The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two competing densities, the null and the alternative distributions. We investigate the use of weighted densities and, in particular, non-local densities as working alternative distributions, to enforce separation from the null and thus refine the screening procedure. We show how these weighted alternatives improve various operating characteristics, such as the Bayesian false discovery rate, of the resulting tests for a fixed mixture proportion with respect to a local, unweighted likelihood approach. Parametric and nonparametric model specifications are proposed, along with efficient samplers for posterior inference. By means of a simulation study, we exhibit how our model compares with both well-established and state-of-the-art alternatives in terms of various operating characteristics. Finally, to illustrate the versatility of our method, we conduct three differential expression analyses with publicly-available datasets from genomic studies of heterogeneous nature.

Denti, F., Peluso, S., Guindani, M., Mira, A. (2023). Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies. STATISTICS IN MEDICINE, 42(12 (30 May 2023)), 1931-1945 [10.1002/sim.9705].

Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies

Denti F.
;
Peluso S.;
2023

Abstract

The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two competing densities, the null and the alternative distributions. We investigate the use of weighted densities and, in particular, non-local densities as working alternative distributions, to enforce separation from the null and thus refine the screening procedure. We show how these weighted alternatives improve various operating characteristics, such as the Bayesian false discovery rate, of the resulting tests for a fixed mixture proportion with respect to a local, unweighted likelihood approach. Parametric and nonparametric model specifications are proposed, along with efficient samplers for posterior inference. By means of a simulation study, we exhibit how our model compares with both well-established and state-of-the-art alternatives in terms of various operating characteristics. Finally, to illustrate the versatility of our method, we conduct three differential expression analyses with publicly-available datasets from genomic studies of heterogeneous nature.
Articolo in rivista - Articolo scientifico
Dirichlet process mixture; multiple hypothesis testing; non-local distributions; two-group model; weight function; weighted density;
English
13-mar-2023
2023
42
12 (30 May 2023)
1931
1945
none
Denti, F., Peluso, S., Guindani, M., Mira, A. (2023). Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies. STATISTICS IN MEDICINE, 42(12 (30 May 2023)), 1931-1945 [10.1002/sim.9705].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/413537
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