Summary the combined DE and model results

hd_plot_feature_heatmap plots a summary bubble-heatmap of the combined differential expression and classification model results. The heatmap shows the log2 fold change and adjusted p-value of the differential expression results, and the scaled importance and sign of the classification model results. The heatmap is ordered and the selected assays are based on the specified control group. * are added to the features that pass the p-value threshold.

Usage

hd_plot_feature_heatmap(
  de_results,
  model_results,
  order_by,
  pval_lim = 0.05,
  logfc_lim = 0
)

Arguments

de_results

A list of differential expression results.

model_results

A list of classification models results.

order_by

The control group to order the heatmap.

pval_lim

The p-value limit to filter the differential expression results of the order_by group.

logfc_lim

The log2 fold change limit to filter the differential expression results of the order_by group.

Value

The summary heatmap of the combined differential expression and classification models results.

Details

It is very important the de_results and model_results are in the same order and in the right format (see examples).

Examples

# Initialize an HDAnalyzeR object
hd_object <- hd_initialize(example_data, example_metadata)

# Run differential expression analysis for AML vs all others
de_results_myel <- hd_de_limma(hd_object, case = "AML", control = "MYEL")
de_results_lungc <- hd_de_limma(hd_object, case = "AML", control = "LUNGC")
de_results_gliom <- hd_de_limma(hd_object, case = "AML", control = "GLIOM")

res_de <- list("MYEL" = de_results_myel,
               "LUNGC" = de_results_lungc,
               "GLIOM" = de_results_gliom)


# Run Classification models
# Split the data into training and test sets
hd_split <- hd_split_data(hd_object, variable = "Disease")
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.

# Run the regularized regression model pipeline
model_results_myel <- hd_model_rreg(hd_split,
                                    variable = "Disease",
                                    case = "AML",
                                    control = "MYEL",
                                    grid_size = 2,
                                    cv_sets = 2,
                                    verbose = FALSE)
#> The groups in the train set are balanced. If you do not want to balance the groups, set `balance_groups = FALSE`.

model_results_lungc <- hd_model_rreg(hd_split,
                                     variable = "Disease",
                                     case = "AML",
                                     control = "LUNGC",
                                     grid_size = 2,
                                     cv_sets = 2,
                                     verbose = FALSE)
#> The groups in the train set are balanced. If you do not want to balance the groups, set `balance_groups = FALSE`.

model_results_gliom <- hd_model_rreg(hd_split,
                                     variable = "Disease",
                                     case = "AML",
                                     control = "GLIOM",
                                     grid_size = 2,
                                     cv_sets = 2,
                                     verbose = FALSE)
#> The groups in the train set are balanced. If you do not want to balance the groups, set `balance_groups = FALSE`.

# The models are in the same order as the DE results
res_model <- list("MYEL" = model_results_myel,
                  "LUNGC" = model_results_lungc,
                  "GLIOM" = model_results_gliom)


# Create the summary heatmap
hd_plot_feature_heatmap(res_de, res_model, order_by = "MYEL")
#> Warning: Removed 26 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 26 rows containing missing values or values outside the scale range
#> (`geom_point()`).