Datasets for "Deep Learning-Based 3D Spatial Transcriptomics with X-Pression"

Deep Learning-Based 3D Spatial Transcriptomics with X-Pression This repository contains spatial transcriptomics and single-cell RNA sequencing data, as well as micro-CT tomograms, used in our manuscript: Demeter Túrós, Lollija Gladiseva, Marius Botos, Chang He, G. Tuba Barut, Inês Berenguer Veiga, Nadine Ebert, Anne Bonnin, Astrid Chanfon, Llorenç Grau-Roma, Alberto Valdeolivas, Sven Rottenberg, Volker Thiel, and Etori Aguiar Moreira."Deep Learning-Based 3D Spatial Transcriptomics with X-Pression." GitHub: https://github.com/rockdeme/x-pression Archive Contents micro_ct: Micro-CT tomograms with predicted gene expression programs L2210914: OTS206-immunised lung, 5 dpc (multichannel TIFF) L2210926: mRNA vaccine-immunised lung, 5 dpc (multichannel TIFF and files required for model training) unet_segmentation: Binary masks and TIFF files for sections used for training models: CNN and U-Net weights single_cell: Combined scRNA-seq datasets as AnnData objects for the infection and challenge cohorts cell_ranger_output: Cell Ranger output folders spatial_transcriptomics: Spatial transcriptomics samples h5ads: Preprocessed and filtered AnnData objects containing Chrysalis and cell2location results hires_images: High-resolution histology images (8K resolution) space_ranger_output: Space Ranger output folders