BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.

ABSTRACT: Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of

SUBMITTER: Fu X 

PROVIDER: S-EPMC10787788 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature