ALS Cross-Platform Classification - CyTOF and scRNA-seq Data

This dataset contains mass cytometry (CyTOF) and single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) of ALS patients and healthy controls, used for developing cross-platform machine learning models to classify disease progression rates. Dataset 1: als_cytof_data.h5ad Technology: Mass cytometry (CyTOF) Samples: 44 samples across 4 batches Groups: Healthy controls, ALS patients (slow, standard, and fast progression) Cell types: 25 immune cell populations profiled Contains: Single-cell protein expression, cell type annotations, clinical metadata (progression rate ALSFRS-R/time, progression group) Dataset 2: Itou2024_scrna_data.h5ad Technology: 10x Genomics scRNA-seq Source: Itou et al. 2024 (GSE244263) Samples: 40 PBMC samples (30 ALS, 10 controls) Groups: Healthy controls, rapid ALS, non-rapid ALS Contains: Single-cell gene expression, cell type annotations, clinical metadata (progression group) These datasets enable cross-platform validation of immune signatures in ALS, demonstrating that disease-associated immune patterns identified in CyTOF data generalize to scRNA-seq data. The datasets support reproducible machine learning analyses for ALS patient stratification based on immune profiling. Manuscript: Peripheral immune patterns enable robust cross-platform prediction of ALS onset and progression