MetaDE (version 1.0.5)

heatmap.sig.genes: A function to plot the heatmap of DE genes detectred at a given FDR threshold from the Meta-analysis.

Description

heatmap.sig.genes,a function to draw the Heatmap of DE genes given a FDR cut point obtained from the Meta-analysis.

Usage

heatmap.sig.genes(result,meta.method, fdr.cut=0.2,color="GR")

Arguments

result

The object file from MetaDE.pvalue,MetaDE.ES or metaDE.minMCC.

meta.method

If multiple methods were chosen for the meta analysis, the user needs to specify which which method is to be used for plotting.

fdr.cut

cut off for FDR for the meta analysis result.

color

The color scheme for the heatmap. "GR" is the default. "GR" stands for green, black,red. "BY" stands for blue,black and yellow.

Value

meta analysis across studies/datasets.

References

Jia Li and George C. Tseng. (2011) An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies. Annals of Applied Statistics. 5:994-1019.

Shuya Lu, Jia Li, Chi Song, Kui Shen and George C Tseng. (2010) Biomarker Detection in the Integration of Multiple Multi-class Genomic Studies. Bioinformatics. 26:333-340. (PMID: 19965884; PMCID: PMC2815659)

Examples

#------example 2: -----------#
# here I generate two pseudo datasets
set.seed(123)
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))

#the input has to be arranged in lists
x<-list(list(exp1,label1),list(exp2,label2))

#here I used the modt test for individual study and used Fisher's method to combine results
#from multiple studies.
meta.res2<-MetaDE.rawdata(x=x,ind.method=c('modt','modt'),meta.method=c('Fisher',"maxP"),nperm=200)
heatmap.sig.genes(meta.res2, meta.method="maxP",fdr.cut=1,color="GR") #plot all genes
heatmap.sig.genes(meta.res2, meta.method="Fisher",fdr.cut=0.05,color="GR")