Single cell RNAseq dataset of MDA-MB-231 xenograft and matched metastasis
Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states within a phenotypic continuum in single-cell data. Unlike traditional archetypal analysis, AAnet learns archetypes in simplex-shaped neural network latent space. Using pre-clinical models and clinical breast cancers, AAnet resolves distinct cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Primary tumor archetypes are recapitulated in matched liver, lung and lymph node metastases. This dataset comprises of the 10X genomics based single cell RNAseq on MDA-MB-231 xenografts and matched metastasis used to perform archetypal analysis to understand tumour hetegeneity as a phenotypic continuum. Overall design: In an MDA-MB-231 mouse model, single-cell RNA-sequencing and spatial RNA-sequencing data were collected for primary tumors, and single-cell RNA-sequencing was collected for lymph node, liver, and lung metastases.