RNA-seq Differential Expression and Gene Ontology Results from Retinal 2-DG Experiment

Mohita Gaur provides RNA-seq differential expression analysis results and functional gene enrichment data from a 2-DG experiment on retinal explants. The dataset includes log2 fold-change values and false discovery rates for genes, plus Gene Ontology Biological Process enrichment statistics. It was last updated in April 2026.

Use Cases

• Validate differential expression analysis methods by comparing logFC and FDR values across experimental conditions.
• Investigate enriched biological processes using the GeneRatio and BgRatio columns from the functional gene enrichment table.
• Benchmark quasi-likelihood negative binomial GLM fits for RNA-seq data against the provided p.adjust (FDR) results.
• Study the impact of 2-DG on retinal gene expression by filtering genes based on the logFC and FDR thresholds.

Strengths

• Data includes statistically adjusted p-values (p.adjust) using the Benjamini-Hochberg false discovery rate method.
• Results are derived from a quasi-likelihood negative binomial generalized log-linear model applied to normalized count data.
• Provides two distinct but related analytical outputs: differential expression and functional enrichment.

Limitations

• The 2.0 MB size suggests a relatively small dataset, potentially with a limited number of genes or samples.
• Row count and specific sample/biological replicate details are unknown, limiting reproducibility assessments.
• Data is specific to retinal explants and a 2-DG intervention, reducing generalizability to other tissues or treatments.

Provenance

Source

Mohita Gaur via figshare.

Collection Method

Differential expression analysis of RNA-seq data from retinal explants treated with 2-DG versus control, followed by functional gene enrichment analysis.

Time Range

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Freshness

Last updated in April 2026, indicating recent availability.

Geography

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