Production-grade multimodal deep learning for biological imaging and spatial transcriptomics, with reproducible data ingestion, image quality control, segmentation, phenotype classification, uncertainty estimation, and transcriptomic interpretation.
Python toolkit for image-based spatial transcriptomics (CosMx, MERFISH, Xenium). Unified data object for expression, polygons, and FOV images, plus QC, clustering, point- and polygon-based spatial statistics, and polygon-native cell-cell communication using real membrane contact geometry.
Workflows and code for spatial and meta-transcriptomic analysis of seronegative hepatitis, autoimmune hepatitis and donor liver samples.
End-to-end R/Seurat pipeline for CosMx SMI spatial transcriptomics: memory-safe object handling, SCTransform, Louvain clustering, InSituType cell typing, and spatialTIME nearest-neighbour G statistics.
single-cell RNA-seq, spatial transcriptomics, and spatial proteomics analysis skills
Analyze biological imaging and spatial transcriptomics data using robust deep learning pipelines and verified dataset auditing.
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