differential-gene-expression-R

Gene expression profiling (LS vs HS samples) using Tidyverse. Includes paired t-tests, fold change calculation, and visualization via Volcano plots and Boxplots.

Differential Gene Expression Analysis (DGE)

This project identifies genes that are significantly up- or down-regulated under different stress conditions (Low Stress vs. High Stress).

Methodology

1. Data Normalization: Converting raw expression data to numeric formats and handling missing values.
2. Statistical Sieve: Applying paired t-tests to calculate significance (p-value).
3. Biological Significance: Calculating Log Fold Change (LFC) to determine the magnitude of change.
4. Classification: Genes are categorized into Upregulated, Downregulated, or Not Significant (p < 0.05).

📂 Click to view all analysis plots

Tech Stack

• R / Tidyverse (dplyr, ggplot2, tidyr)
• Statistical Methods: Paired t-test, Log transformation.

Data Preprocessing: The pipeline includes a specialized step to handle raw CSV files with placeholder columns (double-comma delimiters), ensuring clean data mapping before statistical computation.