plot_dist {DEP} | R Documentation

## Plot Gower's distance matrix

### Description

`plot_dist` generates a distance matrix heatmap using the Gower's distance.

### Usage

    plot_dist(dep, significant = TRUE, pal = "YlOrRd", pal_rev = TRUE,
      indicate = NULL, font_size = 12, plot = TRUE, ...)

### Arguments

`dep` | SummarizedExperiment, Data object for which differentially enriched proteins are annotated (output from `test_diff()` and `add_rejections()`).
`significant` | Logical(1), Whether or not to filter for significant proteins.
`pal` | Character(1), Sets the color panel (from RColorBrewer).
`pal_rev` | Logical(1), Whether or not to invert the color palette.
`indicate` | Character, Sets additional annotation on the top of the heatmap based on columns from the experimental design (colData).
`font_size` | Integer(1), Sets the size of the labels.
`plot` | Logical(1), If `TRUE` (default) the distance matrix plot is produced. Otherwise (if `FALSE`), the data which the distance matrix plot is based on are returned.
`...` | Additional arguments for Heatmap function as depicted in `Heatmap`

### Value

A heatmap plot (generated by `Heatmap`)

### Examples

    # Load example
    data <- UbiLength
    data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",]
    data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")

    # Make SummarizedExperiment
    columns <- grep("LFQ.", colnames(data_unique))
    exp_design <- UbiLength_ExpDesign
    se <- make_se(data_unique, columns, exp_design)

    # Filter, normalize and impute missing values
    filt <- filter_missval(se, thr = 0)
    norm <- normalize_vsn(filt)
    imputed <- impute(norm, fun = "MinProb", q = 0.01)

    # Test for differentially expressed proteins
    diff <- test_diff(imputed, "control", "Ctrl")
    dep <- add_rejections(diff, alpha = 0.05, lfc = 1)

    # Plot correlation matrix
    plot_dist(dep)

[Package DEP version 1.2.0 Index]