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.

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)
#> $`HighFat_water - NormalDiet_water` #> [1] PE 32:1 PE 32:2 PE 34:2 PE 34:3 #> [5] PE 36:5 PE 38:2 PE(O-34:2) PE(O-38:4) #> [9] PE(P-32:0) PE(P-34:1) PE(P-34:2) PE(P-38:3) #> [13] PG 16:0/18:1 PG 16:1/16:0 PG 18:0/16:0 PG 18:1/18:0 #> [17] PG 18:1/18:1 PG 18:2/16:0 PG 18:2/18:0 PG 18:2/18:1 #> [21] PG 20:4/16:0 PG 20:4/16:1 PI 32:2 PI 32:3 #> [25] PI 34:1p PI 34:2 PI 34:3 PI 36:3 #> [29] Sa1P d 18:0 SM 18:1/16:2 SM 18:1/18:0 SM 18:1/18:1 #> [33] SM 18:1/20:0 SM 18:1/20:2 SM 18:1/22:3 SM 18:1/22:4 #> [37] SM 18:1/22:5 SM d18:0/18:0 SM d18:0/20:0 Cer d18:0/C16:0 #> [41] Cer d18:0/C18:0 Cer d18:0/C20:0 Cer d18:0/C22:0 Cer d18:0/C24:0 #> [45] Cer d18:0/C24:1 Cer d18:0/C24:2 Cer d18:1/C16:0 Cer d18:1/C16:1 #> [49] Cer d18:1/C18:0 Cer d18:1/C18:1 Cer d18:1/C20:0 Cer d18:1/C20:1 #> [53] Cer d18:1/C20:2 Cer d18:1/C22:0 Cer d18:1/C22:1 Cer d18:1/C22:2 #> [57] Cer d18:1/C22:3 Cer d18:1/C22:5 Cer d18:1/C22:6 Cer d18:1/C24:1 #> [61] Cer d18:1/C26:0 Cer d18:2/C20:0 PC 36:5 PC 36:6 #> [65] PC 40:2 PC 40:8 LPC 20:0 LPC 20:4 #> [69] LPC 22:0 LPC 26:0 LPE 20:0 LPE 20:5 #> [73] PC(O-34:2) PC(O-34:3) PC(O-38:2) PC(O-40:1) #> [77] PC(P-34:1) PC(P-34:2) PC(P-34:3) PC(P-36:5) #> [81] PC(P-38:0) PC(P-38:1) PC(P-40:0) #> 277 Levels: PE 32:0 PE 32:1 PE 32:2 PE 34:1 PE 34:1 NEG PE 34:2 ... PC(P-40:6) #>
plot_results_volcano(de_results, show.labels = FALSE)