Quantifying the association between multidimensional phenotypes and multidimensional genetic/genomic data
- Date from - to
- 01 Sep 2005 - 31 Dec 2009
- Project leader(s)
- Prof. dr. A.H. (Koos) Zwinderman
- Menezes, de Renee Dr.
- Leunissen, Jack Prof. dr.
- ter Braak, Cajo Prof. Dr.
In this project bioinformatics tools are developed to (quantitatively) study the association between phenotypic variables, and genetic, genomic, or proteomic measurements. The various sub-projects concentrate on the generalisation of existing multivariate statistical techniques to handle data with different measurement scales; modelling the joint distribution through a chain of conditional distributions; the overfit-problem – having many more variables than individuals, which results in a large number of pseudo-relations; and finally, non-parametric smoothing and data visualisation by exploiting the intercorrelations between neighbouring genes.
- Regression by L1 regularization of smart contrasts and sums (ROSCAS) beats PLS and elastic net in latent variable model
- Multivariate analysis of complex gene expression and clinical phenotypes with genetic marker data
- Penalized canonical correlation analysis to quantify the association between gene expression and DNA markers
- Integrated analysis of DNA copy number and gene expression microarray data using gene sets
- Can subtle changes in gene expression be consistently detected with different microarray platforms?