caRpools (version 0.83)

final.table: CaRpools: Generating Table with Analysis Information from all Methods

Description

CaRpools also provides you with a final gene table, which includes p-values, fold changes and ranks by all methods in a single tabular output. This output is **unbiased** and can thus be used for further analysis and data visualization. It takes the output generated by each analysis method, `stat.wilcox`, `stat.DEseq` and `stat.mageck` and combines it into a single tabular representation.

Usage

final.table(wilcox=NULL, deseq=NULL, mageck=NULL, dataset, namecolumn=1,
norm.function=median, type="genes", extractpattern = expression("^(.+?)_.+"))

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`.

dataset

data.frame as created by `load.file` *Default* empty *Values* data frame

namecolumn

In which column are the sgRNA identifiers? *Default* 1 *Values* column number (numeric)

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)

norm.function

The mathematical function to normalize data if `normalize=TRUE`. By default, the median is used. *Default* median *Values* Any mathematical function of R (function)

type

Output generated. *Default* "genes" *Values* "genes"

Value

Returns a data.frame of gene names and all information generated by stat.wilcox, stat.DEseq and stat.mageck.

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)

final.tab = final.table(wilcox=data.wilcox, deseq=data.deseq,
mageck=data.mageck, dataset=CONTROL1.g, namecolumn=1, type="genes")
knitr::kable(final.tab[1:20,])