corr_roc_heatmap

Perform ROC (Receiver Operating Characteristic) analysis for each lipid species or lipid characteristic against specified clinical conditions, and visualize the results as an interactive and static heatmap with hierarchical clustering. Lipids or characteristics with AUC values meeting the specified cutoff are highlighted as significant.

Usage

corr_roc_heatmap(
  processed_se,
  char = NULL,
  condition_col = c("Sex", "Smoking"),
  side_color_char,
  auc_cutoff = 0.5,
  distfun = c("pearson", "spearman", "kendall"),
  hclustfun = c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty",
    "median", "centroid"),
  transform = c("none", "log10", "square", "cube"),
  type = c("Sp", "Char")
)

Arguments

processed_se

A SummarizedExperiment object constructed by as_summarized_experiment and processed by data_process.

char

Character. A lipid characteristic selected from the common list returned by list_lipid_char. Required when type = 'Char'; must be NULL when type = 'Sp'.

condition_col

Character. One or more column names from the group information table representing clinical conditions (e.g., disease status, gene dependency scores) to be used as binary response variables in ROC analysis.

side_color_char

Character. A lipid characteristic used to annotate the side color bar of the heatmap. Must be selected from the common list returned by list_lipid_char. Required when type = 'Sp'; must be NULL when type = 'Char'.

auc_cutoff

Numeric. Minimum AUC threshold for a feature to be considered significant. Must be a value between 0 and 1. Default is 0.5.

distfun

Character. The correlation method used to compute the distance matrix for hierarchical clustering. Allowed methods include 'pearson', 'kendall', and 'spearman'. Default is 'spearman'.

hclustfun

Character. The agglomeration method for hierarchical clustering. Must be one of 'ward.D', 'ward.D2', 'single', 'complete', 'average' (= UPGMA), 'mcquitty' (= WPGMA), 'median' (= WPGMC), or 'centroid' (= UPGMC). Default is 'average'.

transform

Character. Method for data transformation applied before analysis. Allowed methods include 'none', 'log10', 'square', and 'cube'. Select 'none' to skip transformation. Default is 'log10'.

type

Character. Specifies the unit of correlation analysis: 'Sp' for lipid species-level analysis, or 'Char' for lipid characteristic-level analysis.

Value

A list containing the following elements:

all_correlation_result

A data frame of ROC analysis results for all features across all clinical conditions, including AUC, 95\ intervals, and significance flags.

sig_correlation_result

A data frame filtered to features whose AUC meets or exceeds auc_cutoff.

interactive_heatmap

An interactive heatmap widget.

static_heatmap

A static heatmap plot.

heatmap_matrix

The matrix used for heatmap plotting.

Examples

data("corr_data")
processed_se <- data_process(
    corr_data, exclude_missing=TRUE, exclude_missing_pct=70, replace_na_method='min',
    replace_na_method_ref=0.5, normalization='Percentage', transform='log10')
result <- corr_roc_heatmap(processed_se, side_color_char='class',
    condition_col=c('Sex', 'Smoking'), distfun='spearman',
    hclustfun='average', transform='log10', type='Sp')
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