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 Meta-analysis methods to combine the p-values 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 paired-design
, 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 p-value. |
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 p-values in meta-analysis. The default is FALSE. |
... |
Additional arguments. |
The available statistical tests for argument, ind.method
, are:
"regt":
Two-sample t-statistics (unequal variances).
"modt":
Two-sample t-statistics with the variance is modified by adding a fudging parameter.
In our algorithm, we choose the penalized t-statistics 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 t-statistics 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 F-statistics. 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 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).
"SR":
the naive sum of the ranks method.
"PR":
the naive product of the ranks methods.
"minMCC":
the minMCC method.
"FEM":
the Fixed-effect model method.
"REM":
the Random-effect model method.
"roP":
rth p-value method.
"roP.OC":
rth p-value method with one-sided 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 meta-analysis, for those genes with less than miss.tol *100 percent missing,the p-values are calculated if asymptotic is TRUE.
A list with components:
meta.analysis |
a list of the results of meta-analysis with components:
|
ind.stat |
the statistics calculated from individual analysis. This is for |
ind.p |
the p-value 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. 5:994-1019.
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
,
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 log-rank 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
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'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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