Create README.md

README.md

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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}
+}