Integrative Multi-Omics Analysis of Inflammation in Acute Mountain Sickness

A multi-omics dataset from a study investigating inflammation-related molecular networks in acute mountain sickness (AMS). The data includes results from integrated bulk RNA-seq, non-coding RNA-seq, and single-cell RNA-seq analyses, identifying 323 differentially expressed genes. The dataset was created by Zhicheng Xiang and last updated on May 22, 2026.

Use Cases

• Validate predicted miRNA-mRNA regulatory networks based on the identified hsa-miR-375 interactions.
• Analyze cell-type-specific expression patterns based on the single-cell and deconvolution results highlighting monocytes.
• Investigate potential therapeutic targets based on the drug-gene network analysis that highlighted HBEGF.
• Explore enrichment of biological pathways like the ErbB signaling pathway based on the GO and KEGG analysis results.

Strengths

• Integrates multiple omics data types (bulk RNA-seq, non-coding RNA-seq, single-cell RNA-seq) from specified public datasets (GSE75665, GSE90500).
• Analysis identified a specific number of differentially expressed genes (323 DEGs, including 5 inflammation-related genes).
• Includes results from multiple network analyses (protein-protein interaction, miRNA-mRNA, transcription factor, drug-gene).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small (74.9 KB), indicating it likely contains summary results rather than raw sequencing data.

Provenance

Source

figshare

Collection Method

Integrative analysis of public RNA-seq datasets and experimental validation using a mouse model.

Freshness

Last updated 2026-05-22 11:13:42; freshness should be verified.