cytofWorkflow

 

This is the development version of cytofWorkflow; for the stable release version, see cytofWorkflow.

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

Bioconductor version: Development (3.9)

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).

Author: Malgorzata Nowicka [aut, cre], Helena L. Crowell [aut], Mark D. Robinson [aut]

Maintainer: Malgorzata Nowicka <gosia.nowicka.uzh at gmail.com>

Citation (from within R, enter citation("cytofWorkflow")):

Installation

To install this package, start R (version "3.6") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("cytofWorkflow", version = "3.9")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("cytofWorkflow")

 

HTML A workflow for differential discovery in high-throughput high-dimensional cytometry datasets

Details

biocViews ImmunoOncologyWorkflow, SingleCellWorkflow, Workflow
Version 1.7.2
License Artistic-2.0
Depends R (>= 3.4.0)
Imports BiocStyle, knitr, readxl, matrixStats, flowCore, ggplot2, ggridges, reshape2, dplyr, limma, ggrepel, RColorBrewer, pheatmap, ComplexHeatmap, FlowSOM, ConsensusClusterPlus, Rtsne, cowplot, lme4, multcomp
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Suggests knitcitations
SystemRequirements
Enhances
URL https://github.com/gosianow/cytofWorkflow
BugReports https://github.com/gosianow/cytofWorkflow/issues
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package cytofWorkflow_1.7.2.tar.gz
Windows Binary
Mac OS X 10.11 (El Capitan)
Source Repository git clone https://git.bioconductor.org/packages/cytofWorkflow
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/cytofWorkflow
Package Short Url http://bioconductor.org/packages/cytofWorkflow/
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