Package 'cytofWorkflow'
| Title: |
CyTOF workflow: differential discovery in high-throughput
high-dimensional cytometry datasets |
| 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). |
| Authors: |
Malgorzata Nowicka [aut],
Helena L. Crowell [aut],
Mark D. Robinson [aut, cre] |
| Maintainer: |
Mark D. Robinson <[email protected]> |
| License: |
Artistic-2.0 |
| Version: |
1.36.0 |
| Built: |
2026-10-04 17:15:10 UTC |
| Source: |
https://github.com/bioc/cytofWorkflow |
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