Description Usage Arguments Value Functions Examples
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.
1 2 3 4 5 6 7  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)

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

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 
coef 
Column number or column name specifying which coefficient of the linear model is of interest. 
de.results 
Output of 
p.cutoff 
Significance threshold. Default is 
logFC.cutoff 
Cutoff limit for log2 fold change. Default is 
show.labels 
Whether labels should be displayed for
significant lipids. Default is 
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.
significant_molecules
: gets a list of significantly changed lipids for
each contrast.
plot_results_volcano
: plots a volcano chart for differential analysis
results.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  # type ?normalize_pqn to see how to normalize and log2transform 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)

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