Robust Multi-view Co-expression Network Inference
<|reference_start|>Robust Multi-view Co-expression Network Inference: 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.<|reference_end|>
@article{pandeva2024robust,
    title={Robust Multi-view Co-expression Network Inference},
    author={Teodora Pandeva and Martijs Jonker and Leendert Hamoen and Joris Mooij
  and Patrick Forr'e},
    journal={arXiv preprint arXiv:2409.19991},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19991},
    primaryClass={stat.ML cs.LG q-bio.QM stat.AP}
}