The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data
Open accessAbstract
Recent developments in RNA-sequencing (RNA-seq) technology have led to a rapid increase in gene expression data in the form of counts. RNA-seq can be used for a variety of applications, however, identifying differential expression (DE) remains a key task in functional genomics. There have been a number of statistical methods for DE detection for RNA-seq data. One common feature of several leading methods is the use of the negative binomial (Gamma–Poisson mixture) model. That is, the unobserved gene expression is modeled by a gamma random variable and, given the expression, the sequencing read counts are modeled as Poisson. The distinct feature in various methods is how the variance, or dispersion, in the Gamma distribution is modeled and estimated. We evaluate several large public RNA-seq datasets and find that the estimated dispersion in existing methods does not adequately capture the heterogeneity of biological variance among samples. We present a new empirical Bayes shrinkage estimate of the dispersion parameters and demonstrate improved DE detection.
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The International HapMap Project
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Self-citationBullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680
Understanding mechanisms underlying human gene expression variation with RNA sequencing
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GC-Content Normalization for RNA-Seq Data
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Moderated statistical tests for assessing differences in tag abundance
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Self-citationSequencing technology does not eliminate biological variability
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Cited by
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155
Comparison and evaluation of statistical error models for scRNA-seq
Choudhary, S., & Satija, R. (2022) · Genome Biology · doi:10.1186/s13059-021-02584-9 · Cited by 634
Robustly detecting differential expression in RNA sequencing data using observation weights
Zhou, X., Lindsay, H., & Robinson, M. D. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku310 · Cited by 387
Metadata
- Authors
- Wu, H., Wang, C., & Wu, Z.
- Venue
- Biostatistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-09-22
- Volume
- 14
- Issue
- 2
- Pages
- 232-243
- ISSN
- 1465-4644 (ISSN-L)
- 1468-4357
- OpenAlex
- W2110017531
- Cited by
- 242
- References
- 9
- FWCI
- 6.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
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