| proximity_matrix_RF {multiclassPairs} | R Documentation |
Plot binary rule-based heatmaps
Description
proximity_matrix_RF Plot clustering heatmaps showing which out-of-bag samples are predicted in the same class and in the same trees during the training process for the rule-based random forest classifier
Usage
proximity_matrix_RF(object,
classifier,
plot=TRUE,
return_matrix=TRUE,
title = "",
top_anno = c("ref","platform")[1],
classes = NULL,
sam_order = NULL,
ref_col = NULL,
platform_col = NULL,
platforms_ord = NULL,
show_platform = TRUE,
cluster_cols = FALSE,
legend = TRUE,
anno_height = 0.03,
margin = c(0, 5, 0, 5))
Arguments
object |
data_object generated by ReadData function which was used in the training process. |
classifier |
classifier as a rule_based_RandomForest object, generated by train_RF function |
plot |
logical. To plot the proximity matrix or not. Default is TRUE. |
return_matrix |
logical. To return the proximity matrix or not. Default is TRUE. |
title |
Character input as a title for the whole heatmap. Default is "". |
top_anno |
Determine the top annotation level. Samples will be grouped based on the top_anno. Input can be one of two options: "ref", "platform". Default is "ref". |
classes |
Optional vector with the class names. Classes will determine which classes will be plotted and in which order. It is not recommended to use both "classes" and "platforms_ord" arguments together. |
sam_order |
Optional vector with the samples order in the heatmap. |
ref_col |
optional named vector determines the colors of classes for the reference labels. Default is NULL. Vector names should match with the ref labels. |
platform_col |
optional named vector determines the colors of platforms/study labels. Default is NULL. Vector names should match with the platforms/study labels. |
platforms_ord |
Optional vector with the platform/study names. This will determine which platform/study will be plotted and in which order. This will be used when top_anno="platform". It is not recommended to use both "classes" and "platforms_ord" arguments together. |
show_platform |
logical. Determines if the platform/study labels will be plotted or not. If the top_anno argument is "platform" then show_platform will be ignored. |
cluster_cols |
logical. samples will be grouped based on the class then will be Clustered in each class (i.e. not all samples in the cohort). If top_anno is "platform" then the rules from all classes are used to cluster the samples in each platform. |
legend |
logical. Determines if a legend will be plotted under the heatmap. |
anno_height |
Determines the height of the annotations. It is recommended not to go out of this range 0.01<height<0.1. Default is 0.03. |
margin |
Determines the margins of the heatmap. Default is c(0, 5, 0, 5). |
Value
returns the proximity matrix and/or a heatmap plot for the proximity matrix.
Author(s)
Nour-al-dain Marzouka <nour-al-dain.marzouka at med.lu.se>
Examples
# generate random data
Data <- matrix(runif(8000), nrow=100, ncol=80,
dimnames = list(paste0("G",1:100), paste0("S",1:80)))
# generate random labels
L <- sample(x = c("A","B","C","D"), size = 80, replace = TRUE)
# generate random platform labels
P <- sample(c("P1","P2","P3"), size = 80, replace = TRUE)
# create data object
object <- ReadData(Data = Data,
Labels = L,
Platform = P,
verbose = FALSE)
# sort genes
genes_RF <- sort_genes_RF(data_object = object,
seed=123456, verbose = FALSE)
# to get an idea of how many genes we will use
# and how many rules will be generated
# summary_genes_RF(sorted_genes_RF = genes_RF,
# genes_altogether = c(10,20,50,100,150,200),
# genes_one_vs_rest = c(10,20,50,100,150,200))
# creat and sort rules
# rules_RF <- sort_rules_RF(data_object = object,
# sorted_genes_RF = genes_RF,
# genes_altogether = 100,
# genes_one_vs_rest = 100,
# seed=123456,
# verbose = FALSE)
# parameters <- data.frame(
# gene_repetition=c(3,2,1),
# rules_one_vs_rest=0,
# rules_altogether=c(2,3,10),
# run_boruta=c(FALSE,"produce_error",FALSE),
# plot_boruta = FALSE,
# num.trees=c(100,200,300),
# stringsAsFactors = FALSE)
# parameters
# Or you can use expand.grid to generate dataframe with all parameter combinations
# parameters <- expand.grid(
# gene_repetition=c(3,2,1),
# rules_one_vs_rest=0,
# rules_altogether=c(2,3,10),
# num.trees=c(100,500,1000),
# stringsAsFactors = FALSE)
# parameters
# test <- optimize_RF(data_object = object,
# sorted_rules_RF = rules_RF,
# test_object = NULL,
# overall = c("Accuracy"),
# byclass = NULL, verbose = FALSE,
# parameters = parameters)
# test
# test$summary[which.max(test$summary$Accuracy),]
#
# # train the final model
# # it is preferred to increase the number of trees and rules in case you have
# # large number of samples and features
# # for quick example, we have small number of trees and rules here
# # based on the optimize_RF results we will select the parameters
# RF_classifier <- train_RF(data_object = object,
# gene_repetition = 1,
# rules_altogether = 0,
# rules_one_vs_rest = 10,
# run_boruta = FALSE,
# plot_boruta = FALSE,
# probability = TRUE,
# num.trees = 300,
# sorted_rules_RF = rules_RF,
# boruta_args = list(),
# verbose = TRUE)
#
# # training accuracy
# # get the prediction labels
# # if the classifier trained using probability = FALSE
# training_pred <- RF_classifier$RF_scheme$RF_classifier$predictions
# if (is.factor(training_pred)) {
# x <- as.character(training_pred)
# }
#
# # if the classifier trained using probability = TRUE
# if (is.matrix(training_pred)) {
# x <- colnames(training_pred)[max.col(training_pred)]
# }
#
# # training accuracy
# caret::confusionMatrix(data =factor(x),
# reference = factor(object$data$Labels),
# mode = "everything")
# not to run
# visualize the binary rules in training dataset
# plot_binary_RF(Data = object,
# classifier = RF_classifier,
# prediction = NULL, as_training = TRUE,
# show_scores = TRUE,
# top_anno = "ref",
# show_predictions = TRUE,
# title = "Training data")
# not to run
# Extract and plot the proximity matrix from the classifier for the training data
# it takes long time for large data
# proximity_mat <- proximity_matrix_RF(object = object,
# classifier = RF_classifier,
# plot=TRUE,
# return_matrix=TRUE,
# title = "Test",
# cluster_cols = TRUE)
# not to run
# predict
# test_object # any test data
# results <- predict_RF(classifier = RF_classifier, impute = TRUE,
# Data = test_object)
#
# # visualize the binary rules in training dataset
# plot_binary_RF(Data = test_object,
# classifier = RF_classifier,
# prediction = results, as_training = FALSE,
# show_scores = TRUE,
# top_anno = "ref",
# show_predictions = TRUE,
# title = "Test data")