Kyle Coleman

Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP

REMAP reconstructs spatial tissue architecture from scRNA-seq using deep learning and ST references, revealing microenvironmental features and cancer subtypes.

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data

DeepCombat removes batch effects from neuroimaging data using a deep learning approach, outperforming existing methods in preserving biological variation.

Spatial Single-cell Analysis Decodes Cortical Layer and Area Specification

The paper uses spatial single-cell analysis to uncover molecular and structural development of human fetal cortex, revealing early layer and area specification.

Deciphering tumor ecosystems at super-resolution from spatial transcriptomics with TESLA

TESLA annotates tumor microenvironment cells and detects spatial transcriptome differences using spatial transcriptomics data.

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.

Spatially resolved human kidney multi-omics single cell atlas highlights the key role of the fibrotic microenvironment in kidney disease progression

Multi-omic and spatial profiling of 81 human kidney samples resolves four tissue microenvironments; the fibrotic one's gene signature is reported to beat histopathology at predicting outcome.

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.