Figure 3
Jovana Maksimovic
March 03, 2025
Last updated: 2025-03-03
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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 12102741 646.4 19380931 1035.1 13730280 733.3
Vcells 1354185044 10331.7 3693780912 28181.4 3551518786 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$Counts %>%
data.frame %>%
group_by(sample) %>%
summarise(ncells = sum(Freq)),
by = c("sample.id" = "sample")) %>%
distinct() -> info
head(info) %>% knitr::kable()| sample.id | Participant | Disease | Treatment | Severity | Group | Group_severity | Batch | Age | Sex | ncells |
|---|---|---|---|---|---|---|---|---|---|---|
| sample_33.1 | sample_33 | CF | treated (ivacaftor) | severe | CF.IVA | CF.IVA.S | 1 | 5.950685 | M | 2139 |
| sample_25.1 | sample_25 | CF | untreated | severe | CF.NO_MOD | CF.NO_MOD.S | 1 | 4.910000 | F | 3272 |
| sample_29.1 | sample_29 | CF | untreated | severe | CF.NO_MOD | CF.NO_MOD.S | 1 | 5.989041 | F | 1568 |
| sample_27.1 | sample_27 | CF | treated (ivacaftor) | mild | CF.IVA | CF.IVA.M | 1 | 4.917808 | M | 2467 |
| sample_32.1 | sample_32 | CF | untreated | mild | CF.NO_MOD | CF.NO_MOD.M | 1 | 5.926027 | F | 2963 |
| sample_26.1 | sample_26 | CF | untreated | mild | CF.NO_MOD | CF.NO_MOD.M | 1 | 5.049315 | M | 2040 |
Prepare figure panels
# 3A: monocytes, NK-T cells, CD4 Tregs, macro-IGF1
props <- getTransformedProps(clusters = seu$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(
c("CD4 T-reg" = "CD4 T-reg",
"monocytes" = "monocytes*",
"NK-T cells" = "NK-T cells",
"macro-IGF1" = "macro-IGF1*"))
pal <- RColorBrewer::brewer.pal(8, "Set2")[1:4]
names(pal) <- c("CF.IVA","CF.LUMA_IVA","CF.NO_MOD","NON_CF.CTRL")
dat %>%
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) -> p1props <- 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
dat %>%
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 <- "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
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(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
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 = cutoff)) %>% data.frame,
cell = unlist(str_split(str_remove(f, suffix), "/"))[8])
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(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
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" = "macro-alveolar",
"HALLMARK; macro-monocyte-derived" = "macro-monocyte-derived"))
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" = "macro-alveolar",
"REACTOME; macro-CCL" = "macro-CCL",
"REACTOME; macro-lipid" = "macro-lipid",
"REACTOME; macro-monocyte-derived" = "macro-monocyte-derived"))
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" = "macro-alveolar",
"WP; macro-alveolar" = "macro-alveolar",
"FIBROSIS; macrophages" = "macrophages",
"WP; macrophages" = "macrophages"))
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 = 8,
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")) +
wrap_elements(deg_barplot) +
wrap_elements(grid::grid.grabExpr(ComplexHeatmap::draw(plot_lfc))) +
wrap_elements(volc_plot) +
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))) +
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))) +
wrap_elements(ora_plot_fibrosis) +
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: 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