Pratibha Panwar

ClustSIGNAL improves spatial transcriptomics clustering by adapting smoothing based on neighbourhood complexity, enabling scalable analysis of large datasets.

This paper introduces wSIR, a scalable supervised dimensionality reduction method for spatial transcriptomics data, which retains predictive power of spatial coordinates and is demonstrated on breast cancer and embryonic development datasets.

The paper introduces CCIDeconv, a method to deconvolve whole-cell cell-cell interactions into subcellular compartments using scRNA-seq data and spatial transcriptomics datasets.

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

The paper introduces spatialMNN, an algorithm that integrates spatial transcriptomics data to identify spatial domains by incorporating 2D spatial information into mutual nearest neighbors analysis.