🧬 miRNA-DE-multiMiR

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edgeR + multiMiR Pipeline

This repository contains an R workflow for performing differential expression analysis of small RNAs (e.g., miRNAs) using edgeR, followed by retrieval of validated miRNA targets with the multiMiR package.

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📌 Features

• Reads count data and experimental conditions.
• Performs differential expression analysis using edgeR.
• Extracts top differentially expressed miRNAs.
• Retrieves validated miRNA targets from the multiMiR database.
• Outputs results for downstream biological interpretation.

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📦 Requirements

Make sure you have the following R packages installed:

• edgeR
• multiMiR

To install them:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}

# Bioconductor package
BiocManager::install("edgeR")

# CRAN package
install.packages("multiMiR")

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📂 Input Files

The pipeline requires:

1. Counts table (counts_table.tsv) – matrix of raw read counts (genes/miRNAs as rows, samples as columns).
2. Strains/condition factor (strains_factor.tsv) – metadata describing sample groups.

👉 Example datasets are included with the multiMiR package (extdata/).

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🚀 Usage

Run the script:

source("edgeR_multiMiR_pipeline.R")

This will:

1. Load the count data and experimental design.
2. Perform normalization, dispersion estimation, and differential expression analysis with edgeR.
3. Extract the top 10 differentially expressed miRNAs.
4. Query multiMiR for validated targets in mouse (org = "mmu").

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📊 Output

• Top 10 DE miRNAs are printed in the console.
• multiMiR target results are displayed (and can be saved).

Example console output:

✅ Top 10 differentially expressed miRNAs:
[1] "mmu-miR-21a-5p" "mmu-miR-155-5p" "mmu-miR-34a-5p" ...

✅ multiMiR results retrieved:
       mature_mirna_id target_symbol database
1       mmu-miR-21a-5p          PTEN    miRTarBase
2       mmu-miR-155-5p           SOCS1  TarBase
...

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🧬 Customization

• Change org = "mmu" to another organism (e.g., "hsa" for human).
• Adjust the number of top DE miRNAs ([1:10]) as needed.
• Save results with:

write.csv(p_val_DE_edgeR$table, "DE_miRNAs_edgeR.csv")
write.csv(multimir_results@data, "multiMiR_targets.csv")

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📖 References

• edgeR: Robinson MD, McCarthy DJ, Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140.
• multiMiR: Ru Y, Kechris KJ, Tabakoff B, et al. (2014). The multiMiR R package and database: integration of microRNA–target interactions along with their disease and drug associations. Nucleic Acids Research, 42(17).