This is the development version of scShapes; for the stable release version, see scShapes.
A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing DataBioconductor version: Development (3.22)
We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic.
Author: Malindrie Dharmaratne [cre, aut] ORCID: 0000-0002-1694-6496
Maintainer: Malindrie Dharmaratne <malindrie at gmail.com>
Citation (from within R, entercitation("scShapes")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("scShapes")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("scShapes")Follow Installation instructions to use this package in your R session.
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