Naught all zeros in sequence count data are the same.

Journal Article (Journal Article;Review)

Genomic studies feature multivariate count data from high-throughput DNA sequencing experiments, which often contain many zero values. These zeros can cause artifacts for statistical analyses and multiple modeling approaches have been developed in response. Here, we apply different zero-handling models to gene-expression and microbiome datasets and show models can disagree substantially in terms of identifying the most differentially expressed sequences. Next, to rationally examine how different zero handling models behave, we developed a conceptual framework outlining four types of processes that may give rise to zero values in sequence count data. Last, we performed simulations to test how zero handling models behave in the presence of these different zero generating processes. Our simulations showed that simple count models are sufficient across multiple processes, even when the true underlying process is unknown. On the other hand, a common zero handling technique known as "zero-inflation" was only suitable under a zero generating process associated with an unlikely set of biological and experimental conditions. In concert, our work here suggests several specific guidelines for developing and choosing state-of-the-art models for analyzing sparse sequence count data.

Full Text

Duke Authors

Cited Authors

  • Silverman, JD; Roche, K; Mukherjee, S; David, LA

Published Date

  • 2020

Published In

Volume / Issue

  • 18 /

Start / End Page

  • 2789 - 2798

PubMed ID

  • 33101615

Pubmed Central ID

  • PMC7568192

International Standard Serial Number (ISSN)

  • 2001-0370

Digital Object Identifier (DOI)

  • 10.1016/j.csbj.2020.09.014


  • eng

Conference Location

  • Netherlands