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Fig. 2 | BMC Genomics

Fig. 2

From: A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics

Fig. 2

Stemness model using single cell datasets. A UMAP plot of two independent single cell breast cancer datasets (i, ii). Gradient (blue to red) indicates low to high stemness. B Cell clustering of major cell types across each dataset. Colors denote the following cell types seen endothelial, cancer associated fibroblasts (CAFs) B cell, T cells, myeloid, normal epithelial, plasmablasts and cancer epithelial cells (from lightest to darkest blue) in dataset 1 (i) and dataset (ii). C Cell proportion of major cell type clusterings in each breast cancer histotype in both dataset 1 (i) and dataset 2 (ii). Colors align with cell type identification. D UMAP of iPS single cell dataset used for stemness model. Gradient (blue to red) indicates low to high stemness. E Stemness index distribution of single cell iPS (blue) compared to breast cancer cells in dataset 1(pink) and dataset 2(green)

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BMC Genomics

ISSN: 1471-2164

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