Ultrafast clustering of single-cell flow cytometry data using FlowGrid

Xiaoxin Ye, Joshua Wing Kei Ho

Abstract: Flow cytometry is a popular technology for quantitative single-cell profiling of cell surface markers. It enables expression measurement of tens of cell surface protein markers in millions of single cells. It is a powerful tool for discovering cell sub-populations and quantifying cell population heterogeneity. Traditionally, scientists use manual gating to identify cell types, but the process is subjective and is not effective for large multidimensional data. Many clustering algorithms have been developed to analyse these data but most of them are not scalable to very large data sets with more than ten million cells.

External IDs:dblp:journals/bmcsb/YeH19