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Correlation plot between latent components

Source: R/methods_comparison.R
comparison_plot_correlation.Rd

Plots the correlation between either the samples score or the features weight of the latent components obtained from two different integration methods.

Usage

comparison_plot_correlation(
  output_list,
  by = "both",
  latent_dimensions = NULL,
  include_missing_features = FALSE,
  show_cor = TRUE,
  min_show_cor = 0.2,
  round_cor = 2
)

Arguments

output_list

List of length 2 of integration methods output, each generated via the get_output() function. If named, the names will be used to annotate the plot. See details.

by

Character, should the correlation be calculated based on samples score ( by = 'samples') or on features weight (by = 'features'), or both (by = 'both', i.e. two matrices will be plotted). Default value is 'both'.

latent_dimensions

Named list, where each element is a character vector giving the latent dimensions to retain in the corresponding element of output_list. Names must match those of output_list. Can be used to filter latent dimensions only in certain elements from output_list (see examples). If NULL (default value), all latent dimensions will be used.

include_missing_features

Logical, see get_features_weight_correlation for details. Default value is FALSE.

show_cor

Logical, should the correlation values be added to the plot? Default value is TRUE.

min_show_cor

Numeric, the minimum value below which correlation coefficients values are not added to the plot (i.e. only a circle will appear for these values but no text). Ignored if show_cor is FALSE. Default value is 0.2.

round_cor

Integer, how many decimal places to show for the correlation coefficients. Ignored if show_cor is FALSE. Default value is 2.

Value

A ggplot.

Details

If output_list is unnamed, the different elements in the list will be differentiated by the name of the method used to produce them (e.g. DIABLO, sO2PLS, etc). In order to compare different results from a same integration method (e.g. DIABLO applied to the full vs pre-filtered data), it is possible to assign names to the elements of output_list. These names will be used in place of the method name in the plot to identify where the latent dimensions come from.