Package: DCATS 1.3.0
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:
DCATS_1.3.0.tar.gz
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DCATS.pdf |DCATS.html✨
DCATS/json (API)
NEWS
| # Install 'DCATS' in R: |
| install.packages('DCATS', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
- 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-1.3.0(bioc 3.20)DCATS-1.2.0(bioc 3.19)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:1e756e6bde
Exports:create_simMatdcats_GLMdetect_referencegetPhiknn_simMatmultinom_EMsimulator_basesvm_simMat
Dependencies:aodclasscodaDEoptimRe1071latticeMASSMatrixMatrixModelsmatrixStatsmcmcMCMCpackproxyquantregrobustbaseSparseMsurvival
