HolobiomicsLab 
Spectral data preprocessing for lipidomics
Summary
Preprocessing and normalization of lipidomic and metabolomic mass spectrometry data generated by the Multi-ABLE barocycler method to align spectral features across samples and prepare them for multivariate analysis. This skill is essential for converting raw instrument output into a feature table suitable for differential abundance and phenotype association studies.
When to use
You have raw lipidomic and metabolomic spectral data files from a Multi-ABLE barocycler-based concurrent multiomics experiment and need to normalize ion intensities, align retention times and m/z values across samples, and remove noise or low-signal features before statistical comparison of lipid and metabolite profiles between biological conditions (e.g., atherosclerosis phenotypes).
When NOT to use
- Input data are from a different mass spectrometry method or instrument; MultiABLER is specifically designed for Multi-ABLE barocycler output.
- Data have already been preprocessed, normalized, and aligned by another tool; applying this skill would constitute redundant processing.
- Your analysis goal is exploratory or descriptive rather than hypothesis-driven comparison of lipid profiles across conditions.
Inputs
- Raw lipidomic and metabolomic data files from Multi-ABLE method
- Sample metadata or phenotype annotations
- R environment with MultiABLER package installed
Outputs
- Normalized and aligned spectral feature table (lipids and metabolites)
- Quality control plots (normalization and alignment verification)
- Preprocessed data matrix for multivariate analysis
How to apply
Install the MultiABLER R package and its declared dependencies (ProteoMM, limma, Tidyverse) via devtools. Load raw lipidomic and metabolomic data files (output from the Multi-ABLE method) into the R environment. Execute the integrated preprocessing functions provided in the MultiABLER.r file to normalize spectral intensities across samples and align m/z and retention time features. The functions apply standardized normalization to account for signal variation while preserving relative abundance patterns. Quality control plots are generated automatically to verify that normalization did not introduce artifacts and that feature alignment was successful. The output is a preprocessed feature table ready for downstream multivariate analysis to identify differential lipids and metabolites.
Related tools
- MultiABLER (R package providing integrated preprocessing and normalization functions for lipidomic and metabolomic data from Multi-ABLE method) — https://github.com/holab-hku/MultiABLER
- R (Execution environment for MultiABLER functions and data processing pipeline)
- ProteoMM (Dependency for statistical and multivariate analysis functions in MultiABLER) — https://www.bioconductor.org/packages/release/bioc/html/ProteoMM.html
- limma (Dependency for differential abundance testing in MultiABLER) — https://www.bioconductor.org/packages/release/bioc/html/limma.html
- Tidyverse (Dependency for data wrangling and visualization in MultiABLER) — https://www.tidyverse.org
Examples
devtools::install_github("holab-hku/MultiABLER", dependencies = TRUE); source("MultiABLER.r"); # load raw data and execute preprocessing functions as described in tutorial at https://github.com/holab-hku/MultiABLER/blob/main/tutorials/tutorial.html
Evaluation signals
- Quality control plots confirm that normalization reduced inter-sample intensity variation while preserving biological signal (no clustering by batch or artifact).
- Spectral feature alignment is verified by checking that m/z and retention time deviations across samples fall within expected tolerances for the instrument.
- Preprocessed feature table contains no missing values and shows expected lipid and metabolite annotations consistent with Multi-ABLE method performance.
- Statistical summaries (e.g., median fold-change distributions, p-value distributions) are within expected ranges for the biological system under study.
- Downstream multivariate analysis (e.g., PCA, differential abundance testing) reveals expected separation or association with the phenotype of interest (e.g., atherosclerosis stage).
Limitations
- MultiABLER is designed specifically for data generated by the Multi-ABLE method; preprocessing workflows from other lipidomic/metabolomic platforms may require different normalization strategies.
- The skill requires installation of multiple R packages and their dependencies, which may fail in constrained computational environments; a detailed troubleshooting guide is provided in the README.
- Preprocessing parameters (normalization method, alignment tolerance, filtering thresholds) are fixed within the MultiABLER functions and may not be optimal for all tissue types or biological conditions; users are referred to the tutorial for guidance on interpretation.
Evidence
- [readme] MultiABLER is a set of R functions forms a seamless workflow that supports integrative processing and analysis of lipidomic and metabolomic data generated by the Multi-ABLE method: "MultiABLER is a set of R functions forms a seamless workflow that supports integrative processing and analysis of lipidomic and metabolomic data"
- [readme] install the following packges in R and run the funcions in MultiABLER.r: "install the following packges in R and run the funcions in MultiABLER.r"
- [intro] Execute the integrated preprocessing functions to normalize and align spectral features across samples: "Execute the integrated preprocessing functions to normalize and align spectral features across samples"
- [intro] Run the multivariate analysis functions to identify differential lipids and metabolites associated with atherosclerosis phenotypes: "Run the multivariate analysis functions to identify differential lipids and metabolites associated with atherosclerosis phenotypes"
- [intro] Generate output tables, quality control plots, and statistical summaries as produced by the function suite: "Generate output tables, quality control plots, and statistical summaries as produced by the function suite"
- [readme] In case of installation issues, try install the following dependencies: ProteoMM, limma, Tidyverse: "In case of installation issues, try install the following dependencies and run the above code: ProteoMM, limma, Tidyverse"