Figure 3
Jovana Maksimovic
March 23, 2026
Last updated: 2026-03-23
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Knit directory:
paediatric-cf-inflammation-citeseq/
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Modified: output/dge_analysis/macrophages/CAM.FIBROSIS.CF.NO_MOD.SvCF.NO_MOD.M.csv
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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(purrr)
})
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]]))
seuAn 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) limit (Mb) max used (Mb)
Ncells 10387662 554.8 18508525 988.5 NA 12657434 676.0
Vcells 1351269740 10309.4 3689746178 28150.6 65536 3548607425 27073.8Create 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$Counts %>%
data.frame %>%
group_by(sample) %>%
summarise(ncells = sum(Freq)),
by = c("sample.id" = "sample")) %>%
distinct() -> info
head(info) %>% knitr::kable()| sample.id | Participant | Disease | Treatment | Severity | Group | Group_severity | Batch | Age | Sex | ncells |
|---|---|---|---|---|---|---|---|---|---|---|
| sample_33.1 | sample_33 | CF | treated (ivacaftor) | severe | CF.IVA | CF.IVA.S | 1 | 5.950685 | M | 2139 |
| sample_25.1 | sample_25 | CF | untreated | severe | CF.NO_MOD | CF.NO_MOD.S | 1 | 4.910000 | F | 3272 |
| sample_29.1 | sample_29 | CF | untreated | severe | CF.NO_MOD | CF.NO_MOD.S | 1 | 5.989041 | F | 1568 |
| sample_27.1 | sample_27 | CF | treated (ivacaftor) | mild | CF.IVA | CF.IVA.M | 1 | 4.917808 | M | 2467 |
| sample_32.1 | sample_32 | CF | untreated | mild | CF.NO_MOD | CF.NO_MOD.M | 1 | 5.926027 | F | 2963 |
| sample_26.1 | sample_26 | CF | untreated | mild | CF.NO_MOD | CF.NO_MOD.M | 1 | 5.049315 | M | 2040 |
Prepare figure panels
Set up short/better names for cell types and conditions in figure.
lab_map <- c(
"macro-alveolar" = "AM",
"macro-IGF1" = "AM.IGF1",
"macro-CCL" = "AM.CCL",
"macro-lipid" = "AM.Lipid",
"macro-MT" = "AM.MT",
"macro-IFN" = "AM.IFN",
"macro-APOC2+" = "AM.APOC2",
"macro-CCL18" = "AM.CCL18",
"macro-IFI27" = "AM.IFI27",
"macro-monocyte-derived" = "Mac.Mono.Deriv",
"macro-interstitial" = "Mac.Interstitial",
"macro-lipid-APOC2+" = "AM.Lipid.APOC2",
"macro-T" = "Mac.T",
"macro-IFI27+CCL18+" = "AM.IFI27.CCL18",
"macro-IFI27+APOC2+" = "AM.IFI27.APOC2",
"macro-proliferating" = "Mac.Prolif",
"macrophages" = "All Macs (exc. Prolif)",
"CD4 T-reg" = "CD4 T-reg",
"monocytes" = "monocytes",
"NK-T cells" = "NK-T cells"
)
samp_map <-
c(
"CF.IVA" = "CF (iva)",
"CF.LUMA_IVA" = "CF (luma/iva)",
"CF.NO_MOD" = "CF (no mod)",
"NON_CF.CTRL" = "Non-CF control",
"healthy" = "Control",
"mild" = "Mild",
"severe" = "Severe"
)# 3A: monocytes, NK-T cells, CD4 Tregs, macro-IGF1
props <- getTransformedProps(clusters = seu$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.NO_MOD", "NON_CF.CTRL"),
clusters %in% c("monocytes",
"NK-T cells",
"CD4 T-reg")) -> dat
sig_names <- as_labeller(lab_map)
prop.non_macros.fit <- readRDS(here("data/intermediate_objects/prop.ann_level3.non-macrophages.fit.rds"))
prop.macros.fit <- readRDS(here("data/intermediate_objects/prop.ann_level3.macrophages.fit.rds"))
fits <- list(
non_macros = prop.non_macros.fit,
macros = prop.macros.fit
)
stats_all <- map_dfr(
fits,
~ topTable(.x,
coef = "CF.NO_MODvNON_CF.CTRL",
n = Inf) %>%
rownames_to_column("clusters"),
.id = "fit"
)
stat_labels <- stats_all %>%
filter(clusters %in% unique(dat$clusters)) %>%
distinct(clusters, .keep_all = TRUE) %>%
mutate(
sig = case_when(
adj.P.Val < 0.001 ~ "***",
adj.P.Val < 0.01 ~ "**",
adj.P.Val < 0.05 ~ "*",
TRUE ~ ""
),
label = sprintf("p = %.2g\nFDR = %.2g%s",
P.Value, adj.P.Val, sig)
)
y_pos <- dat %>%
group_by(clusters) %>%
summarise(y = max(Freq, na.rm = TRUE) * 0.9)
stat_labels <- left_join(stat_labels, y_pos, by = "clusters")
pal <- setNames(RColorBrewer::brewer.pal(7, "Set2"),
unname(samp_map))
dat %>%
mutate(Group = samp_map[Group]) %>%
ggplot(aes(x = Group,
y = Freq,
colour = Group)) +
geom_jitter(stat = "identity",
width = 0.15,
size = 1) +
theme_classic() +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_text(size = 7),
legend.position = "bottom",
legend.direction = "horizontal",
strip.text = element_text(size = 8)) +
labs(x = "Group", y = "Proportion") +
facet_wrap(~clusters, scales = "free_y", ncol = 4,
labeller = sig_names) +
stat_summary(
geom = "point",
fun.y = "mean",
col = "black",
shape = "_",
size = 10) +
scale_color_manual(values = pal) +
geom_text(
data = stat_labels,
aes(x = 1.5, y = y, label = label),
inherit.aes = FALSE,
size = 3) -> p1
p1
props <- getTransformedProps(clusters = seu$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.NO_MOD", "NON_CF.CTRL"),
clusters %in% c("macro-IGF1")) -> dat
stat_labels <- stats_all %>%
filter(clusters %in% unique(dat$clusters)) %>%
distinct(clusters, .keep_all = TRUE) %>%
mutate(
sig = case_when(
adj.P.Val < 0.001 ~ "***",
adj.P.Val < 0.01 ~ "**",
adj.P.Val < 0.05 ~ "*",
TRUE ~ ""
),
label = sprintf("p = %.2g\nFDR = %.2g%s",
P.Value, adj.P.Val, sig)
)
y_pos <- dat %>%
group_by(clusters) %>%
summarise(y = max(Freq, na.rm = TRUE) * 0.9)
stat_labels <- left_join(stat_labels, y_pos, by = "clusters")
dat %>%
mutate(Group = samp_map[Group]) %>%
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(size = 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) +
geom_text(
data = stat_labels,
aes(x = 1.5, y = y, label = label),
inherit.aes = FALSE,
size = 3) -> p2
layout <- "AAAB"
cf_props <- (p1 +
(p2 + 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.margin = margin(-0.5,0,0,0, unit="lines"),
legend.direction = "horizontal",
legend.position = "bottom")
cf_props
| Version | Author | Date |
|---|---|---|
| aa4438b | Jovana Maksimovic | 2025-09-10 |
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 2048files <- list.files(here("data/intermediate_objects"),
pattern = "macro.*all_samples",
full.names = TRUE)
cutoff <- 0.05
cont_name <- "CF.NO_MODvNON_CF.CTRL"
lfc_cutoff <- log2(1.1)
suffix <- ".all_samples.fit.rds"
get_deg_data(files, cont_name, cell_freq, treat_lfc = lfc_cutoff,
suffix = suffix) -> datdat %>%
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_lfccol_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(`Sig. ≥2 cell types` = (rowSums(!is.na(dat_lfc)) > 1),
check.names = FALSE),
which = "row",
col = list(`Sig. ≥2 cell types` = c("FALSE" = "#fdcce5","TRUE" = "#8bd3c7")),
annotation_legend_param = list(
`Sig. ≥2 cell types` = list(direction = "vertical",
ncol = 1)),
show_annotation_name = FALSE) -> row_ann
colnames(dat_lfc) <- lab_map[colnames(dat_lfc)]
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 |
|---|---|---|
| aa4438b | Jovana Maksimovic | 2025-09-10 |
plot_lfc
| Version | Author | Date |
|---|---|---|
| aa4438b | Jovana Maksimovic | 2025-09-10 |
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") %>%
mutate(cell = lab_map[as.character(cell)]) %>%
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
volc_plot <- draw_treat_volcano_plot(cell = "macrophages",
suffix = suffix,
cutoff = cutoff,
lfc_cutoff = lfc_cutoff)
volc_plot
| Version | Author | Date |
|---|---|---|
| aa4438b | 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)# alveolar macrophages, macro-CCL, macro-lipid, Monocyte-derived macrophages
cell_types <- c("macro-alveolar",
"macro-monocyte-derived")
# TNFA signalling by NFKB, inflammatory responses, MTORC1 signalling, and cholesterol homeostasis
selected <- "ALLOGRAFT_REJECTION|ANDROGEN_RESPONSE"
results_list <- lapply(cell_types, function(cell_type){
read_csv(file = here("output",
"dge_analysis",
cell_type,
"CAM.HALLMARK.CF.NO_MODvNON_CF.CTRL.csv")) %>%
dplyr::filter(!str_detect(Set, selected)) %>%
column_to_rownames(var = "Set") %>%
mutate(cell = cell_type)
})
names(results_list) <- rep("HALLMARK", length(results_list))
labels <- as_labeller(
c("HALLMARK; macro-alveolar" = "AM",
"HALLMARK; macro-monocyte-derived" = "Mac.Mono.Deriv"))
top_camera_sets_by_cell(results_list, num = 4, labeller = labels) +
theme(plot.title = element_blank(),
axis.text.y = element_text(size = 7,
angle = 0)) -> cam_plot_hallmark
cam_plot_hallmark
# alveolar macrophages, macro-CCL, macro-lipid, Monocyte-derived macrophages
cell_types <- c("macro-alveolar", "macro-CCL", "macro-lipid",
"macro-monocyte-derived")
# eukaryotic translation and elongation, SRP dependent cotranslational protein targeting to membrane, SARS-COV-1 and -2 host response and influenza infection
# L1CAM interactions, RHO GTPASES and IL-10 signalling
selected <- "EUKARYOTIC_TRANSLATION_ELONGATION|SRP_DEPENDENT_COTRANSLATIONAL|SARS_COV_1_MODULATES|SARS_COV_2_MODULATES|INFLUENZA_INFECTION|L1CAM_INTERACTIONS|INTERLEUKIN_10_SIGNALING|RHO_GTPASES_ACTIVATE_IQGAPS"
results_list <- lapply(cell_types, function(cell_type){
read_csv(file = here("output",
"dge_analysis",
cell_type,
"CAM.REACTOME.CF.NO_MODvNON_CF.CTRL.csv")) %>%
dplyr::filter(str_detect(Set, selected)) %>%
column_to_rownames(var = "Set") %>%
mutate(cell = cell_type)
})
names(results_list) <- rep("REACTOME", length(results_list))
labels <- as_labeller(
c("REACTOME; macro-alveolar" = "AM",
"REACTOME; macro-CCL" = "AM-CCL",
"REACTOME; macro-lipid" = "AM-lipid",
"REACTOME; macro-monocyte-derived" = "Mac.Mono.Deriv"))
top_camera_sets_by_cell(results_list, num = 5, wrap_width = 50,
labeller = labels) +
theme(plot.title = element_blank(),
axis.text.y = element_text(size = 6,
angle = 30)) -> cam_plot_reactome
cam_plot_reactome 
cell_types <- c("macrophages", "macro-alveolar")
fibrosis <- lapply(cell_types, function(cell_type){
read_csv(file = here("output",
"dge_analysis",
cell_type,
"ORA.FIBROSIS.CF.NO_MODvNON_CF.CTRL.csv")) %>%
column_to_rownames(var = "Set") %>%
mutate(cell = cell_type)
})
names(fibrosis) <- rep("FIBROSIS", length(fibrosis))
wp <- lapply(cell_types, function(cell_type){
read_csv(file = here("output",
"dge_analysis",
cell_type,
"ORA.WP.CF.NO_MODvNON_CF.CTRL.csv")) %>%
dplyr::filter(str_detect(Set, "PROFIBROTIC")) %>%
column_to_rownames(var = "Set") %>%
mutate(cell = cell_type)
})
names(wp) <- rep("WP", length(wp))
results_list <- c(fibrosis, wp)
labels <- as_labeller(
c("FIBROSIS; macro-alveolar" = "AM",
"WP; macro-alveolar" = "AM",
"FIBROSIS; macrophages" = "All Macs (exc. Prolif)",
"WP; macrophages" = "All Macs (exc. Prolif)"))
top_ora_sets_by_cell(results_list, num = 2, wrap_width = 30,
labeller = labels) +
theme(plot.title = element_blank(),
axis.text.y = element_text(size = 7,
angle = 0)) -> ora_plot_fibrosis
ora_plot_fibrosis 
Figure 3
layout <- "
AAAAAAA
BBBCCCC
BBBCCCC
BBBCCCC
DDDCCCC
DDDCCCC
EEECCCC
EEECCCC
EEECCCC
EEECCCC
GGGHHHH
GGGHHHH
"
wrap_elements(cf_props & theme(legend.position = "right",
legend.direction = "vertical",
plot.margin = margin(rep(0,4)))) +
wrap_elements(deg_barplot + theme(plot.margin = margin(rep(0,4)),
axis.text.y = element_text(size = 8))) +
wrap_elements(grid::grid.grabExpr(ComplexHeatmap::draw(plot_lfc,
padding = unit(0, "mm")))) +
wrap_elements(volc_plot + theme(plot.margin = margin(rep(0,4)))) +
wrap_elements(cam_plot_reactome + theme(legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = margin(-0.5,0,0,0, unit="lines"),
legend.key.size = unit(0.5, unit="lines"),
legend.text = element_text(size = 6),
axis.title = element_text(size = 8),
axis.text = element_text(size = 6),
plot.margin = margin(rep(0,4)))) +
wrap_elements(cam_plot_hallmark + theme(legend.position = "bottom",
legend.direction = "horizontal",
legend.box = "vertical",
legend.margin = margin(-0.5,0,0,0, unit="lines"),
legend.key.size = unit(0.5, unit="lines"),
legend.text = element_text(size = 8),
axis.text = element_text(size = 8),
plot.margin = margin(rep(0,4)))) +
wrap_elements(ora_plot_fibrosis + theme(plot.margin = margin(rep(0,4)),
axis.text = element_text(size = 7))) +
plot_layout(design = layout) +
plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 24,
face = "bold",
family = "arial"))
| Version | Author | Date |
|---|---|---|
| aa4438b | Jovana Maksimovic | 2025-09-10 |
Supplemental
library(ComplexHeatmap)
library(circlize)
library(msigdbr)
library(fgsea)
# ── Preparation function ───────────────────────────────────────────────────────
# Run once per cell type, returns all objects needed for plotting
deg_results <- lapply(files, function(f) readRDS(f))
prepare_tnfa <- function(idx, celltype_name, deg_results) {
lrt <- glmLRT(deg_results[[idx]]$fit, contrast = deg_results[[idx]]$contr[,1])
top <- topTags(lrt, n = Inf) %>% data.frame()
entrez <- mapIds(
org.Hs.eg.db,
keys = rownames(lrt),
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
)
mat <- deg_results[[idx]]$adj$normalizedCounts %>% as.data.frame() %>% as.matrix()
mat_log <- log2(mat + 0.5)
mat_z <- t(scale(t(mat_log)))
metadata <- seu@meta.data
metadata[match(colnames(mat_z), metadata$sample.id), ] %>%
remove_rownames() %>%
dplyr::select(sample.id, Group, Treatment, Severity, Age) %>%
distinct() %>%
dplyr::filter(Treatment %in% c("Healthy", "untreated")) -> metadata_subset
sample_anno <- HeatmapAnnotation(
Severity = samp_map[metadata_subset$Severity],
Group = samp_map[metadata_subset$Group],
Age = metadata_subset$Age
)
tnfa_genes <- top[unname(entrez[rownames(top)]) %in%
gene_sets_list$HALLMARK$HALLMARK_TNFA_SIGNALING_VIA_NFKB, ] %>%
arrange(logFC)
ranked_genes <- top %>%
rownames_to_column(var = "gene") %>%
arrange(desc(logFC)) %>%
dplyr::select(gene, logFC) %>%
deframe()
hallmarks <- msigdbr(species = "Homo sapiens", category = "H") %>%
split(x = .$gene_symbol, f = .$gs_name)
set.seed(42)
gsea_res <- fgsea(pathways = hallmarks, stats = ranked_genes,
minSize = 15, maxSize = 500, nperm = 1000)
gsea_res_tidy <- gsea_res %>%
arrange(pval) %>%
#filter(padj < 0.05) %>%
slice_min(pval, n = 20) %>%
mutate(pathway = gsub("HALLMARK_", "", pathway))
tnfa_row <- gsea_res_tidy %>% filter(grepl("TNFA", pathway))
tnfa_genes_leading <- if (nrow(tnfa_row) > 0) unlist(tnfa_row[1, 8]) else NULL
tnfa_genes_min <- c("TNFAIP3", "NFKBIA", "TNF", "IL1B", "IL1A",
"CXCL2", "CXCL3", "NR4A1", "NR4A2")
list(
celltype_name = celltype_name,
lrt = lrt,
entrez = entrez,
mat_z = mat_z,
metadata_subset = metadata_subset,
sample_anno = sample_anno,
tnfa_genes = tnfa_genes,
tnfa_genes_min = tnfa_genes_min,
tnfa_genes_leading = tnfa_genes_leading,
gsea_res_tidy = gsea_res_tidy
)
}
# ── Plot functions ─────────────────────────────────────────────────────────────
plot_barcode <- function(obj) {
statistic <- sign(obj$lrt$table$logFC) * sqrt(obj$lrt$table$LR)
id <- ids2indices(
gene_sets_list$HALLMARK$HALLMARK_TNFA_SIGNALING_VIA_NFKB,
unname(obj$entrez[rownames(obj$lrt)])
)
barcodeplot(
statistics = statistic,
index = unlist(id),
main = glue("HALLMARK_TNFA_SIGNALING_VIA_NFKB — {obj$celltype_name}"),
xlab = glue("Signed LRT statistic (CF (no mod) - Non-CF (ctrl)) — {obj$celltype_name}")
)
}
plot_gsea <- function(obj) {
obj$gsea_res_tidy %>%
mutate(direction = ifelse(NES > 0, "Up", "Down")) %>%
ggplot(aes(x = -log10(padj), y = reorder(pathway, -log10(padj)),
size = abs(NES), colour = direction)) +
geom_point() +
scale_colour_manual(values = c("Up" = "#E41A1C", "Down" = "#377EB8"),
name = "Direction") +
scale_size_continuous(range = c(2, 8), name = "NES") +
geom_vline(xintercept = -log10(0.05), linetype = "dashed", colour = "grey50") +
labs(x = "-log10(adj. p-value)", y = NULL,
title = glue("GSEA — Hallmark gene sets — {obj$celltype_name}")) +
theme_bw() +
theme(axis.text.y = element_text(size = 8))
}
# gene_set: one of "full", "min", "leading"
# split: whether to split columns by Group
plot_tnfa_heatmap <- function(obj, gene_set = c("full", "min", "leading"), split = TRUE) {
gene_set <- match.arg(gene_set)
tnfa_subset <- switch(gene_set,
"full" = obj$tnfa_genes,
"min" = obj$tnfa_genes[rownames(obj$tnfa_genes) %in% obj$tnfa_genes_min, ],
"leading" = {
if (is.null(obj$tnfa_genes_leading)) {
message(glue("TNFa pathway not significant in {obj$celltype_name}"))
return(invisible(NULL))
}
obj$tnfa_genes[rownames(obj$tnfa_genes) %in% obj$tnfa_genes_leading, ]
}
)
label <- switch(gene_set,
"full" = "full TNFa gene set",
"min" = "min TNFa gene set",
"leading" = "leading edge TNFa genes"
)
row_anno <- rowAnnotation(
Significant = tnfa_subset$FDR < 0.05,
logFC = anno_barplot(tnfa_subset$logFC, gp = gpar(fill = "grey40"),
border = FALSE, width = unit(2, "cm"))
)
ht <- Heatmap(
obj$mat_z[rownames(tnfa_subset),
colnames(obj$mat_z) %in% unique(obj$metadata_subset$sample.id)],
name = "z-score",
col = colorRamp2(c(-2, 0, 2), c("blue", "white", "red")),
cluster_rows = FALSE,
cluster_columns = TRUE,
show_row_names = TRUE,
show_column_names = TRUE,
top_annotation = obj$sample_anno,
right_annotation = row_anno,
column_split = if (split) obj$metadata_subset$Group else NULL,
row_names_gp = gpar(fontsize = 8),
column_title = NULL
)
draw(ht, column_title = glue("{obj$celltype_name} — {label}"))
}am <- prepare_tnfa(idx = 1, celltype_name = "macro-alveolar", deg_results)
mono <- prepare_tnfa(idx = 6, celltype_name = "macro-monocyte-derived", deg_results)plot_barcode(am)
plot_barcode(mono)
plot_gsea(am)
plot_gsea(mono)
plot_tnfa_heatmap(am, gene_set = "full", split = TRUE)
plot_tnfa_heatmap(mono, gene_set = "full", split = TRUE)
plot_tnfa_heatmap(am, gene_set = "min")
plot_tnfa_heatmap(mono, gene_set = "min")
plot_tnfa_heatmap(am, gene_set = "leading")
plot_tnfa_heatmap(mono, gene_set = "leading")
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] grid stats4 stats graphics grDevices datasets utils
[8] methods base
other attached packages:
[1] fgsea_1.28.0 msigdbr_26.1.0
[3] circlize_0.4.15 ComplexHeatmap_2.18.0
[5] readxl_1.4.3 ggh4x_0.3.1
[7] dsb_1.0.3 paletteer_1.6.0
[9] tidyHeatmap_1.8.1 speckle_1.2.0
[11] glue_1.8.0 org.Hs.eg.db_3.18.0
[13] AnnotationDbi_1.64.1 patchwork_1.3.1
[15] clustree_0.5.1 ggraph_2.2.0
[17] here_1.0.1 dittoSeq_1.14.2
[19] glmGamPoi_1.14.3 SeuratObject_4.1.4
[21] Seurat_4.4.0 lubridate_1.9.3
[23] forcats_1.0.0 stringr_1.5.1
[25] dplyr_1.1.4 purrr_1.0.2
[27] readr_2.1.5 tidyr_1.3.1
[29] tibble_3.2.1 ggplot2_3.5.2
[31] tidyverse_2.0.0 edgeR_4.0.15
[33] limma_3.58.1 SingleCellExperiment_1.24.0
[35] SummarizedExperiment_1.32.0 Biobase_2.62.0
[37] GenomicRanges_1.54.1 GenomeInfoDb_1.38.6
[39] IRanges_2.36.0 S4Vectors_0.40.2
[41] BiocGenerics_0.48.1 MatrixGenerics_1.14.0
[43] matrixStats_1.2.0 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] Cairo_1.6-2 spatstat.explore_3.2-6 prismatic_1.1.1
[22] labeling_0.4.3 sass_0.4.10 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 blob_1.2.4
[46] promises_1.2.1 crayon_1.5.2 miniUI_0.1.1.1
[49] lattice_0.22-5 cowplot_1.1.3 KEGGREST_1.42.0
[52] pillar_1.9.0 knitr_1.50 rjson_0.2.21
[55] future.apply_1.11.1 codetools_0.2-19 fastmatch_1.1-8
[58] leiden_0.4.3.1 getPass_0.2-4 data.table_1.15.0
[61] vctrs_0.6.5 png_0.1-8 cellranger_1.1.0
[64] gtable_0.3.6 assertthat_0.2.1 rematch2_2.1.2
[67] cachem_1.0.8 xfun_0.52 S4Arrays_1.2.0
[70] mime_0.12 tidygraph_1.3.1 survival_3.5-8
[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_2.23-22 colorspace_2.1-0 DBI_1.2.1
[88] tidyselect_1.2.1 processx_3.8.3 curl_5.2.0
[91] bit_4.0.5 compiler_4.3.3 git2r_0.33.0
[94] DelayedArray_0.28.0 plotly_4.10.4 scales_1.3.0
[97] lmtest_0.9-40 callr_3.7.3 digest_0.6.34
[100] goftest_1.2-3 spatstat.utils_3.0-4 rmarkdown_2.29
[103] XVector_0.42.0 htmltools_0.5.8.1 pkgconfig_2.0.3
[106] fastmap_1.1.1 rlang_1.1.6 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] BiocParallel_1.36.0 mclust_6.1 RCurl_1.98-1.14
[118] magrittr_2.0.3 GenomeInfoDbData_1.2.11 munsell_0.5.0
[121] Rcpp_1.0.12 babelgene_22.9 viridis_0.6.5
[124] reticulate_1.42.0 stringi_1.8.3 zlibbioc_1.48.0
[127] MASS_7.3-60.0.1 plyr_1.8.9 parallel_4.3.3
[130] listenv_0.9.1 ggrepel_0.9.5 deldir_2.0-2
[133] Biostrings_2.70.2 graphlayouts_1.1.0 splines_4.3.3
[136] tensor_1.5 hms_1.1.3 locfit_1.5-9.8
[139] ps_1.7.6 igraph_2.0.1.1 spatstat.geom_3.2-8
[142] reshape2_1.4.4 evaluate_0.23 renv_1.1.4
[145] BiocManager_1.30.22 tzdb_0.4.0 foreach_1.5.2
[148] tweenr_2.0.3 httpuv_1.6.14 RANN_2.6.1
[151] polyclip_1.10-6 future_1.33.1 clue_0.3-65
[154] scattermore_1.2 ggforce_0.4.2 janitor_2.2.0
[157] xtable_1.8-4 later_1.3.2 viridisLite_0.4.2
[160] memoise_2.0.1 cluster_2.1.6 timechange_0.3.0
[163] globals_0.16.2