Robust Multi-view Co-expression Network Inference

• URL: http://arxiv.org/abs/2409.19991v1
• Date: Mon, 30 Sep 2024 06:30:09 GMT
• Title: Robust Multi-view Co-expression Network Inference
• Authors: Teodora Pandeva, Martijs Jonker, Leendert Hamoen, Joris Mooij, Patrick Forré,
• Abstract summary: Inferring gene co-expression networks from transcriptome data presents many challenges. We introduce a robust method for high-dimensional graph inference from multiple independent studies.
• Score: 8.697303234009528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unraveling the co-expression of genes across studies enhances the understanding of cellular processes. Inferring gene co-expression networks from transcriptome data presents many challenges, including spurious gene correlations, sample correlations, and batch effects. To address these complexities, we introduce a robust method for high-dimensional graph inference from multiple independent studies. We base our approach on the premise that each dataset is essentially a noisy linear mixture of gene loadings that follow a multivariate $t$-distribution with a sparse precision matrix, which is shared across studies. This allows us to show that we can identify the co-expression matrix up to a scaling factor among other model parameters. Our method employs an Expectation-Maximization procedure for parameter estimation. Empirical evaluation on synthetic and gene expression data demonstrates our method's improved ability to learn the underlying graph structure compared to baseline methods.

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