carpools.hit.scatter: Plot: Plotting Scatters for hit candidate genes for all...

In caRpools: CRISPR AnalyzeR for Pooled CRISPR Screens

Description

As described before, scatter plots can be generated for all datasets. 'carpools.hit.scatter' serves as a wrapper for 'carpools.read.count.vs' and allows faster plotting for individual candidate genes or all overlapping candidate genes. It generated a pairs plot with the representation of all provided samples and highlights the candidate gene.

Usage

carpools.hit.scatter(wilcox=NULL, deseq=NULL, mageck=NULL, dataset, dataset.names = NULL,
namecolumn=1, fullmatchcolumn=2, title="Read Count", xlab="Readcount Dataset1",
ylab="Readcount Dataset2", labelgenes=NULL, labelcolor="orange",
extractpattern=expression("^(.+?)_.+"),
plotline=TRUE, normalize=TRUE, norm.function=median, offsetplot=1.2,
center=FALSE, aggregated=FALSE, type="enriched",
cutoff.deseq = 0.001, cutoff.wilcox = 0.05,
cutoff.mageck = 0.05, cutoff.override=FALSE, cutoff.hits=NULL,
plot.genes="overlapping", pch=16, col = rgb(0, 0, 0, alpha = 0.65))

Arguments

wilcox	Data output from 'stat.wilcox'. *Default* NULL *Values* Data output from 'stat.wilcox'.
deseq	Data output from 'stat.deseq'. *Default* NULL *Values* Data output from 'stat.deseq'.
mageck	Data output from 'stat.mageck'. *Default* NULL *Values* Data output from 'stat.mageck'.
cutoff.deseq	P-Value threshold used to determine significance. *Default* 0.001 *Values* numeric
cutoff.wilcox	P-Value threshold used to determine significance. *Default* 0.001 *Values* numeric
cutoff.mageck	P-Value threshold used to determine significance. *Default* 0.001 *Values* numeric
dataset	A list of data frames of read-count data as created by load.file(). *Default* none *Values* A list of data frames
namecolumn	In which column are the sgRNA identifiers? *Default* 1 *Values* column number (numeric)
fullmatchcolumn	In which column are the read counts? *Default* 2 *Values* column number (numeric)
dataset.names	A list of names that must be according to the list of data sets given in *dataset*. *Default* NULL *Value* NULL or list of data names (list)
norm.function	The mathematical function to normalize data. By default, the median is used. *Default* median *Values* Any mathematical function of R (function)
extractpattern	PERL regular expression that is used to retrieve the gene identifier from the overall sgRNA identifier. e.g. in **AAK1_107_0** it will extract **AAK1**, since this is the gene identifier beloning to this sgRNA identifier. **Please see: Read-Count Data Files** *Default* expression("^(.+?)(_.+)"), will work for most available libraries. *Values* PERL regular expression with parenthesis indicating the gene identifier (expression)
cutoff.override	Shall the p-value threshold be ignored? If this is TRUE, the top percentage gene of 'cutoff.hits' is used instead. *Default* FALSE *Values* TRUE, FALSE
cutoff.hits	The percentatge of top genes being used if 'cutoff.override=TRUE'. *Default** NULL *Values* numeric
plot.genes	Defines what kind of data is used. By default, overlapping genes are highlighted in red color. *Default* "overlapping" *Values* "overlapping"
type	Defines whether all genes are plotted or only those being enriched or depleted. *Default* "all" *Values* "all", "enriched", "depleted"
labelgenes	For which gene shall the sgRNA effects being plotted? This expects a gene identifier or a vector of gene identifiers. *Default* NULL *Values* A gene identifier or vector of gene identifiers (character)
xlab	Label of X-Axis, only if 'pairs=FALSE' *Default* "X-Axis" *Values* "Label of X-Axis" (character)
ylab	Label of Y-Axism only if 'pairs=FALSE' *Default* "Y-Axis" *Values* "Label of Y-Axis" (character)
pch	The type of point used in the plot. See '?par()'. *Default* 16 *Values* Any number describing the point, e.g. 16 (numeric)
col	The color of the plotted data. Can be any R color or RGB object. See ?rgb() for further information. *Default* rgb(0, 0, 0, alpha = 0.65) *Values* Any R color name or RGB color object (character OR color object)
plotline	You can draw additional lines indicating a fold change of 0, 2, 4. *Default* TRUE *Values** TRUE, FALSE (boolean)
normalize	Whether you would like to normalize read-counts first. Recommended if not done already. *Default* TRUE *Values* TRUE, FALSE (boolean)
offsetplot	Offetplot is used to stretch the x- and y-axis for nicer graphs. This will extend plotting area by offsetplot. *Default* 1.2 (Plotting area is streched to 1.2 times) *Values* any number (numeric)
center	If you like you can center your data within the plot. *Default* FALSE *Values* TRUE, FALSE (boolean)
aggregated	If you want to highlight genes, set this to true if you provide already aggregated gene read count instead of sgRNA read counts. *Default* FALSE *Values* TRUE, FALSE (boolean)
labelcolor	Color to highlight genes stated in 'labelgenes'. *Default* "organge" *Values* Any R color or RGB color object.
title	Title of the plot.

Details

none

Value

Return generic plots. See ?plot and ?pairs.

Note

none

Author(s)

Jan Winter

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)

#Single Gene
plothitsscatter.enriched = carpools.hit.scatter(wilcox=data.wilcox,
deseq=data.deseq, mageck=data.mageck, dataset=list(TREAT1, TREAT2, CONTROL1, CONTROL2),
dataset.names = c(d.TREAT1, d.TREAT2, d.CONTROL1, d.CONTROL2),
namecolumn=1, fullmatchcolumn=2, title="Title", labelgenes="CASP8",
labelcolor="orange", extractpattern=expression("^(.+?)(_.+)"),
normalize=TRUE, norm.function=median, offsetplot=1.2, center=FALSE,
aggregated=FALSE, type="enriched", cutoff.deseq = 0.001,
cutoff.wilcox = 0.05, cutoff.mageck = 0.05, cutoff.override=FALSE,
cutoff.hits=NULL,  pch=16)

#Overlapping candidate genes

plothitsscatter.enriched = carpools.hit.scatter(wilcox=data.wilcox,
deseq=data.deseq, mageck=data.mageck, dataset=list(TREAT1, TREAT2, CONTROL1, CONTROL2),
dataset.names = c(d.TREAT1, d.TREAT2, d.CONTROL1, d.CONTROL2), namecolumn=1,
fullmatchcolumn=2, title="Title", labelgenes=NULL, labelcolor="orange",
extractpattern=expression("^(.+?)(_.+)"), normalize=TRUE, norm.function=median,
offsetplot=1.2, center=FALSE, aggregated=FALSE, type="enriched",
cutoff.deseq = 0.001, cutoff.wilcox = 0.05, cutoff.mageck = 0.05,
cutoff.override=FALSE, cutoff.hits=NULL,  pch=16)

caRpools documentation built on May 2, 2019, 11:26 a.m.