Package: DCATS 1.11.0

Xinyi Lin

DCATS: Differential Composition Analysis Transformed by a Similarity matrix

Methods to detect the differential composition abundances between conditions in singel-cell RNA-seq experiments, with or without replicates. It aims to correct bias introduced by missclaisification and enable controlling of confounding covariates. To avoid the influence of proportion change from big cell types, DCATS can use either total cell number or specific reference group as normalization term.

Authors:Xinyi Lin [aut, cre], Chuen Chau [aut], Yuanhua Huang [aut], Joshua W.K. Ho [aut]

DCATS_1.11.0.tar.gz
DCATS_1.11.0.zip(r-4.7)DCATS_1.11.0.zip(r-4.6)DCATS_1.11.0.zip(r-4.5)
DCATS_1.11.0.tgz(r-4.6-any)DCATS_1.11.0.tgz(r-4.5-any)
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
DCATS/json (API)

# Install 'DCATS' in R:

install.packages('DCATS', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

Haber2017 - Count matrices of intestinal epithelial scRNA-seq data from three conditions
Kang2017 - Count matrices of 8 pooled lupus patient samples within two conditions
Ren2021 - Count matrix and metadata of a large COVID-19 scRNA-seq data cohort.
simulation - Simulated dataset with two conditions

On BioConductor:DCATS-1.11.0(bioc 3.24)DCATS-1.10.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

singlecell normalization

4.65 score 45 scripts 8 exports 17 dependencies

Last updated from:2965358094. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	196
linux-devel-x86_64	NOTE	293
source / vignettes	OK	265
linux-release-x86_64	NOTE	291
macos-release-arm64	NOTE	108
macos-oldrel-arm64	NOTE	133
windows-devel	NOTE	167
windows-release	NOTE	152
windows-oldrel	NOTE	146
wasm-release	OK	195

Exports:create_simMat dcats_GLM detect_reference getPhi knn_simMat multinom_EM simulator_base svm_simMat

Dependencies:aod class coda DEoptimR e1071 lattice MASS Matrix MatrixModels matrixStats mcmc MCMCpack proxy quantreg robustbase SparseM survival

Differential Composition Analysis with DCATS

Rendered fromIntro_to_DCATS.Rmdusingknitr::rmarkdown

Last update: 2023-04-21
Started: 2022-11-16

Help page	Topics
Generate similarity matrix with uniform confusion rate to none-self clusters	create_simMat
A Generalised linear model based likelihood ratio testing	dcats_GLM
Calculate a global phi for all cell types	detect_reference
Calculate a global phi for all cell types	getPhi
Count matrices of intestinal epithelial scRNA-seq data from three conditions	Haber2017
Count matrices of 8 pooled lupus patient samples within two conditions	Kang2017
Calculate stochastic transition matrix between clusters from a KNN connection matrix	knn_simMat
An EM algorithm to fit a multinomial with maximum likelihood	multinom_EM
Count matrix and metadata of a large COVID-19 scRNA-seq data cohort.	Ren2021
Simulated dataset with two conditions	simulation
Composition size simulator	simulator_base
Calculate stochastic transition matrix between clusters from a data frame including information about clustering	svm_simMat

Package: DCATS 1.11.0

DCATS: Differential Composition Analysis Transformed by a Similarity matrix

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)