Single cells make ‘big data’, provoking substantial analytical challenges to decipher underlying biological and clinical insights. Despite striking advances, there is an unmet need for a unified single-cell sequencing data pre-processing and integration approach that is extremely fast and parallelisable, therefore can be efficiently applied to datasets of millions of cells across different modalities, and this project is aimed to tackle this challenge. The proposed method innovatively analyses holistic profiles of sequencing data to account for noise and lack of coverage. It opens a new avenue of cross-disciplinary research in next-generation sequencing data analyses bringing together expertise across diverse disciplines of genomics, oncology, bioinformatics, electrical engineering and artificial intelligence to tackle a major challenge in single cell data analytics with far-reaching applications in precision medicine and personalised therapy.
Fast and scalable denoising, imputation and integration of single cell multi-omics with signal processing based analyses
Chief Investigator:
School of Biotechnology and Biomolecular Sciences, Room 2110 , Level 2 East, Bioscience South E26
Room 412-E, Building K-17 CSE Building, UNSW Kensington Sydney
Samuels Building, Rm 525, UNSW, Sydney NSW 2052, AUSTRALIA