# Cell-type deconvolution

Malignant cell states coexist within a tumor, and single-cell RNA sequencing can resolve them but is costly and hard to scale, while bulk RNA sequencing is cheap but mixes their signals together. CDState uses nonnegative matrix factorization with a sum-to-one constraint on proportions and a cosine-similarity penalty to infer malignant cell states from bulk RNA without reference data; on simulated bulk data from five cancer types it outperformed existing unsupervised methods overall in estimating state expression and proportions. Applied to 9,669 samples across 33 TCGA cancer types, it yielded 193 malignant cell states, with epithelial–mesenchymal transition, MYC targets, and oxidative phosphorylation the most recurrent programs.

Malignant cell states coexist within a tumor, and single-cell RNA sequencing can resolve them but is costly and hard to scale, while bulk RNA sequencing is cheap but mixes their signals together. CDState uses nonnegative matrix factorization with a sum-to-one constraint on proportions and a cosine-similarity penalty to infer malignant cell states from bulk RNA without reference data; on simulated bulk data from five cancer types it outperformed existing unsupervised methods overall in estimating state expression and proportions. Applied to 9,669 samples across 33 TCGA cancer types, it yielded 193 malignant cell states, with epithelial–mesenchymal transition, MYC targets, and oxidative phosphorylation the most recurrent programs.

The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.

The authors present DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation, estimates cell-type proportions from Zc via differentiable non-negative least-squares optimization, shows robust and competitive deconvolution performance across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets and multiple molecular modalities, and further shows that the learned Zc supports chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma.