Robust Multi-view Co-expression Network Inference
This work addresses gene co-expression network inference for computational biology, presenting an incremental method for handling multi-view data.
The paper tackles the problem of inferring gene co-expression networks from multiple transcriptome studies, addressing challenges like spurious correlations and batch effects, and demonstrates improved graph structure learning compared to baselines in empirical evaluations.
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.