A Single-Cell Tumor Immune Atlas for Precision Oncology

<strong>Publication version of the Single-Cell Tumor Immune Atlas</strong> This upload contains: <strong>TICAtlas.rds:</strong> an rds file containing a Seurat object with the whole Atlas <strong>TICAtlas.h5ad:</strong> an h5ad file with the whole Atlas <strong>TICAtlas_downsampled.rds:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas <strong>TICAtlas_downsampled.h5ad:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas <strong>TICAtlas_metadata.csv: </strong>a comma-separated text file with the metadata for each of the cells All the files contain the following patient/sample metadata variables: patient: assigned patient identifiers nCountRNA and nFeatureRNA: number of UMIs and genes per cell percent.mt: percentage of mitochondrial genes gender: the patient's gender (male/female/unknown) source: dataset of origin subtype: cancer type (abbreviations as indicated in the preprint) kmeans_cluster: patients clusters, NA if filtered out before clustering lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types) <pre> </pre> If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the <strong>TICAtlas_metadata.csv </strong>file. For more information, read our paper, check our GitHub and our ShinyApp. h5ad files can be read with Python using Scanpy, rds files can be read in R using Seurat. For format conversion between AnnData and Seurat we recommend SeuratDisk. For other single-cell data formats you can use sceasy.