High dimensional data analysis of flow cytometry data
viSNE and FlowSOM analysis were performed on Cytobank (https://cytobank.org). B cells, non-naïve CD4 T cells, and non-naïve CD8 T cells were analyzed separately. viSNE analysis was performed using equal sampling of 1000 cells from each FCS file, with 5000 iterations, a perplexity of 30, and a theta of 0.5. For B cells, the following markers were used to generate the viSNE maps: CD45RA, IgD, CXCR5, CD138, Eomes, TCF-1, CD38, CD95, CCR7, CD21, KI67, CD27, CX3CR1, CD39, T-bet, HLA-DR, CD16, CD19 and CD20. For non-naïve CD4 and CD8 T cells, the following markers were used: CD45RA, PD1, CXCR5, TCF-1, CD38, CD95, Eomes, CCR7, KI67, CD16, CD27, CX3CR1, CD39, CD20, T-bet, and HLA-DR. Resulting viSNE maps were fed into the FlowSOM clustering algorithm (59). For each cell subset, a new self-organizing map (SOM) was generated using hierarchical consensus clustering on the tSNE axes. For each SOM, 225 clusters and 10 or 15 metaclusters were identified for B cells and T cells respectively.
To group individuals based on B cell landscape, pairwise Earth Mover’s Distance (EMD) value was calculated on the B cell tSNE axes for all COVID-19 day 0 patients, healthy donors, and recovered donors using the emdist package in R as previously described (60). Resulting scores were hierarchically clustered using the hclust package in R.