LipidSigR

“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 five main analysis workflows for analyzing two-group and multi-group data: “Profiling,” “Differential Expression,” “Enrichment,” “Network,” “Machine learning,” and “Correlation.” 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” and “Machine learning” workflow.

Installation

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. Following are the instructions for installing our package.

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

# Step 2: Install BiocManager
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# Step 3: Install LipidSigR
## Update repositories
options(repos = c(
    CRAN = "https://cloud.r-project.org/",
    BiocManager::repositories()))

## Install dependencies and package
devtools::install_github(
    "BioinfOMICS/LipidSigR", 
    dependencies = TRUE)

LipidSigR relies on several dependencies. If an error indicates a missing package, you can install the required packages using the commands below.

# LipidSigR package depends on several packages, which can be installed using the below commands:
BiocManager::install(
    c('fgsea', 'gatom', 'mixOmics', 'S4Vectors', 'BiocGenerics', 
      'SummarizedExperiment', 'rgoslin'))

install.packages(
    c('devtools', 'magrittr', 'plotly', 'tidyverse', 'factoextra', 'ggthemes', 
      'ggforce', 'Hmisc', 'hwordcloud', 'heatmaply', 'iheatmapr', 'Rtsne', 'uwot', 
      'wordcloud', 'rsample', 'ranger', 'caret', 'yardstick', 'fastshap', 
      'SHAPforxgboost', 'visNetwork', 'tidygraph', 'ggraph'))

devtools::install_github("ctlab/mwcsr")

Introduction

After installation, you’re ready to start using LipidSigR. Based on functionality, LipidSigR functions can be categorized as tool functions and 5 analysis workflows. Below is a brief introduction to each section. Please note that only two-group data can conduct the “Network” and “Machine learning” workflow.

  • Tool function: Tool functions are utility functions designed to enhance the convenience of conducting analyses. They include constructing input SummarizedExperiment objects, viewing output results, listing selectable lipid characteristics, performing data processing, and more. Please read vignette("1_tool_function").

  • Profiling: The profiling workflow provides an overview of comprehensive analyses for you to efficiently examine data quality, the clustering of samples, the correlation between lipid characteristics, and the composition of lipid characteristics. Please read vignette("2_profiling").

  • Differential expression: The differential expression workflow integrates many useful lipid-focused analyses for identifying significant lipid species or lipid characteristics. Please read vignette("3_de").

  • Enrichment: The enrichment workflow provides two main approaches: ‘Over Representation Analysis (ORA)’ and ‘Lipid Set Enrichment Analysis (LSEA)’ to illustrates significant lipid species enriched in the categories of lipid class and determine whether an a priori-defined set of lipids shows statistically significant, concordant differences between different biological states (e.g., phenotypes). Please read vignette("4_enrichment").

  • Network: The network workflow provides functions for generates input table for constructing pathway activity network, lipid reaction network and GATOM network. (NOTE: Only provides for two-group data.) Please read vignette("5_network").

  • Machine learning: The machine learning workflow provides functions for building binary classification models and several following analyses to evaluate algorithm performance and identify critical lipid-related variables. (NOTE: Only provides for two-group data.) Please read vignette("6_ml").

  • Correlation: The correlation workflow provides functions such as correlation coefficients and linear regression to analyze continuous clinical features correlating with lipid species or characteristics. Please read vignette("7_corr").

You can analyze data using the tool functions and the five workflow functions. If you need help getting started, try our case examples for two-group and multi-group data! Refer to vignette("8_case_twoGroup") for two-group data analysis and vignette("9_case_multiGroup") for multi-group data analysis. These case examples provide a complete tutorial, from package installation and input data preparation to data analysis and result visualization.

Citation

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.