Arutha Kulasinghe

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.

The paper introduces SM3DD, a 3D spatial transcriptomics analysis method, and finds differences in transcript distances between normal and SARS-CoV-2 patient lung tissues.

The paper integrates 12 spatial and single-cell technologies to analyze cellular interactions and signatures in three skin cancers, identifying key therapeutic targets and genetic associations.

PRISM is a Python package for interactive analysis of multiplexed tissue microarrays, enabling translational and clinical research using spatial proteomic data from multiple platforms.

This study uses spatial transcriptomics to identify placental dysfunction signatures in SARS-CoV-2 infected pregnancies, revealing altered gene expression related to hypoxia, oxidative stress, and preeclampsia-like pathways.

standR is a Bioconductor package that improves the analysis of Nanostring GeoMx DSP spatial transcriptomic data by addressing technical variability and enhancing statistical power.

This paper develops a spatial multi-omics platform to analyze immune responses in SARS-CoV-2-infected lung tissues, revealing macrophage infiltration and spatial heterogeneity in cytokine and interferon signaling.