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Motivation: The pre-estimate of the proportion of null hypotheses (π₀) plays a critical role in controlling false discovery rate (FDR) in multiple hypothesis testing. However, hidden complex dependence structures of many genomics datasets distort the distribution of p-values, rendering existing π₀ estimators less effective.

Results: From the basic non-linear model of the q-value method, we developed a simple linear algorithm to probe local dependence blocks. We uncovered a non-static relationship between tests' p-values and their corresponding q-values that is influenced by data structure and π₀. Using an optimization framework, these findings were exploited to devise a Sliding Linear Model (SLIM) to more reliably estimate π₀ under dependence. When tested on a number of simulation datasets with varying data dependence structures and on microarray data, SLIM was found to be robust in …