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` | 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)

[Package lipidr version 2.6.0 Index]