# ind.analysis

From MetaDE v1.0.5
by Xingbin Wang

##### Identify differentially expressed genes in each individual dataset

`ind.analysis`

is a function to perform individual analysis. The outputs are measures (p-values) for meta-analysis.

##### Usage

```
ind.analysis(x, ind.method = c("f", "regt", "modt", "pairedt",
"pearsonr", "spearmanr", "F", "logrank"), miss.tol =
0.3, nperm = NULL, tail, ...)
```

##### 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

- miss.tol
- The maximum percent missing data allowed in any gene (default 30 percent).
- nperm
- The number of permutations. If nperm is NULL,the results will be based on asymptotic distribution.
- ind.method
- a character vector to specify the statistical test to test if there is association between the variables and the labels (i.e. genes are differentially expressed in each study). see "Details".
- tail
- a character string specifying the alternative hypothesis,must be one of "abs" (default), "low" or "high".
- ...
- 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.

`miss.tol`

, the default is 30 percent. For those genes with less than miss.tol *100 percent missing 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.
##### Value

- a list with components:
stat the value of test statistic for each gene p the p-value for the test for each gene bp the p-value from nperm permutations for each gene. It will be used for the meta analysis. It can be NULL if you chose asymptotic results.

##### 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

`MetaDE.Read`

,`MetaDE.match`

,`MetaDE.merge`

,`MetaDE.filter`

,`MetaDE.pvalue`

and `MetaDE.rawdata`

##### Examples

```
#--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(x=exp1,y=label1),list(x=exp2,y=label2))
# start individual analysis for each study:
#find genes whose expession is higher in class 2 vs class 1 using moderated t test for both studies
test1<-ind.analysis(x,ind.method=c("modt","modt"),tail="high",nperm=100)
#here I want to use two-sample t test for study 1 and moderated t test for study 2.
test2<-ind.analysis(x,ind.method=c("regt","modt"),tail="abs",nperm=100)
#--------time to event---------#
#--generate three pseudo datasets----#
exp1<-matrix(rnorm(20*10),20,10)
time1=c(4,3,1,1,2,2,3,10,5,4)
event1=c(1,1,1,0,1,1,0,0,0,1)
#study 2
exp2<-matrix(rnorm(20*10,1.5),20,10)
time2=c(4,30,1,10,2,12,3,10,50,2)
event2=c(0,1,1,0,0,1,0,1,0,1)
#study 3
exp3<-matrix(rnorm(20*15),20,15)
time3=c(1,27,40,10,2,6,1,10,50,100,20,5,6,8,50)
event3=c(0,1,1,0,0,1,0,1,0,1,1,1,1,0,1)
#the input has to be arranged in lists
test3<-list(list(x=exp1,y=time1,censoring.status=event1),list(x=exp2,y=time2,censoring.status=event2),
list(x=exp3,y=time3,censoring.status=event3))
# start individual analysis for each study: i use log rank test for all studies
test3.res<-ind.analysis(test3,ind.method=rep("logrank",3),nperm=100,tail='abs')
```

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

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