svapls: an R package to correct for hidden factors of variability in gene expression studies

Abstract

BackgroundHidden variability is a fundamentally important issue in the context of gene expression studies. Collected tissue samples may have a wide variety of hidden effects that may alter their transcriptional landscape significantly. As a result their actual differential expression pattern can be potentially distorted, leading to inaccurate results from a genome-wide testing for the important transcripts.ResultsWe present an R package svapls that can be used to identify several types of unknown sample-specific sources of heterogeneity in a gene expression study and adjust for them in order to provide a more accurate inference on the original expression pattern of the genes over different varieties of samples. The proposed method implements Partial Least Squares regression to extract the hidden signals of sample-specific heterogeneity in the data and uses them to find the genes that are actually correlated with the phenotype of interest. We also compare our package with three other popular softwares for testing differential gene expression along with a detailed illustration on the widely popular Golub dataset. Results from the sensitivity analyes on simulated data with widely different hidden variation patterns reveal the improved detection power of our R package compared to the other softwares along with reasonably smaller error rates. Application on the real-life dataset exhibits the efficacy of the R package in detecting potential batch effects from the dataset.ConclusionsOverall, Our R package provides the user with a simplified framework for analyzing gene expression data with a wide range of hidden variation patterns and delivering a differential gene expression analysis with substantially improved power and accuracy.The R package svapls is freely available at http://cran.r-project.org/web/packages/svapls/index.html.

Integrative Identification of Deregulated MiRNA/TF-Mediated Gene Regulatory Loops and Networks in Prostate Cancer

“…As a consequence, many genes that are indeed differentially expressed in the data are not detected, whereas many others are falsely declared as positives [37], [112].…”

“…Standard statistical tests used to identify differentially expressed genes between two conditions in a typical gene expression profiling study (as adopted by previous methods, e.g., see [26] , [27] ) become fundamentally flawed in the presence of unaccounted sources of variability (due to biological and experimental factors among others) [36] – [38] . As a consequence, many genes that are indeed differentially expressed in the data are not detected, whereas many others are falsely declared as positives [37] , [112] .…”

Learning and Imputation for Mass-spec Bias Reduction (LIMBR)

“…LIMBR employs a K nearest neighbors (KNN) based imputation strategy that is both non-parametric and has been shown to be highly effective in the context of proteomics data (Wang et al, 2017b). LIMBR's bias trend modeling procedure is based on surrogate variable analysis (SVA), a proven bias modeling algorithm initially devised for micro-array data (Leek, 2007;Leek et al, 2012;Leek and Storey, 2007) that has been used extensively and successfully in many systems (Benjamin et al, 2017;Lopez et al, 2014;Parsana et al, 2017;Tsang et al, 2014;Wang et al, 2016) and has also spawned many adaptations (Chakraborty et al, 2013;Karpievitch et al, 2009;Parker et al, 2014). LIMBR improves SVA with optimizations specific to large-scale MS experimental designs, particularly in its handling of general and circadian time courses.…”

Learning and Imputation for Mass-spec Bias Reduction (LIMBR)

“…modeling procedure is based on SVA, a proven bias modeling algorithm initially devised for microarray data Leek, 2007;Leek and Storey, 2007b;Leek et al, 2012 that has been used extensively and successfully in many systems Parsana et al, 2017;Benjamin et al, 2017;Wang et al, 2016;Lopez et al, 2014;Tsang et al, 2014 and has also spawned many adaptations Parker et al, 2014;Chakraborty et al, 2013;Karpievitch et al, 2009. LIMBR improves SVA with optimizations specific to large-scale MS experimental designs, particularly in its handling of general and circadian time courses.…”