svplsSurr: svplsSurr

In sutigit21/SVAPLSseq: SVAPLSseq-An R package to adjust for the hidden factors of variability in differential gene expression studies based on RNAseq data

Description

This function extracts the surrogated estimates of the hidden variables in the data by using the partial least squares (PLS) algorithm on two multivariate random matrices. It provides the user with two options:

(1) Unsupervised SVAPLS: Here a standard linear regression model is first used on a transformed version of the expression count matrix to estimate the primary signals of differential expression for all the genes. The fitted model residuals and the transformed count matrix are then organized respectively into two multivariate matrices E and Y, in such a way that each column corresponds to a certain gene. E is then regressed on Y using a Non-linear partial least squares (NPLS) algorithm and the extracted scores in the column-space of Y are deemed as the surrogate variables.

(2) Supervised SVAPLS: In case information on a set of control genes (probes) is provided, this function uses a Non-linear partial least squares (NPLS) algorithm to regress Y on a submatrix of Y (Y.sub) corresponding to the set of controls and scores in the column- space of Y.sub are considered as the surrogate variables.

The function then regresses the first eigenvector of the residual matrix E (for Unsupervised SVAPLS or the control matrix Y.sub for Supervised SVAPLS) on these surrogate variables and tests them for statistical significance with a certain user-specified pvalue cutoff. The variables yielding a pvalue below the cutoff are returned.

Usage

svplsSurr(dat, group, controls = NULL, phi = function(x) log(x + const),
  const = 1, pls.method = "oscorespls", max.surrs = 3, cutoff = 10^-7,
  parallel = FALSE, num.cores = NULL, plot = FALSE)

Arguments

dat	A gene expression count matrix or a 'SummarizedExperiment' object or a 'DGEList' object.
group	a factor representing the sample indices belonging to the two different groups.
controls	The set of control probes with no differential expression between the two groups (set to NULL by default).
phi	The transforming function to be applied on the original gene 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).
pls.method	The non-linear partial least squares method to be used. The different options available are: the classical orthogonal scores algorithm ("oscorespls", default), the kernel algorithm ("kernelpls") and wide kernel algorithm ("widekernelpls"). Using the "oscorespls" option is recommended for producing mutually orthogonal surrogate variables.
max.surrs	The maximum number if surrogate variables to be extracted from the NPLS algorithm (set to 3 by default).
cutoff	The user-specified pvalue cutoff for testing the significance of the extracted surrogate variables (set to 1e-07 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).
plot	Logical, if TRUE a barplot of the variance proportions explained by the significant surrogate variables is returned (set to FALSE by default).

Value

An svplsSurr object.

Examples

##Loading the simulated dataset
data(sim.dat)

##Extracting the significant surrogate variables
group = as.factor(c(rep(1, 10), rep(-1, 10)))
sv <- svplsSurr(dat = sim.dat, group = group)
print(sv)

sutigit21/SVAPLSseq documentation built on May 30, 2019, 8:43 p.m.