A Single-Cell Tumor Immune Atlas for Precision Oncology

Publication version of the Single-Cell Tumor Immune Atlas

This upload contains:

• TICAtlas.rds: an rds file containing a Seurat object with the whole Atlas
• TICAtlas.h5ad: an h5ad file with the whole Atlas
• TICAtlas_downsampled.rds: an rds file containing a downsampled version of the Seurat object of the whole Atlas
• TICAtlas_downsampled.h5ad: an rds file containing a downsampled version of the Seurat object of the whole Atlas
• TICAtlas_metadata.csv: 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)

If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the TICAtlas_metadata.csv 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.