iobr_cor_plot: Integrative Correlation Analysis Between Phenotype and...

In IOBR: Immune Oncology Biological Research

Integrative Correlation Analysis Between Phenotype and Features

Description

Performs comprehensive correlation analysis between phenotype data and feature data, supporting both continuous and categorical phenotypes. Filters features based on statistical significance and generates publication-ready visualizations including box plots, heatmaps, and correlation plots.

Usage

iobr_cor_plot(
  pdata_group,
  id1 = "ID",
  feature_data,
  id2 = "ID",
  target = NULL,
  group = "group3",
  is_target_continuous = TRUE,
  padj_cutoff = 1,
  index = 1,
  category = "signature",
  signature_group = NULL,
  ProjectID = "TCGA",
  palette_box = "nrc",
  cols_box = NULL,
  palette_corplot = "pheatmap",
  palette_heatmap = 2,
  feature_limit = 26,
  character_limit = 60,
  show_heatmap_col_name = FALSE,
  show_col = FALSE,
  show_plot = FALSE,
  path = NULL,
  discrete_x = 20,
  discrete_width = 20,
  show_palettes = FALSE,
  fig.type = "pdf"
)

Arguments

pdata_group	Data frame containing phenotype data with an identifier column.
id1	Character string specifying the column name in 'pdata_group' serving as the sample identifier. Default is '"ID"'.
feature_data	Data frame containing feature data with corresponding identifiers.
id2	Character string specifying the column name in 'feature_data' serving as the sample identifier. Default is '"ID"'.
target	Character string specifying the target variable column name for continuous analysis. Default is 'NULL'.
group	Character string specifying the grouping variable name for categorical analysis. Default is '"group3"'.
is_target_continuous	Logical indicating whether the target variable is continuous, which affects grouping strategy. Default is 'TRUE'.
padj_cutoff	Numeric value specifying the adjusted p-value cutoff for filtering features. Default is '1'.
index	Numeric index used for ordering output file names. Default is '1'.
category	Character string specifying the data category: '"signature"' or '"gene"'.
signature_group	List specifying the grouping variable for signatures. Options include '"sig_group"' for signature grouping or '"signature_collection"'/'"signature_tme"' for gene grouping.
ProjectID	Character string specifying the project identifier for file naming.
palette_box	Character string or integer specifying the color palette for box plots. Default is '"nrc"'.
cols_box	Character vector of specific colors for box plots. Default is 'NULL'.
palette_corplot	Character string or integer specifying the color palette for correlation plots. Default is '"pheatmap"'.
palette_heatmap	Integer specifying the color palette index for heatmaps. Default is '2'.
feature_limit	Integer specifying the maximum number of features to display. Default is '26'.
character_limit	Integer specifying the maximum number of characters for variable labels. Default is '60'.
show_heatmap_col_name	Logical indicating whether to display column names on heatmaps. Default is 'FALSE'.
show_col	Logical indicating whether to display color codes for palettes. Default is 'FALSE'.
show_plot	Logical indicating whether to display plots. Default is 'FALSE'.
path	Character string specifying the directory path for saving output files. Default is 'NULL'.
discrete_x	Numeric threshold for character length beyond which labels are discretized. Default is '20'.
discrete_width	Numeric value specifying the width for label wrapping in plots. Default is '20'.
show_palettes	Logical indicating whether to display color palettes. Default is 'FALSE'.
fig.type	Character string specifying the format for saving figures ('"pdf"', '"png"', etc.). Default is '"pdf"'.

Value

Depending on configuration, returns ggplot2 objects (box plots, heatmaps, correlation plots) and/or a data frame containing statistical analysis results.

Author(s)

Dongqiang Zeng

Examples

set.seed(123)

pdata_group <- data.frame(
  ID = 1:100,
  phenotype_score = rnorm(100)
)

feature_data <- data.frame(
  ID = 1:100,
  Feature1 = rnorm(100),
  Feature2 = rnorm(100),
  Feature3 = rnorm(100)
)

sig_group_example <- list(
  signature = c("Feature1", "Feature2", "Feature3")
)

results <- iobr_cor_plot(
  pdata_group = pdata_group,
  feature_data = feature_data,
  id1 = "ID",
  id2 = "ID",
  target = "phenotype_score",
  is_target_continuous = TRUE,
  category = "signature",
  signature_group = sig_group_example,
  show_plot = FALSE,
  path = tempdir()
)

print(results)

IOBR documentation built on May 30, 2026, 5:07 p.m.