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<p>Cellular deconvolution estimates cell-type fractions from bulk transcriptomic data, but current methods often overlook cell type-specific expression varying across samples, discrepancies between bulk and single-cell data, or lack guidance on reference data selection and integration. Therefore, we present BLEND, a hierarchical Bayesian method that leverages multiple single-cell reference datasets to perform cellular deconvolution. BLEND estimates cellular fractions accurately by learning the most suitable reference for each bulk sample, accounting for the aforementioned issues. BLEND outperforms state-of-the-art methods in comprehensive benchmarking studies using human brain cortex data and provides reliable insights into Alzheimer’s disease progression.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月2日

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  • Penghui Huang,
  • Manqi Cai,
  • Chris McKennan,
  • Jiebiao Wang

摘要

Cellular deconvolution estimates cell-type fractions from bulk transcriptomic data, but current methods often overlook cell type-specific expression varying across samples, discrepancies between bulk and single-cell data, or lack guidance on reference data selection and integration. Therefore, we present BLEND, a hierarchical Bayesian method that leverages multiple single-cell reference datasets to perform cellular deconvolution. BLEND estimates cellular fractions accurately by learning the most suitable reference for each bulk sample, accounting for the aforementioned issues. BLEND outperforms state-of-the-art methods in comprehensive benchmarking studies using human brain cortex data and provides reliable insights into Alzheimer’s disease progression.