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CytoGLMM

Conditional Differential Analysis for Flow and Mass Cytometry Experiments

Bioconductor version: 3.23 · Package version: 1.20.0

The CytoGLMM R package implements two multiple regression strategies: A bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM). Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. As a result, CytoGLMM finds differential proteins in flow and mass cytometry data while reducing biases arising from marker correlations and safeguarding against false discoveries induced by patient heterogeneity.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("CytoGLMM")

Details

MaintainerChristof Seiler <christof.seiler@maastrichtuniversity.nl>
AuthorChristof Seiler [aut, cre] (ORCID: <https://orcid.org/0000-0001-8802-3642>)
LicenseLGPL-3
URLhttps://christofseiler.github.io/CytoGLMM, https://github.com/ChristofSeiler/CytoGLMM
Bug Reportshttps://github.com/ChristofSeiler/CytoGLMM/issues
Downloads rank364
Source branchRELEASE_3_23
biocViewsCellBasedAssays, CellBiology, FlowCytometry, ImmunoOncology, Proteomics, Regression, SingleCell, Software, StatisticalMethod

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageCytoGLMM_1.20.0.tar.gz
Windows binary (x86_64)CytoGLMM_1.20.0.zip
macOS binary (arm64)CytoGLMM_1.20.0.tgz
macOS binary (x86_64)CytoGLMM_1.20.0.tgz
Dependencies

Imports: stats, methods, BiocParallel, RColorBrewer, cowplot, doParallel, dplyr, factoextra, flexmix, ggplot2, magrittr, mbest, pheatmap, stringr, strucchange, tibble, ggrepel, MASS, logging, Matrix, tidyr, caret, rlang, grDevices

Suggests: knitr, rmarkdown, testthat, BiocStyle

Reverse dependencies

Imports Me (1): CyTOFpower