Exploratory Data Analysis and Predictive Modeling with Synthetic Splicing Data

This R project performs exploratory data analysis, visualization, correlation analysis, and predictive modeling using a synthetic gene-splicing dataset.

Objective

The goal is to examine relationships between three splicing factors and a splicing event, then build a linear regression model to predict the splicing event.

Methods

• Summary statistics
• Missing value check
• Outlier detection using boxplots
• Correlation matrix using Pearson correlation
• Histograms with normal curves
• Scatterplots with regression lines
• Linear regression modeling
• Model evaluation using R-squared and Mean Squared Error

Key Findings

• No missing values were found.
• Two outliers were detected.
• SplicingFactor1 was positively associated with the splicing event.
• SplicingFactor2 was negatively associated with the splicing event.
• SplicingFactor3 showed little to no relationship.
• Linear regression showed moderate to strong predictive performance.

Requirements

install.packages(c("ggplot2", "Hmisc", "pastecs"))