Principal-Component-Analysis

Performs Principal Component Analysis (PCA) on gene expression count data generated by featureCounts. It processes multiple .txt files from a specified directory, merges the data, and generates an interactive PCA plot using the DESeq2 and plotly libraries. The resulting plot is saved as an HTML file for easy sharing and visualization. The script uses the vst() function from the DESeq2 package to transform the raw count data. VST applies a transformation that approximates a log2 scale for large counts but avoids issues with zero or low counts. This transformation ensures that variance is stabilized across genes, making it easier to compare samples.

Requirements

Before running the script, ensure you have the following:

1.	R Installed: The script requires R to be installed on your system.

	•	Download R: https://cran.r-project.org/

2.	R Libraries: The script uses several R packages. Install them by running the following command in R:

install.packages(c("ggplot2", "plotly", "RColorBrewer", "htmlwidgets"))

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

BiocManager::install("DESeq2")

Usage

1. Clone or Copy the Script Save the script as pca_analysis.R.
2. Make It Executable Run the following command to make the script executable:

chmod +x pca_analysis.R

Run the Script

Execute the script from the terminal with the following syntax:

./pca_analysis.R <input_directory> <output_file>

• <input_directory>: Path to the directory containing .txt files generated by featureCounts.

• <output_file>: Path where the output HTML file (PCA plot) will be saved.

Example

./pca_analysis.R path/to/your/featurecounts.txt samplePCAplot.html