Abstract:
:Compared to RNA-sequencing transcript differential analysis, gene-level differential expression analysis is more robust and experimentally actionable. However, the use of gene counts for statistical analysis can mask transcript-level dynamics. We demonstrate that 'analysis first, aggregation second,' where the p values derived from transcript analysis are aggregated to obtain gene-level results, increase sensitivity and accuracy. The method we propose can also be applied to transcript compatibility counts obtained from pseudoalignment of reads, which circumvents the need for quantification and is fast, accurate, and model-free. The method generalizes to various levels of biology and we showcase an application to gene ontologies.
journal_name
Genome Bioljournal_title
Genome biologyauthors
Yi L,Pimentel H,Bray NL,Pachter Ldoi
10.1186/s13059-018-1419-zsubject
Has Abstractpub_date
2018-04-12 00:00:00pages
53issue
1eissn
1474-7596issn
1474-760Xpii
10.1186/s13059-018-1419-zjournal_volume
19pub_type
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