DeepPNCC: reconstructing pseudo-spatial cell-cell interaction landscapes from single-cell data to decipher breast cancer pathogenesis.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptomic analysis but loses spatial context due to tissue dissociation, thereby limiting insights into spatially regulated cell-cell interactions critical for tissue function and disease, including cancer. Consequently, developing approaches capable of extracting spatially relevant intercellular interaction information from scRNA-seq data is of substantial importance for elucidating mechanisms underlying disease initiation and progression.

Methods

Here, we present DeepPNCC, a novel deep learning framework that reconstructs pseudo-spatial cell-cell interaction networks (PSCCIs) from scRNA-seq data by leveraging latent spatial cues preserved in undissociated cell aggregates. Built on a variational

SUBMITTER: Li XH 

PROVIDER: S-EPMC12822177 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature