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BASiCS: Bayesian Analysis of Single-Cell Sequencing data
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@catavallejos catavallejos released this 23 Jul 13:18
v0.2.1
19eb335

Introduction

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 unexplained 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 where:

  1. Cell-specific normalization constants are estimated as part of the model parameters,
  2. Technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cells lysate and
  3. The total variability of the expression counts is decomposed into technical and biological components.

BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalized by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by applied users.

This release

This release includes the following changes:

  1. Argument 'GeneNames' has been added to functions 'BASiCS_VarianceDecomp', 'BASiCS_DetectHVG', 'BASiCS_DetectLVG' so that users can specify gene labels or names that will be used for these functions's output.

More details in

Catalina A. Vallejos, John C. Marioni and Sylvia Richardson (2015)
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
PLOS Computational Biology
http://dx.doi.org/10.1371/journal.pcbi.1004333

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