svplsTest: svplsTest

In SVAPLSseq: SVAPLSseq-An R package to estimate the hidden factors of unwanted variability and adjust for them to enable a more powerful and accurate differential expression analysis based on RNAseq data

Description

This function incorporates the significant surrogate variables returned by the function svplsSurr in a linear model along with the group variable in order to estimate the group effects more accurately. The reestimated primary signals (group effects) are then used to test the features for differential expression. The resulting pvalues are further corrected for multiple hypothesis testing at a prespecified FDR level. The significantly differentially expressed features are finally returned along with their uncorrected and corrected pvalues.

Usage

svplsTest(dat, phi = function(x) log(x + const), const = 1,
  normalization = c("TMM", "RLE", "upperquartile", "none"), group, surr,
  test = c("t-test", "LRT"), mht.method = "BH", fdr.level = 0.05,
  parallel = FALSE, num.cores = NULL)

Arguments

dat	The original feature expression count matrix.
phi	The transforming function to be applied on the original feature expression count data (set to be log function with an offset const).
const	The offset parameter for the transforming function phi (set to 1 by default).
normalization	The method to use for normalizing the RNAseq feature expression count data (options are "TMM", "RLE", "upperquartile" or "none").
group	a factor representing the sample indices belonging to the two different groups.
surr	A data.frame of the significant surrogate variables.
test	The test to be used for detecting the differentially expressed features. Options are "t-test" and "Likelihood Ratio Test (LRT)".
mht.method	The method to be used for the multiple hypothesis correction (set to the Benjamini-Hochberg procedure ("BH") by default).
fdr.level	The specified level of the False Discovery Rate (FDR) for the multiple hypothesis testing (set to 0.05 by default).
parallel	Logical, indicating if the computations should be parallelized or not (set to FALSE by default).
num.cores	The requested number of cores to be used in the parallel computations inside the function (used only when parallel is TRUE, NULL by default).

Value

pvs.unadj The uncorrected pvalues corresponding to the genes after adjusting for the signatures of hidden variability.

pvs.adj The multiple hypothesis corrected pvalues after adjusting for the signatures of hidden variability.

sig.features The features detected to be significantly differentially expressed between the two groups.

Examples

##Loading a simulated RNAseq gene expression count dataset
data(sim.dat)

##Fitting a linear model with the surrogate variables and detecting the
##differentially expressed genes
group = as.factor(c(rep(1, 10), rep(-1, 10)))
sv <- svplsSurr(dat = sim.dat, group = group, surr.select = "automatic")
surr = surr(sv)
fit = svplsTest(dat = sim.dat, group = group, surr = surr, normalization = "TMM", test = "t-test")

##The detected genes, hidden effect adjusted pvalues, FDR-corrected pvalues and the positive genes 
##detected from the fitted model are given by:
head(sig.features(fit))

head(pvs.unadj(fit))

head(pvs.adj(fit))

SVAPLSseq documentation built on April 28, 2020, 6:30 p.m.