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Joint Analysis of Single-Cell and Bulk RNA Data via Matrix Factorization

Focus Cell Types

Project Goal

To improve the analysis of single-cell RNA-sequencing data by using matrix factorization methods to jointly analyze RNA-sequencing data along with bulk RNA data.

Results & Resources

This project resulted in multiple publications supporting the biomedical application of single-cell RNA-sequencing, including a publication quantifying the cell states of the mammary gland throughout developmental states using single-cell RNA-sequencing data. With this data, the team has defined and optimized a new method for single-cell dataset alignment. They also reported a new method for supervised principal component analysis (PCA) that simultaneously optimizes PCA and supervised learning objects, leading to performance improvements when compared to previously reported methods.


Lead Investigator

Clayton Scott
Clayton Scott