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c-figure-03.Rmd
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---
title: "Panels of Figure 3: Comparative genomics of the 21 NR-genomes of OsHV-1 isolated from three farming areas"
author: "Delmotte jean"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: TRUE
code_folding: "hide"
theme: united
highlight: tango
number_sections: true
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
# Fonction to install / load package if it's not here
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dependencies = TRUE)
sapply(pkg, require, character.only = TRUE)
}
ipak(unique(
c("data.table", "tidyverse", "ape", "RColorBrewer", "pheatmap", "limma", "glue", "kableExtra", "plotly", "tidytree", "broom",
"hrbrthemes", "viridis", "ggsci", # palette
"cowplot", "scales", "maptools", "gggenes") # plot
) )
```
```{r Base_Path}
base_path <- "~/Documents/OshV-1-molepidemio" # Base location for the folder
```
# Preparation and import of data
Abbreviations :
- GC: comparative genomics
- VC: variant calling
```{r Color_analysis, echo=FALSE, warning = FALSE, message = FALSE}
## Convention
# Color Location Brest:blue | LT:Red | Thau:Green
# show_col(pal_aaas("default")(10))
Brest_color = pal_aaas("default")(10)[1]
LT_color = pal_aaas("default")(10)[2]
Thau_color = pal_aaas("default")(10)[3]
# Color Polymorphisms
# show_col(pal_aaas("default")(10))
SNP = pal_aaas("default")(10)[2]
INDEL = pal_jco("default")(10)[3]
# Order
plot_order_samples <- tibble(ID_EXPERIMENT = c("Brest_2018_NSI_broyage_ind10_noPCR",
"Brest_2018_NSI_broyage_ind2_noPCR",
"Brest_2018_NSI_broyage_ind4_noPCR",
"Brest_2018_NSI_broyage_ind6_noPCR",
"Brest_2018_NSI_broyage_ind9_noPCR",
"LT_2018_NSI_broyage_ind10_noPCR",
"LT_2018_NSI_broyage_ind1_noPCR",
"LT_2018_NSI_broyage_ind3_noPCR",
"LT_2018_NSI_broyage_ind4_noPCR",
"LT_2018_NSI_broyage_ind7_noPCR",
"LT_2018_NSI_broyage_ind8_noPCR",
"LT_2018_NSI_broyage_ind9_noPCR",
"Thau_2018_NSI_broyage_ind10_noPCR",
"Thau_2018_NSI_broyage_ind1_noPCR",
"Thau_2018_NSI_broyage_ind3_noPCR",
"Thau_2018_NSI_broyage_ind4_noPCR",
"Thau_2018_NSI_broyage_ind5_noPCR",
"Thau_2018_NSI_broyage_ind6_noPCR",
"Thau_2018_NSI_broyage_ind7_noPCR",
"Thau_2018_NSI_broyage_ind8_noPCR",
"Thau_2018_NSI_broyage_ind9_noPCR") ) %>%
tidyr::separate(ID_EXPERIMENT, c("LOCATION","YEARS","FAM","TYPE","CONDITION","WGS_PCR"), remove = FALSE) %>%
tidyr::unite(LOCATION, CONDITION, col="Samples",sep="_",remove = FALSE) %>%
dplyr::left_join(., tibble(LOCATION = c("Brest","LT","Thau"),
ORDER= c(1,2,3))) %>%
dplyr::mutate(ORDER_samples = stringr::str_replace_all(CONDITION,"ind",""),
ORDER_samples = as.numeric(ORDER_samples)) %>%
tidyr::unite(LOCATION, YEARS, col="VAR",sep="_",remove = FALSE) %>%
tidyr::unite(LOCATION, CONDITION, col="Samples",sep="_",remove = FALSE) %>%
arrange(ORDER, ORDER_samples) %>%
dplyr::mutate(Samples = stringr::str_replace_all(Samples,"_"," "))
```
```{r TABLE_03}
# importation Comparative genomics
comparative_genomic <- data.table::fread(glue::glue("{base_path}/results/Tables/nucmer_numref.snps"),
skip = 3,
sep = "\t",
fill=TRUE,
quote=FALSE)
colnames(comparative_genomic) <-c("POS", "REF", "ALT","POS_ALT", "BUFF","DIST","R", "Q", "FRM","TAGS", "gREF","ID_EXPERIMENT")
comparative_genomic <- comparative_genomic %>%
dplyr::mutate(UniqueID= glue::glue("{POS}_{REF}>{ALT}")) %>%
as_tibble() %>%
group_by(UniqueID, ALT, ID_EXPERIMENT) %>%
mutate(ALT = paste(ALT, collapse = "")) %>%
data.frame() %>%
select(-POS_ALT, -UniqueID, -DIST) %>%
distinct()
```
# Analysis
```{r}
# Get the genomic position of comparative genomic modification
CG_POS <- comparative_genomic %>%
select(POS) %>%
distinct() %>%
pull()
# In the select, we don't used a regex like `contain()` because we want to be sure of the order
Table_02_binary_matrix <- comparative_genomic %>%
dplyr::mutate(UniqueID= glue::glue("{POS}_{REF}>{ALT}"),
AF = 1) %>%
data.frame() %>%
dplyr::select(UniqueID,
ID_EXPERIMENT,
AF) %>%
distinct() %>%
tidyr::pivot_wider(names_from = ID_EXPERIMENT,
values_from = AF,
values_fill = list(AF = 0)) %>%
as.data.frame() %>%
select(UniqueID,
NR_genome_Brest_2018_NSI_broyage_ind10_noPCR,
NR_genome_Brest_2018_NSI_broyage_ind2_noPCR,
NR_genome_Brest_2018_NSI_broyage_ind4_noPCR,
NR_genome_Brest_2018_NSI_broyage_ind6_noPCR,
NR_genome_Brest_2018_NSI_broyage_ind9_noPCR,
NR_genome_LT_2018_NSI_broyage_ind10_noPCR,
NR_genome_LT_2018_NSI_broyage_ind1_noPCR,
NR_genome_LT_2018_NSI_broyage_ind3_noPCR,
NR_genome_LT_2018_NSI_broyage_ind4_noPCR,
NR_genome_LT_2018_NSI_broyage_ind7_noPCR,
NR_genome_LT_2018_NSI_broyage_ind8_noPCR,
NR_genome_LT_2018_NSI_broyage_ind9_noPCR,
NR_genome_Thau_2018_NSI_broyage_ind10_noPCR,
NR_genome_Thau_2018_NSI_broyage_ind1_noPCR,
NR_genome_Thau_2018_NSI_broyage_ind3_noPCR,
NR_genome_Thau_2018_NSI_broyage_ind4_noPCR,
NR_genome_Thau_2018_NSI_broyage_ind5_noPCR,
NR_genome_Thau_2018_NSI_broyage_ind6_noPCR,
NR_genome_Thau_2018_NSI_broyage_ind7_noPCR,
NR_genome_Thau_2018_NSI_broyage_ind8_noPCR,
NR_genome_Thau_2018_NSI_broyage_ind9_noPCR) %>%
tibble::column_to_rownames("UniqueID") %>%
as.matrix()
```
## Figure 3 B Analysis of all genomic variations at the conchyliculture area level
```{r}
Matrix_SET <- comparative_genomic %>%
dplyr::mutate(ID_EXPERIMENT = stringr::str_replace_all(ID_EXPERIMENT,"NR_genome_","" )) %>%
tidyr::separate(ID_EXPERIMENT, c("LOCATION","YEARS","FAM","STEP","CONDITION","WGS_PCR"), remove = FALSE) %>%
tidyr::unite(LOCATION, YEARS, CONDITION, col="Samples",sep="_",remove = FALSE) %>%
dplyr::mutate(SET = glue::glue("{LOCATION}_{YEARS}"),
SET = as.character(SET)) %>%
dplyr::select(ID_EXPERIMENT, SET) %>%
dplyr::distinct() %>% table()
Table_XXXXXXX<-Table_02_binary_matrix %*% Matrix_SET %>%
as.data.frame() %>%
rownames_to_column("UniqueID")
# impact on the genome
Table_XXXXXXX %>%
tidyr::pivot_longer(-UniqueID) %>%
dplyr::filter(value >0) %>%
separate(UniqueID, c("POS","CHANGE"), sep = "_", remove = FALSE) %>%
mutate(Samples = stringr::str_replace_all(name,"_","")) %>%
ggplot(aes(x = POS, y=value)) +
geom_bar(stat = "identity")
Table_XXXXXXX %>%
tidyr::pivot_longer(-UniqueID) %>%
dplyr::mutate(value = dplyr::case_when(value > 0 ~ 1,
TRUE ~ 0)) %>%
dplyr::mutate(name = stringr::str_replace_all(name,"_2018","")) %>%
tidyr::pivot_wider(names_from = name,
values_from = value,
values_fill = list(value = 0)) %>%
column_to_rownames(var = "UniqueID") %>%
# filter(Brest == 1 & LT == 1 & Thau == 1)
as.matrix() %>%
vennDiagram()
```
mafft \
--thread 4 \
--auto \
oshv_wRef.fna \
> oshv_wRef.faa
Note: Thau lagoon (Thau) and the LT (Marennes Olérons) were exchanged on the article using adobe illustrator.
## Figure 3 C Analysis of all genomic variations at the individual level
```{r}
#### Upsetplot Fig3B
set_order <- plot_order_samples %>%
dplyr::mutate(Samples = stringr::str_replace_all(Samples,"Brest","Br"),
Samples = stringr::str_replace_all(Samples,"LT","LT"),
Samples = stringr::str_replace_all(Samples,"Thau","Th"),
Samples = stringr::str_replace_all(Samples,"_"," ") ) %>%
dplyr::select(Samples) %>% pull()
Figure_03_B_comparative_genomic_nt <- comparative_genomic %>%
dplyr::mutate(UniqueID= glue::glue("{POS}_{REF}>{ALT}")) %>%
as_tibble() %>%
group_by(UniqueID, ALT, ID_EXPERIMENT) %>%
mutate(ALT = paste(ALT, collapse = "")) %>%
data.frame() %>%
select(-UniqueID) %>%
distinct() %>%
dplyr::mutate(ID_EXPERIMENT = stringr::str_replace_all(ID_EXPERIMENT,"NR_genome_",""),
ID_EXPERIMENT = stringr::str_replace_all(ID_EXPERIMENT,"_NSI_broyage_"," "),
ID_EXPERIMENT = stringr::str_replace_all(ID_EXPERIMENT,"_noPCR",""),
ID_EXPERIMENT = stringr::str_replace_all(ID_EXPERIMENT,"_2018",""),
ID_EXPERIMENT = stringr::str_replace_all(ID_EXPERIMENT,"Brest","Br"),
ID_EXPERIMENT = stringr::str_replace_all(ID_EXPERIMENT,"LT","LT"),
ID_EXPERIMENT = stringr::str_replace_all(ID_EXPERIMENT,"Thau","Th"),
UniqueID= glue::glue("{POS}_{REF}>{ALT}"),
AF = 1) %>%
dplyr::select(UniqueID,
ID_EXPERIMENT,
AF) %>%
tidyr::pivot_wider(names_from = ID_EXPERIMENT,
values_from = AF,
values_fill = list(AF = 0)) %>%
as.data.frame() %>%
tibble::column_to_rownames("UniqueID") %>%
as.matrix() %>%
as.data.frame() %>%
UpSetR::upset(nsets = 30,
nintersects = NA,
query.legend = "top",
set_size.show = TRUE,
sets = rev(set_order),
keep.order = TRUE)
```
```{r Figure_03_B_comparative_genomic_nt_export, message=FALSE}
ggsave("Figure_03_B_comparative_genomic_nt.jpeg",
plot = Figure_03_B_comparative_genomic_nt,
device = "jpeg",
path = glue::glue("{base_path}/results/Figures/jpeg/Figure_3"),
width = 180, units = "mm", dpi = 300)
ggsave("Figure_03_B_comparative_genomic_nt.tiff",
plot = Figure_03_B_comparative_genomic_nt,
device = "tiff",
path = glue::glue("{base_path}/results/Figures/tiff/Figure_3"),
width = 180, units = "mm", dpi = 300)
Figure_03_B_comparative_genomic_nt
dev.copy2pdf(file=glue::glue("{base_path}/results/Figures/eps_svg/Figure_3/Figure_03_B_comparative_genomic_nt.pdf"),out.type="cairo", width=18)
writeLines(glue::glue("pdftops -eps {base_path}/results/Figures/eps_svg/Figure_3/Figure_03_B_comparative_genomic_nt.pdf"))
rm(Figure_03_B_comparative_genomic_nt)
```
Note: The figure 3B has been reworked on adobe illustrator
To understand how to dichotomize the sets we have based ourselves on the following table:
| Name | Explanation of genomic changes or polymorphisms | Sets |
|--------------|-------------------------------------------------------------------------------------|-----------------------|
| singletons | Variation find in only one samples | n=1 |
| Shared intra | Variation shared by at least 2 samples and not between all samples of the same area | 1 < n < N_area |
| Common intra | Variation find in all samples of the same area | n=N_area |
| Shared inter | Variation shared by at least 2 samples and not between all samples between area | 1 < n < N_experiment |
| Common inter | Variation find in samples of all area | n=N_experiment |
| Global | Variation which are find in all samples in all area | n=N_area+N_experiment |
```{r}
N_Brest=5
N_LT=7
N_Thau=9
GC_table <- Table_XXXXXXX %>%
dplyr::mutate(Sets =
case_when(
Brest_2018 == 1 & Brest_2018 < N_Brest & Thau_2018 == 0 & LT_2018 == 0 ~ "Br-sing",
Brest_2018 == 0 & Thau_2018 == 0 & LT_2018 == 1 & LT_2018 < N_LT~ "Lt-sing",
Brest_2018 == 0 & Thau_2018 == 1 & Thau_2018 < N_Thau & LT_2018 == 0 ~ "Th-sing",
Brest_2018 > 1 & Brest_2018 < N_Brest & Thau_2018 == 0 & LT_2018 == 0 ~ "Br-shared_intra",
Brest_2018 == 0 & Thau_2018 > 1 & Thau_2018 < N_Thau & LT_2018 == 0 ~ "Th-shared_intra",
Brest_2018 >= 1 & Thau_2018 >= 1 & LT_2018 == 0 ~ "Br Th-shared_inter",
Brest_2018 >= 1 & Thau_2018 == 0 & LT_2018 >= 1 ~ "Br Lt-shared_inter",
Brest_2018 == 0 & Thau_2018 >= 1 & LT_2018 >= 1 ~ "Lt Th-shared_inter",
Brest_2018 == 0 & Thau_2018 == N_Thau & LT_2018 == 0 ~ "Th-common_intra",
Brest_2018 == N_Brest & Thau_2018 == 0 & LT_2018 == 0 ~ "Br-common_intra",
Brest_2018 == 0 & Thau_2018 == 0 & LT_2018 == N_LT ~ "Lt-common_intra",
Brest_2018 == 0 & Thau_2018 == 0 & LT_2018 > 1 & LT_2018 < N_LT~ "Lt-shared_intra",
Brest_2018 >= 1 & Thau_2018 >= 1 & LT_2018 >=1 ~ "Common_inter",
Brest_2018 == N_Brest & Thau_2018 == N_Thau & LT_2018 == N_LT ~ "Global"
)
) %>%
dplyr::mutate(DATASET = "GC")
Global_GC_table <- GC_table %>%
left_join(., Table_02_binary_matrix %>%
as.data.frame() %>%
rownames_to_column(var = "UniqueID")) %>%
as_tibble()
GC_table %>%
left_join(.,Table_02_binary_matrix %>%
as.data.frame() %>%
rownames_to_column("UniqueID")) #%>%
# data.table::fwrite(glue::glue("{base_path}/results/Tables/Table02_comparative_genomic_matrix.csv"),
# sep = "\t",
# quote=FALSE,
# row.names = FALSE,
# col.names = TRUE)
# Global overview
comparative_genomic %>%
group_by(ID_EXPERIMENT) %>%
summarise(N= n()) %>%
dplyr::mutate(ID_EXPERIMENT = stringr::str_replace_all(ID_EXPERIMENT,"NR_genome_","" )) %>%
tidyr::separate(ID_EXPERIMENT, c("LOCATION","YEARS","FAM","STEP","CONDITION","WGS_PCR"), remove = FALSE) %>%
tidyr::unite(LOCATION, YEARS, CONDITION, col="Samples",sep="_",remove = FALSE) %>%
dplyr::mutate(Samples = stringr::str_replace_all(Samples,"_2018_"," " )) %>%
group_by(LOCATION) %>%
summarise(MEAN_N = mean(N),
SD_N = sd(N))
GC_table %>%
dplyr::filter(stringr::str_detect(Sets, "Common")) %>%
dplyr::mutate(sum = Brest_2018 + LT_2018 +Thau_2018 ) %>%
group_by(sum) %>%
summarise(N=n())
GC_table %>%
dplyr::filter(stringr::str_detect(Sets, "sing"))
# Br - LT
GC_table %>%
dplyr::filter(stringr::str_detect(Sets, "Br Lt")) %>%
left_join(., Table_02_binary_matrix %>%
as.data.frame() %>%
rownames_to_column(var = "UniqueID")) %>%
tidyr::pivot_longer(cols = c(-UniqueID, -Brest_2018, -LT_2018, -Thau_2018, -Sets, -DATASET),
names_to = "ID_EXPERIMENT",
values_to = "Pres_abs") %>%
dplyr::filter(Pres_abs > 0) %>%
group_by(ID_EXPERIMENT) %>%
summarise(N=n())
GC_table %>%
dplyr::filter(stringr::str_detect(Sets, "Br Lt")) %>%
left_join(., Table_02_binary_matrix %>%
as.data.frame() %>%
rownames_to_column(var = "UniqueID")) %>%
tidyr::pivot_longer(cols = c(-UniqueID, -Brest_2018, -LT_2018, -Thau_2018, -Sets, -DATASET),
names_to = "ID_EXPERIMENT",
values_to = "Pres_abs") %>%
dplyr::filter(Pres_abs > 0) %>%
group_by(ID_EXPERIMENT) %>%
summarise(N=n()) %>%
tidyr::separate(ID_EXPERIMENT, c("NR","genome","LOCATION","YEARS","NSI","BROYAGE", "IND","PCR"), sep = "_", remove = TRUE) %>%
dplyr::select(-NR, -genome, -YEARS, -NSI, -BROYAGE, -PCR) %>%
unite(LOCATION, IND, col="Samples", sep = " ", remove=FALSE) %>%
ggplot(aes(x = Samples, y = N, fill = LOCATION)) +
geom_bar(stat = "identity") +
geom_text(aes(label=N), hjust=-1) +
theme_minimal()+
scale_fill_manual(values=list(Brest = Brest_color,
LT = LT_color)) +
coord_flip()
# Lt - Th
GC_table %>%
dplyr::filter(stringr::str_detect(Sets, "Lt Th")) %>%
left_join(., Table_02_binary_matrix %>%
as.data.frame() %>%
rownames_to_column(var = "UniqueID")) %>%
tidyr::pivot_longer(cols = c(-UniqueID, -Brest_2018, -LT_2018, -Thau_2018, -Sets, -DATASET),
names_to = "ID_EXPERIMENT",
values_to = "Pres_abs") %>%
dplyr::filter(Pres_abs > 0) %>%
group_by(ID_EXPERIMENT) %>%
summarise(N=n()) #%>%
# data.table::fwrite(glue::glue("{base_path}/results/Tables/Fig03A_analysis_Lt_Th.csv"),
# sep = "\t",
# quote=FALSE,
# row.names = FALSE,
# col.names = TRUE)
GC_table %>%
dplyr::filter(stringr::str_detect(Sets, "Lt Th")) %>%
left_join(., Table_02_binary_matrix %>%
as.data.frame() %>%
rownames_to_column(var = "UniqueID")) %>%
tidyr::pivot_longer(cols = c(-UniqueID, -Brest_2018, -LT_2018, -Thau_2018, -Sets, -DATASET),
names_to = "ID_EXPERIMENT",
values_to = "Pres_abs") %>%
dplyr::filter(Pres_abs > 0) %>%
group_by(ID_EXPERIMENT) %>%
summarise(N=n()) %>%
tidyr::separate(ID_EXPERIMENT, c("NR","genome","LOCATION","YEARS","NSI","BROYAGE", "IND","PCR"), sep = "_", remove = TRUE) %>%
dplyr::select(-NR, -genome, -YEARS, -NSI, -BROYAGE, -PCR) %>%
unite(LOCATION, IND, col="Samples", sep = " ", remove=FALSE) %>%
ggplot(aes(x = Samples, y = N, fill = LOCATION)) +
geom_bar(stat = "identity") +
geom_text(aes(label=N), hjust=-1) +
theme_minimal()+
scale_fill_manual(values=list(Thau = Thau_color,
LT = LT_color)) +
coord_flip()
```
## Location of the positions within the OsHV-1 genome
```{r}
Consensus_size = 186356
var_CG_across_NR_consensus <- Table_02_binary_matrix %>%
as.data.frame() %>%
rownames_to_column("UniqueID") %>%
pivot_longer(-UniqueID) %>%
tidyr::separate(UniqueID, c("POS","MODIFICATION"), sep = "_", remove = FALSE) %>%
dplyr::mutate(POS = as.numeric(POS)) %>%
dplyr::filter(value != 0) %>%
tidyr::separate(name, c("NR","genome","LOCATION","YEARS","NSI","BROYAGE", "IND","PCR"), sep = "_", remove = TRUE) %>%
dplyr::select(-NR, -genome, -YEARS, -NSI, -BROYAGE, -PCR) %>%
unite(LOCATION, IND, col="Samples", remove=FALSE) %>%
left_join(.,GC_table) %>%
dplyr::mutate(Specificite =
case_when(
stringr::str_detect(Sets,"_full") ~ "Specific",
stringr::str_detect(Sets," ") ~ "Shared",
stringr::str_detect(Sets,"Common") ~ "Common",
TRUE ~ "normal"
),
Region = case_when(
POS < 164287 ~ "UL",
POS > 164288 & POS < 171692 ~ "TRL",
POS > 171693 & POS < 173202 ~ "X",
POS > 173203 & POS < 182984 ~ "IRL",
TRUE ~ "US")) %>%
# dplyr::filter(Specificite != "Common") %>%
left_join(., plot_order_samples)
var_CG_across_NR_consensus %>%
ggplot(aes(x = POS, y = forcats::fct_reorder2(Samples, ORDER_samples, ORDER), fill = LOCATION )) +
geom_point(aes(color = LOCATION, shape= Specificite))+
xlim(0, Consensus_size) +
theme_minimal()+
geom_vline(xintercept = Consensus_size, linetype = "dotdash", size = 0.5, color = "#5C5C5C") +
geom_vline(xintercept = 0, size = 0.25, color = "black") +
# geom_hline(yintercept = 0, size = 1, color = "black") +
# facet_grid(Samples~.) +
theme(axis.line = element_line(size = 0.5, colour = "black")) +
scale_color_manual(values=list(Brest = Brest_color,
LT = LT_color,
Thau = Thau_color)) +
scale_shape_manual(values=c(1,4, 3))
# Table_02_binary_matrix %>%
# as.data.frame() %>%
# rownames_to_column("UniqueID") %>%
# pivot_longer(-UniqueID) %>%
# tidyr::separate(UniqueID, c("POS","MODIFICATION"), sep = "_", remove = FALSE) %>%
# dplyr::mutate(POS = as.numeric(POS)) %>%
# dplyr::filter(value != 0) %>%
# tidyr::separate(name, c("NR","genome","LOCATION","YEARS","NSI","BROYAGE", "IND","PCR"), sep = "_", remove = TRUE) %>%
# dplyr::select(-NR, -genome, -YEARS, -NSI, -BROYAGE, -PCR) %>%
# unite(LOCATION, IND, col="Samples", remove=FALSE) %>%
# ggplot(aes(x = POS, fill = LOCATION )) +
# geom_histogram(bins = 1000) +
# xlim(0, Consensus_size) +
# theme_minimal()+
# geom_vline(xintercept = Consensus_size, linetype = "dotdash", size = 0.5, color = "#5C5C5C") +
# geom_vline(xintercept = 0, size = 0.25, color = "black") +
# # geom_hline(yintercept = 0, size = 1, color = "black") +
# # facet_grid(LOCATION~.) +
# theme(axis.line = element_line(size = 0.5, colour = "black")) +
# scale_fill_manual(values=list(Brest = Brest_color,
# LT = LT_color,
# Thau = Thau_color))
```
In future research I encourage the author to use the notions of [jaccard index](https://en.wikipedia.org/wiki/Jaccard_index) for the analysis of inter and intra sensembles variations.
```{r}
sessionInfo()
```