=BB=B4=E7=9A=84=E5=AE=9E=E9=AA=8C=E6=96=B9=E6=B3=95=E6=9D=A5?= =?UTF-8?q?=E5=88=86=E6=9E=90=E5=8D=95=E7=BB=86=E8=83=9ERNA=E6=B5=8B?= =?UTF-8?q?=E5=BA=8F=E6=95=B0=E6=8D=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 从基于液滴的实验方法来分析单细胞RNA测序数据 --- BiocWorkflow_todo/02simpleSingleCell_en/tenx.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/BiocWorkflow_todo/02simpleSingleCell_en/tenx.Rmd b/BiocWorkflow_todo/02simpleSingleCell_en/tenx.Rmd index 62e193e..974f861 100644 --- a/BiocWorkflow_todo/02simpleSingleCell_en/tenx.Rmd +++ b/BiocWorkflow_todo/02simpleSingleCell_en/tenx.Rmd @@ -166,7 +166,7 @@ hist(all.out$PValue[all.out$Total <= limit & all.out$Total > 0], ``` 接近零的大峰值表明具有低于`lower`的值的条形码的总数并非全部来自外部环境。 -这可以通过进一步减少 `lower`的值去进一步排除条形码对应于具有非常小的细胞的液滴。 +这可以通过进一步减少 `lower`的值去进一步排除条形码，其所对应于具有非常小的细胞的液滴。 # 细胞的质控 From 705b6a3dbb66535e3fe58eff8848dd5c0e34b207 Mon Sep 17 00:00:00 2001 From: lakeseafly <21230309@student.uwa.edu.au> Date: Thu, 1 Aug 2019 14:08:51 +0800 Subject: [PATCH 7/8] =?UTF-8?q?=E5=88=86=E6=9E=90=E5=90=AB=E6=9C=89UMI?= =?UTF-8?q?=E8=AE=A1=E6=95=B0=E7=9A=84=E5=8D=95=E7=BB=86=E8=83=9ERNA-seq?= =?UTF-8?q?=E6=95=B0=E6=8D=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 分析含有UMI计数的单细胞RNA-seq数据 --- BiocWorkflow_todo/02simpleSingleCell_en/umis.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/BiocWorkflow_todo/02simpleSingleCell_en/umis.Rmd b/BiocWorkflow_todo/02simpleSingleCell_en/umis.Rmd index b65c726..d69fa90 100644 --- a/BiocWorkflow_todo/02simpleSingleCell_en/umis.Rmd +++ b/BiocWorkflow_todo/02simpleSingleCell_en/umis.Rmd @@ -96,7 +96,7 @@ stopifnot(identical(endo.data$metadata$cell_id, spike.data$metadata$cell_id)) # stopifnot(all(endo.data$metadata$cell_id==mito.data$metadata$cell_id)) # should now be the same. ``` -在该特定数据集中，一些基因由对应于可供选择的基因位置的多行来表示。 +在该给定数据集中，一些基因由对应于可供选择的基因位置的多行来表示。 为了便于解释，我们将对应于单个基因的所有行的计数相加。 ```{r} From fa51fc9775e696899b90a5df74895357fbb1e1ca Mon Sep 17 00:00:00 2001 From: lakeseafly <21230309@student.uwa.edu.au> Date: Wed, 16 Oct 2019 11:52:48 +0800 Subject: [PATCH 8/8] Add files via upload --- .../Haifei Hu\342\200\231s CV.pdf" | Bin 0 -> 883436 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 "BiocWorkflow_todo/02simpleSingleCell_en/Haifei Hu\342\200\231s CV.pdf" diff --git "a/BiocWorkflow_todo/02simpleSingleCell_en/Haifei Hu\342\200\231s CV.pdf" "b/BiocWorkflow_todo/02simpleSingleCell_en/Haifei Hu\342\200\231s CV.pdf" new file mode 100644 index 0000000000000000000000000000000000000000..819fad518e0e058e086e74b3033f6c0400a6269b GIT binary patch literal 883436 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