signifLRT.TcGSA {TcGSA}

Identifying the Significant Gene Sets

Description

A function that identifies the significant gene sets in an object of class 'TcGSA'.

Usage

signifLRT.TcGSA(
  tcgsa,
  threshold = 0.05,
  myproc = "BY",
  nbsimu_pval = 1e+06,
  write = F,
  txtfilename = NULL,
  directory = NULL,
  exact = TRUE
)

Arguments

tcgsa

a tcgsa object.

threshold

the threshold at which the FDR or the FWER should be controlled.

myproc

a vector of character strings containing the names of the multiple testing procedures for which adjusted p-values are to be computed. This vector should include any of the following: "Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY", "ABH", "TSBH" or "none". "none" indicates no adjustment for multiple testing. See mt.rawp2adjp for details. Default is "BY", the Benjamini & Yekutieli (2001) step-up FDR-controlling procedure (general dependency structures). In order to control the FWER(in case of an analysis that is more a hypothesis confirmation than an exploration of the expression data), we recommend to use "Holm", the Holm (1979) step-down adjusted p-values for strong control of the FWER.

nbsimu_pval

the number of observations under the null distribution to be generated in order to compute the p-values. Default is 1e+06.

write

logical flag enabling the export of the results as a table in a .txt file. Default is FALSE.

txtfilename

a character string with the name of the .txt file in which the results table is to be written, if write is TRUE. Default is NULL.

directory

if write is TRUE, a character string with the directory of the .txt file in which the results table is to be written, if write is TRUE. Default is NULL.

exact

logical flag indicating whether the raw p-values should be computed from the exact asymptotic mixture of chi-square, or simulated (longer and not better). Default is TRUE and should be preferred.

Value

signifLRT.TcGSA returns a list.

The first element mixedLRTadjRes is data frame with p rows (one row for each significant gene set) and the 3 following variables:

• GeneSet the significant gene set name from the gmt object.

• AdjPval the adjusted p-value corresponding to the significant gene set.

• desc the significant gene set description from the gmt object.

The second element multCorProc passes along the multiple testing procedure used (from the argument myproc).

The third element threshold passes along the significance threshold used (from the argument threshold).

Author(s)

Boris P. Hejblum

References

Hejblum BP, Skinner J, Thiebaut R, (2015) Time-Course Gene Set Analysis for Longitudinal Gene Expression Data. PLOS Comput. Biol. 11(6):e1004310. doi: 10.1371/journal.pcbi.1004310

See Also

multtest.TcGSA, TcGSA.LR

Examples

if(interactive()){
data(data_simu_TcGSA)

tcgsa_sim_1grp <- TcGSA.LR(expr=expr_1grp, gmt=gmt_sim, design=design, 
                          subject_name="Patient_ID", time_name="TimePoint",
                          time_func="linear", crossedRandom=FALSE)
                          
sgnifs <- signifLRT.TcGSA(tcgsa_sim_1grp, threshold = 0.05, myproc = "BY",
                         nbsimu_pval = 1000, write=FALSE)
sgnifs
}