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

A transcriptomic dataset from a 2026 study analyzing airway epithelium and immune cells from pediatric asthma patients. The data was generated by Yuchen Wen and includes results from bulk RNA-seq, single-cell RNA-seq, and machine learning analyses. It focuses on the downregulation of HLA-DPA1 and HLA-DPB1 genes and their link to macrophage immune-metabolic dysregulation.

Use Cases

• Validate gene expression signatures for pediatric asthma diagnosis based on HLA-DPA1/DPB1 downregulation.
• Study macrophage heterogeneity and cell-cell communication using the single-cell RNA-seq results mentioned.
• Benchmark machine learning algorithms for feature selection in transcriptomic data, referencing the SVM-RFE, Boruta, LASSO, and XGBoost methods used.
• Investigate the role of the MIF signaling axis in immune cell crosstalk as described in the analysis.

Strengths

• Data is validated across independent datasets (GSE152004 and GSE65204) and includes in vitro experimental confirmation.
• Analysis integrates multiple omics approaches, including bulk RNA-seq, single-cell RNA-seq, and four machine learning algorithms.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 121.4 KB file size suggests a limited scope, likely containing summary-level results rather than raw sequencing data.

Provenance

Source

figshare

Collection Method

Analysis of bulk and single-cell RNA-sequencing data from public repositories (GSE152004, GSE65204, GSE254127), combined with machine learning and in vitro validation.

Freshness

Last updated 2026-06-03 04:54:37.