tests/testthat/test-plot_pca.R

In luciorq/txomics: Functional Analysis and Visualization of Transcriptomic Data

testthat::context("Testing plot_pca function")

#   set.seed(42)
#
#   imported_tx <- dplyr::tibble(
#     gene = paste0("gene", stringr::str_pad(1:100, 3, pad = "0")),
#     `sample 1` = stats::rnbinom(n = 100, size = 1, prob = 0.05),
#     `sample X` = stats::rnbinom(n = 100, size = 5, prob = 0.05),
#     `_31` = stats::rnbinom(n = 100, size = 1, prob = 0.05),
#     `X12` = stats::rnbinom(n = 100, size = 10, prob = 0.05),
#     `SAMPLE.FIVE` = stats::rnbinom(n = 100, size = 10, prob = 0.05)
#   )
#   sample_df <- dplyr::tibble(
#     sample = names(imported_tx)[2:length(imported_tx)],
#     condition = c("A", "2", "|", "A", "A"),
#     color = c("red", "green", "green", "red", "green")
#   )
#
#   plot_pca(imported_tx,
#     sample_table = sample_df,
#     color_by = "color", num = 30
#   )
#
#   plot_pca(imported_tx,
#     sample_table = sample_df,
#     color_by = "sample", num = -10
#   )
#
#   testthat::expect_error(plot_pca(imported_tx, num = 0))
#
#   res2 <- plot_pca(head(imported_tx, 5))
#
#
#   testthat::expect_equal(res2$data$value, c(
#     -0.141589618299063, -0.180204758946487, 0.402524792746824,
#     -0.933770590153989, 0.853040174652716, -0.262972431433991, 0.752433817191767,
#     -0.30550222096098, -0.211692682937508, 0.0277335181407126
#   ))
#
#   res3 <- plot_pca(imported_tx, label = TRUE)
#
#
#   testthat::expect_equal(res3$data$PC1, c(
#     -9.08978758902333, -1.36567534552286, -8.65240409661001, 10.1086525613534,
#     8.99921446980285, -9.08978758902333, -1.36567534552286, -8.65240409661001,
#     10.1086525613534, 8.99921446980285
#   ))
#   # imported_tx_df <- data.frame(
#   #  gene = paste0("gene",stringr::str_pad(1:100 ,3,pad = "0")),
#   #  `sample 1` = stats::rnbinom(n = 100,size = 1,prob = 0.05),
#   #  `sample X` = stats::rnbinom(n = 100,size = 5,prob = 0.05),
#   #  `_31` = stats::rnbinom(n = 100,size = 1,prob = 0.05),
#   #  `X12` = stats::rnbinom(n = 100,size = 10,prob = 0.05)
#   # )
# })

# tx <- imported_transcripts; sample_table <- samples_metadata
# color_by = c("condition","population"); num = 500
# breaks_x <- base::pretty(df$log2FoldChange)
# if( isTRUE(max(-log10(df$pvalue)) <= 4) ) {
#  range_y <- c(0,4)
# } else{
#  range_y <- -log10(df$pvalue)
# }
# breaks_y <- base::pretty(range_y)
# ggplot2::scale_x_continuous( breaks = breaks_x, limits = base::range(breaks_x*1.1)) +
# ggplot2::scale_y_continuous( breaks = breaks_y, limits = base::range(breaks_y))

testthat::test_that("Test if plot_pca runs", {
  TRUE
})