dcGSA {dcGSA} | R Documentation |
Perform gene set analysis for longitudinal gene expression profiles.
dcGSA(data = NULL, geneset = NULL, nperm = 10, c = 0, KeepPerm=FALSE, parallel = FALSE, BPparam = MulticoreParam(workers = 4))
data |
A list with ID (a character vector for subject ID), pheno (a data frame with each column being one clinical outcome), gene (a data frame with each column being one gene). |
geneset |
A list of gene sets of interests (the output of
|
nperm |
An integer number of permutations performed to get P values. |
c |
An integer cutoff value for the overlapping number of genes between the data and the gene set. |
KeepPerm |
A logical value indicating if the permutation statistics are kept. If there are a large number of gene sets and the number of permutation is large, the matrix of the permutation statistics could be large and memory demanding. |
parallel |
A logical value indicating if parallel computing is wanted. |
BPparam |
Parameters to configure parallel evaluation environments
if parallel is TRUE. The default value is to use 4 cores in a single
machine. See |
Returns a data frame with following columns, if KeepPerm=FALSE; otherwise, returns a list with two objects: "res" object being the following data frame and "stat" being the permutation statistics.
Geneset |
Names for the gene sets. |
TotalSize |
The original size of each gene set. |
OverlapSize |
The overlapping number of genes between the data and the gene set. |
Stats |
Longitudinal distance covariance between the clinical outcomes and the gene set. |
NormScore |
Only available when permutation is performed. Normalized longitudinal distance covariance using the mean and standard deviation of permutated values. |
P.perm |
Only available when permutation is performed. Permutation P values. |
P.approx |
P values obtained using normal distribution to approximate the null distribution. |
FDR.approx |
FDR based on the P.approx. |
Distance-correlation based Gene Set Analysis in Longitudinal Studies. Jiehuan Sun, Jose Herazo-Maya, Xiu Huang, Naftali Kaminski, and Hongyu Zhao.
data(dcGSAtest) fpath <- system.file("extdata", "sample.gmt.txt", package="dcGSA") GS <- readGMT(file=fpath) system.time(res <- dcGSA(data=dcGSAtest,geneset=GS,nperm=100)) head(res)