CA Vallejos, S Richardson, JC Marioni
Journal name: 
Genome Biol
Citation info: 
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
Research group: 
Marioni Group
E-pub date: 
15 Apr 2016
Users with this publication listed: 
John Marioni