Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 123 - Unraveling Tissue Heterogeneity for Analyzing Omics Data in Cancer Research
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: WNAR
Abstract #309614
Title: Clustering of Single Cells Based on Expressions of Subsets of Genes
Author(s): Sha Cao* and Changlin Wan and Chi Zhang and Anru Zhang
Companies: Indiana University School of Medicine and Purdue University and Indiana University School of Medicine and University of Wisconsin-Madison
Keywords: Single cell RNA-Seq; Subspace clustering; Feature selection; Local low rank

Single cell gene expression profiles exhibit high dimensional features of the cells, part of which contribute to cell type specificities and identities, while the rest are purely stochastic. Further, the count based single cell expression data are often subject to high drop-out events. These remain as the two challenges in clustering single cells and predicting their cell (sub)type identities. In other words, high dimensional gene features contain far too many noisy ones that tend to obscure the true cluster identities; and the drop out events make it challenging to directly apply clustering on the original data as their distance measures are highly sensitive to outliers. We assumed a composite model for single cell expression data based on multinomial and Poisson distribution. Then we turn to look for a sparse local low rank structure on the parameter space of the resulted entry-wise Poisson distributed matrix . This resulted in subspace clusters on the parameters space instead of on the original count data, which particularly handles the aforementioned two challenges in single cell clustering.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program