get_pca_plot: Generate a PCA plot.

In cparsania/parcutils: Utility Functions To Deal Day To Day Bioinformatics Tasks.

Generate a PCA plot.

Description

Generate a PCA plot.

Usage

get_pca_plot(
  x,
  samples = NULL,
  genes = NULL,
  circle_size = 10,
  label_replicates = FALSE,
  sample_colors = FALSE,
  sample.seed = 123
)

Arguments

x	an abject of class "parcutils". This is an output of the function run_deseq_analysis().
samples	a character vector denoting samples to plot in PCA plot, default NULL. If set to NULL all samples are accounted.
genes	a character vector denoting genes to consider for PCA plot, default NULL. If set to NULL all genes are accounted.
circle_size	a numeric value, default 10, denoting size of the circles in PCA plot.
label_replicates	logical, default FALSE, denoting whether to label each replicate in the plot.
sample_colors	a logical, default FALSE, denoting whether to shuffle colors of dots in PCA plot.
sample.seed	an integer, default 123, denoting a value for set.seed().

Value

an object of ggplot2.

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_pca_plot(x = res,sample_colors = TRUE) %>% print()

 # reproduce sampled colors with argument `sample.seed`
 get_pca_plot(x = res,sample_colors = TRUE, sample.seed = 345) %>% print()

# label replicates

get_pca_plot(x = res, sample_colors = FALSE,label_replicates =  TRUE)

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