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We introduce a dual-view graph neural network (GNN) framework called scNET that integrates scRNA-seq data with protein–protein interaction networks. This approach enhances the characterization of gene functions, pathways and gene–gene relationships and improves cell clustering and the identification of differentially activated biological pathways across conditions.
The authors present a computational framework that leverages mechanical force inference and spatial transcriptomics to enable analyses of the interplay between the transcriptomic and mechanical state.
scNET combines single-cell gene expression information with protein–protein interaction networks using a dual-view architecture based on graph neural networks to better characterize changes in cellular pathways and complexes across cellular conditions.
This Resource describes data, code and tools developed for the Human Reference Atlas and the Human BioMolecular Atlas Program for building and navigating a multiscale human atlas.
This analysis provides a collection of sequencing datasets generated from long-read and short-read RNA sequencing, serving as a valuable resource for transcriptome profiling.
Spotiphy (spot imager with pseudo-single-cell-resolution histology) is a computational toolkit that transforms sequencing-based spatially resolved transcriptomics data into whole-transcriptome images with single-cell resolution.
DREDge is a software tool for motion correction of high-density electrophysiology recordings. It can handle action potential or local field potential data and is demonstrated on a variety of acute or chronic recordings from humans, nonhuman primates and mice.
