---
name: bio-small-rna-seq-differential-mirna
description: 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.
tool_type: r
primary_tool: DESeq2
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
  - read_file
  - run_shell_command
---

<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA

-->


# 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 significance
# |log2FC| > 1: 2-fold change minimum
sig <- subset(res_shrunk, padj < 0.05 & abs(log2FoldChange) > 1)
sig <- sig[order(sig$padj), ]

# Separate up and down-regulated
up <- subset(sig, log2FoldChange > 0)
down <- subset(sig, log2FoldChange < 0)

cat('Upregulated:', nrow(up), '\n')
cat('Downregulated:', nrow(down), '\n')
```

## edgeR Alternative

```r
library(edgeR)

# Create DGEList
dge <- DGEList(counts = counts, group = coldata$condition)

# Filter low expression
keep <- filterByExpr(dge)
dge <- dge[keep, , keep.lib.sizes = FALSE]

# Normalize
dge <- calcNormFactors(dge)

# Design matrix
design <- model.matrix(~ condition, data = coldata)

# Estimate dispersion
dge <- estimateDisp(dge, design)

# Fit model and test
fit <- glmQLFit(dge, design)
qlf <- glmQLFTest(fit, coef = 2)

# Get results
res_edger <- topTags(qlf, n = Inf)$table
```

## Visualization

```r
library(ggplot2)
library(EnhancedVolcano)

# Volcano plot
EnhancedVolcano(
    res_shrunk,
    lab = rownames(res_shrunk),
    x = 'log2FoldChange',
    y = 'padj',
    pCutoff = 0.05,
    FCcutoff = 1,
    title = 'Differential miRNA Expression'
)

# MA plot
plotMA(res_shrunk, ylim = c(-4, 4))
```

## Heatmap of DE miRNAs

```r
library(pheatmap)

# Get normalized counts
vsd <- vst(dds, blind = FALSE)

# Select significant miRNAs
sig_mirnas <- rownames(sig)
mat <- assay(vsd)[sig_mirnas, ]

# Z-score scale rows
mat_scaled <- t(scale(t(mat)))

pheatmap(
    mat_scaled,
    annotation_col = coldata['condition'],
    cluster_rows = TRUE,
    cluster_cols = TRUE,
    show_rownames = nrow(mat) < 50
)
```

## Export Results

```r
# Full results with normalized counts
res_df <- as.data.frame(res_shrunk)
res_df$miRNA <- rownames(res_df)
res_df$baseMean_norm <- rowMeans(counts(dds, normalized = TRUE)[rownames(res_df), ])

write.csv(res_df, 'DE_miRNAs_full.csv', row.names = FALSE)

# Significant only
write.csv(as.data.frame(sig), 'DE_miRNAs_significant.csv')
```

## 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


<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

---
Source attribution: GPTomics
Original project: https://github.com/GPTomics/bioSkills
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 FridrichMethod

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

---

NOTE ON BUNDLED CONTENT

This repository aggregates skills from multiple upstream projects (see the
README for the source list). Each `skills/<name>/` directory may carry its
own license inherited from its upstream source. The MIT license above
applies to this repository's curation and the scripts/workflows in `/`,
`/scripts/`, and `/.github/` — not to the upstream skill content itself.
Consult the upstream repository linked in the README and any LICENSE
file inside a skill directory before reusing skill content commercially.



# Mega AI Hub resource metadata

Creator / publisher: GPTomics
Original project: https://github.com/GPTomics/bioSkills
Attribution basis: Exact package in biomedical source index and publisher source table

Fields of work: Data & Analytics
Roles: Business Intelligence Analyst, Data Scientist, Market Research Analyst
Specialties: Design, Frontend, Testing
Tools: Deseq2, Edger
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 86e7a60fcbfe9056fdcf3ff6466c2f2b4b59306f29bc013fafafebff5fc19a3c
Metadata reviewed: 2026-10-27
This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.