plot_heatmap_diagnostic: Plot the heatmap of samples (cols) vs features (rows)

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

Description

Plot the heatmap of samples (cols) vs features (rows)

Usage

plot_heatmap_diagnostic(
  data_matrix,
  sample_annotation = NULL,
  sample_id_col = "FullRunName",
  factors_to_plot = NULL,
  fill_the_missing = -1,
  color_for_missing = "black",
  heatmap_color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
  cluster_rows = TRUE,
  cluster_cols = FALSE,
  color_list = NULL,
  peptide_annotation = NULL,
  feature_id_col = "peptide_group_label",
  factors_of_feature_ann = c("KEGG_pathway", "evolutionary_distance"),
  color_list_features = NULL,
  filename = NULL,
  width = 7,
  height = 7,
  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).
factors_to_plot	vector of technical and biological factors to be plotted in this diagnostic plot (assumed to be present in sample_annotation)
fill_the_missing	numeric value that the missing values are substituted with, or NULL if features with missing values are to be excluded.
color_for_missing	special color to make missing values. Usually black or white, depending on heatmap_color
heatmap_color	vector of colors used in heatmap (typicall a gradient)
cluster_rows	boolean value determining if rows should be clustered
cluster_cols	boolean value determining if columns should be clustered
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.
peptide_annotation	long format data frame with peptide ID and their corresponding protein and/or gene annotations. See help("example_peptide_annotation").
feature_id_col	name of the column with feature/gene/peptide/protein ID used in the long format representation df_long. In the wide formatted representation data_matrix this corresponds to the row names.
factors_of_feature_ann	vector of factors that characterize features, as listed in peptide_annotation
color_list_features	list, as returned by sample_annotation_to_colors, but mapping peptide_annotation where each item contains a color vector for each factor to be mapped to the color.
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 link[pheatmap]{pheatmap}

Value

object returned by link[pheatmap]{pheatmap}

See Also

sample_annotation_to_colors, pheatmap

Examples

log_transformed_matrix = log_transform_dm(example_proteome_matrix)
heatmap_plot <- plot_heatmap_diagnostic(log_transformed_matrix, 
example_sample_annotation, 
factors_to_plot = c("MS_batch",  "digestion_batch", "Diet", 'DateTime'), 
cluster_cols = TRUE, cluster_rows = FALSE,
show_rownames = FALSE, show_colnames = FALSE)

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

log_transformed_matrix = log_transform_dm(example_proteome_matrix)
heatmap_plot <- plot_heatmap_diagnostic(log_transformed_matrix, 
example_sample_annotation, 
factors_to_plot = c("MS_batch",  "digestion_batch", "Diet", 'DateTime'), 
cluster_cols = TRUE, cluster_rows = FALSE,
color_list = color_list,
show_rownames = FALSE, show_colnames = FALSE)

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