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]