RNA-seq differential expression studies: more sequence or more replication?

By Yuwen Liu, Jie Zhou, Kevin P. White. Bioinform., 2014

474 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/17868f976dd014be13acaf1ceff94c802abf14a0

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In Molecular Biology

Citations in the corpus

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, background): “• For well-controlled experiments with expected changes of >twofold in many genes, three or more independent samples per condition are recommended 175,219 .”
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).”
RNA Sequencing and Analysis (2015, unknown): “greatly improve the robustness of the experimental design (Liu et al. 2014).”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, background): “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.”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, background): “An increased number of reads only marginally increases the coverage of lowly expressed genes and therefore the statistical power to detect differential expression (DE) does not improve considerably, especially if the experiment already comprises ~10 million reads per sample [23] .”
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? (2015, background): “…(e.g., Anders and Huber 2010; Hardcastle and Kelly 2010; Robinson et al. 2010; Wang et al. 2010; Tarazona et al. 2011; Li et al. 2012; Lund et al. 2012; Trapnell et al. 2012; Leng et al. 2013; Li and Tibshirani 2013; Frazee et al. 2014; Law et al. 2014; Love et al. 2014; Moulos and Hatzis 2015).”
rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data (2014, background): “suggested that 10 million reads would be the appropriate sequencing depth for differential expression studies (30).”
A survey of best practices for RNA-seq data analysis (2016, background): “Increasing sequencing depth also can improve statistical power for lowly expressed genes [10, 194], and for any given sample there exists a level of sequencing at which power improvement is best achieved by increasing the number of replicates [195].”
Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis (2014, background): “Moreover, the relatively high cost of sequencing has led to study designs that often emphasize per sample sequencing depth over biological replication [45], despite advice to the contrary [46,47].”