get_corr_heatbox: Generate a correlation heat plot.
In cparsania/parcutils: Utility Functions To Deal Day To Day Bioinformatics Tasks.

get_corr_heatbox

R Documentation

Generate a correlation heat plot.

Description

For all (or selected) samples this function generate a correlation heat box type plot.

Usage

get_corr_heatbox(
  x,
  samples = NULL,
  genes = NULL,
  corr_method = "pearson",
  plot_type = "full",
  vis_method = "square",
  show_diagonal = TRUE,
  show_corr_values = FALSE,
  col_corr_values = "yellow",
  size_corr_values = 5,
  cluster_samples = FALSE,
  scale_range = NULL
)

Arguments

`x`	an object of class parcutils.
`samples`	a character vector denoting samples to plot in scatter plot, default `NULL`. If set to NULL all samples are accounted.
`genes`	a character vector denoting genes to consider in scatter plot, default `NULL`. If set to NULL all genes are accounted.
`corr_method`	a character string, default `"pearson"`, denoting a value for correlation method. Value can be one of these `"pearson", "kendall", "spearman"`.
`plot_type`	a character string, default `"square"`, denoting value for plot type. Value can be one of these `"full", "lower", "upper"`.
`vis_method`	a character string, default `"full"`, denoting a value type of visualization. Value can be one of these `"square", "circle"`.
`show_diagonal`	a logical, default `TRUE`, denoting whether to show diagonal values or not.
`show_corr_values`	a logical, default `TRUE`, denoting whether to show corr values or not.
`col_corr_values`	a character string, default `"yellow"`, denoting a valid color string for corr values.
`size_corr_values`	a numeric, default `5`, denoting a size for corr values.
`cluster_samples`	a logical, default `TRUE`, denoting whether to cluster samples or not.
`scale_range`	a numeric vector of length two denoting minimum and maximum value for the color scale, default `NULL`.

Value

a corr plot.

Examples

count_file <- system.file("extdata","toy_counts.txt" , package = "parcutils")
count_data <- readr::read_delim(count_file, delim = "\t")

sample_info <- count_data %>% colnames() %>% .[-1]  %>%
 tibble::tibble(samples = . , groups = rep(c("control" ,"treatment1" , "treatment2"), each = 3) )


res <- run_deseq_analysis(counts = count_data ,
                         sample_info = sample_info,
                         column_geneid = "gene_id" ,
                         group_numerator = c("treatment1", "treatment2") ,
                         group_denominator = c("control"))

get_corr_heatbox(res,samples = c("treatment1","control"),cluster_samples = FALSE,show_corr_values =TRUE,
size_corr_values = 4)

cparsania/parcutils documentation built on Oct. 27, 2024, 4:55 a.m.