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.
- Bayes Theorem
- Gene Expression Profiling/methods
- Genetic Heterogeneity
- Mouse Embryonic Stem Cells/cytology
- Sequence Analysis, RNA/methods
- Single-Cell Analysis/methods
- Web Browser
FingerprintDive into the research topics of 'Beyond comparisons of means: understanding changes in gene expression at the single-cell level'. Together they form a unique fingerprint.
Person: Academic: Research Active