de_analysis {lipidr}R Documentation

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

SkylineExperiment object created by read_skyline(), 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

Name of the column containing sample names. Default is Area.

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.

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

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)

[Package lipidr version 1.0.0 Index]