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Keyword Search Criteria: RNA-Seq returned 44 record(s)
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Monday, 07/30/2018
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Per-Gene Normalization Method (UQ-PgQ2) Improves the Specificity for the Analysis of Differential Gene Expression in RNA-Seq Data
Xiaohong Li, University of Louisville; Nigel G.F. Cooper, University of Louisville; Dongfeng Wu, University of Louisville; Eric C. Rouchka, University of Louisville; Shesh N. Rai, University of Louisville
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Changing Mixtures Does Not Always Change Margins: An Application to Single-Cell RNA-Seq
Michael Newton, University of Wisconsin at Madison; XIuyu Ma, University of Wisconsin at Madison; Christina Kendziorski, University of Wisconsin - Madison
9:25 AM
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A Bayesian Approach to Analyzing Differential Gene Expression in Heterogeneous Tissue Samples
Megan Stefanski, University of Missouri - Kansas City; David Spade, University of Missouri - Kansas City
10:35 AM
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Identifying Biomarkers in Heterogeneous Samples Without Known Reference Cell Type Profiles
Kelly Mosesso, Harvard University ; Martin Aryee, Harvard University
11:35 AM
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SAME-Clustering: Single-Cell Aggregated Clustering via Mixture Model Ensemble
Ruth Huh, University of North Carolina at Chapel Hill; Yuchen Yang, University of North Carolina at Chapel Hill; Houston Culpepper, University of North Carolina at Chapel Hill; Yun Li, University of North Carolina at Chapel Hill
2:05 PM
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A Correlated Random Effects Hurdle Model for Detecting Differentially Expressed Genes in Discrete Single Cell RNA Sequencing Data
Michael Sekula, University of Louisville; Jeremy Gaskins, University of Louisville; Susmita Datta, University of Florida
2:05 PM
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Tuesday, 07/31/2018
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Visualization Methods for RNA-Sequencing Data Analysis
Lindsay Rutter, Iowa State University; Dianne Cook, Monash University
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An Ensemble RNA-Seq Differential Analysis Method for False Discovery Rate Control
Dongmei Li, University of Rochester; Ananta Paine, University of Rochester; Timothy D. Dye, University of Rochester
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Integrative Statistical Analysis Pipeline for RNA-Seq and NanoString with Application to Gene Expression Data of Cancer Patients
Jeea Choi, Novartis Pharmaceuticals; Catarina D. Campbell, Novartis Institutes for BioMedical Research; Xiaoshan Wang, Novartis Pharmaceuticals; He Wei, Novartis Pharmaceuticals; Robinson Douglas, Novartis Pharmaceuticals; Stephane Wong, Novartis Pharmaceuticals; Bin Fu, Novartis Pharmaceuticals; Rebecca Leary, Novartis Institutes for BioMedical Research; Kavitha Venkatesan, Novartis Institutes for BioMedical Research; Ying A Wang, Novartis Pharmaceuticals
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Three-Component Dissection of Tumor Cellular Heterogeneity by a Bayesian Hierarchical Model
Tao Wang, UT Southwestern Medical Center
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Integrative Statistical Analysis Pipeline for RNA-Seq and NanoString with Application to Gene Expression Data of Cancer Patients
Jeea Choi, Novartis Pharmaceuticals; Catarina D. Campbell, Novartis Institutes for BioMedical Research; Xiaoshan Wang, Novartis Pharmaceuticals; He Wei, Novartis Pharmaceuticals; Robinson Douglas, Novartis Pharmaceuticals; Stephane Wong, Novartis Pharmaceuticals; Bin Fu, Novartis Pharmaceuticals; Rebecca Leary, Novartis Institutes for BioMedical Research; Kavitha Venkatesan, Novartis Institutes for BioMedical Research; Ying A Wang, Novartis Pharmaceuticals
8:40 AM
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Three-Component Dissection of Tumor Cellular Heterogeneity by a Bayesian Hierarchical Model
Tao Wang, UT Southwestern Medical Center
8:50 AM
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Visualization Methods for RNA-Sequencing Data Analysis
Lindsay Rutter, Iowa State University; Dianne Cook, Monash University
8:55 AM
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Improving the Value of Public Data with Recount2 and Phenotype Prediction
Shannon Ellis, Johns Hopkins University, Bloomberg School of Public Health
8:55 AM
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An Ensemble RNA-Seq Differential Analysis Method for False Discovery Rate Control
Dongmei Li, University of Rochester; Ananta Paine, University of Rochester; Timothy D. Dye, University of Rochester
9:20 AM
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The Most Informative Spacing Statistic Identifies Biologically Relevant Patterns in Transcript Level Distributions
Stanley Pounds, St. Jude Children's Research Hospital
9:20 AM
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Statistical Methods for Single-Cell RNA-Seq in Studies of Mammalian Development
Christina Kendziorski, University of Wisconsin - Madison; Zijian Wang, University of Wisconsin - Madison; Ron Stewart, Morgridge Institute for Research; Chris Barry, Morgridge Institute for Research; Li-Fang Chu, Morgridge Institute for Research
10:35 AM
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General and Flexible Methods for Signal Extraction from Single-Cell RNA-Seq Data
Davide Risso, Weill Cornell Medicine
11:00 AM
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Using Genomic Features to Make Smart Clinical Decisions: The Power of Machine Learning with RNA-Seq
Jing Huang, Veracyte Inc; Su yeon Kim , Veracyte Inc; Yangyang Hao, Veracyte Inc; Jing Lu, Veracyte Inc; Joshua Babiarz, Veracyte Inc; Sean Walsh, Veracyte Inc; Giulia Kennedy, Veracyte Inc
11:00 AM
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Detecting Developmental Expression Switches from Transcriptomic and Epigenomic Data
Claudia Kleinman, McGill University; Marie Forest, Lady Davis Research Institute, McGill University; Selin Jessa, McGill University; Celia M.T. Greenwood, Lady Davis Research Institute, McGill University
2:25 PM
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Fully Bayesian Analysis of Hierarchical Count Regression Models
Jarad Niemi, Iowa State University; William Landau, Eli Lilly and Company; Dan Nettleton, Iowa State University
2:50 PM
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Wednesday, 08/01/2018
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SAME-Clustering: Single-Cell Aggregated Clustering via Mixture Model Ensemble
Ruth Huh, University of North Carolina at Chapel Hill; Yuchen Yang, University of North Carolina at Chapel Hill; Houston Culpepper, University of North Carolina at Chapel Hill; Jin Szatkiewicz, University of North Carolina at Chapel Hill; Yun Li, University of North Carolina at Chapel Hill
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TWO-SIGMA: a Two-Component Generalized Linear Mixed Model for ScRNA-Seq Association Analysis
Eric Van Buren, UNC Chapel Hill; Yun Li, University of North Carolina at Chapel Hill; Ming Hu, Cleveland Clinic Foundation; Di Wu, UNC Chapel HIll
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Testing Differential Gene Expression from Single-Cell RNA-Seq Data Using Bayes Deconvolusion
Jingyi Zhai, University of Michigan; Hui Jiang, University of Michigan
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Bayesian Hierarchical Modeling of Clustered or Longitudinal RNA Sequencing Experiments
Brian Vestal, National Jewish Health; Camille Moore, National Jewish Health; Katerina Kechris, Colorado School of Public Health; Laura Saba, University of Colorado Anschutz Medical Campus; Tasha Fingerlin, National Jewish Health
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Probabilistic Inference of Clonal Gene Expression Through Integration of RNA and DNA-Seq at Single-Cell Resolution
Kieran Campbell, University of British Columbia; Sohrab P Shah, BC Cancer Agency; Alexandre Bouchard-Côté, University of British Columbia
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Joint Modeling of Multiple RNA-Seq Samples for Accurate Isoform Quantification
Wei Li, University of California, Los Angeles; Jingyi Li, University of California, Los Angeles; Anqi Zhao, Harvard University; Shihua Zhang, Chinese Academy of Sciences
8:35 AM
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ScImpute: Accurate and Robust Imputation for Single Cell RNA-Seq Data
Jingyi Li, University of California, Los Angeles; Wei Li, University of California, Los Angeles
8:55 AM
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Using RNA-Seq Data to Study Patients' Response on Tumor Immunotherapy
Wei Sun, Fred Hutchinson Cancer Research Center; Chong Jin, UNC-Chapel Hill; Paul Little, UNC Chapel Hill; Danyu Lin, University of North Carolina; Mengjie Chen, University of Chicago
9:15 AM
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Mitigating the Adverse Impact of Batch Effects in Sample Pattern Detection
Teng Fei, Emory University; Tengjiao Zhang, School of Life Sciences and Technology, Tongji University; Weiyang Shi, School of Life Sciences and Technology, Tongji University; Tianwei Yu, Emory University
9:20 AM
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Normalization of Transcript Degradation Improves Accuracy in RNA-Seq Analysis
Ji-Ping Wang, Northwestern University; Bin Xiong, Northwestern University; Yiben Yang, Northwestern University
9:35 AM
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Sequencing Data, Repeated Measures and Genetic Heritability
Katerina Kechris, Colorado School of Public Health; Brian Vestal, National Jewish Health; Wen Jenny Shi, University of Colorado Anschutz Medical Campus; Pratyaydipta Rudra, University of Colorado at Denver; Pamela Russell, University of Colorado Anschutz Medical Campus; Laura Saba, University of Colorado Anschutz Medical Campus
9:55 AM
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GLMM-Seq: Detection of Population-Based Gene Level Allele-Specific Expression by RNA-Seq
Jiaxin Fan, University of Pennsylvania; Jian Hu, University of Pennsylvania; Muredach Reilly, Columbia University; Rui Xiao, University of Pennsylvania; Mingyao Li, University of Pennsylvania
11:35 AM
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Genotype Prediction for All Publicly Available RNA-Seq Data
Siruo Wang, Johns Hopkins Bloomberg SPH; Jeffrey Leek, Johns Hopkins Bloomberg School of Public Health
11:50 AM
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Differential Expression Analysis of RNA-Seq Data with Integrated Likelihood Method
Yilun Zhang, University of Clifornia, Davis; David Rocke, University of California, Davis
2:05 PM
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Cell Type-Aware Differential Expression Analysis for RNA-Seq Data
Chong Jin, UNC-Chapel Hill; Wei Sun, Fred Hutchinson Cancer Research Center; Mengjie Chen, University of Chicago; Danyu Lin, University of North Carolina
2:20 PM
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DiPhiSeq: Robust Comparison of Expression Levels on RNA-Seq Data with Large Sample Sizes
Alicia Lamere, Bryant University; Jun Li, University of Notre Dame
2:35 PM
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Testing for Differentially Expressed Genetic Pathways with Single-Subject N-Of-1 Data in the Presence of Inter-Gene Correlation
Alfred Schissler, University of Nevada, Reno; Walter W Piegorsch, University of Arizona; Yves A Lussier, University of Arizona
2:50 PM
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Finding Best Low Dimensional Angles for Visualizing High-Dimensional Data
Yanming Di, Oregon State University; Wanli Zhang, Oregon State University
3:05 PM
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A Data Adjustment-Tolerant Strategy for RNA-Seq Differential Gene Expression Analysis
Guoshuai Cai, Arnold School of Public Health, University of South Carolina; Jennifer M. Franks, Geisel School of Medicine at Dartmouth; Michael L. Whitfield, Geisel School of Medicine at Dartmouth
3:20 PM
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