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fix(report): correct per-cell exon-count percentages and drop dead bins figure #38

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Merged
CarlosBlancoGo merged 1 commit into from
Jul 18, 2026
Merged

fix(report): correct per-cell exon-count percentages and drop dead bins figure #38

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66 changes: 3 additions & 63 deletions src/report_assets/SQANTI-sc_report.R
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Original file line number Diff line number Diff line change
Expand Up @@ -2440,68 +2440,7 @@ generate_sqantisc_plots <- function(SQANTI_cell_summary, Classification_file, Ju
override_outline_vars = c("Genic")
)

# 3) Exon count bins per structural category across cells (HTML)
exon_bin_levels <- c("1", "2-3", "4-5", ">=6")
bin_fill_map <- setNames(c("#6BAED6", "#78C679", "#FC8D59", "#969696"), exon_bin_levels)
gg_exon_bins_by_category <<- list()
for (ck in all_cats) {
cat_df <- cls_valid %>% filter(cat_key == ck)
if (nrow(cat_df) == 0) {
next
}
bins_by_cell <- cat_df %>%
mutate(
exons_n = as.numeric(exons),
bin = dplyr::case_when(
exons_n <= 1 ~ "1",
exons_n <= 3 ~ "2-3",
exons_n <= 5 ~ "4-5",
TRUE ~ ">=6"
)
) %>%
group_by(CB, bin) %>%
summarise(n = sum(count, na.rm = TRUE), .groups = "drop") %>%
group_by(CB) %>%
mutate(perc = ifelse(sum(n) > 0, 100 * n / sum(n), NA_real_)) %>%
ungroup() %>%
tidyr::complete(CB, bin = exon_bin_levels)

df_long_bins <- data.frame(
Variable = factor(bins_by_cell$bin, levels = exon_bin_levels),
Value = bins_by_cell$perc
)

pretty_name <- switch(ck,
FSM = "FSM",
ISM = "ISM",
NIC = "NIC",
NNC = "NNC",
Genic = "Genic Genomic",
Antisense = "Antisense",
Fusion = "Fusion",
Intergenic = "Intergenic",
Genic_intron = "Genic Intron",
ck
)

gg_exon_bins_by_category[[pretty_name]] <<- build_violin_plot(
df_long = df_long_bins,
title = paste0("Exon Count Bins in ", pretty_name, " Across Cells"),
x_labels = exon_bin_levels,
fill_map = bin_fill_map,
y_label = paste(entity_label_plural, ", %", sep = ""),
legend = FALSE,
ylim = c(0, 100),
violin_alpha = 0.7,
box_alpha = 0.3,
box_width = 0.05,
x_tickangle = 45,
violin_outline_fill = TRUE,
box_outline_default = "grey20"
)
}

# 4) Exon count profile per category across cells (median + IQR)
# 3) Exon count profile per category across cells (median + IQR)
K <- 20
min_reads <- 5
gg_exon_profile_by_category <<- list()
Expand All @@ -2527,7 +2466,8 @@ generate_sqantisc_plots <- function(SQANTI_cell_summary, Classification_file, Ju
group_by(CB, exons_n) %>%
summarise(n = sum(count, na.rm = TRUE), .groups = "drop") %>%
group_by(CB) %>%
mutate(perc = ifelse(sum(n) > 0, 100 * n / sum(n), 0)) %>%
# Elementwise recycling of the scalar sum(n) -- see the note above.
mutate(perc = 100 * n / sum(n)) %>%
ungroup() %>%
tidyr::complete(CB, exons_n = seq_len(K), fill = list(n = 0, perc = 0))
# Aggregate across cells
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