Biologically valid linear factor models of gene expression.
BACKGROUND
The identification of physiological processes underlying and generating the expression pattern observed in microarray experiments is a major challenge. Principal component analysis (PCA) is a linear multivariate statistical method that is regularly employed for that purpose as it provides a reduced-dimensional representation for subsequent study of possible biological processes responding to the particular experimental conditions. Making explicit the data assumptions underlying PCA highlights their lack of biological validity thus making biological interpretation of the principal components problematic. A microarray data representation which enables clear biological interpretation is a desirable analysis tool.


RESULTS
We address this issue by employing the probabilistic interpretation of PCA and proposing alternative linear factor models which are based on refined biological assumptions. A practical study on two well-understood microarray datasets highlights the weakness of PCA and the greater biological interpretability of the linear models we have developed.
Girolami
Mark
M
Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, UK. girolami@dcs.gla.ac.uk
Breitling
Rainer
R
D000465:Algorithms; D018797:Cell Cycle Proteins / Q000378:metabolism*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016014:Linear Models; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018384:Oxidative Stress / Q000502:physiology; D025341:Principal Component Analysis; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism
D018797:Cell Cycle Proteins; D029701:Saccharomyces cerevisiae Proteins