L1-regularisation, motif regression and ChIP-on-chip data analysis
Seminar Room 1, Newton Institute
Motivated by the proposed format of talks, we include the following: (i) a review of statistical facts about L1-regularization for high-dimensional problems; (ii) some adaptations of motif regression (Conlon, Liu, Lieb & Liu, 2003) for scoring potential motifs or for presence/absence of other biological targets of interest (e.g. proteins) by integrating multiple data sources; (iii) using the concepts for analyzing ChIP-on-chip data from human liver cells (with a side remark on signal extraction) for HIF-dependent transcriptional networks.
Issue (i) deals with a general purpose method for variable selection or feature extraction which is potentially useful for a broad variety of (multiple) bio-molecular and high-dimensional data. Issue (ii) is - in our experience - an interesting method to improve upon some chosen "standard" methodology by making use of additional data sources. Finally, issue (iii) is work in progress with the Ricci lab at ETH Zurich: it is an illustration for statisticians and - of course - the "real thing" for biologists.
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