Beyond comparisons of means: understanding changes in gene expression at the single-cell level.

ABSTRACT: Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarchical model that builds upon BASiCS to study changes that lie beyond comparisons of means, incorporating built-in normalization and quantifying technical artifacts by borrowing information from spike-in genes. Using a probabilistic approach, we highlight genes undergoing changes in cell-to-cell heterogeneity but whose overall expression remains unchanged. Control experiments validate our method's performance and a case study suggests that novel biological insights can be revealed. Our method is implemented in R and available at https://github.com/catavallejos/BASiCS.

SUBMITTER: Vallejos CA 

PROVIDER: S-EPMC4832562 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature