Robust Multi-view Co-expression Network Inference
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910625069793280 |
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| author | Pandeva, Teodora Jonker, Martijs Hamoen, Leendert Mooij, Joris Forré, Patrick |
| author_facet | Pandeva, Teodora Jonker, Martijs Hamoen, Leendert Mooij, Joris Forré, Patrick |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_19991 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Multi-view Co-expression Network Inference Pandeva, Teodora Jonker, Martijs Hamoen, Leendert Mooij, Joris Forré, Patrick Machine Learning Quantitative Methods Applications 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. |
| title | Robust Multi-view Co-expression Network Inference |
| topic | Machine Learning Quantitative Methods Applications |
| url | https://arxiv.org/abs/2409.19991 |