Description Usage Arguments Details Value Note Author(s) Examples
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.
1 2 | final.table(wilcox=NULL, deseq=NULL, mageck=NULL, dataset, namecolumn=1,
norm.function=median, type="genes", extractpattern = expression("^(.+?)_.+"))
|
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" |
none
Returns a data.frame of gene names and all information generated by stat.wilcox, stat.DEseq and stat.mageck.
none
Jan Winter
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | 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,])
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.