CimpleG (version 1.0.1)

diffmeans_sumvariance_plot: 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
)

Value

a ggplot2 object with the dmsv plot.

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

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)