Journal
PHYSICAL REVIEW RESEARCH
Volume 4, Issue 2, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevResearch.4.023240
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Funding
- Alfred P. Sloan Foundation
- Simons Foundation
- National Institutes of Health [R01EB026943]
- National Science Foundation, through the Center for the Physics of Biological Function [PHY-1734030]
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Statistical inference is crucial for scientific research, but its working mechanism is still not fully understood. This study investigates the efficacy of direct coupling analysis (DCA) in inferring pairwise interactions from amino acid sequence data. The results reveal that the accuracy of inference depends strongly on the nature of data-generating distributions. Importantly, it is found that DCA is not always superior to traditional local-statistics-based methods when data are limited.
Statistical inference is central to many scientific endeavors, yet how it works remains unresolved. Answering this requires a quantitative understanding of the intrinsic interplay between statistical models, inference methods, and the structure in the data. To this end, we characterize the efficacy of direct coupling analysis (DCA)-a highly successful method for analyzing amino acid sequence data-in inferring pairwise interactions from samples of ferromagnetic Ising models on random graphs. Our approach allows for physically motivated exploration of qualitatively distinct data regimes separated by phase transitions. We show that inference quality depends strongly on the nature of data-generating distributions: optimal accuracy occurs at an intermediate temperature where the detrimental effects from macroscopic order and thermal noise are minimal Importantly our results indicate that DCA does not always outperform its local-statistics-based predecessors; while DCA excels at low temperatures, it becomes inferior to simple correlation thresholding at virtually all temperatures when data are limited. Our findings offer insights into the regime in which DCA operates so successfully, and more broadly, how inference interacts with the structure in the data.
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