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

Shuang Li published a dataset on figshare in 2026 containing results from a multi-omics integration study of myasthenia gravis. The data includes six signature genes identified by machine learning as associated with a potentially pathogenic monocyte subset. The dataset is 10.8 KB in size and is available under a CC-BY-4.0 license.

Use Cases

• Validate monocyte-related biomarkers in myasthenia gravis based on the six signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, CFD).
• Investigate the role of mTORC1 signaling and complement pathways in disease based on the functional associations described.
• Benchmark machine learning models for feature selection in single-cell omics based on the seven algorithms mentioned.
• Study intercellular communication dynamics in immune cells based on the described activity of CFD+CD14+ monocytes.

Strengths

• The dataset is derived from an integrated analysis of multiple public omics databases (GEO, GWAS catalog, GTEx).
• It includes validation from a newly generated scRNA-seq dataset on patient peripheral blood.
• Six specific signature genes are identified through a benchmark of seven machine learning algorithms.

Limitations

• The dataset is very small at 10.8 KB, indicating limited scope.
• Row count and column-level documentation are absent, requiring manual inspection after download.
• The description is detailed but lacks sample data or explicit column definitions.

Provenance

Source

figshare

Collection Method

Integrated multi-omics data from GEO, GWAS catalog, and GTEx databases, with validation from newly generated scRNA-seq data.

Freshness

Last updated 2026-05-01 05:25:22.