Identifying molecular subtypes of breast cancer using single cell RNA-seq data integration and random forest classification
by Peter Jerome Ishmael V. Paulino; Muhammad Sufyan
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 20, No. 5, 2024

Abstract: Single-cell RNA sequencing (scRNA-seq) has been invaluable in advancing our understanding of various cancers, including breast cancer. The extensive analysis of scRNA-seq data from multiple independent breast cancer studies helped build an integrated single-cell gene expression atlas encompassing over 60,000 cells. Unsupervised clustering and classification algorithms including t-SNE, UMAP, and random forest were applied to identify molecular subtypes and classify new tumour samples. Integrated analysis identified six major breast cancer subtypes consistent with known luminal, HER2-enriched, and basal-like classifications. Random forest classification using a panel of discriminative genes achieved over 90% accuracy in classifying held-out tumour samples into known subtypes. Further substructure within subtypes revealed novel candidate cell states. The study also demonstrated the feasibility and advantages of integrating multiple scRNA-seq datasets to generate a comprehensive breast cancer atlas. The results of this study provide insights into breast cancer biology with potential applications in precision oncology.

Online publication date: Tue, 01-Oct-2024

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