ArticleNucleic acids research2015
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 478 papers, 1 of them a synthesis that pooled it.
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Who cites it
478 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Confidence: a web app for cross-platform differential gene expression analysis, gene scoring, and enrichment analysis.Scientific reports · 2026Article
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- Single-nucleus multiomic profiling of the aging mouse substantia nigra reveals conserved gene alterations linked to Parkinson's disease.Genome research · 2026Article
- Loss of long-range co-expression is a common feature in cancer.NPJ systems biology and applications · 2026Article
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418 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.