Automated population identification and sorting algorithms for high-dimensional single-cell data

Benedict Anchang; Sylvia Plevritis

doi:10.1101/046664

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bioRxiv · 10.1101/046664

Automated population identification and sorting algorithms for high-dimensional single-cell data

Benedict Anchang·Sylvia Plevritis

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Abstract

Cell sorting or gating homogenous subpopulations from single-cell data enables cell-type specific characterization, such as cell-type genomic profiling as well as the study of tumor progression. This highlight summarizes recently developed automated gating algorithms that are optimized for both population identification and sorting homogeneous single cells in heterogeneous single-cell data. Data-driven gating strategies identify and/or sort homogeneous subpopulations from a heterogeneous population without relying on expert knowledge thereby removing human bias and variability. We further describe an optimized cell sorting strategy called CCAST based on Clustering, Classification and Sorting Trees which identifies the relevant gating markers, gating hierarchy and partitions that define underlying cell subpopulations. CCAST identifies more homogeneous subpopulations in several applications compared to prior sorting strategies and reveals simultaneous intracellular signaling across different lineage subtypes under different experimental conditions.

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Benedict Anchang, Sylvia Plevritis. 2016-03-31. Automated population identification and sorting algorithms for high-dimensional single-cell data. https://doi.org/10.1101/046664

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