Computational & Statistical Systems Biology Laboratory
We develop computational and statistical solutions for the analysis of high-dimensional molecular data in various contexts of biomedical research. These tools are motivated by a wide range of research topics, including
Network inference and interpretable integration of multi-omics data in biological experiments and clinical studies
Development of novel informatics tools for large-scale untargeted and targeted mass spectrometry (MS) data processing in metabolomics
Effect size-driven analysis approaches for protein modifications, interactions, and other applications of proteomics
Using Atlas data sets as a prior information to make cell population-level inference from bulk -omics data
Clinical application of sequencing and mass spectrometry-based omics technologies
To see examples of our work, check out the CSSB LAB GitHub page!
15 March 2023: Check out our upgraded iOmicsPASS+ software for network-based multi-omic integration and classification
15 March 2023: We have implemented a number of changes in our untargeted metabolomics analysis software MetaboKit, including MS1 signal-based fingerprinting for peaks without fragmentation and annotation of metabolites through HMDB and LipidMaps databases.
15 March 2023: Collaborators at Claridge-Chang lab (Duke-NUS, Singapore) improved the popular data analysis and visualization tool DABEST with a number of additional functionalities. Check it out!
HC is an associate editor at Molecular Omics -- feel free to discuss submission of your manuscript. We are looking for reviews and research articles in single cell and spatial omics and novel multi-omics integration approaches.