PMC:7417788 / 73296-74109 JSONTXT

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{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7417788","sourcedb":"PMC","sourceid":"7417788","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7417788","text":"Mass cytometry visualizing and clustering\nWe created mass cytometry datasets for analysis by concatenating cells from all individuals for each cell type. In this way, we created downsampled datasets of 95,316 TCs, 35,254 NKs, 22,042 BCs, 39,144 MCs and 8,244 DCs in cohort-1 and 57,910 TCs, 34,857 NKs, 13,812 BCs, 45,431 MCs and 7,990 DCs in cohort-2 for analysis. We used FlowCore (65 flowCore: Basic structures for flow cytometry data.) to read and process FCS files for further analysis. For sample with more than 20,000 cells, we randomly selected 20,000 cells to ensure that samples were equally represented. At last, we run the t-SNE dimensionality reduction algorithm on a combined data sample using the Seurat package based on harmony embedding (https://github.com/immunogenomics/harmony, version 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