Description Usage Arguments Details Value References See Also Examples
View source: R/individual_analysis03282012.r
ind.analysis
is a function to perform individual analysis. The outputs are measures (pvalues) for metaanalysis.
1 2 3  ind.analysis(x, ind.method = c("f", "regt", "modt", "pairedt",
"pearsonr", "spearmanr", "F", "logrank"), miss.tol =
0.3, nperm = NULL, tail, ...)

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

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. 
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.
For the argument, 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.
a list with components:
stat 
the value of test statistic for each gene 
p 
the pvalue for the test for each gene 
bp 
the pvalue from nperm permutations for each gene. It will be used for the meta analysis. It can be NULL if you chose asymptotic results. 
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.Read
,MetaDE.match
,MetaDE.merge
,MetaDE.filter
,MetaDE.pvalue
and MetaDE.rawdata
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  #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 twosample 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')

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