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44 Mon, 8/3/2020, 10:00 AM - 2:00 PM Virtual
Statistical Methods in Gene Expression Data Analysis I — Contributed Papers
Section on Statistics in Genomics and Genetics
Chair(s): Qiongshi Lu, University of Wisconsin-Madison
Detecting Allele-Specific Expression and Alterations of Allele-Specific Expression by a Bivariate Bayesian Hidden Markov Model
Tieming Ji, University of Missouri At Columbia
Decoding Heterogeneity in Bulk Tumor Gene Expression Data
Daiwei Tang, Yale University; Seyoung Park, Sungkyunkwan University; Hongyu Zhao, Yale University
Prioritizing SNP Sets by Joint Model Selection
Juhyun Kim, University of California, Los Angeles; Judong Shen, Merck & Co., Inc.; Anran Wang, Merck & Co., Inc. ; Devan Mehrotra, Merck; Jin Zhou, University of Arizona; Hua Zhou, University of California, Los Angeles
Time-Varying Stochastic Block Models via Kernel Smoothing, with Application to RNA-Seq Data and Cell Development
Kevin Lin, Carnegie Mellon University; Jing Lei, Carnegie Mellon University; Kathryn Roeder, Carnegie Mellon University
Bayesian Subject-Level Bulk Expression Deconvolution and Application to Cell-Type-Specific Differential Expression Analysis
Jiebiao Wang, University of Pittsburgh; Bernie Devlin, University of Pittsburgh; Kathryn Roeder, Carnegie Mellon University
MuSiC2: Multi-Condition Bulk RNA-Seq Cell Type Deconvolution
Jiaxin Fan, University of Pennsylvania; Yafei Lyu, University of Pennsylvania; Rui Xiao, University of Pennsylvania; Mingyao Li, University of Pennsylvania