iobr_cor_plot: Integrative Correlation Between Phenotype and Features

In IOBR/IOBR: Immune Oncology Biological Research

Integrative Correlation Between Phenotype and Features

Description

Performs integrative correlation analysis between phenotype data and feature data, supporting both continuous and categorical phenotypes. This function filters features based on adjusted p-value cutoffs and can visualize results in various plot formats 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 = sig_group,
  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	A data frame containing phenotype data including an identifier column.
id1	Column name in 'pdata_group' serving as the identifier.
feature_data	A data frame containing feature data corresponding to the identifiers.
id2	Column name in 'feature_data' serving as the identifier.
target	Optional; the column name for the target variable if continuous. Default is NULL.
group	The grouping variable name used for categorical analysis, default is "group3".
is_target_continuous	Logical; specifies if the target variable is continuous, affecting grouping strategy.
padj_cutoff	Cutoff for adjusted p-values to filter features, default is 1.
index	Numeric index used to order file names for output.
category	Specifies if the data pertains to 'signature' or 'gene'.
signature_group	Grouping variable for signatures; differentiates between 'sig_group' for signature grouping or 'signature_collection'/'signature_tme' for gene grouping.
ProjectID	Identifier for the project, used in file naming.
palette_box	Color palette for box plots.
cols_box	Optional; specific color settings for the box, default is NULL.
palette_corplot	Color palette for correlation plots.
palette_heatmap	Index for heatmap color palette.
feature_limit	Maximum number of features to consider, default is 26.
character_limit	Maximum number of characters for variable labels, default is 60.
show_heatmap_col_name	Logical; if TRUE, shows column names on the heatmap.
show_col	Logical; if TRUE, shows color codes for palettes.
show_plot	Logical; if TRUE, prints plots to the display.
path	Optional; path to save output files. Default is NULL.
discrete_x	Numeric threshold for character length beyond which labels will be discretized.
discrete_width	Numeric; specifies the width for label wrapping in plots.
show_palettes	Logical; if TRUE, displays color palettes used.
fig.type	Format for saving figures, default is 'pdf', can be changed to 'png'.

Value

Depending on the configuration, this function returns various plots such as box plots, heatmaps, and correlation plots, and may also return a dataframe containing statistical analysis results.

Author(s)

Dongqiang Zeng

Examples

# Assuming 'pdata_group' and 'feature_data' are predefined:
pdata_group <- data.frame(ID = 1:100, phenotype = sample(c("Type1", "Type2"), 100, replace = TRUE))
feature_data <- data.frame(ID = 1:100, Feature1 = rnorm(100), Feature2 = rnorm(100))
results <- iobr_cor_plot(pdata_group = pdata_group, feature_data = feature_data,
                         id1 = "ID", id2 = "ID", target = "Feature1", is_target_continuous = TRUE,
                         show_plot = TRUE, path = "path/to/save/results")
print(results)

IOBR/IOBR documentation built on Sept. 9, 2024, 8:36 p.m.