The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data
Open accessAbstract
We discuss the identification of features that are associated with an outcome in RNA-Sequencing (RNA-Seq) and other sequencing-based comparative genomic experiments. RNA-Seq data takes the form of counts, so models based on the normal distribution are generally unsuitable. The problem is especially challenging because different sequencing experiments may generate quite different total numbers of reads, or ‘sequencing depths’. Existing methods for this problem are based on Poisson or negative binomial models: they are useful but can be heavily influenced by ‘outliers’ in the data. We introduce a simple, non-parametric method with resampling to account for the different sequencing depths. The new method is more robust than parametric methods. It can be applied to data with quantitative, survival, two-class or multiple-class outcomes. We compare our proposed method to Poisson and negative binomial-based methods in simulated and real data sets, and find that our method discovers more consistent patterns than competing methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
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
Moderated statistical tests for assessing differences in tag abundance
Robinson, M. D., & Smyth, G. K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm453 · Cited by 856
Normalization, testing, and false discovery rate estimation for RNA-sequencing data
Li, J., Witten, D. M., Johnstone, I. M., & Tibshirani, R. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr031 · Cited by 369 · cited 7×
“([14]) did a detailed comparison of many methods on simulated data sets, including (1) their own method, PoissonSeq, (2) SAM (Significance Analysis of Microarrays, [31]) applied to the square root of normalized data, (3) The Poisson distribution based method proposed by [16], which is implemented in an R package DEGSe…”
BackgroundMethodInfluentialSelf-citation
Research works printed but not linked: 29
- 10.1126/science.278.5338.680 · DOI · Paper page
- 10.1091/mbc.9.12.3273 · DOI · Paper page
- 10.1016/S0076-6879(99)03014-1 · DOI · Paper page
- 10.1038/4462 · DOI · Paper page
- Dudoit S, Yang YH, Callow MJ and Speed TP. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 2002; 12: 111–139.
- 10.1089/10665270050514954 · DOI · Paper page
- 10.1089/106652701300099074 · DOI · Paper page
- 10.1073/pnas.091062498 · DOI · Paper page
- 10.1126/science.1158441 · DOI · Paper page
- 10.1038/nmeth0708-585 · DOI · Paper page
- 10.1016/j.ymeth.2009.03.016 · DOI · Paper page
- 10.1186/1471-2164-10-221 · DOI · Paper page
- 10.1093/nar/gkn705 · DOI · Paper page
- 10.1101/gr.079558.108 · DOI · Paper page
- 10.1093/bioinformatics/btp612 · DOI · Paper page
- 10.1186/1471-2105-11-422 · DOI · Paper page
- Li J, 2011, Biostatistics
- 10.1093/biostatistics/kxm030 · DOI · Paper page
- 10.1186/gb-2010-11-3-r25 · DOI · Paper page
- 10.1186/gb-2010-11-10-r106 · DOI · Paper page
- 10.1186/1471-2105-5-144 · DOI
- 10.1186/1471-2105-6-165 · DOI
- 10.1093/bioinformatics/btp616 · DOI · Paper page
- 10.1186/1741-7007-8-58 · DOI · Paper page
- Benjamini Y, 1995, J R Stat Soc B Stat Meth, 85, 289, 10.1111/j.2517-6161.1995.tb02031.x · DOI · Paper page
- 10.1111/1467-9868.00346 · DOI · Paper page
- Storey J and Tibshirani R. Sam thresholding and false discovery rates for detecting differential gene expression indna microarrays. In: Parmigiani G Garrett ES Irizarry RA Zeger SL (eds). The analysis of gene expression data: methods and software . New York Springer. 2002, pp. 272–290. · DOI
- 10.1214/aos/1074290335 · DOI · Paper page
- 10.2307/1402659 · DOI · Paper page
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
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
Integrated genomic characterization of pancreatic ductal adenocarcinoma
Raphael, B. J., Hruban, R. H., Aguirre, A. J., Moffitt, R. A., Yeh, J. J., Stewart, C., Robertson, A. G., Cherniack, A. D., Gupta, M., Getz, G., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.07.007 · Cited by 2,019
Differential analysis of RNA-seq incorporating quantification uncertainty
Pimentel, H., Bray, N. L., Puente, S., Melsted, P., & Pachter, L. (2017) · Nature Methods · doi:10.1038/nmeth.4324 · Cited by 1,534
Fernandes, A. D., Reid, J. N., Macklaim, J. M., McMurrough, T. A., Edgell, D. R., & Gloor, G. B. (2014) · Microbiome · doi:10.1186/2049-2618-2-15 · Cited by 1,329
Metadata
- Authors
- Li, J., & Tibshirani, R.
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2011-11-28
- Volume
- 22
- Issue
- 5
- Pages
- 519-536
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- OpenAlex
- W2098734857
- Cited by
- 437
- References
- 6
- FWCI
- 8.46
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. https://app.cochat.ai/papers/10.1177/0962280211428386
- The paper
- Li, J., & Tibshirani, R. (2013). Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. Statistical Methods in Medical Research. https://doi.org/10.1177/0962280211428386
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
