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