Script for generating sample-correlation plot

Sample correlation plot visualizes the relationships between multiple variables within a dataset by displaying the correlation coefficients between each pair of variables. Each cell in the plot represents the correlation between two variables, with colors indicating the strength and direction of the correlation. Positive correlations are shown in one color range, while negative correlations are displayed in another, allowing for quick identification of strongly correlated and inversely correlated pairs. The plot provides an intuitive overview of how variables interact with each other, facilitating the detection of patterns, redundancies, and potential multicollinearity within the data. This type of visualization is particularly useful for exploratory data analysis, feature selection, and understanding complex interrelationships in high-dimensional datasets.

This script useful while trying to integrate multiple datasets.

1. Working Dependencies:

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R version 4.0

1. Installation:

• install.packages ("ggplot2") # click here for more detailed understanding

• Example Input file: File1 & File2

2. File preparation: In input file please provide .txt file extension with "\t" (tab) seperation

• File1.txt

    example: Col1    Col2
  
            Gene1    1234
    
            Gene2    1112
  

• File2.txt

    example: Col1    Col3
  
            Gene1    1222
    
            Gene2     900
  

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                               SCRIPT USAGE GUIDE  

2. Install & Load library

    library(ggplot2)

3. Reading files

    File1 <- read.table("File1.txt", header = TRUE)

    File2 <- read.table("File2.txt", header = TRUE)

4. Data merge

    merged_data <- merge(rna_data, chip_data, by = "Col1")

    correlation <- cor(merged_data$Col2, merged_data$Col3)

    plot <- ggplot(merged_data, aes(x = Col2, y = Col3)) +
    
       geom_point() +
       
       geom_smooth(method = "lm", se = FALSE, color = "blue") +
       
       labs(title = paste("Correlation: ", round(correlation, 2)),
       
            x = "Col2_targets",
            
            y = "Col3_targets") +
            
       theme_minimal()

5. Save plot

    ggsave(filename = "Pairwise_correlation_plot.pdf", plot = plot, width = 8, height = 6) 

A correlation suggests that there might be some degree of relationship between the gene expression levels and the binding intensity of the protein.

    Correlation scale:

    Low::Moderate::High - Correlation

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Note: Please be careful while copy pasting indentation might cause trouble for you!