lipid-class-abundance-comparison
Summary
Compare abundance patterns of lipid classes (e.g., phosphatidylcholines, cardiolipins, triglycerides) across sample groups to identify which lipid classes are systematically up- or down-regulated in disease versus control states. This skill enables discovery of lipid biomarker signatures by quantifying and visualizing differential regulation at the lipid class level rather than individual lipid species.
When to use
Apply this skill when you have a LipidomicsExperiment object with logged and normalized Area measurements, sample-level phenotype annotations (e.g., SampleType: Cancer, Benign, Metastasis), and you want to identify which lipid classes show consistent directional change across all samples within a group. Particularly useful after outlier removal and when prior PCA or quality control suggests biological separation between groups.
When NOT to use
- Input data is unnormalized or not log-transformed; normalize and log the data first using set_normalized() and set_logged().
- Sample size is very small (n < 3 per group); limma requires sufficient replication to estimate variance reliably.
- You want to identify individual lipid species biomarkers rather than class-level trends; use univariate species-level analysis instead.
Inputs
- LipidomicsExperiment object (logged and normalized Area measurements)
- Sample phenotype annotations (SampleType, Stage, Race, or other grouping variable)
- Outlier sample identifiers for removal (optional but recommended)
Outputs
- Differential expression results (log2-fold-change, adjusted p-value, test statistic per lipid)
- Volcano plot(s) showing log2FC vs. -log10(p-value) with lipids colored by class
- Summary table of lipid class regulation patterns (up-, down-, or non-regulated classes per contrast)
How to apply
Load a LipidomicsExperiment object from lipidr with pre-processed, logged, and normalized Area measurements. Remove outlier samples using column subsetting (e.g., exclude flagged samples 18 and 42). Perform two-group differential expression analysis using de_analysis() with contrasts of interest (e.g., Cancer-Benign, Cancer-Metastasis), which leverages the limma package to compute log2-fold-change and adjusted p-values per lipid. Generate volcano plots using plot_results_volcano() to visualize log2FC and -log10(p-value) for each lipid, then aggregate results by lipid class to identify classes where the majority of members are significantly up- or down-regulated in the same direction (e.g., PCs and PGs up-regulated, CLs and TGs down-regulated). Interpret the pattern as evidence of coordinated class-level regulation rather than random individual lipid shifts.
Related tools
- lipidr (Loads LipidomicsExperiment objects, wraps limma differential analysis via de_analysis(), and generates volcano plots via plot_results_volcano() to visualize lipid class regulation.) — https://github.com/ahmohamed/lipidr
- limma (Performs statistical differential expression testing (log2FC, adjusted p-value computation) for each lipid species using moderated t-statistics.)
Examples
two_group <- de_analysis(d, Cancer~Benign, Cancer~Metastasis); plot_results_volcano(two_group)
Evaluation signals
- Volcano plot shows distinct clusters of lipids by class, with consistent direction (e.g., all PCs in upper right, all CLs in lower left), indicating real class-level regulation rather than scattered noise.
- Adjusted p-values for class members are consistently small (< 0.05) and log2FC values have the same sign within each class, confirming coordinated regulation.
- Number of significantly regulated lipids per class is consistent with biological prior (e.g., multiple PCs and PGs simultaneously up-regulated in cancer vs. benign is plausible for membrane remodeling).
- Comparison across multiple contrasts (e.g., Cancer-Benign and Cancer-Metastasis) shows that class-level patterns are reproducible in the first contrast but not in the second (as shown in source article), supporting specificity.
- Visual inspection of volcano plot reproduces the published finding (e.g., PCs and PGs up-regulated, CLs and TGs down-regulated in cancer tissues).
Limitations
- Lipid class abundance comparison assumes that lipid class membership is correctly annotated in the input LipidomicsExperiment; mis-annotated or ambiguously named lipids (flagged during parsing) should be manually reviewed or removed before aggregation.
- The skill does not account for confounder variables (e.g., Race, Cancer Stage) automatically; use de_design() with model formulae to adjust for covariates if needed.
- Class-level aggregation can mask heterogeneous regulation within a class (e.g., some PC species up-regulated, others down-regulated); inspect individual species volcano plots to verify.
- Small effect sizes or high within-group variance can reduce power to detect class-level regulation; quality control and outlier removal are essential pre-requisites.
Evidence
- [other] Phosphatidylcholines (PCs) and phosphatidylglycerols (PGs) are up-regulated while cardiolipins (CLs) and triglycerides (TGs) are down-regulated in cancer tissues compared to benign samples.: "Phosphatidylcholines (PCs) and phosphatidylglycerols (PGs) are up-regulated while cardiolipins (CLs) and triglycerides (TGs) are down-regulated in cancer tissues compared to benign samples."
- [other] Perform two-group differential expression analysis using de_analysis() with Cancer-Benign and Cancer-Metastasis contrasts, leveraging the limma package for statistical testing.: "Perform two-group differential expression analysis using de_analysis() with Cancer-Benign and Cancer-Metastasis contrasts, leveraging the limma package for statistical testing."
- [other] Generate volcano plots using plot_results_volcano() to visualize log2-fold-change and adjusted p-values, highlighting lipid classes (PCs, PGs, CLs, TGs) with differential regulation.: "Generate volcano plots using plot_results_volcano() to visualize log2-fold-change and adjusted p-values, highlighting lipid classes (PCs, PGs, CLs, TGs) with differential regulation."
- [intro] A fairly large difference is observed between cancer and benign samples, with PCs and PGs up-regulated and CLs and TGs down-regulated in cancer tissues.: "A fairly large difference is observed between cancer and benign samples, with PCs and PGs up-regulated and CLs and TGs down-regulated in cancer tissues."
- [intro] This step of the workflow requires the limma package to be installed.: "This step of the workflow requires the limma package to be installed."
- [readme] lipidr represents lipidomics datasets as a LipidomicsExperiment, which extends SummarizedExperiment, to facilitate integration with other Bioconductor packages.: "lipidr represents lipidomics datasets as a LipidomicsExperiment, which extends SummarizedExperiment, to facilitate integration with other Bioconductor packages."
1---2name: lipid-class-abundance-comparison3description: Use when you have a LipidomicsExperiment object with logged and normalized Area measurements, sample-level phenotype annotations (e.g., SampleType: Cancer, Benign, Metastasis), and you want to identify which lipid classes show consistent directional change across all samples within a group.4license: CC-BY-4.05---67# lipid-class-abundance-comparison89## Summary1011Compare abundance patterns of lipid classes (e.g., phosphatidylcholines, cardiolipins, triglycerides) across sample groups to identify which lipid classes are systematically up- or down-regulated in disease versus control states. This skill enables discovery of lipid biomarker signatures by quantifying and visualizing differential regulation at the lipid class level rather than individual lipid species.1213## When to use1415Apply this skill when you have a LipidomicsExperiment object with logged and normalized Area measurements, sample-level phenotype annotations (e.g., SampleType: Cancer, Benign, Metastasis), and you want to identify which lipid classes show consistent directional change across all samples within a group. Particularly useful after outlier removal and when prior PCA or quality control suggests biological separation between groups.1617## When NOT to use1819- Input data is unnormalized or not log-transformed; normalize and log the data first using set_normalized() and set_logged().20- Sample size is very small (n < 3 per group); limma requires sufficient replication to estimate variance reliably.21- You want to identify individual lipid species biomarkers rather than class-level trends; use univariate species-level analysis instead.2223## Inputs2425- LipidomicsExperiment object (logged and normalized Area measurements)26- Sample phenotype annotations (SampleType, Stage, Race, or other grouping variable)27- Outlier sample identifiers for removal (optional but recommended)2829## Outputs3031- Differential expression results (log2-fold-change, adjusted p-value, test statistic per lipid)32- Volcano plot(s) showing log2FC vs. -log10(p-value) with lipids colored by class33- Summary table of lipid class regulation patterns (up-, down-, or non-regulated classes per contrast)3435## How to apply3637Load a LipidomicsExperiment object from lipidr with pre-processed, logged, and normalized Area measurements. Remove outlier samples using column subsetting (e.g., exclude flagged samples 18 and 42). Perform two-group differential expression analysis using de_analysis() with contrasts of interest (e.g., Cancer-Benign, Cancer-Metastasis), which leverages the limma package to compute log2-fold-change and adjusted p-values per lipid. Generate volcano plots using plot_results_volcano() to visualize log2FC and -log10(p-value) for each lipid, then aggregate results by lipid class to identify classes where the majority of members are significantly up- or down-regulated in the same direction (e.g., PCs and PGs up-regulated, CLs and TGs down-regulated). Interpret the pattern as evidence of coordinated class-level regulation rather than random individual lipid shifts.3839## Related tools4041- **lipidr** (Loads LipidomicsExperiment objects, wraps limma differential analysis via de_analysis(), and generates volcano plots via plot_results_volcano() to visualize lipid class regulation.) — https://github.com/ahmohamed/lipidr42- **limma** (Performs statistical differential expression testing (log2FC, adjusted p-value computation) for each lipid species using moderated t-statistics.)4344## Examples4546```47two_group <- de_analysis(d, Cancer~Benign, Cancer~Metastasis); plot_results_volcano(two_group)48```4950## Evaluation signals5152- Volcano plot shows distinct clusters of lipids by class, with consistent direction (e.g., all PCs in upper right, all CLs in lower left), indicating real class-level regulation rather than scattered noise.53- Adjusted p-values for class members are consistently small (< 0.05) and log2FC values have the same sign within each class, confirming coordinated regulation.54- Number of significantly regulated lipids per class is consistent with biological prior (e.g., multiple PCs and PGs simultaneously up-regulated in cancer vs. benign is plausible for membrane remodeling).55- Comparison across multiple contrasts (e.g., Cancer-Benign and Cancer-Metastasis) shows that class-level patterns are reproducible in the first contrast but not in the second (as shown in source article), supporting specificity.56- Visual inspection of volcano plot reproduces the published finding (e.g., PCs and PGs up-regulated, CLs and TGs down-regulated in cancer tissues).5758## Limitations5960- Lipid class abundance comparison assumes that lipid class membership is correctly annotated in the input LipidomicsExperiment; mis-annotated or ambiguously named lipids (flagged during parsing) should be manually reviewed or removed before aggregation.61- The skill does not account for confounder variables (e.g., Race, Cancer Stage) automatically; use de_design() with model formulae to adjust for covariates if needed.62- Class-level aggregation can mask heterogeneous regulation within a class (e.g., some PC species up-regulated, others down-regulated); inspect individual species volcano plots to verify.63- Small effect sizes or high within-group variance can reduce power to detect class-level regulation; quality control and outlier removal are essential pre-requisites.6465## Evidence6667- [other] Phosphatidylcholines (PCs) and phosphatidylglycerols (PGs) are up-regulated while cardiolipins (CLs) and triglycerides (TGs) are down-regulated in cancer tissues compared to benign samples.: "Phosphatidylcholines (PCs) and phosphatidylglycerols (PGs) are up-regulated while cardiolipins (CLs) and triglycerides (TGs) are down-regulated in cancer tissues compared to benign samples."68- [other] Perform two-group differential expression analysis using de_analysis() with Cancer-Benign and Cancer-Metastasis contrasts, leveraging the limma package for statistical testing.: "Perform two-group differential expression analysis using de_analysis() with Cancer-Benign and Cancer-Metastasis contrasts, leveraging the limma package for statistical testing."69- [other] Generate volcano plots using plot_results_volcano() to visualize log2-fold-change and adjusted p-values, highlighting lipid classes (PCs, PGs, CLs, TGs) with differential regulation.: "Generate volcano plots using plot_results_volcano() to visualize log2-fold-change and adjusted p-values, highlighting lipid classes (PCs, PGs, CLs, TGs) with differential regulation."70- [intro] A fairly large difference is observed between cancer and benign samples, with PCs and PGs up-regulated and CLs and TGs down-regulated in cancer tissues.: "A fairly large difference is observed between cancer and benign samples, with PCs and PGs up-regulated and CLs and TGs down-regulated in cancer tissues."71- [intro] This step of the workflow requires the limma package to be installed.: "This step of the workflow requires the limma package to be installed."72- [readme] lipidr represents lipidomics datasets as a LipidomicsExperiment, which extends SummarizedExperiment, to facilitate integration with other Bioconductor packages.: "lipidr represents lipidomics datasets as a LipidomicsExperiment, which extends SummarizedExperiment, to facilitate integration with other Bioconductor packages."
HolobiomicsLab