Supplementary Figures
Jovana Maksimovic
September 10, 2025
Last updated: 2025-09-10
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Knit directory:
paediatric-cf-inflammation-citeseq/
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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"))Load data
files <- list.files(here("data/C133_Neeland_merged"),
pattern = "C133_Neeland_full_clean.*(t_cells|other_cells)_annotated_diet.SEU.rds",
full.names = TRUE)
seuLst <- lapply(files, function(f) readRDS(f))
seu <- merge(seuLst[[1]],
y = seuLst[[2]])
seuAn object of class Seurat
19973 features across 29198 samples within 1 assay
Active assay: RNA (19973 features, 0 variable features) used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 10080959 538.4 18039965 963.5 NA 12878867 687.9
Vcells 135392576 1033.0 371694648 2835.9 65536 325548377 2483.8Prepare figure panels
seu@meta.data %>%
data.frame %>%
dplyr::select(ann_level_1) %>%
group_by(ann_level_1) %>%
count() %>%
arrange(-n) %>%
dplyr::rename(cell = ann_level_1) -> cell_freq
cell_freq# A tibble: 14 × 2
# Groups: cell [14]
cell n
<chr> <int>
1 CD8 T cells 7268
2 CD4 T cells 5482
3 DC cells 4094
4 B cells 3769
5 monocytes 3225
6 epithelial cells 1847
7 innate lymphocyte 1481
8 NK cells 695
9 neutrophils 477
10 proliferating T/NK 250
11 gamma delta T cells 244
12 dividing innate cells 176
13 mast cells 99
14 NK-T cells 91files <- list.files(here("data/intermediate_objects"),
pattern = ".*all_samples",
full.names = TRUE)
files <- files[!str_detect(files, "macro")]
cutoff <- 0.05
cont_name <- "CF.NO_MODvNON_CF.CTRL"
lfc_cutoff <- 0
suffix <- ".all_samples.fit.rds"
get_deg_data(files, cont_name, cell_freq, treat_lfc = lfc_cutoff,
suffix = suffix) -> datZero log2-FC threshold detected. Switch to glmLRT() instead.
Zero log2-FC threshold detected. Switch to glmLRT() instead.
Zero log2-FC threshold detected. Switch to glmLRT() instead. bind_rows(lapply(files, function(f){
deg_results <- readRDS(f)
lrt <- glmLRT(deg_results$fit,
contrast = deg_results$contr[,cont_name])
tmp <- cbind(summary(decideTests(lrt, p.value = cutoff)) %>% data.frame,
cell = str_extract(basename(f), "^[^.]+"))
tmp
})) -> dat_degdat_deg %>%
left_join(cell_freq) -> dat_deg
pal_dt <- c(paletteer::paletteer_d("RColorBrewer::Set1")[2:1], "grey")
dat_deg %>%
dplyr::filter(Var1 != "NotSig") %>%
ggplot(aes(x = fct_reorder(cell, -n), y = Freq, fill = Var1)) +
geom_col(position = "dodge") +
scale_fill_manual(values = pal_dt) +
theme_classic() +
theme(legend.position = "top") +
geom_text(aes(label = Freq),
position = position_dodge(width = 0.9),
vjust = -0.5,
size = 2.5) +
labs(x = "Cell Type",
y = "No. DEG (FDR < 0.05)",
fill = "Direction") -> deg_barplot
deg_barplot
get_deg_data(files, cont_name, cell_freq, treat_lfc = lfc_cutoff,
suffix = suffix, cutoff = 1) -> dat_allZero log2-FC threshold detected. Switch to glmLRT() instead.
Zero log2-FC threshold detected. Switch to glmLRT() instead.
Zero log2-FC threshold detected. Switch to glmLRT() instead. dat_all %>%
left_join(cell_freq) %>%
mutate(Direction = as.factor(ifelse(sig == -1, "Down",
ifelse(sig == 1, "Up", "N.S."))),
cell = fct_reorder(cell, -n)) -> dat_all
ggplot(dat_all, aes(x = logFC, y = -log10(FDR), colour = Direction)) +
geom_point(size = 0.5) +
facet_wrap(~cell, ncol = 4) +
theme_classic() +
scale_color_manual(values = pal_dt[c(1,3,2)]) +
ggrepel::geom_text_repel(data = dat_all[dat_all$sig != 0,],
aes(label = gene), size = 2) -> volc_plot
volc_plot
dat_all %>%
dplyr::select(-sig, -n, -Direction) %>%
dplyr::filter(FDR < cutoff) %>%
group_by(cell) %>%
arrange(FDR, .by_group = TRUE) %>%
gt() %>%
tab_header(title = "Differentially expressed genes by cell type",
subtitle = cont_name) %>%
tab_style(cell_text(size = px(10)),
locations = list(cells_body())) %>%
tab_style(cell_text(size = px(12), weight = "bold"),
locations = list(cells_column_labels())) %>%
tab_style(cell_text(size = px(12), weight = "bold"),
locations = list(cells_row_groups())) -> tab
tab| Differentially expressed genes by cell type | ||
| CF.NO_MODvNON_CF.CTRL | ||
| gene | logFC | FDR |
|---|---|---|
| CD8 T cells | ||
| ADGRG1 | 2.5021012 | 0.00758679 |
| SULF2 | -1.9413497 | 0.01467086 |
| CTSB | 1.7399768 | 0.01467086 |
| REL | 0.8640961 | 0.03411823 |
| ANKRD36C | 1.1218820 | 0.03411823 |
| DC cells | ||
| CEACAM4 | 2.2610736 | 0.02775101 |
| SLC16A10 | 2.9741874 | 0.02775101 |
| ALDH1A2 | 4.8973876 | 0.02775101 |
| TGM2 | 3.6728039 | 0.02868435 |
| CCL5 | 3.3089485 | 0.04566559 |
Supplementary Figure 3
layout <- "
A
B
C
"
(wrap_elements(deg_barplot + theme(axis.title.x = element_blank(),
legend.text = element_text(size = 8),
plot.margin = margin(rep(0,4))))) +
wrap_elements(volc_plot + theme(strip.text = element_text(size = 7),
plot.margin = margin(rep(0,4)))) +
wrap_table(tab, panel = "full") +
plot_layout(design = layout) +
plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 24,
face = "bold",
family = "arial"))
Prepare figure panels
seu@meta.data %>%
data.frame %>%
dplyr::select(ann_level_1) %>%
group_by(ann_level_1) %>%
count() %>%
arrange(-n) %>%
dplyr::rename(cell = ann_level_1) -> cell_freq
cell_freq# A tibble: 14 × 2
# Groups: cell [14]
cell n
<chr> <int>
1 CD8 T cells 7268
2 CD4 T cells 5482
3 DC cells 4094
4 B cells 3769
5 monocytes 3225
6 epithelial cells 1847
7 innate lymphocyte 1481
8 NK cells 695
9 neutrophils 477
10 proliferating T/NK 250
11 gamma delta T cells 244
12 dividing innate cells 176
13 mast cells 99
14 NK-T cells 91files <- list.files(here("data/intermediate_objects"),
pattern = ".*CF_samples",
full.names = TRUE)
files <- files[!str_detect(files, "macro")]
cutoff <- 0.05
cont_name <- "CF.NO_MOD.SvCF.NO_MOD.M"
lfc_cutoff <- 0
suffix <- ".CF_samples.fit.rds"
get_deg_data(files, cont_name, cell_freq, treat_lfc = lfc_cutoff,
suffix = suffix) -> datZero log2-FC threshold detected. Switch to glmLRT() instead.
Zero log2-FC threshold detected. Switch to glmLRT() instead.
Zero log2-FC threshold detected. Switch to glmLRT() instead. bind_rows(lapply(files, function(f){
deg_results <- readRDS(f)
lrt <- glmLRT(deg_results$fit,
contrast = deg_results$contr[,cont_name])
tmp <- cbind(summary(decideTests(lrt, p.value = cutoff)) %>% data.frame,
cell = str_extract(basename(f), "^[^.]+"))
tmp
})) -> dat_degdat_deg %>%
left_join(cell_freq) -> dat_deg
pal_dt <- c(paletteer::paletteer_d("RColorBrewer::Set1")[2:1], "grey")
dat_deg %>%
dplyr::filter(Var1 != "NotSig") %>%
ggplot(aes(x = fct_reorder(cell, -n), y = Freq, fill = Var1)) +
geom_col(position = "dodge") +
scale_fill_manual(values = pal_dt) +
theme_classic() +
theme(legend.position = "top") +
geom_text(aes(label = Freq),
position = position_dodge(width = 0.9),
vjust = -0.5,
size = 2.5) +
labs(x = "Cell Type",
y = "No. DEG (FDR < 0.05)",
fill = "Direction") -> deg_barplot
deg_barplot
get_deg_data(files, cont_name, cell_freq, treat_lfc = lfc_cutoff,
suffix = suffix, cutoff = 1) -> dat_allZero log2-FC threshold detected. Switch to glmLRT() instead.
Zero log2-FC threshold detected. Switch to glmLRT() instead.
Zero log2-FC threshold detected. Switch to glmLRT() instead. dat_all %>%
left_join(cell_freq) %>%
mutate(Direction = as.factor(ifelse(sig == -1, "Down",
ifelse(sig == 1, "Up", "N.S."))),
cell = fct_reorder(cell, -n)) -> dat_all
ggplot(dat_all, aes(x = logFC, y = -log10(FDR), colour = Direction)) +
geom_point(size = 0.5) +
facet_wrap(~cell, ncol = 4) +
theme_classic() +
scale_color_manual(values = pal_dt[c(1,3,2)]) +
ggrepel::geom_text_repel(data = dat_all[dat_all$sig != 0,],
aes(label = gene), size = 2) -> volc_plot
volc_plot
dat_all %>%
dplyr::select(-sig, -n, -Direction) %>%
dplyr::filter(FDR < cutoff) %>%
group_by(cell) %>%
arrange(FDR, .by_group = TRUE) %>%
gt() %>%
tab_header(title = "Differentially expressed genes by cell type",
subtitle = cont_name) %>%
tab_style(cell_text(size = px(10)),
locations = list(cells_body())) %>%
tab_style(cell_text(size = px(12), weight = "bold"),
locations = list(cells_column_labels())) %>%
tab_style(cell_text(size = px(12), weight = "bold"),
locations = list(cells_row_groups())) -> tab
tab| Differentially expressed genes by cell type | ||
| CF.NO_MOD.SvCF.NO_MOD.M | ||
| gene | logFC | FDR |
|---|---|---|
| CD8 T cells | ||
| IER2 | -0.6465572 | 0.0152957508 |
| CMC1 | 1.2609872 | 0.0184005852 |
| MSMO1 | -1.1619299 | 0.0368874786 |
| FBLN5 | -1.9805997 | 0.0399874695 |
| CST7 | 1.0002086 | 0.0399874695 |
| CD4 T cells | ||
| AREG | -3.6207178 | 0.0382842050 |
| DC cells | ||
| FABP4 | 3.0135376 | 0.0009380894 |
| SCD | 2.9594586 | 0.0268390379 |
Supplementary Figure 5
layout <- "
A
B
C
"
(wrap_elements(deg_barplot + theme(axis.title.x = element_blank(),
legend.text = element_text(size = 8),
plot.margin = margin(rep(0,4))))) +
wrap_elements(volc_plot + theme(strip.text = element_text(size = 7),
plot.margin = margin(rep(0,4)))) +
wrap_table(tab, panel = "full") +
plot_layout(design = layout) +
plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 24,
face = "bold",
family = "arial"))
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, 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, 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, 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_MODvNON_CF.CTRL",
y = "log2FC CF.IVAvNON_CF.CTRL") +
scale_colour_identity(guide = "legend",
breaks = c("#FF6B6B", "#20A4A4", "#CC8E00","lightgrey"),
labels = c("Sig. in both",
"Sig. in CF.IVAvNON_CF.CTRL",
"Sig. in CF.NO_MODvNON_CF.CTRL",
"N.S. in either"),
name = "Statistical significance") +
theme_classic() +
theme(legend.position = "right",
legend.direction = "vertical") -> p1
p1
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 <- rbind(read_csv(file = here("output",
"dge_analysis",
"macrophages",
"ORA.HALLMARK.CF.IVAvNON_CF.CTRL.csv")) %>%
slice_head(n = num) %>%
mutate(contrast = "CF.IVAvNON_CF.CTRL",
Rank = 1:min(num, 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_MODvNON_CF.CTRL",
Rank = 1:min(num, n()))) %>%
mutate(dups = duplicated(Set) | duplicated(Set, fromLast = TRUE)) %>%
mutate(Set = str_wrap(str_replace_all(Set, "_", " "), width = 75),
Set = str_remove_all(Set, "GO |REACTOME |HALLMARK |WP "))
pal <- c(paletteer::paletteer_d("RColorBrewer::Set1")[2:1], "grey")
sub <- 1:10
hallmark[sub, ]%>%
ggplot(aes(x = -log10(FDR), y = -Rank, colour = GR)) +
geom_point(aes(size = N)) +
geom_point(shape = 8, colour = "white", size = 3,
data = hallmark[sub,][hallmark$dups[sub],],
aes(x = -log10(FDR), y = -Rank)) +
geom_vline(xintercept = -log10(0.05),
linetype = "dashed") +
facet_wrap(~contrast) +
scale_colour_viridis_c(option = "cividis") +
scale_y_continuous(breaks = -hallmark$Rank[sub],
labels = hallmark$Set[sub]) +
labs(y = "Hallmark Gene Set", size = "Set size") +
theme_classic(base_size = 10) -> p2
sub <- 11:20
hallmark[sub, ]%>%
ggplot(aes(x = -log10(FDR), y = -Rank, colour = GR)) +
geom_point(aes(size = N)) +
geom_point(shape = 8, colour = "white", size = 3,
data = hallmark[sub,][hallmark$dups[sub],],
aes(x = -log10(FDR), y = -Rank)) +
geom_vline(xintercept = -log10(0.05),
linetype = "dashed") +
facet_wrap(~contrast) +
scale_colour_viridis_c(option = "cividis") +
scale_y_continuous(breaks = -hallmark$Rank[sub],
labels = hallmark$Set[sub]) +
labs(y = "Hallmark Gene Set", size = "Set size") +
theme_classic(base_size = 10) -> p3
p2 / p3
Supplementary Figure 6
layout <- "
AAA
AAA
BBB
CCC
"
wrap_elements(p1 + theme(text = element_text(size = 8))) +
wrap_elements(p2 + theme(text = element_text(size = 8),
legend.margin = margin(-0.5,0,0,0, unit="lines"),
legend.key.size = unit(1, "lines"))) +
wrap_elements(p3 + theme(text = element_text(size = 8),
legend.margin = margin(-0.5,0,0,0, unit="lines"),
legend.key.size = unit(1, "lines"))) +
plot_layout(design = layout) +
plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 16,
face = "bold",
family = "arial"))
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] gt_1.0.0 readxl_1.4.3
[3] ggh4x_0.3.1 dsb_1.0.3
[5] paletteer_1.6.0 tidyHeatmap_1.8.1
[7] speckle_1.2.0 glue_1.8.0
[9] org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1
[11] patchwork_1.3.1 clustree_0.5.1
[13] ggraph_2.2.0 here_1.0.1
[15] dittoSeq_1.14.2 glmGamPoi_1.14.3
[17] SeuratObject_4.1.4 Seurat_4.4.0
[19] lubridate_1.9.3 forcats_1.0.0
[21] stringr_1.5.1 dplyr_1.1.4
[23] purrr_1.0.2 readr_2.1.5
[25] tidyr_1.3.1 tibble_3.2.1
[27] ggplot2_3.5.2 tidyverse_2.0.0
[29] edgeR_4.0.15 limma_3.58.1
[31] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
[33] Biobase_2.62.0 GenomicRanges_1.54.1
[35] GenomeInfoDb_1.38.6 IRanges_2.36.0
[37] S4Vectors_0.40.2 BiocGenerics_0.48.1
[39] MatrixGenerics_1.14.0 matrixStats_1.2.0
[41] 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 prismatic_1.1.1
[22] sass_0.4.10 spatstat.data_3.0-4 ggridges_0.5.6
[25] pbapply_1.7-2 parallelly_1.37.0 rstudioapi_0.15.0
[28] RSQLite_2.3.5 generics_0.1.3 shape_1.4.6
[31] vroom_1.6.5 ica_1.0-3 spatstat.random_3.2-2
[34] dendextend_1.17.1 Matrix_1.6-5 fansi_1.0.6
[37] abind_1.4-5 lifecycle_1.0.4 whisker_0.4.1
[40] yaml_2.3.8 SparseArray_1.2.4 Rtsne_0.17
[43] grid_4.3.3 blob_1.2.4 promises_1.2.1
[46] crayon_1.5.2 miniUI_0.1.1.1 lattice_0.22-5
[49] cowplot_1.1.3 KEGGREST_1.42.0 pillar_1.9.0
[52] knitr_1.50 ComplexHeatmap_2.18.0 rjson_0.2.21
[55] future.apply_1.11.1 codetools_0.2-19 leiden_0.4.3.1
[58] getPass_0.2-4 data.table_1.15.0 vctrs_0.6.5
[61] png_0.1-8 cellranger_1.1.0 gtable_0.3.6
[64] rematch2_2.1.2 cachem_1.0.8 xfun_0.52
[67] S4Arrays_1.2.0 mime_0.12 tidygraph_1.3.1
[70] survival_3.5-8 pheatmap_1.0.12 iterators_1.0.14
[73] statmod_1.5.0 ellipsis_0.3.2 fitdistrplus_1.1-11
[76] ROCR_1.0-11 nlme_3.1-164 bit64_4.0.5
[79] RcppAnnoy_0.0.22 rprojroot_2.0.4 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] scales_1.3.0 lmtest_0.9-40 callr_3.7.3
[97] digest_0.6.34 goftest_1.2-3 spatstat.utils_3.0-4
[100] rmarkdown_2.29 XVector_0.42.0 htmltools_0.5.8.1
[103] pkgconfig_2.0.3 fastmap_1.1.1 rlang_1.1.6
[106] GlobalOptions_0.1.2 htmlwidgets_1.6.4 shiny_1.8.0
[109] farver_2.1.1 jquerylib_0.1.4 zoo_1.8-12
[112] jsonlite_1.8.8 mclust_6.1 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] later_1.3.2 viridisLite_0.4.2 memoise_2.0.1
[157] cluster_2.1.6 timechange_0.3.0 globals_0.16.2