Investigate the Role of Transcriptional Regulation in Breast Cancer Heterogeneity Using Single Cell RNA Sequencing (scRNA-seq) Data
Breast cancer heterogeneity is not due to chromosome abnormalities or mutations, which affect the primary DNA sequence, but is mostly driven by epigenetic alterations leading to aberrant transcriptional regulation. It has thus been speculated that breast cancer subtypes might represent arrests at different stages of epithelial cell differentiation. In this context, the goal of Marjolaine David’s project is to study the regulation of key transcription factors that control cellular differentiation using scRNA-seq data. As the Drop-Seq technology was newly introduced in the lab, her goal is also to provide some guidelines in the experimental design and computational analysis for further scRNA-seq experiments in the lab. She mainly focuses on the sequencing depth needed to detect the biological signal of interest, and on the performance of machine learning methods compared to more standard ones for the computational analysis.
Keywords: transcriptomics, bioinformatics, machine learning, computer sciences, breast cancer, scRNA-seq, Drop-Seq, transcription factors