Data-Driven Soft Labeling Scales DNA Read Classification to Whole-Body Cell-Type Deconvolution
2026-07-06 • Machine Learning
Machine Learning
AI summaryⓘ
The authors address the problem of figuring out cell type proportions in mixed biological samples using DNA methylation data. Existing methods either lose detailed information by averaging or struggle with many cell types because individual DNA reads can match multiple cell types. They introduce a new approach, Syto, which uses 'soft labels' to represent probabilities rather than hard assignments for each DNA read, helping the method learn better. On tests with many human cell types, Syto outperformed previous methods and worked well even on new data. This approach could improve analysis of complex biological samples and is adaptable to other problems with ambiguous data-label relationships.
cell-type deconvolutionDNA methylationepigenetic marksread-level classificationsoft labelsconditional distributionheterogeneous samplesclassifier convergencemany-to-many mappingwhole-body atlas
Authors
Dmytro Rizdvanetskyi, Nathan Ross, Pavlo Lutsik
Abstract
Cell-type deconvolution, the task of estimating the proportions of constituent cell types in a heterogeneous biological sample, is a core problem in computational biology. Methods that rely on epigenetic marks such as DNA methylation typically operate on aggregated methylation estimates, discarding the pattern-level information carried by individual DNA reads. Existing read-level approaches that exploit this information are scarce, and all remain restricted to few-class settings; scaling them further is an open problem because, at scale, non-discriminative reads dominate and hard labels conflict with the many-to-many mapping between methylation patterns and cell types, preventing classifier convergence. To overcome this, we propose data-driven soft labels that estimate the conditional cell-type distribution for each read, and integrate this scheme into Syto, a new modular framework for read-level classification-based deconvolution. On a whole-body atlas of 39 human cell types, Syto reduces MSE by 2.56$\times$ over SoTA, with gains transferring to an out-of-distribution dataset spanning 16 tissues. Syto lays the foundation for modeling increasingly large cell-type panels, with improved applications in biology and healthcare. The proposed soft-labeling scheme is further translatable to any setting with a many-to-many signal-to-label mapping.