Description Usage Arguments Value References Examples
View source: R/utility03282012.r
heatmap.sig.genes
,a function to draw the
Heatmap of DE genes given a FDR cut point obtained from the Meta-analysis.
1 | heatmap.sig.genes(result,meta.method, fdr.cut=0.2,color="GR")
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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. |
A figure shows the standardized expression levels for the DE genes detected by meta analysis across studies/datasets.
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)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | #------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")
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