Mouse vs. Human (Fig. 4)
Last updated: 2020-12-28
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Knit directory: meta-liver/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e4603ab | christianholland | 2020-12-28 | Added script for fig 4 |
Introduction
Here we generate publication-ready plots for the comparison of the chronic CCl4 model and patient cohorts.
Libraries and sources
These libraries and sources are used for this analysis.
library(tidyverse)
library(tidylog)
library(here)
library(AachenColorPalette)
library(VennDiagram)
library(gridExtra)
library(ggpubr)
library(scales)
library(lemon)
library(ComplexHeatmap)
library(ggwordcloud)
library(circlize)
library(patchwork)
source(here("code/utils-plots.R"))
source(here("code/utils-utils.R"))Definition of global variables and functions that are used throughout this analysis.
# i/o
data_path <- "data/meta-mouse-vs-human"
output_path <- "output/meta-mouse-vs-human"
# graphical parameters
# fontsize
fz <- 7
# color function for heatmaps
col_fun <- colorRamp2(
c(-4, 0, 4),
c(aachen_color("blue"), "white", aachen_color("red"))
)
# keys to annotate contrasts
key_mm <- readRDS(here("data/meta-chronic-vs-acute/contrast_annotation.rds"))
key_hs <- readRDS(here("data/meta-mouse-vs-human/contrast_annotation.rds"))Similarity of patient cohort gene sets
keys <- key_hs %>%
distinct(contrast, label, source, phenotype)
j <- readRDS(here(output_path, "gene_set_similarity.rds")) %>%
separate(set1, into = c("source", "phenotype", "contrast"), sep = "-") %>%
inner_join(keys, by = c("contrast", "source", "phenotype")) %>%
select(-source, -phenotype, -contrast) %>%
rename(set1 = label) %>%
separate(set2, into = c("source", "phenotype", "contrast"), sep = "-") %>%
inner_join(keys, by = c("contrast", "source", "phenotype")) %>%
rename(set2 = label) %>%
select(set1, set2, similarity)
patient_gs_sim <- j %>%
mutate(set1 = fct_rev(set1)) %>%
ggplot(aes(
x = set1, y = set2, fill = similarity,
label = round(similarity, 3)
)) +
geom_tile(color = "black") +
scale_fill_gradient(low = "white", high = aachen_color("green")) +
labs(x = NULL, y = NULL, fill = "Jaccard\nIndex") +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.line = element_blank(),
axis.ticks = element_blank()
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
geom_text(size = (fz - 2) / (14 / 5)) +
my_theme(fsize = fz, grid = "no")
patient_gs_sim
Interstudy enrichment of patient cohorts
keys <- key_hs %>%
distinct(contrast, label, source, phenotype)
gsea_res <- readRDS(here(output_path, "interstudy_enrichment.rds")) %>%
separate(signature, into = c("source", "phenotype", "contrast"), sep = "-") %>%
inner_join(keys, by = c("contrast", "source", "phenotype")) %>%
select(-source, -phenotype, -contrast) %>%
rename(signature = label) %>%
separate(geneset, into = c("source", "phenotype", "contrast"), sep = "-") %>%
inner_join(keys, by = c("contrast", "source", "phenotype")) %>%
rename(geneset = label)
patient_interstudy_enrichment <- gsea_res %>%
mutate(direction = fct_rev(str_to_title(direction))) %>%
mutate(label = gtools::stars.pval(padj)) %>%
ggplot(aes(x = signature, y = geneset, fill = ES)) +
geom_tile() +
geom_text(aes(label = label), size = fz / (14 / 5), vjust = 1) +
facet_wrap(~direction, ncol = 2) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.line = element_blank(),
axis.ticks = element_blank()
) +
scale_fill_gradient2(
low = aachen_color("blue"), mid = "white",
high = aachen_color("red")
) +
my_theme(grid = "no", fsize = fz) +
labs(x = "Signature", y = "Gene Set", fill = "ES") +
guides(fill = guide_colorbar(title = "ES"))
patient_interstudy_enrichment
Mouse enrichment in patients
contrast_keys_mouse <- key_mm %>%
distinct(signature = contrast, label2)
contrast_keys_human <- key_hs %>%
unite(geneset, source, phenotype, contrast, sep = "-") %>%
distinct(geneset, label)
gsea_res <- readRDS(here(output_path, "gsea_res.rds")) %>%
inner_join(contrast_keys_mouse, by = "signature") %>%
select(-signature) %>%
rename(signature = label2) %>%
inner_join(contrast_keys_human, by = "geneset") %>%
select(-geneset) %>%
rename(geneset = label) %>%
mutate(
label = gtools::stars.pval(padj),
direction = fct_rev(str_to_title(direction))
)
mm_enrichment_in_hs <- gsea_res %>%
ggplot(aes(x = signature, y = geneset, fill = ES, label = label)) +
geom_tile() +
facet_wrap(~direction) +
scale_fill_gradient2(
low = aachen_color("blue"), mid = "white",
high = aachen_color("red")
) +
my_theme(grid = "no", fsize = fz) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.line = element_blank(),
axis.ticks = element_blank()
) +
geom_text(size = fz / (14 / 5), vjust = 1) +
labs(x = "Signature", y = "Gene Set")
mm_enrichment_in_hs
Overlap of leading-edge genes
keys <- key_mm %>%
distinct(signature = contrast, class = time_label2)
le <- readRDS(here(output_path, "leading_edges.rds")) %>%
inner_join(keys, by = "signature") %>%
rename(regulation = direction) %>%
arrange(class)
tables <- le %>%
group_split(class)
# extract labellers
c1 <- tables[[1]] %>%
distinct(class) %>%
pull() %>%
as.character()
c2 <- tables[[2]] %>%
distinct(class) %>%
pull() %>%
as.character()
c3 <- tables[[3]] %>%
distinct(class) %>%
pull() %>%
as.character()
t1 <- tables[[1]] %>% count(regulation)
t2 <- tables[[2]] %>% count(regulation)
t3 <- tables[[3]] %>% count(regulation)
le_overlap_plots <- c("up", "down") %>%
map(function(r) {
# set sizes of regulated genes
a1 <- t1 %>%
filter(regulation == r) %>%
pull(n)
a2 <- t2 %>%
filter(regulation == r) %>%
pull(n)
a3 <- t3 %>%
filter(regulation == r) %>%
pull(n)
a12 <- purrr::reduce(
list(
tables[[1]] %>% filter(regulation == r) %>% pull(gene),
tables[[2]] %>% filter(regulation == r) %>% pull(gene)
),
intersect
) %>%
length()
a23 <- purrr::reduce(
list(
tables[[2]] %>% filter(regulation == r) %>% pull(gene),
tables[[3]] %>% filter(regulation == r) %>% pull(gene)
),
intersect
) %>%
length()
a13 <- purrr::reduce(
list(
tables[[1]] %>% filter(regulation == r) %>% pull(gene),
tables[[3]] %>% filter(regulation == r) %>% pull(gene)
),
intersect
) %>%
length()
a123 <- purrr::reduce(
list(
tables[[1]] %>% filter(regulation == r) %>% pull(gene),
tables[[2]] %>% filter(regulation == r) %>% pull(gene),
tables[[3]] %>% filter(regulation == r) %>% pull(gene)
),
intersect
) %>%
length()
grid.newpage()
grid.grabExpr(draw.triple.venn(
area1 = a1, area2 = a2, area3 = a3,
n12 = a12, n23 = a23, n13 = a13,
n123 = a123,
category = c(c1, c2, c3),
# lty = "blank",
cex = 1 / 12 * fz,
fontfamily = rep("sans", 7),
# fill = aachen_color(c("purple", "petrol", "red")),
# cat.col = aachen_color(c("purple", "petrol", "red")),
cat.cex = 1 / 12 * (fz + 1),
cat.fontfamily = rep("sans", 3),
cat.pos = c(0, 0, 180),
cat.prompts = T
)) %>%
as_ggplot() %>%
grid.arrange(top = textGrob(str_to_title(r), gp = gpar(
fontsize = fz,
fontface = "bold"
)))
})
le_gene_overlap <- wrap_plots(le_overlap_plots)Top human-mouse consistent genes
Heatmap
contrast_keys_mouse <- key_hs %>%
unite(contrast, source, phenotype, contrast, sep = "-") %>%
distinct(contrast, label)
contrast_keys_human <- key_mm %>%
filter(str_detect(contrast, "pure")) %>%
distinct(contrast, label)
keys <- bind_rows(contrast_keys_mouse, contrast_keys_human)
# get degs from single cell data
sc_degs <- readRDS(here(data_path, "single_cell_degs.rds")) %>%
enframe("celltype") %>%
unnest(value) %>%
filter(p_val_adj <= 0.05) %>%
mutate(adjusted_logfc = case_when(
avg_logFC >= 0 & cluster == "Uninjured" ~ -avg_logFC,
avg_logFC < 0 & cluster == "Uninjured" ~ -avg_logFC,
TRUE ~ avg_logFC
))
# load consistent genes
df <- readRDS(here(output_path, "consistent_genes.rds")) %>%
left_join(sc_degs, by = "gene") %>%
mutate(celltype = fct_explicit_na(celltype, na_level = "Unknown"))
# extract top 100 consistent genes
top_genes_df <- df %>%
filter(rank <= 100) %>%
inner_join(keys, by = "contrast") %>%
mutate(label = fct_drop(label))
# build matrix for heatmap
m <- top_genes_df %>%
distinct(gene, label, logFC) %>%
spread(label, logFC) %>%
data.frame(row.names = 1, check.names = F) %>%
as.matrix()
# build cell type annotation
celltype_anno <- top_genes_df %>%
distinct(gene, celltype, adjusted_logfc) %>%
spread(celltype, adjusted_logfc, fill = 0) %>%
data.frame(row.names = 1, check.names = F) %>%
select(-Unknown)
col_fun_anno <- col_fun
ha <- HeatmapAnnotation(
df = celltype_anno,
show_legend = F,
border = F,
gap = unit(0.25, "mm"),
col = list(
MPs = col_fun_anno, pDCs = col_fun_anno, ILCs = col_fun_anno,
Tcells = col_fun_anno, Bcells = col_fun_anno,
`Plasma Bcells` = col_fun_anno, `Mast cells` = col_fun_anno,
Endothelia = col_fun_anno, Mesenchyme = col_fun_anno,
Mesothelia = col_fun_anno, Hepatocytes = col_fun_anno,
Cholangiocytes = col_fun_anno
),
annotation_name_gp = gpar(fontsize = fz - 1),
simple_anno_size = unit(0.15, "cm")
)
# build heatmap
h_hmap <- Heatmap(
t(as.matrix(m)),
col = col_fun,
cluster_rows = T,
cluster_columns = T,
show_row_dend = F,
show_column_dend = T,
row_names_gp = gpar(fontsize = fz), column_names_gp = gpar(fontsize = fz - 2),
name = "logFC",
heatmap_legend_param = list(
title_gp = gpar(
fontface = "plain",
fontsize = fz + 1
),
labels_gp = gpar(fontsize = fz)
),
row_gap = unit(2.5, "mm"),
border = T,
top_annotation = ha,
row_split = c(rep("Mouse", 3), rep("Human", 15)),
row_title_gp = gpar(fontsize = fz + 1),
)
h_hmap
Characteriatzion via TFs
ora_res <- readRDS(here(
output_path,
"leading_edges_characterization.rds"
)) %>%
filter(group %in% c("dorothea") & fdr <= 0.2) %>%
mutate(regulation = str_to_title(regulation))
tfs_up <- ora_res %>%
filter(regulation == "Up") %>%
filter(geneset %in% c("SP1", "RELA", "NFKB1")) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "TF", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
# no significant hits
# tfs_down <- ora_res %>%
# filter(regulation == "Down") %>%
# slice_min(order_by = p.value, n = 3) %>%
# ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
# geom_col() +
# geom_text(aes(label = geneset),
# color = "white", angle = 90, hjust = 1.5,
# size = fz / (14 / 5)
# ) +
# theme(axis.text.x = element_blank()) +
# labs(x = "TF", y = expression(-log["10"] * "(p-value)")) +
# my_theme(grid = "y", fsize = fz) +
# facet_rep_wrap(~regulation)
tfs_up
Characteriatzion via pathways
ora_res <- readRDS(here(
output_path,
"leading_edges_characterization.rds"
)) %>%
filter(group %in% c("progeny") & fdr <= 0.2) %>%
mutate(regulation = str_to_title(regulation))
pw_up <- ora_res %>%
filter(regulation == "Up") %>%
filter(geneset %in% c("TGFb", "TNFa")) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "Pathway", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
pw_down <- ora_res %>%
filter(regulation == "Down") %>%
filter(geneset %in% c("Androgen")) %>%
ggplot(aes(x = fct_reorder(geneset, p.value), y = -log10(p.value))) +
geom_col() +
geom_text(aes(label = geneset),
color = "white", angle = 90, hjust = 1.5,
size = fz / (14 / 5)
) +
theme(axis.text.x = element_blank()) +
labs(x = "Pathway", y = expression(-log["10"] * "(p-value)")) +
my_theme(grid = "y", fsize = fz) +
facet_rep_wrap(~regulation)
pw_up + pw_down
Characterization via GO terms
# get wordcounts for go terms
wordcounts <- readRDS(here(output_path, "go_wordcounts.rds"))
# get ranking of go clusters
cluster_ranking <- readRDS(here(output_path, "go_cluster_ranking.rds")) %>%
group_by(cluster) %>%
mutate(label = str_c(n(), " ", "GO terms")) %>%
ungroup() %>%
mutate(regulation = fct_rev(str_to_title(regulation))) %>%
arrange(desc(cluster)) %>%
mutate(description = fct_rev(as_factor(description)))
cluster_anno <- cluster_ranking %>%
nest(data = c(rank, term)) %>%
# find max peak for each cluster
mutate(peak = data %>% map(function(data) {
max(density(data$rank)$y)
})) %>%
unnest(c(peak)) %>%
group_by(regulation) %>%
mutate(max_peak = max(peak)) %>%
ungroup() %>%
distinct(
cluster, regulation, description, peak, max_peak, max_rank,
label
) %>%
arrange(description) %>%
group_by(regulation) %>%
mutate(n_clusters = row_number()) %>%
mutate(x_coord = case_when(
n_clusters == 1 ~ 0,
n_clusters == 2 ~ 1 * max_rank
)) %>%
ungroup()
# up-regulated genes
up_dens <- cluster_ranking %>%
filter(regulation == "Up") %>%
plot_go_rank_density() +
my_theme(grid = "no", fsize = fz) +
geom_text(
data = filter(cluster_anno, regulation == "Up"),
aes(x = x_coord, y = 1.1 * max_peak, label = label, color = description),
inherit.aes = F, size = fz / (14 / 5), hjust = "inward",
show.legend = F
) +
scale_color_manual(values = aachen_color(c("green", "purple"))) +
guides(color = guide_legend(nrow = 2)) +
theme(legend.box.margin = margin(-10, 0, -20, 0))
up_cloud <- wordcounts %>%
filter(regulation == "up") %>%
slice_max(order_by = n, n = 15) %>%
plot_wordcloud(fontsize = fz)
consistent_up <- up_dens +
inset_element(up_cloud,
left = 0, bottom = 0, right = 1, top = 1,
align_to = "panel"
)
# down-regulated genes
down_dens <- cluster_ranking %>%
filter(regulation == "Down") %>%
plot_go_rank_density() +
my_theme(grid = "no", fsize = fz) +
geom_text(
data = filter(cluster_anno, regulation == "Down"),
aes(x = x_coord, y = 1.1 * max_peak, label = label, color = description),
inherit.aes = F, size = fz / (14 / 5), hjust = "inward",
show.legend = F
) +
scale_color_manual(values = aachen_color("purple")) +
guides(color = guide_legend(nrow = 2)) +
theme(legend.box.margin = margin(-10, 0, -20, 0))
down_cloud <- wordcounts %>%
filter(regulation == "down") %>%
slice_max(order_by = n, n = 15) %>%
plot_wordcloud(fontsize = fz)
consistent_down <- down_dens +
inset_element(down_cloud,
left = 0, bottom = 0, right = 1, top = 1,
align_to = "panel"
)
consistent_up + consistent_down
Expression of consistent genes in mouse
keys <- key_mm %>%
distinct(contrast, label = time_label2)
le <- readRDS(here(output_path, "leading_edges_mgi.rds")) %>%
rename(contrast = signature)
c <- readRDS(here("output/meta-chronic-vs-acute/limma_result.rds")) %>%
filter(treatment == "pure_ccl4") %>%
left_join(le) %>%
select(-regulation) %>%
rename(regulation = direction) %>%
replace_na(list(regulation = "ns")) %>%
mutate(regulation = factor(regulation, levels = c("up", "down", "ns"))) %>%
mutate(regulation = fct_recode(regulation,
Up = "up", Down = "down",
n.s. = "ns"
)) %>%
inner_join(keys, by = "contrast") %>%
arrange(desc(regulation))
consistent_volcano_main <- c %>%
filter(label == "Month 12") %>%
ggplot(aes(x = logFC, y = -log10(pval), color = regulation, alpha = regulation)) +
geom_point() +
facet_rep_wrap(~label, scales = "free") +
my_theme(grid = "y", fsize = fz) +
scale_alpha_manual(values = c(0.7, 0.7, 0.2), guide = "none", drop = F) +
scale_color_manual(
values = c(aachen_color(c("red", "blue", "black50"))),
breaks = c("Up", "Down"), labels = c("Up", "Down"),
drop = F
) +
labs(
x = "logFC", y = expression(-log["10"] * "(p-value)"),
color = str_wrap("Regulation", width = 15)
)
consistent_volcano_supp <- c %>%
filter(label != "Month 12") %>%
ggplot(aes(x = logFC, y = -log10(pval), color = regulation, alpha = regulation)) +
geom_point() +
facet_rep_wrap(~label, scales = "free") +
my_theme(grid = "y", fsize = fz) +
scale_alpha_manual(values = c(0.7, 0.7, 0.2), guide = "none", drop = F) +
scale_color_manual(
values = c(aachen_color(c("red", "blue", "black50"))),
breaks = c("Up", "Down"), labels = c("Up", "Down"),
drop = F
) +
labs(
x = "logFC", y = expression(-log["10"] * "(p-value)"),
color = str_wrap("Regulation", width = 15)
)
consistent_volcano_main + consistent_volcano_supp
Collage
Figure 4
Main Figure.
fig4 <- patient_interstudy_enrichment /
((mm_enrichment_in_hs | le_overlap_plots[[1]] | le_overlap_plots[[2]]) +
plot_layout(widths = c(1, 2.25, 1.75))) /
grid.grabExpr(draw(h_hmap)) /
((pw_up | tfs_up | consistent_up | consistent_volcano_main) +
plot_layout(width = c(0.2, 0.3, 1.5, 1.5))) +
plot_layout(height = c(0.75, 0.75, 1.75, 0.75)) +
plot_annotation(tag_levels = list(c("A", "B", "C", "", "D", "E", "F", "G", "", "H"))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
fig4
ggsave(here("figures/Figure 4.pdf"), fig4,
width = 21, height = 29.7, units = c("cm")
)
ggsave(here("figures/Figure 4.png"), fig4,
width = 21, height = 29.7, units = c("cm")
)Supplementary Figure 4.1
Gene similarity of patient cohorts.
sfig4_1 <- patient_gs_sim +
theme(
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
sfig4_1
ggsave(here("figures/Supplementary Figure 4.1.pdf"), sfig4_1,
width = 21, height = 10, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 4.1.png"), sfig4_1,
width = 21, height = 10, units = c("cm")
)Supplementary Figure 4.2
Characterization of down-regulated consistent genes.
sfig4_2 <- pw_down + consistent_down +
plot_layout(widths = c(1, 8)) +
plot_annotation(tag_levels = list(c("A", "B"))) &
theme(
plot.tag = element_text(size = fz + 3, face = "bold"),
legend.key.height = unit(11.5, "pt"),
legend.key.width = unit(12.5, "pt")
)
sfig4_2
ggsave(here("figures/Supplementary Figure 4.2.pdf"), sfig4_2,
width = 21, height = 10, units = c("cm")
)
ggsave(here("figures/Supplementary Figure 4.2.png"), sfig4_2,
width = 21, height = 10, units = c("cm")
)Time spend to execute this analysis: 00:31 minutes.
sessionInfo()
#> R version 4.0.2 (2020-06-22)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Mojave 10.14.5
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] grid stats graphics grDevices datasets utils methods
#> [8] base
#>
#> other attached packages:
#> [1] patchwork_1.1.1 circlize_0.4.11 ggwordcloud_0.5.0
#> [4] ComplexHeatmap_2.4.3 lemon_0.4.5 scales_1.1.1
#> [7] ggpubr_0.4.0 gridExtra_2.3 VennDiagram_1.6.20
#> [10] futile.logger_1.4.3 AachenColorPalette_1.1.2 here_1.0.1
#> [13] tidylog_1.0.2 forcats_0.5.0 stringr_1.4.0
#> [16] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
#> [19] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
#> [22] tidyverse_1.3.0 workflowr_1.6.2
#>
#> loaded via a namespace (and not attached):
#> [1] colorspace_2.0-0 ggsignif_0.6.0 rjson_0.2.20
#> [4] ellipsis_0.3.1 rio_0.5.16 rprojroot_2.0.2
#> [7] GlobalOptions_0.1.2 fs_1.5.0 clue_0.3-58
#> [10] rstudioapi_0.13 farver_2.0.3 fansi_0.4.1
#> [13] lubridate_1.7.9.2 xml2_1.3.2 codetools_0.2-16
#> [16] knitr_1.30 jsonlite_1.7.2 broom_0.7.3
#> [19] cluster_2.1.0 dbplyr_2.0.0 png_0.1-7
#> [22] compiler_4.0.2 httr_1.4.2 backports_1.2.1
#> [25] assertthat_0.2.1 cli_2.2.0 later_1.1.0.1
#> [28] formatR_1.7 htmltools_0.5.0 tools_4.0.2
#> [31] gtable_0.3.0 glue_1.4.2 Rcpp_1.0.5
#> [34] carData_3.0-4 cellranger_1.1.0 vctrs_0.3.6
#> [37] xfun_0.19 openxlsx_4.2.3 rvest_0.3.6
#> [40] lifecycle_0.2.0 renv_0.12.3 gtools_3.8.2
#> [43] rstatix_0.6.0 clisymbols_1.2.0 hms_0.5.3
#> [46] promises_1.1.1 parallel_4.0.2 lambda.r_1.2.4
#> [49] RColorBrewer_1.1-2 yaml_2.2.1 curl_4.3
#> [52] stringi_1.5.3 zip_2.1.1 shape_1.4.5
#> [55] rlang_0.4.9 pkgconfig_2.0.3 evaluate_0.14
#> [58] lattice_0.20-41 labeling_0.4.2 cowplot_1.1.0
#> [61] tidyselect_1.1.0 plyr_1.8.6 magrittr_2.0.1
#> [64] R6_2.5.0 generics_0.1.0 DBI_1.1.0
#> [67] pillar_1.4.7 haven_2.3.1 whisker_0.4
#> [70] foreign_0.8-80 withr_2.3.0 abind_1.4-5
#> [73] modelr_0.1.8 crayon_1.3.4 car_3.0-10
#> [76] futile.options_1.0.1 rmarkdown_2.6 GetoptLong_1.0.5
#> [79] readxl_1.3.1 data.table_1.13.4 git2r_0.27.1
#> [82] reprex_0.3.0 digest_0.6.27 httpuv_1.5.4
#> [85] munsell_0.5.0