Package 'lipidr'

Add sample annotation to Skyline data frame

Description

Add sample annotation to Skyline data frame

Usage

add_sample_annotation(data, annot_file)

Arguments

data	LipidomicsExperiment object.
annot_file	CSV file or a data.frame with at least 2 columns, sample names & group(s).

Value

LipidomicsExperiment with sample group information.

Examples

datadir <- system.file("extdata", package = "lipidr")

# all csv files
filelist <- list.files(datadir, "data.csv", full.names = TRUE)
d <- read_skyline(filelist)

# Add clinical info to existing LipidomicsExperiment object
clinical_file <- system.file("extdata", "clin.csv", package = "lipidr")
d <- add_sample_annotation(d, clinical_file)
colData(d)
d$group

# Subset samples using clinical information
# Note we are subsetting columns
d[, d$group == "QC"]

# Subset lipids using lipid annotation
# Note we are subsetting rows
d[rowData(d)$istd, ]

---

Parse molecule names to extract lipid class and chain information.

Description

Parse lipid names to return a data.frame containing lipid class, total chain length and unsaturation. Lipids should follow the pattern 'class xx:x/yy:y', with class referring to the abbreviated lipid class, xx:x as the composition of the first chain and yy:y as the second chain. Alternatively, lipids can be supplied following the pattern 'class zz:z', where zz:z indicates the combined chain length and unsaturation information.

Usage

annotate_lipids(molecules, no_match = c("warn", "remove", "ignore"))

Arguments

molecules	A character vector containing lipid molecule names.
no_match	How to handle lipids that cannot be parsed? Default is to give warnings.

Value

A data.frame with lipid annotations as columns. Input lipid names are given in a column named "Molecule".

Examples

lipid_list <- c(
  "Lyso PE 18:1(d7)",
  "PE(32:0)",
  "Cer(d18:0/C22:0)",
  "TG(16:0/18:1/18:1)"
)
annotate_lipids(lipid_list)

---

Convert data.frame/matrix to LipidomicsExperiment

Description

Convert data.frame/matrix to LipidomicsExperiment

Usage

as_lipidomics_experiment(df, logged = FALSE, normalized = FALSE)

Arguments

df	A data.frame or matrix where rows are lipids and columns are samples. Lipid names should be provided in the first column or in rownames of df. Measurements should be numeric. The data is considered summarized unless at least one lipid is duplicated (has > 1 row).
logged	Whether the data is log-transformed
normalized	Whether the data is normalized

Value

LipidomicsExperiment

---

Differential analysis of lipids between sample groups

Description

de_analysis and de_design perform differential analysis of measured lipids that are associated with a sample group (annotation). de_analysis accepts a list of contrasts, while de_design allows users to define a design matrix, useful for complex experimental designs or for adjusting possible confounding variables.

Usage

de_analysis(data, ..., measure = "Area", group_col = NULL)

de_design(data, design, ..., coef = NULL, measure = "Area")

significant_molecules(de.results, p.cutoff = 0.05, logFC.cutoff = 1)

plot_results_volcano(de.results, show.labels = TRUE)

Arguments

data	LipidomicsExperiment object, should be normalized and log2 transformed.
...	Expressions, or character strings which can be parsed to expressions, specifying contrasts. These are passed to limma::makeContrasts.
measure	Which measure to use as intensity, usually Area (default).
group_col	Name of the column containing sample groups. If not provided, defaults to first sample annotation column.
design	Design matrix generated from model.matrix(), or a design formula.
coef	Column number or column name specifying which coefficient of the linear model is of interest.
de.results	Output of de_analysis().
p.cutoff	Significance threshold. Default is 0.05.
logFC.cutoff	Cutoff limit for log2 fold change. Default is 1. Ignored in multi-group (ANOVA-style) comparisons.
show.labels	Whether labels should be displayed for significant lipids. Default is TRUE.

Value

TopTable as returned by limma package

significant_molecules returns a character vector with names of significantly differentially changed lipids.

plot_results_volcano returns a ggplot object.

Functions

• significant_molecules(): gets a list of significantly changed lipids for each contrast.

• plot_results_volcano(): plots a volcano chart for differential analysis results.

Examples

# type ?normalize_pqn to see how to normalize and log2-transform your data
data(data_normalized)

# Specifying contrasts
de_results <- de_analysis(
  data_normalized,
  HighFat_water - NormalDiet_water,
  measure = "Area"
)
# Using formula
de_results_formula <- de_design(
  data = data_normalized,
  design = ~group,
  coef = "groupHighFat_water",
  measure = "Area"
)

# Using design matrix
design <- model.matrix(~group, data = colData(data_normalized))
de_results_design <- de_design(
  data = data_normalized,
  design = design,
  coef = "groupHighFat_water",
  measure = "Area"
)
significant_molecules(de_results)
plot_results_volcano(de_results, show.labels = FALSE)

---

Remove molecules with CV larger that a threshold

Description

Remove molecules with CV larger that a threshold

Usage

filter_by_cv(data, cv.cutoff = 20, measure = "Area")

Arguments

data	LipidomicsExperiment object.
cv.cutoff	CV threshold (numeric). Default is 20.
measure	Which measure used to calculate CV, usually Area (default).

Value

LipidomicsExperiment object with molecules filtered.

Examples

data(data_normalized)
filter_by_cv(data_normalized)

---

Generate lipid sets from lipid molecule names

Description

Generate lipid sets from lipid molecule names

Usage

gen_lipidsets(molecules, min_size = 2)

Arguments

molecules	A character vector containing lipid molecule names.
min_size	Minimum number of molecules in a set to be included in enrichment.

Value

List of lipid sets

Examples

data(data_normalized)
molecules <- rowData(data_normalized)$Molecule
gen_lipidsets(molecules)

---

Impute missing values in a LipidomicsExperiment

Description

Impute missing values in a LipidomicsExperiment

Usage

impute_na(
  data,
  measure = "Area",
  method = c("knn", "svd", "mle", "QRILC", "minDet", "minProb", "zero"),
  ...
)

Arguments

data	LipidomicsExperiment object.
measure	Which measure to use as intensity, usually Area, Area Normalized or Height. Default is Area.
method	The imputation method to use. All methods are wrappers for imputeLCMD package. These include knn Wraps imputeLCMD::impute.wrapper.KNN(). Default. This requires an additional argument K (Number of neighbors used to infer the missing data). svd Wraps imputeLCMD::impute.wrapper.SVD(). This requires an additional argument K (Number of principal components to use). mle Wraps imputeLCMD::impute.wrapper.MLE(), minDet Wraps imputeLCMD::impute.MinDet(), minProb Wraps imputeLCMD::impute.MinProb(), zero Wraps imputeLCMD::impute.ZERO(),
...	Other arguments passed to the imputation method.

Value

LipidomicsExperiment object with missing values imputed.

Examples

data(data_normalized)

# Replace with values calculated using K-nearest neighbors
impute_na(data_normalized, "Area", "knn", 10)

# Replace with values calculated from the first K principal components
impute_na(data_normalized, "Area", "svd", 3)

# Replace with Maximum likelihood estimates
impute_na(data_normalized, "Area", "mle")

# Replace with randomly drawn values from a truncated distribution
impute_na(data_normalized, "Area", "QRILC")

# Replace with a minimal value
impute_na(data_normalized, "Area", "minDet")

# Replace with randomly drawn values from a Gaussian distribution
# cerntered around a minimal value
impute_na(data_normalized, "Area", "minProb")

# Replace with zero (not recommended)
impute_na(data_normalized, "Area", "zero")

---

Functions to get and set attributes of LipidomicsExperiment objects

Description

Functions to get and set attributes of LipidomicsExperiment objects

Usage

is_logged(data, measure)

set_logged(data, measure, val)

is_normalized(data, measure)

set_normalized(data, measure, val)

is_summarized(data)

set_summarized(data, val)

Arguments

data	LipidomicsExperiment object.
measure	Which measure to get / set attributes of.
val	Value to be assigned to the attribute.

Value

Modified LipidomicsExperiment.

Examples

data(data_normalized)
is_logged(data_normalized, "Area")
is_summarized(data_normalized)

---

Constructor for Lipidomics experiment from list of assays

Description

Constructor for Lipidomics experiment from list of assays

Usage

LipidomicsExperiment(assay_list, metadata, colData = NULL, rowData = NULL)

Arguments

assay_list	A list or SimpleList of matrix-like elements, or a matrix-like object. Passed to SummarizedExperiment().
metadata	A list containing arbitrary information about the experiment. It should at least contain 2 elements: dimnames 2-element character vector with dimension names summarized Has transitions been summarized?
colData	An optional DataFrame describing the samples (contains clinical information). Row names, if present, become the column names of the LipidomicsExperiment.
rowData	A DataFrame object describing the rows (contains generated lipid annotations). Row names, if present, become the row names of the SummarizedExperiment object. The number of rows of the DataFrame must be equal to the number of rows of the matrices in assays.

Value

LipidomicsExperiment object

---

LipidomicsExperiment object

Description

LipidomicsExperiment object

---

Metabolomics Workbench integration

Description

These functions use Metabolomics Workbench REST API to support data mining of publicly available lipidomics datasets.

Usage

list_mw_studies(keyword = "lipid")

fetch_mw_study(study_id)

read_mwTab(mwTab)

read_mw_datamatrix(file)

Arguments

keyword	A keyword to search for in Metabolomics Workbench studies.
study_id	The Metabolomics Workbench study ID.
mwTab	File path or url for a mwTab file.
file	File path or url for the file containing the data matrix.

Value

list_mw_studies returns a data frame with studies matching the keyword. Study ID, title, author and details are retrieved.

All other functions return a LipidomicsExperiment object containing clinical and lipid intensity data.

Functions

• list_mw_studies(): retrieves a list of lipidomics studies from Metabolomics Workbench.

• fetch_mw_study(): downloads and parse full data for a study from Metabolomics Workbench using a study ID. The function returns a LipidomicsExperiment where users can directly apply lipidr analysis workflow.

• read_mwTab(): parses mwTab file into a LipidomicsExperiment.

• read_mw_datamatrix(): parses a Metabolomics Workbench data matrix into a LipidomicsExperiment. Data matrix downloaded from Metabolomics Workbench are parsed into a LipidomicsExperiment object to enable lipidr workflow analysis.

Examples

## Not run: 
list_mw_studies()

## End(Not run)
## Not run: 
fetch_mw_study("ST001111")

## End(Not run)

---

Lipid set enrichment analysis (LSEA)

Description

Lipid set enrichment analysis (LSEA)

Usage

lsea(
  de.results,
  rank.by = c("logFC", "P.Value", "adj.P.Val"),
  min_size = 2,
  ...
)

significant_lipidsets(enrich.results, p.cutoff = 0.05, size.cutoff = 2)

plot_class_enrichment(de.results, significant.sets, measure = "logFC")

plot_enrichment(
  de.results,
  significant.sets,
  annotation = c("class", "length", "unsat"),
  measure = "logFC"
)

Arguments

de.results	Output of de_analysis().
rank.by	Statistic used to rank the lipid list. Default is logFC.
min_size	Minimum number of molecules in a set to be included in enrichment.
...	Extra parameters passed to fgsea::fgsea().
enrich.results	Output of lsea().
p.cutoff	Significance threshold. Default is 0.05.
size.cutoff	Minimum number of lipids in a set tested for enrichment. Default is 2.
significant.sets	List of significantly changed lipid sets (output of significant_lipidsets()).
measure	Which measure to plot the distribution of: logFC, P.Value, Adj.P.Val. Default is logFC.
annotation	Which lipid set collection to plot.

Value

lsea returns enrichment results (data.frame) as returned from fgsea::fgsea(). The results also contain the following attributes:

• de.results Original de.results input.

• rank.by Measure used to rank lipid molecules.

• sets Lipid sets tested, with their member molecules.

significant_lipidsets returns a list of character vectors of significantly enriched sets for each contrast.

plot_enrichment returns a ggplot object.

Functions

• significant_lipidsets(): gets a list of significantly changed lipid sets

• plot_enrichment(): is usually used to look at log2 fold change distribution of lipids in each class, chain length or unsaturation, marking significantly enriched sets. It can also be used to plot P.Value or Adj.P.Val.

Examples

data(data_normalized)
de_results <- de_analysis(
  data_normalized,
  HighFat_water - NormalDiet_water,
  measure = "Area"
)
enrich_results <- lsea(
  de_results,
  rank.by = "logFC", min_size = 4, nperm = 1000
)
sig_lipidsets <- significant_lipidsets(enrich_results)
plot_enrichment(de_results, sig_lipidsets, annotation="class")
plot_enrichment(de_results, sig_lipidsets, annotation="length")

---

Perform multivariate analyses to investigate sample clustering

Description

mva performs multivariate analysis using several possible methods. The available methods are PCA, PCoA, OPLS and OPLS-DA. The OPLS method requires a numeric y-variable, whilst OPLS-DA requires two groups for comparison. By default, for OPLS and OPLS-DA the number of predictive and orthogonal components are set to 1. Blank samples are automatically detected (using TIC) and excluded. Missing data are imputed using average lipid intensity across all samples.

Usage

mva(
  data,
  measure = "Area",
  method = c("PCA", "PCoA", "OPLS", "OPLS-DA"),
  group_col = NULL,
  groups = NULL,
  ...
)

plot_mva(
  mvaresults,
  components = c(1, 2),
  color_by = NULL,
  ellipse = TRUE,
  hotelling = TRUE
)

plot_mva_loadings(
  mvaresults,
  components = c(1, 2),
  color_by = NULL,
  top.n = nrow(mvaresults$loadings)
)

top_lipids(mvaresults, top.n = 10)

Arguments

data	LipidomicsExperiment object.
measure	Which measure to use as intensity, usually Area (default). The measure should be already summarized and normalized.
method	Either PCA, PCoA, OPLS or OPLS-DA. Default is PCA.
group_col	Sample annotation to use as grouping column. If not provided, samples are treated independently.
groups	A numeric grouping (OPLS) or two groups to be used for supervised analysis (OPLS-DA), ignored in other methods.
...	Extra arguments to be passed to opls() for OPLS-DA, ignored in other methods.
mvaresults	Results obtained from mva().
components	Which components to plot. Ignored for PCoA, OPLS and OPLS-DA results. Default is first 2 components.
color_by	Sample annotation (or lipid annotation in case of plot_mva_loadings) to use as color. Defaults to individual samples / lipids
ellipse	Whether to plot ellipses around groups
hotelling	Whether to plot Hotelling T2.
top.n	Number of top ranked features to highlight in the plot. If omitted, returns top 10 lipids.

Value

Multivariate analysis results in mvaresults object. The object contains the following:

• scores Sample scores

• loadings Feature or component loadings (not for PCoA)

• method Multivariate method that was used

• row_data Lipid molecule annotations

• col_data Sample annotations

• original_object Original output object as returned by corresponding analysis methods

plot_mva returns a ggplot of the sample scores.

plot_mva_loadings returns a ggplot of the loadings.

top_lipids returns s dataframe of top.n lipids with their annotations.

Functions

• plot_mva(): plots a multivariate scatterplot of sample scores to investigate sample clustering.

• plot_mva_loadings(): Plot a multivariate scatterplot of feature loadings to investigate feature importance.

• top_lipids(): extracts top lipids from OPLS-DA results

Examples

data(data_normalized)

# PCA
mvaresults <- mva(data_normalized, measure = "Area", method = "PCA")
plot_mva(mvaresults, color_by = "group")
# NOT RUN
# plot_mva(mvaresults, color_by = "Diet", components = c(2, 3))

# PCoA
mvaresults <- mva(data_normalized, measure = "Area", method = "PCoA")
# NOT RUN
# plot_mva(mvaresults, color_by = "group")

# OPLS-DA
mvaresults <- mva(
  data_normalized,
  method = "OPLS-DA", group_col = "Diet", groups = c("HighFat", "Normal")
)
plot_mva(mvaresults, color_by = "group")
plot_mva_loadings(mvaresults, color_by = "Class", top.n = 10)
top_lipids(mvaresults, top.n = 10)

---

Get a list of molecules that couldn't be parsed by lipidr

Description

Get a list of molecules that couldn't be parsed by lipidr

Usage

non_parsed_molecules(data)

Arguments

data	LipidomicsExperiment object.

Value

A character vector of the molecule names that could not be parsed.

Examples

data(data_normalized)
non_parsed_molecules(data_normalized)

---

Normalize each class by its corresponding internal standard(s).

Description

Normalize each class by its corresponding internal standard(s). Lipid classes are normalized using corresponding internal standard(s) of the same lipid class. If no corresponding internal standard is found the average of all measured internal standards is used instead.

Usage

normalize_istd(data, measure = "Area", exclude = "blank", log = TRUE)

Arguments

data	LipidomicsExperiment object.
measure	Which measure to use as intensity, usually Area, Area Normalized or Height. Default is Area.
exclude	Samples to exclude, can be either: "blank" - automatically detected blank samples and exclude them logical vector with the same length as samples. Default.
log	whether the normalized values should be log2 transformed. Default is TRUE.

Value

A LipidomicsExperiment object with normalized values. Each molecule is normalized against the internal standard from the same class.

Examples

datadir <- system.file("extdata", package = "lipidr")
filelist <- list.files(datadir, "data.csv", full.names = TRUE)
d <- read_skyline(filelist)
clinical_file <- system.file("extdata", "clin.csv", package = "lipidr")
d <- add_sample_annotation(d, clinical_file)
d_summarized <- summarize_transitions(d, method = "average")

# Normalize data that have been summarized (single value per molecule).
data_norm_istd <- normalize_istd(
  d_summarized,
  measure = "Area", exclude = "blank", log = TRUE
)

---

Perform Probabilistic Quotient Normalization for intensities.

Description

Perform Probabilistic Quotient Normalization (PQN) for sample intensities. The PQN method determines a dilution factor for each sample by comparing the distribution of quotients between samples and a reference spectrum, followed by sample normalization using this dilution factor. The reference spectrum in this method is the average lipid abundance of all samples (excluding blanks).

Usage

normalize_pqn(data, measure = "Area", exclude = "blank", log = TRUE)

Arguments

data	LipidomicsExperiment object.
measure	Which measure to use as intensity, usually Area, Area Normalized or Height. Default is Area.
exclude	Samples to exclude, can be either: "blank" - automatically detected blank samples and exclude them logical vector with the same length as samples. Default.
log	Whether the normalized values should be log2 transformed. Default is TRUE.

Value

A LipidomicsExperiment object with normalized values

References

Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H. (2006). Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical chemistry, 78(13), 4281-4290.

Examples

datadir <- system.file("extdata", package = "lipidr")
filelist <- list.files(datadir, "data.csv", full.names = TRUE)
d <- read_skyline(filelist)
clinical_file <- system.file("extdata", "clin.csv", package = "lipidr")
d <- add_sample_annotation(d, clinical_file)
d_summarized <- summarize_transitions(d, method = "average")

# Normalize data that have been summarized (single value per molecule).
data_normalized <- normalize_pqn(
  d_summarized,
  measure = "Area", exclude = "blank", log = TRUE
)

---

Plot logFC of lipids per class showing chain information

Description

Plot a chart of (log2) fold changes of lipids per class showing chain lengths and unsaturations. If multiple molecules with the same total chain length and unsaturation are present in the dataset, the measure is averaged, and the number of molecules is indicated on the plot.

Usage

plot_chain_distribution(de_results, contrast = NULL, measure = "logFC")

Arguments

de_results	Output of de_analysis().
contrast	Which comparison to plot. if not provided, defaults to the the first comparison.
measure	Which measure to plot the distribution of: logFC, P.Value, Adj.P.Val. Default is logFC

Value

A ggplot object.

Examples

data(data_normalized)
de_results <- de_analysis(
  data_normalized,
  HighFat_water - NormalDiet_water,
  measure = "Area"
)
plot_chain_distribution(de_results)

---

Informative plots to investigate lipid classes

Description

lipidr supports two types of plots for to visualize at lipid classes.

sd plots a bar chart for standard deviation of a certain measure in each class. This plot type is usually used to look at standard deviations of intensity in each class, but can also be used to look at different measures such as ⁠Retention Time⁠, to ensure all lipids are eluted within the expected range. To assess instrumental variation apply the function to technical quality control samples.

boxplot Plots a boxplot chart to examine the distribution of values per class. This plot type is usually used to look at the intensity distribution in each class, but can also be used to look at different measures, such as ⁠Retention Time⁠ or Background.

Usage

plot_lipidclass(data, type = c("boxplot", "sd"), measure = "Area", log = TRUE)

Arguments

data	LipidomicsExperiment object.
type	plot type, either boxplot or sd. Default is boxplot.
measure	Which measure to plot the distribution of: usually Area, Area Normalized, Height or Retention Time. Default is Area
log	Whether values should be log2 transformed. Default is TRUE (Set FALSE for retention time).

Value

A ggplot object.

Examples

data(data_normalized)

d_qc <- data_normalized[, data_normalized$group == "QC"]
plot_lipidclass(d_qc, "sd", "Area", log = TRUE)
plot_lipidclass(d_qc, "sd", "Retention Time", log = FALSE)
plot_lipidclass(d_qc, "boxplot", "Area", log = TRUE)
plot_lipidclass(d_qc, "boxplot", "Retention Time", log = FALSE)

---

Informative plots to investigate individual lipid molecules

Description

lipidr supports three types of plots for to visualize at lipid molecules.

cv plots a bar chart for coefficient of variation of lipid molecules. This plot type is usually used to investigate the CV in lipid intensity or retention time, in QC samples.

sd plots a bar chart for standard deviations of a certain measure in each lipid. This plot type is usually used to look at standard deviation of intensity for each lipid, but can also be used to look at different measures such as ⁠Retention Time⁠, to ensure all lipids elute within expected range.

boxplot plots a boxplot chart to examine the distribution of values per lipid. This plot type is usually used to look at intensity distribution for each lipid, but can also be used to look at different measures, such as ⁠Retention Time⁠ or Background.

Usage

plot_molecules(
  data,
  type = c("cv", "sd", "boxplot"),
  measure = "Area",
  log = TRUE,
  color = "Class"
)

Arguments

data	LipidomicsExperiment object.
type	plot type, either cv, sd or boxplot. Default is cv.
measure	Which measure to plot the distribution of: usually Area, Area Normalized or Height. Default is Area
log	Whether values should be log2 transformed (Set FALSE for retention time). Default is TRUE
color	The column name of a row annotation to be used as color

Value

A ggplot object.

Examples

data(data_normalized)
d_qc <- data_normalized[, data_normalized$group == "QC"]

# plot the variation in intensity and retention time of all measured
#   lipids in QC samples
plot_molecules(d_qc, "cv", "Area")
plot_molecules(d_qc, "cv", "Retention Time", log = FALSE)

# plot the variation in intensity, RT of ISTD (internal standards)
#   in QC samples
d_istd_qc <- data_normalized[
  rowData(data_normalized)$istd,
  data_normalized$group == "QC"
]
plot_molecules(d_istd_qc, "sd", "Area")
plot_molecules(d_istd_qc, "sd", "Retention Time", log = FALSE)

plot_molecules(d_istd_qc, "boxplot")
plot_molecules(d_istd_qc, "boxplot", "Retention Time", log = FALSE)

---

Informative plots to investigate samples

Description

lipidr supports two types of plots for sample quality checking.

tic plots a bar chart for total sample intensity.

boxplot plots a boxplot chart to examine the distribution of values per sample.

Usage

plot_samples(
  data,
  type = c("tic", "boxplot"),
  measure = "Area",
  log = TRUE,
  color = NULL
)

Arguments

data	LipidomicsExperiment object.
type	plot type, either tic or boxplot. Default is tic.
measure	Which measure to use as intensity, usually Area, Area Normalized or Height. Default is Area
log	Whether values should be log2 transformed. Default is TRUE
color	The column name of a sample annotation to be used as color

Value

A ggplot object.

Examples

data(data_normalized)

plot_samples(data_normalized, type = "tic", "Area", log = TRUE)
plot_samples(data_normalized, type = "tic", "Background", log = FALSE)
plot_samples(
  data_normalized[, data_normalized$group == "QC"],
  type = "boxplot",
  measure = "Retention Time", log = FALSE
)

---

Plot a regulation trend line between logFC and chain annotation

Description

Fit and plot a regression line of (log2) fold changes and total chain lengths or unsaturations. If multiple comparisons are included, one regression is plotted for each.

Usage

plot_trend(de_results, annotation = c("length", "unsat"))

Arguments

de_results	Output of de_analysis.
annotation	Whether to fit trend line against chain length or unsat.

Value

A ggplot object.

Examples

data(data_normalized)
de_results <- de_analysis(
  data_normalized,
  HighFat_water - NormalDiet_water,
  NormalDiet_DCA - NormalDiet_water,
  measure = "Area"
)
plot_trend(de_results, "length")

---

Read Skyline exported files

Description

Read Skyline exported files

Usage

read_skyline(files)

Arguments

files

Character vector with filepaths to Skyline exported files in CSV format.

Value

LipidomicsExperiment object.

Examples

datadir <- system.file("extdata", package = "lipidr")

# all csv files
filelist <- list.files(datadir, "data.csv", full.names = TRUE)
d <- read_skyline(filelist)

# View automatically generated lipid annotations
rowData(d)

---

Remove molecules that couldn't be parsed by lipidr from the dataset

Description

Remove molecules that couldn't be parsed by lipidr from the dataset

Usage

remove_non_parsed_molecules(data)

Arguments

data	LipidomicsExperiment object.

Value

A filtered LipidomicsExperiment object.

Examples

data(data_normalized)
remove_non_parsed_molecules(data_normalized)

---

Summarize transitions

Description

Calculate a single intensity for molecules with multiple transitions, by determining the average or maximum intensity.

Usage

summarize_transitions(data, method = c("max", "average"))

Arguments

data	LipidomicsExperiment object.
method	Choose to summarize multiple transitions by taking the average or maximum intensity. Default is max

Value

A LipidomicsExperiment object with single intensities per lipid molecule

Examples

datadir <- system.file("extdata", package = "lipidr")
filelist <- list.files(datadir, "data.csv", full.names = TRUE)
d <- read_skyline(filelist)
clinical_file <- system.file("extdata", "clin.csv", package = "lipidr")
d <- add_sample_annotation(d, clinical_file)
d_summarized <- summarize_transitions(d, method = "average")

---

Rename molecules in a dataset.

Description

This function enables users to rename selected molecules in the dataset, so that they can be parsed correctly by lipidr or modify the lipid class. lipidr automatically updates the annotation for the renamed molecules.

Usage

update_molecule_names(data, old, new)

Arguments

data	LipidomicsExperiment object.
old	A character vector of the molecule names to be renamed.
new	A character vector of the new molecule names.

Value

A LipidomicsExperiment object with molecules name and annotation updated.

Examples

data(data_normalized)
old_names <- rowData(data_normalized)$Molecule
# replace PCO with plasmenylPC
new_names <- sub("^LPE", "LysoPE", old_names)
update_molecule_names(data_normalized, old_names, new_names)

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Activate interactive graphics

Description

Use this function to turn on/off interactive graphics plotting. Interactive plots require plotly to be installed. Interactive graphics are disabled by default.

Usage

use_interactive_graphics(interactive = TRUE)

Arguments

interactive

Should interactive plots be displayed? Default is TRUE.

Value

None

Examples

data(data_normalized)
use_interactive_graphics()

# plot the variation in intensity and retention time of all measured
#  lipids in QC samples
d_qc <- data_normalized[, data_normalized$group == "QC"]
# plot_molecules(d_qc, "cv", "Area")

# turn off interactivity
use_interactive_graphics(interactive = FALSE)

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