Last updated: 2026-07-15

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Load fusion statistics

Load the merged fusion statistics table produced by MCF7_exp1_data_preparation.Rmd. This table contains per-fusion single-cell expression statistics, Arriba bulk split read counts, probe sequence information, and breakpoint proximity to 10x probes.

file  <- here("data", "RDS", "mcf7_fusion_stats.rds")
stats <- readRDS(file)

Helper functions

Three helper functions used throughout the probe quality plots: longest_run finds the longest consecutive homopolymer in a probe sequence (runs ≥ 4 bp are penalised in probe design); check_gc bins a GC fraction into qualitative categories aligned with the 10x recommended range of 44–72%; and y_residual computes a probe’s signed deviation from the fitted bulk–single-cell regression line.

longest_run <- function(x) {
  runs    <- rle(unlist(str_split(x, "")))
  max(runs$lengths)
}
check_gc <- function(x) {
  x <- x * 100
  if      (x >= 44 & x <  45) "very low"
  else if (x >= 45 & x <  50) "low"
  else if (x >= 50 & x <  55) "good"
  else if (x >= 55 & x <  60) "ok"
  else if (x >= 60 & x <  65) "high"
  else if (x >= 65 & x <= 72) "very high"
  else NA_character_
}
check_y_val <- function(x, actual_y, intercept, gradient) {
  y         <- gradient * x + intercept
  actual_y - y
}

y_residual <- function(object, intercept, slope) {
  object <- as.data.frame(t(object))
  check_y_val(
    log2(as.numeric(object$average_sum_split_reads)),
    log2(as.numeric(object$fusion_reads_mcf7)),
    intercept,
    slope
  )
}

Single-cell UMI count vs. number of expressing cells (Figure 2D)

A near-perfect correlation between total UMI count and number of expressing cells is expected if probes are working as intended — more expressing cells should yield proportionally more reads. The R² and Spearman ρ values reported here correspond to those in Figure 1D.

# Prepare cleaned labels and flag well-established MCF7 fusions for labelling
stats_clean <- stats %>%
  { if (!"fusion_id" %in% colnames(.)) rownames_to_column(., "fusion_id") else . } %>%
  mutate(
    clean_label = str_extract(fusion_id, "^[^-]+-[^-]+") %>%
      str_replace("-", "::"),
    established = clean_label %in% c(
      "BCAS4::BCAS3", "ARFGEF2::SULF2", "ZNF217::SULF2", "NCOA3::AL499605"
    )
  )

# Linear model and Spearman correlation
m_cells   <- lm(log2(fusion_reads_mcf7) ~ log2(fusions_cell_nums), stats_clean)
intercept <- coef(m_cells)[1]
slope     <- coef(m_cells)[2]
r_sq      <- summary(m_cells)$r.squared
resid_sd  <- sd(residuals(m_cells))

cor_cells <- cor.test(
  stats_clean$fusions_cell_nums,
  stats_clean$fusion_reads_mcf7,
  method = "spearman",
  exact  = FALSE
)

# Ribbon data (±1 and ±2 SD bands)
x_range_c <- range(log2(stats_clean$fusions_cell_nums))
ribbon_c  <- data.frame(x = seq(x_range_c[1], x_range_c[2], length.out = 200)) %>%
  mutate(
    fitted = intercept + slope * x,
    sd1_hi = fitted + 1 * resid_sd,
    sd1_lo = fitted - 1 * resid_sd,
    sd2_hi = fitted + 2 * resid_sd,
    sd2_lo = fitted - 2 * resid_sd
  )

stats_clean <- stats_clean %>%
  mutate(residual_cells = residuals(m_cells))

r_sq_label <- sprintf("italic(R)^2 == %.2f", r_sq)
rho_label  <- sprintf(
  "italic(ρ) == %.3f ~ ',' ~ italic(p) == %.2e",
  cor_cells$estimate,
  cor_cells$p.value
)
x_annot <- x_range_c[1] + 0.1
y_annot <- max(log2(stats_clean$fusion_reads_mcf7))

ggplot(stats_clean,
       aes(x = log2(fusions_cell_nums), y = log2(fusion_reads_mcf7))) +

  geom_ribbon(data = ribbon_c, aes(x = x, ymin = sd2_lo, ymax = sd2_hi),
              fill = "#1A7A8A", alpha = 0.10, inherit.aes = FALSE) +
  geom_ribbon(data = ribbon_c, aes(x = x, ymin = sd1_lo, ymax = sd1_hi),
              fill = "#1A7A8A", alpha = 0.18, inherit.aes = FALSE) +
  geom_line(data = ribbon_c, aes(x = x, y = fitted),
            color = "grey30", linetype = "dashed", linewidth = 0.6, inherit.aes = FALSE) +

  geom_point(aes(size = established),
             shape = 21, fill = "grey40", color = "white", stroke = 0.3, alpha = 0.85) +
  scale_size_manual(values = c("TRUE" = 3, "FALSE" = 2), guide = "none") +

  geom_text(data = stats_clean %>% filter(established), aes(label = clean_label),
            size = 2.25, color = "grey15", hjust = 0, nudge_x = 0.05, nudge_y = 0.12) +

  annotate("text", x = x_range_c[1],
           y = intercept + slope * x_range_c[1] + 1 * resid_sd + 0.15,
           label = "±1 SD", hjust = 0, size = 2.8, color = "#1A7A8A", fontface = "italic") +
  annotate("text", x = x_range_c[1],
           y = intercept + slope * x_range_c[1] + 2 * resid_sd + 0.15,
           label = "±2 SD", hjust = 0, size = 2.8, color = "#1A7A8A", fontface = "italic") +

  annotate("text", x = x_annot, y = y_annot - 0.3,
           label = r_sq_label, parse = TRUE, hjust = 0, vjust = 1, size = 3.2, color = "grey30") +
  annotate("text", x = x_annot, y = y_annot - 1.1,
           label = rho_label, parse = TRUE, hjust = 0, vjust = 1, size = 3.2, color = "grey30") +

  coord_cartesian(clip = "off") +
  labs(
    x     = expression(log[2] ~ "(No. cells expressing fusion)"),
    y     = expression(log[2] ~ "(Single-cell fusion UMI count)"),
    title = "Fusion UMI count vs. number of expressing cells"
  ) +
  theme_classic(base_size = 10) +
  theme(
    plot.title       = element_text(size = 10, face = "bold", color = "grey15", margin = margin(b = 8)),
    plot.margin      = margin(12, 80, 8, 8),
    axis.title       = element_text(size = 9, color = "grey20"),
    axis.title.x     = element_text(margin = margin(t = 6)),
    axis.title.y     = element_text(margin = margin(r = 6)),
    axis.text        = element_text(size = 8, color = "grey30"),
    axis.line        = element_line(color = "grey60", linewidth = 0.4),
    axis.ticks       = element_line(color = "grey60", linewidth = 0.3),
    panel.grid.major = element_line(color = "grey95", linewidth = 0.4),
    panel.grid.minor = element_blank()
  )

Version Author Date
1b5bb97 Jovana Maksimovic 2026-05-11

Concordance between bulk (Arriba) and single-cell fusion detection (Figure 2E)

Compares total single-cell UMI counts per fusion to the Arriba split read count from bulk RNA-seq. Higher Spearman ρ indicates better concordance between the two detection methods. The R² and ρ reported here correspond to Figure 1E.

# Prepare cleaned labels; this plot labels 6 established fusions (vs. 4 in Figure 2D above),
# adding RPS6KB1::DIAPH3 and FOXA1::TTC6 which have reliable Arriba support
stats_clean <- stats %>%
  { if (!"fusion_id" %in% colnames(.)) rownames_to_column(., "fusion_id") else . } %>%
  mutate(
    clean_label = str_extract(fusion_id, "^[^-]+-[^-]+") %>%
      str_replace("-", "::"),
    established = clean_label %in% c(
      "BCAS4::BCAS3", "ARFGEF2::SULF2", "ZNF217::SULF2",
      "RPS6KB1::DIAPH3", "FOXA1::TTC6", "NCOA3::AL499605"
    )
  )

# Linear model on log2 scale and Spearman correlation
m         <- lm(log2(fusion_reads_mcf7) ~ log2(average_sum_split_reads), stats_clean)
intercept <- coef(m)[1]
slope     <- coef(m)[2]
r_sq      <- summary(m)$r.squared
resid_sd  <- sd(residuals(m))

cor_result <- cor.test(
  stats_clean$average_sum_split_reads,
  stats_clean$fusion_reads_mcf7,
  method = "spearman",
  exact  = FALSE
)

# Ribbon data
x_range   <- range(log2(stats_clean$average_sum_split_reads))
ribbon_df <- data.frame(x = seq(x_range[1], x_range[2], length.out = 200)) %>%
  mutate(
    fitted = intercept + slope * x,
    sd1_hi = fitted + 1 * resid_sd,
    sd1_lo = fitted - 1 * resid_sd,
    sd2_hi = fitted + 2 * resid_sd,
    sd2_lo = fitted - 2 * resid_sd
  )

stats_clean <- stats_clean %>%
  mutate(residual = residuals(m))

r_sq_label <- sprintf("italic(R)^2 == %.2f", r_sq)
rho_label  <- sprintf(
  "italic(ρ) == %.2f ~ ',' ~ p == %.3f",
  cor_result$estimate,
  cor_result$p.value
)
x_annot <- x_range[1] + 0.2
y_annot <- max(log2(stats_clean$fusion_reads_mcf7))

ggplot(stats_clean,
       aes(x = log2(average_sum_split_reads), y = log2(fusion_reads_mcf7))) +

  geom_ribbon(data = ribbon_df, aes(x = x, ymin = sd2_lo, ymax = sd2_hi),
              fill = "#1A7A8A", alpha = 0.10, inherit.aes = FALSE) +
  geom_ribbon(data = ribbon_df, aes(x = x, ymin = sd1_lo, ymax = sd1_hi),
              fill = "#1A7A8A", alpha = 0.18, inherit.aes = FALSE) +
  geom_line(data = ribbon_df, aes(x = x, y = fitted),
            color = "grey30", linetype = "dashed", linewidth = 0.6, inherit.aes = FALSE) +

  geom_point(aes(size = established),
             shape = 21, fill = "grey40", color = "white", stroke = 0.3, alpha = 0.85) +
  scale_size_manual(values = c("TRUE" = 3, "FALSE" = 2), guide = "none") +

  geom_text(data = stats_clean %>% filter(established), aes(label = clean_label),
            size = 2.25, color = "grey15", hjust = 0, nudge_x = 0.1, nudge_y = 0.15) +

  annotate("text", x = x_range[1],
           y = intercept + slope * x_range[1] + 1 * resid_sd + 0.15,
           label = "±1 SD", hjust = 0, size = 2.8, color = "#1A7A8A", fontface = "italic") +
  annotate("text", x = x_range[1],
           y = intercept + slope * x_range[1] + 2 * resid_sd + 0.15,
           label = "±2 SD", hjust = 0, size = 2.8, color = "#1A7A8A", fontface = "italic") +

  annotate("text", x = x_annot, y = y_annot - 0.3,
           label = r_sq_label, parse = TRUE, hjust = 0, vjust = 1, size = 3.2, color = "grey30") +
  annotate("text", x = x_annot, y = y_annot - 1.1,
           label = rho_label, parse = TRUE, hjust = 0, vjust = 1, size = 3.2, color = "grey30") +

  labs(
    x     = expression(log[2] ~ "(Arriba split read count)"),
    y     = expression(log[2] ~ "(Single-cell fusion UMI count)"),
    title = "Concordance between bulk and single-cell fusion detection"
  ) +
  theme_classic(base_size = 10) +
  coord_cartesian(clip = "off") +
  theme(
    plot.title       = element_text(size = 10, face = "bold", color = "grey15", margin = margin(b = 8)),
    plot.margin      = margin(12, 80, 8, 8),
    axis.title       = element_text(size = 9, color = "grey20"),
    axis.title.x     = element_text(margin = margin(t = 6)),
    axis.title.y     = element_text(margin = margin(r = 6)),
    axis.text        = element_text(size = 8, color = "grey30"),
    axis.line        = element_line(color = "grey60", linewidth = 0.4),
    axis.ticks       = element_line(color = "grey60", linewidth = 0.3),
    panel.grid.major = element_line(color = "grey95", linewidth = 0.4),
    panel.grid.minor = element_blank()
  )

Version Author Date
1b5bb97 Jovana Maksimovic 2026-05-11

Compute the residual of each fusion from the regression line for use in the probe quality plots below.

stats$y_residual <- apply(stats, 1, y_residual, intercept, slope)

Probe sequence characteristics

The following analyses examine whether probe sequence properties — GC content, ligation dinucleotide, homopolymer run length, and breakpoint position — are associated with deviation from the expected bulk–single-cell concordance. Greater deviation (positive or negative residual) may suggest the probe is under- or over-performing.

GC content

Average GC content of the two 25 bp probe halves. The 10x recommended range is 44–72%.

stats$average_gc <- (stats$GC_first_half + stats$GC_second_half) / 2
stats$binned_gc  <- unlist(lapply(stats$average_gc, check_gc))
stats$binned_gc  <- with(stats, reorder(binned_gc, average_gc))

ggplot(stats) +
  aes(x = average_gc, y = y_residual, color = binned_gc) +
  geom_point() +
  geom_abline(intercept = 0, slope = 0, color = "black", linetype = "dashed", linewidth = 0.5) +
  labs(
    title = "GC content vs. deviation from expected expression",
    x     = "Average GC content",
    y     = "Residual (deviation from regression)",
    color = "GC bin"
  )

Version Author Date
1b5bb97 Jovana Maksimovic 2026-05-11

Ligation dinucleotide

Probes are split by whether their ligation junction dinucleotide is in the recommended set. Points are coloured by GC content bin.

ggplot(stats) +
  aes(x = dinucleotide_recommended, y = y_residual,
      group = dinucleotide_recommended, color = binned_gc) +
  geom_violin() +
  geom_point(position = position_jitter(seed = 1, width = 0.05)) +
  labs(
    title = "Ligation dinucleotide vs. deviation",
    x     = "Recommended dinucleotide?",
    y     = "Residual",
    color = "GC bin"
  )

Version Author Date
1b5bb97 Jovana Maksimovic 2026-05-11 
ggplot(stats) +
  aes(x = dinucleotide, y = y_residual, color = dinucleotide) +
  geom_point() +
  geom_boxplot(width = 0.1) +
  geom_abline(intercept = 0, slope = 0, color = "black", linetype = "dashed", linewidth = 0.5) +
  labs(
    title = "Ligation dinucleotide vs. deviation",
    x     = "Dinucleotide",
    y     = "Residual"
  )

Version Author Date
1b5bb97 Jovana Maksimovic 2026-05-11

Homopolymer run length

Long homopolymer runs (≥ 4 bp) are penalised in the probe design. Here we check whether probes with longer runs show greater deviation from expected expression.

stats$longest_run_length <- unlist(lapply(stats$Probes, longest_run))

ggplot(stats) +
  aes(x = as.character(longest_run_length), y = y_residual,
      color = as.character(longest_run_length)) +
  geom_violin() +
  geom_point(position = position_jitter(seed = 1, width = 0.05)) +
  labs(
    title = "Homopolymer run length vs. deviation",
    x     = "Longest homopolymer run (bp)",
    y     = "Residual",
    color = "Run length"
  )

Version Author Date
1b5bb97 Jovana Maksimovic 2026-05-11

Breakpoint position within the probe

The ligation breakpoint is nominally at position 25 of the 50 bp probe. Here we examine whether deviation of the breakpoint from the probe centre is associated with performance.

ggplot(stats) +
  aes(x = Deviation_of_break_in_Probe, y = y_residual, color = dinucleotide_recommended) +
  geom_point() +
  labs(
    title = "Breakpoint offset from probe centre vs. deviation",
    x     = "Deviation of breakpoint from probe centre (bp)",
    y     = "Residual",
    color = "Recommended dinucleotide?"
  )

Version Author Date
1b5bb97 Jovana Maksimovic 2026-05-11

Distance from breakpoint to nearest 10x probe

For each fusion, the distance from each breakpoint to the nearest standard 10x probe in the Chromium Human Transcriptome set. Short distances may increase competition from endogenous probe binding.

ggplot(stats) +
  geom_point(aes(x = log10(fusion1_bp_distance), y = y_residual, color = "Gene 1 breakpoint")) +
  geom_point(aes(x = log10(fusion2_bp_distance), y = y_residual, color = "Gene 2 breakpoint")) +
  labs(
    title  = "Distance from breakpoint to nearest 10x probe vs. deviation",
    x      = expression(log[10] ~ "(Distance from 10x probe, bp)"),
    y      = "Residual",
    color  = NULL
  )

Version Author Date
1b5bb97 Jovana Maksimovic 2026-05-11

Session info

The analysis and this document were prepared using the following software (click triangle to expand) 
sessioninfo::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       macOS 15.5
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Australia/Melbourne
 date     2026-07-15
 pandoc   3.8.3 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
 quarto   1.9.38 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 ! package      * version   date (UTC) lib source
 P BiocManager    1.30.27   2025-11-14 [?] CRAN (R 4.4.3)
 P bslib          0.9.0     2025-01-30 [?] CRAN (R 4.4.1)
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 P cli            3.6.5     2025-04-23 [?] CRAN (R 4.4.1)
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 P dplyr        * 1.1.4     2023-11-17 [?] CRAN (R 4.4.0)
 P evaluate       1.0.3     2025-01-10 [?] CRAN (R 4.4.1)
 P farver         2.1.2     2024-05-13 [?] CRAN (R 4.4.1)
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 P generics       0.1.4     2025-05-09 [?] CRAN (R 4.4.1)
 P getPass        0.2-4     2023-12-10 [?] CRAN (R 4.4.1)
 P ggplot2      * 4.0.2     2026-02-03 [?] CRAN (R 4.4.3)
 P git2r          0.36.2    2025-03-29 [?] CRAN (R 4.4.1)
 P glue           1.8.0     2024-09-30 [?] CRAN (R 4.4.1)
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 P here         * 1.0.1     2020-12-13 [?] CRAN (R 4.4.1)
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 P httpuv         1.6.16    2025-04-16 [?] CRAN (R 4.4.1)
 P httr           1.4.7     2023-08-15 [?] CRAN (R 4.4.0)
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 P knitr          1.50      2025-03-16 [?] CRAN (R 4.4.1)
 P labeling       0.4.3     2023-08-29 [?] CRAN (R 4.4.1)
 P later          1.4.2     2025-04-08 [?] CRAN (R 4.4.1)
 P lifecycle      1.0.4     2023-11-07 [?] CRAN (R 4.4.1)
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 P pillar         1.10.2    2025-04-05 [?] CRAN (R 4.4.1)
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 P promises       1.3.3     2025-05-29 [?] CRAN (R 4.4.1)
 P ps             1.9.1     2025-04-12 [?] CRAN (R 4.4.1)
 P R6             2.6.1     2025-02-15 [?] CRAN (R 4.4.1)
 P RColorBrewer   1.1-3     2022-04-03 [?] CRAN (R 4.4.1)
 P Rcpp           1.1.1-1.1 2026-04-24 [?] CRAN (R 4.4.1)
   renv           1.1.4     2025-03-20 [1] CRAN (R 4.4.1)
 P rlang          1.1.6     2025-04-11 [?] CRAN (R 4.4.1)
 P rmarkdown      2.29      2024-11-04 [?] CRAN (R 4.4.1)
 P rprojroot      2.0.4     2023-11-05 [?] CRAN (R 4.4.1)
 P rstudioapi     0.17.1    2024-10-22 [?] CRAN (R 4.4.1)
 P S7             0.2.0     2024-11-07 [?] CRAN (R 4.4.1)
 P sass           0.4.10    2025-04-11 [?] CRAN (R 4.4.1)
 P scales         1.4.0     2025-04-24 [?] CRAN (R 4.4.1)
 P sessioninfo    1.2.3     2025-02-05 [?] CRAN (R 4.4.1)
 P stringi        1.8.7     2025-03-27 [?] CRAN (R 4.4.1)
 P stringr      * 1.6.0     2025-11-04 [?] CRAN (R 4.4.1)
 P tibble       * 3.2.1     2023-03-20 [?] CRAN (R 4.4.0)
 P tidyselect     1.2.1     2024-03-11 [?] CRAN (R 4.4.0)
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 [1] /Users/maksimovicjovana/Library/CloudStorage/OneDrive-PeterMac/Desktop/projects/my_research/flex-fusion-probes/renv/library/macos/R-4.4/aarch64-apple-darwin20
 [2] /Users/maksimovicjovana/Library/Caches/org.R-project.R/R/renv/sandbox/macos/R-4.4/aarch64-apple-darwin20/f7156815

 * ── Packages attached to the search path.
 P ── Loaded and on-disk path mismatch.

──────────────────────────────────────────────────────────────────────────────

sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS 15.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Melbourne
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
[1] tibble_3.2.1    stringr_1.6.0   ggplot2_4.0.2   here_1.0.1     
[5] dplyr_1.1.4     workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] gtable_0.3.6        jsonlite_2.0.0      compiler_4.4.1     
 [4] BiocManager_1.30.27 renv_1.1.4          promises_1.3.3     
 [7] tidyselect_1.2.1    Rcpp_1.1.1-1.1      git2r_0.36.2       
[10] callr_3.7.6         later_1.4.2         jquerylib_0.1.4    
[13] scales_1.4.0        yaml_2.3.10         fastmap_1.2.0      
[16] R6_2.6.1            labeling_0.4.3      generics_0.1.4     
[19] knitr_1.50          rprojroot_2.0.4     RColorBrewer_1.1-3 
[22] bslib_0.9.0         pillar_1.10.2       rlang_1.1.6        
[25] cachem_1.1.0        stringi_1.8.7       httpuv_1.6.16      
[28] xfun_0.56           S7_0.2.0            getPass_0.2-4      
[31] fs_1.6.6            sass_0.4.10         cli_3.6.5          
[34] withr_3.0.2         magrittr_2.0.3      ps_1.9.1           
[37] grid_4.4.1          digest_0.6.37       processx_3.8.6     
[40] rstudioapi_0.17.1   lifecycle_1.0.4     vctrs_0.6.5        
[43] evaluate_1.0.3      glue_1.8.0          farver_2.1.2       
[46] whisker_0.4.1       sessioninfo_1.2.3   rmarkdown_2.29     
[49] httr_1.4.7          tools_4.4.1         pkgconfig_2.0.3    
[52] htmltools_0.5.8.1