Integrative Multi-Omics Data on HLA-DPA1/DPB1 in Pediatric Asthma

A 191.6 KB CSV file contains integrative multi-omics analysis results identifying HLA-DPA1 and HLA-DPB1 as candidate key genes in pediatric asthma. The data was generated by Yuchen Wen and published on figshare under a CC-BY-4.0 license, with a last update timestamp of 2026-06-03. The analysis integrates bulk RNA-seq, single-cell RNA-seq, and machine learning algorithms to explore macrophage immune-metabolic dysregulation.

Use Cases

• Validate diagnostic models for pediatric asthma based on HLA-DPA1/DPB1 expression levels.
• Investigate immune-metabolic crosstalk in macrophages using the identified candidate genes.
• Benchmark machine learning algorithms (SVM-RFE, Boruta, LASSO, XGBoost) for feature selection in transcriptomic data.
• Analyze cell-cell communication networks, specifically the MIF signaling axis, between epithelial cells and immune cells.

Strengths

• Integrates data from multiple public transcriptomic datasets (GSE152004, GSE65204, GSE254127).
• Applies four distinct machine learning algorithms for consensus gene identification.
• Includes experimental validation from in vitro IL-13 stimulation and patient samples.

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment.
• The dataset is small (191.6 KB), suggesting it contains processed results rather than raw sequencing data.
• Freshness should be verified as the last update is dated in the future (2026-06-03).

Provenance

Source

figshare

Collection Method

Analysis of bulk and single-cell RNA-sequencing data from public repositories, combined with machine learning and experimental validation.

Freshness

Last updated 2026-06-03 04:54:38; freshness should be verified.