Retinal RNA-seq Differential Expression Results for Glucose Supplementation

Mohita Gaur provides RNA-seq differential expression analysis results comparing retinal explants under 25 mM versus 5 mM glucose conditions. The dataset includes log2 fold-change values, false discovery rates, and mean gene expression counts per million. It was uploaded to figshare in April 2026.

Use Cases

• Identify significantly upregulated or downregulated genes under high glucose using the logFC and FDR columns.
• Validate gene expression levels by comparing mean CPM values in the 5 mM_CPM_mean and 25 mM_CPM_mean columns.
• Prioritize candidate genes for further experimental study based on combined metrics of fold-change and statistical significance.

Strengths

• Data includes key statistical metrics for differential expression analysis: logFC and Benjamini-Hochberg FDR.
• Provides directly comparable mean expression values (CPM) for two experimental conditions.

Limitations

• The dataset is very small at 25.1 KB, indicating a limited number of genes or rows.
• Results are specific to a single experimental setup (retinal explants, glucose supplementation), limiting generalizability.
• No row count or explicit gene list is provided, making scope assessment difficult.

Provenance

Source

Mohita Gaur via figshare.

Collection Method

Analysis of RNA-seq data using a quasi-likelihood negative binomial generalized log-linear model on normalized count data.

Time Range

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Freshness

Last updated in April 2026.

Geography

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