Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter are quantified via a biological over-dispersion parameter that measures residual over-dispersion (with respect to Poisson sampling) after normalisation and technical noise removal.
|Bioconductor views||Bayesian CellBiology DifferentialExpression GeneExpression Normalization RNASeq Sequencing SingleCell Software Transcriptomics|
|Maintainer||Catalina A. Vallejos <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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