--- dataset_info: features: - name: donor_id dtype: string - name: embedding sequence: float32 - name: disease_label dtype: string - name: dataset_id dtype: string splits: - name: train num_bytes: 6025971 num_examples: 2804 download_size: 7646553 dataset_size: 6025971 configs: - config_name: default data_files: - split: train path: data/train-* license: mit --- # Dataset Card for DONORxEMBED-aligned This dataset contains pre-computed PULSAR donor embeddings for 2,804 peripheral blood mononuclear cell (PBMC) samples from 41 independent studies, totaling over 10 million cells with paired diagnostic annotations. The embeddings are generated using PULSAR (Patient Understanding Leveraging Single-cell universAl Representation), a multi-scale, multi-cellular foundation model designed to transform PBMC scRNA-seq samples into unified donor representations. This dataset hosts the disease-aligned model (PULSAR-aligned) embeddings. A zero-shot variant is also available (see https://huggingface.co/datasets/KuanP/PULSAR_DONORxEMBED_zero_shot). ## Dataset Details ### Dataset Description - **Curated by:** Kuan Pang (Stanford University, kuanpang@stanford.edu) - **License:** MIT ## Uses ### Direct Use This dataset is intended for: - Disease classification through reference-based mapping using k-nearest neighbor search in the embedding space - Building searchable vector databases for flexible, scalable disease classification - Benchmarking machine learning models for immune-related disease prediction - Cross-cohort generalization studies in immunology - Population-level analysis of immune variation across diseases ## Dataset Structure The dataset contains 2,804 samples with the following fields: - donor_id (string): Unique identifier for each donor/sample - embedding (sequence of float32): 512-dimensional PULSAR donor embedding vector - disease_label (string): Disease annotation following Mondo Disease Ontology standards - dataset_id (string): Source dataset identifier ## Dataset Creation ### Curation Rationale This dataset was created to enable reference-based disease classification and to demonstrate PULSAR's capacity to capture disease-relevant donor-level signatures. The comprehensive reference cohort allows for scalable disease classification through vector database search, enabling continuous expansion without model re-training. ### Source Data The dataset was assembled from 41 sources: - 32 datasets from CZ CELLxGENE Census database - 1 dataset from Single Cell Portal (SCP) - 8 additional curated sources ## Citation **BibTeX:** [More Information Needed]