context('add_genoppi_trace')
data("example_data")
test_that('basic test to see if function works',{
# perform moderated t-test
stats_df <- calc_mod_ttest(example_data)
# identify enriched proteins
sig_df <- id_enriched_proteins(stats_df)
# generate volcano plot with bait protein labeled
p <- plot_volcano_basic(sig_df)
p <- plot_overlay(p, as.bait('BCL2'))
# ensure data and overlay has appropiate columns for plotting
data = to_overlay_data(append_to_column(p$data))
overlay = to_overlay_data(append_to_column(p$overlay, sig_text = 'sig_text'))
overlay$color = ifelse(overlay$significant, as.character(overlay$col_significant), as.character(overlay$col_other))
# get the global symbol and color mapping and save in local environemnt
sizes = c(min(c(p$data$size, p$overlay$size)), max(c(p$data$size, p$overlay$size)))
global_colors = set_names_by_dataset(data, overlay, marker = 'color')
global_symbols = set_names_by_dataset(data, overlay, marker= 'symbol')
ggparams = p
params = environment()
# interactive volcano plot
result = plot_ly(source = source, sizes = sizes) %>% # sizes=c(7,29)
add_genoppi_trace(data[data$gene %nin% overlay$gene,], params)
# look at nnotations
m = result$x$attrs[[2]]
expect_true(!is.null(result$x))
expect_equal(quo_name(m$x),'logFC')
expect_equal(quo_name(m$y),'-log10(pvalue)')
expect_equal(quo_name(m$text),'~gene')
})
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