We propose a general and formal statistical framework for the multiple tests of associations between known fixed features of a genome and unknown parameters of the distribution of variable features of this genome in a population of interest. The known fixed gene-annotation profiles, corresponding to the fixed features of the genome, may concern Gene Ontology (GO) annotation, pathway membership, regulation by particular transcription factors, nucleotide sequences, or protein sequences. The unknown gene-parameter profiles, corresponding to the variable features of the genome, may be, for example, regression coefficients relating genome-wide transcript levels or DNA copy numbers to possibly censored biological and clinical outcomes and covariates. A generic question of great interest in current genomic research, regarding the detection of associations between biological annotation metadata and genome-wide expression measures, may then be translated into the multiple tests of hypotheses concerning association measures between gene-annotation and gene-parameter profiles. A general and rigorous formulation of the statistical inference question allows us to apply the multiple testing methodology developed in Dudoit and van der Laan (2006) and related articles, to control a broad class of Type I error rates, in testing problems involving general data generating distributions (with arbitrary dependence structures among variables), null hypotheses, and test statistics. Resampling-based single-step and stepwise multiple testing procedures, that take into account the joint distribution of the test statistics, are provided to control Type I error rates defined as tail probabilities for arbitrary functions of the numbers of false positives and rejected hypotheses.

The proposed statistical and computational methods are illustrated using the acute lymphoblastic leukemia (ALL) microarray dataset of Chiaretti et al. (2004), with the aim of relating GO annotation to differential gene expression between B-cell ALL with the BCR/ABL fusion and cytogenetically normal NEG B-cell ALL. The sensitivity of the identified lists of GO terms to the choice of association parameter between GO annotation and differential gene expression demonstrates the importance of translating the biological question in terms of suitable gene-annotation profiles, gene-parameter profiles, and association measures. In particular, the results show the limitations of binary gene-parameter profiles of differential expression indicators, which are still the norm for combined GO annotation and microarray data analyses. Procedures based on such binary gene-parameter profiles tend to be conservative and lack robustness with respect to the estimator for the set of differentially expressed genes.

WWW companion: www.stat.berkeley.edu/~sandrine/Docs/Papers/DFF06/DFF.html


Biostatistics | Genetics | Laboratory and Basic Science Research | Multivariate Analysis | Statistical Methodology | Statistical Models | Statistical Theory