Dataset Information

ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data.

ABSTRACT: High-dimensional cytometry is an innovative tool for immune monitoring in health and disease, and it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here, we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster), an R package for immune profiling cellular heterogeneity in high-dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a nonspecialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users' needs. The protocol consists of three core computational stages: (1) data import and quality control; (2) dimensionality reduction and unsupervised clustering; and (3) annotation and differential testing, all contained within an R-based open-source framework.

SUBMITTER: Opzoomer JW

PROVIDER: S-EPMC8112868 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

<i>ImmunoCluster</i> provides a computational framework for the nonspecialist to profile high-dimensional cytometry data.

Opzoomer James W JW Timms Jessica A JA Blighe Kevin K Mourikis Thanos P TP Chapuis Nicolas N Bekoe Richard R Kareemaghay Sedigeh S Nocerino Paola P Apollonio Benedetta B Ramsay Alan G AG Tavassoli Mahvash M Harrison Claire C Ciccarelli Francesca F Parker Peter P Fontenay Michaela M Barber Paul R PR Arnold James N JN Kordasti Shahram S

eLife 20210430

High-dimensional cytometry is an innovative tool for immune monitoring in health and disease, and it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here, we describe <i>ImmunoCluster</i> (https://github.com/kordastilab/ImmunoCluster), an R package for immune profiling cellular heterogeneity in high-dimensional liquid and imaging mas ...[more]

PMID: 33929322

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Project description:Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level, widely used in basic research and routine clinical diagnostics. Traditionally, data analysis is carried out using manual gating, in which cut-offs are defined manually for each marker. Recent technical advances, including the introduction of mass cytometry, have increased the number of proteins that can be simultaneously assessed in each cell. To tackle the resulting escalation in data complexity, numerous new analysis algorithms have been developed. However, many of these show limitations in terms of providing statistical testing, data sharing, cross-experiment comparability integration with clinical data. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of clinical meta data. MetaGate allows manual gating to take place in traditional cytometry analysis software, while providing a combinatorial gating system for simple and transparent definition of biologically relevant cell populations. We demonstrate the utility of MetaGate through a comprehensive analysis of peripheral blood immune cells from 28 patients with diffuse large B-cell lymphoma (DLBCL) along with 17 age- and sex-matched healthy controls using two mass cytometry panels made of a total of 55 phenotypic markers. In a two-step process, raw data from 143 FCS files is first condensed through a data reduction algorithm and combined with information from manual gates, user-defined cellular populations and clinical meta data. This results in one single small project file containing all relevant information to allow rapid statistical calculation and visualization of any desired comparison, including box plots, heatmaps and volcano plots. Our detailed characterization of the peripheral blood immune cell repertoire in patients with DLBCL corroborate previous reports showing expansion of monocytic myeloid-derived suppressor cells, as well as an inverse correlation between NK cell numbers and disease progression.

Dataset Information

ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data.

Publications

<i>ImmunoCluster</i> provides a computational framework for the nonspecialist to profile high-dimensional cytometry data.

Similar Datasets

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