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CaRpools-PDF.Rmd
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CaRpools-PDF.Rmd
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---
title: "CaRpools Report - CRISPR Screen Analysis"
output:
pdf_document:
fig_height: 6
fig_width: 11
highlight: null
keep_tex: yes
latex_engine: xelatex
toc: yes
toc_depth: 3
---
```{r loadlibs, echo=FALSE, error=FALSE, warning=FALSE, message=FALSE}
if("caRpools" %in% rownames(installed.packages()) == FALSE) {install.packages("caRpools")}
library(caRpools,warn.conflicts = FALSE, quietly = TRUE,verbose =FALSE)
load.packages()
```
\newpage
\begin{center}
\includegraphics{CaRpools.png}
\end{center}
```{r settings-setup, echo=FALSE, error=FALSE, warning=FALSE, message=FALSE}
#options(RCurlOptions=list(proxy="YOURproxyHERE", http.version=1))
### Load PARAMETER FILE
# This name can be changed to your needs
if(!exists("miaccs.file"))
{miaccs.file = "MIACCS.xls"}
miaccs = load.file(miaccs.file, type="xlsx")
#### global options to produce the report
knitr::opts_chunk$set(sanitize=TRUE)
knitr::opts_chunk$set(dev=miaccs["carpools.device",3]) # ImageMagick needs to be installed!
knitr::opts_chunk$set(dpi=as.numeric(miaccs["carpools.dpi",3]))
knitr::opts_chunk$set(results="asis")
options(scipen = 0)
#### Screen Information
screen.id = as.character(miaccs["screening.id",3])
screen.date = as.character(miaccs["screening.date",3])
screen.hypothesis = as.character(miaccs["hypothesis",3])
screen.description = as.character(miaccs["assay.description",3])
screen.organism = as.character(miaccs["assay.organism",3])
screen.cell = as.character(miaccs["assay.cellline",3])
screen.drug = as.character(miaccs["assay.treated",3])
screen.control = as.character(miaccs["assay.untreated",3])
screen.targets = as.character(miaccs["assay.genes",3])
screen.plasmids = as.character(miaccs["assay.plasmids",3])
screen.library = as.character(miaccs["assay.library",3])
screen.NGS = as.character(miaccs["lib.sequencing",3])
screen.despergene = as.character(miaccs["assay.designs",3])
#### FILES AND DATASETS
## PATHs
# abolsute path to where CRISPR-extract.pl and CRISPR-mapping.pl are located
scriptpath=as.character(miaccs["carpools.scriptpath",3])
# abolsute path to where fastq / read count and mapping files are located
datapath = as.character(miaccs["carpools.datapath",3])
## File Names
# name of fastq files of the datasets WITHOUT extension .fastq
# in general, the presence of two replicates for each group is MANDATORY.
# If mapping = TRUE and extract = FALSE, these files need to be already extracted fastq files.
# If mapping = FALSE and extract = FALSE, these files need to be final .txt files with read count data and .txt extension.
fileCONTROL1 = as.character(miaccs["carpools.untreated1",3])
d.CONTROL1 = as.character(miaccs["carpools.untreated1.desc",3])
fileCONTROL2 = as.character(miaccs["carpools.untreated2",3])
d.CONTROL2 = as.character(miaccs["carpools.untreated2.desc",3])
fileTREAT1 = as.character(miaccs["carpools.treated1",3])
d.TREAT1 = as.character(miaccs["carpools.treated1.desc",3])
fileTREAT2 = as.character(miaccs["carpools.treated2",3])
d.TREAT2 = as.character(miaccs["carpools.treated2.desc",3])
# name of reference library .fasta file WITHOUT extension .fasta
referencefile = as.character(miaccs["carpools.lib.ref",3])
## Analysis Name
# This name will be used as file name for all tabular, PDF and HTML files.
analysis.name = stringi::stri_replace_all_fixed(miaccs["assay.name",3], " ", "_")
knitr::opts_chunk$set(fig.path=paste(datapath,"/",analysis.name, "/", sep=""))
#### PERL REGULAR EXPRESSIONS
## Gene Identifier Discrimination from sgRNA name
# Regular expression that is returning the gene identifier from the sgRNA name
# e.g. expression("^(.+?)(_.+)")
g.extractpattern = as.character(miaccs["carpools.regex.gene",3])
# Pattern to extract data if extract=TRUE, for more information see manual
seq.pattern = as.character(miaccs["carpools.regex.extract",3])
# Pattern of READ ID (depends on sequencing maschine), e.g. M01100
maschine.pattern = as.character(miaccs["carpools.regex.maschine",3])
#### DATA EXTRACTION AND MAPPING
## Extract fastq files from fastq raw data?
extract = as.logical(miaccs["carpools.extract.fastq",3])
# is the data within the fastq file in reverse complement oriantation (e.g. paired end read)
reversecomplement = as.logical(miaccs["carpools.fastq.rev",3])
## Mapping
# Shall the extracted fastq file be mapped to the reference library file using the generated or already present .SAM file and count files created?
mapping = as.logical(miaccs["carpools.mapping",3])
## Mapping extracted data
# Create a bowtie2 index file? TRUE if NO bowtie2 index file has been generated for the reference library fasta file, so that a new one is created
createindex = as.logical(miaccs["carpools.bt2.index",3])
# How many threads shall be used for bowtie2 usage?
threads = as.integer(miaccs["carpools.bt2.threads",3])
# Additional parameters for bowtie2 alignment
bowtieparams = as.character(miaccs["carpools.bt2.additional",3])
if(is.na(bowtieparams) || bowtieparams == "NA" || bowtieparams == "none" || is.null(bowtieparams))
{bowtieparams = ""}
# Sensitivity string for bowtie2 alignment, e.g. --very-sensitive-local, --local, --very-sensitive -> have a look in the bowtie2 manual!
sensitivity = as.character(miaccs["carpools.bt2.sens",3])
# How good must the alignment of the 20 nt CRISPR Oligo must be in order to be considered as present in the data?
match = as.character(miaccs["carpools.align.quality",3])
#### REPORT GENERATION
## CONVERT GENE IDENTIFIER?
g.convert = as.logical(miaccs["carpools.gconvert",3])
# Which dataset shall be used?
# See biomaRt for more information on dataset and type
a.database = as.character(miaccs["carpools.bm.database",3])
a.dataset = as.character(miaccs["carpools.bm.dataset",3])
# What kind of identifier is used? e.g. EnsemblID or HGNC Symbol?
# See biomaRt Filter for additional identifiers (filters)
g.identifier = as.character(miaccs["carpools.bm.identifier",3])
# Convert it to?
# see biomaRt for additional attributes
g.identifier.new = as.character(miaccs["carpools.bm.identifier.new",3])
## Anootate final hit candidates?
a.annotate.hits = as.logical(miaccs["carpools.annotate",3])
a.annotate = unlist(strsplit(stringi::stri_replace_all_fixed(miaccs["carpools.bm.attributes",3]," ","", fixed=TRUE), ",", fixed=TRUE))
#### GENERAL
# Add statistics to plots?
plot.statistic = as.logical(miaccs["carpools.statistic",3])
# Column in which the gene/sgRNA identifier is
namecolumn = as.numeric(miaccs["carpools.namecolumn",3])
# Column in which the read count data is
fullmatchcolumn = as.numeric(miaccs["carpools.readcolumn",3])
# Normalize read count of datasets in the plots?
normalize = as.logical(miaccs["carpools.normalize",3])
# Normalization function used to normalize datasets
norm.function = eval(parse(text = miaccs["carpools.normfun",3]))
# color to plot highlighted genes / sgRNAs
plot.labelcolor = as.character(miaccs["carpools.labelcolor",3])
put.names = as.logical(miaccs["carpools.labelnames",3])
#### DATA ANALYSIS
# Targeting Controls
controls.target = as.character(unlist(strsplit(stringi::stri_replace_all_fixed(miaccs["carpools.pos",3]," ", "", fixed=TRUE), ",", fixed=TRUE)))
if(is.na(controls.target) || controls.target == "NULL" || controls.target == "none")
{
controls.target = NULL
}
# Non-Targeting Controls
controls.nontarget = as.character(unlist(strsplit(stringi::stri_replace_all_fixed(miaccs["carpools.non-targeting",3]," ", "", fixed=TRUE), ",", fixed=TRUE)))
if(is.na(controls.nontarget) || controls.nontarget == "NULL" || controls.nontarget == "none" || controls.nontarget == "")
{
controls.nontarget = NULL
}
## wilcoxn
control.picks = as.numeric(miaccs["carpools.randompicks",3])
## CUT OFF
sig.pval.deseq = as.numeric(miaccs["carpools.deseq2",3])
sig.pval.mageck = as.numeric(miaccs["carpools.mageck",3])
sig.pval.wilcox = as.numeric(miaccs["carpools.wilcox",3])
## HIT VISUALIZATION
# This will plot FOUR scatter plots for each enriched or depleted hit automatically and label the genes and sgRNAs
sgRNAs.top.deseq = as.numeric(miaccs["carpools.topdeseq",3])
sgRNAs.top.mageck = as.numeric(miaccs["carpools.topmageck",3])
cutoff.override = as.logical(miaccs["carpools.override",3])
# Percentage of TOP enriched or depleted genes to plot in general
number.hits.plot.enriched = as.numeric(miaccs["carpools.top.enriched",3])
number.hits.plot.depleted = as.numeric(miaccs["carpools.top.depleted",3])
# Comparing Methods
compare.cutoff = as.numeric(miaccs["carpools.overrride.genes",3])
```
\newpage
# Screen
Screen | Information
------ | --------
__Screen ID__ | `r screen.id`
__Screening Date__ | `r screen.date`
__Organism__ | `r screen.organism`
__Cell Line__ | `r screen.cell`
__Drug__ | `r screen.drug`
__Control__ | `r screen.control`
__Number of Target__ | `r screen.targets`
__Designs per Gene__ | `r screen.despergene`
__Library__ | `r screen.library`
__Library Reference File__ | `r referencefile`
__NGS__ | `r screen.NGS`
__MIACCS__
The MIACCS-file can be found at
`r paste("_", datapath, "/", "_" , sep="")`
`r paste("__", "MIACCS.xls" ,"__", sep="")`
__Description__
`r screen.description`
```{r plasmid-screen-overview, echo=FALSE}
## Prepare for plotting overviews
if(miaccs["assay.plasmids.image",3] != "none" && !is.na(miaccs["assay.plasmids.image",3]))
{
plasmid.image = strsplit(miaccs["assay.plasmids.image",3], split=",")
for(i in 1:length(unlist(plasmid.image)))
{
cat("\n")
cat("\\newpage","\n")
cat("\n")
cat("#","Plasmid Overview","\n")
cat("![](",paste(datapath, plasmid.image[[1]][i], sep="/"), ")" )
}
}
if(miaccs["cell.setup.image",3] != "none"&& !is.na(miaccs["cell.setup.image",3]))
{
screen.image = strsplit(miaccs["cell.setup.image",3], split=",")
for(i in 1:length(unlist(screen.image)))
{
cat("\n")
cat("\\newpage","\n")
cat("\n")
cat("#","Experimental Setup","\n")
cat("![](",paste(datapath, screen.image[[1]][i], sep="/"),")" )
}
}
```
\newpage
# Load Data
```{r datainput, echo=FALSE, error=FALSE, warning=FALSE, message=FALSE, results='hide'}
# Do we need to map the data?
if(identical(mapping, TRUE))
{
# Extract Data?
fileCONTROL1 = data.extract(scriptpath, datapath, fastqfile=fileCONTROL1, extract, seq.pattern, maschine.pattern, createindex, referencefile, mapping, reversecomplement, threads, bowtieparams, sensitivity, match)
fileCONTROL2 = data.extract(scriptpath, datapath, fastqfile=fileCONTROL2, extract, seq.pattern, maschine.pattern, createindex, referencefile, mapping, reversecomplement, threads, bowtieparams, sensitivity, match)
fileTREAT1 = data.extract(scriptpath, datapath, fastqfile=fileTREAT1, extract, seq.pattern, maschine.pattern, createindex, referencefile, mapping, reversecomplement, threads, bowtieparams, sensitivity, match)
fileTREAT2 = data.extract(scriptpath, datapath, fastqfile=fileTREAT2, extract, seq.pattern, maschine.pattern, createindex, referencefile, mapping, reversecomplement, threads, bowtieparams, sensitivity, match)
# Data will be mapped, so we start mapping and then get back the data!
CONTROL1 = load.file(paste(datapath, fileCONTROL1, sep="/"))
CONTROL2 = load.file(paste(datapath, fileCONTROL2, sep="/"))
TREAT1 = load.file(paste(datapath, fileTREAT1, sep="/"))
TREAT2 = load.file(paste(datapath, fileTREAT2, sep="/"))
libFILE = load.file(paste(datapath, paste(referencefile,".fasta",sep=""), sep="/"),header = FALSE, type="fastalib")
} else {
# Extracted and mapped read count data is present, so ONLY load files already there using the filename fileCONTROL etc.
CONTROL1 = load.file(paste(datapath, fileCONTROL1, sep="/"))
CONTROL2 = load.file(paste(datapath, fileCONTROL2, sep="/"))
TREAT1 = load.file(paste(datapath, fileTREAT1, sep="/"))
TREAT2 = load.file(paste(datapath, fileTREAT2, sep="/"))
libFILE = load.file(paste(datapath, paste(referencefile,".fasta",sep=""), sep="/"),header = FALSE, type="fastalib")
}
```
```{r geneidentifier, echo=FALSE, echo=FALSE, error=FALSE, warning=FALSE, message=FALSE, results='hide'}
if(identical(g.convert, TRUE))
{
cat("## Convert Gene Identifiers", "\n")
cat("Gene identifiers are converted from","\n", "__", g.identifier, "__" ,"to __",g.identifier.new,"__", "using the __", a.dataset,"__ database." ,"\n")
# convert from ENSEMBL ID to human genome nomenclature symbol
CONTROL1 = get.gene.info(CONTROL1, namecolumn=namecolumn, extractpattern=g.extractpattern, database=a.database, dataset=a.dataset, filters=g.identifier, attributes = c(g.identifier.new), return.val = "dataset")
CONTROL2 = get.gene.info(CONTROL2, namecolumn=namecolumn, extractpattern=g.extractpattern, database=a.database, dataset=a.dataset, filters=g.identifier, attributes = c(g.identifier.new), return.val = "dataset")
TREAT1 = get.gene.info(TREAT1, namecolumn=namecolumn, extractpattern=g.extractpattern, database=a.database, dataset=a.dataset, filters=g.identifier, attributes = c(g.identifier.new), return.val = "dataset")
TREAT2 = get.gene.info(TREAT2, namecolumn=namecolumn, extractpattern=g.extractpattern, database=a.database, dataset=a.dataset, filters=g.identifier, attributes = c(g.identifier.new), return.val = "dataset")
# convert control list
controls.target = get.gene.info(as.data.frame(controls.target), namecolumn=namecolumn, extractpattern=g.extractpattern, database=a.database, dataset=a.dataset, filters=g.identifier, attributes = c(g.identifier.new), return.val = "dataset", controls=TRUE)
controls.target = controls.target[,1]
# Library file
libFILE = get.gene.info(libFILE, namecolumn=namecolumn, extractpattern=g.extractpattern, database=a.database, dataset=a.dataset, filters=g.identifier, attributes = c(g.identifier.new), return.val = "dataset")
}
# aggregate to genes for later plots
CONTROL1.g=aggregatetogenes(CONTROL1, agg.function=sum, extractpattern = g.extractpattern)
CONTROL2.g=aggregatetogenes(CONTROL2, agg.function=sum, extractpattern = g.extractpattern)
TREAT1.g=aggregatetogenes(TREAT1, agg.function=sum, extractpattern = g.extractpattern)
TREAT2.g=aggregatetogenes(TREAT2, agg.function=sum, extractpattern = g.extractpattern)
# Add genes names to normal count file (if not present)
CONTROL1=aggregatetogenes(CONTROL1, extractpattern = g.extractpattern, type="annotate")
CONTROL2=aggregatetogenes(CONTROL2, extractpattern = g.extractpattern, type="annotate")
TREAT1=aggregatetogenes(TREAT1, extractpattern = g.extractpattern,type="annotate")
TREAT2=aggregatetogenes(TREAT2, extractpattern = g.extractpattern, type="annotate")
# Calculate Percentage into gene rows
number.enriched.counter = ceiling((nrow(CONTROL1.g)/100)*number.hits.plot.enriched)
number.depleted.counter = ceiling((nrow(CONTROL1.g)/100)*number.hits.plot.depleted)
```
\newpage
# Stats
All file-based output (e.g. tables) is stored in:
_`r datapath`_.
## General
General stats can be found in
__`r paste(analysis.name, "STATS.xls", sep="-")`__.
The following read count statistics were calculated for the single datasets.
```{r stats-general, echo=FALSE}
# General
U1.stats = stats.data(dataset=CONTROL1, namecolumn = namecolumn, fullmatchcolumn = fullmatchcolumn, extractpattern=g.extractpattern, type="stats")
U2.stats = stats.data(dataset=CONTROL2, namecolumn = namecolumn, fullmatchcolumn = fullmatchcolumn, extractpattern=g.extractpattern, type="stats")
T1.stats = stats.data(dataset=TREAT1, namecolumn = namecolumn, fullmatchcolumn = fullmatchcolumn, extractpattern=g.extractpattern, type="stats")
T2.stats = stats.data(dataset=TREAT2, namecolumn = namecolumn, fullmatchcolumn = fullmatchcolumn, extractpattern=g.extractpattern, type="stats")
combined.stats = cbind.data.frame(U1.stats[,1:2], U2.stats[,2], T1.stats[,2], T2.stats[,2])
colnames(combined.stats) = c("Readcount", d.CONTROL1, d.CONTROL2, d.TREAT1, d.TREAT2)
# output to report
knitr::kable(combined.stats)
# output to file
xlsx::write.xlsx(combined.stats, file=paste(datapath, paste(analysis.name, "STATS.xls", sep="_"), sep="/"), sheetName="Combined Stats", row.names=FALSE)
```
\newpage
```{r stats-dropout, echo=FALSE}
# General
U1.unmapped = unmapped.genes(data=CONTROL1, namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, genes=NULL, extractpattern=g.extractpattern)
U1.unmapped = U1.unmapped[order(U1.unmapped$sgRNA, decreasing=TRUE),]
U2.unmapped = unmapped.genes(data=CONTROL2, namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, genes=NULL, extractpattern=g.extractpattern)
U2.unmapped = U2.unmapped[order(U2.unmapped$sgRNA, decreasing=TRUE),]
T1.unmapped = unmapped.genes(data=TREAT1, namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, genes=NULL, extractpattern=g.extractpattern)
T1.unmapped = T1.unmapped[order(T1.unmapped$sgRNA, decreasing=TRUE),]
T2.unmapped = unmapped.genes(data=TREAT2, namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, genes=NULL, extractpattern=g.extractpattern)
T2.unmapped = T2.unmapped[order(T2.unmapped$sgRNA, decreasing=TRUE),]
# output to file
xlsx::write.xlsx(U1.unmapped, file=paste(datapath, paste(analysis.name, "DROPOUT.xls", sep="_"), sep="/"), sheetName=d.CONTROL1, row.names=FALSE)
xlsx::write.xlsx(U2.unmapped, file=paste(datapath, paste(analysis.name, "DROPOUT.xls", sep="_"), sep="/"), sheetName=d.CONTROL2, append=TRUE, row.names=FALSE)
xlsx::write.xlsx(T1.unmapped, file=paste(datapath, paste(analysis.name, "DROPOUT.xls", sep="_"), sep="/"), sheetName=d.TREAT1, append=TRUE, row.names=FALSE)
xlsx::write.xlsx(T2.unmapped, file=paste(datapath, paste(analysis.name, "DROPOUT.xls", sep="_"), sep="/"), sheetName=d.TREAT2, append=TRUE, row.names=FALSE)
# Output single sgRNAs of each gene
U1.unmapped.singles = unmapped.genes(data=CONTROL1, namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, genes=U1.unmapped[,"name"], extractpattern=g.extractpattern)
U1.unmapped.singles = U1.unmapped.singles[order(U1.unmapped.singles$sgRNA, decreasing=TRUE),]
xlsx::write.xlsx(U1.unmapped.singles, file=paste(datapath, paste(analysis.name, "DROPOUT.xls", sep="_"), sep="/"), sheetName=paste(d.CONTROL1,"sgRNA"), row.names=FALSE, append=TRUE)
U2.unmapped.singles = unmapped.genes(data=CONTROL2, namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, genes=U2.unmapped[,"name"], extractpattern=g.extractpattern)
U2.unmapped.singles = U2.unmapped.singles[order(U2.unmapped.singles$sgRNA, decreasing=TRUE),]
xlsx::write.xlsx(U2.unmapped.singles, file=paste(datapath, paste(analysis.name, "DROPOUT.xls", sep="_"), sep="/"), sheetName=paste(d.CONTROL2,"sgRNA"), row.names=FALSE, append=TRUE)
T1.unmapped.singles = unmapped.genes(data=TREAT1, namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, genes=T1.unmapped[,"name"], extractpattern=g.extractpattern)
T1.unmapped.singles = T1.unmapped.singles[order(T1.unmapped.singles$sgRNA, decreasing=TRUE),]
xlsx::write.xlsx(T1.unmapped.singles, file=paste(datapath, paste(analysis.name, "DROPOUT.xls", sep="_"), sep="/"), sheetName=paste(d.TREAT1,"sgRNA"), row.names=FALSE, append=TRUE)
T2.unmapped.singles = unmapped.genes(data=TREAT2, namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, genes=T2.unmapped[,"name"], extractpattern=g.extractpattern)
T2.unmapped.singles = T2.unmapped.singles[order(T2.unmapped.singles$sgRNA, decreasing=TRUE),]
xlsx::write.xlsx(T2.unmapped.singles, file=paste(datapath, paste(analysis.name, "DROPOUT.xls", sep="_"), sep="/"), sheetName=paste(d.TREAT2,"sgRNA"), row.names=FALSE, append=TRUE)
```
## Missing sgRNAs in Datasets
Information of how many sgRNA per gene were not present in the mapped datasets is stored in:
`r paste("_", datapath, "/", "_", sep="")`
`r paste("__", paste(analysis.name, "DROPOUT.xls", sep="_") ,"__", sep="") `
In brief, the following number of sgRNAs had a _read count of 0_ in the dataset:
__`r d.CONTROL1`:__ __`r sum(U1.unmapped[,2])`__ missing sgRNAs
__`r d.CONTROL2`:__ __`r sum(U2.unmapped[,2])`__ missing sgRNAs
__`r d.TREAT1`:__ __`r sum(T1.unmapped[,2])`__ missing sgRNAs
__`r d.TREAT2`:__ __`r sum(T2.unmapped[,2])`__ missing sgRNAs
## All Stats
In depth dataset read count stats can be found in
`r paste("_", datapath, "/", "_" , sep="")`
`r paste("__", paste(analysis.name, "STATS.xls", sep="-") ,"__", sep="" ) `
```{r, stats-all, echo=FALSE}
U1.allstats = stats.data(dataset=CONTROL1, namecolumn = namecolumn, fullmatchcolumn = fullmatchcolumn, extractpattern=g.extractpattern, type="dataset")
U2.allstats = stats.data(dataset=CONTROL2, namecolumn = namecolumn, fullmatchcolumn = fullmatchcolumn, extractpattern=g.extractpattern, type="dataset")
T1.allstats = stats.data(dataset=TREAT1, namecolumn = namecolumn, fullmatchcolumn = fullmatchcolumn, extractpattern=g.extractpattern, type="dataset")
T2.allstats = stats.data(dataset=TREAT2, namecolumn = namecolumn, fullmatchcolumn = fullmatchcolumn, extractpattern=g.extractpattern, type="dataset")
combined.stats.mean = cbind.data.frame(U1.allstats[,"Name"], U1.allstats[,"readcount.mean"],U2.allstats[,"readcount.mean"],T1.allstats[,"readcount.mean"],T2.allstats[,"readcount.mean"])
colnames(combined.stats.mean) = c("Gene",d.CONTROL1, d.CONTROL2, d.TREAT1, d.TREAT2)
combined.stats.median = cbind.data.frame(U1.allstats[,"Name"], U1.allstats[,"readcount.median"],U2.allstats[,"readcount.median"],T1.allstats[,"readcount.median"],T2.allstats[,"readcount.median"])
colnames(combined.stats.median) = c("Gene",d.CONTROL1, d.CONTROL2, d.TREAT1, d.TREAT2)
combined.stats.min = cbind.data.frame(U1.allstats[,"Name"], U1.allstats[,"readcount.min"],U2.allstats[,"readcount.min"],T1.allstats[,"readcount.min"],T2.allstats[,"readcount.min"])
colnames(combined.stats.min) = c("Gene",d.CONTROL1, d.CONTROL2, d.TREAT1, d.TREAT2)
combined.stats.max = cbind.data.frame(U1.allstats[,"Name"], U1.allstats[,"readcount.max"],U2.allstats[,"readcount.max"],T1.allstats[,"readcount.max"],T2.allstats[,"readcount.max"])
colnames(combined.stats.max) = c("Gene",d.CONTROL1, d.CONTROL2, d.TREAT1, d.TREAT2)
xlsx::write.xlsx(combined.stats.mean, file=paste(datapath, paste(analysis.name, "STATS.xls", sep="_"), sep="/"), sheetName="Mean Read Count", append=TRUE ,row.names=FALSE)
xlsx::write.xlsx(combined.stats.median, file=paste(datapath, paste(analysis.name, "STATS.xls", sep="_"), sep="/"), sheetName="Median Read Count", append=TRUE, row.names=FALSE)
xlsx::write.xlsx(combined.stats.min, file=paste(datapath, paste(analysis.name, "STATS.xls", sep="_"), sep="/"), sheetName="Minimum Read Count", append=TRUE, row.names=FALSE)
xlsx::write.xlsx(combined.stats.max, file=paste(datapath, paste(analysis.name, "STATS.xls", sep="_"), sep="/"), sheetName="Maximum Read Count", append=TRUE, row.names=FALSE)
```
\newpage
# Quality Control
## Read Distribution
These plots show how the read count of the sgRNAs for each dataset is distributed.
Depending on the treatment stringency, e.g. in resistance or dropout screens, the data can show asymmetry.
However, the major population should be more or less normally distributed.
```{r QC-distribution, echo=FALSE, sanitize=TRUE}
par(mfrow=c(1,1))
carpools.read.distribution(CONTROL1, fullmatchcolumn=fullmatchcolumn,breaks=200, title=d.CONTROL1, xlab="log2 Readcount", ylab="# sgRNAs",statistics=plot.statistic)
carpools.read.distribution(CONTROL2, fullmatchcolumn=fullmatchcolumn,breaks=200, title=d.CONTROL2, xlab="log2 Readcount", ylab="# sgRNAs",statistics=plot.statistic)
# add new page
cat("\\newpage", "\n")
carpools.read.distribution(TREAT1, fullmatchcolumn=fullmatchcolumn,breaks=200, title=d.TREAT1, xlab="log2 Readcount", ylab="# sgRNAs",statistics=plot.statistic)
carpools.read.distribution(TREAT2, fullmatchcolumn=fullmatchcolumn,breaks=200, title=d.TREAT2, xlab="log2 Readcount", ylab="# sgRNAs",statistics=plot.statistic)
```
\pagebreak
## Read Depth
The following plot shows the read count for each gene normalized to the number of sgRNAs. Spikes indicate a higher read count per sgRNA for this particular gene.
One would expect no outstanding spikes within the untreated data samples, however spikes within the treated datasets indicate a read count enrichment for this particular gene.
If a non-targeting control has been set in the MIACCS file, this control is highlighted in __orange__ color.
```{r QC-readdepth, echo=FALSE, sanitize=TRUE}
#, fig.width=10, fig.height=20
#par(mfrow=c(4,1))
carpools.read.depth(datasets = list(CONTROL1,CONTROL2,TREAT1,TREAT2), namecolumn=namecolumn ,fullmatchcolumn=fullmatchcolumn, dataset.names=list(d.CONTROL1,d.CONTROL2,d.TREAT1,d.TREAT2), extractpattern=g.extractpattern, xlab="Genes", ylab="Read Count per sgRNA",statistics=plot.statistic, labelgenes = NULL, controls.target = controls.target, controls.nontarget=controls.nontarget)
par(mfrow=c(1,1))
```
\pagebreak
## Designs per Gene
These plots provide an overview of the representation of sgRNAs per gene within your data.
Depending on the number of sgRNAs per gene in the library, one would expect a representation of more than 80 % of sgRNAs per gene in the untreated samples.
Moreover, genes with a low percentage of present sgRNAs will also show a reduced readcount.
```{r QC-despergene, echo=FALSE, sanitize=TRUE}
#, fig.width=10, fig.height=10
par(mfrow=c(2,2))
control1.readspergene = carpools.reads.genedesigns(CONTROL1, fullmatchcolumn=fullmatchcolumn, namecolumn=namecolumn, title=paste("% sgRNAs:", d.CONTROL1, sep=" "), xlab="% of sgRNAs present", ylab="# of Genes")
control2.readspergene = carpools.reads.genedesigns(CONTROL2, fullmatchcolumn=fullmatchcolumn, namecolumn=namecolumn, title=paste("% sgRNAs:", d.CONTROL2, sep=" "), xlab="% of sgRNAs present", ylab="# of Genes")
treat1.readspergene = carpools.reads.genedesigns(TREAT1, fullmatchcolumn=fullmatchcolumn, namecolumn=namecolumn, title=paste("% sgRNAs:", d.TREAT1, sep=" "), xlab="% of sgRNAs present", ylab="# of Genes")
treat2.readspergene = carpools.reads.genedesigns(TREAT2, fullmatchcolumn=fullmatchcolumn, namecolumn=namecolumn, title=paste("% sgRNAs:", d.TREAT2, sep=" "), xlab="% of sgRNAs present", ylab="# of Genes")
par(mfrow=c(1,1))
```
\newpage
```{r echo=FALSE, eval=FALSE}
if(!is.null(controls.nontarget))
{
cat("\n")
#cat("\\blandscape","\n")
}
```
## Controls
### Non-Targeting
Non-targeting controls are sgRNAs that do either not target the genome at all (so called random or scramble sgRNAs) or target a gene that does not show a phenotype in the screen.
Therefore, the scatter for these, which are highlighted in __blue__, will be distributed within the main cloud of scatter points.
`r if(is.null(controls.nontarget)) {print("Unfortunately, no Non-Targeting controls were set.")}`
```{r QC-non-targeting-scatter, echo=FALSE, sanitize=TRUE, fig.width=8, fig.height=8}
# Plot non-targeting controls
#, fig.height=20, fig.width=30,
if(!is.null(controls.nontarget))
{
cat("Non-targeting control: ", controls.nontarget ,"\n")
cat("\n")
carpools.read.count.vs(dataset=list(TREAT1, TREAT2, CONTROL1, CONTROL2), dataset.names = c(d.TREAT1, d.TREAT2, d.CONTROL1, d.CONTROL2), pairs=TRUE, namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, title=analysis.name, pch=16, normalize=normalize, norm.function=norm.function, labelgenes=controls.nontarget, labelcolor="blue", center=FALSE, aggregated=FALSE)
} else {
cat("No non-targeting control has been set in the MIACCS file.")
}
```
```{r echo=FALSE, eval=FALSE}
if(!is.null(controls.nontarget))
{
cat("\n")
#cat("\\elandscape","\n")
}
```
\newpage
### Positive Controls
SgRNAs targeting genes that will show a phenotype in the screening setup can be used as positive controls.
These will show either an enrichment (in resistance screens) or a depletion (in dropout screens) in the treatment.
Within the scatter
```{r QC-targeting-scatter, echo=FALSE, sanitize=TRUE, fig.width=8, fig.height=8}
# Plot non-targeting controls
#, fig.height=20, fig.width=30
if(!is.null(controls.target))
{
cat("Positive Control: ", as.character(controls.target) ,"\n")
cat("\n")
carpools.read.count.vs(dataset=list(TREAT1.g, TREAT2.g, CONTROL1.g, CONTROL2.g), dataset.names = c(d.TREAT1, d.TREAT2, d.CONTROL1, d.CONTROL2), pairs=TRUE, namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, title=analysis.name, pch=16, normalize=normalize, norm.function=norm.function, labelgenes=controls.target, labelcolor="red", center=FALSE, aggregated=TRUE)
} else {
cat("No positive control has been set in the MIACCS file.")
}
```
\newpage
# Hit Analysis
Hit analsysis is performed using three different methods:
* Wilcox
* DESeq2
* MAGeCK
For each analysis method, separate plots will be created and analysis files will be written to __`r paste(analysis.name, "HIT-CALLING.xls", sep="_")`__. See below for further information.
The following adjusted p-values are used to determine significance levels:
Method | p-value
------ | -----
Wilcox | `r sig.pval.wilcox`
DESeq2 | `r sig.pval.deseq`
MAGeCK | `r sig.pval.mageck`
__Wilcox__
Within this approach, the read counts of all sgRNAs in one dataset are first normalized by the function set in the MIACCS file. By default, normalization is done by read count division with the dataset median.
Then, the fold change of each population of sgRNAs for a gene is tested against the population of either the non-targeting controls or randomly picked sgRNAs, as defined by the random picks option within the MIACCS file, using a two-sided Mann-Whitney test with FDR correction.
__DESeq2__
For the DESeq2 analysis implementation, the read counts of all sgRNAs for a given gene are first summed up to increase the available read count.
Then, DESeq2 analysis is perfomed, which includes the estimation of size-factors, the variance stabilization using a parametric fit and a Wald-Test for differnece in log2 fold changes between the untreated and treated data.
More information about this can be found in _Love et al._
[Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2](http://www.ncbi.nlm.nih.gov/pubmed/25516281)
_Genome Biology_ 2014
__MAGeCK__
MAGeCK analysis uses a rank-based model to test for a change in abundance of sgRNAs after median normalization of the dataset.
Further information can be found at the [MAGeCK Homepage](http://sourceforge.net/projects/mageck/).
\newpage
## Wilcox
All analysis data can be found in the __`r paste(analysis.name, "HIT-CALLING.xls", sep="_")`__ file.
```{r HT-method-wilcox, echo=FALSE}
# wilcox
if(is.null(controls.nontarget))
{
cat("\n")
cat(paste("**Since no non-targeting controls were set,", control.picks, "sgRNAs are picked from the datset as reference population.**", sep=" "))
cat("\n")
}
data.wilcox = stat.wilcox(untreated.list = list(CONTROL1, CONTROL2), treated.list = list(TREAT1,TREAT2), namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, normalize=normalize, norm.fun=norm.function, sorting=FALSE, controls=controls.nontarget, control.picks=control.picks)
xlsx::write.xlsx(data.wilcox, file=paste(datapath, paste(analysis.name, "HIT-CALLING.xls", sep="_"), sep="/"), sheetName="Wilcox")
```
### P-Value Distribution
For all genes present in the data, the -log10 corrected p-values are plotted to estimate how well the analysis method performed for this screen. A straight line of points with only small differences in the p-value indicates that the analysis method did not perform well.
Genes that resulted in a p-value below the threshold set in the MIACCS file are highlighted in red color.
```{r HT-wilcox-pval-distribution, echo=FALSE}
carpools.waterfall.pval(type="wilcox", dataset=data.wilcox, pval=sig.pval.wilcox, log=TRUE)
```
### Enriched
The following genes showed enrichment in the treatment datasets with a __p-value smaller than `r sig.pval.wilcox`__:
```{r HT-method-wilcox-enriched, echo=FALSE}
data.wilcox.plot.enriched = data.wilcox[data.wilcox$foldchange > 1,]
data.wilcox.plot.enriched = data.wilcox.plot.enriched[order(data.wilcox.plot.enriched$p.value, na.last=TRUE),]
if(nrow(data.wilcox.plot.enriched[data.wilcox.plot.enriched$p.value < sig.pval.wilcox,]) > 0)
{
knitr::kable(data.wilcox.plot.enriched[data.wilcox.plot.enriched$p.value < sig.pval.wilcox,])
} else { cat("**No genes showed significant enrichment with a p-value lower than", sig.pval.wilcox, "**")}
```
According to the value set, the __top `r number.hits.plot.enriched` % enriched genes__ were:
`r knitr::kable(data.wilcox.plot.enriched[1:number.enriched.counter,])`
\newpage
### Depleted
The following genes showed depletion in the treatment datasets with a __p-value smaller than `r sig.pval.wilcox`__:
```{r HT-method-wilcox-depleted, echo=FALSE}
data.wilcox.plot.depleted = data.wilcox[data.wilcox$foldchange < 1,]
data.wilcox.plot.depleted = data.wilcox.plot.depleted[order(data.wilcox.plot.depleted$p.value, na.last=TRUE),]
if(nrow(data.wilcox.plot.depleted[data.wilcox.plot.depleted$p.value < sig.pval.wilcox,]) > 0)
{
knitr::kable(data.wilcox.plot.depleted[data.wilcox.plot.depleted$p.value < sig.pval.wilcox,])
} else { cat("**No genes showed significant depletion with a p-value lower than", sig.pval.wilcox, "**")}
```
According to the value set, the __top `r number.hits.plot.depleted` % depleted genes__ were:
`r knitr::kable(data.wilcox.plot.depleted[1:number.depleted.counter,])`
\newpage
## DESeq2
All analysis data can be found in the __`r paste(analysis.name, "HIT-CALLING.xls", sep="_")`__ file.
```{r HT-method-DESeq2, echo=FALSE, message=FALSE,warning=FALSE}
# DESeq2
data.deseq = stat.DESeq(untreated.list = list(CONTROL1, CONTROL2), treated.list = list(TREAT1,TREAT2), namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, extractpattern=g.extractpattern, sorting=FALSE, filename.deseq = paste(analysis.name, "-ANALYSIS-DESeq2-sgRNA.tab", sep=""), sgRNA.pval = sig.pval.deseq, fitType="mean")
xlsx::write.xlsx(as.data.frame(data.deseq$genes), file=paste(datapath, paste(analysis.name, "HIT-CALLING.xls", sep="_"), sep="/"), sheetName="DESeq2", append=TRUE)
```
### P-Value Distribution
For all genes present in the data, the -log10 corrected p-values are plotted to estimate how well the analysis method performed for this screen. A straight line of points with only small differences in the p-value indicates that the analysis method did not perform well.
Genes that resulted in a p-value below the threshold set in the MIACCS file are highlighted in red color.
```{r HT-deseq-pval-distribution, echo=FALSE}
carpools.waterfall.pval(type="deseq2", dataset=data.deseq, pval=sig.pval.wilcox, log=TRUE)
```
### Enriched
The following genes showed enrichment in the treatment datasets with a __p-value smaller than `r sig.pval.deseq`__:
```{r HT-method-deseq-enriched, echo=FALSE}
data.deseq.plot.enriched = as.data.frame(data.deseq[[1]][data.deseq[[1]]$log2FoldChange > 0,])
data.deseq.plot.enriched = data.deseq.plot.enriched[order(data.deseq.plot.enriched$padj, na.last=TRUE),]
if(nrow(data.deseq.plot.enriched[data.deseq.plot.enriched$padj < sig.pval.deseq,c(2,3,6,8)]) > 0)
{
knitr::kable(data.deseq.plot.enriched[data.deseq.plot.enriched$padj < sig.pval.deseq,c(2,3,6,8)])
} else { cat("**No genes showed significant enrichment with a p-value lower than", sig.pval.deseq, "**")}
```
According to the value set, the __top `r number.hits.plot.enriched` % enriched genes__ were:
`r knitr::kable(data.deseq.plot.enriched[1:number.enriched.counter,c(2,3,6,8)])`
\newpage
### Depleted
The following genes showed depletion in the treatment datasets with a __p-value smaller than `r sig.pval.deseq`__:
```{r HT-method-deseq-depleted, echo=FALSE}
data.deseq.plot.depleted = data.deseq[[1]][data.deseq[[1]]$log2FoldChange < 0,]
data.deseq.plot.depleted = data.deseq.plot.depleted[order(data.deseq.plot.depleted$padj, na.last=TRUE),]
if(nrow(data.deseq.plot.depleted[data.deseq.plot.depleted$padj < sig.pval.deseq,c(2,3,6,8)]) > 0)
{
knitr::kable(data.deseq.plot.depleted[data.deseq.plot.depleted$padj < sig.pval.deseq,c(2,3,6,8)])
} else { cat("**No genes showed significant depletion with a p-value lower than", sig.pval.deseq, "**")}
```
According to the value set, the __top `r number.hits.plot.depleted` % depleted genes__ were:
`r knitr::kable(data.deseq.plot.depleted[1:number.depleted.counter,c(2,3,6,8)])`
\newpage
## MAGeCK
All analysis data for MAGeCK can be found in the __`r paste(analysis.name, "HIT-CALLING.xls", sep="_")`__ file.
```{r HT-method-MAGeCK, echo=FALSE}
# MAGeCK
data.mageck = stat.mageck(untreated.list = list(CONTROL1, CONTROL2), treated.list = list(TREAT1,TREAT2), namecolumn=namecolumn, fullmatchcolumn=fullmatchcolumn, norm.fun="median", extractpattern=g.extractpattern, mageckfolder=NULL, sort.criteria="neg", adjust.method="fdr", filename = paste(analysis.name, "-ANALYSIS-MAGeCK-RAW", sep=""), fdr.pval=sig.pval.mageck)
xlsx::write.xlsx(as.data.frame(data.mageck$genes), file=paste(datapath, paste(analysis.name, "HIT-CALLING.xls", sep="_"), sep="/"), sheetName="MAGeCK", append=TRUE)
# get number of sig gene
mageck.cutoff = nrow(data.mageck[[1]][data.mageck[[1]]$pos < sig.pval.mageck,]) + nrow(data.mageck[[1]][data.mageck[[1]]$neg < sig.pval.mageck,])
```
### P-Value Distribution
For all genes present in the data, the -log10 corrected p-values are plotted to estimate how well the analysis method performed for this screen. A straight line of points with only small differences in the p-value indicates that the analysis method did not perform well.
Genes that resulted in a p-value below the threshold set in the MIACCS file are highlighted in red color.
```{r HT-mageck-pval-distribution, echo=FALSE}
carpools.waterfall.pval(type="mageck", dataset=data.mageck, pval=sig.pval.wilcox, log=TRUE)
```
### Enriched
The following genes showed enrichment in the treatment datasets with __a p-value smaller than `r sig.pval.mageck`__:
```{r HT-method-mageck-enriched, echo=FALSE}
data.mageck.plot.enriched = data.mageck[[1]][data.mageck[[1]]$pos < sig.pval.mageck,]
data.mageck.plot.enriched = data.mageck.plot.enriched[order(data.mageck.plot.enriched$rank.pos, na.last=TRUE),]
if(nrow(data.mageck.plot.enriched) > 0)
{
knitr::kable(data.mageck.plot.enriched[,c(2,3,4,5,6,7)])
} else { cat("**No genes showed significant enrichment with a p-value lower than", sig.pval.mageck, "**")}
data.mageck.plot.enriched = data.mageck[[1]][order(data.mageck[[1]]$rank.pos, na.last=TRUE),]
```
According to the value set, the __top `r number.hits.plot.enriched` % enriched genes__ were:
`r knitr::kable(data.mageck.plot.enriched[1:number.enriched.counter,c(2,3,4,5,6,7)])`
\newpage
### Depleted
The following genes showed depletion in the treatment datasets with a __p-value smaller than `r sig.pval.mageck`__:
```{r HT-method-mageck-depleted, echo=FALSE}
data.mageck.plot.depleted = data.mageck[[1]][data.mageck[[1]]$neg < sig.pval.mageck,]
data.mageck.plot.depleted = data.mageck.plot.depleted[order(data.mageck.plot.depleted$rank.neg, na.last=TRUE),]
if(nrow(data.mageck.plot.depleted) > 0)
{
knitr::kable(data.mageck.plot.depleted[,c(2,3,4,5,6,7)])
} else { cat("**No genes showed significant depletion with a p-value lower than", sig.pval.mageck, "**")}
data.mageck.plot.depleted = data.mageck[[1]][order(data.mageck[[1]]$rank.neg, na.last=TRUE),]
```
According to the value set, the __top `r number.hits.plot.depleted` % depleted genes__ were:
`r knitr::kable(data.mageck.plot.depleted[1:number.depleted.counter,c(2,3,4,5,6,7)])`
\newpage
# Hit Candidate Overview
## Overview
Genes which showed enrichment or depletion within the individual analysis methods are presented in the following section.
All genes that showed _significant_ __enrichment__ within Wilcox, DESeq2 and MAGeCK with the given p-value cutoffs are highlighted in __red__ color.
All genes that showed _significant_ __depletion__ within Wilcox, DESeq2 and MAGeCK with the given p-value cutoffs are highlighted in __blue__ color.
Moreover, all genes that showed up as _significantly_ enriched or depleted in any of the analysis methods are highlighted in __orange__ color.
Genes that showed no significant effect are presented in grey or black color.
```{r HT-overview-hits, echo=FALSE, sanitize=TRUE}
carpools.hit.overview(wilcox=data.wilcox, deseq=data.deseq, mageck=data.mageck, cutoff.deseq = sig.pval.deseq, cutoff.wilcox = sig.pval.wilcox, cutoff.mageck = sig.pval.mageck, cutoff.override=cutoff.override, cutoff.hits=NULL, plot.genes="overlapping")
```
```{r HT-overlaps, echo=FALSE, sanitize=TRUE}
overlap.enriched = generate.hits(wilcox=data.wilcox, deseq=data.deseq, mageck=data.mageck, type="enriched", cutoff.deseq = sig.pval.deseq, cutoff.wilcox = sig.pval.wilcox, cutoff.mageck = sig.pval.mageck, cutoff.override=cutoff.override, cutoff.hits=compare.cutoff, plot.genes="overlapping")
overlap.depleted = generate.hits(wilcox=data.wilcox, deseq=data.deseq, mageck=data.mageck, type="depleted", cutoff.deseq = sig.pval.deseq, cutoff.wilcox = sig.pval.wilcox, cutoff.mageck = sig.pval.mageck, cutoff.override=cutoff.override, cutoff.hits=compare.cutoff, plot.genes="overlapping")
# Combine datasets for overlapping hits
overlap.enriched.dataset = data.frame(
gene = overlap.enriched,
log2FC = data.deseq[[1]][rownames(data.deseq[[1]]) %in% overlap.enriched, "log2FoldChange"],
wilcox.pval = data.wilcox[rownames(data.wilcox) %in% overlap.enriched,"p.value"],
deseq2.pval = data.deseq[[1]][rownames(data.deseq[[1]]) %in% overlap.enriched,"padj"],
mageck.pval = data.mageck[[1]][rownames(data.mageck[[1]]) %in% overlap.enriched,"pos"],
stringsAsFactors=FALSE)
overlap.depleted.dataset = data.frame(
gene = overlap.depleted,
log2FC = data.deseq[[1]][rownames(data.deseq[[1]]) %in% overlap.depleted, "log2FoldChange"],
wilcox.pval = data.wilcox[rownames(data.wilcox) %in% overlap.depleted,"p.value"],
deseq2.pval = data.deseq[[1]][rownames(data.deseq[[1]]) %in% overlap.depleted,"padj"],
mageck.pval = data.mageck[[1]][rownames(data.mageck[[1]]) %in% overlap.depleted,"neg"],
stringsAsFactors=FALSE)
```
\newpage
## Overlaps in Enrichment Analysis
```{r HT-comparevenn-enriched, echo=FALSE, sanitize=TRUE}
if(nrow(overlap.enriched.dataset) > 0)
{
cat("The following genes showed enrichment in all three analysis methods.", "\n")
knitr::kable(overlap.enriched.dataset)
cutoff.override.enriched = cutoff.override
} else {
cat("\n")
cat("**ATTENTION** \n")
cat("**No overlapping enriched genes were found between all three methods.** ", "\n \n")
cat("Therefore, the overlap from the", number.hits.plot.enriched, "% of genes within all methods is used for plotting possible gene candidates.","\n" )
cat("\n")
cat("We strongly advise you to have a closer look at the individual outputs of the analysis methods and to carefully look at the following candidate genes.")
cutoff.override.enriched=TRUE
overlap.enriched = generate.hits(wilcox=data.wilcox, deseq=data.deseq, mageck=data.mageck, type="enriched", cutoff.deseq = sig.pval.deseq, cutoff.wilcox = sig.pval.wilcox, cutoff.mageck = sig.pval.mageck, cutoff.override= cutoff.override.enriched, cutoff.hits=number.enriched.counter, plot.genes="overlapping")
overlap.enriched.dataset = data.frame(
gene = overlap.enriched,
log2FC = data.deseq[[1]][rownames(data.deseq[[1]]) %in% overlap.enriched, "log2FoldChange"],
wilcox.pval = data.wilcox[rownames(data.wilcox) %in% overlap.enriched,"p.value"],
deseq2.pval = data.deseq[[1]][rownames(data.deseq[[1]]) %in% overlap.enriched,"padj"],
mageck.pval = data.mageck[[1]][rownames(data.mageck[[1]]) %in% overlap.enriched,"pos"],
stringsAsFactors=FALSE)
}
```
Within the __top enriched hits__, the overlap of enriched hits per analysis method is displayed as follows:
```{r HT-compare-enriched-overlap, echo=FALSE,sanitize=TRUE, message=FALSE,warning=FALSE}
venn.enriched = compare.analysis(wilcox=data.wilcox, deseq=data.deseq, mageck=data.mageck, type="enriched", cutoff.deseq = sig.pval.deseq, cutoff.wilcox = sig.pval.wilcox, cutoff.mageck = sig.pval.mageck, cutoff.override=cutoff.override.enriched, cutoff.hits=number.enriched.counter, output="venn")
require(VennDiagram)
grid::grid.draw(VennDiagram::venn.diagram(venn.enriched, file=NULL, fill=c("lightgreen","lightblue2","lightgray"), na="remove", cex=2,lty=2, cat.cex=2))
```
\newpage
## Overlaps in Depletion Analysis
```{r HT-comparevenn-depleted, echo=FALSE, sanitize=TRUE}
if(nrow(overlap.depleted.dataset) > 0)
{
cat("The following genes showed depletion in all three analysis methods.", "\n")
knitr::kable(overlap.depleted.dataset)
cutoff.override.depleted=cutoff.override
} else {
cat("\n")
cat("**ATTENTION** \n")
cat("**No overlapping depleted genes were found between all three methods.**", "\n \n")
cat("Therefore, the overlap from the", number.hits.plot.depleted, "% of genes within all methods is used for plotting possible hit candidates.","\n" )
cat("\n")
cat("We strongly advise you to have a closer look at the individual outputs of the analysis methods and to carefully look at the following candidate genes.")
cutoff.override.depleted = TRUE
overlap.depleted = generate.hits(wilcox=data.wilcox, deseq=data.deseq, mageck=data.mageck, type="depleted", cutoff.deseq = sig.pval.deseq, cutoff.wilcox = sig.pval.wilcox, cutoff.mageck = sig.pval.mageck, cutoff.override=cutoff.override.depleted, cutoff.hits=number.depleted.counter, plot.genes="overlapping")
overlap.depleted.dataset = data.frame(
gene = overlap.depleted,
log2FC = data.deseq[[1]][rownames(data.deseq[[1]]) %in% overlap.depleted, "log2FoldChange"],
wilcox.pval = data.wilcox[rownames(data.wilcox) %in% overlap.depleted,"p.value"],
deseq2.pval = data.deseq[[1]][rownames(data.deseq[[1]]) %in% overlap.depleted,"padj"],
mageck.pval = data.mageck[[1]][rownames(data.mageck[[1]]) %in% overlap.depleted,"neg"],
stringsAsFactors=FALSE)
}
```
Within the __top depleted hits__, the overlap of depleted hits per analysis method is displayed as follows:
```{r HT-compare-depleted-overlap, echo=FALSE,sanitize=TRUE, message=FALSE,warning=FALSE}
venn.depleted = compare.analysis(wilcox=data.wilcox, deseq=data.deseq, mageck=data.mageck, type="depleted", cutoff.deseq = sig.pval.deseq, cutoff.wilcox = sig.pval.wilcox, cutoff.mageck = sig.pval.mageck, cutoff.override=cutoff.override.depleted, cutoff.hits=number.depleted.counter, output="venn")
require(VennDiagram)
grid::grid.draw(VennDiagram::venn.diagram(venn.depleted, file=NULL, fill=c("lightgreen","lightblue2","lightgray"), na="remove", cex=2,lty=2, cat.cex=2))
```
```{r annotate-for-hit-candidate, echo=FALSE, message=FALSE,warning=FALSE}
if(identical(g.convert,TRUE))
{
g.identifier = g.identifier.new
}
if(identical(a.annotate.hits,TRUE))
{