Dharmesh Dinesh Bhuva

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.

cellNexus enhances Human Cell Atlas data by adding quality control, metadata, normalization, and analysis layers to enable multi-study analyses and large-scale biological discovery.

The paper introduces smartid, a method for accurately detecting markers in rare cell populations using a modified TF-IDF approach with Gaussian Mixture Model, improving cell type annotation in single-cell RNA-seq data.

mastR identifies markers from bulk RNA-seq data using a permutation test and signal-to-noise ratio approach, outperforming existing methods in benchmark tests.

SpaNorm improves spatial transcriptomics data normalisation by separating spatial library size effects from biological signals, enhancing domain and spatially variable gene identification.

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.

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

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

The paper uses spatial transcriptomics to analyze how CDK4/6 inhibition affects tumor response in SHH medulloblastoma by mapping cellular states and spatial organization.

The paper introduces stingscore, a method that uses stably-expressed genes to score transcriptomic signatures from individual samples, improving normalization and enabling clinical applications.

The paper introduces a single-sample scoring method for gene set analysis that is stable across varying sample compositions and demonstrates improved computational efficiency compared to existing methods.