CFD+CD14+ Monocyte Signature Genes in Myasthenia Gravis from Multi-Omics Analysis

A 2026 study by Shuang Li integrates transcriptome-wide association study (TWAS) and single-cell RNA sequencing (scRNA-seq) data to investigate myasthenia gravis pathogenesis. The dataset, shared under a CC-BY-4.0 license, contains results from a machine learning benchmark that identified six signature genes associated with a pathogenic monocyte subset. It is derived from multi-omics data sourced from GEO, GWAS catalog, and GTEx databases.

Use Cases

• Validate CFD+CD14+ monocyte signature genes based on the six identified robust genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, CFD).
• Investigate monocyte subpopulation activity in autoimmune disease based on described TWAS activity across cell types.
• Benchmark machine learning algorithms for feature selection in omics data based on the use of seven algorithms and the 'mlr3' R package.
• Study pathway associations in immune cells based on described enrichment in mTORC1 signaling, complement, and inflammatory response pathways.

Strengths

• Dataset is derived from a multi-omics integration of scRNA-seq, GWAS, and GTEx data.
• Results are validated by a separate scRNA-seq dataset generated from peripheral blood of MG patients and healthy controls.
• Feature selection was performed using a benchmark of seven machine learning algorithms.

Limitations

• The dataset is very small at 2.3 KB, indicating limited scope, likely containing summary results rather than raw data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale analysis.

Provenance

Source

Integrated data from GEO, GWAS catalog, and Genotype-Tissue Expression (GTEx) databases.

Collection Method

Multi-omics integration, TWAS analysis, and machine learning feature selection performed on scRNA-seq data.

Freshness

Last updated 2026-05-01 05:25:24.