# MetaDE.rawdata

From MetaDE v1.0.5
by Xingbin Wang

##### Identify differentially expressed genes by integrating multiple studies(datasets)

`MetaDE.rawdata`

Identify differentially expressed genes by integrating multiple studies(datasets).

##### Usage

```
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, ...)
```

##### Arguments

- x
- a list of studies. Each study is a list with components:
**x**: the gene expression matrix.**y**: the outcome variable. For a binary outcome, 0 refers to "normal" and 1 to "diseased". For a multiple class outcome, the first leve

- 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
`paired`

should be TRUE otherwise FALSE. - 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.

##### Details

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.

`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.

##### Value

- A list with components:
meta.analysis a list of the results of meta-analysis with components: - meta.stat: the statistics for the chosen meta analysis method
- pval: the p-value for the above statistic. It is calculated from permutation.
- FDR: the p-values corrected by Benjamini-Hochberg.
- AW.weight: The optimal weight assigned to each dataset/study for each gene if the '
`AW`

' or '`AW.OC`

' method was chosen.

ind.stat the statistics calculated from individual analysis. This is for `meta.method`

expecting "`REM`

","`FEM`

","`minMCC`

" and "`rankProd`

".ind.p the p-value matrix calculated from individual analysis. This is for `meta.method`

expecting "`REM`

","`FEM`

","`minMCC`

" and "`rankProd`

".ind.ES the effect size matrix calculated from indvidual analysis. This is only `meta.method`

, "REM" and "FEM".ind.Var the corresponding variance matrix calculated from individual analysis. This is only `meta.method`

, "`REM`

" and "`FEM`

".raw.data the raw data of your input. That's `x`

. This part will be used for plotting.

##### 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

##### Examples

```
#---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))
```

*Documentation reproduced from package MetaDE, version 1.0.5, License: GPL-2*

### Community examples

Looks like there are no examples yet.