Robust Multi-view Co-expression Network Inference (2409.19991v1)

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.

• The paper presents MVTLASSO, a robust probabilistic method for inferring multi-view gene co-expression networks.
• The approach utilizes an EM procedure combined with Graphical Lasso to estimate a shared sparse precision matrix.
• Empirical tests show that MVTLASSO outperforms baseline methods in reconstructing accurate gene networks from noisy data.

Robust Multi-view Co-expression Network Inference: An Overview

The paper "Robust Multi-view Co-expression Network Inference" by Pandeva et al. presents an advanced method, called MVTLASSO, for inferring gene co-expression networks from high-dimensional gene expression data obtained from multiple independent studies. This technique aims to address significant challenges in the field, including spurious gene correlations, sample correlations, and batch effects, which are common obstacles in transcriptome data analysis.

Methodology

The authors propose a novel probabilistic model built upon the premise that each dataset is essentially a noisy linear mixture of gene loadings that follow a multivariate $t$-distribution with a shared sparse precision matrix across studies. This model extends the TLASSO framework by Finegold et al. to a multi-view setting, capturing covariances at both the sample and the variable levels. The sparse precision matrix, which represents the gene co-expression network (GCN), is identifiable up to a scaling factor. The identifiability guarantees, as formalized in the paper, underpin the method's ability to recover true model parameters.

The estimation of model parameters is carried out through an Expectation-Maximization (EM) procedure:

1. E-step: Computes conditional expectations given current parameter estimates.
2. M-step: Updates parameter estimates by solving a series of convex optimization problems, including a Graphical Lasso (GLASSO) step for estimating the sparse precision matrix.

Numerical Analysis

The efficacy of MVTLASSO is validated through extensive empirical evaluations on both synthetic and real-world gene expression data.

Synthetic Data

In simulations with 200 variables and 100 samples, MVTLASSO consistently outperforms baseline methods (GLASSO and TLASSO) by more accurately reconstructing the underlying graph structures, even as the ratio of noise to signal loadings increases. The simulations also demonstrate that increasing the number of views (data sources) enhances the performance of MVTLASSO, as indicated by improved ROC curves.

Real Gene Expression Data

For practical validation, the method is applied to infer GCNs for the bacterium Bacillus subtilis using two well-controlled transcriptome compendia (BSB1 and PY79). To benchmark MVTLASSO against other methods, the authors apply various preprocessing techniques, such as standardization and ICA, before employing GLASSO. MVTLASSO produces more true positive edges across different penalty parameter settings compared to the baseline methods when validated against ground truth data from SubtiWiki.

Implications and Future Directions

MVTLASSO stands out as a robust and reliable method for inferring GCNs from complex multi-view transcriptome data. The approach accommodates noise and confounding factors inherent in real data better than traditional methods, thereby offering researchers improved tools for dissecting gene regulatory mechanisms.

The theoretical implications of this work extend to the broader field of high-dimensional statistics, particularly in developing more robust inference techniques for problems characterized by multi-source data. In practical terms, this method could significantly enhance the reliability of inferred genetic interactions, facilitating advancements in understanding cellular processes and disease mechanisms.

Future work in this domain could focus on refining hyperparameter selection procedures to streamline the computational process further and integrating experimental metadata into the modeling framework for even more accurate GCN inference. Advanced techniques for dimensionality reduction and noise filtering could also be integrated to enhance the robustness of the EM procedure.

Conclusion

The paper by Pandeva et al. makes a significant contribution to the methods available for inferring gene co-expression networks in the context of high-dimensional, multi-view data. By leveraging a robust probabilistic approach and rigorous numerical validation, the authors provide a powerful tool for researchers in the field of computational biology and beyond. This robust method not only advances our capability to infer complex genetic networks but also lays the groundwork for future developments in high-dimensional data analysis.