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license: GPL-3.0-only
title: 'scShapes: A Statistical Framework for Modeling and Identifying Differential
  Distributions in Single-cell RNA-sequencing Data'
version: 1.11.0
abstract: 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.
authors:
- family-names: Dharmaratne
  given-names: Malindrie
  email: malindrie@gmail.com
  orcid: https://orcid.org/0000-0002-1694-6496
repository: https://bioc.r-universe.dev
repository-code: https://github.com/Malindrie/scShapes
url: https://github.com/Malindrie/scShapes
contact:
- family-names: Dharmaratne
  given-names: Malindrie
  email: malindrie@gmail.com
  orcid: https://orcid.org/0000-0002-1694-6496