Institute for Machine Learning

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.