Sonali Andani

DeepSpot predicts spatial transcriptomics from H&E images, improving gene correlations across cancer datasets and generating virtual spatial transcriptomics samples.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.