MetaDE.pvalue: Identify differentially expressed genes by combining p-values

Description Usage Arguments Details Value References See Also Examples

View source: R/meta_analysis03282012.r

Description

MetaDE.pvalue Identify differentially expressed genes by integrating multiple studies(datasets). The data consists of p-values from your own method/calculations.

Usage

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MetaDE.pvalue(x, meta.method = c("maxP", "maxP.OC", "minP",
                 "minP.OC", "Fisher", "Fisher.OC", "AW", "AW.OC",
                 "roP", "roP.OC", "Stouffer", "Stouffer.OC", "SR",
                 "PR"), rth = NULL, miss.tol = 0.3, asymptotic = FALSE)
		

Arguments

x

a list with components:

  • p: a list of p values for each dataset.

  • bp: a list of p values calculated from permutation for each dataset. This part can be NULL if you just have the p-values from your own method.

meta.method

a character to specify the type of Meta-analysis methods to combine the p-values or effect sizes. See "Detials".

rth

this is the option for roP and roP.OC method. rth means the rth smallest p-value.

miss.tol

The maximum percent missing data allowed in any gene (default 30 percent).

asymptotic

A logical values to specify whether the parametric methods is chosen to calculate the p-values in meta-analysis. The default is FALSE.

Details

The options for argument,mete.method,are listed below:

For those genes with less than miss.tol *100 percent missing,the p-values are calculated using parametric metod if asymptotic is TRUE. Otherwise, , the p-values for genes without missing values are calculated using permutation methold.

Value

A list containing:

stat

a matrix with rows reprenting genes. It is the statistic for the selected meta analysis method of combining p-values.

pval

the p-value from meta analysis for each gene for the above stat.

FDR

the FDR of the p-value for each gene for the above stat.

AW.weight

The optimal weight assigned to each dataset/study for each gene if the 'AW' or 'AW.OC' method was chosen.

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)

See Also

MetaDE.minMCC, MetaDE.pvalue,MetaDE.ES,plot.FDR,heatmap.sig.genes

Examples

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#---example 1: Meta analysis of Differentially expressed genes between two classes----------#
# here I generate two pseudo datasets
label1<-rep(0:1,each=5)
label2<-rep(0:1,each=5)
exp1<-cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,2),20,5))
exp2<-cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,1.5),20,5))

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

# start individual analysis for each dataset: here I used modt to generate p-values. 
DEgene<-ind.analysis(x,ind.method=c("modt","modt"),tail="high",nperm=100)
#you don't have to use our ind.analysis for the analysis for individual study. you can input 
#p-values to MetaDE.pvalue for meta analysis only. But the input has to be specified in the
# same format as the DEgene in the example above

#--then you can use meta analysis method to combine the above p-values:here I used the Fisher's method
MetaDE.pvalue(DEgene,meta.method='Fisher')

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