A 2026 multi-omics integration study identifies a potential pathogenic monocyte subset in myasthenia gravis. The dataset, authored by Shuang Li and shared on figshare, results from integrating scRNA-seq, GWAS, and GTEx data with machine learning analysis. It highlights six signature genes, including CFD, associated with high TWAS activity in CD14+ monocytes from MG patients.
Use Cases
- Validate monocyte subset markers based on the six signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, CFD) identified in the description.
- Investigate intercellular communication patterns based on the described enhanced signal activity of CFD+CD14+ monocytes.
- Explore pathway enrichment in monocytes based on the mTORC1 signaling, complement, and inflammatory response pathways mentioned.
- Benchmark machine learning models for feature selection in single-cell omics based on the seven algorithms used in the study.
Strengths
- Integrates multi-omics data from GEO, GWAS catalog, and GTEx databases.
- Identifies six specific signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, CFD) through machine learning analysis.
- Includes a validation dataset from scRNA-seq performed on peripheral blood from MG patients and healthy controls.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- The dataset is very small (2.0 KB), suggesting limited scope, likely containing summary results rather than raw data.
Provenance
- Source
- figshare
- Collection Method
- Integration of multi-omics data from GEO, GWAS catalog, and GTEx databases, followed by machine learning analysis.
- Freshness
- Last updated 2026-05-01 05:25:24; freshness should be verified.