LipidAnalyst: A Comprehensive Tool for Lipidomic Data Visualization and Analysis

Introduction: Proper analysis of high-throughput lipidomic data requires specialized tools for data processing, normalization, visualization, and statistical and bioinformatic analysis. However, limitations in lipid parsing, data processing, and visualization capabilities in existing software packages create challenges for comprehensive lipidomic data analysis. To address these limitations, we developed LipidAnalyst (v 1.0.3), a user-friendly tool designed to facilitate efficient lipid parsing and processing, visualization, and analysis of lipidomic datasets. Methods: LipidAnalyst was developed using the R Shiny framework. It is hosted on MiServer for online work but can also be downloaded from GitHub. Results: LipidAnalyst provides functionalities in three major areas: data processing, visualization, and statistical analysis. Data processing features include quality control filtering, normalization, internal standard-based quantification, and unique capabilities for missing-value imputation, lipid parsing, and aggregation. Visualization tools include box and violin plots for data distribution assessment, principal component analysis (PCA) plots, hierarchical clustering and differential abundance heatmaps, volcano plots, correlation plots, and Debiased Sparse Partial Correlation (DSPC) clustering plots. Statistical analysis modules include t-test, analysis of variance (ANOVA), Partial Least Squares Differential Analysis (PLS-DA), Orthogonal Partial Least Squares Differential Analysis (OPLS-DA), and Random Forest (RF) modeling. Conclusions: LipidAnalyst is a comprehensive platform for optimal processing, visualization, and analysis of lipidomic data. By integrating advanced data processing workflows with extensive visualization and statistical analysis capabilities, LipidAnalyst enables researchers to explore lipidomic datasets more effectively and develop informed analytical strategies.