Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.

ABSTRACT:

Motivation

Cells exhibit a wide array of morphological features, enabling computer vision methods to identify and track relevant parameters. Morphological analysis has long been implemented to identify specific cell types and cell responses. Here we asked whether morphological features might also be used to classify transcriptomic subpopulations within in vitro cancer cell lines. Identifying cell subpopulations furthers our understanding of morphology as a reflection of underlying cell phenotype and could enable a better understanding of how subsets of cells compete and cooperate in disease progression and treatment.

Results

We demonstrate that cell morphology can reflect underlying transcriptomic differences in vitro using convolutional neural networks. First,

SUBMITTER: Jost TA 

PROVIDER: S-EPMC11245002 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature