Malvika Kharbanda

bgnorm improves signal separation and marker positivity classification in multiplex spatial proteomics by addressing technical variation through background correction, normalization, and quality control.

hoodscanR identifies and analyzes cell neighborhoods in spatial transcriptomics data, revealing transcriptional differences in tumor cells across neighborhoods.

This study shows that library size in spatial transcriptomics data confounds biological signals and recommends against normalizing it unless necessary.

vissE identifies and visualizes higher-order molecular phenotypes from functional enrichment analysis by summarizing redundancies into biological themes.

scDECAF identifies cell types, states, and programs in single-cell data by learning gene set representations, showing effectiveness in disease and immune response contexts.