DOI: 10.5281/zenodo.15350417
title: Suco - TIL

description: Suco (Single cell universal classification omnibus) is a large standardized reference dataset for cell type classification in single cell RNA sequencing data. Suco seeks to tackle the lack of standardized datasets for classification tasks in the single cell genomics field. In other fields of artificial intelligence, like computer vision, standardized datasets such as MNIST or ImageNet have transformed the development of powerful new machine learning methods.

Suco features manual uniform standardized hierarchical cell type annotations in independently analyzed datasets. This collection of independent datasets ensures that machine learning classifiers can be tested using statistically independent data & labels. 

Here, we present the tumor infiltrating leukocyte dataset within Suco which includes > 1000 independent manual cell type cluster labels from 8 datasets totalling 290 individuals and >0.5 millions cells. 

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Structure of the dataset:

.zip compressed folder containing datasets from individual studies which have been reprocessed, clustered and annotated independently by two different expert raters (human immunology)



Filenames: DATASET_ID.h5ad



The dataset ID has the following format (each line followed by ‘-X-‘ separator



Tissue/cell type: here PBMC

Disease context

Publication year

First author (optional: followed by _BATCHNAME)

DOI (/ in DOI is replace by _ for compatibility with file systems



the .h5ad files have the following structure



load using the scanpy python package adata = sc.read(FILE_PATH)

cell barcode (adata.obs_names)

Study-ID + '-X-' + internal barcode

adata.obs[‘sample_id’]



the sample ID should be the patient ID + '-X-' separator + internal sample ID



e.g. TIL-X-BRCA-X-scRNAseq-X-Bassez-X-2021-X-10.1038_s41591-021-01323-8-X-2-X-Pre



with TIL-X-BRCA-X-scRNAseq-X-Bassez-X-2021-X-10.1038_s41591-021-01323-8-X-2 being the patient ID

-X-  the separator







adata.obs['patient_id']



dataset id followed by an '-X-' separator and the internal patient id

e.g. TIL-X-BRCA-X-scRNAseq-X-Bassez-X-2021-X-10.1038_s41591-021-01323-8-X-35

-X- is the separator

35 is the internal patient



adata.obs[cluster_final']



final clustering used for the cell type annotation

granularity can differ between subsets --> e.g. clustering from myeloid cells can originate from myeloid subset, clustering from TNK from TNK subset and epithelial from all leukocyte subset

should be preceeded by the prefix used for subtyping e.g. 'TNK' for TNK cells followed by a '_' seperator and the cluster number:·  

e.g.



cluster 0 in TNK would be 'TNK_0'

cluster 1 in M would be 'M_1'





adata.obs[cluster_all']



containing coarse clustering format 'all_CLUSTERNUMBER'     



adata.obs[‘annotation’]



Most granular annotation based on adata.obs[‘cluster_final’]



adata.obs[‘annotation_all’]



 annotation based on adata.obs[‘cluster_all’]








 

______________________________________________________________________________________________________The dataset contains reprocessed and reannotated data from the following studies: 

1.             Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nature Medicine (2021). https://doi.org:10.1038/s41591-021-01323-8

2.             Zhang, Y. et al. Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer. Cancer Cell 39, 1578-1593.e1578 (2021). https://doi.org:10.1016/j.ccell.2021.09.010

3.             Che, L.-H. et al. A single-cell atlas of liver metastases of colorectal cancer reveals reprogramming of the tumor microenvironment in response to preoperative chemotherapy. Cell Discovery 7, 80 (2021). https://doi.org:10.1038/s41421-021-00312-y

4.             Moorman, A. et al. Progressive plasticity during colorectal cancer metastasis. Nature 637, 947-954 (2025). https://doi.org:10.1038/s41586-024-08150-0

5.             Wang, F. et al. Single-cell and spatial transcriptome analysis reveals the cellular heterogeneity of liver metastatic colorectal cancer. Science Advances 9, eadf5464 (2023). https://doi.org:doi:10.1126/sciadv.adf5464

6.             Peng, J. et al. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res 29, 725-738 (2019). https://doi.org:10.1038/s41422-019-0195-y

7.             Steele, N. G. et al. Multimodal mapping of the tumor and peripheral blood immune landscape in human pancreatic cancer. Nature Cancer 1, 1097-1112 (2020). https://doi.org:10.1038/s43018-020-00121-4

8.             Werba, G. et al. Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment. Nature Communications 14, 797 (2023). https://doi.org:10.1038/s41467-023-36296-4