# Robust Multi-view Co-expression Network Inference

arXiv: https://arxiv.org/abs/2409.19991 · HTML: https://mlsanity.com/paper/2409.19991/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Trivializations for Gradient-Based Optimization on Manifolds](https://mlsanity.com/paper/1909.09501/) (2019)
- [Common and Distinct Components in Data Fusion](https://mlsanity.com/paper/1607.02328/) (2016)
- [Partial Correlation Graphical LASSO](https://mlsanity.com/paper/2104.10099/) (2021)

## Built on by (most cited first, 1 of 1)

- [Foundations of Independent Component Analysis](https://mlsanity.com/paper/2608.13229/) (2026)

## Abstract (arXiv)

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.