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Hongzhe Li Seminar
University of Pennsylvania Department of Biostatistics and Epidemiology
| What | |
|---|---|
| When |
2009-04-09 14:06
2009-04-09 15:10
2009-04-09 from 14:06 to 15:10 |
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1 Abstract
“High Dimensional Statistics in Genomics:
Some Problems and Solutions”
Abstract:
Large-scale systematic genomic datasets have been generated to inform our biological understanding of both the normal workings of organisms in biology and disrupted pathways which cause human disease. The integrative analysis of these vast amounts of diverse types of quantitative data, which has become an increasingly important part of genomics and systems biology research, poses many interesting statistical problems, largely driven by the complex inter-relationships between these high-dimensional genomic measurements. In this talk, I will present three problems in genomics research that require the development of new statistical methods: (1) identification of active transcription factors in microarray time-course experiments; (2) identification of sub-networks that are associated with clinical outcomes; and (3) identification of genetic variants that explain higher-order gene co-expression modules. I will present several regularized estimation methods to address these questions and demonstrate their applications using real data examples. I will also discuss some theoretical properties of these procedures.
Thursday, April 9, 2009
2:00 pm
RRI 101
Host: Fengzhu Sun
2 Log
LD graph for GWAS. r2>0.4
coefficients of adjacent genes expected to be "smooth"
- penality
- markov random field
2.1 publications
- Wang L, Li H and Huang J (2008): Variable selection in nonparametric varying-coefficient models for analysis of repeated measurements. Journal of the American Statistical Association, 103: 1556-1569.
- Wei Z and Li H (2008): A Hidden Spatial-temporal Markov Random Field Model for Network-based Analysis of Time Course Gene Expression Data. Annals of Applied Statistics, 2(1), 408-429.
- Li H (2008): Statistical methods for inference of genetic networks and regulatory modules. Analysis of Microarray Data: Network-based Approaches. Edited by Emmert-Streid and Dehmer. Wiley VCH. pp 143-167.
- Li C and Li H (2008): Network-constraint regularization and variable selection for analysis of genomic data. Bioinformatics, 24: 1175-1182.
Gene Regulatory Networks Special Feature (PNAS)