Dataset card for TCGA digital spatial transcriptomics data This repository contains results from the paper "DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H\&E Images". Authors : Kalin Nonchev, Sebastian Dawo, Karina Selina, Holger Moch, Sonali Andani, Tumor Profiler Consortium, Viktor Hendrik Koelzer, and Gunnar Rätsch The preprint is available here. What is TCGA digital spatial transcriptomics? We trained a model using available spatial transcriptomics data to predict gene expression for both fresh frozen (FF) and formalin fixed paraffin embedded (FFPE) slides from TCGA SKCM (skin melanoma) and TCGA KIRC (kidney cancer) datasets. More information can be found at: https://github.com/ratschlab/DeepSpot. Fig: DeepSpot predicts spatial transcriptomics from H&E images by leveraging recent foundation models in pathology and spatial multi level tissue context. 1: DeepSpot is trained to predict 5 000 genes, with hyperparameters optimized using cross validation. 2: DeepSpot can be used for de novo spatial transcriptomics prediction or for correcting existing spatial transcriptomics data. 3: Validation involves nested leave one out patient cross v…
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