Integrative Multi-Omics Analysis of HLA-DPA1/DPB1 in Pediatric Asthma

A 1.6 MB CSV file containing results from a multi-level transcriptomic analysis of pediatric asthma. The dataset, authored by Yuchen Wen and last updated in June 2026, integrates bulk RNA-seq, single-cell RNA-seq, and experimental validation to identify candidate key genes. It was published on figshare under a CC-BY-4.0 license.

Use Cases

• Validate diagnostic biomarkers for pediatric asthma based on identified downregulated genes HLA-DPA1 and HLA-DPB1.
• Study macrophage functional heterogeneity and pseudotime trajectory analysis based on single-cell RNA-seq results.
• Investigate cell-cell communication networks, such as the MIF signaling axis, based on the described intercellular analysis.
• Replicate machine learning screening of candidate genes using SVM-RFE, Boruta, LASSO, and XGBoost algorithms as described.

Strengths

• Integrates multiple data types, including bulk RNA-seq (GSE152004, GSE65204) and single-cell RNA-seq (GSE254127).
• Validation includes in vitro IL-13 stimulation experiments and patient peripheral blood mononuclear cell samples.
• Candidate genes were identified by consensus of four distinct machine learning algorithms.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect bias inherent to the specific gene expression datasets and patient cohorts used.

Provenance

Source

figshare

Collection Method

Analysis of public RNA-sequencing datasets (GSE152004, GSE65204, GSE254127) combined with experimental validation.

Freshness

Last updated 2026-06-03 04:54:39; freshness should be verified.