plot_expression_similarity {noisyr}

Plot the similarity against expression levels

Description

Creates the expression-similarity line and box plots for each sample.

Usage

plot_expression_similarity(
  expression.summary,
  sample.names = NULL,
  similarity.name = "Pearson correlation",
  log.transform = TRUE,
  min.y = NULL,
  max.y = NULL,
  smooth.span = 0.1,
  only.boxplot = FALSE,
  binsize = 1,
  last.together = 0,
  show.counts = TRUE,
  add.threshold = NULL,
  file.name = NULL
)

Arguments

expression.summary

list containing expression_levels and expression_levels_similarity matrices, as calculated by calculate_expression_similarity_counts or calculate_expression_similarity_transcript

sample.names

names for the plots, defaults to the column names of the expression matrix

similarity.name

similarity metric used (for the y-axis title)

log.transform

should the count matrix be log-transformed? If not, boxplot is skipped

min.y, max.y

limits for the y axis. If unset default to symmetric including all values in expression.levels.similarity; min is set to 0 if there are no negative values

smooth.span

span to be used for smoothing in the line plot; defaults to 0.1

only.boxplot

option to skip the line plot (usually a good idea if there are too many points and lines are too erratic); sets log.transform to TRUE

binsize

size of each bin in the boxplot; defaults to 0.5

last.together

groups observations so the highest abundance bin has at least this many

show.counts

whether to show how many observations are in each bin

add.threshold

adds a horizontal line at this value

file.name

name of pdf to output the plots; if not provided (default), no printing is done

Value

A list of all the plots (returned silently)

Examples

plots <- plot_expression_similarity(
  expression.summary=list(
    "expression.levels" = matrix(2^(10*seq(0,1,length.out=100))),
    "expression.levels.similarity" = matrix(seq(0,1,length.out=100)+(runif(100)/5))))
plots[[1]]
plots[[2]]