Table 4_CFD+CD14+ monocytes: potential pathogenic subset in myasthenia gravis uncovered by

A 2026 dataset by Shuang Li presents results from a multi-omics integration study of myasthenia gravis. It identifies six signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, and CFD) associated with a pathogenic monocyte subset. The data was generated by integrating scRNA-seq, GWAS, and GTEx data, followed by machine learning analysis.

Use Cases

• Validate CFD+CD14+ monocyte signatures in myasthenia gravis based on the six identified signature genes.
• Investigate mTORC1 signaling and complement pathway activity in monocytes based on the described functional associations.
• Benchmark machine learning algorithms for feature selection in single-cell omics data based on the seven algorithms used.
• Study intercellular communication dynamics in immune cells based on the described signal reception and transmission capacity.

Strengths

• Data integrates multiple public omics sources (GEO, GWAS catalog, GTEx) and a validation scRNA-seq dataset.
• Analysis employed seven machine learning algorithms and a benchmarked model selection process.
• Results focus on a specific, novel cell subset (CFD+CD14+ monocytes) with detailed functional pathway associations.

Limitations

• The dataset is extremely small (881.0 B), suggesting it contains only summary results, not raw or processed single-cell 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 analyses.

Provenance

Source

figshare

Collection Method

Multi-omics data integration from public databases and original scRNA-seq validation.

Freshness

Last updated 2026-05-01 05:25:23.