Amelia R. Schroeder

COSIE is a computational framework that integrates multiple spatial omics modalities to generate high-resolution molecular landscapes, outperforming existing methods across diverse datasets.

UTOPIA quantifies confidence in virtual spatial transcriptomics predictions across scales, improving reliability and preventing false conclusions.

Pixel2Gene improves spatial gene expression reconstruction and prediction by integrating histology images with spatial transcriptomics data.

HistoSweep improves tissue quality control in gigapixel images by removing artifacts and preserving biologically relevant structures, enhancing analysis accuracy in digital pathology and spatial omics.

S2Omics automatically selects regions of interest from histology images to maximize molecular information content in spatial omics experiments, improving reproducibility and downstream biological discovery.

iSCALE predicts super-resolution gene expression and annotates tissue architecture in large tissues, revealing features missed by conventional spatial transcriptomics.

SpaDecon improves cell-type deconvolution in spatial transcriptomics using semi-supervised learning, outperforming existing methods on real and pseudo datasets.

This paper introduces SpaGCN, a graph convolutional network that identifies spatial domains and spatially variable genes by integrating gene expression, spatial location, and histology in spatial transcriptomics data.