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Activity Number:
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418
- Statistical Methods for Single Cell Genomics and Spatial Transcriptomics
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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International Indian Statistical Association
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Abstract #323547
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Title:
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Bayesian Analysis of Single-Nuclei Dose Response Data
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Author(s):
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Samiran Sinha* and Tapabrata Maiti and Satabdi Saha
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Companies:
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Texas A&M University and Michigan State University and Michigan State University
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Keywords:
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Approximate Bayes;
Negative Binomial;
Random effect;
Spline;
Taylor's expansion;
Zero-inflation
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Abstract:
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In this talk I will describe a novel approach of analyzing single-nuclei dose-response data from various genes and various cell types. There are two aspects of the analysis, 1) a sensible model for the single-nuclei data and 2) and a feasible computational approach to do the computation based on this huge dataset. We use the negative Binomial model with regression splines to model the response, and use an approximate Bayes method to do fast computation. The proposed method will be accompanied by simulation studies and a real data analysis.
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Authors who are presenting talks have a * after their name.
