Abstract
Extensive evaluation of RNA-seq methods have demonstrated that no single algorithm consistently outperforms all others. Removal of unwanted variation (RUV) has also been proposed as a method for stabilizing differential expression (DE) results. Despite this, it remains a challenge to run multiple RNA-seq algorithms to identify significant differences common to multiple algorithms, whilst also integrating and assessing the impact of RUV into all algorithms. consensusDE was developed to automate the process of identifying significant DE by combining the results from multiple algorithms with minimal user input and with the option to automatically integrate RUV. consensusDE only requires a table describing the sample groups, a directory containing BAM files or preprocessed count tables and an optional transcript database for annotation. It supports merging of technical replicates, paired analyses and outputs a compendium of plots to guide the user in subsequent analyses. Herein, we assess the ability of RUV to improve DE stability when combined with multiple algorithms and between algorithms, through application to real and simulated data. We find that, although RUV increased fold change stability between algorithms, it demonstrated improved FDR in a setting of low replication for the intersect, the effect was algorithm specific and diminished with increased replication, reinforcing increased replication for recovery of true DE genes. We finish by offering some rules and considerations for the application of RUV in a consensus-based setting. consensusDE is freely available, implemented in R and available as a Bioconductor package, under the GPL-3 license, along with a comprehensive vignette describing functionality: http://bioconductor.org/packages/consensusDE/.
Highlights
Differential gene expression (DE) analysis aims to identify transcripts or features that are expressed differently between conditions
Multi_de_pairs Automatically performs DE analysis on all possible pairs of ‘‘groups’’ defined in a provided sample table using all available DE methods and outputs a summary table that merges the results of all methods into one table
Options are provided for annotations, including an option to annotate from gtf files, and users are provided the option to remove unwanted sources of variation by RUVr (Risso et al, 2014)
Summary
Differential gene expression (DE) analysis aims to identify transcripts or features that are expressed differently between conditions. They found that no single DE method was stable in all cases, with permutation or bootstrap strategies being limited by replicate number and computational demands Another technique often used to assess performance of DE methods is ‘False DE’, where genes not expected to exhibit significant DE are examined (Rapaport et al, 2013; Seyednasrollah, Laiho & Elo, 2015). A comparison of 11 methods found that uniquely identified DE genes are often attributed to low fold changes (Soneson & Delorenzi, 2013), but that methods largely (with some exceptions) ranked genes (Soneson & Delorenzi, 2013) These findings support a ‘‘combined’’ or consensus-based approach or at the least a need to compare and ideally benchmark results with known truth between different methods
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