Create README.md

README.md

@@ -0,0 +1,53 @@
+# 🧬 sc-ImmuAging – Human PBMC Single-Cell Aging Clock Dataset
+
+This dataset includes curated feature selections from peripheral blood mononuclear cells (PBMCs) used to train aging clock models across five major immune cell types. It was derived from the **sc-ImmuAging** project to understand how aging affects the immune system at the single-cell level using machine learning models.
+
+---
+
+## 📦 Dataset Contents
+
+- `sc-ImmuAging.parquet` — Long-format data containing gene features per immune cell type:
+  - CD4 T cells
+  - CD8 T cells
+  - Monocytes
+  - NK cells
+  - B cells
+
+---
+
+## 💡 Use Cases
+
+- 🧠 **Aging Clock Development**: Train regression models to predict biological age per cell type.
+- 🔬 **Immune System Aging Analysis**: Study gene-level contributions to age-related changes across immune subsets.
+- 🧬 **Biomarker Discovery**: Identify robust transcriptomic signatures of aging in blood-derived cells.
+- 🛠 **Feature Selection Benchmarking**: Compare machine learning models and feature selection strategies in scRNA-seq datasets.
+- 📊 **Multi-Omics Integration**: Align transcriptomic aging features with epigenetic clocks or proteomics.
+
+---
+
+## 📖 Citation
+
+If you use this dataset, please cite the original study:
+
+**Dos Santos, Osorio et al. (2022).**  
+"A single-cell transcriptomic atlas of the human immune system reveals age-related changes in PBMC composition and function."  
+*Science Advances*, 8(45):eabq3784.  
+https://doi.org/10.1126/sciadv.abq3784
+
+---
+
+## 🧬 Dataset Description
+
+This dataset was extracted from the **sc-ImmuAging** study that built predictive aging clocks using PBMC single-cell RNA-seq profiles. The features represent selected gene markers associated with aging across five immune cell types. Each list was curated for machine learning model input.
+
+**Original Data Source**:  
+[GitHub Repository](https://github.com/dosorio/sc-ImmuAging)  
+[Published Paper](https://www.science.org/doi/10.1126/sciadv.abq3784)
+
+---
+
+## 🙏 Acknowledgments
+
+- Original authors of the sc-ImmuAging dataset and publication.
+- Curated and converted to `parquet` format by **Iris Lee** for ease of machine learning usage. ### 🧑‍💻 Team: MultiModalMillenials. Iris Lee (`@iris8090`)
+- Thanks to the open science community enabling downstream applications of single-cell data.