R/Rcppsva-package.R

In xinchoubiology/Rcppsva: Rcpp Integration Surrogate Variable Analysis

#' Accelerate version of sva packaga on Bioconductor
#'
#'
#' The Rcppsva package contains functions for removing batch
#' effects and other unwanted variation in high-throughput
#' experiment. Specifically, the sva package contains functions
#' for the identifying and building surrogate variables for
#' high-dimensional data sets. Surrogate variables are covariates
#' constructed directly from high-dimensional data (like gene
#' expression/RNA sequencing/methylation/brain imaging data) that
#' can be used in subsequent analyses to adjust for unknown,
#' unmodeled, or latent sources of noise. The sva package can be
#' used to remove artifacts in three ways: (1) identifying and
#' estimating surrogate variables for unknown sources of variation
#' in high-throughput experiments (Leek and Storey 2007 PLoS
#' Genetics,2008 PNAS), (2) directly removing known batch
#' effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing
#' batch effects with known control probes (Leek 2014 biorXiv).
#' Removing batch effects and using surrogate variables in
#' differential expression analysis have been shown to reduce
#' dependence, stabilize error rate estimates, and improve
#' reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008
#' PNAS or Leek et al. 2011 Nat. Reviews Genetics).
#'
#' @docType package
#' @name Rcppsva
#' @aliases Rcppsva package-Rcppsva
#' @useDynLib Rcppsva
#' @import Rcpp RcppEigen methods
#' @author Xin Zhou \url{xinchoubiology@@gmail.com}
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