Package: cytofWorkflow
Title: CyTOF workflow: differential discovery in high-throughput
        high-dimensional cytometry datasets
Version: 1.0.4
Date: 2017-10-19
Authors@R: c(person(role=c("aut", "cre"), "Malgorzata", "Nowicka",
        email = "gosia.nowicka.uzh@gmail.com"), person(role=c("aut"), "Mark
        D.", "Robinson", email = "mark.robinson@imls.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 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 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 (>= 3.4.0)
License: Artistic-2.0
Encoding: UTF-8
LazyData: true
biocViews: WorkflowStep, SingleCell
VignetteBuilder: knitr
Imports: BiocStyle, knitr, readxl, matrixStats, flowCore, ggplot2,
        ggridges, reshape2, dplyr, limma, ggrepel, RColorBrewer,
        pheatmap, ComplexHeatmap, FlowSOM, ConsensusClusterPlus, Rtsne,
        cowplot, lme4, multcomp
Suggests: knitcitations
NeedsCompilation: no
Workflow: true
URL: https://github.com/gosianow/cytofWorkflow
BugReports: https://github.com/gosianow/cytofWorkflow/issues
Packaged: 2017-11-29 12:24:35 UTC; SYSTEM
Author: Malgorzata Nowicka [aut, cre],
  Mark D. Robinson [aut]
Maintainer: Malgorzata Nowicka <gosia.nowicka.uzh@gmail.com>
Built: R 3.4.2; ; 2017-11-29 12:25:42 UTC; windows
