Single Cell Mosaic Mutation Atlas of Human Organ

This project has three major research goals: 1) Develop robust computational methods to accurately detect rare mosaic mutations from scRNA-seq data. This includes a Bayesian method MosaiCopy for detection of copy number variations, a toolkit MosaiTect for discovery of allele-specific point mutations, and a model-based method MosaiMtTect to detect mutations in mtDNAs in individual cells. 2) Estimate the functional effects of mosaic mutations in rare cells by developing a machine-learning software scGPS (single cell Genotype-Phenotype Synergy). Additionally, this method will quantify the threshold of phenotype manifestation for each mosaic mutation. 3) Genotype HCA datasets to investigate the cell type and cell state specific mutations and their functions in affected cells of human organs. As a case study and validation of the results, the in-house heart cell atlas datasets will be generated from healthy hearts (collected during heart transplantation). The overall goal of this project is to develop novel computational methods to investigate the global pictures of mosaic mutations and functional effects on cells of human organs. Successful completion of this project will lead to new insights into the effects of genomic diversification on cell functions within human body.