A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments.
BACKGROUND
Multiclass response (MCR) experiments are those in which there are more than two classes to be compared. In these experiments, though the null hypothesis is simple, there are typically many patterns of gene expression changes across the different classes that led to complex alternatives. In this paper, we propose a new strategy for selecting genes in MCR that is based on a flexible mixture model for the marginal distribution of a modified F-statistic. Using this model, false positive and negative discovery rates can be estimated and combined to produce a rule for selecting a subset of genes. Moreover, the method proposed allows calculation of these rates for any predefined subset of genes.


RESULTS
We illustrate the performance our approach using simulated datasets and a real breast cancer microarray dataset. In this latter study, we investigate predefined subset of genes and point out interesting differences between three distinct biological pathways.


BACKGROUND
http://www.bgx.org.uk/software.html
Broët
Philippe
P
INSERM U472, 16 Avenue Paul Vaillant Couturier, 94807 Villejuif Cedex, France. broet@vjf.inserm.fr
Lewin
Alex
A
Richardson
Sylvia
S
Dalmasso
Cyril
C
Magdelenat
Henri
H
D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
D009363:Neoplasm Proteins