Application of Bayesian decomposition for analysing microarray data.
BACKGROUND
Microarray and gene chip technology provide high throughput tools for measuring gene expression levels in a variety of circumstances, including cellular response to drug treatment, cellular growth and development, tumorigenesis, among many other processes. In order to interpret the large data sets generated in experiments, data analysis techniques that consider biological knowledge during analysis will be extremely useful. We present here results showing the application of such a tool to expression data from yeast cell cycle experiments.


RESULTS
Originally developed for spectroscopic analysis, Bayesian Decomposition (BD) includes two features which make it useful for microarray data analysis: the ability to assign genes to multiple coexpression groups and the ability to encode biological knowledge into the system. Here we demonstrate the ability of the algorithm to provide insight into the yeast cell cycle, including identification of five temporal patterns tied to cell cycle phases as well as the identification of a pattern tied to an approximately 40 min cell cycle oscillator. The genes are simultaneously assigned to the patterns, including partial assignment to multiple patterns when this is required to explain the expression profile.


BACKGROUND
The application is available free to academic users under a material transfer agreement. Go to http://bioinformatics.fccc.edu/ for more details.
Moloshok
T D
TD
Bioinformatics Working Group, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. td_moloshok@fccc.edu
Klevecz
R R
RR
Grant
J D
JD
Manion
F J
FJ
Speier
W F
WF
Ochs
M F
MF
D000465:Algorithms; D001499:Bayes Theorem; D002453:Cell Cycle / Q000235:genetics; D030541:Databases, Genetic; D005786:Gene Expression Regulation; D016681:Genome, Fungal; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D010507:Periodicity; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012680:Sensitivity and Specificity