Statistics Meeting Focused on RNA-Seq Data Handling

Joint Statistical Meeting July 30th-August 4th, 2011
Miami Beach, Fl

Sharing Information Across Genes to Estimate Overdispersion in RNA-seq Data
Steven Peder Lund, Iowa State University; Dan Nettleton, Iowa State University31/2011

Differential expression analysis for paired RNA-seq data
Lisa M Chung, Department of Epidemiology and Public Health, Yale University ; John Ferguson, Department of Epidemiology and Public Health, Yale University ; Hongyu Zhao, Yale University

How to characterize dynamic bayesian networks across multiple species from time series mRNA-Seq count gene expression profiles:An intelligent Dynamic Bayesian Networks (IDBNs)
sunghee OH, Yale University; Hongyu Zhao, Yale University; James P. Noonan, Yale University

Statistical methods for the analysis of next-generation sequencing data
Karthik Devarajan, Fox Chase Cancer Center

MEAN-VARIANCE MODELING OF RNA-SEQ TRANSCRIPTIONAL COUNT DATA
Yihui Zhou, Univ North Carolina; Fred Andrew Wright, Univ North Carolina

On Differential Gene Expression Using RNA-Seq Data
Ju Hee Lee, University of Texas, MD Anderson Cancer Center; Yuan Ji, MD Anderson Cancer Centre – University of Texas; Shoudan Liang, University of Texas, MD Anderson Cancer Center; Guoshuai Cai, University of Texas, MD Anderson Cancer Center; Peter Mueller, MD Anderson Cancer Center

A Bayesian nonparametric method for differential expression analysis of RNA-seq data
Yiyi Wang, Department of Statistics, Texas A&M University; David B. Dahl, Department of Statistics, Texas A&M University

Statistical strategy for eQTL mapping using RNA-seq data
Wei Sun, University of North Carolina, Chapel Hill

Joint analyses of high-throughput DNA and RNA-seq data from cancer samples
Su Yeon Kim, University of California, Berkeley; Terence Speed, University of California, Berkeley

Significance Analysis of time-series gene expression profiles :via differential/trajectory models in temporal mRNA-Seq data
sunghee OH, Yale University; Hongyu Zhao, Yale University; James P. Noonan, Yale University

Model-Based Clustering for RNA-Seq Data
Yaqing Si, Iowa State University; Peng Liu, Iowa State University

An integrative approach to comparing and normalizing gene expression data generated from RNA-seq, microarray, and RT-PCR technologies
Zhaonan Sun, Department of Statistics, Purdue University; Yu Zhu, Department of Statistics, Purdue University

Normalization, testing, and false discovery rate estimation for RNA-sequencing data
Jun Li, Department of Statistics, Stanford University; Daniela Witten, University of Washington; Iain M Johnstone, Stanford University; Robert Tibshirani, Dept of Health Research and Policy, & Statistics, Stanford University

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