diffmeans_sumvariance_plot: Creates the old version of the difference in means by sum of...

In CimpleG: A Method to Identify Single CpG Sites for Classification and Deconvolution

Creates the old version of the difference in means by sum of variances plot

Description

Represent CpGs in the difference in means, sum of variances space. This plot is often used to select CpGs that would be good classifiers. These CpGs are often located on the bottom left and bottom right of this plot.

Usage

diffmeans_sumvariance_plot(
  data,
  xcol = "diff_means",
  ycol = "sum_variance",
  feature_id_col = "id",
  is_feature_selected_col = NULL,
  label_var1 = "Target",
  label_var2 = "Others",
  target_vector = NULL,
  mean_cutoff = NULL,
  var_cutoff = NULL,
  threshold_func = NULL,
  func_factor = NULL,
  feats_to_highlight = NULL,
  cpg_ranking_df = NULL,
  color_all_points = NULL,
  plot_density = TRUE,
  density_type = c("density", "histogram", "boxplot", "violin", "densigram"),
  plot_dir = NULL,
  id_tag = NULL,
  file_tag = NULL,
  custom_mods = FALSE
)

Arguments

data	Data to create difference in means, sum of variances plot. Either a data.frame with 'xcol','ycol' and 'feature_id_col' or, if 'target_vector' is not 'NULL' a matrix with beta values from which, given the target, the difference in means between the target and others, and the sum of variances within the target and others will be calculated.
xcol	Column with x-axis data
ycol	Column with y-axis data
feature_id_col	Column with the feature ID
is_feature_selected_col	NULL or column with TRUE/FALSE for features which should be highlighted as selected
label_var1	Label of the target class
label_var2	Label of the other classes
target_vector	if not NULL a vector target class assignment, see data
mean_cutoff	a numeric draw mean cutoff at given position
var_cutoff	a numeric draw variance cutoff at given position
threshold_func	specification of the parabola function, see examples
func_factor	argument to be passed to the parabola function, see examples
feats_to_highlight	features (CpGs) to be highlighted in the plot
cpg_ranking_df	data.frame with ranked features (CpGs) to be highlighted in the plot, if present must have the following columns: .id, predType, Rank and DiffAndFoldScaledAUPR
color_all_points	color that all non-highlighted points should have, argument defaults to NULL, the default color is black
plot_density	A boolean, if TRUE (default) the function will produce density plots on top/side of scatterplot
density_type	One of "density", "histogram", "boxplot", "violin" or "densigram". Defines the type of density plot if 'plot_density = TRUE'
plot_dir	path to directory where to save the plot. If NULL (default), plot will not be saved.
id_tag	character string to identify plots, is displayed in the plot and present in the file name
file_tag	character string to identify plots, tags only the file name
custom_mods	a boolean, if TRUE will add some custom labels to the plot. Default is FALSE

Value

a ggplot2 object with the dmsv plot.

Examples

library("CimpleG")

# read data
data(train_data)
data(train_targets)

# make basic plot
plt <- diffmeans_sumvariance_plot(
  train_data,
  target_vector = train_targets$blood_cells == 1
)
print(plt)

# make plot with parabola, colored and highlighted features
df_dmeansvar <- compute_diffmeans_sumvar(
  train_data,
  target_vector = train_targets$blood_cells==1
)
parab_param <- .7
df_dmeansvar$is_selected <- select_features(
    x = df_dmeansvar$diff_means,
    y = df_dmeansvar$sum_variance,
    a = parab_param
)

plt <- diffmeans_sumvariance_plot(
  data=df_dmeansvar,
  label_var1="Leukocytes",
  color_all_points="red",
  is_feature_selected_col="is_selected",
  feats_to_highlight=c("cg10456121"),
  threshold_func=function(x,a) (a*x)^2,
  func_factor=parab_param
)
print(plt)

CimpleG documentation built on Dec. 7, 2025, 1:07 a.m.