Figure 4
Jovana Maksimovic
March 04, 2025
Last updated: 2025-03-04
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Knit directory: paed-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)
})
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) max used (Mb)
Ncells 12102327 646.4 19380334 1035.1 13729724 733.3
Vcells 1354183067 10331.6 3693778179 28181.3 3551516809 27096.0Create 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$Counts %>%
data.frame %>%
group_by(sample) %>%
summarise(ncells = sum(Freq)),
by = c("sample.id" = "sample")) %>%
distinct() -> info
head(info) %>% knitr::kable()| sample.id | Participant | Disease | Treatment | Severity | Group | Group_severity | Batch | Age | Sex | ncells |
|---|---|---|---|---|---|---|---|---|---|---|
| sample_33.1 | sample_33 | CF | treated (ivacaftor) | severe | CF.IVA | CF.IVA.S | 1 | 5.950685 | M | 2139 |
| sample_25.1 | sample_25 | CF | untreated | severe | CF.NO_MOD | CF.NO_MOD.S | 1 | 4.910000 | F | 3272 |
| sample_29.1 | sample_29 | CF | untreated | severe | CF.NO_MOD | CF.NO_MOD.S | 1 | 5.989041 | F | 1568 |
| sample_27.1 | sample_27 | CF | treated (ivacaftor) | mild | CF.IVA | CF.IVA.M | 1 | 4.917808 | M | 2467 |
| sample_32.1 | sample_32 | CF | untreated | mild | CF.NO_MOD | CF.NO_MOD.M | 1 | 5.926027 | F | 2963 |
| sample_26.1 | sample_26 | CF | untreated | mild | CF.NO_MOD | CF.NO_MOD.M | 1 | 5.049315 | M | 2040 |
Prepare figure panels
# HSP+ B, CD4 T-IFN
props <- getTransformedProps(clusters = seu$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_severity %in% c("CF.NO_MOD.S", "CF.NO_MOD.M"),
clusters %in% c("CD4 T-IFN",
"HSP+ B cells")) -> dat
sig_names <- as_labeller(
c("CD4 T-IFN" = "CD4 T-IFN",
"HSP+ B cells" = "HSP+ B cells"))
pal <- RColorBrewer::brewer.pal(8, "Accent")[1:2]
names(pal) <- c("CF.NO_MOD.S", "CF.NO_MOD.M")
dat %>%
ggplot(aes(x = Group_severity,
y = Freq,
colour = Group_severity)) +
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",
colour = "Group") +
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
p1
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.*CF_samples",
full.names = TRUE)
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.
Zero 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.
Zero log2-FC threshold detected. Switch to glmLRT() instead.
Zero log2-FC threshold detected. Switch to glmLRT() instead. genes <- c("ARHGEF5",
"GPD1",
"ITGA1",
"SIRPB1",
"TLCD4",
"GBP3",
"CLIC2",
"LILRB2",
"MMP14",
"SLC28A3",
"SLC46A1",
"ARPIN",
"CTSK",
"VAMP5")
dat %>%
dplyr::select(gene, cell, logFC) %>%
distinct() %>%
dplyr::filter(gene %in% sort(genes)) %>%
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% .$cell]))) %>%
column_to_rownames(var = "gene") -> dat_lfccol_fun <- circlize::colorRamp2(seq(0, 100000, length.out = 9),
(RColorBrewer::brewer.pal(9, "PiYG")))
col_split <- c(rep("aggregate", 1), rep("sub-type", ncol(dat_lfc) - 1))
pal_dt <- c(paletteer::paletteer_d("RColorBrewer::Set1")[2:1], "grey")
col_lfc_fun <- circlize::colorRamp2(seq(-2, 2, length.out = 3),
c(pal_dt[1], "white", pal_dt[2]))
ComplexHeatmap::HeatmapAnnotation(df = cell_freq %>%
dplyr::filter(cell %in% colnames(dat_lfc)) %>%
column_to_rownames(var = "cell") %>%
dplyr::rename(`No. cells` = n),
which = "column",
show_annotation_name = FALSE,
col = list(`No. cells` = col_fun),
annotation_legend_param = list(
`No. cells` = list(direction = "vertical",
labels_gp = grid::gpar(fontsize = 7)))) -> col_ann
ComplexHeatmap::Heatmap(dat_lfc[order(rowSums(dat_lfc, na.rm = TRUE),
decreasing = TRUE),],
name = "logFC",
column_split = col_split,
column_title = NULL,
cluster_rows = FALSE,
cluster_columns = FALSE,
rect_gp = grid::gpar(col = "white", lwd = 1),
row_names_gp = grid::gpar(fontsize = 7),
column_names_gp = grid::gpar(fontsize = 8),
column_names_rot = 90,
top_annotation = col_ann,
col = col_lfc_fun,
#right_annotation = row_ann,
heatmap_legend_param = list(direction = "vertical",
labels_gp = grid::gpar(fontsize = 7))) -> plot_lfc
ComplexHeatmap::draw(as(list(plot_lfc), "HeatmapList"),
heatmap_legend_side = "right",
annotation_legend_side = "right",
merge_legends = TRUE) -> plot_lfc
plot_lfc
bind_rows(lapply(files, function(f){
deg_results <- readRDS(f)
lrt <- glmLRT(deg_results$fit,
contrast = deg_results$contr[,cont_name])
tmp <- cbind(summary(decideTests(lrt, p.value = 0.05)) %>% data.frame,
cell = unlist(str_split(str_remove(f, suffix), "/"))[8])
tmp
})) -> dat_degbind_rows(lapply(files, function(f){
deg_results <- readRDS(f)
lrt <- glmLRT(deg_results$fit,
contrast = deg_results$contr[,cont_name])
tmp <- cbind(summary(decideTests(lrt, p.value = cutoff)) %>% data.frame,
cell = unlist(str_split(str_remove(f, suffix), "/"))[8])
tmp
})) -> dat_degdat_deg %>%
left_join(cell_freq) -> dat_deg
dat_deg %>%
dplyr::filter(Var1 != "NotSig") %>%
ggplot(aes(x = fct_reorder(cell, -n), y = Freq, fill = Var1)) +
geom_col(position = "dodge") +
scale_fill_manual(values = pal_dt) +
theme_classic() +
theme(axis.text.x = element_text(angle = 30,
hjust = 1,
vjust = 1,
size = 7),
legend.position = "top") +
geom_text(aes(label = Freq),
position = position_dodge(width = 0.9),
vjust = -0.5,
size = 2.5) +
labs(x = "Cell Type",
y = "No. DEG (FDR < 0.05)",
fill = "Direction") -> deg_barplot
deg_barplot
Figure 4
layout <- "
ABB
CCC
"
wrap_elements(p1 + theme(legend.direction = "vertical")) +
(wrap_elements(deg_barplot + theme(axis.title.x = element_blank(),
legend.text = element_text(size = 8)))) +
#wrap_elements(volc_plot + theme(strip.text = element_text(size = 7))) +
wrap_elements(grid::grid.grabExpr(ComplexHeatmap::draw(plot_lfc))) +
plot_layout(design = layout) +
plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 16,
face = "bold",
family = "arial"))
Supplementary Figure X
dat %>%
dplyr::select(gene, cell, logFC) %>%
distinct() %>%
pivot_wider(
names_from = cell, # Column whose values become new column names
values_from = logFC,
values_fill = list(logFC = NA)) %>%
arrange(across(all_of(cell_freq$cell[cell_freq$cell %in% .$cell]))) %>%
column_to_rownames(var = "gene") -> dat_lfc_suppcol_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_supp) - 1))
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_supp)) %>%
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",
labels_gp = grid::gpar(fontsize = 7)))) -> col_ann
ComplexHeatmap::HeatmapAnnotation(df = data.frame(multiple = (rowSums(!is.na(dat_lfc_supp)) > 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_supp,
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 = 8),
column_names_rot = 90,
top_annotation = col_ann,
col = col_lfc_fun,
right_annotation = row_ann,
heatmap_legend_param = list(direction = "vertical",
labels_gp = grid::gpar(fontsize = 7))) -> plot_lfc
ComplexHeatmap::draw(as(list(plot_lfc), "HeatmapList"),
heatmap_legend_side = "right",
annotation_legend_side = "right",
merge_legends = TRUE) -> plot_lfc_supp
plot_lfc_supp
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.
Zero 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.
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
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 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 snakecase_0.11.1 SparseArray_1.2.4
[43] Rtsne_0.17 grid_4.3.3 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_1.45 ComplexHeatmap_2.18.0
[55] rjson_0.2.21 future.apply_1.11.1 codetools_0.2-19
[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_0.3.4 rematch2_2.1.2 cachem_1.0.8
[67] xfun_0.42 S4Arrays_1.2.0 mime_0.12
[70] tidygraph_1.3.1 survival_3.7-0 pheatmap_1.0.12
[73] iterators_1.0.14 statmod_1.5.0 ellipsis_0.3.2
[76] fitdistrplus_1.1-11 ROCR_1.0-11 nlme_3.1-164
[79] bit64_4.0.5 RcppAnnoy_0.0.22 rprojroot_2.0.4
[82] bslib_0.6.1 irlba_2.3.5.1 KernSmooth_2.23-24
[85] colorspace_2.1-0 DBI_1.2.1 tidyselect_1.2.1
[88] processx_3.8.3 bit_4.0.5 compiler_4.3.3
[91] git2r_0.33.0 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_2.25 XVector_0.42.0 htmltools_0.5.8.1
[103] pkgconfig_2.0.3 highr_0.10 fastmap_1.1.1
[106] rlang_1.1.4 GlobalOptions_0.1.2 htmlwidgets_1.6.4
[109] shiny_1.8.0 farver_2.1.1 jquerylib_0.1.4
[112] zoo_1.8-12 jsonlite_1.8.8 mclust_6.1
[115] RCurl_1.98-1.14 magrittr_2.0.3 GenomeInfoDbData_1.2.11
[118] munsell_0.5.0 Rcpp_1.0.12 viridis_0.6.5
[121] reticulate_1.35.0 stringi_1.8.3 zlibbioc_1.48.0
[124] MASS_7.3-60.0.1 plyr_1.8.9 parallel_4.3.3
[127] listenv_0.9.1 ggrepel_0.9.5 deldir_2.0-2
[130] Biostrings_2.70.2 graphlayouts_1.1.0 splines_4.3.3
[133] tensor_1.5 hms_1.1.3 circlize_0.4.15
[136] locfit_1.5-9.8 ps_1.7.6 igraph_2.0.1.1
[139] spatstat.geom_3.2-8 reshape2_1.4.4 evaluate_0.23
[142] renv_1.0.3 BiocManager_1.30.22 tzdb_0.4.0
[145] foreach_1.5.2 tweenr_2.0.3 httpuv_1.6.14
[148] RANN_2.6.1 polyclip_1.10-6 future_1.33.1
[151] clue_0.3-65 scattermore_1.2 ggforce_0.4.2
[154] janitor_2.2.0 xtable_1.8-4 later_1.3.2
[157] viridisLite_0.4.2 memoise_2.0.1 cluster_2.1.6
[160] timechange_0.3.0 globals_0.16.2