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bio-small-rna-seq-differential-mirna
Maintainer FreedomIntelligence · Last updated April 1, 2026
Differential miRNA expression analysis with DESeq2/edgeR.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-small-rna-seq-differential-mirna
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- counts <- read.csv('miR.Counts.csv', row.names = 1).
- coldata <- data.frame( sample = colnames(counts), condition = factor(c('control', 'control', 'treated', 'treated')), row.names = colnames(counts) ).
Source Doc
Excerpt From SKILL.md
Create DESeq2 dataset
dds <- DESeqDataSetFromMatrix( countData = round(counts), # DESeq2 requires integers colData = coldata, design = ~ condition )
Keep miRNAs with at least 10 reads across samples
keep <- rowSums(counts(dds)) >= 10 dds <- dds[keep, ]
Get results
res <- results(dds, contrast = c('condition', 'treated', 'control')) res <- res[order(res$padj), ]
Use cases
- Use bio-small-rna-seq-differential-mirna for genomics and bioinformatics workflows.
- Apply bio-small-rna-seq-differential-mirna to sequencing, variant, or omics analysis tasks.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- mirge3-analysis - Get miRNA counts
- mirdeep2-analysis - Alternative quantification
- target-prediction - Predict targets of DE miRNAs
- differential-expression/deseq2-basics - General DE analysis concepts
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