powsimR: power analysis for bulk and single cell RNA-seq experiments
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Abstract
Summary: Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses. Availability and implementation: The R package and associated tutorial are freely available at https://github.com/bvieth/powsimR. Supplementary information: Supplementary data are available at Bioinformatics online.
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scRNA-seq data are not in fact log-normally distributed.
“While scRNA‐seq data are not in fact log‐normally distributed (Vieth et al, 2017), these three effects make the log transformation a crude, but useful tool.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Vieth et al, 2017
Vieth et al showed that read and count data are best fit by different models.
“For example, Vieth et al (2017) showed that read and count data are best fit by different models.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Vieth et al, 2017
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Cites
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 · see citing works
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159 · see citing works
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
Single-cell mRNA quantification and differential analysis with Census
Qiu, X., Hill, A. J., Packer, J. S., Lin, D., Ma, Y.-A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4150 · Cited by 1,885 · see citing works
De novo assembly of bacterial transcriptomes from RNA-seq data
Tjaden, B. C. (2015) · Genome biology · doi:10.1186/s13059-014-0572-2 · Cited by 1,695 · see citing works
Comparative Analysis of Single-Cell RNA Sequencing Methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494 · see citing works
Self-citationBayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · see citing works
EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments
Leng, N., Dawson, J. A., Thomson, J. A., Ruotti, V., Rissman, A. I., Smits, B. M. G., Haag, J. D., Gould, M. N., Stewart, R. M., & Kendziorski, C. M. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt087 · Cited by 1,304 · see citing works
CEL-seq: single-cell RNA-seq by multiplexed linear amplification
Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.08.003 · Cited by 1,231 · see citing works
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts
L. Lun, A. T., Bach, K., & Marioni, J. C. (2016) · Genome Biology · doi:10.1186/s13059-016-0947-7 · Cited by 1,108 · 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
Comparative Analysis of Single-Cell RNA Sequencing MethodsPreprint
Ziegenhain, C., Vieth, B., Parekh, S., et al. (2016) · bioRxiv · doi:10.1101/035758 · Cited by 951 · see citing works
Self-citationMassively parallel digital transcriptional profiling of single cellsPreprint
Zheng, G. X. Y., Terry, J. M., Belgrader, P., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/065912 · Cited by 897 · see citing works
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package
Tarazona, S., Furió‐Tarí, P., Turrà, D., Pietro, A. D., Nueda, M. J., Ferrer, A., & Conesa, A. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv711 · Cited by 757 · see citing works
Validation of noise models for single-cell transcriptomics
Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721 · see citing works
Quantitative assessment of single-cell RNA-sequencing methods
Wu, A. R., Neff, N. F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M. E., Mburu, F. M., Mantalas, G. L., Sim, S., Clarke, M. F., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2694 · Cited by 696 · see citing works
Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation
Kolodziejczyk, A. A., Kim, J. K., Tsang, J. C. H., Ilicic, T., Henriksson, J., Natarajan, K. N., Tuck, A. C., Gao, X., Bühler, M., Liu, P., et al. (2015) · Cell Stem Cell · doi:10.1016/j.stem.2015.09.011 · Cited by 570 · see citing works
Design and computational analysis of single-cell RNA-sequencing experiments
Bacher, R. L., & Kendziorski, C. M. (2016) · Genome biology · doi:10.1186/s13059-016-0927-y · Cited by 437 · see citing works
Statistical Design and Analysis of RNA Sequencing Data
Auer, P. L., & Doerge, R. W. (2010) · Genetics · doi:10.1534/genetics.110.114983 · Cited by 393 · see citing works
A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
Korthauer, K. D., Chu, L.-F., Newton, M. A., Li, Y., Thomson, J., Stewart, R., & Kendziorski, C. (2016) · Genome Biology · doi:10.1186/s13059-016-1077-y · Cited by 273 · see citing works
A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data
Wu, H., Wang, C., & Wu, Z. J. (2012) · Biostatistics · doi:10.1093/biostatistics/kxs033 · Cited by 241 · see citing works
Characterization of directed differentiation by high-throughput single-cell RNA-SeqPreprint
Soumillon, M., Cacchiarelli, D., Semrau, S., van Oudenaarden, A., & Mikkelsen, T. S. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/003236 · Cited by 217 · see citing works
Beta-Poisson model for single-cell RNA-seq data analyses
Vu, T. N., Wills, Q. F., Kalari, K. R., Niu, N., Wang, L., Rantalainen, M., & Pawitan, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw202 · Cited by 157 · cited 1× · see citing works
“…8 R-packages for DE analysis for bulk and single cell data (limma [21], edgeR [22], DESeq2 [13], ROTS [24], baySeq [6], DSS [29], NOISeq [26], EBSeq [12]) and five packages that were 3/8 specifically developed for single-cell RNA-seq (MAST [4], scde [8], BPSC [27], scDD [11], monocle [20]).”
MethodROTS: reproducible RNA-seq biomarker detector—prognostic markers for clear cell renal cell cancer
Seyednasrollah, F., Rantanen, K., Jaakkola, P., & Elo, L. L. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv806 · Cited by 75 · see citing works
Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology
Hardcastle, T. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv569 · Cited by 44 · see citing works
Feasibility of sample size calculation for RNA-seq studies
Poplawski, A., & Binder, H. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw144 · Cited by 33 · see citing works
Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data
Mi, G., Di, Y., & Schafer, D. W. (2015) · PLoS ONE · doi:10.1371/journal.pone.0119254 · Cited by 22 · see citing works
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Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231 · see citing works
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071 · see citing works
Visualizing structure and transitions in high-dimensional biological data
Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., Elzen, A. V. D., Hirn, M. J., Coifman, R. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0336-3 · Cited by 1,065 · see citing works
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945 · see citing works
Bias, robustness and scalability in single-cell differential expression analysis
Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770 · see citing works
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 · see citing works
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Svensson, V. (2019) · doi:10.1101/582064 · Cited by 356 · see citing works
A systematic evaluation of single cell RNA-seq analysis pipelines
Vieth, B., Parekh, S., Ziegenhain, C., Enard, W., & Hellmann, I. (2019) · Nature Communications · doi:10.1038/s41467-019-12266-7 · Cited by 265 · see citing works
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Chen, W., Li, Y., Easton, J., Finkelstein, D., Wu, G., & Chen, X. (2018) · Genome Biology · doi:10.1186/s13059-018-1438-9 · Cited by 113 · see citing works
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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
- Authors
- Vieth, B., Ziegenhain, C., Parekh, S., Enard, W., & Hellmann, I.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- Corresponding author
- Vieth, Beate
- Hellmann, Ines
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-07-11
- Volume
- 33
- Issue
- 21
- Pages
- 3486-3488
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2949086748
- Cited by
- 155
- References
- 34
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 10 · 2023: 16 · 2022: 26 · 2021: 23 · 2020: 21 · 2019: 19 · 2018: 15 · 2017: 4
- FWCI
- 6.59
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft
- DFG (SFB1243)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- 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
- Keywords
- power analysis, single-cell RNA sequencing, bulk RNA-seq, differential gene expression, sample size simulation, a priori power analysis, R package
- MeSH terms
- Software, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis
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