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bioRxiv · 10.1101/2024.10.18.619153

p-ClustVal: A Novel p-adic Approach for Enhanced Clustering of High-Dimensional scRNASeq Data

Sharma, P.·Mishra, S.·Kurban, H.·Dalkilic, M.
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Abstract

This paper introduces p-ClustVal, a novel data transformation technique inspired by p-adic number theory that significantly enhances cluster discernibility in genomics data, specifically Single Cell RNA Sequencing (scRNASeq). By leveraging p-adic-valuation, p-ClustVal integrates with and augments widely used clustering algorithms and dimension reduction techniques, amplifying their effectiveness in discovering meaningful structure from data. The transformation uses a data-centric heuristic to determine optimal parameters, without relying on ground truth labels, making it more user-friendly. p-ClustVal reduces overlap between clusters by employing alternate metric spaces inspired by p-adic-valuation, a significant shift from conventional methods. Our comprehensive evaluation spanning 30 experiments and over 1400 observations, shows that p-ClustVal improves performance in 91% of cases, and boosts the performance of classical and state of the art (SOTA) methods. This work contributes to data analytics and genomics by introducing a unique data transformation approach, enhancing downstream clustering algorithms, and providing empirical evidence of p-ClustVals efficacy. The study concludes with insights into the limitations of p-ClustVal and future research directions.

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Sharma, P., Mishra, S., Kurban, H., Dalkilic, M.. 2024-10-22. p-ClustVal: A Novel p-adic Approach for Enhanced Clustering of High-Dimensional scRNASeq Data. https://doi.org/10.1101/2024.10.18.619153

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