An interpretable and adaptive autoencoder for efficient tissue deconvolution.

ABSTRACT: Deconvolution models are a powerful tool for extracting cell-type-specific information from bulk gene expression profiles. Current methods leverage advanced machine learning models and high-resolution sequencing, like single-cell RNA-sequencing, showing promising results across diverse tissues and conditions. However, they still present important limitations: First, many depend on selecting a robust reference, which can strongly affect the deconvolution. Second, pseudobulk data used for training and real bulk RNA-seq samples often exhibit strong distribution shifts, which are currently unaccounted for. Finally, most deconvolution approaches behave as black boxes, which can compromise the reliability of the results. Here, we present Sweetwater, an adaptive and interpretable autoencoder that

SUBMITTER: de la Fuente J 

PROVIDER: S-EPMC12400930 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature