A novel means of using gene clusters in a two-step empirical Bayes method for predicting classes of samples.
BACKGROUND
The classification of samples using gene expression profiles is an important application in areas such as cancer research and environmental health studies. However, the classification is usually based on a small number of samples, and each sample is a long vector of thousands of gene expression levels. An important issue in parametric modeling for so many gene expression levels is the control of the number of nuisance parameters in the model. Large models often lead to intensive or even intractable computation, while small models may be inadequate for complex data.


METHODS
We propose a two-step empirical Bayes classification method as a solution to this issue. At the first step, we use the model-based cluster algorithm with a non-traditional purpose of assigning gene expression levels to form abundance groups. At the second step, by assuming the same variance for all the genes in the same group, we substantially reduce the number of nuisance parameters in our statistical model.


RESULTS
The proposed model is more parsimonious, which leads to efficient computation under an empirical Bayes estimation procedure. We consider two real examples and simulate data using our method. Desired low classification error rates are obtained even when a large number of genes are pre-selected for class prediction.
Ji
Yuan
Y
Department of Biostatistics and Applied Mathematics, The University of Texas M.D. Anderson Cancer Center Houston, TX 77030, USA. yuanji@mdanderson.org
Tsui
Kam-Wah
KW
Kim
Kyungmann
K
D000465:Algorithms; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000378:metabolism; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D007938:Leukemia / Q000175:diagnosis / Q000378:metabolism; D008954:Models, Biological; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins