Description Usage Arguments Details Value References See Also Examples
View source: R/meta_analysis03282012.r
MetaDE.pvalue
Identify differentially expressed genes by integrating multiple studies(datasets). The data
consists of pvalues from your own method/calculations.
1 2 3 4 5  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)

x 
a list with components:

meta.method 
a character to specify the type of Metaanalysis methods to combine the pvalues or effect sizes. See "Detials". 
rth 
this is the option for roP and roP.OC method. rth means the rth smallest pvalue. 
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 pvalues in metaanalysis. The default is FALSE. 
The options for argument,mete.method
,are listed below:
"maxP": the maximum of p value method.
"maxP.OC": the maximum of p values with onesided correction.
"minP": the minimum of p values from "test" across studies.
"minP.OC": the minimum of p values with onesided correction.
"Fisher": Fisher's method (Fisher, 1932),the summation of log(pvalue) across studies.
"Fisher.OC": Fisher's method with onesided correction (Fisher, 1932),the summation of log(pvalue) across studies.
"AW": Adaptivelyweighted method (Li and Tseng, 2011).
"AW.OC": Adaptivelyweighted method with onesided correction (Li and Tseng, 2011).
"roP": rth pvalue method.
"roP.OC": rth pvalue method with onesided correction.
"Stouffer": the minimum of p values from "test" across studies.
"Stouffer.OC": the minimum of p values with onesided correction.
"SR": the naive sum of the ranks method.
"PR": the naive product of the ranks method.
For those genes with less than miss.tol *100 percent missing,the pvalues are calculated using parametric metod if asymptotic is TRUE. Otherwise, , the pvalues for genes without missing values are calculated using permutation methold.
A list containing:
stat 
a matrix with rows reprenting genes. It is the statistic for the selected meta analysis method of combining pvalues. 
pval 
the pvalue from meta analysis for each gene for the above stat. 
FDR 
the FDR of the pvalue for each gene for the above stat. 
AW.weight 
The optimal weight assigned to each dataset/study for each gene if the ' 
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. 5:9941019.
Shuya Lu, Jia Li, Chi Song, Kui Shen and George C Tseng. (2010) Biomarker Detection in the Integration of Multiple Multiclass Genomic Studies. Bioinformatics. 26:333340. (PMID: 19965884; PMCID: PMC2815659)
MetaDE.minMCC, MetaDE.pvalue,MetaDE.ES,plot.FDR,heatmap.sig.genes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  #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 pvalues.
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
#pvalues 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 pvalues:here I used the Fisher's method
MetaDE.pvalue(DEgene,meta.method='Fisher')

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.