Probabilistic matrix clustering with feature priors for unbiased control selection
https://doi.org/10.17586/2226-1494-2025-25-5-999-1001
Abstract
We propose a probabilistic matrix-clustering method that leverages a prior distribution of features and dimensionality reduction (Singular Value Decomposition, SVD). The approach identifies, within a large control pool, a cluster statistically comparable to the test cohort, thereby reducing systematic bias in downstream comparative analyses. We show that the method correctly selects control groups in scenarios where standard nearest-neighbor matching produces false positives. The method has been used to construct control groups in studies based on the Russian Biobank at the Almazov National Medical Research Centre (Ministry of Health of the Russian Federation).
About the Author
D. A. Usoltsev
Institute for Genomic Medicine, Nationwide Children’s Hospital; ITMO University
United States
United States
Dmitrii A. Usoltsev — Senior Researcher; PhD Student
sc 57279360300
Columbus, 43205
Saint Petersburg, 197101
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Review
For citations:
Usoltsev D.A. Probabilistic matrix clustering with feature priors for unbiased control selection. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2025;25(5):999-1001. (In Russ.) https://doi.org/10.17586/2226-1494-2025-25-5-999-1001
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