Uthsav Chitra

This study evaluates spatial transcriptomics foundation models' ability to capture multi-scale spatial variation using SAFFRON, finding that one model better captures global gradients but not local patterns.

The paper introduces GLACIER, a method that infers spatially encoded transcriptional and signaling relationships using spatial Granger causality, applied to mouse cerebellum single-cell spatial transcriptomics data.

This paper introduces SLOPER, a method to learn spatial gradients from spatial transcriptomics data, improving the identification of tissue organization and spatially variable gene modules.

This study maps spatial metabolic gradients in murine liver and intestine using integrated experimental-computational methods, revealing distinct metabolic patterns and fructose-induced metabolic derangements.

GASTON-Mix identifies spatial domains and gradients in SRT data more accurately than existing methods, revealing biological insights in brain and tumor tissues.

This paper introduces GASTON, an interpretable deep learning method to map spatial gene expression topography, identifying gradients and domains in spatial transcriptomics data.

The paper identifies issues with the chimeric formula for quantifying epistasis, showing it can lead to incorrect biological interpretations in higher-order interactions.

DIALECT improves the identification of mutual exclusivity and co-occurrence between driver mutations by distinguishing them from passenger mutations in cancer genomics data.

Belayer models both continuous and discrete spatial variation in gene expression from spatially resolved transcriptomics data, accurately identifying tissue layers and spatially varying genes.

NetMix2 unifies network propagation and altered subnetwork approaches to identify disease-related subnetworks, outperforming existing methods in simulated and real data.

NetMix improves subnetwork identification by reducing statistical bias in altered subnetwork estimation, outperforming existing methods on simulated and real data.