Using Supervised Learning Methods for Gene Selection in RNA-Seq Case-Control Studies

Abstract

Whole transcriptome studies typically yield large amounts of data, with expression values for all genes or transcripts of the genome. The search for genes of interest in a particular study setting can thus be a daunting task, usually relying on automated computational methods. Moreover, most biological questions imply that such a search should be performed in a multivariate setting, to take into account the inter-genes relationships. Differential expression analysis commonly yields large lists of genes deemed significant, even after adjustment for multiple testing, making the subsequent study possibilities extensive. Here, we explore the use of supervised learning methods to rank large ensembles of genes defined by their expression values measured with RNA-Seq in a typical 2 classes sample set. First, we use one of the variable importance measures generated by the random forests classification algorithm as a metric to rank genes. Second, we define the EPS (extreme pseudo-samples) pipeline, making use of VAEs (Variational Autoencoders) and regressors to extract a ranking of genes while leveraging the feature space of both virtual and comparable samples. We show that, on 12 cancer RNA-Seq data sets ranging from 323 to 1,210 samples, using either a random forests-based gene selection method or the EPS pipeline outperforms differential expression analysis for 9 and 8 out of the 12 datasets respectively, in terms of identifying subsets of genes associated with survival. These results demonstrate the potential of supervised learning-based gene selection methods in RNA-Seq studies and highlight the need to use such multivariate gene selection methods alongside the widely used differential expression analysis.

Novel Insights into the Role of UBE3A in Regulating Apoptosis and Proliferation

“…For this, we used a random forest approach, utilizing the transcriptome data from the mouse hippocampi we recently produced (see Methods) [57]. Machine-learning methods are progressively being applied to rank ensembles of genes defined by their expression values measured with RNA-seq [74]. Using importance measures generated by the random forest algorithm, we identified groups of 40 apoptosis-related genes and 10 proliferation-related genes that together differentiate between the adult Angelman syndrome model mice and the wild-type (WT) littermates.…”

Rewiring of gene expression in circulating white blood cells is associated with pregnancy outcome in heifers (Bos taurus)

“…We tested this hypothesis by applying machine learning algorithms on two groups of heifers that were bred in 2015 (year one) and 2016 (year two). Parallel random forest emerged as the algorithm with over 90% efficiency of classification nearly all trials executed, which confirms the potential of accurate classification of samples using RNA-seq data under the case-control framework 60,61 . The results show that while not one single gene emerges as a potential biomarker, the accumulated information of transcript abundance from multiple genes can be powerful for the identification of fertility potential in cattle.…”

Rewiring of gene expression in circulating white blood cells is associated with pregnancy outcome in heifers (Bos taurus)

“…We tested this hypothesis by applying machine learning algorithms on two groups of heifers that were bred in 2015 (year one) and 2016 (year two). Parallel random forest emerged as the algorithm with over 90% efficiency of classification nearly all trials executed, which confirms the potential of accurate classification of samples using RNA-seq data under the case-control framework 69,70 . The results show that while not one single gene emerges as a potential biomarker, the accumulated information of transcript abundance from multiple genes can be powerful for the identification of fertility potential in cattle.…”