multiclassPairs (version 0.4.3)

plot_binary_TSP: Plot binary rule-based heatmaps

Description

plot_binary_TSP Plot binary heatmaps for datasets based on one-vs-rest multiclass top score pairs classifier

Usage

plot_binary_TSP(Data,
                classifier,
                ref              = NULL,
                prediction       = NULL,
                platform         = NULL,
                classes          = NULL,
                platforms_ord    = NULL,
                top_anno         = c("ref", "prediction", "platform")[1],
                title            = "",
                binary_col       = c("white", "black", "gray"),
                ref_col          = NULL,
                pred_col         = NULL,
                platform_col     = NULL,
                show_ref         = TRUE,
                show_predictions = TRUE,
                show_platform    = TRUE,
                show_scores      = TRUE,
                show_rule_name   = TRUE,
                legend           = TRUE,
                cluster_cols     = TRUE,
                cluster_rows     = TRUE,
                anno_height      = 0.03,
                score_height     = 0.03,
                margin           = c(0, 5, 0, 5))

Arguments

Data

a matrix, dataframe, where samples as columns and row as features/genes. Can also take ExpressionSet, or data_object generated by ReadData function.

classifier

classifier as a OnevsrestScheme_TSP object, generated by train_one_vs_rest_TSP function

ref

Optional vector with the reference labels. Ref labels in data_object will be used if not ref input provided. For ExpressionSet, the name of the variable in the pheno data.

prediction

Optional dataframe with class "OneVsRestTSP prediction" generated by predict_one_vs_rest_TSP function with the scores and the predicted labels.

platform

Optional vector with the platform/study labels or any additional annotation. Platform labels in data_object will be used if no platform input provided. For ExpressionSet, the name of the variable in the pheno data.

classes

Optional vector with the class names. This will determine which classes will be plotted and in which order. It is not recommended to use both "classes" and "platforms_ord" arguments together.

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.

top_anno

Determine the top annotation level. Samples will be grouped based on the top_anno. Input can be one of three options: "ref", "prediction", "platform". Default is "ref".

title

Carachter input as a title for the whole heatmap. Default is "".

binary_col

vector determines the colors of the binary heatmap. Default is c("white", "black", "gray"). First color for the color when the rule is false in the sample. Second color for the color when the rule is true. Third color is for NAs.

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.

pred_col

optional named vector determines the colors of classes for the prediction labels. Default is NULL. Vector names should match with the prediction labels in the prediction 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.

show_ref

logical. Determines if the ref labels will be plotted or not. If the top_anno argument is "ref" then show_ref will be ignored.

show_predictions

logical. Determines if the prediction labels will be plotted or not. If the top_anno argument is "prediction" then show_predictions will be ignored.

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.

show_scores

logical. Determines if the prediction scores will be plotted or not.

show_rule_name

logical. Determines if the rule names will be plotted on the left side of the heatmapp or not.

legend

logical. Determines if a legend will be plotted under the heatmap.

cluster_cols

logical. Clustering the samples in each class (i.e. not all samples in the cohort) based on the binary rules for that class. If top_anno is "platform" then the rules from all classes are used to cluster the samples in each platform.

cluster_rows

logical. Clustering the rules in each class.

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.

score_height

Determines the height of the score bars. 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 a heatmap plot for the binary rule

Examples

# NOT RUN {
# random data
Data <- matrix(runif(10000), nrow=100, ncol=100,
               dimnames = list(paste0("G",1:100), paste0("S",1:100)))

# labels
L <- sample(x = c("A","B","C"), size = 100, replace = TRUE)

# study/platform
P <- sample(c("P1","P2"), size = 100, replace = TRUE)

object <- ReadData(Data = Data,
             Labels = L,
                   Platform = P)

# not to run
# switchBox package from Bioconductor is needed
# Visit their website or install switchBox package using:
# if(!requireNamespace("switchBox", quietly = TRUE)){
#       if (!requireNamespace('BiocManager', quietly = TRUE)) {
#       install.packages('BiocManager')
#      }
#      BiocManager::install('switchBox')", call. = FALSE)
#  }

#filtered_genes <- filter_genes_TSP(data_object = object,
#                                   filter = "one_vs_rest",
#                                  platform_wise = FALSE,
#                                  featureNo = 10,
#                                  UpDown = TRUE,
#                                  verbose = FALSE)

# training
# classifier <- train_one_vs_rest_TSP(data_object = object,
#                              filtered_genes = filtered_genes,
#                              k_range = 2:50,
#                              include_pivot = FALSE,
#                              one_vs_one_scores = FALSE,
#                              platform_wise_scores = FALSE,
#                              seed = 1234,
#                              verbose = FALSE)

# results <- predict_one_vs_rest_TSP(classifier = classifier,
#                                   Data = object,
#                                   tolerate_missed_genes = TRUE,
#                                   weighted_votes = TRUE,
#                                   verbose = FALSE)

# Confusion Matrix and Statistics on training data
#  caret::confusionMatrix(data = factor(results$max_score, levels = unique(L)),
#                         reference = factor(L, levels = unique(L)),
#                         mode="everything")

# plot_binary_TSP(Data = object, classes=c("A","B","C"),
#                 classifier = classifier,
#                 prediction = results,
#                 title = "Test")
# }