A table of results from a multi-omics integration and machine learning analysis of myasthenia gravis. The dataset was created by Shuang Li and last updated on 2026-05-01. It is derived from scRNA-seq data, GWAS summary data, and expression quantitative trait loci integrated from GEO, GWAS catalog, and GTEx databases.
Use Cases
- Validate signature genes for myasthenia gravis based on the six identified robust signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, CFD).
- Investigate monocyte subpopulations in autoimmune disease based on the CFD+CD14+ monocyte subset identified.
- Benchmark machine learning algorithms for feature selection in omics data based on the seven algorithms used in the study.
- Study pathway enrichment in immune cells based on the mTORC1 signaling, complement, and inflammatory response pathways mentioned.
Strengths
- Data is derived from multi-omics integration of scRNA-seq, GWAS, and GTEx data.
- Analysis identified six robust signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, CFD) using seven machine learning algorithms.
- Includes a validation dataset from scRNA-seq performed on peripheral blood from MG patients and healthy controls.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- The dataset is very small at 1.5 KB, indicating limited scope.
Provenance
- Source
- Integrated from GEO, GWAS catalog, and Genotype-Tissue Expression (GTEx) databases.
- Collection Method
- Multi-omics integration and machine learning analysis.
- Freshness
- Last updated 2026-05-01 05:25:25; freshness should be verified.