Singlecell mRNA sequencing can uncover novel celltocell 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 SingleCell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of singlecell 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 builtin data normalisation (global scaling) and technical noise quantification (based on spikein 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 prespecified 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 celltocell heterogeneity. The latter can be quantified via a biological overdispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/overdispersion confounding that is typically observed for scRNAseq datasets, BASiCS also tests for changes in residual overdispersion, defined by residual values with respect to a global mean/overdispersion trend.
Package details 


Bioconductor views  Bayesian CellBiology DifferentialExpression GeneExpression ImmunoOncology Normalization RNASeq Sequencing SingleCell Software Transcriptomics 
Maintainer  
License  GPL3 
Version  2.13.5 
URL  https://github.com/catavallejos/BASiCS 
Package repository  View on GitHub 
Installation 
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