Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

By Franck Rapaport, Raya Khanin, Yupu Liang, Mono Pirun, A. Krek, Paul Zumbo, Christopher E. Mason, Nicholas D. Socci, Doron Betel. Genome Biology, 2013

737 citations recorded by the source corpus.

A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b5860253e015c27e8b125cf9b7ba2cb543dc36ed

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Citations in the corpus

featureCounts: an efficient general purpose program for assigning sequence reads to genomic features (2013, methodology): “Recent comparisons have concluded that the read count methods perform well relative to model-based methods for the purposes of gene level diﬀerential expression (Nookaew et al. , 2012; Rapaport et al. , 2013) or detection of splice variation (Anders et al. , 2012).”
Robustly detecting differential expression in RNA sequencing data using observation weights (2013, background): “(6,7)) and those that accompany every new method published, is that they are a snapshot in time.”
limma powers differential expression analyses for RNA-sequencing and microarray studies (2015, result): “The resulting pipeline gives comparable performance to the best of the negative binomial-based software packages but with greater speed and reliability for large data sets (10,21).”
A survey of best practices for RNA-seq data analysis (2016, neutral): “Most comparison studies have focused on simulated datasets [56, 208, 209] or on samples to which exogenous RNA ( ‘ spike-in ’ ) has been added in known quantities [63, 196].”
SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data (2015, methodology): “In recent years edgeR and a previous version of DESeq2, DESeq [4], have been included in several benchmark studies [5, 6] and have shown to perform well in replicated experiments.”
Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis (2017, background): “Unlike other studies 7 , our work not only compared various differential analysis tools, but also studied the impact of different alignment-based and alignment-free approaches on the accuracy of differential analysis.”
Effects of library size variance, sparsity, and compositionality on the analysis of microbiome data (2015, background): “…(Cameron & Trivedi), and there is debate about which distribution to use 201 (Auer & Doerge 2010; Cheung 2002; Connolly et al. 2009; Holmes et al. 2012; McMurdie & 202 Holmes 2014; Paulson et al. 2013; Rapaport et al. 2013; Soneson & Delorenzi 2013; White et al. 203 2009; Yu et al. 2013).”
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package (2015, background): “The relevance of sufficient replication has also been brought to attention in recent work that evaluated experimental design considerations (20,56,57), a conclusion that was echoed by the SEQC project (58).”
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? (2015, methodology): “The majority of studies based on analysis of experimental RNA-seq data rely on data from experiments with fewer than five replicates per condition (Marioni et al. 2008; Bullard et al. 2010; Kvam et al. 2012; Li et al. 2012; Busby et al. 2013; Dillies et al. 2013; Rapaport et al. 2013; SEQC/MAQC-III ”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, result): “As our results show (Figures 2 and 4) and as others have reported [40,42], the overall impact of the sequencing depth is not as critical as the number of biological replicates.”
MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing (2014, background): “Several studies have been published comparing the differential expression methods and concluded that there are substantial differences in terms of sensitivity and specificity among the methods [33-35].”
Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions (2016, methodology): “[15] All were equivalent (DESeq, PS, UQ, TMM, Q, CuffDiff) Introduces DEGES ROC curves and AUC (real and simulated data).”