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Activity Number: 527 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #304270
Title: Sparse Probabilistic NMF for Single Cell RNA Sequencing
Author(s): Xiaotian Wu* and Zhijin Wu
Companies: Brown University and Brown University
Keywords: single cell RNA sequencing; non-negative matrix factorization; penalization

Single cell RNA sequencing (scRNA-seq) is a newly developed technology that provides cell level resolution of gene expression. The scRNA-seq data are count tables of all genes' expression across all samples. We propose a novel sparse probabilistic non-negative matrix factorization (NMF) for extracting latent biological functional topics and topic frequencies for each cell. The proposed model is applied to scRNA-seq datasets for dimensionality reduction and cell type identification.

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

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