Publications

BLEND: probabilistic cellular deconvolution with individualized single-cell reference integration

Published in Genome Biology, 2025

BLEND is a hierarchical Bayesian method that integrates multiple single-cell reference datasets to accurately deconvolve bulk omics data. By learning the best reference for each sample and modeling cell-type profiles within the convex hull of references, BLEND outperforms existing methods and provides new insights into disease progression. R package

EMixed: Probabilistic Multi-Omics Cellular Deconvolution of Bulk Omics Data

Published in Journal of Data Science, 2025

EMixed integrates gene expression and DNA methylation data to perform multi-omics deconvolution, improving accuracy in estimating cell-type fractions beyond single-modality approaches. R package

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Accurate estimation of rare cell-type fractions from tissue omics data via hierarchical deconvolution

Published in Annals of Applied Statistics, 2024

HiDecon leverages single-cell references and a hierarchical cell-type tree to deliver accurate, interpretable estimates of cell-type fractions, especially for highly correlated and rare cell types. R package

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