MetaDE.pvalue: Identify differentially expressed genes by combining p-values
In MetaDE: MetaDE: Microarray meta-analysis for differentially expressed gene detection

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 p-values from your own method/calculations.

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: 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.

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

"maxP": the maximum of p value method.
"maxP.OC": the maximum of p values with one-sided correction.
"minP": the minimum of p values from "test" across studies.
"minP.OC": the minimum of p values with one-sided correction.
"Fisher": Fisher's method (Fisher, 1932),the summation of -log(p-value) across studies.
"Fisher.OC": Fisher's method with one-sided correction (Fisher, 1932),the summation of -log(p-value) across studies.
"AW": Adaptively-weighted method (Li and Tseng, 2011).
"AW.OC": Adaptively-weighted method with one-sided correction (Li and Tseng, 2011).
"roP": rth p-value method.
"roP.OC": rth p-value method with one-sided correction.
"Stouffer": the minimum of p values from "test" across studies.
"Stouffer.OC": the minimum of p values with one-sided 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 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.

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.

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)

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

#---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')

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, cbind, colMeans, colSums, colnames, 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
$meta.analysis
$meta.analysis$stat
             Fisher
gene 1  0.001000250
gene 2  0.153157428
gene 3  0.231025978
gene 4  0.293636258
gene 5  0.066079708
gene 6  0.111025420
gene 7  0.009010266
gene 8  0.018081490
gene 9  0.283127129
gene 10 0.020100672
gene 11 0.055384840
gene 12 0.016064344
gene 13 0.007006258
gene 14 0.030227276
gene 15 0.009020311
gene 16 0.110515971
gene 17 0.373051966
gene 18 0.014037145
gene 19 0.180221441
gene 20 0.007012279

$meta.analysis$pval
        Fisher
gene 1  1.0000
gene 2  0.9955
gene 3  0.9890
gene 4  0.9865
gene 5  0.9980
gene 6  0.9975
gene 7  1.0000
gene 8  1.0000
gene 9  0.9875
gene 10 1.0000
gene 11 0.9985
gene 12 1.0000
gene 13 1.0000
gene 14 0.9995
gene 15 1.0000
gene 16 0.9975
gene 17 0.9820
gene 18 1.0000
gene 19 0.9935
gene 20 1.0000

$meta.analysis$FDR
        Fisher
gene 1       1
gene 2       1
gene 3       1
gene 4       1
gene 5       1
gene 6       1
gene 7       1
gene 8       1
gene 9       1
gene 10      1
gene 11      1
gene 12      1
gene 13      1
gene 14      1
gene 15      1
gene 16      1
gene 17      1
gene 18      1
gene 19      1
gene 20      1

$meta.analysis$AW.weight
[1] NA

attr(,"nstudy")
[1] 2
attr(,"meta.method")
[1] "Fisher"

$ind.p
        dataset1 dataset2
gene 1    1.0000   0.9995
gene 2    0.9960   0.9300
gene 3    0.9910   0.8990
gene 4    0.8865   0.9740
gene 5    1.0000   0.9675
gene 6    1.0000   0.9460
gene 7    0.9980   0.9975
gene 8    1.0000   0.9910
gene 9    1.0000   0.8680
gene 10   1.0000   0.9900
gene 11   0.9850   0.9875
gene 12   1.0000   0.9920
gene 13   0.9980   0.9985
gene 14   1.0000   0.9850
gene 15   1.0000   0.9955
gene 16   0.9740   0.9715
gene 17   0.9980   0.8315
gene 18   0.9990   0.9940
gene 19   0.9960   0.9175
gene 20   1.0000   0.9965

attr(,"class")
[1] "MetaDE.pvalue"