bio-small-rna-seq-differential-mirna
Perform differential expression analysis of miRNAs between conditions using DESeq2 or edgeR with small RNA-specific considerations. Use when identifying miRNAs that change between treatment groups, disease states, or developmental stages.
npx skills add majiayu000/claude-skill-registry --skill differential-mirna --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Differential miRNA Expression ## Load miRNA Count Data ```r library(DESeq2) # Load miRge3 or miRDeep2 counts counts <- read.csv('miR.Counts.csv', row.names = 1) # Create sample metadata coldata <- data.frame( sample = colnames(counts), condition = factor(c('control', 'control', 'treated', 'treated')), row.names = colnames(counts) ) ``` ## DESeq2 Analysis ```r # Create DESeq2 dataset dds <- DESeqDataSetFromMatrix( countData = round(counts), # DESeq2 requires integers colData = coldata, design = ~ condition ) # Filter low-expressed miRNAs # miRNAs typically have fewer total counts than mRNAs # Keep miRNAs with at least 10 reads across samples keep <- rowSums(counts(dds)) >= 10 dds <- dds[keep, ] # Run DESeq2 dds <- DESeq(dds) # Get results res <- results(dds, contrast = c('condition', 'treated', 'control')) res <- res[order(res$padj), ] ``` ## Apply Shrinkage for Effect Sizes ```r # apeglm shrinkage for more accurate log2 fold changes # Particularly important for low-count miRNAs library(apeglm) res_shrunk <- lfcShrink( dds, coef = 'condition_treated_vs_control', type = 'apeglm' ) ``` ## Filter Significant miRNAs ```r # Standard thresholds for miRNA DE # padj < 0.05: FDR-corrected
- Load miRNA Count Data
- DESeq2 Analysis
- Apply Shrinkage for Effect Sizes
- Filter Significant miRNAs
- edgeR Alternative
- Visualization
- Heatmap of DE miRNAs
- Export Results
- Related Skills
What does the bio-small-rna-seq-differential-mirna skill do?
Perform differential expression analysis of miRNAs between conditions using DESeq2 or edgeR with small RNA-specific considerations. Use when identifying miRNAs that change between treatment groups, disease states, or developmental stages.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill differential-mirna --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.