Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression
Open accessAbstract
Motivation: A common question arises at the beginning of every experiment where RNA-Seq is used to detect differential gene expression between two conditions: How many reads should we sequence? Results: Scotty is an interactive web-based application that assists biologists to design an experiment with an appropriate sample size and read depth to satisfy the user-defined experimental objectives. This design can be based on data available from either pilot samples or publicly available datasets. Availability: Scotty can be freely accessed on the web at http://euler.bc.edu/marthlab/scotty/scotty.php Contact: gabor.marth@bc.edu Supplementary information: Supplementary data are is available at Bioinformatics online.
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The exact statistical power depends on the differential expression method, and software packages can provide theoretical power estimates over a range of variables given the within-group variance.
“The exact power will depend on the method used for differential expression analysis, and software packages exist that provide a theoretical estimate of power over a range of variables, given the within-group variance of the samples, which is intrinsic to the experiment [190, 191].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [191]
Tools such as Scotty can calculate the best trade-off between sequencing depth and replicate number under budgetary constraints.
“Tools such as Scotty are available to calculate the best trade-off between sequencing depth and replicate number given some budgetary constraints [191].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [191]
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Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works
Design and validation issues in RNA-seq experiments
Fang, Z., & Cui, X. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr004 · Cited by 229 · see citing works
An interactive power analysis tool for microarray hypothesis testing and generation
Seo, J., Gordish‐Dressman, H., & Hoffman, E. P. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btk052 · Cited by 153 · see citing works
A NOTE ON SAMPLE SIZE CALCULATION FOR MEAN COMPARISONS BASED ON NONCENTRAL t-STATISTICS
Chow, S., Shao, J., & Wang, H. (2002) · Journal of Biopharmaceutical Statistics · doi:10.1081/bip-120016229 · Cited by 60 · see citing works
Sample Size and Power Calculations using the Noncentral t-distribution
Harrison, D. A., & Brady, T. (2004) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0400400205 · Cited by 33 · see citing works
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A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
Schurch, N. J., Schofield, P., Gierliński, M., Cole, C., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G. G., Owen-Hughes, T., et al. (2016) · RNA · doi:10.1261/rna.053959.115 · Cited by 966 · see citing works
Power analysis and sample size estimation for RNA-Seq differential expression
Ching, T., Huang, S., & Garmire, L. X. (2014) · RNA · doi:10.1261/rna.046011.114 · Cited by 263 · see citing works
Moisiadis, V. G., Constantinof, A., Kostaki, A., Szyf, M., & Matthews, S. G. (2017) · Scientific Reports · doi:10.1038/s41598-017-11635-w · Cited by 122 · see citing works
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · see citing works
Liu, H., Naxerova, K., Pinter, M., Incio, J., Lee, H., Shigeta, K., Ho, W. W., Crain, J. A., Jacobson, A., Michelakos, T., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-0256 · Cited by 96 · see citing works
RNA‐seq Data: Challenges in and Recommendations for Experimental Design and Analysis
Williams, A. G., Thomas, S., Wyman, S. K., & Holloway, A. K. (2014) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg1113s83 · Cited by 93 · see citing works
subSeq: Determining Appropriate Sequencing Depth Through Efficient Read Subsampling
Robinson, D. G., & Storey, J. D. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu552 · Cited by 57 · see citing works
- Authors
- Busby, M. A., Stewart, C., Miller, C. A., Grzeda, K. R., & Marth, G. T.
- Affiliations
- Boston College (US)
- Department of Biology, Boston College, Chestnut Hill, MA 02467, USA
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-01-12
- Volume
- 29
- Issue
- 5
- Pages
- 656-657
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3582267
- OpenAlex
- W2161318011
- Cited by
- 140
- References
- 5
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 3 · 2023: 10 · 2022: 9 · 2021: 8 · 2020: 8 · 2019: 16 · 2018: 19 · 2017: 19 · 2016: 16 · 2015: 11 · 2014: 9 · 2013: 4 · 2012: 1
- FWCI
- 5.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Human Genome Research Institute (R01HG004719)
- 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, differential gene expression, sample size determination, sequencing depth, experimental design, web-based tool, pilot data, public datasets
- MeSH terms
- Humans, Software, Gene Expression, Sequence Analysis, RNA, Internet, Gene Expression Profiling
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