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Activity Number:
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81
- Contributed Poster Presentations: Section on Statistics in Epidemiology
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #313783
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Title:
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Trajectory Based Gene Regulation Inference on Single Cell RNA-Seq
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Author(s):
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Wenxuan Deng* and Biqing Zhu and Taylor S Adams and Naftali Kaminski and Hongyu Zhao
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Companies:
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Yale University and Yale University and Yale University School of Medicine and Yale University School of Medicine and Yale University
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Keywords:
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Single Cell;
Trajectory;
Gene Regulatory;
Variable selection;
IPF;
COPD
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Abstract:
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While many methods have been developed for differential expression analysis using single cell RNA-seq data, there is a lack of methods to infer gene regulatory networks (GRN) using these data. In this presentation, we described a statistical approach to the inference of gene regulations based on the estimated pseudotime and cell lineages through single cell RNA-seq data while incorporating prior knowledge. Our method can handle both linear and nonlinear regulations. We demonstrate the usefulness of our approach through its application to monocyte-macrophage differentiation and T-cell lineage for samples collected from both the Idiopathic Pulmonary Fibrosis (IPF) and Chronic Obstructive Pulmonary Disease (COPD) patients. We show that our new method can better identify the differential gene regulations in IPF and COPD patients compared with healthy controls.
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Authors who are presenting talks have a * after their name.
