Iterative Random Forests to detect predictive and stable high-order interactions (1706.08457v4)

Abstract: Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on Random Forests (RF), Random Intersection Trees (RITs), and through extensive, biologically inspired simulations, we developed the iterative Random Forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with same order of computational cost as RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human derived cell lines. In Drosophila, among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, novel third-order interactions, e.g. between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF re-discovered a central role of H3K36me3 in chromatin-mediated splicing regulation, and identified novel 5th and 6th order interactions, indicative of multi-valent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens new avenues of inquiry into the molecular mechanisms underlying genome biology.

• The paper presents the iRF method, which integrates iterative feature weighting with Random Intersection Trees to detect high-order interactions in genomic data.
• It applies the approach to Drosophila and human cell datasets, validating 80% of known interactions and uncovering novel regulatory associations.
• The study demonstrates that balancing computational efficiency with interpretability can enhance reproducibility and insights in high-dimensional biological research.

Insights into High-Order Interactions with Iterative Random Forests

The paper presents an innovative methodology called iterative Random Forests (iRF) tailored to unearth high-order interactions within complex biological datasets. Anchored by advances in genomics, the focus is on identifying the nuanced interactions that lead to meaningful biological functions such as gene regulation.

Methodological Contributions

iRF advances the conventional Random Forests framework by introducing an iterative, feature-weighting mechanism designed to adaptively focus on pertinent features across iterations. The approach achieves this without exacerbating computational costs, maintaining efficiency akin to that of standard Random Forests. The integration of Random Intersection Trees (RIT) further refines the identification of stable high-order interactions, extending the applicability to problems characterized by high-dimensional feature spaces.

Biological Relevance and Case Studies

Conventionally, the paper of genomic interactions has wrestled with the substantial complexity imbued by high-order interactions among molecular entities. iRF is applied to two biologically significant cases: enhancer activity in early Drosophila embryos and mRNA splicing in human cell lines. The algorithm effectively discovers known interactions, such as those between Drosophila transcription factors, while also surfacing novel higher-order interactions involving key elements such as the Zelda (Zld) transcription factor.

In identifying these interactions, the algorithm substantiates 80% of its Drosophila transcription factor pairwise findings with pre-established physical interactions. For human-derived cells, iRF discerns complex chromatin interactions pivotal for splicing regulation. Here, iRF demonstrates its prowess by discovering significant high-order interactions that extend our understanding of chromatin-mediated regulation.

Implications and Future Directions

The theoretical utility of iRF lies in its potential for unveiling the intricate layers of functional genomics, specifically where direct experimental validation of these high-order processes is experimentally demanding. Practically, this tool provides a pathway for hypothesizing the roles of molecular interactions and guides further empirical investigations into cellular regulation processes.

Moreover, the stability principle embedded in iRF aligns with the demand for reproducibility in computational biology, an essential factor given the variance and multidimensionality characteristic of biological data. The exploration of local feature importance and the application of similar ensemble techniques across multidimensional datasets represents an exciting avenue for prospective developments.

Overall, the iRF method not only enhances the actionable insights gleaned from genomic data but also fortifies the methodological toolbox available to computational biologists tackling complex biological systems. As genomic datasets continue to expand in volume and complexity, strategies such as iRF that balance computational efficiency with interpretative power will be pivotal in driving biological discovery forward.