plot_sample_corr_heatmap: Sample correlation matrix (heatmap)

Description Usage Arguments Value See Also Examples

View source: R/correlation-based_diagnostics.R

Description

Plot correlation of selected samples

Usage

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plot_sample_corr_heatmap(
  data_matrix,
  samples_to_plot = NULL,
  sample_annotation = NULL,
  sample_id_col = "FullRunName",
  factors_to_plot = NULL,
  cluster_rows = FALSE,
  cluster_cols = FALSE,
  heatmap_color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
  color_list = NULL,
  filename = NULL,
  width = NA,
  height = NA,
  units = c("cm", "in", "mm"),
  plot_title = sprintf("Correlation matrix of%s samples",
    ifelse(is.null(samples_to_plot), "", " selected")),
  ...
)

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"))

samples_to_plot

string vector of samples in data_matrix to be used in the plot

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 covariates to be plotted in this diagnostic plot (assumed to be present in sample_annotation)

cluster_rows

boolean values determining if rows should be clustered or hclust object

cluster_cols

boolean values determining if columns should be clustered or hclust object

heatmap_color

vector of colors used in heatmap.

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.

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))

...

parameters for the pheatmap visualisation, for details see examples and help to corresponding functions

Value

pheatmap object

See Also

pheatmap

Examples

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specified_samples = example_sample_annotation$FullRunName[
which(example_sample_annotation$order %in% 110:115)] 

sample_corr_heatmap <- plot_sample_corr_heatmap(example_proteome_matrix, 
samples_to_plot = specified_samples, 
factors_to_plot = c('MS_batch','Diet', 'DateTime', 'digestion_batch'),
 cluster_rows= FALSE, cluster_cols=FALSE,
 annotation_names_col = TRUE, annotation_legend = 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'))
 sample_corr_heatmap_annotated <- plot_sample_corr_heatmap(log_transform_dm(example_proteome_matrix), 
 sample_annotation = example_sample_annotation,
 factors_to_plot = c('MS_batch','Diet', 'DateTime', 'digestion_batch'),
 cluster_rows= FALSE, cluster_cols=FALSE,
 annotation_names_col = TRUE, 
 show_colnames = FALSE, color_list = color_list)

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