ArticleNature communications2023
Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.Nature communications · 2026Article
- A mutational signature for brain metastasis tropism in lung adenocarcinoma.Discover oncology · 2026Article
- Unveiling tissue heterogeneity through genomic interaction-encoded image representation of RNA-sequencing data.American journal of human genetics · 2025Article
- Article
- Exploring structured molecular landscape from single-cell multi-omics data by an explainable multimodal model.iScience · 2024Article
- Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions.Scientific reports · 2024Article
- Bladder Cancer and Artificial Intelligence: Emerging Applications.The Urologic clinics of North America · 2024Review
- Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.Briefings in bioinformatics · 2024Article
- Revealing hidden patterns in deep neural network feature space continuum via manifold learning.Nature communications · 2023Article
- scapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data.PLoS biology · 2023Article
- Leveraging cell-cell similarity for high-performance spatial and temporal cellular mappings from gene expression data.Patterns (New York, N.Y.) · 2023Article
- Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data.Nature communications · 2023Article
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2 authors.
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Abstract
Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques.
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