Integrative Correlation Analysis Between Phenotype and Features

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

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()
)
#> ℹ Processing signature: "signature"
#> tidyHeatmap says: (once per session) from release 1.7.0 the scaling is set to "none" by default. Please use scale = "row", "column" or "both" to apply scaling
#> tidyHeatmap says: If you use tidyHeatmap for scientific research, please cite: Mangiola, S. and Papenfuss, A.T., 2020. 'tidyHeatmap: an R package for modular heatmap production based on tidy principles.' Journal of Open Source Software. doi:10.21105/joss.02472.
#> This message is displayed once per session.
#> Warning: `when()` was deprecated in purrr 1.0.0.
#> ℹ Please use `if` instead.
#> ℹ The deprecated feature was likely used in the tidyHeatmap package.
#>   Please report the issue at
#>   <https://github.com/stemangiola/tidyHeatmap/issues>.
#> ℹ Heatmap3 palettes: pheatmap, peach, blues, virids, reds, normal

#> ℹ Computing spearman correlation for 3 features
#> ✔ Correlation analysis complete

print(results)
#> # A tibble: 3 × 6
#>   sig_names p.value statistic p.adj log10pvalue stars
#>   <chr>       <dbl>     <dbl> <dbl>       <dbl> <fct>
#> 1 Feature2    0.225  -0.122   0.493      0.647  "+"  
#> 2 Feature3    0.328  -0.0987  0.493      0.484  "+"  
#> 3 Feature1    0.969   0.00390 0.969      0.0136 ""