Supplementary Figures
Jovana Maksimovic
April 01, 2026
Last updated: 2026-04-01
Checks: 7 0
Knit directory:
paediatric-cf-inflammation-citeseq/
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Unstaged changes:
Modified: .DS_Store
Modified: analysis/13.0_DGE_analysis_macrophages.Rmd
Modified: analysis/13.1_DGE_analysis_macro-alveolar.Rmd
Modified: analysis/13.2_DGE_analysis_macro-APOC2+.Rmd
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Modified: analysis/13.4_DGE_analysis_macro-IFI27.Rmd
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Modified: analysis/13.6_DGE_analysis_macro-monocyte-derived.Rmd
Modified: analysis/13.7_DGE_analysis_macro-proliferating.Rmd
Modified: analysis/14.0_DGE_analysis_CD4-T-cells.Rmd
Modified: analysis/14.1_DGE_analysis_CD8-T-cells.Rmd
Modified: analysis/14.2_DGE_analysis_DC-cells.Rmd
Modified: analysis/15.0_proportions_analysis_ann_level_1.Rmd
Modified: analysis/15.1_proportions_analysis_ann_level_3_non-macrophages.Rmd
Modified: analysis/15.2_proportions_analysis_ann_level_3_macrophages.Rmd
Modified: output/dge_analysis/macrophages/CAM.FIBROSIS.CF.IVAvCF.NO_MOD.csv
Modified: output/dge_analysis/macrophages/CAM.FIBROSIS.CF.IVAvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/CAM.FIBROSIS.CF.LUMA_IVAvCF.NO_MOD.csv
Modified: output/dge_analysis/macrophages/CAM.FIBROSIS.CF.NO_MOD.SvCF.NO_MOD.M.csv
Modified: output/dge_analysis/macrophages/CAM.FIBROSIS.CF.NO_MODvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/CAM.GO.CF.IVAvCF.NO_MOD.csv
Modified: output/dge_analysis/macrophages/CAM.GO.CF.IVAvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/CAM.GO.CF.LUMA_IVAvCF.NO_MOD.csv
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Modified: output/dge_analysis/macrophages/CAM.GO.CF.NO_MODvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/CAM.HALLMARK.CF.IVAvCF.NO_MOD.csv
Modified: output/dge_analysis/macrophages/CAM.HALLMARK.CF.IVAvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/CAM.HALLMARK.CF.LUMA_IVAvCF.NO_MOD.csv
Modified: output/dge_analysis/macrophages/CAM.HALLMARK.CF.NO_MOD.SvCF.NO_MOD.M.csv
Modified: output/dge_analysis/macrophages/CAM.HALLMARK.CF.NO_MODvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/CAM.REACTOME.CF.IVAvCF.NO_MOD.csv
Modified: output/dge_analysis/macrophages/CAM.REACTOME.CF.IVAvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/CAM.REACTOME.CF.LUMA_IVAvCF.NO_MOD.csv
Modified: output/dge_analysis/macrophages/CAM.REACTOME.CF.NO_MOD.SvCF.NO_MOD.M.csv
Modified: output/dge_analysis/macrophages/CAM.REACTOME.CF.NO_MODvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/CAM.WP.CF.IVAvCF.NO_MOD.csv
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Modified: output/dge_analysis/macrophages/CAM.WP.CF.NO_MODvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/CF.IVAvCF.NO_MOD.csv
Modified: output/dge_analysis/macrophages/CF.IVAvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/CF.LUMA_IVAvCF.NO_MOD.csv
Modified: output/dge_analysis/macrophages/CF.NO_MOD.SvCF.NO_MOD.M.csv
Modified: output/dge_analysis/macrophages/CF.NO_MODvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/ORA.GO.CF.IVAvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/ORA.GO.CF.NO_MOD.SvCF.NO_MOD.M.csv
Modified: output/dge_analysis/macrophages/ORA.GO.CF.NO_MODvNON_CF.CTRL.csv
Modified: output/dge_analysis/macrophages/ORA.HALLMARK.CF.IVAvCF.NO_MOD.csv
Modified: output/dge_analysis/macrophages/ORA.REACTOME.CF.NO_MODvNON_CF.CTRL.csv
Modified: output/pdf_figures/Figure_1.pdf
Modified: output/pdf_figures/Figure_2.pdf
Modified: output/pdf_figures/Figure_3.pdf
Modified: output/pdf_figures/Figure_4.pdf
Modified: output/pdf_figures/Figure_5.pdf
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5879432 | Jovana Maksimovic | 2026-04-01 | wflow_publish(c("analysis/16.0_Figure_1.Rmd", "analysis/16.1_Figure_2.Rmd", |
| html | 66498fe | Jovana Maksimovic | 2025-09-10 | Build site. |
| Rmd | 68c82b7 | Jovana Maksimovic | 2025-09-10 | wflow_publish("analysis/16.5_Figure_6.Rmd") |
Load libraries.
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(edgeR)
library(tidyverse)
library(ggplot2)
library(Seurat)
library(glmGamPoi)
library(dittoSeq)
library(here)
library(clustree)
library(patchwork)
library(AnnotationDbi)
library(org.Hs.eg.db)
library(glue)
library(speckle)
library(tidyHeatmap)
library(paletteer)
library(dsb)
library(ggh4x)
library(readxl)
library(gt)
})
source(here("code/utility.R"))Prepare figure panels
file <- here("data",
"intermediate_objects",
"macrophages.all_samples.fit.rds")
deg_results <- readRDS(file = file)
contr <- deg_results$contr[,1:2]
lapply(1:ncol(contr), function(i) {
lrt <- glmLRT(deg_results$fit, contrast = contr[,i])
topTags(lrt, n = Inf) %>%
data.frame %>%
rownames_to_column(var = "Symbol") %>%
dplyr::arrange(Symbol) %>%
dplyr::rename_with(~ paste0(.x, ".", i))
}) %>% bind_cols -> all_lrtall_lrt %>%
mutate(IVA = ifelse(FDR.1 < 0.05 & FDR.2 < 0.05, "#FF6B6B",
ifelse(FDR.1 < 0.05 & FDR.2 >= 0.05, "#CC8E00",
ifelse(FDR.1 >= 0.05 & FDR.2 < 0.05, "#20A4A4",
"lightgrey")))) -> all_lrt
ggplot(all_lrt, aes(x = logFC.1,
y = logFC.2)) +
geom_point(data = subset(all_lrt, IVA %in% "lightgrey"),
aes(colour = "lightgrey"),
alpha = 0.25) +
geom_point(data = subset(all_lrt, IVA %in% "#20A4A4"),
aes(colour = "#20A4A4"),
alpha = 0.5) +
geom_point(data = subset(all_lrt, IVA %in% "#CC8E00"),
aes(colour = "#CC8E00"),
alpha = 0.5) +
geom_point(data = subset(all_lrt, IVA %in% "#FF6B6B"),
aes(colour = "#FF6B6B")) +
ggrepel::geom_text_repel(data = subset(all_lrt, (IVA %in% "#20A4A4")),
aes(x = logFC.1, y = logFC.2,
label = Symbol.1),
size = 2.5, colour = "#20A4A4", max.overlaps = 5) +
ggrepel::geom_text_repel(data = subset(all_lrt, (IVA %in% "#CC8E00")),
aes(x = logFC.1, y = logFC.2,
label = Symbol.1),
size = 2.5, colour = "#CC8E00", max.overlaps = 5) +
ggrepel::geom_text_repel(data = subset(all_lrt, (IVA %in% "#FF6B6B")),
aes(x = logFC.1, y = logFC.2,
label = Symbol.1),
size = 2.5, colour = "#FF6B6B", max.overlaps = Inf) +
geom_hline(yintercept = 0, linetype = "dashed", colour = "darkgrey") +
geom_vline(xintercept = 0, linetype = "dashed", colour = "darkgrey") +
labs(x = "log2FC CF (no mod) vs Non-CF ctrl",
y = "log2FC CF (iva) vs Non-CF ctrl") +
scale_colour_identity(guide = "legend",
breaks = c("#FF6B6B", "#20A4A4", "#CC8E00","lightgrey"),
labels = c("Sig. in both",
"Only in CF (iva) vs Non-CF ctrl",
"Only in CF (no mod) vs Non-CF ctrl",
"Not Sig."),
name = "Statistical Significance") +
theme_classic() +
theme(legend.position = "bottom",
legend.direction = "vertical",
legend.title = element_text(hjust = 0.5)) +
guides(colour = guide_legend(ncol = 4)) -> p1
p1
| Version | Author | Date |
|---|---|---|
| 66498fe | Jovana Maksimovic | 2025-09-10 |
Hs.c2.all <- convert_gmt_to_list(here("data/c2.all.v2024.1.Hs.entrez.gmt"))
Hs.h.all <- convert_gmt_to_list(here("data/h.all.v2024.1.Hs.entrez.gmt"))
Hs.c5.all <- convert_gmt_to_list(here("data/c5.all.v2024.1.Hs.entrez.gmt"))
fibrosis <- create_custom_gene_lists_from_file(here("data/fibrosis_gene_sets.csv"))
# add fibrosis sets from REACTOME and WIKIPATHWAYS
fibrosis <- c(lapply(fibrosis, function(l) l[!is.na(l)]),
Hs.c2.all[str_detect(names(Hs.c2.all), "FIBROSIS")])
gene_sets_list <- list(HALLMARK = Hs.h.all,
GO = Hs.c5.all,
REACTOME = Hs.c2.all[str_detect(names(Hs.c2.all), "REACTOME")],
WP = Hs.c2.all[str_detect(names(Hs.c2.all), "^WP")],
FIBROSIS = fibrosis)num <- 10
hallmark_ora <- rbind(read_csv(file = here("output",
"dge_analysis",
"macrophages",
"ORA.HALLMARK.CF.IVAvNON_CF.CTRL.csv")) %>%
slice_head(n = num) %>%
mutate(contrast = "CF (iva) vs. Non-CF control",
Rank = 1:n()),
read_csv(file = here("output",
"dge_analysis",
"macrophages",
"ORA.HALLMARK.CF.NO_MODvNON_CF.CTRL.csv")) %>%
slice_head(n = num) %>%
mutate(contrast = "CF (no mod) vs. Non-CF control",
Rank = 1:n()))
library(dplyr)
library(ggplot2)
library(tidytext) # for reorder_within() / scale_y_reordered()
library(scales) # for squish
# df is your tibble
dotdat <- hallmark_ora %>%
mutate(
score = -log10(pmax(FDR, .Machine$double.xmin)), # avoid Inf if FDR==0
de_prop = DE / N,
Set = gsub("^HALLMARK_", "(H) ", Set), # replace prefix
Set = gsub("_", " ", Set), # remove underscores
# order within each contrast by Rank (1 = top), shown at top of facet
Set_ord = reorder_within(Set, -Rank, contrast)
)
ggplot(dotdat, aes(x = score, y = Set_ord, size = N, color = de_prop)) +
geom_point() +
scale_y_reordered() +
facet_wrap(~ contrast, scales = "free_y", ncol = 1) +
scale_size(range = c(1, 3), name = "Gene Set Size") +
scale_color_viridis_c(name = "Gene Ratio", oob = squish,
option = "plasma") +
geom_vline(xintercept = -log10(0.05),
linetype = "dashed") +
labs(
x = expression(-log[10](FDR)),
y = NULL,
) +
theme_classic(base_size = 10) -> p2
p2
| Version | Author | Date |
|---|---|---|
| 66498fe | Jovana Maksimovic | 2025-09-10 |
library(dplyr)
library(ggplot2)
library(tidytext) # for reorder_within / scale_y_reordered
library(scales)
dotdat <- hallmark_ora %>%
mutate(
score = -log10(pmax(FDR, .Machine$double.xmin)),
de_prop = DE / N,
# clean set labels
Set_lbl = gsub("^HALLMARK_", "(H) ", Set),
Set_lbl = gsub("_", " ", Set_lbl)
) %>%
group_by(Set_lbl) %>%
mutate(in_both = dplyr::n_distinct(contrast) > 1) %>%
ungroup() %>%
# keep facet-wise ordering by Rank
mutate(Set_ord = reorder_within(Set_lbl, -Rank, contrast))
p2 <- ggplot(dotdat, aes(x = score, y = Set_ord, size = N)) +
# Base layer: filled points, no outline
geom_point(
aes(fill = de_prop),
shape = 21,
colour = "transparent", # ensure no border is drawn
stroke = 0,
alpha = 0.95
) +
# Outline layer: only for sets present in both contrasts
geom_point(
data = dplyr::filter(dotdat, in_both),
aes(fill = de_prop),
shape = 21,
colour = "grey",
stroke = 1.2,
alpha = 0.95
) +
scale_y_reordered() +
facet_wrap(~ contrast, scales = "free_y", ncol = 1) +
scale_size(range = c(1, 4), name = "Gene Set Size") +
scale_fill_viridis_c(name = "Gene Ratio", oob = squish, option = "plasma") +
geom_vline(xintercept = -log10(0.05), linetype = "dashed") +
labs(x = expression(-log[10](FDR)), y = NULL) +
theme_classic(base_size = 10)
p2
| Version | Author | Date |
|---|---|---|
| 66498fe | Jovana Maksimovic | 2025-09-10 |
Figure 6
layout <- "
AAA
AAA
AAA
BBB
BBB
"
wrap_elements(p1 + theme(text = element_text(size = 10),
plot.margin = margin(rep(0, 4)))) +
wrap_elements(p2 + theme(text = element_text(size = 10),
legend.margin = margin(-0.5,0,0,0, unit="lines"),
legend.key.size = unit(1, "lines"),
plot.margin = margin(rep(0, 4)))) +
plot_layout(design = layout) +
plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 24,
face = "bold",
family = "arial")) -> fig6
fig6
| Version | Author | Date |
|---|---|---|
| 66498fe | Jovana Maksimovic | 2025-09-10 |
Save figure as PDF.
ggsave(here("output/pdf_figures/Figure_6.pdf"),
plot = fig6, width = 7, height = 10, units = "in", device = cairo_pdf)Session info
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] scales_1.3.0 tidytext_0.4.3
[3] gt_1.0.0 readxl_1.4.3
[5] ggh4x_0.3.1 dsb_1.0.3
[7] paletteer_1.6.0 tidyHeatmap_1.8.1
[9] speckle_1.2.0 glue_1.8.0
[11] org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1
[13] patchwork_1.3.1 clustree_0.5.1
[15] ggraph_2.2.0 here_1.0.1
[17] dittoSeq_1.14.2 glmGamPoi_1.14.3
[19] SeuratObject_4.1.4 Seurat_4.4.0
[21] lubridate_1.9.3 forcats_1.0.0
[23] stringr_1.5.1 dplyr_1.1.4
[25] purrr_1.0.2 readr_2.1.5
[27] tidyr_1.3.1 tibble_3.2.1
[29] ggplot2_3.5.2 tidyverse_2.0.0
[31] edgeR_4.0.15 limma_3.58.1
[33] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
[35] Biobase_2.62.0 GenomicRanges_1.54.1
[37] GenomeInfoDb_1.38.6 IRanges_2.36.0
[39] S4Vectors_0.40.2 BiocGenerics_0.48.1
[41] MatrixGenerics_1.14.0 matrixStats_1.2.0
[43] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.6 spatstat.sparse_3.0-3 bitops_1.0-7
[4] httr_1.4.7 RColorBrewer_1.1-3 doParallel_1.0.17
[7] tools_4.3.3 sctransform_0.4.1 utf8_1.2.4
[10] R6_2.5.1 lazyeval_0.2.2 uwot_0.1.16
[13] GetoptLong_1.0.5 withr_3.0.0 sp_2.1-3
[16] gridExtra_2.3 progressr_0.14.0 cli_3.6.5
[19] spatstat.explore_3.2-6 labeling_0.4.3 sass_0.4.10
[22] spatstat.data_3.0-4 ggridges_0.5.6 pbapply_1.7-2
[25] parallelly_1.37.0 rstudioapi_0.15.0 RSQLite_2.3.5
[28] generics_0.1.3 shape_1.4.6 vroom_1.6.5
[31] ica_1.0-3 spatstat.random_3.2-2 dendextend_1.17.1
[34] Matrix_1.6-5 fansi_1.0.6 abind_1.4-5
[37] lifecycle_1.0.4 whisker_0.4.1 yaml_2.3.8
[40] SparseArray_1.2.4 Rtsne_0.17 grid_4.3.3
[43] blob_1.2.4 promises_1.2.1 crayon_1.5.2
[46] miniUI_0.1.1.1 lattice_0.22-5 cowplot_1.1.3
[49] KEGGREST_1.42.0 pillar_1.9.0 knitr_1.50
[52] ComplexHeatmap_2.18.0 rjson_0.2.21 future.apply_1.11.1
[55] codetools_0.2-19 leiden_0.4.3.1 getPass_0.2-4
[58] data.table_1.15.0 vctrs_0.6.5 png_0.1-8
[61] cellranger_1.1.0 gtable_0.3.6 rematch2_2.1.2
[64] cachem_1.0.8 xfun_0.52 S4Arrays_1.2.0
[67] mime_0.12 tidygraph_1.3.1 survival_3.5-8
[70] pheatmap_1.0.12 iterators_1.0.14 statmod_1.5.0
[73] ellipsis_0.3.2 fitdistrplus_1.1-11 ROCR_1.0-11
[76] nlme_3.1-164 bit64_4.0.5 RcppAnnoy_0.0.22
[79] rprojroot_2.0.4 SnowballC_0.7.1 bslib_0.6.1
[82] irlba_2.3.5.1 KernSmooth_2.23-22 colorspace_2.1-0
[85] DBI_1.2.1 tidyselect_1.2.1 processx_3.8.3
[88] bit_4.0.5 compiler_4.3.3 git2r_0.33.0
[91] xml2_1.3.6 DelayedArray_0.28.0 plotly_4.10.4
[94] lmtest_0.9-40 callr_3.7.3 digest_0.6.34
[97] goftest_1.2-3 spatstat.utils_3.0-4 rmarkdown_2.29
[100] XVector_0.42.0 htmltools_0.5.8.1 pkgconfig_2.0.3
[103] fastmap_1.1.1 rlang_1.1.6 GlobalOptions_0.1.2
[106] htmlwidgets_1.6.4 shiny_1.8.0 farver_2.1.1
[109] jquerylib_0.1.4 zoo_1.8-12 jsonlite_1.8.8
[112] mclust_6.1 tokenizers_0.3.0 RCurl_1.98-1.14
[115] magrittr_2.0.3 GenomeInfoDbData_1.2.11 munsell_0.5.0
[118] Rcpp_1.0.12 viridis_0.6.5 reticulate_1.42.0
[121] stringi_1.8.3 zlibbioc_1.48.0 MASS_7.3-60.0.1
[124] plyr_1.8.9 parallel_4.3.3 listenv_0.9.1
[127] ggrepel_0.9.5 deldir_2.0-2 Biostrings_2.70.2
[130] graphlayouts_1.1.0 splines_4.3.3 tensor_1.5
[133] hms_1.1.3 circlize_0.4.15 locfit_1.5-9.8
[136] ps_1.7.6 igraph_2.0.1.1 spatstat.geom_3.2-8
[139] reshape2_1.4.4 evaluate_0.23 renv_1.1.4
[142] BiocManager_1.30.22 tzdb_0.4.0 foreach_1.5.2
[145] tweenr_2.0.3 httpuv_1.6.14 RANN_2.6.1
[148] polyclip_1.10-6 future_1.33.1 clue_0.3-65
[151] scattermore_1.2 ggforce_0.4.2 xtable_1.8-4
[154] janeaustenr_1.0.0 later_1.3.2 viridisLite_0.4.2
[157] memoise_2.0.1 cluster_2.1.6 timechange_0.3.0
[160] globals_0.16.2