This repository contains all source code and processed data required to reproduce the figures and supplementary results presented in:

LipidTrend: A Structure-Aware Framework for Detecting Continuous Trends in Lipidomic Remodeling

LipidTrend is a structure-aware statistical framework for detecting continuous lipidomic remodeling in chain-length × double-bond structural space.

This repository provides:

• All R scripts used to generate main and supplementary figures
• Processed input data (as described in the manuscript)
• A fully locked computational environment via renv

No new datasets were generated in this study. All lipidomics datasets were obtained from previously published studies as detailed in the manuscript.

All analyses were performed under:

• R 4.5.2
• LipidTrend (Bioconductor release version used in manuscript)
• Additional packages recorded in renv.lock

To reproduce the computational environment:

install.packages("renv")
renv::restore()

This restores all package versions exactly as used for figure generation.

If renv restoration fails due to R version mismatch, users may install required packages manually according to renv.lock.

To reproduce a specific figure:

source("scripts/[dataset]/[comparison]/[lipid class]/[chain_dimension_*.R]")

All scripts:

• Load data from data/
• Perform structure-aware smoothing and permutation testing
• Apply BH-FDR correction
• Save figures and tables to results/

Each analysis follows the workflow described in the manuscript:

1. Species-level statistical testing.
2. Signed log-transformed regional scoring.
3. Gaussian kernel smoothing in structural space.
4. Permutation-based null estimation.
5. Benjamini–Hochberg FDR control.
6. Significant zone identification (FDR < 0.05).

Supported analysis modes demonstrated in this repository:

• One-dimensional (chain length or unsaturation)
• Two-dimensional (chain length × double bonds)
• Abundance-weighted and unweighted smoothing
• Odd–even chain stratification

All datasets analyzed in this study were obtained from previously published work and are described in the manuscript and Supplementary Table 1.

No raw data redistribution beyond published material is included.

The LipidTrend R package is publicly available:

• Bioconductor: https://bioconductor.org/packages/LipidTrend
• GitHub: https://github.com/BioinfOMICS/LipidTrend

This repository specifically documents the exact scripts and processed inputs used to generate the figures in the manuscript.

The diagram below summarizes the relationship between datasets, analysis modules, and manuscript figures.