A Single-Cell Tumor Immune Atlas for Precision Oncology

Preprint version of the Single-Cell Tumor Immune Atlas

This upload contains:

• TICAtlas.rds: an rds file containing a Seurat object with the whole Atlas (317111 cells, RNA and integrated assays, PCA and UMAP reductions)
• TICAtlas.h5ad: an h5ad file with the whole Atlas (317111 cells, RNA assay, PCA and UMAP)
• TICAtlas_RNA.rds: an rds file containing a Seurat object of the whole Atlas but only the RNA assay (317111 cells, UMAP embedding)
• TICAtlas_downsampled_1000.rds: an rds file containing a downsampled version of the Seurat object of the whole Atlas (24834 cells, RNA and integrated assay, PCA  and UMAP reductions)
• TICAtlas_downsampled_1000.h5ad: an rds file containing a downsampled version of the Seurat object of the whole Atlas (24834 cells, RNA assay, PCA  and UMAP reductions)
• TICAtlas_metadata.csv: a comma-separated text file with the metadata for each of the cells

For the h5ad files, the .X slot contains the normalized data, while the .X.raw slot contains the raw counts as they were in the original datasets.

All the files contain the following patient/sample metadata variables:

• patient: assigned patient identifiers
• gender: the patient's gender (male/female/unknown)
• source: dataset of origin
• subtype: cancer type (abbreviations as indicated in the preprint)
• cluster_kmeans_k6: patients clusters, NA if filtered out
• cell_type: annotated cell type for each of the cells

If you have any issues with the metadata you can use the TICAtlas_metadata.csv file.

For more information, read our preprint and check our GitHub.

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.