HolobiomicsLab R Function Workflow Execution
License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.
Summary
Execute an integrated suite of R functions to process, normalize, and analyze lipidomic and metabolomic data from the Multi-ABLE method. This skill involves installing R package dependencies, loading function definitions, and sequentially running preprocessing and multivariate analysis functions to produce normalized feature tables, differential abundance results, and quality control visualizations.
When to use
You have raw lipidomic and metabolomic data files generated by the Multi-ABLE barocycler-based concurrent multiomics method and need to perform integrative preprocessing (spectral normalization and alignment across samples) followed by multivariate analysis to identify differential lipids and metabolites associated with a phenotype of interest (e.g., atherosclerosis).
When NOT to use
- Your data originates from a method other than Multi-ABLE (e.g., different barocycler protocols, non-concurrent lipidomic/metabolomic acquisition, or different mass spectrometry platforms) — MultiABLER is specifically designed for Multi-ABLE output.
- Your lipidomic and metabolomic data have already been individually preprocessed and normalized by their respective vendor software; MultiABLER is designed for raw, unaligned spectral features from the Multi-ABLE workflow.
- You require real-time or streaming data processing — MultiABLER is a batch-mode R function suite designed for complete datasets loaded into memory.
Inputs
- Raw lipidomic data files from Multi-ABLE method (e.g., peak intensity tables with m/z, retention time, sample annotations)
- Raw metabolomic data files from Multi-ABLE method (e.g., feature abundance matrices with metabolite identifiers)
- Sample metadata table (sample IDs, phenotype labels, batch/instrument information)
- MultiABLER.r function definition file
Outputs
- Normalized and aligned lipidomic feature abundance table (samples × features)
- Normalized and aligned metabolomic feature abundance table (samples × features)
- Differential abundance statistical results (test statistics, p-values, log fold-changes for lipids and metabolites)
- Quality control plots (intensity distributions, sample clustering, feature correlation heatmaps)
- Statistical summary tables and visualizations of significantly altered lipids/metabolites associated with phenotype
How to apply
Begin by installing required R packages (including ProteoMM, limma, and Tidyverse) via devtools or Bioconductor. Load the MultiABLER.r function definitions into your R environment. Import your raw lipidomic and metabolomic data files as R objects. Execute the integrated preprocessing functions in sequence to normalize intensities and align spectral features across all samples, ensuring batch and instrumental drift effects are corrected. Subsequently run the multivariate analysis functions (e.g., differential abundance testing using limma framework) to identify lipids and metabolites significantly associated with your phenotype groups. Validate preprocessing success by inspecting quality control plots (e.g., before/after intensity distributions, sample clustering dendrograms) and checking that feature abundance tables meet expected ranges and contain no missing values after imputation.
Related tools
- MultiABLER (R package providing integrated preprocessing and multivariate analysis functions for lipidomic and metabolomic data from Multi-ABLE) — https://github.com/holab-hku/MultiABLER
- R (Programming environment and runtime for executing MultiABLER functions)
- limma (Bioconductor package used by MultiABLER for differential abundance statistical testing of lipids and metabolites) — https://www.bioconductor.org/packages/release/bioc/html/limma.html
- ProteoMM (Bioconductor dependency providing statistical methods for multiomics data analysis) — https://www.bioconductor.org/packages/release/bioc/html/ProteoMM.html
- Tidyverse (R metapackage for data manipulation and visualization dependencies) — https://www.tidyverse.org
Examples
devtools::install_github("holab-hku/MultiABLER", dependencies = TRUE); source("MultiABLER.r"); results <- run_multiABLER(lipid_data, metabolite_data, sample_metadata)
Evaluation signals
- Quality control plots show expected patterns: intensity distributions are symmetric and centered after normalization; sample clustering dendrograms show phenotype-driven separation (e.g., atherosclerosis-prone vs. resistant animals cluster distinctly); no systematic batch effects visible.
- Normalized feature abundance tables contain no missing values after imputation and all intensities fall within biologically expected ranges (no negative values, no saturated signals).
- Differential abundance results include multiple significant features (p < 0.05 after multiple testing correction) with effect sizes consistent with known biology of atherosclerosis-associated lipid and metabolite changes.
- Function execution completes without errors or warnings; output file formats and column names match tutorial specifications and README documentation.
- Cross-validation: re-running the workflow on the same input data produces bit-identical or statistically equivalent output tables and visualizations (accounting for stochastic components like random seeds).
Limitations
- MultiABLER is specifically designed for the Multi-ABLE barocycler-based concurrent multiomics protocol and may not generalize to other lipidomic or metabolomic methods without modification.
- Installation may require manual installation of Bioconductor dependencies (ProteoMM, limma) if automatic dependency resolution fails; users must diagnose and resolve R package environment conflicts.
- The workflow assumes raw data files follow the Multi-ABLE format specifications; misaligned file formats or missing metadata (e.g., sample phenotype labels, batch identifiers) will cause function failures.
- Preprocessing assumes spectral features are aligned by m/z and retention time; highly divergent instrument configurations or calibration drift between batches may reduce alignment quality.
- Statistical power for differential abundance detection depends on sample size and effect magnitude; small sample sizes or subtle phenotype-associated changes may result in low numbers of significant features.
Evidence
- [other] MultiABLER implements its integrative workflow by providing R functions that users install as packages and then execute from the MultiABLER.r file, with detailed instructions provided in a tutorial.: "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"
- [readme] The workflow requires sequential installation and execution of R packages and functions to preprocess and analyze lipidomic and metabolomic data.: "To run MultiABLER, install the following packges in R and run the funcions in MultiABLER.r. A detailed tutorial on how to run MultiABLER can be found in here"
- [other] Preprocessing functions normalize and align spectral features across samples; multivariate analysis functions identify differential lipids and metabolites.: "Execute the integrated preprocessing functions to normalize and align spectral features across samples. 5. Run the multivariate analysis functions to identify differential lipids and metabolites"
- [other] The workflow produces normalized feature tables, quality control plots, and statistical summaries.: "Generate output tables, quality control plots, and statistical summaries as produced by the function suite"
- [readme] Specific Bioconductor dependencies may need manual installation if automatic resolution fails.: "In case of installation issues, try install the following dependencies and run the above code: * ProteoMM * limma * Tidyverse"