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

Description Usage Arguments Value See Also Examples

View source: R/proteome_wide_diagnostics.R

Description

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

Usage

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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:

  1. sample_id_col (this can be repeated as row names)

  2. biological covariates

  3. 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

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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.