Xi Jiang

This paper introduces iMOSCATO, a Bayesian model that integrates single-cell RNA-seq and spatial transcriptomics data to decompose cell-type compositions and detect spatial domains.

The paper introduces a Bayesian clustering method with feature selection for spatially resolved transcriptomics data, improving accuracy through a zero-inflated negative binomial model and geospatial integration.

The paper introduces a Bayesian model to detect spatially variable genes in imaging-based spatial transcriptomics data, showing high accuracy in identifying genes with novel spatial patterns.

This paper introduces BayesDeep, a Bayesian model that reconstructs spatial transcriptomics data at single-cell resolution using histology images and SRT datasets.

ScopeViewer provides a browser-based tool for visualizing spatial transcriptomics data with features for interactive exploration, dual viewing, optimized bandwidth, and data security.

The paper introduces iIMPACT, a method that integrates histology images and molecular data to improve clustering and domain detection in spatial transcriptomics.

STAr provides an integrated platform with curated datasets and methods to evaluate and develop spatial transcriptomics analysis tools.