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Activity Number:
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55
- Statistical methods for data from single cell technologies
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Type:
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Contributed
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Date/Time:
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Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #318225
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Title:
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Deconvolution of Spatial Transcriptomics Data Using Penalized Bayesian Non-Negative Matrix Factorization
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Author(s):
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Roopali Singh* and Xiang Zhu and Qunhua Li
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Companies:
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The Pennsylvania State University and The Pennsylvania State University and The Pennsylvania State University
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Keywords:
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Spatial Transcriptomics;
Deconvolution;
Single cell RNA-seq;
Cell-type Composition;
Non-negative matrix factorization;
Bayesian Non-negative matrix factorization
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Abstract:
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Spatial transcriptomics (ST) technology allows scientists to measure all the gene activity in a tissue sample while maintaining the spatial information of the cells in the tissue. However, these technologies currently do not yet have a single-cell resolution but rather produce a group of potentially heterogeneous cells at each location. One of the challenging problems associated with the spatial transcriptomics data is to deconvolve these heterogeneous mixtures of cells to get the cell type composition at each location. In this paper, we develop a penalized Bayesian non-negative matrix factorization to deconvolve spatial transcriptomics data and obtain the cell type composition of each location. Using simulations, we show that our method is more accurate in estimating cell-type compositions than existing deconvolution techniques. We illustrate the usefulness of our method using Mouse Brain Cerebellum and Human Intestine data.
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Authors who are presenting talks have a * after their name.
