plot_hierarchical_clustering {proBatch} | R Documentation

## cluster the data matrix to visually inspect which confounder dominates

### Description

cluster the data matrix to visually inspect which confounder dominates

### Usage

    plot_hierarchical_clustering(data_matrix, color_df,
      distance = "euclidean", agglomeration = "complete",
      label_samples = TRUE, label_font = 0.2, plot_title = NULL, ...)

### Arguments

`data_matrix` | features (in rows) vs samples (in columns) matrix, with feature IDs in rownames and file/sample names as colnames. in most function, it is assumed that this is the log transformed version of the original data
`color_df` | data frame of colors, as created by `sample_annotation_to_colors`
`distance` | distance metric used for clustering
`agglomeration` | agglomeration methods as used by `hclust`
`label_samples` | if `TRUE` sample IDs (column names of `data_matrix`) will be printed
`label_font` | size of the font. Is active if `label_samples` is `TRUE`, ignored otherwise
`plot_title` | Title of the plot (usually, processing step + representation level (fragments, transitions, proteins))
`...` | other parameters of `plotDendroAndColors` from `WGCNA` package

### Value

No return

### See Also

`hclust`, `sample_annotation_to_colors`, `plotDendroAndColors`

### Examples

    color_scheme <- sample_annotation_to_colors (example_sample_annotation,
    factor_columns = c('MS_batch','EarTag', "Strain", "Diet", "digestion_batch", "Sex"),
    not_factor_columns = 'DateTime',
    numeric_columns = c('order'))

    color_annotation <- color_scheme$color_df

    hiarchical_clustering_plot <- plot_hierarchical_clustering(
    example_proteome_matrix, color_annotation,
    distance = "euclidean", agglomeration = 'complete',
    label_samples = FALSE)

[Package proBatch version 1.0.0 Index]