LIPIDIFy
2026-09-24
Package
LIPIDIFy 0.99.7
1 Introduction
LIPIDIFy provides an end-to-end toolkit for mass-spectrometry lipidomics data analysis: missing-value imputation, batch-effect correction, multiple normalization strategies, differential abundance analysis (limma and edgeR), gene-set enrichment analysis, and extensive visualization. Lipid names are automatically classified by class, subclass, and fatty-acid saturation, and the package supports user-defined classification schemes and enrichment sets for lipidomes that don’t fit a fixed ontology.
1.1 Why LIPIDIFy?
Several excellent packages already support lipidomics analysis in
Bioconductor, most notably
lipidr, which centres on a
SummarizedExperiment-derived class for annotation, normalization, and
class-level statistical testing. LIPIDIFy is a complementary tool rather than
a replacement, aimed at two audiences at once:
- Bench biologists, via
launch_lipidomics_app(), an interactive Shiny dashboard that requires no R programming: upload a CSV, click through normalization/QC/differential/enrichment steps, and export a report. - Bioinformaticians, via the same underlying functions used programmatically, for reproducible, scriptable analyses.
Relative to existing workflows, LIPIDIFy additionally emphasises:
- Flexible classification and enrichment. Lipid classes are inferred automatically from standard nomenclature, but users can supply their own classification table (any hierarchy, any number of columns) and custom enrichment sets, rather than being tied to one fixed ontology.
- Side-by-side pipeline comparison.
create_pipeline_plot()lets users visually compare two full normalization pipelines (e.g. TIC+Log2 vs. PQN+Log2) before committing to one, which is otherwise a manual, repetitive step. - Imputation and batch correction as first-class steps, with multiple
interchangeable methods (
impute_missing_values(),correct_batch_effects()) integrated into the same workflow as normalization and differential analysis, rather than requiring separate tooling.
1.2 Interoperability with the Bioconductor ecosystem
Internally, LIPIDIFy represents data as plain matrices and data frames
(list(data, metadata, numeric_data)) rather than a custom S4 class, which
keeps the API approachable for users who are not already familiar with
Bioconductor’s class system. This representation converts trivially to a
SummarizedExperiment, so results can be handed off to other Bioconductor
tools (or a lipidr LipidomicsExperiment, which extends
SummarizedExperiment) for further analysis. See the
Interoperability section near
the end of this vignette for a worked example.
1.3 Installation
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("LIPIDIFy")library(LIPIDIFy)The same workflow below is available without any coding via the Shiny application:
launch_lipidomics_app()2 Example Data
This vignette uses a real, publicly available lipidomics dataset rather than
simulated data: Metabolomics Workbench study
ST001359,
“Monophasic lipidomics extraction in cancer cell lines” (Rodriguez Blanco G.,
Beatson Institute for Cancer Research; Project PR000929, Analysis AN002263;
licensed CC BY 4.0). It profiles HepG2 hepatocellular carcinoma cells by
LC-MS (reversed phase, positive ion mode) under two conditions: untreated
controls and cells treated with an SDC (syndecan) inhibitor, 3 replicates
each. The processed data matrix is bundled with the package at
inst/extdata/ST001359_lipidomics.csv; the script that downloaded and
prepared it is inst/scripts/prepare_ST001359_lipidomics.R.
file_path <- system.file("extdata", "ST001359_lipidomics.csv", package = "LIPIDIFy")
raw_data <- load_lipidomics_data(
file_path,
metadata_columns = c("Sample Name", "Sample Group")
)
cat("Samples :", nrow(raw_data$numeric_data), "\n")
#> Samples : 6
cat("Lipids :", ncol(raw_data$numeric_data), "\n")
#> Lipids : 446
cat("Groups :", paste(unique(raw_data$metadata$`Sample Group`), collapse = ", "), "\n")
#> Groups : Control, SDC_iload_lipidomics_data() returns a named list with three elements:
$numeric_data- matrix of lipid abundances (samples x lipids)$metadata- data frame of sample metadata$data- the original full data frame
head(raw_data$metadata, 6)
#> Sample Name Sample Group
#> VV_13_HEpG2_C1 VV_13_HEpG2_C1 Control
#> VV_14_HEpG2_C2 VV_14_HEpG2_C2 Control
#> VV_15_HEpG2_C3 VV_15_HEpG2_C3 Control
#> VV_16_HEpG2_SDC1 VV_16_HEpG2_SDC1 SDC_i
#> VV_17_HEpG2_SDC2 VV_17_HEpG2_SDC2 SDC_i
#> VV_18_HEpG2_SDC3 VV_18_HEpG2_SDC3 SDC_i
raw_data$numeric_data[, 1:4]
#> AC(12:0) AC(14:0) AC(14:1) AC(16:0)
#> VV_13_HEpG2_C1 7.82 9.87 8.71 10.59
#> VV_14_HEpG2_C2 7.83 9.95 8.88 10.70
#> VV_15_HEpG2_C3 7.88 9.98 8.83 10.79
#> VV_16_HEpG2_SDC1 7.31 9.61 6.60 10.83
#> VV_17_HEpG2_SDC2 7.62 9.65 6.89 10.75
#> VV_18_HEpG2_SDC3 7.83 10.19 7.33 11.19Values are the study’s own “peak area, normalized” units, which the submitters already log2-scaled; we use them unchanged (not re-normalized or back-transformed) to preserve fidelity to the deposited data. This matters below: a second application of Log2 or TIC normalization would not be meaningful on data that is already log-scaled and normalized, so we apply only light median-centring further on.
If you would rather experiment with a larger, fully synthetic dataset (20
samples, 4 groups, un-transformed intensities), generate_example_data()
creates one on the fly – it is used in this vignette only for the
normalization-pipeline comparison in the next section, where raw-scale
intensities make the effect of each method clearer.
3 Lipid Classification
classify_lipids() parses lipid names and assigns three classification
levels:
- LipidGroup - broad category (e.g., Glycerophospholipids, Sphingolipids)
- LipidType - specific subclass (e.g., Phosphatidylcholine)
- Saturation - SFA (0 double bonds), MUFA (1), PUFA (> 1)
lipid_names <- colnames(raw_data$numeric_data)
classification <- classify_lipids(lipid_names)
head(classification, 8)
#> Lipid LipidGroup LipidType Saturation
#> 1 AC(12:0) Other/Unclassified Other SFA
#> 2 AC(14:0) Other/Unclassified Other SFA
#> 3 AC(14:1) Other/Unclassified Other MUFA
#> 4 AC(16:0) Other/Unclassified Other SFA
#> 5 AC(18:0) Other/Unclassified Other SFA
#> 6 AC(18:1) Other/Unclassified Other MUFA
#> 7 Alkenyl-TG(P-18:0_16:0_18:1) Other/Unclassified Other MUFA
#> 8 Alkenyl-TG(P-48:0) Other/Unclassified Other SFAcat("Lipid groups:\n")
#> Lipid groups:
print(sort(table(classification$LipidGroup), decreasing = TRUE))
#>
#> Glycerophospholipids Other/Unclassified Glycerolipids
#> 179 114 79
#> Sphingolipids Sterol Lipids
#> 60 14
cat("\nFatty-acid saturation:\n")
#>
#> Fatty-acid saturation:
print(table(classification$Saturation))
#>
#> MUFA PUFA SFA
#> 85 284 77Real-world lipid identifiers are rarely as tidy as textbook nomenclature.
Here, roughly a quarter of features (e.g. BMP, CL, acylcarnitines, ether
lipids reported as Plasmanyl-/Plasmenyl-) fall outside the built-in
pattern set and are labelled "Other/Unclassified". Rather than guessing,
you can supply your own classification table with load_custom_classification()
(any number of columns, any hierarchy) to cover exactly the lipid space of
your dataset.
4 Raw Data Visualization
Examining distributions before normalization helps detect outlier samples, batch effects, and scaling differences.
raw_list <- list(numeric_data = raw_data$numeric_data, metadata = raw_data$metadata)
visualize_raw_data_improved(
raw_list,
plot_type = "boxplot",
view_mode = "sample",
metadata = raw_data$metadata,
group_column = "Sample Group"
)
Figure 1: Per-sample intensity distributions before normalization
Boxes are coloured by group.
visualize_raw_data_improved(
raw_list,
plot_type = "density",
view_mode = "sample",
metadata = raw_data$metadata,
group_column = "Sample Group"
)
Figure 2: Intensity density curves before normalization, one curve per sample
5 Normalization
5.1 What methods are available?
get_normalization_methods()
#> [1] "TIC" "PQN" "Quantile" "VSN" "Log2Median"
#> [6] "Median" "Mean" "Log2" "Log10" "Sqrt"
#> [11] "None"5.2 VSN and Log2Median are different methods
Two of these deserve a note, because their names invite confusion:
"Log2Median"applies a fixed transformation:log2(x + 1), then a per-sample median shift onto a common baseline, back-transformed to the original scale. Nothing is estimated from the data."VSN"performs genuine variance-stabilising calibration withvsn::justvsn()from the Bioconductor vsn package (Huber et al., Bioinformatics 18, S96–S104, 2002). It fits an arcsinh (generalised-log) transformation whose parameters are estimated by robust maximum likelihood, simultaneously calibrating between-sample differences and stabilising the variance-versus-mean relationship.
VSN may be appropriate for positive, intensity-like measurements whose variance grows with the mean – a common pattern in raw MS lipidomics data. It is not simply another name for a log transformation, and it is not automatically the best choice for every lipidomics dataset; compare pipelines on your own data before committing to one.
Practical restrictions:
- The Bioconductor
vsnpackage must be installed (BiocManager::install("vsn")). If it is missing,"VSN"raises an error – it never silently falls back to another method. - At least 2 samples and at least 42 lipid features are required (vsn’s
minDataPointsPerStratumdefault), and values must be finite. Infinite values, samples or features that are entirely missing, and samples with fewer than two observed values are rejected with a descriptive error. - Missing values are permitted: vsn fits on the observed values and returns
NAin the same positions. They are never imputed. - Negative values are passed to vsn unchanged but produce a warning, since they usually indicate data that has already been log-transformed or background-subtracted.
- The output is already on a log-like scale, so do not chain a
"Log2"step after"VSN".
synthetic_obj_vsn <- load_lipidomics_data_from_df(generate_example_data())
vsn_norm <- apply_normalizations(synthetic_obj_vsn$numeric_data, "VSN")
dim(vsn_norm) # samples x lipids - orientation and dimnames preserved
#> [1] 20 301
# Distinct from Log2Median, which is a different method:
l2m_norm <- apply_normalizations(synthetic_obj_vsn$numeric_data, "Log2Median")
isTRUE(all.equal(vsn_norm, l2m_norm))
#> [1] FALSE5.3 Comparing pipelines side-by-side
It is good practice to compare normalization strategies visually before committing to one. This is most informative on raw-scale intensities, so we illustrate it here with the built-in synthetic dataset, comparing TIC+Log2 (a common starting point) against PQN+Log2 (more robust when large fold-changes are expected):
synthetic_df <- generate_example_data()
synthetic_obj <- load_lipidomics_data_from_df(synthetic_df)
pipeline1 <- apply_normalizations(synthetic_obj$numeric_data, c("TIC", "Log2"))
pipeline2 <- apply_normalizations(synthetic_obj$numeric_data, c("PQN", "Log2"))
p1 <- create_pipeline_plot(pipeline1,
title = "Pipeline 1: TIC + Log2",
metadata = synthetic_obj$metadata,
plot_type = "boxplot"
)
p2 <- create_pipeline_plot(pipeline2,
title = "Pipeline 2: PQN + Log2",
metadata = synthetic_obj$metadata,
plot_type = "boxplot"
)
gridExtra::grid.arrange(p1, p2, ncol = 1)
Figure 3: Side-by-side comparison of two normalization pipelines applied to the same raw, synthetic data
5.4 Applying normalization to the real dataset
The ST001359 data is already log2-scaled and normalized by the original submitters, so we apply only median-centring here rather than a second TIC/PQN/Log2 pass, which would not be meaningful on top of an existing log-scale normalization:
norm_data <- apply_normalizations(raw_data$numeric_data, methods = "Median")
dim(norm_data) # samples x lipids - same dimensions as raw data
#> [1] 6 446norm_list <- list(numeric_data = norm_data, metadata = raw_data$metadata)
visualize_raw_data_improved(
norm_list,
plot_type = "boxplot",
view_mode = "sample",
metadata = raw_data$metadata,
group_column = "Sample Group"
)
Figure 4: Per-sample distributions after median-centring
6 Missing Value Imputation
Missing values in lipidomics data typically arise when a lipid’s signal
falls below the instrument’s limit of detection. LIPIDIFy provides six
imputation strategies via impute_missing_values(). Imputing after
normalisation ensures that imputed values sit on the same scale as observed
values.
cat("Missing values in this dataset:", sum(is.na(norm_data)), "\n")
#> Missing values in this dataset: 0
cat("Imputation methods available:\n")
#> Imputation methods available:
print(get_imputation_methods())
#> [1] "half_min" "min" "zero" "mean" "median" "knn"# For demonstration: introduce 5% artificial missing values
demo_norm <- norm_data
set.seed(42)
demo_norm[sample(length(demo_norm), size = round(0.05 * length(demo_norm)))] <- NA
cat(
"Missingness introduced:", sum(is.na(demo_norm)),
sprintf("(%.1f%%)\n", 100 * mean(is.na(demo_norm)))
)
#> Missingness introduced: 134 (5.0%)
imputed_norm <- impute_missing_values(demo_norm, method = "half_min")
cat("Missing after imputation:", sum(is.na(imputed_norm)), "\n")
#> Missing after imputation: 0Choosing a method:
| Method | Best for |
|---|---|
half_min |
Most lipidomics data; models the LOD on normalised scale |
knn |
Data missing at random (MAR); requires impute Bioconductor package |
zero |
Only when absence of signal truly means zero |
mean / median |
Small proportions of missing values |
For the remainder of this vignette we use the complete normalised data without artificial missingness.
7 Batch Effect Correction
When samples are processed across multiple runs, instruments, or operators,
technical batch effects can obscure biological differences.
correct_batch_effects() removes these effects while preserving biological
signal, and should be applied after normalisation and imputation, then
verified with PCA. The ST001359 dataset has no recorded batch structure, so
this step is illustrated but not evaluated here.
Two methods are available:
| Method | When to use |
|---|---|
"limma" |
Default; fast and reliable for balanced designs |
"combat" |
Better for large or unbalanced batch effects; requires sva package |
# Simulate batch labels (real data would have this in the metadata file)
raw_data$metadata$Batch <- rep(c("Run1", "Run2"), each = 3)
norm_corrected <- correct_batch_effects(
data_matrix = norm_data,
metadata = raw_data$metadata,
batch_column = "Batch",
group_column = "Sample Group",
method = "limma"
)
# Verify: PCA should now show clustering by biology, not by batch
pca_corrected <- perform_pca(norm_corrected, raw_data$metadata, "Sample Group")
create_pca_plot_with_ellipses(
pca_data = pca_corrected$pca_data,
variance_explained = pca_corrected$variance_explained,
ellipse_type = "confidence",
title = "PCA after Batch Correction"
)8 Quality Control with PCA
PCA on normalized data should show samples clustering by biological group rather than by technical artefact.
pca_res <- perform_pca(norm_data, raw_data$metadata, "Sample Group")
create_pca_plot_with_ellipses(
pca_data = pca_res$pca_data,
variance_explained = pca_res$variance_explained,
ellipse_type = "none",
show_sample_labels = TRUE,
title = "PCA - Control vs. SDC Inhibition"
)
Figure 5: PCA of the normalized ST001359 data
With only 3 replicates per group, confidence ellipses are not meaningful
here (ellipse_type = "none"); with larger sample sizes,
ellipse_type = "confidence" draws 95% confidence regions per group.
9 Differential Analysis
9.1 Automatic pairwise contrasts
create_default_contrasts() generates all pairwise comparisons from the
groups present in your data. You can also specify contrasts manually, or
supply your own design matrix (see ?perform_differential_analysis) when
the default ~0 + group design doesn’t fit your experiment (e.g. paired
samples, additional covariates).
groups <- unique(raw_data$metadata$`Sample Group`)
contrasts <- create_default_contrasts(groups)
print(contrasts)
#> [1] "SDC_i - Control"9.2 Choosing a method: limma or edgeR?
diff_res <- perform_differential_analysis(
data_matrix = norm_data,
metadata = raw_data$metadata,
group_column = "Sample Group",
contrasts_list = contrasts,
method = "limma"
)limma is the recommended default for mass-spectrometry lipidomics: it models continuous, approximately log-normal intensities directly.
edgeR is provided only as an exploratory alternative. edgeR’s
negative-binomial model was designed for integer RNA-seq count data;
applying it to continuous MS intensities is an approximation, and
perform_differential_analysis(..., method = "edger") emits a warning to
that effect every time it is called. Consider edgeR only if you specifically
want to cross-check limma’s results against a second modelling assumption,
or if you are integrating lipidomics into a broader pipeline that already
standardises on edgeR for consistency – not as a first choice.
cat(sprintf("%-20s %5s %5s %4s %5s\n", "Contrast", "Total", "Sig", "Up", "Down"))
#> Contrast Total Sig Up Down
cat(strrep("-", 48), "\n")
#> ------------------------------------------------
for (nm in names(diff_res$results)) {
res <- diff_res$results[[nm]]
n_sig <- sum(res$adj.P.Val < 0.05, na.rm = TRUE)
n_up <- sum(res$adj.P.Val < 0.05 & res$logFC > 0, na.rm = TRUE)
n_down <- sum(res$adj.P.Val < 0.05 & res$logFC < 0, na.rm = TRUE)
cat(sprintf("%-20s %5d %5d %4d %5d\n", nm, nrow(res), n_sig, n_up, n_down))
}
#> SDC_i - Control 446 283 146 137first_contrast <- names(diff_res$results)[1]
res_df <- diff_res$results[[first_contrast]]
res_df <- res_df[order(res_df$adj.P.Val), ]
top10 <- utils::head(res_df[, c("logFC", "AveExpr", "adj.P.Val")], 10)
top10$logFC <- round(top10$logFC, 3)
top10$AveExpr <- round(top10$AveExpr, 2)
top10$adj.P.Val <- signif(top10$adj.P.Val, 3)
cat("Top 10 features in:", first_contrast, "\n\n")
#> Top 10 features in: SDC_i - Control
print(top10)
#> logFC AveExpr adj.P.Val
#> PC(37:0) 6.891 8.23 1.17e-07
#> PC(34:4) -2.526 11.75 7.11e-06
#> PC(36:0) 2.530 12.46 7.11e-06
#> SHexCer(d38:5) -2.899 7.27 7.11e-06
#> DG(16:0_18:0) 3.992 11.85 9.85e-06
#> PG(16:0_18:0) 4.690 4.74 9.85e-06
#> PI(42:9) 1.890 8.26 9.85e-06
#> PC(34:0) 1.772 17.15 9.85e-06
#> LysoPC(17:0) 1.980 8.27 9.85e-06
#> BMP(38:6) 1.520 7.88 1.30e-0510 Results Visualization
10.1 Volcano plot
Significant lipids (|logFC| > 1 and adj.P.Val < 0.05) are coloured by lipid class. The top 10 most significant are labelled.
create_volcano_plot_labeled(
results = diff_res$results[[first_contrast]],
title = paste("Volcano:", first_contrast),
logfc_threshold = 1,
pval_threshold = 0.05,
top_labels = 10,
classification_data = classification,
color_by = "LipidGroup"
)
Figure 6: Volcano plot coloured by lipid class
10.2 Heatmap of significant features
sig_lipids <- rownames(res_df)[
!is.na(res_df$adj.P.Val) &
res_df$adj.P.Val < 0.05 &
abs(res_df$logFC) > 1
]
if (length(sig_lipids) >= 5) {
top_n <- min(30, length(sig_lipids))
avail <- intersect(sig_lipids[seq_len(top_n)], colnames(norm_data))
hm_mat <- t(norm_data[, avail, drop = FALSE])
create_heatmap_robust(
data_matrix = hm_mat,
metadata = raw_data$metadata,
group_column = "Sample Group",
top_n = top_n,
title = paste("Significant lipids:", first_contrast)
)
} else {
message("Fewer than 5 significant lipids at these thresholds.")
}11 Lipid Expression
create_lipid_expression_barplot() shows per-sample abundance of individual
lipids, sorted by group. It returns a single ggplot object when one lipid is
selected, or a named list of ggplot objects when multiple lipids are
selected.
top_lipids <- utils::head(rownames(res_df)[!is.na(res_df$adj.P.Val)], 3)
cat("Selected lipids:\n")
#> Selected lipids:
print(top_lipids)
#> [1] "PC(37:0)" "PC(34:4)" "PC(36:0)"
expr_result <- create_lipid_expression_barplot(
data_matrix = norm_data,
metadata = raw_data$metadata,
selected_lipids = top_lipids,
group_column = "Sample Group",
data_type = "normalized"
)
if (inherits(expr_result, "ggplot")) {
print(expr_result)
} else {
for (lipid_plot in expr_result) print(lipid_plot)
}
Figure 7: Per-sample abundance of the 3 most significant lipids
Figure 8: Per-sample abundance of the 3 most significant lipids
Figure 9: Per-sample abundance of the 3 most significant lipids
12 Enrichment Analysis
Enrichment analysis tests whether lipids in a particular class are collectively more increased or decreased than expected by chance, using the fgsea algorithm.
enrich_res <- perform_enrichment_analysis(
results_list = diff_res$results,
classification_data = classification,
min_set_size = 3,
max_set_size = 500
)
cat("Categories per contrast:", paste(names(enrich_res[[1]]), collapse = ", "), "\n")
#> Categories per contrast: LipidGroup, LipidType, Saturationgrp_enrich <- enrich_res[[first_contrast]][["LipidGroup"]]
if (!is.null(grp_enrich) && nrow(grp_enrich) > 0) {
grp_enrich <- grp_enrich[order(grp_enrich$pval), ]
out <- utils::head(grp_enrich[, c("pathway", "NES", "pval", "padj", "size")], 5)
out$NES <- round(out$NES, 3)
out$pval <- signif(out$pval, 3)
out$padj <- signif(out$padj, 3)
print(out, row.names = FALSE)
} else {
cat("No enrichment results available.\n")
}
#> pathway NES pval padj size
#> <char> <num> <num> <num> <int>
#> Glycerolipids -1.436 0.0194 0.0968 79
#> Sphingolipids 1.043 0.3790 0.6260 60
#> Sterol Lipids -0.957 0.5190 0.6260 14
#> Other/Unclassified 0.930 0.5890 0.6260 114
#> Glycerophospholipids 0.932 0.6260 0.6260 179if (!is.null(grp_enrich) && nrow(grp_enrich) > 0) {
create_enrichment_dotplot(
enrichment_data = grp_enrich,
title = paste("Lipid Group Enrichment:", first_contrast),
max_pathways = 10
)
}
Figure 10: Enrichment dot plot for the first contrast
Dot size reflects set size; colour reflects statistical significance.
Custom, user-defined pathway sets (not tied to LipidGroup/LipidType) can
be supplied via load_custom_enrichment_sets() and passed as custom_sets.
13 Interoperability with SummarizedExperiment
LIPIDIFy’s list-of-matrices representation converts directly to a
SummarizedExperiment, for interoperability with the rest of the
Bioconductor ecosystem (or with lipidr, whose LipidomicsExperiment class
extends SummarizedExperiment):
if (requireNamespace("SummarizedExperiment", quietly = TRUE)) {
se <- SummarizedExperiment::SummarizedExperiment(
assays = list(normalized = t(norm_data)),
colData = raw_data$metadata
)
print(se)
}
#> class: SummarizedExperiment
#> dim: 446 6
#> metadata(0):
#> assays(1): normalized
#> rownames(446): AC(12:0) AC(14:0) ... TG(62:3) TG(62:4)
#> rowData names(0):
#> colnames(6): VV_13_HEpG2_C1 VV_14_HEpG2_C2 ... VV_17_HEpG2_SDC2
#> VV_18_HEpG2_SDC3
#> colData names(2): Sample Name Sample GroupFrom here, se can be passed to any Bioconductor function expecting a
SummarizedExperiment.
14 Funding
This work was supported by a grant to A/Prof Karen Sheppard from the National Health and Medical Research Council of Australia (#2020050).
15 Session Information
sessionInfo()
#> R version 4.6.1 Patched (2026-06-24 r90190)
#> Platform: x86_64-apple-darwin20
#> Running under: macOS Ventura 13.7.8
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.6-x86_64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.6-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
#>
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] LIPIDIFy_0.99.7 BiocStyle_2.41.0
#>
#> loaded via a namespace (and not attached):
#> [1] gridExtra_2.3.1 sandwich_3.1-3
#> [3] rlang_1.3.0 magrittr_2.0.5
#> [5] shinydashboard_0.7.3 multcomp_1.4-32
#> [7] otel_0.2.0 matrixStats_1.5.0
#> [9] compiler_4.6.1 vctrs_0.7.3
#> [11] stringr_1.6.0 sysfonts_0.8.9
#> [13] pkgconfig_2.0.3 fastmap_1.2.0
#> [15] XVector_0.53.0 magick_2.9.1
#> [17] labeling_0.4.3 promises_1.5.0
#> [19] rmarkdown_2.32 preprocessCore_1.75.1
#> [21] tinytex_0.61 purrr_1.2.2
#> [23] xfun_0.61 showtext_0.9-8
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