Protein Analysis and Gene Annotation Repository

Introduction

This repository contains three scripts for protein analysis and gene annotation. These scripts are written in R and Python and perform various tasks, including data manipulation, statistical analysis, and gene annotation.

Script 1: Protein Analysis in R

Purpose: This R script is designed for protein data analysis, focusing on statistical testing and visualization. Key Steps: Data loading from an Excel file Grouping and combining data for statistical comparison Statistical tests with multiple comparisons adjustment Generation of volcano plots for visualizing differentially expressed proteins Usage: Ensure you have the required R packages installed and set the working directory to the input folder before executing the script. Script 2: Correlation Analysis in R

Purpose: This R script analyzes the correlation between proteins in a dataset and generates a heatmap for visualization. Key Steps: Reading proteomics data from an Excel file Computing the correlation matrix Identifying top correlated variables Creating a heatmap to visualize the correlation matrix Usage: Set the working directory to the location of your data and modify the filename as needed before running the script. Script 3: Gene Annotation in Python

Purpose: This Python script annotates genes with their respective KEGG IDs. Key Steps: Installation of necessary Python libraries Conversion of gene names to KEGG IDs using the BioServices library Saving the annotated data as a TSV file Usage: Install the required libraries using pip and execute the script to annotate genes and generate a TSV file with KEGG IDs.