Amelia R. Schroeder

Toward Computationally Complete Spatial Omics

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

Multiscale confidence quantification for virtual spatial transcriptomics with UTOPIA

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

Pixel2Gene enables histology-guided reconstruction and prediction of spatial gene expression

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

HistoSweep enables cellular-resolution tissue quality control for gigapixel images in digital pathology and spatial omics

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.

Designing smart spatial omics experiments with S2Omics

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

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

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

SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning

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

Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

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.