Visualizing Spatial Transcriptomics with U-CIE Color Encoding

YouTube Excerpt: https://cran.r-project.org/web/packages/ucie/index.html https://u-cie.jensenlab.org/ Spatial transcriptomics is a cutting-edge technique that enables the analysis of gene expression patterns within specific regions of tissue or organs. However, analyzing the large and complex datasets generated from spatial transcriptomics experiments remains a challenge. Here we propose U-CIE, a method for visualizing high-dimensional data by encoding it as colors using a combination of dimensionality reduction and the CIELAB color space. U-CIE allows genome-wide expression patterns within tissue or organ sections to be visualized and highlights the distribution of different cell types across the spatial transcriptomics data. U-CIE first uses UMAP to reduce high-dimensional gene expression data to three dimensions while preserving the spatial information. Next, the resulting three-dimensional representation is embedded within the CIELAB color space, generating a color encoding that captures much of the original structure of the data. U-CIE has been successfully applied to a mouse brain section dataset to highlight the distribution of different cell types across the spatial transcriptomics data and provide insights into the organization of these cells within brain regions. U-CIE has the potential to be a powerful tool for exploring spatial transcriptomics data and gaining new insights into cellular organization and function. 0:00 Introduction 2:32 CIE L * a * b * color space 3:51 Optimization algorithm 6:58 Web resource 7:58 R package 8:51 Application on genetic ancestries 10:43 Spatial Transcriptomics 12:48 U-CIE on spatial transcriptomics 14:34 Cell type composition 18:00 Reference color map of cell types 22:00 scverse 22:57 Acknowledgment

https://cran.r-project.org/web/packages/ucie/index.html https://u-cie.jensenlab.org/ Spatial transcriptomics is a cutting-edge technique that...

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