• Load data
  • Prepare figure panels
  • Figure 5
  • Session info

Last updated: 2025-01-08

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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)
})

source(here("code/utility.R"))

Load data

files <- list.files(here("data/C133_Neeland_merged"),
                    pattern = "C133_Neeland_full_clean.*(macrophages|t_cells|other_cells)_annotated_diet.SEU.rds",
                    full.names = TRUE)

seuLst <- lapply(files[2:4], function(f) readRDS(f))

seu <- merge(seuLst[[1]], 
             y = c(seuLst[[2]], 
                   seuLst[[3]]))
seu
An object of class Seurat 
21568 features across 194407 samples within 1 assay 
Active assay: RNA (21568 features, 0 variable features)
             used    (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   12101517   646.3   20150177  1076.2   13844582   739.4
Vcells 1354183553 10331.7 3693778851 28181.3 3551517295 27096.0

Create sample meta data table.

props <- getTransformedProps(clusters = seu$ann_level_3,
                             sample = seu$sample.id, transform="asin")

seu@meta.data %>%
  dplyr::select(sample.id,
                Participant,
                Disease,
                Treatment,
                Severity,
                Group,
                Group_severity,
                Batch, 
                Age, 
                Sex) %>%
     left_join(props$Counts %>% 
                 data.frame %>%
                 group_by(sample) %>%
                 summarise(ncells = sum(Freq)),
               by = c("sample.id" = "sample")) %>%
    distinct() -> info

head(info) %>% knitr::kable()
sample.id Participant Disease Treatment Severity Group Group_severity Batch Age Sex ncells
sample_33.1 sample_33 CF treated (ivacaftor) severe CF.IVA CF.IVA.S 1 5.950685 M 2139
sample_25.1 sample_25 CF untreated severe CF.NO_MOD CF.NO_MOD.S 1 4.910000 F 3272
sample_29.1 sample_29 CF untreated severe CF.NO_MOD CF.NO_MOD.S 1 5.989041 F 1568
sample_27.1 sample_27 CF treated (ivacaftor) mild CF.IVA CF.IVA.M 1 4.917808 M 2467
sample_32.1 sample_32 CF untreated mild CF.NO_MOD CF.NO_MOD.M 1 5.926027 F 2963
sample_26.1 sample_26 CF untreated mild CF.NO_MOD CF.NO_MOD.M 1 5.049315 M 2040

Prepare figure panels

props <- getTransformedProps(clusters = seu$ann_level_3[!str_detect(seu$ann_level_3, "macro")],
                             sample = seu$sample.id[!str_detect(seu$ann_level_3, "macro")], transform="asin")

# IVA 5A: NK-T cells, CD4 T-IFN, monocytes
props$Proportions %>% data.frame %>%
  left_join(info,
            by = c("sample" = "sample.id")) %>%
  dplyr::filter(Group %in% c("CF.IVA", "CF.NO_MOD", "NON_CF.CTRL"),
                clusters %in% c("NK-T cells",
                                "CD4 T-IFN",
                                "monocytes")) -> dat

sig_names <- as_labeller(
     c("CD4 T-IFN" = "CD4 T-IFN",
       "monocytes" = "monocytes",
       "NK-T cells" = "NK-T cells"))

pal <- RColorBrewer::brewer.pal(8, "Set2")[1:4]
names(pal) <- c("CF.IVA","CF.LUMA_IVA","CF.NO_MOD","NON_CF.CTRL")
  
dat %>%
dplyr::filter(Group %in% c("CF.IVA", "CF.NO_MOD"),
                clusters %in% "NK-T cells") %>%
ggplot(aes(x = Group,
                y = Freq,
                colour = Group)) +
  geom_jitter(stat = "identity",
              width = 0.15,
              size = 1) +
  theme_classic() +
  theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.text.y = element_text(7),
          legend.position = "bottom",
          legend.direction = "horizontal",
          strip.text = element_text(size = 8)) +
  labs(x = "Group", y = "Proportion") +
  facet_wrap(~clusters, scales = "free_y", ncol = 4,
             labeller = sig_names) +
  stat_summary(
    geom = "point",
    fun.y = "mean",
    col = "black",
    shape = "_",
    size = 10) +
  scale_color_manual(values = pal) -> p1

dat %>%
dplyr::filter(Group %in% c("CF.IVA", "NON_CF.CTRL"),
                clusters %in% c("CD4 T-IFN",
                                "monocytes")) %>%
ggplot(aes(x = Group,
                y = Freq,
                colour = Group)) +
  geom_jitter(stat = "identity",
              width = 0.15,
              size = 1) +
  theme_classic() +
  theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.text.y = element_text(7),
          legend.position = "bottom",
          legend.direction = "horizontal",
          strip.text = element_text(size = 8)) +
  labs(x = "Group", y = "Proportion") +
  facet_wrap(~clusters, scales = "free_y", ncol = 4,
             labeller = sig_names) +
  stat_summary(
    geom = "point",
    fun.y = "mean",
    col = "black",
    shape = "_",
    size = 10) +
  scale_color_manual(values = pal) -> p2

layout <- "ABB"
iva_props <- (p1 + 
                (p2 + theme(axis.title.y = element_blank()))) + 
  plot_layout(design = layout) &
  theme(axis.text.y = element_text(size = 6,
                                   angle = 90,
                                   hjust = 0.5),
        legend.text = element_text(size = 8),
        legend.key.spacing = unit(0, "lines"),
        legend.margin = margin(-0.5,0,0,0, unit="lines"),
        legend.direction = "horizontal") &
  guides(colour = guide_legend(title.position="top", title.hjust = 0.5))

iva_props

Version Author Date
bb42dbf Jovana Maksimovic 2025-01-06 
# LUMA-IVA 5B: NK-T cells, CD4 Tregs, monocytes, CD4 T-IFN, macro-CCL18

props <- getTransformedProps(clusters = seu$ann_level_3[!str_detect(seu$ann_level_3, "macro")],
                             sample = seu$sample.id[!str_detect(seu$ann_level_3, "macro")], transform="asin")

props$Proportions %>% data.frame %>%
  left_join(info,
            by = c("sample" = "sample.id")) %>%
  dplyr::filter(Group %in% c("CF.LUMA_IVA", "CF.NO_MOD", "NON_CF.CTRL"),
                clusters %in% c("NK-T cells",
                                "CD4 T-reg",
                                "CD4 T-IFN",
                                "monocytes")) -> dat

sig_names <- as_labeller(
     c("CD4 T-IFN" = "CD4 T-IFN",
       "CD4 T-reg" = "CD4 T-reg",
       "monocytes" = "monocytes",
       "macro-CCL18" = "macro-CCL18",
       "NK-T cells" = "NK-T cells"))

dat %>%
dplyr::filter(Group %in% c("CF.LUMA_IVA", "CF.NO_MOD"),
                clusters %in% "NK-T cells") %>%
ggplot(aes(x = Group,
                y = Freq,
                colour = Group)) +
  geom_jitter(stat = "identity",
              width = 0.15,
              size = 1,
              show.legend = FALSE) +
  theme_classic() +
  theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.text.y = element_text(7),
          legend.position = "bottom",
          legend.direction = "horizontal",
          strip.text = element_text(size = 8)) +
  labs(x = "Group", y = "Proportion") +
  facet_wrap(~clusters, scales = "free_y", ncol = 4,
             labeller = sig_names) +
  stat_summary(
    geom = "point",
    fun.y = "mean",
    col = "black",
    shape = "_",
    size = 10) +
  scale_color_manual(values = pal) -> p1

dat %>%
dplyr::filter(Group %in% c("CF.LUMA_IVA", "NON_CF.CTRL"),
                clusters %in% c("CD4 T-IFN",
                                "CD4 T-reg",
                                "monocytes")) %>%
ggplot(aes(x = Group,
                y = Freq,
                colour = Group)) +
  geom_jitter(stat = "identity",
              width = 0.15,
              size = 1,
              show.legend = FALSE) +
  theme_classic() +
  theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.text.y = element_text(7),
          legend.position = "bottom",
          legend.direction = "horizontal",
          strip.text = element_text(size = 8)) +
  labs(x = "Group", y = "Proportion") +
  facet_wrap(~clusters, scales = "free_y", ncol = 4,
             labeller = sig_names) +
  stat_summary(
    geom = "point",
    fun.y = "mean",
    col = "black",
    shape = "_",
    size = 10) +
  scale_color_manual(values = pal) -> p2
props <- getTransformedProps(clusters = seu$ann_level_3[str_detect(seu$ann_level_3, "macro")],
                             sample = seu$sample.id[str_detect(seu$ann_level_3, "macro")], transform="asin")

props$Proportions %>% data.frame %>%
  left_join(info,
            by = c("sample" = "sample.id")) %>%
  dplyr::filter(Group %in% c("CF.LUMA_IVA", "CF.NO_MOD", "NON_CF.CTRL"),
                clusters %in% "macro-CCL18") -> dat

dat %>%
dplyr::filter(Group %in% c("CF.LUMA_IVA", "CF.NO_MOD", "NON_CF.CTRL"),
                clusters %in% c("macro-CCL18")) %>%
ggplot(aes(x = Group,
                y = Freq,
                colour = Group)) +
  geom_jitter(stat = "identity",
              width = 0.15,
              size = 1) +
  theme_classic() +
  theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.text.y = element_text(7),
          legend.position = "bottom",
          legend.direction = "horizontal",
          strip.text = element_text(size = 8)) +
  labs(x = "Group", y = "Proportion") +
  facet_wrap(~clusters, scales = "free_y", ncol = 4,
             labeller = sig_names) +
  stat_summary(
    geom = "point",
    fun.y = "mean",
    col = "black",
    shape = "_",
    size = 10) +
  scale_color_manual(values = pal) -> p3

layout <- "ABBBC"
lumaiva_props <- (p1 + 
                    (p2 + theme(axis.title.y = element_blank())) + 
                    (p3 + theme(axis.title.y = element_blank()))) + 
  plot_layout(design = layout, guides = "collect") &
  theme(axis.text.y = element_text(size = 6,
                                   angle = 90,
                                   hjust = 0.5),
        legend.text = element_text(size = 8),
        legend.key.spacing = unit(0, "lines"),
        legend.position = "bottom",
        legend.margin = margin(-0.5,0,0,0, unit="lines"))

lumaiva_props

Version Author Date
bb42dbf Jovana Maksimovic 2025-01-06 
seu@meta.data %>% 
    data.frame %>% 
    dplyr::select(ann_level_2) %>%
    dplyr::filter(str_detect(ann_level_2, "macro")) %>%
    group_by(ann_level_2) %>% 
    count() %>%
    janitor::adorn_totals(name = "macrophages") %>%
    arrange(-n) %>%
    dplyr::rename(cell = ann_level_2) -> cell_freq

cell_freq
                   cell      n
            macrophages 165209
         macro-alveolar  52563
            macro-IFI27  24864
              macro-CCL  21246
 macro-monocyte-derived  13461
           macro-APOC2+  13354
            macro-lipid  12452
             macro-IGF1   8229
    macro-proliferating   6821
               macro-MT   4037
     macro-interstitial   3412
                macro-T   2722
              macro-IFN   2048
files <- list.files(here("data/intermediate_objects"),
            pattern = "macro.*CF_samples", 
            full.names = TRUE) 
 
cutoff <- 0.05 
cont_name <- "CF.IVAvCF.NO_MOD"
lfc_cutoff <- log2(1.2)
suffix <- ".CF_samples.fit.rds"
  
get_deg_data(files, cont_name, cell_freq, treat_lfc = lfc_cutoff,
             suffix = suffix) -> dat
dat %>%
  dplyr::select(gene, cell, logFC) %>%
  distinct() %>%
  pivot_wider(
    names_from = cell,        # Column whose values become new column names
    values_from = logFC,
    values_fill = list(logFC = NA)) %>%
   arrange(across(all_of(cell_freq$cell[cell_freq$cell %in% dat$cell]))) %>%
  column_to_rownames(var = "gene") -> dat_lfc
col_fun <- circlize::colorRamp2(seq(0, 100000, length.out = 9), 
                                (RColorBrewer::brewer.pal(9, "PiYG")))
col_split <- c(rep("aggregate", 1), rep("sub-type", ncol(dat_lfc) - 1))

pal_dt <- c(paletteer::paletteer_d("RColorBrewer::Set1")[2:1], "grey") 
col_lfc_fun <- circlize::colorRamp2(seq(-2, 2, length.out = 3), 
                                    c(pal_dt[1], "white", pal_dt[2]))

ComplexHeatmap::HeatmapAnnotation(df = cell_freq %>%
                                    dplyr::filter(cell %in% colnames(dat_lfc)) %>%
                                    column_to_rownames(var = "cell") %>%
                                    dplyr::rename(`No. cells` = n),
                                  which = "column",
                                  show_annotation_name = FALSE,
                                  col = list(`No. cells` = col_fun),
                                  annotation_legend_param = list(
                                    `No. cells` = list(direction = "vertical"))) -> col_ann

ComplexHeatmap::HeatmapAnnotation(df = data.frame(multiple = (rowSums(!is.na(dat_lfc)) > 1)),
                                  which = "row",
                                  col = list(multiple = c("FALSE" = "#fdcce5","TRUE" = "#8bd3c7")),
                                  annotation_legend_param = list(
                                    multiple = list(direction = "vertical",
                                                    ncol = 1))) -> row_ann

ComplexHeatmap::Heatmap(dat_lfc, 
                        name = "logFC",
                        column_split = col_split,
                        column_title = NULL,
                        cluster_rows = FALSE,
                        cluster_columns = FALSE,
                        rect_gp = grid::gpar(col = "white", lwd = 1),
                        row_names_gp = grid::gpar(fontsize = 7),
                        column_names_gp = grid::gpar(fontsize = 7),
                        col = col_lfc_fun,
                        top_annotation = col_ann,
                        right_annotation = row_ann,
                        heatmap_legend_param = list(direction = "vertical")) -> plot_lfc

ComplexHeatmap::draw(as(list(plot_lfc), "HeatmapList"), 
                     heatmap_legend_side = "right", 
                     annotation_legend_side = "right",
                     merge_legends = TRUE) -> plot_lfc

Version Author Date
bb42dbf Jovana Maksimovic 2025-01-06 
plot_lfc

Version Author Date
bb42dbf Jovana Maksimovic 2025-01-06 
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 = unlist(str_split(str_remove(f, ".CF_samples.fit.rds"), "/"))[8])
  tmp
})) -> dat_deg
dat_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(axis.text.y = element_text(angle = -45,
                                   hjust = 1,
                                   vjust = 1,
                                   size = 7),
        legend.position = "top") +
  geom_text(aes(label = Freq), 
            position = position_dodge(width = 0.9),
            vjust = -0.5,
            angle = 270,
            size = 2.5) +
  labs(x = "Cell Type",
       y = "No. DEG (FDR < 0.05)",
       fill = "Direction") +
  coord_flip() -> deg_barplot

deg_barplot

Version Author Date
bb42dbf Jovana Maksimovic 2025-01-06 
volc_plot <- draw_treat_volcano_plot(cell = "macro-alveolar",
                                     suffix = suffix,
                                     cutoff = cutoff,
                                     lfc_cutoff = lfc_cutoff)
volc_plot

Version Author Date
bb42dbf Jovana Maksimovic 2025-01-06 
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)
hallmark <- read_csv(file = here("output",
                                 "dge_analysis",
                                 "macro-alveolar",
                                 "CAM.HALLMARK.CF.IVAvCF.NO_MOD.csv"))

reactome <- read_csv(file = here("output",
                                 "dge_analysis",
                                 "macro-alveolar",
                                 "CAM.REACTOME.CF.IVAvCF.NO_MOD.csv"))

results_list <- list(HALLMARK = hallmark %>% column_to_rownames(var = "Set"),
                REACTOME = reactome %>% column_to_rownames(var = "Set"))

top_camera_sets(results_list, num = 4) + 
  theme(plot.title = element_blank(),
        axis.text.y = element_text(size = 7,
                                   angle = 35)) -> cam_plot

cam_plot

Version Author Date
bb42dbf Jovana Maksimovic 2025-01-06 
reactome <- read_csv(file = here("output",
                                 "dge_analysis",
                                 "macro-alveolar",
                                 "ORA.REACTOME.CF.IVAvCF.NO_MOD.csv"))
go <- read_csv(file = here("output",
                                 "dge_analysis",
                                 "macro-alveolar",
                                 "ORA.GO.CF.IVAvCF.NO_MOD.csv"))

results_list <- list(REACTOME = reactome %>% column_to_rownames(var = "Set"),
                     GO = go %>% column_to_rownames(var = "Set"))

top_ora_sets(results_list, num = 3) + 
  theme(plot.title = element_blank(),
        axis.text.y = element_text(size = 7,
                                   angle = 35)) -> ora_plot

ora_plot

Version Author Date
bb42dbf Jovana Maksimovic 2025-01-06 
genes <- c("LILRB2", "C3", "PADI2", "SVIP", "FCN1", "CDA", "ADGRE3", "CAPN11", "LCN2", "CLEC12A", "LYZ")

f <- files[1]
deg_results <- readRDS(f)
norm_counts <- deg_results$adj$normalizedCounts
group <- deg_results$fit$samples[,"group", drop = FALSE]

cpm(norm_counts, log = TRUE) %>% 
  data.frame %>%
  rownames_to_column(var = "gene") %>%
  pivot_longer(-gene, 
               names_to = "sample", 
               values_to = "logCPM") %>%
  left_join(group %>%
              data.frame %>%
              rownames_to_column(var = "sample")) %>%
  dplyr::filter(gene %in% genes) %>%
  mutate(group = str_remove_all(group, "(.M|.S)$")) %>%
  dplyr::filter(group %in% str_split(cont_name, "v")[[1]]) -> dat

ggplot(dat, aes(x = group, y = logCPM, colour = group)) +
 # geom_violin() +
  geom_jitter(width = 0.15,
              size = 1) +
  stat_summary(geom = "point",
               fun.y = "mean",
               col = "black",
               shape = "_",
               size = 10) +
  facet_wrap(~gene, nrow = 1, scales = "free_y") + 
  theme_classic() +
    theme(axis.text.x = element_blank(),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.text.y = element_text(7),
          legend.position = "bottom",
          legend.direction = "horizontal",
          strip.text = element_text(size = 8)) +
  labs(colour = "Group") +
  scale_color_manual(values = pal) -> gene_plot

gene_plot

Version Author Date
bb42dbf Jovana Maksimovic 2025-01-06

Figure 5

layout <- "
AAABBBB
AAABBBB
CCCDDDD
CCCDDDD
CCCDDDD
CCCDDDD
EEEDDDD
EEEDDDD
EEEDDDD
FFFDDDD
FFFDDDD
FFFDDDD
GGGDDDD
GGGDDDD
GGGDDDD
HHHHHHH
HHHHHHH
"

wrap_elements(iva_props) +
  wrap_elements(lumaiva_props) +
  wrap_elements(deg_barplot) +
  wrap_elements(grid::grid.grabExpr(ComplexHeatmap::draw(plot_lfc))) +
  wrap_elements(volc_plot) +
    wrap_elements(cam_plot + theme(legend.position = "bottom",
                                 legend.direction = "horizontal",
                                 legend.box = "vertical",
                                 legend.margin = margin(-0.5,0,0,0, unit="lines"),
                        legend.text = element_text(size = 8))) +
  wrap_elements(ora_plot + 
                  theme(legend.position = "bottom",
                        legend.direction = "horizontal",
                        legend.box = "vertical",
                        legend.margin = margin(-0.5,0,0,0, unit="lines"),
                        legend.text = element_text(size = 7))) +
  wrap_elements(gene_plot) +
  plot_layout(design = layout) +
  plot_annotation(tag_levels = "A")  &
  theme(plot.tag = element_text(size = 16,
                                face = "bold",
                                family = "arial"))

Version Author Date
bb42dbf Jovana Maksimovic 2025-01-06

Session info


sessionInfo()
R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices datasets  utils     methods  
[8] base     

other attached packages:
 [1] readxl_1.4.3                ggh4x_0.2.8                
 [3] dsb_1.0.3                   paletteer_1.6.0            
 [5] tidyHeatmap_1.8.1           speckle_1.2.0              
 [7] glue_1.8.0                  org.Hs.eg.db_3.18.0        
 [9] AnnotationDbi_1.64.1        patchwork_1.3.0            
[11] clustree_0.5.1              ggraph_2.2.0               
[13] here_1.0.1                  dittoSeq_1.14.2            
[15] glmGamPoi_1.14.3            SeuratObject_4.1.4         
[17] Seurat_4.4.0                lubridate_1.9.3            
[19] forcats_1.0.0               stringr_1.5.1              
[21] dplyr_1.1.4                 purrr_1.0.2                
[23] readr_2.1.5                 tidyr_1.3.1                
[25] tibble_3.2.1                ggplot2_3.5.0              
[27] tidyverse_2.0.0             edgeR_4.0.15               
[29] limma_3.58.1                SingleCellExperiment_1.24.0
[31] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[33] GenomicRanges_1.54.1        GenomeInfoDb_1.38.6        
[35] IRanges_2.36.0              S4Vectors_0.40.2           
[37] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[39] matrixStats_1.2.0           workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.5                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_3.6.3              
 [19] Cairo_1.6-2             spatstat.explore_3.2-6  prismatic_1.1.1        
 [22] labeling_0.4.3          sass_0.4.9              spatstat.data_3.0-4    
 [25] ggridges_0.5.6          pbapply_1.7-2           parallelly_1.37.0      
 [28] rstudioapi_0.15.0       RSQLite_2.3.5           generics_0.1.3         
 [31] shape_1.4.6             vroom_1.6.5             ica_1.0-3              
 [34] spatstat.random_3.2-2   dendextend_1.17.1       Matrix_1.6-5           
 [37] fansi_1.0.6             abind_1.4-5             lifecycle_1.0.4        
 [40] whisker_0.4.1           yaml_2.3.8              snakecase_0.11.1       
 [43] SparseArray_1.2.4       Rtsne_0.17              grid_4.3.3             
 [46] blob_1.2.4              promises_1.2.1          crayon_1.5.2           
 [49] miniUI_0.1.1.1          lattice_0.22-5          cowplot_1.1.3          
 [52] KEGGREST_1.42.0         pillar_1.9.0            knitr_1.45             
 [55] ComplexHeatmap_2.18.0   rjson_0.2.21            future.apply_1.11.1    
 [58] codetools_0.2-19        leiden_0.4.3.1          getPass_0.2-4          
 [61] data.table_1.15.0       vctrs_0.6.5             png_0.1-8              
 [64] cellranger_1.1.0        gtable_0.3.4            rematch2_2.1.2         
 [67] cachem_1.0.8            xfun_0.42               S4Arrays_1.2.0         
 [70] mime_0.12               tidygraph_1.3.1         survival_3.7-0         
 [73] pheatmap_1.0.12         iterators_1.0.14        statmod_1.5.0          
 [76] ellipsis_0.3.2          fitdistrplus_1.1-11     ROCR_1.0-11            
 [79] nlme_3.1-164            bit64_4.0.5             RcppAnnoy_0.0.22       
 [82] rprojroot_2.0.4         bslib_0.6.1             irlba_2.3.5.1          
 [85] KernSmooth_2.23-24      colorspace_2.1-0        DBI_1.2.1              
 [88] tidyselect_1.2.1        processx_3.8.3          bit_4.0.5              
 [91] compiler_4.3.3          git2r_0.33.0            DelayedArray_0.28.0    
 [94] plotly_4.10.4           scales_1.3.0            lmtest_0.9-40          
 [97] callr_3.7.3             digest_0.6.34           goftest_1.2-3          
[100] spatstat.utils_3.0-4    rmarkdown_2.25          XVector_0.42.0         
[103] htmltools_0.5.8.1       pkgconfig_2.0.3         highr_0.10             
[106] fastmap_1.1.1           rlang_1.1.4             GlobalOptions_0.1.2    
[109] htmlwidgets_1.6.4       shiny_1.8.0             farver_2.1.1           
[112] jquerylib_0.1.4         zoo_1.8-12              jsonlite_1.8.8         
[115] mclust_6.1              RCurl_1.98-1.14         magrittr_2.0.3         
[118] GenomeInfoDbData_1.2.11 munsell_0.5.0           Rcpp_1.0.12            
[121] viridis_0.6.5           reticulate_1.35.0       stringi_1.8.3          
[124] zlibbioc_1.48.0         MASS_7.3-60.0.1         plyr_1.8.9             
[127] parallel_4.3.3          listenv_0.9.1           ggrepel_0.9.5          
[130] deldir_2.0-2            Biostrings_2.70.2       graphlayouts_1.1.0     
[133] splines_4.3.3           tensor_1.5              hms_1.1.3              
[136] circlize_0.4.15         locfit_1.5-9.8          ps_1.7.6               
[139] igraph_2.0.1.1          spatstat.geom_3.2-8     reshape2_1.4.4         
[142] evaluate_0.23           renv_1.0.3              BiocManager_1.30.22    
[145] tzdb_0.4.0              foreach_1.5.2           tweenr_2.0.3           
[148] httpuv_1.6.14           RANN_2.6.1              polyclip_1.10-6        
[151] future_1.33.1           clue_0.3-65             scattermore_1.2        
[154] ggforce_0.4.2           janitor_2.2.0           xtable_1.8-4           
[157] later_1.3.2             viridisLite_0.4.2       memoise_2.0.1          
[160] cluster_2.1.6           timechange_0.3.0        globals_0.16.2