Differential expression in RNA-seq: a matter of depth.

By Sonia Tarazona, Fernando García-Alcalde, Joaquín Dopazo, Alberto Ferrer, Ana Conesa. Genome Research, 2011

1,522 citations recorded by the source corpus.

Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2cd8dc8fe9e89af280eef7e5b00cecd73e564da9

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Citations in the corpus

Comparison of software packages for detecting differential expression in RNA-seq studies (2013, methodology): “[6], non-parametric approaches such as NOIseq [7] and SAMseq [8], transformations of gene-level read”
Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series (2014, methodology): “…et al. , 2010) and DEseq (Anders and Huber, 2010) updated microarray analysis to RNA-seq by incorporating appropriate statistical models, whereas other methodologies were developed specifically for the new technology (Roberts and Pachter, 2013; Tarazona et al. , 2011; Trapnell et al. , 2012).”
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].”
FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer (2014, methodology): “When provided with gene expression files, our module calls NOISeq [54] when having RPKM values and DESeq [55] with raw read counts (from reads-mapping tools) to detect differentially expressed genes.”
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package (2015, methodology): “These experimental procedures may result in RNA-seq data having a non-uniform RNA composition that may not be directly comparable (25).”
A comparison of methods for differential expression analysis of RNA-seq data (2013, methodology): “The two non-parametric methods evaluated here, NOISeq and SAMseq, do not assume any particular distribution for the data.”
RNA Sequencing and Analysis (2015, positive): “Furthermore, replicates in RNA-Seq experiments are crucial for measuring variability and improving estimations for the model parameters (Tarazona et al. 2011; Glaus et al. 2012).”
A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae (2012, methodology): “We first address the impact of different aligners in detecting DGE: Gsnap (33), Stampy (34) and TopHat (35) and successively evaluate the impact of using five different statistical methods: (i) baySeq (31), (ii) Cuffdiff (23), (iii) DESeq (30), (iv) edgeR (25) and (v) NOISeq (32).”
TCC: an R package for comparing tag count data with robust normalization strategies (2013, methodology): “The normalized data can directly be used as, for example, the input data of another package NOISeq [13].”
A survey of best practices for RNA-seq data analysis (2016, methodology): “Finally, non-parametric approaches such as NOISeq [10] or SAMseq [61] make minimal assumptions about the data and estimate the null distribution for inferential analysis from the actual data alone.”
De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity (2013, background): “Deep RNA-seq produces vast numbers of reads for transcripts that are already well represented at lower sequencing depths in addition to providing increased sensitivity for the detection of rarer transcripts 36,46 .”
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? (2015, methodology): “…is somewhat surprising that scientists would contemplate doing DGE analysis without replicated data, but for completeness we note that several DGE analysis tools advertise that they can work with a single replicate per condition (Anders and Huber 2010; Robinson et al. 2010; Tarazona et al. 2011).”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, result): “However, as expected, the number of DEGs discovered by each tool decreased with a reduction in sequencing depth (Figure 5 A-B), which is in line with a previous study [41].”