Exploratory Data Analysis of the most popular human gene#
Data#
require(tidyverse)
require(ggpubr)
require(ComplexHeatmap)
ROOT = here::here()
Description#
According to this article, TP53 is the most popular gene in the human genome by far. In the practical part of the talk, we will all dive into pubicly available data on this gene.
We collected harmonized multi-omic data from the XenaBrowser’s TCGA Pan Cancer (PANCAN) dataset:
Gene expression: log2(norm_value+1)
Exon expression: log2(RPKM+1)
Gene methylation 450k: beta value
Copy Number Variation: log( tumor / normal )
Mutations
Additionally, we downloaded a protein Multiple Sequence Alignment (MSA) of 153 TP53 mammal orthologues from NCBI, generated through their aligner COBALT.
Overview#
The pre-processed data sets can be found in data/prep.
omics = read_tsv(file.path(ROOT,'data','prep','data_multiomics.tsv.gz'))
muts = read_tsv(file.path(ROOT,'data','prep','data_somatic_mutations.tsv.gz'))
The data sets contain data on all these cancer types:
omics %>% distinct(cancer_type) %>% pull()
## [1] "GBM" "OV" "LUAD" "LUSC" "PRAD" "UCEC" "BLCA" "TGCT" "ESCA" "PAAD"
## [11] "KIRP" "LIHC" "CESC" "SARC" "BRCA" "THYM" "MESO" "COAD" "STAD" "SKCM"
## [21] "CHOL" "KIRC" "THCA" "HNSC" "LAML" "READ" "LGG" "DLBC" "KICH" "UCS"
## [31] "ACC" "PCPG" "UVM"
I pre-processed different omic data sets into a long format data frame in which every “value” is linked to an “omic_type”, which in turn can have several “omic_names”. Additionally, we added information on the sample metadata.
# available variables
colnames(omics)
## [1] "sample" "omic_name"
## [3] "value" "omic_type"
## [5] "_primary_disease" "age_at_initial_pathologic_diagnosis"
## [7] "clinical_stage" "gender"
## [9] "histological_grade" "histological_type"
## [11] "OS" "OS.time"
## [13] "residual_tumor" "sample_type"
## [15] "tumor_status" "_PATIENT"
## [17] "cancer_type"
# omic data types
omics %>% group_by(omic_type) %>% count()
## # A tibble: 4 × 2
## # Groups: omic_type [4]
## omic_type n
## <chr> <int>
## 1 copy_number_variation 10873
## 2 exon_expression 125508
## 3 gene_expression 11060
## 4 methylation 318050
In the cases of exon expression and methylation, there are multiple “omic_name”s that correspond to different exons and methylation sites.
# exons
omics %>% filter(omic_type=='exon_expression') %>% distinct(omic_name) %>% pull()
## [1] "chr17:7580643-7580745:-" "chr17:7577019-7577155:-"
## [3] "chr17:7577499-7577608:-" "chr17:7571720-7573008:-"
## [5] "chr17:7576525-7576657:-" "chr17:7578371-7578811:-"
## [7] "chr17:7590695-7590863:-" "chr17:7565097-7565332:-"
## [9] "chr17:7579312-7579590:-" "chr17:7579700-7579940:-"
## [11] "chr17:7576853-7576926:-" "chr17:7573927-7574033:-"
# methylation sites
omics %>% filter(omic_type=='methylation') %>% distinct(omic_name) %>% pull()
## [1] "cg21050342" "cg26539663" "cg17461511" "cg12373934" "cg06365412"
## [6] "cg16397722" "cg14351634" "cg25896754" "cg18198734" "cg02045224"
## [11] "cg10792831" "cg22949073" "cg02166782" "cg12041075" "cg13468400"
## [16] "cg15206330" "cg05479194" "cg00807143" "cg05348746" "cg16203911"
## [21] "cg18311066" "cg02087342" "cg22175811" "cg06587969" "cg08119584"
## [26] "cg13169780" "cg06317056" "cg02690969" "cg02842899" "cg02855142"
## [31] "cg04009932" "cg25053252" "cg07991600"
Then, the mutations data set is structured as a list of mutations that occur inthis gene in every sample:
colnames(muts)
## [1] "sample" "chr"
## [3] "start" "end"
## [5] "gene" "reference"
## [7] "alt" "altGene"
## [9] "effect" "aminoAcid"
## [11] "rnaVaf" "dnaVaf"
## [13] "_primary_disease" "age_at_initial_pathologic_diagnosis"
## [15] "clinical_stage" "gender"
## [17] "histological_grade" "histological_type"
## [19] "OS" "OS.time"
## [21] "residual_tumor" "sample_type"
## [23] "tumor_status" "_PATIENT"
## [25] "cancer_type"
muts %>% drop_na(effect)
## # A tibble: 9,104 × 25
## sample chr start end gene reference alt altGene effect aminoAcid
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 TCGA-02-000… chr17 7577… 7577… TP53 G A <NA> Misse… p.R282W
## 2 TCGA-02-003… chr17 7577… 7577… TP53 C T <NA> Misse… p.R248Q
## 3 TCGA-02-004… no v… no v… no v… no v… no varia… no v… no var… no va… no varia…
## 4 TCGA-02-005… chr17 7578… 7578… TP53 C T <NA> Misse… p.V216M
## 5 TCGA-02-247… no v… no v… no v… no v… no varia… no v… no var… no va… no varia…
## 6 TCGA-02-248… chr17 7577… 7577… TP53 C T <NA> Misse… p.R273H
## 7 TCGA-02-248… chr17 7577… 7577… TP53 C T <NA> Misse… p.R248Q
## 8 TCGA-02-248… no v… no v… no v… no v… no varia… no v… no var… no va… no varia…
## 9 TCGA-04-135… no v… no v… no v… no v… no varia… no v… no var… no va… no varia…
## 10 TCGA-04-136… chr17 7577… 7577… TP53 C A <NA> Misse… p.R280I
## # ℹ 9,094 more rows
## # ℹ 15 more variables: rnaVaf <chr>, dnaVaf <chr>, `_primary_disease` <chr>,
## # age_at_initial_pathologic_diagnosis <chr>, clinical_stage <chr>,
## # gender <chr>, histological_grade <chr>, histological_type <chr>, OS <chr>,
## # OS.time <dbl>, residual_tumor <chr>, sample_type <chr>, tumor_status <chr>,
## # `_PATIENT` <chr>, cancer_type <chr>
Exploratory Data Analysis#
In this section, we will try to answer the questions using functions from the packages introduced previously. These solutions are completely subjective, so feel free to find your own best ways of combining and visualizing the different types of data.
Counting#
How many samples per cancer type are there?#
ggbarplot(omics %>% group_by(cancer_type) %>% count(), x = 'cancer_type', y = 'n',
fill = 'cancer_type',
ggtheme = theme_pubr(x.text.angle = 50, base_size = 10))
How many samples per cancer type and sample type are there?#
ggbarplot(omics %>% group_by(cancer_type, sample_type) %>% count(), x = 'cancer_type', y = 'n',
fill = 'sample_type', palette = 'uchicago',
ggtheme = theme_pubr(x.text.angle = 50, base_size = 10, legend = 'right'))
Distributions#
What are the distributions of values for every omic type across cancer types?#
omic_types = unique(omics$omic_type)
#omic_type_oi = 'gene_expression'
plts = list()
for (omic_type_oi in omic_types){
omic_names = omics %>% filter(omic_type==omic_type_oi) %>% distinct(omic_name) %>% pull()
for (omic_name_oi in omic_names){
# plot
pltTitle = paste0(omic_type_oi,'-',omic_name_oi)
plt = ggboxplot(omics %>% filter(omic_type==omic_type_oi & omic_name==omic_name_oi),
x = 'cancer_type', y = 'value',
color = 'cancer_type',
ggtheme = theme_pubr(x.text.angle = 50, base_size = 10, legend = 'right')) +
ggtitle(pltTitle)
# save
plts[[pltTitle]] = plt
}
}
names(plts)
## [1] "copy_number_variation-TP53 (average)"
## [2] "exon_expression-chr17:7580643-7580745:-"
## [3] "exon_expression-chr17:7577019-7577155:-"
## [4] "exon_expression-chr17:7577499-7577608:-"
## [5] "exon_expression-chr17:7571720-7573008:-"
## [6] "exon_expression-chr17:7576525-7576657:-"
## [7] "exon_expression-chr17:7578371-7578811:-"
## [8] "exon_expression-chr17:7590695-7590863:-"
## [9] "exon_expression-chr17:7565097-7565332:-"
## [10] "exon_expression-chr17:7579312-7579590:-"
## [11] "exon_expression-chr17:7579700-7579940:-"
## [12] "exon_expression-chr17:7576853-7576926:-"
## [13] "exon_expression-chr17:7573927-7574033:-"
## [14] "gene_expression-TP53"
## [15] "methylation-cg21050342"
## [16] "methylation-cg26539663"
## [17] "methylation-cg17461511"
## [18] "methylation-cg12373934"
## [19] "methylation-cg06365412"
## [20] "methylation-cg16397722"
## [21] "methylation-cg14351634"
## [22] "methylation-cg25896754"
## [23] "methylation-cg18198734"
## [24] "methylation-cg02045224"
## [25] "methylation-cg10792831"
## [26] "methylation-cg22949073"
## [27] "methylation-cg02166782"
## [28] "methylation-cg12041075"
## [29] "methylation-cg13468400"
## [30] "methylation-cg15206330"
## [31] "methylation-cg05479194"
## [32] "methylation-cg00807143"
## [33] "methylation-cg05348746"
## [34] "methylation-cg16203911"
## [35] "methylation-cg18311066"
## [36] "methylation-cg02087342"
## [37] "methylation-cg22175811"
## [38] "methylation-cg06587969"
## [39] "methylation-cg08119584"
## [40] "methylation-cg13169780"
## [41] "methylation-cg06317056"
## [42] "methylation-cg02690969"
## [43] "methylation-cg02842899"
## [44] "methylation-cg02855142"
## [45] "methylation-cg04009932"
## [46] "methylation-cg25053252"
## [47] "methylation-cg07991600"
plts$`methylation-cg25053252`
Could you now split them by sample type?#
omic_types = unique(omics$omic_type)
#omic_type_oi = 'gene_expression'
plts = list()
for (omic_type_oi in omic_types){
omic_names = omics %>% filter(omic_type==omic_type_oi) %>% distinct(omic_name) %>% pull()
for (omic_name_oi in omic_names){
# plot
pltTitle = paste0(omic_type_oi,'-',omic_name_oi)
plt = ggboxplot(omics %>% filter(omic_type==omic_type_oi & omic_name==omic_name_oi),
x = 'cancer_type', y = 'value',
fill = 'sample_type',
ggtheme = theme_pubr(x.text.angle = 50, base_size = 10, legend = 'right')) +
ggtitle(pltTitle)
# save
plts[[pltTitle]] = plt
}
}
names(plts)
## [1] "copy_number_variation-TP53 (average)"
## [2] "exon_expression-chr17:7580643-7580745:-"
## [3] "exon_expression-chr17:7577019-7577155:-"
## [4] "exon_expression-chr17:7577499-7577608:-"
## [5] "exon_expression-chr17:7571720-7573008:-"
## [6] "exon_expression-chr17:7576525-7576657:-"
## [7] "exon_expression-chr17:7578371-7578811:-"
## [8] "exon_expression-chr17:7590695-7590863:-"
## [9] "exon_expression-chr17:7565097-7565332:-"
## [10] "exon_expression-chr17:7579312-7579590:-"
## [11] "exon_expression-chr17:7579700-7579940:-"
## [12] "exon_expression-chr17:7576853-7576926:-"
## [13] "exon_expression-chr17:7573927-7574033:-"
## [14] "gene_expression-TP53"
## [15] "methylation-cg21050342"
## [16] "methylation-cg26539663"
## [17] "methylation-cg17461511"
## [18] "methylation-cg12373934"
## [19] "methylation-cg06365412"
## [20] "methylation-cg16397722"
## [21] "methylation-cg14351634"
## [22] "methylation-cg25896754"
## [23] "methylation-cg18198734"
## [24] "methylation-cg02045224"
## [25] "methylation-cg10792831"
## [26] "methylation-cg22949073"
## [27] "methylation-cg02166782"
## [28] "methylation-cg12041075"
## [29] "methylation-cg13468400"
## [30] "methylation-cg15206330"
## [31] "methylation-cg05479194"
## [32] "methylation-cg00807143"
## [33] "methylation-cg05348746"
## [34] "methylation-cg16203911"
## [35] "methylation-cg18311066"
## [36] "methylation-cg02087342"
## [37] "methylation-cg22175811"
## [38] "methylation-cg06587969"
## [39] "methylation-cg08119584"
## [40] "methylation-cg13169780"
## [41] "methylation-cg06317056"
## [42] "methylation-cg02690969"
## [43] "methylation-cg02842899"
## [44] "methylation-cg02855142"
## [45] "methylation-cg04009932"
## [46] "methylation-cg25053252"
## [47] "methylation-cg07991600"
plts$`exon_expression-chr17:7573927-7574033:-`
Can you find a type of omic with statistically different values across cancer types when comparing “Primary Tumor” vs. “Solid Tissue Normal” sample types?#
omic_types = unique(omics$omic_type)
sample_types_oi = c('Primary Tumor','Solid Tissue Normal')
plts = list()
for (omic_type_oi in omic_types){
omic_names = omics %>% filter(omic_type==omic_type_oi) %>% distinct(omic_name) %>% pull()
for (omic_name_oi in omic_names){
# plot
pltTitle = paste0(omic_type_oi,'-',omic_name_oi)
plt = ggboxplot(omics %>% filter(omic_type==omic_type_oi & omic_name==omic_name_oi & sample_type%in%sample_types_oi),
x = 'cancer_type', y = 'value',
fill = 'sample_type',
ggtheme = theme_pubr(x.text.angle = 50, base_size = 10)) +
stat_compare_means(aes(color = sample_type), label = "p.signif", method = 'wilcox.test')
ggtitle(pltTitle)
# save
plts[[pltTitle]] = plt
}
}
names(plts)
## [1] "copy_number_variation-TP53 (average)"
## [2] "exon_expression-chr17:7580643-7580745:-"
## [3] "exon_expression-chr17:7577019-7577155:-"
## [4] "exon_expression-chr17:7577499-7577608:-"
## [5] "exon_expression-chr17:7571720-7573008:-"
## [6] "exon_expression-chr17:7576525-7576657:-"
## [7] "exon_expression-chr17:7578371-7578811:-"
## [8] "exon_expression-chr17:7590695-7590863:-"
## [9] "exon_expression-chr17:7565097-7565332:-"
## [10] "exon_expression-chr17:7579312-7579590:-"
## [11] "exon_expression-chr17:7579700-7579940:-"
## [12] "exon_expression-chr17:7576853-7576926:-"
## [13] "exon_expression-chr17:7573927-7574033:-"
## [14] "gene_expression-TP53"
## [15] "methylation-cg21050342"
## [16] "methylation-cg26539663"
## [17] "methylation-cg17461511"
## [18] "methylation-cg12373934"
## [19] "methylation-cg06365412"
## [20] "methylation-cg16397722"
## [21] "methylation-cg14351634"
## [22] "methylation-cg25896754"
## [23] "methylation-cg18198734"
## [24] "methylation-cg02045224"
## [25] "methylation-cg10792831"
## [26] "methylation-cg22949073"
## [27] "methylation-cg02166782"
## [28] "methylation-cg12041075"
## [29] "methylation-cg13468400"
## [30] "methylation-cg15206330"
## [31] "methylation-cg05479194"
## [32] "methylation-cg00807143"
## [33] "methylation-cg05348746"
## [34] "methylation-cg16203911"
## [35] "methylation-cg18311066"
## [36] "methylation-cg02087342"
## [37] "methylation-cg22175811"
## [38] "methylation-cg06587969"
## [39] "methylation-cg08119584"
## [40] "methylation-cg13169780"
## [41] "methylation-cg06317056"
## [42] "methylation-cg02690969"
## [43] "methylation-cg02842899"
## [44] "methylation-cg02855142"
## [45] "methylation-cg04009932"
## [46] "methylation-cg25053252"
## [47] "methylation-cg07991600"
plts$`gene_expression-TP53`
## Warning: The dot-dot notation (`..p.signif..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(p.signif)` instead.
## ℹ The deprecated feature was likely used in the ggpubr package.
## Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
Mutations#
How many samples have at least a mutation?#
muts %>% filter(!is.na(effect) & effect != 'no variant') %>% group_by(sample) %>% count() %>% nrow()
## [1] 3325
Which are the most common effects of mutations?#
df = muts %>% filter(!is.na(effect) & effect != 'no variant')
ggbarplot(df %>% group_by(effect) %>% count(), x = 'effect', y = 'n',
fill='effect', palette='simpsons', ggtheme = theme_pubr(x.text.angle = 50, base_size = 10))
How are mutation effects associated to certain positions? And cancer_types?#
plt = gghistogram(df %>% mutate(start=as.numeric(start)), x = 'start', stat="density", facet.by = 'effect', fill = 'effect', palette = 'simpsons', numeric.x.axis=TRUE)
ggpar(plt, font.xtickslab = 8, font.ytickslab = 8)
Pairwise associations#
Could mutation effects be associated with gene expression?#
omic_type_oi = 'gene_expression'
df = merge(omics %>% filter(omic_type==omic_type_oi), muts %>% filter(!is.na(effect) & effect != 'no variant') %>% select(sample, effect), by = 'sample')
ggboxplot(df, x = 'effect', y = 'value', fill = 'sample_type', ggtheme = theme_pubr(x.text.angle = 50, base_size = 8, legend = 'right'), palette = 'simpsons') +
ggtitle(omic_type_oi)
Could gene expression be associated with copy number variation?#
cancer_types_oi = c('BRCA','OV','GBM','PRAD','LUAD')
omic_types_oi = c('gene_expression','copy_number_variation')
df = omics %>% filter(omic_type%in%omic_types_oi & cancer_type%in%cancer_types_oi) %>%
pivot_wider(id_cols = c(sample, cancer_type, sample_type), names_from = omic_type, values_from = value) %>%
drop_na()
ggscatter(df, x = 'gene_expression', y = 'copy_number_variation', size = 1,
color = 'sample_type', palette = 'uchicago', alpha = 0.5,
facet.by = 'cancer_type',
add = 'reg.line',
ggtheme = theme_pubr(legend = 'right')) +
stat_cor(aes(color = sample_type), method='spearman', label.x.npc = 'left', label.y.npc = 'bottom') +
theme(aspect.ratio = 1)
Visualize other pairwise associations that you found interesting#
Try to visualize them considering different sample types, cancer types and omic types and names.
Generating data overviews#
Could you use ComplexHeatmap to get an overview of the distributions of every omic type across samples? Could you also include “cancer_type” and “omic_name” as row and column annotations?#
# subset by cancer types (my computer does not have enough memory)
cancer_types_oi = c('BRCA')
# prepare annotations
annotation_col = omics %>%
drop_na(sample_type) %>%
select(omic_type, omic_name) %>%
distinct() %>%
as.data.frame()
annotation_row = omics %>%
drop_na(sample_type) %>%
filter(cancer_type%in%cancer_types_oi) %>%
select(sample, sample_type, cancer_type) %>%
distinct() %>%
select(-sample) %>%
as.data.frame()
# prepare matrix of values, sorted by gene expression
df = omics %>%
drop_na(sample_type) %>%
filter(cancer_type%in%cancer_types_oi) %>%
pivot_wider(id_cols = c(sample, cancer_type, sample_type),
names_from = omic_name,
values_from = value)
df = df %>% arrange(TP53) # gene expression
mat = as.matrix(df[,annotation_col$omic_name])
rownames(mat) = df$sample
# prepare annotation colors
annotation_colors = list(
omic_name = setNames(get_palette(k=length(unique(annotation_col$omic_name))), unique(annotation_col$omic_name)),
omic_type = setNames(get_palette('lancet',length(unique(annotation_col$omic_type))), unique(annotation_col$omic_type)),
sample_type = setNames(get_palette('npg',length(unique(annotation_row$sample_type))), unique(annotation_row$sample_type)),
cancer_type = setNames(get_palette('simpsons',length(unique(annotation_row$cancer_type))), unique(annotation_row$cancer_type))
)
plts = list()
for (omic_type_oi in omic_types){
# get omic names corresponding to this omic type
idx = annotation_col$omic_type==omic_type_oi
omic_names_oi = annotation_col$omic_name[idx]
# build annotations
annot_color = annotation_colors
annot_color$omic_type = annot_color$omic_type[omic_type_oi]
annot_color$omic_name = annot_color$omic_name[omic_names_oi]
h_annotation_col = HeatmapAnnotation(df=annotation_col, col=annot_color[colnames(annotation_col)], which="column")
h_annotation_row = HeatmapAnnotation(df=annotation_row, col=annot_color[colnames(annotation_row)], which="row")
plts[[omic_type_oi]] = Heatmap(
mat[,omic_names_oi, drop=FALSE],
right_annotation = h_annotation_row, top_annotation = h_annotation_col[idx],
cluster_rows = FALSE, cluster_columns = FALSE,
show_row_names = FALSE, show_column_names = FALSE,
name=" "
)
}
png('images/heatmaps.png', height = 400, width = 800, units = 'mm', res = 100)
plts[['gene_expression']] + plts[['exon_expression']] + plts[['methylation']] + plts[['copy_number_variation']]
dev.off()
References#
Session Info#
sessionInfo()
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so; LAPACK version 3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=es_ES.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=es_ES.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Madrid
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ComplexHeatmap_2.9.3 ggpubr_0.4.0 lubridate_1.9.2
## [4] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.1
## [7] purrr_0.3.4 readr_2.1.5 tidyr_1.2.1
## [10] tibble_3.2.1 ggplot2_3.5.0 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 farver_2.1.1 fastmap_1.1.1
## [4] digest_0.6.29 timechange_0.3.0 lifecycle_1.0.4
## [7] cluster_2.1.4 ellipsis_0.3.2 Cairo_1.6-1
## [10] magrittr_2.0.3 compiler_4.3.1 rlang_1.1.0
## [13] tools_4.3.1 utf8_1.2.4 yaml_2.3.8
## [16] knitr_1.45 ggsignif_0.6.4 labeling_0.4.3
## [19] bit_4.0.5 here_1.0.1 RColorBrewer_1.1-3
## [22] abind_1.4-5 withr_3.0.0 BiocGenerics_0.48.1
## [25] stats4_4.3.1 fansi_1.0.3 colorspace_2.0-3
## [28] scales_1.3.0 iterators_1.0.14 cli_3.4.0
## [31] rmarkdown_2.26 crayon_1.5.2 generics_0.1.3
## [34] tzdb_0.3.0 rjson_0.2.21 splines_4.3.1
## [37] parallel_4.3.1 matrixStats_1.0.0 vctrs_0.6.1
## [40] Matrix_1.6-5 carData_3.0-5 car_3.1-2
## [43] IRanges_2.30.1 hms_1.1.3 GetoptLong_1.0.5
## [46] S4Vectors_0.40.1 bit64_4.0.5 rstatix_0.7.2
## [49] clue_0.3-65 magick_2.8.3 foreach_1.5.2
## [52] glue_1.6.2 codetools_0.2-19 stringi_1.7.8
## [55] gtable_0.3.4 shape_1.4.6.1 munsell_0.5.0
## [58] pillar_1.9.0 htmltools_0.5.7 circlize_0.4.16
## [61] R6_2.5.1 doParallel_1.0.17 rprojroot_2.0.4
## [64] lattice_0.21-8 vroom_1.6.5 evaluate_0.23
## [67] highr_0.10 png_0.1-8 backports_1.4.1
## [70] broom_1.0.5 ggsci_3.0.1 Rcpp_1.0.8.3
## [73] nlme_3.1-164 mgcv_1.9-0 xfun_0.42
## [76] pkgconfig_2.0.3 GlobalOptions_0.1.2