caRpools (version 0.83)

carpools.hitident: Visualization of hit analysis performed by Wilcox, DESeq2 and MAGeCK

Description

The output from `stat.wilcox`, `stat.DEseq` and `stat.mageck` can be visualized with `carpools.hitident`. In this case, log2 fold changes are plotted against the gene names for all methods as well as the number of significant sgRNAs for data analyzed with DESeq2 or MAGeCK.

Usage

carpools.hitident(data, type="deseq2", title="DESeq2 plot", print.names=FALSE,
cutoff=c(0,0,0,0), inches=0.1, offsetplot=1.2, plot.p=0.01, sgRNA.top=1, separate=FALSE)

Arguments

data

Output data from either `stat.wilcox`, `stat.DEseq` or `stat.mageck`. *Default* empty *Values* Output from either `stat.wilcox`, `stat.DEseq` or `stat.mageck`.

type

Which type of analysis method was used? *Default* deseq2 *Values* "wilcox", "deseq2", "mageck"

title

Title of the plot. *Default* "DESeq2 plot" *Values* (character)

print.names

Shall the names of significant or top candidates being plotted? *Default* FALSE *Values* TRUE, FALSE (boolean)

cutoff

A vector containing plotting cutoffs if `print.names=TRUE`. c("top enriched", "top depleted", "most sgRNA enriched", "most sgRNA depleted"). *Default* c(0,0,0,0) *Values* Vector of length 4 (numeric)

inches

see `?par`. *Default* 0.1 *Values* (numeric)

offsetplot

Multiplication factor for stretching the plotting area to get a better plot experience. *Default* 1.2 *Values* > 1 (numeric)

plot.p

Which p-value shall be plotted and used for visualization? *Default* 0.05 *Values* (numeric)

sgRNA.top

For sgRNA plots, this indicates how many genes will be labeled (the top X genes). *Default* 1 *Values* (numeric, integer)

separate

Gene that showed enrichment can be plotted separately from those that have shown a depletion for better overview, works only for wilcox. *Default* FALSE *Values* TRUE, FALSE

Value

carpools.hitident returns a generic plot, which can be passed on to any device.

Details

none

Examples

data(caRpools)


data.wilcox = stat.wilcox(untreated.list = list(CONTROL1, CONTROL2),
  treated.list = list(TREAT1,TREAT2), namecolumn=1, fullmatchcolumn=2,
  normalize=TRUE, norm.fun=median, sorting=FALSE, controls="random",
  control.picks=NULL)
  
data.deseq = stat.DESeq(untreated.list = list(CONTROL1, CONTROL2),
  treated.list = list(TREAT1,TREAT2), namecolumn=1,
  fullmatchcolumn=2, extractpattern=expression("^(.+?)(_.+)"),
  sorting=FALSE, filename.deseq = "ANALYSIS-DESeq2-sgRNA.tab",
  fitType="parametric")
  
data.mageck = stat.mageck(untreated.list = list(CONTROL1, CONTROL2),
treated.list = list(TREAT1,TREAT2), namecolumn=1, fullmatchcolumn=2,
norm.fun="median", extractpattern=expression("^(.+?)(_.+)"), 
mageckfolder=NULL, sort.criteria="neg", adjust.method="fdr",
filename = "TEST" , fdr.pval = 0.05)

mageck.result = carpools.hitident(data.mageck, type="mageck",
title="MAGeCK", inches=0.1, print.names=TRUE, plot.p=0.05, offsetplot=1.2, sgRNA.top=1)

wilcox.result = carpools.hitident(data.wilcox, type="wilcox",
title="Wilcox", inches=0.1, print.names=TRUE, plot.p=0.05, offsetplot=1.2, sgRNA.top=1)