Package: cytofWorkflow
Title: CyTOF workflow: differential discovery in high-throughput
        high-dimensional cytometry datasets
Version: 1.37.0
Date: 2023-10-28
Authors@R: c(
	person(role=c("aut"), "Malgorzata", "Nowicka", email = "gosia.nowicka.uzh@gmail.com"), 
	person(role="aut", "Helena L.", "Crowell", email = "helena@crowell.eu"),
	person(role=c("aut", "cre"), "Mark D.", "Robinson", email = "mark.robinson@mls.uzh.ch"))
Description: High-dimensional mass and flow cytometry (HDCyto)
        experiments have become a method of choice for high-throughput
        interrogation and characterization of cell populations. Here,
        we present an updated R-based pipeline for differential
        analyses of HDCyto data, largely based on Bioconductor
        packages. We computationally define cell populations using
        FlowSOM clustering, and facilitate an optional but reproducible
        strategy for manual merging of algorithm-generated clusters.
        Our workflow offers different analysis paths, including
        association of cell type abundance with a phenotype or changes
        in signaling markers within specific subpopulations, or
        differential analyses of aggregated signals. Importantly, the
        differential analyses we show are based on regression
        frameworks where the HDCyto data is the response; thus, we are
        able to model arbitrary experimental designs, such as those
        with batch effects, paired designs and so on. In particular, we
        apply generalized linear mixed models or linear mixed models to
        analyses of cell population abundance or
        cell-population-specific analyses of signaling markers,
        allowing overdispersion in cell count or aggregated signals
        across samples to be appropriately modeled. To support the
        formal statistical analyses, we encourage exploratory data
        analysis at every step, including quality control (e.g.,
        multi-dimensional scaling plots), reporting of clustering
        results (dimensionality reduction, heatmaps with dendrograms)
        and differential analyses (e.g., plots of aggregated signals).
Depends: R (>= 3.6.0), BiocStyle, knitr, readxl, CATALYST, diffcyt,
        HDCytoData, uwot, cowplot
License: Artistic-2.0
Encoding: UTF-8
biocViews: ImmunoOncologyWorkflow, Workflow, SingleCellWorkflow
VignetteBuilder: knitr
Suggests: knitcitations, markdown, rmarkdown
NeedsCompilation: no
Workflow: true
URL: https://github.com/markrobinsonuzh/cytofWorkflow
BugReports: https://github.com/markrobinsonuzh/cytofWorkflow/issues
Config/pak/sysreqs: cmake libfontconfig1-dev libfreetype6-dev
        libglpk-dev make libicu-dev libpng-dev libuv1-dev libxml2-dev
        libssl-dev perl zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 12:40:24 UTC
RemoteUrl: https://github.com/bioc/cytofWorkflow
RemoteRef: HEAD
RemoteSha: bd00dfcf2284b72b718d1761dfa241650155f6d4
Packaged: 2026-10-01 15:39:33 UTC; root
Author: Malgorzata Nowicka [aut],
  Helena L. Crowell [aut],
  Mark D. Robinson [aut, cre]
Maintainer: Mark D. Robinson <mark.robinson@mls.uzh.ch>
