Software
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Single-Cell Sketching
‘Sketching’ means good downsampling for single-cell data. In immunology, and especially for working with flow and mass cytometry data, we want to preserve all major cell-types and their relative frequencies. Our sketches based on Kernel Herding do this very effectively! Work led my Vishal Baskaran and Jolene Ranek in collaboration with Junier Oliva (ACM BCB 2022).
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Cytocoarsening
Many single-cell analysis algorithms for linking immune cells to clinical outcomes rely on a graph representation of the data. To make such analyses more scalable, we developed a single-cell graph coarsening approach where redundant cells are aggregated based on graph structure and clinical outcomes. Work led by Chi-Jane Chen. (PSB,2023)
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CKME
Finding prototypical cells for a clinical phenotype using kernel mean embedding. In addition, we use a random Fourier feature representation for each cell and a mean embedding approach to define a single vector representation for each profiled sample. Work led by Siyuan Shan and in collaboration with Junier Oliva (Under Review, 2022).
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