GO Enrichment Results from PsychENCODE Schizophrenia Transcriptomic Analysis

Costas Bampos published GO enrichment results on 2026-04-15. The dataset contains functional enrichment analysis outputs from a multi-method study of PsychENCODE transcriptomic data focused on schizophrenia. The 5.5 KB XLS file likely contains Gene Ontology and KEGG pathway enrichment scores for genes identified through co-expression network and dimensionality reduction analyses.

Use Cases

• Validate and compare biological pathway enrichments based on results from three orthogonal analytical methods (igraph-based, WGCNA, PCA) described in the study.
• Identify hub genes and pathways related to immune response, synaptic signaling, and epigenetic processes in schizophrenia based on the described enrichment findings.
• Support meta-analysis of schizophrenia transcriptomics by providing pre-computed GO and KEGG enrichment scores from the PsychENCODE consortium data.
• Benchmark new network analysis or dimensionality reduction techniques against the enrichment signatures identified in this multi-method study.

Strengths

• Results derived from the PsychENCODE consortium's multi-omic human brain dataset, a recognized resource in neuropsychiatry.
• Enrichment analysis performed using two standard databases: Gene Ontology (GO) and KEGG pathways.
• Findings are based on three independent analytical approaches applied to the same dataset, as described in the source study.

Limitations

• Dataset is very small (5.5 KB), indicating it contains summary-level enrichment results rather than raw or intermediate analysis data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain analyses.

Provenance

Source

Analysis by Costas Bampos, derived from PsychENCODE consortium transcriptomic data.

Collection Method

Functional enrichment analysis (GO and KEGG) performed on gene lists identified via Surrogate Variable Analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and Principal Component Analysis (PCA).

Freshness

Last updated 2026-04-15 17:47:17; freshness should be verified.