Use of Data Visualization Techniques in Bioinformatics for Time-Based Gene Expression Pattern Analysis

Abstract

This study aims to explore data visualization techniques in bioinformatics to analyze time-based gene expression patterns. The research seeks to answer how different visualization approaches can improve the interpretation of large-scale temporal gene expression data. A time-series gene expression dataset consisting of 4381 genes across 24 time intervals was used. The methods applied include heatmaps to identify gene correlations, Principal Component Analysis (PCA) for dimensionality reduction, volcano plots to detect significant expression changes between conditions, and dendrograms to classify genes into functional clusters. The PCA results revealed that two principal components (PC1 and PC2) accounted for 42.32% of the total variance. Volcano plot analysis identified differentially expressed genes with log2 fold change > 1 and p-value < 0.05, while the dendrogram visualization revealed several major gene clusters with similar expression behaviors. These findings demonstrate that combining multiple visualization methods provides comprehensive insights into temporal gene expression dynamics. The application of these methods offers a solution to the challenges of interpreting complex biological data by simplifying correlation patterns, identifying candidate biomarkers, and supporting the development of personalized therapeutic strategies. This research confirms the value of visual analytics in bioinformatics and recommends the integration of these tools in future large-scale omics studies.