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\name{BASiCS-package} | ||
\alias{BASiCS-package} | ||
\alias{BASiCS} | ||
\docType{package} | ||
\title{ | ||
Bayesian Analysis of Single Cell Sequencing data | ||
} | ||
\description{ | ||
BASiCS (Bayesian Analysis of Single Cell Sequencing data) provides a tool | ||
for analysis datasets generated by single-cell sequencing experiments. | ||
} | ||
\details{ | ||
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: (ii) | ||
cell-specific normalization constants are estimated as part of the model | ||
parameters, (ii) technical variability is quantified based on spike-in | ||
genes that are artificially introduced to each analysed cells lysate and | ||
(iii) 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. | ||
} | ||
\author{ | ||
Catalina A. Vallejos | ||
|
||
Maintainer: Catalina A. Vallejos <catalina.vallejos@mrc-bsu.cam.ac.uk> | ||
} | ||
\references{ | ||
Vallejos, Marioni and Richardson (2015). Bayesian Analysis of Single Cell Sequencing data. | ||
} | ||
% Optionally other standard keywords, one per line, | ||
% from the file KEYWORDS in the R documentation. | ||
\keyword{ package } | ||
\examples{ | ||
# See vignette | ||
} |
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