Correlation-Compressed Direct Coupling Analysis

TL;DR

This paper introduces a scalable approach for inferring pairwise interactions in large Ising and Potts models by pre-filtering data with empirical correlations, enabling analysis of whole genomes efficiently.

Contribution

The study presents a novel combined method that uses empirical correlation filtering before inference, allowing analysis of larger datasets than previously feasible.

Findings

Method achieves similar accuracy to full inference on large datasets.

Successfully applied to whole-genome epistatic coupling data.

Enables computationally feasible analysis of genome-wide pairwise dependencies.

Abstract

Learning Ising or Potts models from data has become an important topic in statistical physics and computational biology, with applications to predictions of structural contacts in proteins and other areas of biological data analysis. The corresponding inference problems are challenging since the normalization constant (partition function) of the Ising/Potts distributions cannot be computed efficiently on large instances. Different ways to address this issue have hence given size to a substantial methodological literature. In this paper we investigate how these methods could be used on much larger datasets than studied previously. We focus on a central aspect, that in practice these inference problems are almost always severely under-sampled, and the operational result is almost always a small set of leading (largest) predictions. We therefore explore an approach where the data is pre-filtered based on empirical correlations, which can be computed directly even for very large problems. Inference is only used on the much smaller instance in a subsequent step of the analysis. We show that in several relevant model classes such a combined approach gives results of almost the same quality as the computationally much more demanding inference on the whole dataset. We also show that results on whole-genome epistatic couplings that were obtained in a recent computation-intensive study can be retrieved by the new approach. The method of this paper hence opens up the possibility to learn parameters describing pair-wise dependencies in whole genomes in a computationally feasible and expedient manner.