The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.
Statistical Design and Analysis of RNA Sequencing Data
Open accessAbstract
Next-generation sequencing technologies are quickly becoming the preferred approach for characterizing and quantifying entire genomes. Even though data produced from these technologies are proving to be the most informative of any thus far, very little attention has been paid to fundamental design aspects of data collection and analysis, namely sampling, randomization, replication, and blocking. We discuss these concepts in an RNA sequencing framework. Using simulations we demonstrate the benefits of collecting replicated RNA sequencing data according to well known statistical designs that partition the sources of biological and technical variation. Examples of these designs and their corresponding models are presented with the goal of testing differential expression.
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“These effects can be minimized by appropriate experimental design [51] or, alternatively, removed by batch-correction methods such as COMBAT [52] or ARSyN [20, 53].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [51]
In the citing paper
“In the absence of careful experimental design, distinguishing gene expression variability due to technical artifacts and biological signal is challenging (Auer and Doerge, 2010).”
From Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015) · cites this paper as Auer and Doerge, 2010
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Cites
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Bioconductor: open software development for computational biology and bioinformatics
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1 of the 53 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- 2009, Nature, 457, 769
- 2009, Nat. Rev. Genet., 10, 669
- 2008, Biostatistics, 9, 321
- 2004, Stat. Appl. Genet. Mol. Biol., 3, 3
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Items 401–406 of 406
Verspagen, N., Frapin, M., Roy, R. D., Mäkinen, H., Saastamoinen, M., & DiLeo, M. F. (2026) · Molecular Ecology · doi:10.1111/mec.70309
36-gene blood transcriptomic signature captures intrinsic mortality risk in early sepsis
Kumar, P. (2026) · Artificial Intelligence in Emergency Medicine · doi:10.1016/j.aiemed.2026.100035
Radovich, M. (2012) · IUScholarWorks (Indiana University) · doi:10.7912/c2/1947
Metadata
- Authors
- Auer, P. L., & Doerge, R. W.
- Affiliations
- Purdue University West Lafayette (US)
- Corresponding author
- Doerge, R W
- ORCID
- Auer, Paul L
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-06-01
- Volume
- 185
- Issue
- 2
- Pages
- 405-416
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- PMC
- PMC2881125
- OpenAlex
- W1995070233
- Cited by
- 393
- References
- 53
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 12 · 2023: 11 · 2022: 14 · 2021: 17 · 2020: 24 · 2019: 18 · 2018: 43 · 2017: 25 · 2016: 32 · 2015: 49 · 2014: 42 · 2013: 39 · 2012: 28
- FWCI
- 12.26
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Keywords
- RNA sequencing, statistical design, sampling, randomization, replication, blocking, differential gene expression, biological variation, technical variability, experimental design, gene expression quantification
- MeSH terms
- Base Sequence, Research Design, Clinical Laboratory Techniques
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