plot_hierarchical_clustering: cluster the data matrix to visually inspect which confounder...

In proBatch: Tools for Diagnostics and Corrections of Batch Effects in Proteomics

Description

cluster the data matrix to visually inspect which confounder dominates

Usage

plot_hierarchical_clustering(
  data_matrix,
  sample_annotation,
  sample_id_col = "FullRunName",
  color_list = NULL,
  factors_to_plot = NULL,
  fill_the_missing = 0,
  distance = "euclidean",
  agglomeration = "complete",
  label_samples = TRUE,
  label_font = 0.2,
  filename = NULL,
  width = 38,
  height = 25,
  units = c("cm", "in", "mm"),
  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. See "example_proteome_matrix" for more details (to call the description, use help("example_proteome_matrix"))
sample_annotation	data frame with: sample_id_col (this can be repeated as row names) biological covariates technical covariates (batches etc) . See help("example_sample_annotation")
sample_id_col	name of the column in sample_annotation table, where the filenames (colnames of the data_matrix are found).
color_list	list, as returned by sample_annotation_to_colors, where each item contains a color vector for each factor to be mapped to the color.
factors_to_plot	vector of technical and biological covariates to be plotted in this diagnostic plot (assumed to be present in sample_annotation)
fill_the_missing	numeric value determining how missing values should be substituted. If NULL, features with missing values are excluded.
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
filename	path where the results are saved. If null the object is returned to the active window; otherwise, the object is save into the file. Currently only pdf and png format is supported
width	option determining the output image width
height	option determining the output image width
units	units: 'cm', 'in' or 'mm'
plot_title	title of the plot (e.g., processing step + representation level (fragments, transitions, proteins) + purpose (meanplot/corrplot etc))
...	other parameters of plotDendroAndColors from WGCNA package

Value

No return

See Also

hclust, sample_annotation_to_colors, plotDendroAndColors

Examples

selected_batches = example_sample_annotation$MS_batch %in% 
                                              c('Batch_1', 'Batch_2')
selected_samples = example_sample_annotation$FullRunName[selected_batches]
test_matrix = example_proteome_matrix[,selected_samples]

hierarchical_clustering_plot <- plot_hierarchical_clustering(
example_proteome_matrix, example_sample_annotation,
factors_to_plot = c('MS_batch', 'Diet', 'DateTime'),
color_list = NULL,  
distance = "euclidean", agglomeration = 'complete',
label_samples = FALSE)

#with defined color scheme:
color_list <- sample_annotation_to_colors (example_sample_annotation, 
factor_columns = c('MS_batch', "Strain", "Diet", "digestion_batch"),
numeric_columns = c('DateTime', 'order'))
hierarchical_clustering_plot <- plot_hierarchical_clustering(
example_proteome_matrix, example_sample_annotation,
factors_to_plot = c('MS_batch', "Strain", 'DateTime', "digestion_batch"),
color_list = color_list,  
distance = "euclidean", agglomeration = 'complete',
label_samples = FALSE)

proBatch documentation built on Nov. 8, 2020, 4:55 p.m.