Sparsity modelling in gene expression pathway studies
Duration: 1 hour 14 mins 1 sec
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| Description: |
West, M (Duke)
Tuesday 01 April 2008, 14:00-15:00 High Dimensional Statistics in Biology |
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| Created: | 2008-04-07 11:59 | ||
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| Collection: | Statistical Theory and Methods for Complex, High-Dimensional Data | ||
| Publisher: | Isaac Newton Institute | ||
| Copyright: | West, M | ||
| Language: | eng (English) | ||
| Distribution: |
World
(downloadable)
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| Credits: |
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| Explicit content: | No | ||
| Aspect Ratio: | 4:3 | ||
| Screencast: | No | ||
| Bumper: | UCS Default | ||
| Trailer: | UCS Default | ||
| Abstract: | I will discuss aspects of large-scale multivariate modelling utilising sparsity priors for anova, regression and latent factor analysis in gene expression studies. Specific attention will be given to the development of experimental gene expression signatures in cell lines and animal models, and their extrapolation/evaluation in gene pathway-focused analyses of data from human disease contexts. The role of sparse statistical modelling in signature identification, and in evaluation of complex interacting "sub pathway" related patterns in gene expression in observational data sets, will behighlighted. I will draw on data and examples from some of our projects in cancer and cardiovascular genomics. |
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