count.DEnumber: Count the number of differentially expressed (DE) genes

In MetaDE: MetaDE: Microarray meta-analysis for differentially expressed gene detection

Description

a function to summary the number of DE genes at given p-value or FDR thresholds.

Usage

count.DEnumber(result, p.cut, q.cut)

Arguments

result	A p-value matrix or an object file from metaDE.pvalue,metaDE.minMCC, metaDE.ES
p.cut	a numeric vecter to specify the p-value thresholds at which the DE number is counted.
q.cut	a numeric vecter to specify the FDR thresholds at which the DE number is counted.

Details

To count the DE number at FDR thresholds, the p-values were corrected by Benjamini-Hochberg procedure.

Value

a list with components:

pval.table	a table contains the DE numbers counted at given p-value thresholds.
FDR.table	a table contains the DE numbers counted at given FDR thresholds.

Author(s)

Jia Li and Xingbin Wang

References

Benjamini Y, Hochberg Y: Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological 1995, 57(1):289-300.

See Also

draw.DEnumber

Examples

#---example 1: Meta analysis of Differentially expressed genes between two classes----------#
label1<-rep(0:1,each=5)
label2<-rep(0:1,each=5)
exp1<-cbind(matrix(rnorm(5*200),200,5),matrix(rnorm(5*200,2),200,5))
exp2<-cbind(matrix(rnorm(5*200),200,5),matrix(rnorm(5*200,1.5),200,5))
x<-list(list(exp1,label1),list(exp2,label2))

# here I used modt to generate p-values. 
DEgene<-ind.analysis(x,ind.method=rep("regt",2),tail="abs",nperm=100)

#--then you can use Fisher's method to combine the above p-values
res<-MetaDE.pvalue(DEgene,meta.method='Fisher')
draw.DEnumber(res,FDR=TRUE,maxcut=0.1)
count.DEnumber(res,p.cut=c(0.01,0.05),q.cut=c(0.01,0.05))

Example output

Loading required package: survival
Loading required package: impute
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colMeans, colSums, colnames,
    dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
    rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: combinat

Attaching package: 'combinat'

The following object is masked from 'package:utils':

    combn

Loading required package: tools
dataset 1 is done
dataset 2 is done
$pval.table
       dataset1 dataset2 Fisher
p=0.01       67       54    128
p=0.05      152      111    177

$FDR.table
         dataset1 dataset2 Fisher
FDR=0.01        0        3    102
FDR=0.05      127       62    173

MetaDE documentation built on May 29, 2017, 9 a.m.