Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package

By Sonia Tarazona, Pedro Furió-Tarí, D. Turrà, A. Pietro, María José Nueda, Alberto Ferrer, Ana Conesa. Nucleic Acids Research, 2015

729 citations recorded by the source corpus.

As the use of RNA-seq has popularized, there is an increasing consciousness of the importance of experimental design, bias removal, accurate quantification and control of false positives for proper data analysis. We introduce the NOISeq R-package for quality control and analysis of count data. We show how the available diagnostic tools can be used to monitor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the non-parametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods. NOISeq is a comprehensive resource that meets current needs for robust data-aware analysis of RNA-seq differential expression.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/432c172e73949fac6bdd8788115140553ceff26a

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In Molecular Biology

Citations in the corpus

TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository (2021, methodology): “Nor-malization using the TMM method was performed on count data generated from tximport with the ‘tmm’ function in Bioconductor package NOISeq [22].”
A survey of best practices for RNA-seq data analysis (2016, methodology): “A number of R packages (such as NOISeq [19] or EDA-Seq [20]) provide useful plots for quality control of count data.”