The R script will give the Differential expression analysis of miRNA counts from two samples with two conditions, followed by QC analysis such as Pheatmap, 3D plot, Dispersion plot, PCA, scree plot, plotcounts, sample to sample distance plot, volcano plot

Both input and output (individual) files were uploaded for the reference.

This will give results of TWO WAY ANOVA

PS: This is an sample script and not to be used for any publication reference.

This repository contains a script for performing RNA-Seq data analysis and quality control using the DESeq2 package in R. The script provides various functions to analyze and visualize differential gene expression, perform quality control checks, and generate informative plots.

1. Clone this repository to your local machine using the following command:

   git clone https://github.com/sivkri/miRNASeq-DE-Analysis-QC
   

2. Make sure you have the necessary dependencies installed (listed in the Prerequisites section).

1. Place your count table file (in tab-delimited format) in the same directory as the script.
2. Modify the script's parameters, such as the file path, as per your dataset and requirements.
3. Run the script using R or an R script editor.

Make sure you have the following dependencies installed:

• R
• DESeq2 package
• gplots package
• ggplot2 package
• pheatmap package
• RColorBrewer package
• scatterplot3d package

You can install the packages using the following commands in R:

install.packages("DESeq2")
install.packages("gplots")
install.packages("ggplot2")
install.packages("pheatmap")
install.packages("RColorBrewer")
install.packages("scatterplot3d")

The required R libraries are imported at the beginning of the script to enable the use of specific functions for data analysis and visualization.

The script reads the count table file in tab-delimited format and prepares it for further analysis. Make sure to modify the file path in the script to match the location of your count table.

The script performs differential expression analysis using the DESeq2 package. It sets up the experimental design, runs the DESeq function, and extracts the results. The analysis can be customized by modifying the experimental conditions and genotype levels.

The script includes several visualization functions to explore and visualize the results. These include PCA plots, pheatmap (alternative to heatmap), sample-to-sample distance, MA plot, dispersion plot, scatterplot, and more. Each plot provides insights into the data and helps identify patterns and differentially expressed genes.

The script generates various output files in PDF format, including the results of differential expression analysis, quality control plots, and visualization plots. The output files are saved with meaningful names to aid in result interpretation and reporting.

Contributions to this project are welcome. If you have any suggestions, improvements, or bug fixes, feel free to open an issue or submit a pull request.

This project is licensed under the MIT License.

Feel free to customize and expand upon this template to provide more