Create README.md

README.md

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+---
+dataset_name: ainciburu2023_hematopoiesis_aging_mds
+annotations_creators:
+  - expert-generated
+language:
+  - en
+multilinguality: "no"
+pretty_name: Single-cell Profiling of Human Hematopoiesis Across Aging and Myeloid Malignancies (Ainciburu et al. 2023)
+task_categories:
+  - cell-type-classification
+  - trajectory-inference
+  - regulatory-network-analysis
+size_categories:
+  - 100K<n<1M
+license: cc-by-4.0
+dataset_type: biomedical
+---
+
+# Dataset Card for `ainciburu_processed`
+
+## Dataset Summary
+
+This dataset comprises ~115,000 CD34+ hematopoietic stem and progenitor cells (HSPCs) profiled via 10x Genomics single-cell RNA-seq from healthy young, elderly, and myelodysplastic syndrome (MDS) patients. The data originates from:
+
+> *Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single-cell resolution*  
+> — Ainciburu et al., *eLife* (2023)  
+> [PMCID: PMC9904760](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9904760/)  
+> [DOI: 10.7554/eLife.79363](https://doi.org/10.7554/eLife.79363)
+
+## Transformation Summary
+
+The raw files were processed using the following pipeline:
+
+1. **Data Acquisition**:
+   - Extracted from GEO accession `GSE180298`, including raw matrix `.h5` files and associated metadata (`*_metadata.txt.gz`).
+
+2. **Data Parsing and Merging**:
+   - Read individual 10x `.h5` matrices per sample.
+   - Assigned unique cell barcodes including the sample identifier.
+   - Merged all into a single `AnnData` object with batch annotations.
+
+3. **Metadata Alignment**:
+   - Loaded metadata for young, elderly, and MDS samples.
+   - Used barcode-sample combinations to merge metadata with cell observations.
+   - Mapped `cell_type`, `patient_id`, and manually defined `patient_age`.
+
+4. **Final Output**:
+   - Saved unified dataset as `processed/ainciburu_processed.h5ad`.
+
+## Supported Tasks and Benchmarks
+
+- **Aging and Disease Comparison**: Track HSPC shifts from youth to old age and MDS.
+- **Trajectory Inference**: Includes pseudotime, lineage tracing via STREAM and Palantir.
+- **GRN Analysis**: SCENIC-based regulon detection per age/disease group.
+- **Subtype Classification**: Includes supervised label transfer from healthy to diseased donors.
+- **Differential Expression and GSEA**: Precomputed per lineage and age/disease state.
+
+## Languages
+
+All annotations and metadata are in English.
+
+## Dataset Structure
+
+### Data Instances
+
+Each row is a single cell with:
+
+- Raw gene expression (UMIs)
+- Cell type annotation (e.g., HSC, MEP, GMP)
+- Sample-level metadata (patient ID, condition, age)
+- Batch label (sample ID)
+
+### Data Splits
+
+No formal splits; users may stratify by:
+
+- `patient_id`
+- `patient_age` (continuous)
+- `condition`: `"young"`, `"elderly"`, `"mds"`
+
+## Dataset Creation
+
+### Curation Rationale
+
+Aimed to reveal regulatory, transcriptional, and population-level changes in hematopoiesis across the lifespan and in disease (MDS), using high-resolution single-cell RNA-seq and computational modeling.
+
+### Source Data
+
+- Bone marrow CD34+ cells from 5 young (19–23y), 3 elderly (61–74y), and 4 MDS patients (54–83y).
+- Sorted, sequenced using 10x Genomics Chromium.
+- Raw data from GEO accession `GSE180298`.
+
+### Preprocessing Details
+
+- Metadata aligned by barcode + sample combination
+- Cell type labels derived from supervised classification and manual annotation
+- Cell-level age assignments based on patient identity
+- Final object stored as a single `.h5ad` file for interoperability
+
+## Licensing Information
+
+This dataset is released under the **Creative Commons BY 4.0** license. Please cite the original publication when using this dataset in your work.
+
+## Citation
+
+```bibtex
+@article{ainciburu2023aging,
+  title={Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single-cell resolution},
+  author={Ainciburu, Marina and Ezponda, Teresa and Berastegui, Nerea and others},
+  journal={eLife},
+  volume={12},
+  pages={e79363},
+  year={2023},
+  publisher={eLife Sciences Publications Limited},
+  doi={10.7554/eLife.79363}
+}