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43 results for “graph learning”
SpaMask: Dual Masking Graph Autoencoder with Contrastive Learning for Spatial Transcriptomics
<p>Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled the analysis of gene expression while preserving the spatial context within tissues. Spatial domain characterization is a critical first step in SRT data analysis, providing the foundation for subsequent analyses and insights into biological implications. Graph neural networks (GNNs) have emerged as a common tool for addressing this challenge due to the structural nature of SRT data. However, current graph-based deep learning approaches often overlook the instability caused by the high sparsity of SRT data. <strong>Masking mechanisms</strong>, as an effective self-supervised learning strategy, can enhance the robustness of these models. To this end, we propose <strong>SpaMask, dual masking graph autoencoder with contrastive learning for SRT analysis</strong>. Unlike previous GNNs, SpaMask masks a portion of spot nodes and spot-to-spot edges to enhance its performance and robustness. SpaMask combines <strong>Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL)</strong> modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. We conducted a comprehensive evaluation of SpaMask on <strong>eight datasets from five different platforms</strong>. Compared to existing methods, SpaMask achieves superior clustering accuracy and effective batch correction.</p>
Graphed data in the manuscript "Visuo-frontal interactions during social learning in freely moving macaques" by Franch et al., Nature 2024
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SMMGCL: A novel multi-scale graph contrastive learning framework for integrating spatial multi-omics data
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.