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")
#> Rows: 5000 Columns: 10
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: "\t"
#> chr (1): gene_id
#> dbl (9): control_rep1, control_rep2, control_rep3, treat1_rep1, treat1_rep2,...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

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"))
#> ℹ Running DESeq2 ...
#> converting counts to integer mode
#> Warning: some variables in design formula are characters, converting to factors
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> ✔ Done.
#> ℹ Summarizing DEG ...
#> ✔ Done.


 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)