CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets [version 3; peer review: 2 approved]
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 c...
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doaj-6251be81e1054b31a51e83cd94d3667b2020-11-25T03:21:41ZengF1000 Research LtdF1000Research2046-14022019-05-01610.12688/f1000research.11622.320556CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets [version 3; peer review: 2 approved]Malgorzata Nowicka0Carsten Krieg1Helena L. Crowell2Lukas M. Weber3Felix J. Hartmann4Silvia Guglietta5Burkhard Becher6Mitchell P. Levesque7Mark D. Robinson8Institute for Molecular Life Sciences, University of Zurich, Zurich, 8057, SwitzerlandInstitute of Experimental Immunology, University of Zurich, Zurich, 8057, SwitzerlandSIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, SwitzerlandInstitute for Molecular Life Sciences, University of Zurich, Zurich, 8057, SwitzerlandInstitute of Experimental Immunology, University of Zurich, Zurich, 8057, SwitzerlandDepartment of Experimental Oncology, European Institute of Oncology, Via Adamello 16, Milan, I-20139, ItalyInstitute of Experimental Immunology, University of Zurich, Zurich, 8057, SwitzerlandDepartment of Dermatology, University Hospital Zurich, Zurich, CH-8091, SwitzerlandInstitute for Molecular Life Sciences, University of Zurich, Zurich, 8057, SwitzerlandHigh-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 signalling 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).https://f1000research.com/articles/6-748/v3BioinformaticsBiomacromolecule-Ligand InteractionsCell SignalingCell Signaling & Trafficking StructuresChemical Biology of the CellMembranes & SortingNuclear Structure & Function |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Malgorzata Nowicka Carsten Krieg Helena L. Crowell Lukas M. Weber Felix J. Hartmann Silvia Guglietta Burkhard Becher Mitchell P. Levesque Mark D. Robinson |
spellingShingle |
Malgorzata Nowicka Carsten Krieg Helena L. Crowell Lukas M. Weber Felix J. Hartmann Silvia Guglietta Burkhard Becher Mitchell P. Levesque Mark D. Robinson CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets [version 3; peer review: 2 approved] F1000Research Bioinformatics Biomacromolecule-Ligand Interactions Cell Signaling Cell Signaling & Trafficking Structures Chemical Biology of the Cell Membranes & Sorting Nuclear Structure & Function |
author_facet |
Malgorzata Nowicka Carsten Krieg Helena L. Crowell Lukas M. Weber Felix J. Hartmann Silvia Guglietta Burkhard Becher Mitchell P. Levesque Mark D. Robinson |
author_sort |
Malgorzata Nowicka |
title |
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets [version 3; peer review: 2 approved] |
title_short |
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets [version 3; peer review: 2 approved] |
title_full |
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets [version 3; peer review: 2 approved] |
title_fullStr |
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets [version 3; peer review: 2 approved] |
title_full_unstemmed |
CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets [version 3; peer review: 2 approved] |
title_sort |
cytof workflow: differential discovery in high-throughput high-dimensional cytometry datasets [version 3; peer review: 2 approved] |
publisher |
F1000 Research Ltd |
series |
F1000Research |
issn |
2046-1402 |
publishDate |
2019-05-01 |
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 signalling 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). |
topic |
Bioinformatics Biomacromolecule-Ligand Interactions Cell Signaling Cell Signaling & Trafficking Structures Chemical Biology of the Cell Membranes & Sorting Nuclear Structure & Function |
url |
https://f1000research.com/articles/6-748/v3 |
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