Bayesian Meta-Regression Model Using Heavy-Tailed Random-effects with Missing Sample Sizes for Self-thinning Meta-data.
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ABSTRACT:
SUBMITTER: Ma Z
PROVIDER: S-EPMC8315582 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationNetwork-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when estimating multiple networks across heterogeneous sub-populations, varying sample sizes pose a challenge in the estimation and inference, as network differences may be driven by differences in power. We are particularly interested in addressing this challenge in the context of proteomic networks for related cancers, as the number of subjects available for rare cancer (sub-)types is often limited.ResultsWe develop NExUS (Network Estimation across Unequal Sample sizes), a Bayesian method that enables joint learning of multiple networks while avoiding artefactual relationship between sample size and network sparsity. We demonstrate through simulations that NExUS outperforms existing network estimation methods in this context, and apply it to learn network similarity and shared pathway activity for groups of cancers with related origins represented in The Cancer Genome Atlas (TCGA) proteomic data.Availability and implementationThe NExUS source code is freely available for download at https://github.com/priyamdas2/NExUS.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BackgroundRandom-effects (RE) models are commonly applied to account for heterogeneity in effect sizes in gene expression meta-analysis. The degree of heterogeneity may differ due to inconsistencies in sample quality. High heterogeneity can arise in meta-analyses containing poor quality samples. We applied sample-quality weights to adjust the study heterogeneity in the DerSimonian and Laird (DSL) and two-step DSL (DSLR2) RE models and the Bayesian random-effects (BRE) models with unweighted and weighted data, Gibbs and Metropolis-Hasting (MH) sampling algorithms, weighted common effect, and weighted between-study variance. We evaluated the performance of the models through simulations and illustrated application of the methods using Alzheimer's gene expression datasets.ResultsSample quality adjusting within study variance (wP6) models provided an appropriate reduction of differentially expressed (DE) genes compared to other weighted functions in classical RE models. The BRE model with a uniform(0,1) prior was appropriate for detecting DE genes as compared to the models with other prior distributions. The precision of DE gene detection in the heterogeneous data was increased with the DSLR2wP6 weighted model compared to the DSLwP6 weighted model. Among the BRE weighted models, the wP6weighted- and unweighted-data models and both Gibbs- and MH-based models performed similarly. The wP6 weighted common-effect model performed similarly to the unweighted model in the homogeneous data, but performed worse in the heterogeneous data. The wP6weighted data were appropriate for detecting DE genes with high precision, while the wP6weighted between-study variance models were appropriate for detecting DE genes with high overall accuracy. Without the weight, when the number of genes in microarray increased, the DSLR2 performed stably, while the overall accuracy of the BRE model was reduced. When applying the weighted models in the Alzheimer's gene expression data, the number of DE genes decreased in all metadata sets with the DSLR2wP6weighted and the wP6weighted between study variance models. Four hundred and forty-six DE genes identified by the wP6weighted between study variance model could be potentially down-regulated genes that may contribute to good classification of Alzheimer's samples.ConclusionsThe application of sample quality weights can increase precision and accuracy of the classical RE and BRE models; however, the performance of the models varied depending on data features, levels of sample quality, and adjustment of parameter estimates.
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