A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data.

By V. Kvam, Peng Liu, Yaqing Si. American-Eurasian journal of botany, 2012

306 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/37cae00437f3d00a8aff2417fe6354137fc04999

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Citations in the corpus

Corset: enabling differential gene expression analysis for de novo assembled transcriptomes (2014, methodology): “Similarly, the choice of method for performing count-based statistical testing for differential expression has been evaluated [16,17].”
ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq (2013, methodology): “The analysis of single-organism RNA-Seq has largely been informative with fixedeffect models, although the recent versions of both DESeq and edgeR have incorporated statistical methods to better deal with intra-condition variation[23,38,42].”
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data (2013, methodology): “which rely on simulated data generated by specific statistical distribution or limited experimental datasets [23,33,34], we used the SEQC experimental dataset where a large fraction of the differentially expressed genes were validated by qRT-PCR and biological replicates from three cell lines profil”
A survey of best practices for RNA-seq data analysis (2016, background): “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].”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, methodology): “Recently, several published studies have evaluated the performance of RNA-Seq software tools for differential expression analysis, although they all confined their analyses to methods that are applicable to count matrices (also known as count tables that contain raw counts for each feature) [28-30].”
Comparison of software packages for detecting differential expression in RNA-seq studies (2013, background): “were reported to fit well to a Poisson distribution [3, 12, 13].”
A comparison of methods for differential expression analysis of RNA-seq data (2013, methodology): “This is concordant with the findings in a previous study [19] where the type I error rate control of edgeR, DESeq and NBPSeq were compared.”