Single-Cell Atlas of COVID-19 Lung Pathology
Reanalysis of single-nucleus RNA-seq data from Melms et al. (2021) examining lung tissue from 26 COVID-19 patients and healthy controls. Identifies myeloid expansion, epithelial depletion, and pro-fibrotic remodeling across 94,027 cells using scVI batch correction and Leiden clustering, with an interactive 8-tab Shiny app for exploring gene expression and cell-cell interactions.
DeepAMR: AI-Powered Antimicrobial Resistance Prediction
A deep learning platform that analyzes genomic sequences to predict antimicrobial resistance patterns, built for the Bangladesh healthcare system. Delivers AMR predictions in under 15 minutes with 95%+ accuracy, identifying resistance genes and mutations across 12+ organisms and 50+ antibiotics — reducing traditional culture-based testing from days to hours.
Fezf2-Mediated Cortical Development: Multi-Omics Analysis
Integrative analysis of bulk and single-cell RNA-Seq data to elucidate Fezf2’s role in cortical neurogenesis. Leveraging the GSE153164 dataset (Di Bella et al. 2021) to investigate temporal and cell-type-specific mechanisms by which Fezf2 mutations disrupt cortical development.
Single-Cell Meta-Analysis of Microglial Activation in Alzheimer’s Disease
Meta-analysis of single-cell RNA-Seq datasets to identify microglial states and activation patterns in Alzheimer’s disease. Integrating multiple cohorts to characterize disease-associated microglia subtypes and their transcriptomic signatures across disease stages.
Integrative Spatial-scRNA-seq Atlas of Immunotherapy Resistance Across Cancer Types
Comprehensive spatial transcriptomics and single-cell analysis to map tumor microenvironment and resistance mechanisms in immunotherapy. Investigating how cell-type interactions influence tumor development and immunotherapeutic response, including SPP1-expressing macrophage-mediated resistance through adenosine pathway activation.