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Gene-level differential analysis at transcript-level resolution

Lynn Yi, Harold Pimentel, Nicolas L. Bray, Lior Pachter
doi: https://doi.org/10.1101/190199
Lynn Yi
1UCLA-Caltech Medical Science Training Program, Los Angeles, CA
2Division of Biology and Biological Engineering, Caltech, Pasadena, CA
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Harold Pimentel
3Department of Genetics, Stanford University, Palo Alto, CA
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Nicolas L. Bray
4Innovative Genomics Institute, Berkeley, Berkeley, CA
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  • For correspondence: nicolas.bray@gmail.com lpachter@caltech.edu
Lior Pachter
2Division of Biology and Biological Engineering, Caltech, Pasadena, CA
5Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA
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  • For correspondence: nicolas.bray@gmail.com lpachter@caltech.edu
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Abstract

Gene-level differential expression analysis based on RNA-Seq is more robust, powerful and biologically actionable than transcript-level differential analysis. However aggregation of transcript counts prior to analysis results can mask transcript-level dynamics. We demonstrate that aggregating the results of transcript-level analysis allow for gene-level analysis with transcript-level resolution. We also show that p-value aggregation methods, typically used for meta-analyses, greatly increase the sensitivity of gene-level differential analyses. Furthermore, such aggregation can be applied directly to transcript compatibility counts obtained during pseudoalignment, thereby allowing for rapid and accurate model-free differential testing. The methods are general, allowing for testing not only of genes but also of any groups of transcripts, and we showcase an example where we apply them to perturbation analysis of gene ontologies.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 18, 2017.
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Gene-level differential analysis at transcript-level resolution
Lynn Yi, Harold Pimentel, Nicolas L. Bray, Lior Pachter
bioRxiv 190199; doi: https://doi.org/10.1101/190199
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Gene-level differential analysis at transcript-level resolution
Lynn Yi, Harold Pimentel, Nicolas L. Bray, Lior Pachter
bioRxiv 190199; doi: https://doi.org/10.1101/190199

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