We are interested in developing probabilistic frameworks that can integrate mutation allele frequency from bulk sequencing and mutation profile from single-cell sequencing for better understanding the heterogeneous subpopulations in cancer tissue and inferring the temporal order in which the somatic mutations were acquired. See here for details.
We develop novel probabilistic tree inference algorithms that integrate different data modalities (e.g., mutation and gene-expression data) for elucidating the cell lineage of whole organism. See here for details.
Recent advances in single-cell sequencing technologies are generating multitude of datasets measuring single or multiple modalities (e.g., DNA sequence, gene expression, surface proteins, etc.) in large number of cells. We are interested in developing computational frameworks for disentangling the cellular diversity and development by elucidating the relationship between the cellular biomolecules from these large-scale single-cell datasets. See here for details.
Oral cancer is among the top three ranking cancers in India, posing a major health concern and financial burden. Despite its high incidence, the mutational and transcriptional landscape of oral cancer and its impact on patient survival, treatment and relapse remain poorly explored. Using integrated DNA and RNA sequencing data, we investigate the evolutionary dynamics of oral cancer to resolve the cancer cell populations, their mutational signatures and underlying mutational processes, association with gene expression and functional impact on therapeutic response.