"LipidSigR" is an R package developed based on LipidSig web-based tool https://lipidsig.bioinfomics.org/.

This package integrates a comprehensive analysis for streamlined data mining of lipidomic datasets. We provide four main analysis workflows for analyzing two-group and multi-group data: "Profiling," "Differential Expression," "Enrichment," and "Network." Each section offers unique aspects to analyzing lipidome profiling data based on various characteristics, including lipid class, chain length, unsaturation, hydroxyl groups, and fatty acid composition. Please note that only two-group data can conduct the "Network" workflow.

We assume that you have already installed the R program (see the R project at http://www.r-project.org and are familiar with it. You need to have R 4.2.0 or a later version installed for running LipidSigR.

Our package is available at the github https://github.com/BioinfOMICS/LipidSigR. There are 2 recommended ways to install our package.

# Step 1: Install devtools
install.packages("devtools")
library(devtools)

# Step 2: Install LipidSigR
devtools::install_github("BioinfOMICS/LipidSigR")

# LipidSigR package depends on several packages, which can be installed using the below commands:
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c('fgsea', 'gatom', 'mixOmics', 'S4Vectors', 'SummarizedExperiment', 'rgoslin'))

install.packages(c('devtools', 'magrittr', 'plotly', 'tidyverse'))
devtools::install_github("ctlab/mwcsr")

git clone https://github.com/BioinfOMICS/LipidSigR.git
R CMD build LipidSigR
R CMD INSTALL LipidSigR_0.7.0.tar.gz

You can cite the LipidSigR publication as follows:

Chia-Hsin Liu, Pei-Chun Shen, Wen-Jen Lin, Hsiu-Cheng Liu, Meng-Hsin Tsai, Tzu-Ya Huang, I-Chieh Chen, Yo-Liang Lai, Yu-De Wang, Mien-Chie Hung, Wei-Chung Cheng, LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis, Nucleic Acids Research, Volume 52, Issue W1, 5 July 2024, Pages W390–W397, doi: 10.1093/nar/gkae335; PMID: 38709887.