Normalise for IC50 with K-Clustering: +Visualisation
+Kate Chiang
+2024-05-17
+ +This document allows visualisation of the use of k-clustering to +select the best mean for normalising neutralisation data and account for +dodgy control values.
+#Download Packages
+library(ggplot2) #to make ggplot
+library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
+## ✔ dplyr 1.1.4 ✔ readr 2.1.5
+## ✔ forcats 1.0.0 ✔ stringr 1.5.1
+## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
+## ✔ purrr 1.0.2 ✔ tidyr 1.3.1
+## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
+## ✖ dplyr::filter() masks stats::filter()
+## ✖ dplyr::lag() masks stats::lag()
+## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidyr)
+library(dplyr)
+library(Ckmeans.1d.dp) #for k-clustering1 Setting up
+input_file <- here::here("Validation/anomaly_detection/example1kcv2.xlsx") #CHANGE to example file in your directory for troubleshooting
+input_directory = here::here("Validation/anomaly_detection/") #CHANGE this to directory of input .xlsx data filesrotate_by <- 0 # clockwise degrees, multiples of 90, useful when doing 12 fold dilutions.
+control_neg_column <- c(1) # default negative control column is on first column from the left
+control_pos_column <- c(2) # default positive control column is on second column from the leftGetting the filenames from our directory of anomalous data
+anomaly_detection_files <- Sys.glob(paste0(
+ paste0(input_directory,"/*.xlsx")) # find all .xlsx file in input directory
+)
+anomaly_detection_files## [1] "/Users/katie/Documents/GitHub/Visualise/Validation/anomaly_detection//example1kc.xlsx"
+## [2] "/Users/katie/Documents/GitHub/Visualise/Validation/anomaly_detection//example1kcv2.xlsx"2 Example case: +examplekcv2
+Examplekcv2 contains anomalously low values in the positive control +column that are not ideal for normalisation. Although the majority of +the values are low, the positive control values can be clustered into +two groups with the higher average being better for normalisation - as +long as the cluster used in normalisation contains at least three +values.
+df <- NormaliseForIC50::read_promega_plate_excel(input_promega_excel_file_path = input_file)
+df## neg pos sample one sample one.1 sample two NA sample three
+## A 90 376100 182200 229100 275900 186500 80
+## B 80 27750 289200 292000 100900 245400 100
+## C 100 28720 288400 292000 278800 277700 380
+## D 110 301300 309500 301100 304800 313300 24090
+## E 50 28060 293000 286400 309400 292500 99030
+## F 90 27200 266000 289600 303000 309200 175700
+## G 100 25120 295100 286500 311100 283200 244100
+## H 50 348100 350900 345000 386400 364800 343600
+## sample three.1 9 10 11 12
+## A 90 133200 143600 55510 96660
+## B 110 266000 269900 259500 298000
+## C 830 291200 293900 275200 327900
+## D 27390 314900 289400 314200 397400
+## E 119800 300700 322300 293400 360300
+## F 203000 302600 294500 293000 379000
+## G 239600 287200 307500 295000 372400
+## H 351700 360300 332200 350300 3683003 Clean dataset
+Isolate the positive and negative control values and the three +largest and three smallest values from each sample column
+#'Use get_top_bottom function to isolate top 3 and bottom 3 values
+get_top_bottom <- function(column) {
+ top_values <- sort(column, decreasing = TRUE)[1:3]
+ bottom_values <- sort(column)[1:3]
+ return(c(top_values, bottom_values))
+}
+top_bottom_values <- lapply(df[c(3:12)], get_top_bottom) #Apply get_top_bottom function
+top_bottom_df <- as.data.frame(top_bottom_values) #Create data frame
+df.vis <- top_bottom_df %>% gather(key=y, value=x)
+data_min_max <- df.vis %>% #Rearrange to get three columns with data labeled as 'min' or 'max'
+ arrange(x) %>%
+ group_by(y) %>%
+ mutate(Label = case_when(
+ row_number() <= 3 ~ "min",
+ row_number() > n() - 3 ~ "max",
+ TRUE ~ NA_character_
+ ))
+
+#'Isolate and cluster positive control values (The third column refers to which cluster the value belongs to)
+#'Ensure that the third column "Label" is a character
+control_pos_values <- df[c(2)] %>% gather(key=y, value=x)
+poscontrolmeans <- Ckmeans.1d.dp::Ckmeans.1d.dp(as.numeric(unlist(df[c(2)])))
+control_pos_values$Label <- poscontrolmeans$cluster
+control_pos_values<-transform(control_pos_values, Label = as.character(Label)) #"Label" column is denoted as <character>
+
+#'Isolate and cluster negative control values (The third column refers to which cluster the value belongs to)
+control_neg_values <- df[c(1)] %>% gather(key=y, value=x)
+negcontrolmeans <- Ckmeans.1d.dp::Ckmeans.1d.dp(as.numeric(unlist(df[c(1)])))
+control_neg_values$Label <- negcontrolmeans$cluster
+control_neg_values <- transform(control_neg_values, Label = as.character(Label)) #"Label" column is denoted as <character>4 K-clustering
+Select the cluster mean from the positive and negative control that +is closest to the average of the sample maximum and sample minimum +values, respectively. A warning will be thrown if none of the k-clusters +have at least 3 values.
+source(here::here("R/k_clustering.R"))
+source(here::here("R/normalise_plate.R"))
+source(here::here("R/mean_without_outliers.R"))
+#exclude positive and negative control columns
+ df.values<-df[,-c(control_neg_column,control_pos_column)]
+
+ # Throw warning
+ check_max(df.values,filename = filename) #warning if last three row values are not the largest in the column
+ check_min(df.values,filename = filename) #warning if first three row values are not the smallest in the column
+ lim <- data.frame(
+ lower_lim = apply(df.values, MARGIN = 2, min),
+ upper_im = apply(df.values, MARGIN = 2, max)
+ )Isolate suitable positive and negative control clusters
+control.neg <- k_clustering(unlist(df[,control_neg_column]), lim$lower_lim, type = "negative", na.rm=TRUE) ## 2negativeclusters: sized2,6control.pos <- k_clustering(unlist(df[,control_pos_column]), lim$upper_im, type = "positive", na.rm=TRUE)## 2positiveclusters: sized5,35 Plot
+Combine control and sample data into dataset containing samples, +values, and labels.
+#'Combine labeled control and sample graphs
+df.visualize <- control_neg_values %>%
+ bind_rows(control_pos_values)%>%
+ bind_rows(data_min_max)The plot shows boxplots for the minimum and maximum three values for +each sample. Boxplots in the pos and neg control columns represent the +identified clusters. An hline() is created for the mean of a select +cluster generated via the k-clustering function. From the positive +control, the “Positive Control Cluster Mean” is the mean of the cluster +that is closest to the average of the sample maximums (highest values on +the “max” boxplots). From the negative control, the “Negative Control +Cluster Mean” is the mean of the cluster that is closest to the average +of the sample minimums (lowest values on the “min” boxplots).
+ggplot(df.visualize, suppressWarnings(aes(x=df.visualize$y, y=df.visualize$x, color=Label)))+
+ labs(title = "Control Cluster Means for Normalisation",
+ x="Samples", y="Titers", color = "Legend")+
+ geom_boxplot(position = position_dodge(0.8))+
+ geom_jitter(position = position_dodge(0.8))+
+ scale_x_discrete(limits=c("neg", "pos", "sample.one", "sample.one.1", "sample.two", "NA.", "sample.three", "sample.three.1", "X9", "X10", "X11","X12"))+
+ geom_hline(yintercept = control.pos, color="maroon", lty="dashed", lwd=1)+
+ geom_hline(yintercept = control.neg, color="brown", lty="dashed", lwd=1)+
+ geom_label(label="Positive Control Cluster Mean",
+ x = "X9",
+ y = 380000,
+ label.padding = unit(0.01, "lines"),
+ label.size = 0.05,
+ color = "maroon",
+ size = 3)+
+ geom_label(label="Negative Control Cluster Mean",
+ x = "X9",
+ y = 30000,
+ label.padding = unit(0.01, "lines"),
+ label.size = 0.05,
+ color = "brown",
+ size = 3)
Compared to simple means, the cluster mean appears to work better for +normalisation of widely varying control data since it selects a mean +that is closest to the average maximum and minimum samples values. The +hline for the positive control simple mean is much lower and not as +close to the sample maximums as the selected cluster mean.
+ggplot(df.visualize, suppressWarnings(aes(x=df.visualize$y, y=df.visualize$x, color=Label)))+
+ labs(title = "Control Simple Means for Normalisation",
+ x="Samples", y="Titers", color = "Legend")+
+ geom_boxplot(position = position_dodge(0.8))+
+ geom_jitter(position = position_dodge(0.8))+
+ scale_x_discrete(limits=c("neg", "pos", "sample.one", "sample.one.1", "sample.two", "NA.", "sample.three", "sample.three.1", "X9", "X10", "X11","X12"))+
+ geom_hline(yintercept = mean(unlist(df[,control_pos_column]),na.rm=TRUE), color="maroon", lty="dashed", lwd=1)+
+ geom_hline(yintercept = mean(unlist(df[,control_neg_column]),na.rm=TRUE), color="brown", lty="dashed", lwd=1)+
+ geom_label(label="Positive Control Simple Mean",
+ x = "X9",
+ y = 105000,
+ label.padding = unit(0.3, "lines"),
+ label.size = 0.05,
+ color = "maroon",
+ size = 3)+
+ geom_label(label="Negative Control Simple Mean",
+ x = "X9",
+ y = 30000,
+ label.padding = unit(0.3, "lines"),
+ label.size = 0.05,
+ color = "brown",
+ size = 3)