Gut Microbiota and Metabolomic Profiles in Myasthenia Gravis Patients and Controls

Yunan Shan's dataset from 2026 contains integrated 16S rRNA sequencing and untargeted metabolomics data from fecal samples of 29 myasthenia gravis patients and 10 healthy controls. It includes results from LEfSe analysis identifying 232 discriminative taxa and 567 altered metabolites. The data was used to build a Random Forest metabolite model and explore correlations with clinical severity scores.

Use Cases

• Training machine learning models for disease classification based on the reported 567 altered metabolite features.
• Analyzing associations between specific microbial taxa (e.g., Faecalibacterium prausnitzii, Klebsiella) and clinical severity in autoimmune conditions.
• Investigating metabolic pathway disturbances (e.g., bile secretion, sphingolipid signaling) implicated by KEGG analysis.
• Validating the reported diagnostic performance (AUC = 1.0) of the metabolite-based Random Forest model on independent cohorts.

Strengths

• Includes data from two complementary omics techniques: 16S rRNA sequencing and UHPLC-Q-TOF MS metabolomics.
• Reports specific, quantified findings: 232 discriminative taxa and 567 altered metabolites (424 downregulated).
• Applies multiple analytical methods (LEfSe, Random Forest, Spearman correlation) with reported statistical thresholds (VIP > 1.0, P < 0.05, FDR q < 0.05).

Limitations

• The dataset is very small (19.5 KB), suggesting it contains summary results or a document, not the raw sequencing or spectral data.
• Row count and column-level documentation are unknown, limiting assessment of data structure and field semantics.
• The sample size is limited to 39 individuals (29 patients, 10 controls), which may affect statistical power and generalizability.

Provenance

Source

Yunan Shan via figshare.

Collection Method

Fecal samples analyzed via 16S rRNA sequencing and UHPLC-Q-TOF MS untargeted metabolomics.

Freshness

Last updated 2026-04-23 05:35:44.