Statistical Modeling of RNA-Seq Data
Open accessAbstract
Recently, ultra high-throughput sequencing of RNA (RNA-Seq) has been developed as an approach for analysis of gene expression. By obtaining tens or even hundreds of millions of reads of transcribed sequences, an RNA-Seq experiment can offer a comprehensive survey of the population of genes (transcripts) in any sample of interest. This paper introduces a statistical model for estimating isoform abundance from RNA-Seq data and is flexible enough to accommodate both single end and paired end RNA-Seq data and sampling bias along the length of the transcript. Based on the derivation of minimal sufficient statistics for the model, a computationally feasible implementation of the maximum likelihood estimator of the model is provided. Further, it is shown that using paired end RNA-Seq provides more accurate isoform abundance estimates than single end sequencing at fixed sequencing depth. Simulation studies are also given.
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Collapsing fragments into equivalence classes greatly reduces the time required for iterative optimization in transcript quantification.
“Collapsing fragments into equivalence classes is a well-established idea in the transcript quantification literature, and numerous different notions of equivalence classes have been previously introduced, and shown to greatly reduce the time required to perform iterative optimization such as that described in Offline phase.”
From Salmon provides fast and bias-aware quantification of transcript expression (2017) · cites this paper as [25]
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References 25 · Cited by 68
Research works printed but not linked: 1
- Lehmann EL 1998 Theory of point estimation 2 Springer
- Authors
- Salzman, J., Jiang, H., & Wong, W. H.
- Affiliations
- Stanford University (US)
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2011-02-01
- Volume
- 26
- Issue
- 1
- Pages
- 62-83
- ISSN
- 0883-4237 (ISSN-L)
- 2168-8745
- PMC
- PMC3846358
- OpenAlex
- W2068568257
- Cited by
- 68
- References
- 25
- Citations by year
- 2025: 5 · 2023: 4 · 2022: 3 · 2021: 2 · 2020: 3 · 2018: 3 · 2017: 6 · 2016: 7 · 2015: 10 · 2014: 12 · 2013: 4 · 2012: 6
- FWCI
- 3.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Statistical Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (0805157, 0821823)
- National Institutes of Health (HG000205)
- National Human Genome Research Institute (R01HG004634, P01HG000205)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- RNA-seq, gene expression, paired-end sequencing, single-end sequencing, length bias, maximum likelihood estimation, sufficient statistics, statistical models
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