ChromHMM
Renee Matthews
2026-01-23
Last updated: 2026-01-23
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Knit directory: DXR_continue/
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library(tidyverse)
library(GenomicRanges)
library(plyranges)
library(genomation)
library(readr)
library(rtracklayer)
library(stringr)
library(ggrepel)
library(DT)
library(readxl)
library(ChIPseeker)This is the AIC/BIC plots genrated to figure out how many states we wanted to use for our modeling of our data:
aic_bic_table_TACC <-read_delim("C:/Users/renee/Other_projects_data/DXR_data/TACC_models/model_selection_summary.tsv",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)
aic_bic_table_TACC %>%
pivot_longer(cols=c(AIC,BIC), names_to = "test_type",values_to = "calculation") %>%
ggplot(.,aes(x=States,y=calculation))+
geom_point()+
geom_line()+
facet_wrap(~test_type)
The results point to 16 states yielding the best results.
I ran the models for 16 states and here is the outcome of those results:
Here is the emission table of the results:
emission_16state <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/TACC_models/Chrom_model_16states_final/emissions_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)
emission_16state %>%
mutate(`State (Emission order)`=factor(`State (Emission order)`, levels=c(1:16))) %>%
pivot_longer(., !`State (Emission order)`, names_to = "Histone",values_to = "emission") %>%
mutate(Histone=factor(Histone,levels=c("H3K27ac","H3K36me3","H3K27me3","H3K9me3"))) %>%
ggplot(., aes(x=Histone, y=`State (Emission order)`, fill=emission))+
geom_tile(color = "grey9",
lwd = .1,
linetype = 1)+
scale_fill_gradient(
low = "white",
high = "#08306B",
limits = c(0, 1), # <- KEY
oob = scales::squish,
na.value = "white", # <- this sets NAs to white
name = "scale"
)+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0), limits = rev)+
coord_fixed()+
theme_classic()+
ggtitle("Emission Parameters")+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Here is the transition plot of the results:
transition_16state <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/TACC_models/Chrom_model_16states_final/transitions_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)
transition_16state %>%
mutate(`State (from\\to) (Emission order)`=factor(`State (from\\to) (Emission order)`, levels=c(1:16)))%>%
dplyr::rename(state_from_to=`State (from\\to) (Emission order)`) %>%
pivot_longer(., !state_from_to, names_to = "States",values_to = "transition") %>%
mutate(States=factor(States, levels=c(1:16))) %>%
ggplot(., aes(x=States, y=state_from_to, fill=transition))+
geom_tile(color = "grey9",
lwd = .1,
linetype = 1)+
scale_fill_gradient(
low = "white",
high = "#08306B",
limits = c(0, 1), # <- KEY
oob = scales::squish,
na.value = "white", # <- this sets NAs to white
name = "scale"
)+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
scale_x_discrete(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0), limits = rev)+
coord_fixed()+
theme_classic()+
ggtitle("Transition Parameters")+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
### Enrichment across the states: looking at enrichment of Features
across the states by each group. This helps with the classification and
interpretation of what each state represents.
DOX_24T_full <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/rerun_regions/24t_DOX_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="DOX_24T")
DOX_24R_full <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/rerun_regions/24R_DOX_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="DOX_24R")
DOX_144R_full <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/rerun_regions/144R_DOX_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE) %>% mutate(group="DOX_144R")
VEH_24T_full <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/rerun_regions/24t_VEH_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="VEH_24T")
VEH_24R_full <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/rerun_regions/24R_VEH_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="VEH_24R")
VEH_144R_full <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/rerun_regions/144R_VEH_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="VEH_144R") DOX_24T_gene <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/16-internal-enrichment/24t_DOX_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="DOX_24T")
DOX_24R_gene <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/16-internal-enrichment/24R_DOX_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="DOX_24R")
DOX_144R_gene <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/16-internal-enrichment/144R_DOX_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE) %>% mutate(group="DOX_144R")
VEH_24T_gene <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/16-internal-enrichment/24t_VEH_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="VEH_24T")
VEH_24R_gene <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/16-internal-enrichment/24R_VEH_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="VEH_24R")
VEH_144R_gene <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/16-internal-enrichment/144R_VEH_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="VEH_144R")
all_genes_states <-
VEH_144R_gene %>%
bind_rows(DOX_144R_gene) %>%
bind_rows(DOX_24R_gene) %>%
bind_rows(VEH_24R_gene) %>%
bind_rows(DOX_24T_gene) %>%
bind_rows(VEH_24T_gene) DOX_24T_ZFP <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/ZFP_run/24t_DOX_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="DOX_24T")
DOX_24R_ZFP <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/ZFP_run/24R_DOX_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="DOX_24R")
DOX_144R_ZFP <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/ZFP_run/144R_DOX_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE) %>% mutate(group="DOX_144R")
VEH_24T_ZFP <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/ZFP_run/24t_VEH_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="VEH_24T")
VEH_24R_ZFP <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/ZFP_run/24R_VEH_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="VEH_24R")
VEH_144R_ZFP <- read_delim("C:/Users/renee/Other_projects_data/DXR_data/overlap_enrichment_all/ZFP_run/144R_VEH_16.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)%>% mutate(group="VEH_144R")
all_ZFPs_states <-
VEH_144R_ZFP %>%
bind_rows(DOX_144R_ZFP) %>%
bind_rows(DOX_24R_ZFP) %>%
bind_rows(VEH_24R_ZFP) %>%
bind_rows(DOX_24T_ZFP) %>%
bind_rows(VEH_24T_ZFP)
just_ZFP <- all_ZFPs_states[,c("State (Emission order)","ZFP_proteins.bed","group")]
just_ZFP %>%
dplyr::filter(`State (Emission order)` !="Base")# A tibble: 96 × 3
`State (Emission order)` ZFP_proteins.bed group
<chr> <dbl> <chr>
1 E2 0.212 VEH_144R
2 E1 0.712 VEH_144R
3 E4 2.06 VEH_144R
4 E3 1.82 VEH_144R
5 E9 0.745 VEH_144R
6 E7 0.649 VEH_144R
7 E8 0.774 VEH_144R
8 E6 0.764 VEH_144R
9 E5 0.561 VEH_144R
10 E11 1.64 VEH_144R
# ℹ 86 more rowsall_files_states <-
VEH_144R_full %>%
bind_rows(DOX_144R_full) %>%
bind_rows(DOX_24R_full) %>%
bind_rows(VEH_24R_full) %>%
bind_rows(DOX_24T_full) %>%
bind_rows(VEH_24T_full)
long_file <- all_files_states %>%
dplyr::select(`State (Emission order)`,
`Genome %`,
`GRCh38-cCREs.bed`,
`SCREEN_hg38_CA-CTCF.bed`:SCREEN_hg38_pELS.bed,
Set_1_H3K27ac_ROI.bed:Set_3_H3K27ac_ROI.bed,
all_H3K27ac_H3K27ac_ROI.bed,
LINE_rptmasker.bed,
SINE_rptmasker.bed,
LTR_rptmasker.bed,
DNA_rptmasker.bed,
Retroposon_rptmasker.bed,
RC_rptmasker.bed,
Low_complexity_rptmasker.bed,
RNA_rptmasker.bed,
Satellite_rptmasker.bed,
Simple_repeat_rptmasker.bed,
Unknown_rptmasker.bed,
rRNA_rptmasker.bed:group) %>%
mutate(`State (Emission order)`=factor(`State (Emission order)`, levels = c(paste0("E", 1:16),"Base"))) %>%
dplyr::filter(`State (Emission order)` != "Base") %>%
pivot_longer(., cols=!c(`State (Emission order)`,group), names_to = "Identity",values_to = "Fold_enrichment") %>%
mutate(Identity=str_remove(Identity,".bed")) %>%
mutate(Identity=factor(Identity,
levels=c("Genome %",
"GRCh38-cCREs",
"SCREEN_hg38_CA-CTCF",
"SCREEN_hg38_CA-H3K4me3",
"SCREEN_hg38_CA-TF",
"SCREEN_hg38_CA",
"SCREEN_hg38_PLS",
"SCREEN_hg38_TF",
"SCREEN_hg38_pELS",
"SCREEN_hg38_dELS",
"all_H3K27ac_H3K27ac_ROI",
"Set_1_H3K27ac_ROI",
"Set_2_H3K27ac_ROI",
"Set_3_H3K27ac_ROI",
"LINE_rptmasker",
"SINE_rptmasker",
"LTR_rptmasker",
"DNA_rptmasker",
"Retroposon_rptmasker",
"RC_rptmasker",
"Low_complexity_rptmasker",
"RNA_rptmasker",
"Satellite_rptmasker",
"Simple_repeat_rptmasker",
"Unknown_rptmasker",
"rRNA_rptmasker",
"scRNA_rptmasker",
"snRNA_rptmasker",
"srpRNA_rptmasker",
"tRNA_rptmasker"))) %>%
mutate(group=factor(group,levels=c("VEH_24T","VEH_24R","VEH_144R","DOX_24T", "DOX_24R", "DOX_144R")))# columns to pivot (exclude ID columns)
id_cols <- c("State (Emission order)", "group")
all_genes_states_long <-
all_genes_states %>%
mutate(`State (Emission order)`=paste0("E",`State (Emission order)`)) %>%
left_join(just_ZFP) %>%
mutate(`State (Emission order)` = factor(`State (Emission order)`,
levels = c(paste0("E",1:16),"EBase"))) %>%
filter(`State (Emission order)` != "EBase") %>%
pivot_longer(
cols = -all_of(id_cols),
names_to = "Identity",
values_to = "Fold_enrichment"
) %>%
# preserve original order
mutate(Identity = factor(Identity, levels =c( names(all_genes_states)[!names(all_genes_states) %in% id_cols],"ZFP_proteins.bed"))) %>%
# create cleaned label for plotting
mutate(Identity_label = case_when(
str_detect(Identity, "\\.hg38\\.bed\\.gz$") ~ str_remove(Identity, "\\.hg38\\.bed\\.gz$"),
str_detect(Identity, "\\.bed$") ~ str_remove(Identity, "\\.bed$"),
TRUE ~ Identity # keep everything else unchanged
),
group = factor(group,
levels = c("VEH_24T","VEH_24R","VEH_144R",
"DOX_24T","DOX_24R","DOX_144R")))
final_order <- unique(all_genes_states_long$Identity_label)
all_genes_states_long <- all_genes_states_long %>%
mutate(Identity_label=factor(Identity_label,levels=final_order))Looking a genomic features:
all_genes_states_long %>%
group_by(Identity) %>% # i.e. per annotation column
mutate(
FE_min = min(Fold_enrichment, na.rm = TRUE),
FE_max = max(Fold_enrichment, na.rm = TRUE),
chromhmm_scaled = (Fold_enrichment - FE_min) / (FE_max - FE_min)
) %>%
ggplot(.,aes(x=Identity_label,y=`State (Emission order)`, fill=chromhmm_scaled))+
geom_tile(color = "grey9",
lwd = .1,
linetype = 1)+
scale_fill_gradient(
low = "white",
high = "#08306B",
limits = c(0, 1), # <- KEY
oob = scales::squish,
na.value = "white", # <- this sets NAs to white
name = "transformed scale"
)+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
facet_wrap(~group, nrow=1, ncol=6)+
scale_y_discrete(limits=rev)+
ggtitle("Genic region enrichment across states")+
coord_fixed()
long_file %>%
dplyr::filter(stringr::str_detect(Identity, "SCREEN")) %>%
group_by(Identity) %>% # i.e. per annotation column
mutate(
FE_min = min(Fold_enrichment, na.rm = TRUE),
FE_max = max(Fold_enrichment, na.rm = TRUE),
chromhmm_scaled = (Fold_enrichment - FE_min) / (FE_max - FE_min)
) %>%
ggplot(.,aes(x=Identity,y=`State (Emission order)`, fill=chromhmm_scaled))+
geom_tile(color = "grey9",
lwd = .1,
linetype = 1)+
scale_fill_gradient(
low = "white",
high = "#08306B",
limits = c(0, 1), # <- KEY
oob = scales::squish,
na.value = "white", # <- this sets NAs to white
name = "transformed scale"
)+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
facet_wrap(~group, nrow=1, ncol=6)+
scale_y_discrete(limits=rev)+
ggtitle("Enhancer enrichment acros states")+
coord_fixed()
### Cormotif set enrichment across states
Each Histone will have slices of their own states. Here are all sets next to all states
order <- c("Genome %",
"all_H3K27ac_H3K27ac_ROI.bed",
"Set_1_H3K27ac_ROI.bed" ,
"Set_2_H3K27ac_ROI.bed",
"Set_3_H3K27ac_ROI.bed",
"all_H3K36me3_regions_H3K36me3_ROI.bed",
"Set_1_H3K36me3_ROI.bed",
"Set_2_H3K36me3_ROI.bed",
"all_H3K27me3_regions_H3K27me3_ROI.bed",
"Set_1_H3K27me3_ROI.bed",
"all_H3K9me3_regions_H3K9me3_ROI.bed",
"Set_2_H3K27me3_ROI.bed" ,
"Set_1_H3K9me3_ROI.bed",
"Set_2_H3K9me3_ROI.bed",
"Set_3_H3K9me3_ROI.bed" ,
"ZFP_proteins.bed")
all_ZFPs_states %>%
mutate(
`State (Emission order)` = as.character(`State (Emission order)`),
`State (Emission order)` = case_when(
`State (Emission order)` %in% as.character(1:16) ~ paste0("E", `State (Emission order)`),
TRUE ~ `State (Emission order)`)) %>%
mutate(`State (Emission order)` = factor(`State (Emission order)`,
levels = c(paste0("E",1:16), "Base"))) %>%
filter(`State (Emission order)` != "Base") %>%
pivot_longer(., -c(`State (Emission order)`,group), names_to = "Identity", values_to="Fold_enrichment") %>%
mutate(group=factor(group,levels=c("VEH_24T","VEH_24R","VEH_144R","DOX_24T", "DOX_24R", "DOX_144R"))) %>%
mutate(Identity=factor(Identity, levels=order)) %>%
dplyr::filter(Identity!="ZFP_proteins.bed") %>%
group_by(Identity) %>% # i.e. per annotation column
mutate(
FE_min = min(Fold_enrichment, na.rm = TRUE),
FE_max = max(Fold_enrichment, na.rm = TRUE),
chromhmm_scaled = (Fold_enrichment - FE_min) / (FE_max - FE_min)
) %>%
ggplot(.,aes(x=Identity,y=`State (Emission order)`, fill=chromhmm_scaled))+
geom_tile(color = "grey9",
lwd = .1,
linetype = 1)+
scale_fill_gradient(
low = "white",
high = "#08306B",
limits = c(0, 1), # <- KEY
oob = scales::squish,
na.value = "white", # <- this sets NAs to white
name = "transformed scale"
)+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
facet_wrap(~group, nrow=2, ncol=3)+
scale_y_discrete(limits=rev)+
ggtitle("Cormotif across states")+
coord_fixed()
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ChIPseeker_1.42.1 readxl_1.4.5 DT_0.33
[4] ggrepel_0.9.6 rtracklayer_1.66.0 genomation_1.38.0
[7] plyranges_1.26.0 GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
[10] IRanges_2.40.1 S4Vectors_0.44.0 BiocGenerics_0.52.0
[13] lubridate_1.9.4 forcats_1.0.0 stringr_1.5.1
[16] dplyr_1.1.4 purrr_1.1.0 readr_2.1.5
[19] tidyr_1.3.1 tibble_3.3.0 ggplot2_3.5.2
[22] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3
[2] rstudioapi_0.17.1
[3] jsonlite_2.0.0
[4] magrittr_2.0.3
[5] ggtangle_0.0.7
[6] GenomicFeatures_1.58.0
[7] farver_2.1.2
[8] rmarkdown_2.29
[9] fs_1.6.6
[10] BiocIO_1.16.0
[11] zlibbioc_1.52.0
[12] vctrs_0.6.5
[13] memoise_2.0.1
[14] Rsamtools_2.22.0
[15] RCurl_1.98-1.17
[16] ggtree_3.14.0
[17] htmltools_0.5.8.1
[18] S4Arrays_1.6.0
[19] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[20] plotrix_3.8-4
[21] curl_7.0.0
[22] cellranger_1.1.0
[23] SparseArray_1.6.2
[24] gridGraphics_0.5-1
[25] sass_0.4.10
[26] KernSmooth_2.23-26
[27] bslib_0.9.0
[28] htmlwidgets_1.6.4
[29] plyr_1.8.9
[30] impute_1.80.0
[31] cachem_1.1.0
[32] GenomicAlignments_1.42.0
[33] igraph_2.1.4
[34] whisker_0.4.1
[35] lifecycle_1.0.4
[36] pkgconfig_2.0.3
[37] Matrix_1.7-3
[38] R6_2.6.1
[39] fastmap_1.2.0
[40] GenomeInfoDbData_1.2.13
[41] MatrixGenerics_1.18.1
[42] enrichplot_1.26.6
[43] digest_0.6.37
[44] aplot_0.2.8
[45] colorspace_2.1-1
[46] patchwork_1.3.2
[47] AnnotationDbi_1.68.0
[48] ps_1.9.1
[49] rprojroot_2.1.1
[50] RSQLite_2.4.3
[51] labeling_0.4.3
[52] timechange_0.3.0
[53] httr_1.4.7
[54] abind_1.4-8
[55] compiler_4.4.2
[56] bit64_4.6.0-1
[57] withr_3.0.2
[58] BiocParallel_1.40.2
[59] DBI_1.2.3
[60] gplots_3.2.0
[61] R.utils_2.13.0
[62] rappdirs_0.3.3
[63] DelayedArray_0.32.0
[64] rjson_0.2.23
[65] caTools_1.18.3
[66] gtools_3.9.5
[67] tools_4.4.2
[68] ape_5.8-1
[69] httpuv_1.6.16
[70] R.oo_1.27.1
[71] glue_1.8.0
[72] restfulr_0.0.16
[73] callr_3.7.6
[74] nlme_3.1-168
[75] GOSemSim_2.32.0
[76] promises_1.3.3
[77] getPass_0.2-4
[78] gridBase_0.4-7
[79] reshape2_1.4.4
[80] fgsea_1.32.4
[81] generics_0.1.4
[82] gtable_0.3.6
[83] BSgenome_1.74.0
[84] tzdb_0.5.0
[85] R.methodsS3_1.8.2
[86] seqPattern_1.38.0
[87] data.table_1.17.8
[88] hms_1.1.3
[89] utf8_1.2.6
[90] XVector_0.46.0
[91] pillar_1.11.0
[92] vroom_1.6.5
[93] yulab.utils_0.2.1
[94] later_1.4.2
[95] splines_4.4.2
[96] treeio_1.30.0
[97] lattice_0.22-7
[98] bit_4.6.0
[99] tidyselect_1.2.1
[100] GO.db_3.20.0
[101] Biostrings_2.74.1
[102] knitr_1.50
[103] git2r_0.36.2
[104] SummarizedExperiment_1.36.0
[105] xfun_0.52
[106] Biobase_2.66.0
[107] matrixStats_1.5.0
[108] stringi_1.8.7
[109] UCSC.utils_1.2.0
[110] lazyeval_0.2.2
[111] boot_1.3-32
[112] ggfun_0.2.0
[113] yaml_2.3.10
[114] evaluate_1.0.5
[115] codetools_0.2-20
[116] qvalue_2.38.0
[117] ggplotify_0.1.2
[118] cli_3.6.5
[119] processx_3.8.6
[120] jquerylib_0.1.4
[121] dichromat_2.0-0.1
[122] Rcpp_1.1.0
[123] png_0.1-8
[124] XML_3.99-0.18
[125] parallel_4.4.2
[126] blob_1.2.4
[127] DOSE_4.0.1
[128] bitops_1.0-9
[129] tidytree_0.4.6
[130] scales_1.4.0
[131] crayon_1.5.3
[132] rlang_1.1.6
[133] fastmatch_1.1-6
[134] cowplot_1.2.0
[135] KEGGREST_1.46.0