Description Usage Arguments Details Value References See Also Examples
View source: R/meta_analysis03282012.r
MetaDE.rawdata
Identify differentially expressed genes by integrating multiple studies(datasets).
1 2 3 4 5 6 7 8  MetaDE.rawdata(x, ind.method = c("modt", "regt", "pairedt", "F",
"pearsonr", "spearmanr", "logrank"), meta.method =
c("maxP", "maxP.OC", "minP", "minP.OC", "Fisher",
"Fisher.OC", "AW", "AW.OC", "roP", "roP.OC",
"Stouffer", "Stouffer.OC", "SR", "PR", "minMCC",
"FEM", "REM", "rankProd"), paired = NULL, miss.tol =
0.3, rth = NULL, nperm = NULL, ind.tail = "abs",
asymptotic = FALSE, ...)

x 
a list of studies. Each study is a list with components:

ind.method 
a character vector to specify the statistical test to test whether there is association between the variables and the labels (i.e. genes are differentially expressed in each study). see "Details". 
ind.tail 
a character string specifying the alternative hypothesis, must be one of "abs" (default), "low" or "high". 
meta.method 
a character to specify the type of Metaanalysis methods to combine the pvalues or effect sizes. See "Detials". 
paired 
a vector of logical values to specify that whether the design of ith study is paired or not. If the ith study is paireddesign
, the correponding element of 
miss.tol 
The maximum percent missing data allowed in any gene (default 30 percent). 
rth 
this is the option for roP and roP.OC method. rth means the rth smallest pvalue. 
nperm 
The number of permutations. If nperm is NULL,the results will be based on asymptotic distribution. 
asymptotic 
A logical values to specify whether the parametric methods is chosen to calculate the pvalues in metaanalysis. The default is FALSE. 
... 
Additional arguments. 
The available statistical tests for argument, ind.method
, are:
"regt":
Twosample tstatistics (unequal variances).
"modt":
Twosample tstatistics with the variance is modified by adding a fudging parameter.
In our algorithm, we choose the penalized tstatistics used in Efron et al.(2001) and Tusher et al. (2001). The fudge parameter s0 is chosen to be the median
variability estimator in the genome.
"pairedt":
Paired tstatistics for the design of paired samples.
"pearsonr":
, Pearson's correlation. It is usually chosen for quantitative outcome.
"spearmanr":
, Spearman's correlation. It is usually chosen for quantitative outcome.
"F":
, the test is based on Fstatistics. It is usually chosen where there are 2 or more classes.
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).
"SR":
the naive sum of the ranks method.
"PR":
the naive product of the ranks methods.
"minMCC":
the minMCC method.
"FEM":
the Fixedeffect model method.
"REM":
the Randomeffect model method.
"roP":
rth pvalue method.
"roP.OC":
rth pvalue method with onesided correction.
"rankProd":
rank Product method.
For the argument, miss.tol, the default is 30 percent. In individual analysis, for those genes with less than miss.tol *100 percent, missing values are imputed using KNN method in package,impute; for those genes with more than or equal miss.tol*100 percent missing are igmored for the further analysis. In metaanalysis, for those genes with less than miss.tol *100 percent missing,the pvalues are calculated if asymptotic is TRUE.
A list with components:
meta.analysis 
a list of the results of metaanalysis with components:

ind.stat 
the statistics calculated from individual analysis. This is for 
ind.p 
the pvalue matrix calculated from individual analysis. This is for 
ind.ES 
the effect size matrix calculated from indvidual analysis. This is only 
ind.Var 
the corresponding variance matrix calculated from individual analysis. This is only 
raw.data 
the raw data of your input. That's 
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
,
draw.DEnumber
,
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  #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))
#here I used the modt test for individual study and used Fisher's method to combine results
#from multiple studies.
meta.res1<MetaDE.rawdata(x=x,ind.method=c('modt','modt'),meta.method='Fisher',nperm=20)
#example 2: genes associated with survival#
# here I generate two pseudo datasets
exp1<cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,2),20,5))
time1=c(4,3,1,1,2,2,3,10,5,4)
event1=c(1,1,1,0,1,1,0,0,0,1)
exp2<cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,1.5),20,4))
time2=c(4,30,1,10,2,12,3,10,50)
event2=c(0,1,1,0,0,1,0,1,0)
#again,the input has to be arranged in lists
test2 <list(list(x=exp1,y=time1,censoring.status=event1),list(x=exp2,y=time2,censoring.status=event2))
#here I used the logrank test for individual study and used Fisher's method to combine results
#from multiple studies.
meta.res2<MetaDE.rawdata(x=test2,ind.method=c('logrank','logrank'),meta.method='Fisher',nperm=20)
#example 3: Fixed effect model for two studies from paired design#
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))
x<list(list(x=exp1,y=label1),list(x=exp2,y=label2))
test< MetaDE.rawdata(x,nperm=1000, meta.method="FEM", paired=rep(FALSE,2))

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
Please make sure the following is correct:
*You input 2 studies
*You selected modt modt for your 2 studies respectively
*They are not paired design
* Fisher was chosen to combine the 2 studies,respectively
dataset 1 is done
dataset 2 is done
Permutation was used instead of the asymptotic estimation
Please make sure the following is correct:
*You input 2 studies
*You selected logrank logrank for your 2 studies respectively
*They are not paired design
* Fisher was chosen to combine the 2 studies,respectively
dataset 1 is done
dataset 2 is done
Permutation was used instead of the asymptotic estimation
Please make sure the following is correct:
*You input 2 studies
* FEM was chosen to combine the 2 studies,respectively
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