Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.
In most analyses of large-scale genomic data sets, differential expression analysis is typically assessed by testing for differences in the mean of the distributions between 2 groups. A recent finding by Tomlins and others (2005) is of a different type of pattern of differential expression in which a fraction of samples in one group have overexpression relative to samples in the other group. In this work, we describe a general mixture model framework for the assessment of this type of expression, called outlier profile analysis. We start by considering the single-gene situation and establishing results on identifiability. We propose 2 nonparametric estimation procedures that have natural links to familiar multiple testing procedures. We then develop multivariate extensions of this methodology to handle genome-wide measurements. The proposed methodologies are compared using simulation studies as well as data from a prostate cancer gene expression study.
Department of Statistics and Department of Public Health Sciences, Pennsylvania State University, University Park, PA 16802, USA. ghoshd@psu.edu
Debashis
D
Ghosh
Arul M
AM
Chinnaiyan
D001699:Biometry / Q000379:methods; D016001:Confidence Intervals; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011471:Prostatic Neoplasms / Q000235:genetics; D015203:Reproducibility of Results; D018401:Sample Size; D018709:Statistics, Nonparametric; D035501:Uncertainty