Visualize Correlation for Multiple Identifiers

NOTE: the dataset must be dense matrix in UCSC Xena data hubs.

Usage

vis_identifier_multi_cor(
  dataset,
  ids,
  samples = NULL,
  matrix.type = c("full", "upper", "lower"),
  type = c("parametric", "nonparametric", "robust", "bayes"),
  partial = FALSE,
  sig.level = 0.05,
  p.adjust.method = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
    "none"),
  color_low = "#E69F00",
  color_high = "#009E73",
  ...
)

Arguments

dataset

the dataset to obtain identifiers.

ids

the molecule identifiers.

samples

default is NULL, can be common sample names for two datasets.

matrix.type

Character, "upper" (default), "lower", or "full", display full matrix, lower triangular or upper triangular matrix.

type

A character specifying the type of statistical approach:

• "parametric"

• "nonparametric"

• "robust"

• "bayes"

You can specify just the initial letter.

partial

Can be TRUE for partial correlations. For Bayesian partial correlations, "full" instead of pseudo-Bayesian partial correlations (i.e., Bayesian correlation based on frequentist partialization) are returned.

sig.level

Significance level (Default: 0.05). If the p-value in p-value matrix is bigger than sig.level, then the corresponding correlation coefficient is regarded as insignificant and flagged as such in the plot.

p.adjust.method

Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".

color_low

the color code for lower value mapping.

color_high

the color code for higher value mapping.

...

other parameters passing to ggstatsplot::ggcorrmat.

Value

a (gg)plot object.

Examples

if (FALSE) { # \dontrun{
dataset <- "TcgaTargetGtex_rsem_isoform_tpm"
ids <- c("TP53", "KRAS", "PTEN")
vis_identifier_multi_cor(dataset, ids)
} # }