Statistical approaches for differential expression analysis in metatranscriptomics | Zendy

Yancong Zhang | Zendy; Kelsey N. Thompson | Zendy; Curtis Huttenhower | Zendy; Eric A. Franzosa | Zendy

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Statistical approaches for differential expression analysis in metatranscriptomics

Author(s) -

Yancong Zhang,

Kelsey N. Thompson,

Curtis Huttenhower,

Eric A. Franzosa

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab327

Subject(s) - interpretability , computational biology , normalization (sociology) , biology , covariate , population , metagenomics , computer science , data mining , gene , machine learning , genetics , sociology , anthropology , demography

Metatranscriptomics (MTX) has become an increasingly practical way to profile the functional activity of microbial communities in situ. However, MTX remains underutilized due to experimental and computational limitations. The latter are complicated by non-independent changes in both RNA transcript levels and their underlying genomic DNA copies (as microbes simultaneously change their overall abundance in the population and regulate individual transcripts), genetic plasticity (as whole loci are frequently gained and lost in microbial lineages) and measurement compositionality and zero-inflation. Here, we present a systematic evaluation of and recommendations for differential expression (DE) analysis in MTX.

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