rename package to ChIPseqSpikeInFree

.gitignore

@@ -1,2 +1,3 @@
 *.Rproj
 .Rproj.user
+.gitignore

DESCRIPTION

@@ -1,13 +1,13 @@
-Package: ChIPseqSpikeFree
+Package: ChIPseqSpikeInFree
 Title: A Spike-in Free ChIP-Seq Normalization Approach for Detecting Global Changes in Histone Modifications
-Version: 1.0.7
-Date: 2019-02-12
+Version: 1.0.8
+Date: 2019-02-18
 Authors@R: 
     person(given = "Hongjian",
            family = "Jin",
            role = c("aut", "cre"),
            email = "[email protected]")
-Description:The detection of global histone modification changes can be addressed using exogenous reference Spike-in controls. However, many thousands of ChIP-seq data available in public depositories nowadays were done without including Spike-in procedure. In order to do quantitative comparisons between these data, researchers have to regenerate whole data set using spike-in ChIP-seq protocols – this is an infeasible solution sometime. A basic scaling factor calculation for these scenarios remains a problem with surprisingly few solutions presented so far.We pesent ChIPseqSpikeFree , a simple ChIP-seq normalization method to effectively determine scaling factors for samples across different conditions or treatments, which doesn't rely on exogenous spike-in chromatin or peak detection to reveal global changes in histone modification occupancy. It can reveal same magnitude of global changes compared to spike-In method.
+Description:The detection of global histone modification changes can be addressed using exogenous reference Spike-in controls. However, many thousands of ChIP-seq data available in public depositories nowadays were done without including Spike-in procedure. In order to do quantitative comparisons between these data, researchers have to regenerate whole data set using spike-in ChIP-seq protocols – this is an infeasible solution sometime. A basic scaling factor calculation for these scenarios remains a problem with surprisingly few solutions presented so far.We pesent ChIPseqSpikeInFree , a novel ChIP-seq normalization method to effectively determine scaling factors for samples across different conditions or treatments, which doesn't rely on exogenous spike-in chromatin or peak detection to reveal global changes in histone modification occupancy. It can reveal same magnitude of global changes compared to spike-In method.
 Depends:
   GenomicAlignments,
   GenomicRanges,  

NAMESPACE

@@ -2,7 +2,7 @@
 
 export(BoxplotSF)
 export(CalculateSF)
-export(ChIPseqSpikeFree)
+export(ChIPseqSpikeInFree)
 export(CountRawReads)
 export(GenerateBins)
 export(ParseReadCounts)

README.md

@@ -1,13 +1,13 @@
-## ChIPseqSpikeFree, 
+## ChIPseqSpikeInFree, 
 A Spike-in Free ChIP-Seq Normalization Approach for Detecting Global Changes in Histone Modifications
 
 ## Background
 
-The detection of global histone modification changes can be addressed using exogenous reference Spike-in controls. However, many thousands of ChIP-seq data available in public depositories nowadays were done without including Spike-in procedure. In order to do quantitative comparisons between these data, researchers have to regenerate whole data set using spikein ChIP-seq protocols – this is an infeasible solution sometime. A basic scaling factor calculation for these scenarios remains a problem with surprisingly few solutions presented so far. We pesent ChIPseqSpikeFree , a simple ChIP-seq normalization method to effectively determine scaling factors for samples across different conditions or treatments, which doesn't rely on exogenous spike-in chromatin or peak detection to reveal global changes in histone modification occupancy. It can reveal same magnitude of global changes compared to spike-In method.
+The detection of global histone modification changes can be addressed using exogenous reference Spike-in controls. However, many thousands of ChIP-seq data available in public depositories nowadays were done without including Spike-in procedure. In order to do quantitative comparisons between these data, researchers have to regenerate whole data set using spikein ChIP-seq protocols – this is an infeasible solution sometime. A basic scaling factor calculation for these scenarios remains a problem with surprisingly few solutions presented so far. We pesent ChIPseqSpikeInFree , a simple ChIP-seq normalization method to effectively determine scaling factors for samples across different conditions or treatments, which doesn't rely on exogenous spike-in chromatin or peak detection to reveal global changes in histone modification occupancy. It can reveal same magnitude of global changes compared to spike-In method.
 
 ## Prerequisites
 
-ChIPseqSpikeFree depends on Rsamtools,GenomicRanges and GenomicAlignments to count reads from bam file.
+ChIPseqSpikeInFree depends on Rsamtools,GenomicRanges and GenomicAlignments to count reads from bam file.
 To install these packages, start R (version "3.4") and enter:
 ```
 >source("https://bioconductor.org/biocLite.R")
@@ -29,12 +29,12 @@ If you use R (version "3.5") and enter:
 If you use R, enter
 ```
 #Option 1. intall this package from CRAN
->install.packages("ChIPseqSpikeFree")
+>install.packages("ChIPseqSpikeInFree")
 
 #Option 2. intall this package from GitHub
 >install.packages("devtools")
 >library(devtools)
->install_github("hongjianjin/ChIPseqSpikeFree")
+>install_github("hongjianjin/ChIPseqSpikeInFree")
 ```
 
 ### Usage
@@ -43,7 +43,7 @@ A simple workflow in R environment.
 
 ###### 0. load package
 ```
->library("ChIPseqSpikeFree")
+>library("ChIPseqSpikeInFree")
 ```
 ###### 1. generate a sample_meta.txt (tab-delimited txt file) as follows
 ##save as "/your/path/sample_meta.txt"
@@ -60,9 +60,9 @@ A simple workflow in R environment.
 >bams <- c("ChIPseq1.bam","ChIPseq2.bam")
 ```
 
-###### 3. run ChIPseqSpikeFree pipeline (when your bam files correspond to the human reference hg19) 
+###### 3. run ChIPseqSpikeInFree pipeline (when your bam files correspond to the human reference hg19) 
 ```
->ChIPseqSpikeFree(bamFiles=bams, chromFile="hg19",metaFile=metaFile,prefix="test")
+>ChIPseqSpikeInFree(bamFiles=bams, chromFile="hg19",metaFile=metaFile,prefix="test")
 ```
 
 ### Input
@@ -71,7 +71,7 @@ In the simple usage scenario, the user should have ChIP-seq Bam files ready and
 
 ##### 1.bamFiles: a vector of bam filenames
 
-User should follow ChIP-seq guidelines suggested by EMCODE consortium(Landt, et al., 2012) and check the data quality. We recommend to remove low-quality or non-unique reads from your bam files before you run ChIPseqSpikeFree normalization.
+User should follow ChIP-seq guidelines suggested by EMCODE consortium(Landt, et al., 2012) and check the data quality. We recommend to remove low-quality or non-unique reads from your bam files before you run ChIPseqSpikeInFree normalization.
 
 ##### 2.chromFile:chromosome sizes of reference genome. 
 "hg19", "mm9","mm10","hg19" are included in the package.
@@ -88,9 +88,9 @@ A tab-delimited text file having three columns: ID, ANTIBODY and GROUP. Where ID
 
 ### Output
 
-After you successfully run following ChIPseqSpikeFree pipeline 
+After you successfully run following ChIPseqSpikeInFree pipeline 
 ```
->ChIPseqSpikeFree(bamFiles=bams, chromFile="hg19",metaFile=metaFile,prefix="test")
+>ChIPseqSpikeInFree(bamFiles=bams, chromFile="hg19",metaFile=metaFile,prefix="test")
 ```
 Output will include: (in case that you set prefix ="test")
 ##### 1. test_SF.txt (text result)