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

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
)
#> Warning: Use of `sp_df$x` is discouraged.
#> ℹ Use `x` instead.
#> Warning: Use of `sp_df$x` is discouraged.
#> ℹ Use `x` instead.
#> Warning: Removed 10 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_label()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_label_repel()`).
#> Warning: Use of `sp_df$x` is discouraged.
#> ℹ Use `x` instead.
#> Warning: Removed 10 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_label()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_label_repel()`).
#> Warning: Use of `sp_df$x` is discouraged.
#> ℹ Use `x` instead.
#> Warning: Use of `sp_df$x` is discouraged.
#> ℹ Use `x` instead.
#> Warning: Removed 10 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_label_repel()`).
#> Warning: Use of `sp_df$x` is discouraged.
#> ℹ Use `x` instead.
#> Warning: Removed 10 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_label_repel()`).
print(plt)