voom: precision weights unlock linear model analysis tools for RNA-seq read counts
New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. Thi...
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Published in: | Genome biology Vol. 15; no. 2; p. R29 |
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Main Authors: | , , , |
Format: | Journal Article |
Language: | English |
Published: |
England
Springer-Verlag
03-02-2014
BioMed Central |
Subjects: | |
Online Access: | Get full text |
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Summary: | New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods. |
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Bibliography: | http://dx.doi.org/10.1186/gb-2014-15-2-r29 ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
ISSN: | 1465-6906 1474-760X 1465-6914 1474-760X |
DOI: | 10.1186/gb-2014-15-2-r29 |