Singapore:
PI, NMRC CS-IRG, March 1st, 2026- Feb 28, 2029, SGD$1,500,000
PI, MOE Tier 1, March 1st, 2025 - Feb 29, 2028, SGD$240,000
PI, Block fund, SGD$$$
US:
PI, NIH/NIA, R03, USD$200,000
PI, Mount Sinai, Distinguished Scholar Award, USD$50,000
>10 collaborative grants
Spatially Resolved Transcriptomics (SRT)
QuadST (Identifying cell-cell interaction changed genes on single-cell SRT data)
RECCIPE (cell-cell interaction on multi-cell SRT data) -- No standalone package; implemented in Giotto Suite
sCCIgen (SRT data simulator for cell-cell interactions beyond using SRT data)
Integrative -Omic Data Analysis
MiXcan (cell-type-aware transcriptome-wide association analysis)
S-MiXCan (cell-type-aware omics-wide association analysis with GWAS summary statistics)
iProMix (cell-type specific association analysis for bulk profiling data)
iProFun (integrating multi-omics data to identify function consequences of DNA alterations)
ProTrack (Interactive web tool to visualize multi-omic data)
CPTACdream (Package of NCI-CPTAC DREAM Proteogenomics Challenge to predict protein levels from copy number and transcrpit levels and phosphorylation levels from protein levels.)
Quantile Regression
AI in omics data analysis
To be disclosed.
Spatial Transcriptomics
We are developing several tools for studying cell-cell interactions using single-cell and multi-cell spatial transcriptomics data. We have applied them to study neuron-microgial cell-cell interactions in brain tissues and tumor immune microenvironment in different cancer types.
Proteogenomics Data Analysis
We are active members of the Proteogenomic Data Analysis Center for The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC). We have developed many statistical methods for the integrative analysis of proteogenomic data, and applied them for the comprehensive characterization of proteogenomic profiles of multiple cancer types.
Cell-type Specific Association Analyses with Bulk Profiling Data
We have developed a few tools for performing cell-type specific association analysis using data profiled at bulk tissue levels, and have applied them to study COVID-19, breast cancer and Alzhimer's disease.
Transcriptomic and Network Analysis for Mammographic Density
We are analyzing one of the world's largest genomic data set for mammographic density from Kaiser Permanente, to identify genes, pathways and networks associated with mammographic density and breast cancer risk.
Genetic, Transcriptomic and Network Analysis for Prostate Cancer Survival
We have compiled valuable genomic and transcriptomic data sets that have been folllowed-up for decades to study genes, pathways and networks responsible for prostate cancer survival.