Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expres...

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Veröffentlicht in:	Nature biotechnology 2024-09, Vol.42 (9), p.1372-1377
Hauptverfasser:	Zhang, Daiwei, Schroeder, Amelia, Yan, Hanying, Yang, Haochen, Hu, Jian, Lee, Michelle Y. Y., Cho, Kyung S., Susztak, Katalin, Xu, George X., Feldman, Michael D., Lee, Edward B., Furth, Emma E., Wang, Linghua, Li, Mingyao
Format:	Artikel
Sprache:	eng
Schlagworte:	631/114/1305 631/1647/245/2160 631/61/212/2019 Accuracy Agriculture Algorithms Animals Annotations Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Breast cancer Brief Communication Communication Datasets Gene expression Gene Expression Profiling - methods Gene mapping Genomics Histology Humans Image Processing, Computer-Assisted - methods Image resolution Life Sciences Medicine Neural networks Performance evaluation Research methodology Single-Cell Analysis - methods Transcriptome - genetics Transcriptomics
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