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---
dataset_name: riken2018_pbmc_supercentenarians
annotations_creators:
- expert-generated
language:
- en
multilinguality: "no"
pretty_name: PBMCs from Supercentenarians and Controls (Hashimoto et al. 2018)
task_categories:
- cell-type-classification
size_categories:
- 100K<n<1M
license: cc-by-4.0
dataset_type: biomedical
---
# Dataset Card for `riken2018_processed`
## Dataset Summary
This dataset is derived from single-cell RNA-seq of peripheral blood mononuclear cells (PBMCs) from supercentenarians (ages 110+) and younger controls, processed from the study:
> *Single-cell transcriptomics reveals expansion of cytotoxic CD4 T cells in supercentenarians*
> — Hashimoto et al., *PNAS* (2019)
> [DOI:10.1073/pnas.1907883116](https://doi.org/10.1073/pnas.1907883116)
The final object includes cells from three experimental batches (firstrun, SC1, SC2) and supports aging-focused immunology research.
## Transformation Summary
Raw data was downloaded from [http://gerg.gsc.riken.jp/SC2018/](http://gerg.gsc.riken.jp/SC2018/) and processed with the following steps:
1. **Download and Extraction**:
- Retrieved UMI matrix (`01.UMI.txt.gz`) and cell barcodes (`03.Cell.Barcodes.txt.gz`).
- Extracted expression matrices from `SC1.tar` and `SC2.tar` using `scanpy.read_10x_mtx`.
2. **UMI Matrix Assembly**:
- Constructed an `AnnData` object from the UMI count matrix and matched barcodes.
- Gene names were mapped to Ensembl IDs using SC1 reference.
3. **Batch Merging**:
- Merged `firstrun`, `SC1`, and `SC2` into one `AnnData` object using `scanpy.concat`, with batch labels preserved.
4. **Metadata Curation**:
- Filled in missing columns for sample origin (`SC1`, `SC2`, etc.) based on batch labels.
- Added standardized columns: `age_int`, `assay_simple`, `cell_type`, and `centenarian_status`.
5. **Output**:
- Saved final object to `raw/riken2018/processed/riken2018_processed.h5ad`.
## Supported Tasks and Benchmarks
- **Immune Aging Analysis**: Especially suited for studying the immune profile of extreme aging.
- **CD4 T Cell Phenotyping**: Detects rare CD4 cytotoxic T cell expansions.
- **Batch Integration**: Includes multiple experimental runs merged with consistent annotations.
## Languages
Textual metadata and annotations are in English.
## Dataset Structure
### Data Instances
Each instance represents a single PBMC with:
- Raw UMI expression data
- Batch origin (firstrun, SC1, SC2)
- Age group metadata (`centenarian_status`, `age_int`)
- Cell type label (`PBMC`)
### Data Splits
- No formal train/test split provided. Users may stratify by `centenarian_status`.
## Dataset Creation
### Curation Rationale
The dataset was assembled to study cellular aging phenotypes by comparing PBMC populations between centenarians and controls, with a focus on adaptive immunity.
### Source Data
- 7 supercentenarians and 5 controls
- Collected and sequenced using 10x Genomics Chromium 3' technology
- Published by RIKEN Center for Integrative Medical Sciences
### Preprocessing Details
- Raw count matrix: `01.UMI.txt.gz`
- Barcode matching and gene name alignment using 10x `SC1` reference
- Minor cleanup of missing metadata and consistent column naming
- Concatenation with `scanpy` after aligning gene and barcode identifiers
## Licensing Information
This dataset is distributed under the **Creative Commons BY 4.0** license. Please cite the original paper when using this dataset in publications.
## Citation
```bibtex
@article{hashimoto2019supercentenarians,
title={Single-cell transcriptomics reveals expansion of cytotoxic CD4 T cells in supercentenarians},
author={Hashimoto, Kosuke and Kouno, Tsukasa and Ikawa, Tomokatsu and et al.},
journal={Proceedings of the National Academy of Sciences},
volume={116},
number={48},
pages={24242--24251},
year={2019},
publisher={National Academy of Sciences}
}
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