Data Sheet 1_A multi-omics case–control study identifying oropharyngeal microbiome–metabol

Hong Zhang's dataset comprises multi-omics profiles from 236 influenza patients in Jiangsu Province, China, collected during the 2022–2023 influenza season. It includes 59 cases of secondary bacterial pneumonia and 177 uncomplicated controls, with data from 16S rRNA sequencing and UPLC-MS/MS plasma metabolomics. The study used machine learning to identify microbial and metabolic patterns associated with pneumonia progression.

Use Cases

• Training classifiers to predict secondary pneumonia risk based on oropharyngeal microbiome composition.
• Identifying metabolic pathway dysregulation (e.g., lipid depletion, catabolic pathways) associated with disease progression.
• Validating microbial indicators like Peptococcus abundance as risk factors using SHAP analysis.

Strengths

• Includes 236 patient records with matched microbiome and metabolome data.
• Applies seven machine-learning algorithms and reports LASSO logistic regression results with cross-validation.
• Identifies specific microbial taxa (Synergistota, Bifidobacteriaceae) and metabolites (PC(O-16:0/0:0), PS(14:0/18:3)) with discriminatory power.

Limitations

• Row count for the underlying feature tables is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is from a single province in China during one influenza season, which may limit generalizability.

Provenance

Source

figshare, author Hong Zhang.

Collection Method

Cross-sectional case-control study with oropharyngeal swab 16S rRNA sequencing and plasma UPLC-MS/MS metabolomics.

Time Range

Influenza season 2022–2023, patient enrollment October 2023–December 2024.

Freshness

Last updated 2026-05-22 04:28:08; freshness should be verified.

Geography

Jiangsu Province, China.