ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data.
James W OpzoomerJessica A TimmsKevin BligheThanos P MourikisNicolas ChapuisRichard BekoeSedigeh KareemaghayPaola NocerinoBenedetta ApollonioAlan G RamsayMahvash TavassoliClaire HarrisonFrancesca CiccarelliPeter ParkerMichaela FontenayPaul R BarberJames N ArnoldShahram KordastiPublished in: eLife (2021)
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.
Keyphrases
- single cell
- rna seq
- flow cytometry
- healthcare
- electronic health record
- quality control
- high resolution
- public health
- machine learning
- high throughput
- big data
- randomized controlled trial
- induced apoptosis
- risk assessment
- data analysis
- climate change
- palliative care
- health information
- cell proliferation
- mass spectrometry
- cell cycle arrest
- endoplasmic reticulum stress
- human health
- liquid chromatography
- social media