knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# RESULTADOS PLASMIDFINDER CON ENSAMBLADOS ANTES DE CERRAR GENOMA/PLASMIDS
library(readxl)
library(tidyverse)
## āā Attaching packages āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā tidyverse 1.3.1 āā
## ā ggplot2 3.3.6 ā purrr 0.3.4
## ā tibble 3.1.7 ā dplyr 1.0.9
## ā tidyr 1.2.0 ā stringr 1.4.0
## ā readr 2.1.2 ā forcats 0.5.1
## āā Conflicts āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā tidyverse_conflicts() āā
## ā dplyr::filter() masks stats::filter()
## ā dplyr::lag() masks stats::lag()
library(ggplot2)
library(ggpubr)
barcodes <- read.delim2("~/MEGA/Tesis/In_vivolution/Bioinfo/strains_metadata.tsv")
plasmidfinder <-
read_excel("~/MEGA/Tesis/In_vivolution/Bioinfo/tabla_all_plasmidfinder.xlsx")
data <-
read_excel("~/MEGA/Tesis/In_vivolution/Bioinfo/invivolution.xlsx",
skip =)
plasmidfinder <- plasmidfinder %>%
select(strain, Plasmid, Contig) %>%
#
mutate(Plasmid = ifelse(
Plasmid == "IncFIB(K)",
yes = "IncFIB(K)/IncFII(K)",
no = ifelse(Plasmid == "IncFII(K)", yes = "IncFIB(K)/IncFII(K)", no = Plasmid)
)) %>%
unique()
atbresistance <-
read_excel("~/MEGA/Tesis/In_vivolution/Bioinfo/tabla_all_resfinder.xlsx")
atbresistance$`#FILE` = str_remove(string = atbresistance$`#FILE`, "../spades/")
atbresistance$`#FILE` = str_remove(string = atbresistance$`#FILE`, "_careful/scaffolds.fasta")
atbresistance$`#FILE` = str_remove(string = atbresistance$`#FILE`, "/scaffolds.fasta")
atbresistance <- atbresistance %>%
mutate(strain = `#FILE`) %>%
select(-`#FILE`) %>%
mutate(Contig = SEQUENCE) %>%
select(-SEQUENCE)
# En esta tabla solo salen los genes de resistencia de los plƔsmidos IncF e IncQ
TOTAL <-
left_join(plasmidfinder, atbresistance, by = c("strain", "Contig"))
TOTAL <- left_join(TOTAL, data, by = c("strain"))
TOTAL <-
TOTAL %>% mutate(strain_type = ifelse(grepl("P", strain), yes = "Portadores", no = "InfecciĆ³n"))
TOTAL %>%
filter(!is.na(GENE)) %>%
# filter(strain_type == "InfecciĆ³n") %>%
ggplot(aes(
x = Plasmid,
y = GENE,
color = species,
fill = species
)) +
geom_point() +
facet_wrap( ~ strain) +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
atbresistance <- left_join(atbresistance, data, by = c("strain"))
atbresistance %>% filter(GENE != "GENE") %>%
ggplot(aes(
x = strain,
y = GENE,
color = species,
fill = species
)) +
geom_tile() +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# HEATMAPS
# Heatmap genes de resistencia
atbresistance <- atbresistance %>%
mutate(aislado = ifelse(grepl("P", strain), yes= "Portadores", no = "Infeccion")) %>%
filter(aislado == "Infeccion")
adj <-
atbresistance %>% select(strain, species, tratamiento) %>% distinct()
adj <- column_to_rownames(adj, var = 'strain')
zz <-
atbresistance %>% select(GENE, strain) %>% filter(GENE != 'GENE')
zz$value <- 1
zz_matrix <-
zz %>% distinct() %>% pivot_wider(names_from = GENE,
values_from = value,
values_fill = 0) %>% column_to_rownames(var = "strain")
f1 <- pheatmap::pheatmap(zz_matrix,
clustering_method = "ward.D2",
annotation_row = adj)
# Heatmap x plasmido
adj <- plasmidfinder %>% select(strain) %>% distinct()
adj <- column_to_rownames(adj, var = 'strain')
zz <- plasmidfinder %>% select(Plasmid, strain)
zz$value <- 1
zz_matrix <-
zz %>% distinct() %>% pivot_wider(names_from = Plasmid,
values_from = value,
values_fill = 0) %>% column_to_rownames(var = "strain")
pheatmap::pheatmap(zz_matrix, clustering_method = "ward.D2")
# Heatmap genes x plasmido
adj <-
TOTAL %>% select(strain, species, tratamiento) %>% distinct()
adj <- column_to_rownames(adj, var = 'strain')
zz <- TOTAL %>% select(GENE, Plasmid) %>% filter(GENE != 'GENE')
zz$value <- 1
zz_matrix <-
zz %>% distinct() %>% pivot_wider(names_from = GENE,
values_from = value,
values_fill = 0) %>% column_to_rownames(var = "Plasmid")
pheatmap::pheatmap(zz_matrix)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# PLOTS
# Cepas por fecha
position <-
c(
"23/11/2020",
"6/12/2020",
"16/12/2020",
"21/1/2021",
"6/4/2021",
"8/4/2021",
"13/4/2021",
"14/4/2021"
)
TOTAL %>%
ggplot(aes(
x = GENE,
y = Plasmid,
color = strain,
fill = strain
)) +
geom_point(alpha = 0.4, size = 7) +
# scale_x_discrete(limits=position) +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
# Genes de resistencia por cepas
position1 <-
c(
"C1A1",
"C1B1",
"C2C1",
"C3A1",
"C3B1",
"C4C1",
"C5.1C1",
"C5.2C1",
"C5.2C2",
"P1C1",
"P2C2",
"P3C1",
"P4C1",
"P5C1.I",
"P6C1",
"P7C1",
"P8C1",
"P9C1",
"P10C1",
"C6A1",
"C6B1"
)
atbresistance %>%
filter(GENE != 'GENE') %>%
filter(species == "Ec") %>%
ggplot(aes(
x = strain,
y = GENE,
color = species,
fill = species
)) +
geom_point(alpha = 0.6, size = 7) +
# facet_wrap(~species) +
scale_x_discrete(limits = position1) +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
TOTAL %>%
ggplot(aes(
x = strain,
y = Plasmid,
color = species,
fill = species
)) +
geom_tile() +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
TOTAL %>%
filter(!is.na(GENE)) %>%
ggplot(aes(
x = strain,
y = GENE,
color = Plasmid,
fill = Plasmid
)) +
geom_tile() +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
#Genes de resistencia y plƔsmidos por contig
a1 <- TOTAL %>% filter(!is.na(Plasmid)) %>% filter(strain =="C1B1") %>%
ggplot(aes(x = Contig, y = Plasmid, color = strain, fill = strain)) +
geom_tile() +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
# scale_fill_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF")) +
# scale_color_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF"))
a2 <- atbresistance %>% filter(strain =="C1B1") %>%
ggplot(aes(x = Contig, y = PRODUCT, color = strain, fill = strain)) +
geom_tile() +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
# scale_fill_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF")) +
# scale_color_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF"))
ggarrange(a1, a2)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# ANALISIS SNIPPY_CONTIGS GENOMAS CERRADOS
# Leemos la tabla del snippy para las cepas con el plƔsmido
Invivolution.snps <- read.delim2("~/MEGA/Tesis/In_vivolution/Ensamblados/Snippy/compilation_tabs/tabla_all_SNPS_variantesInvivolution.txt")
Invivolution.snps = Invivolution.snps %>%
separate(filename.CHROM,
into = c("strain", "contig"),
sep = ",") %>%
separate(strain, into = c("strain", "cosa"), sep = "_") %>%
select(-cosa) %>%
filter(strain != "filename")
Invivolution.snps = Invivolution.snps %>% full_join(data)
Invivolution.snps.count = Invivolution.snps %>%
group_by(strain, TYPE, species) %>%
summarise(NumberSNPs = n())
# Number snps per strain
Invivolution.snps.count %>%
filter(!is.na(TYPE)) %>%
ggplot() +
geom_col(aes(strain, NumberSNPs, color = species, fill = species)) +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
# TYPE snps per strain
Invivolution.snps.count %>%
filter(!is.na(TYPE)) %>%
ggplot() +
geom_col(aes(TYPE, NumberSNPs)) +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# ANALISIS SNIPPY CON LOS READS SIN LIMPIAR
# Colis contra C3A1 como referencia Y Klebsiellas contra C1C1 como referencia
Invivolution.snps <- read.delim2("~/MEGA/Tesis/In_vivolution/Bioinfo/Snippy/tabla_all_SNPS_reads_Invivolution.txt")
Invivolution.snps = Invivolution.snps %>%
separate(filename.CHROM,
into = c("fastq", "contig"),
sep = ",") %>%
separate(fastq, into = c("fastq", "cosa"), sep = "_snps") %>%
select(-cosa) %>%
filter(fastq != "filename")
Invivolution.snps = Invivolution.snps %>% full_join(barcodes)
# Limpiamos los SNPs de las referencias contra ellas mismas
SNPs.referencias <- Invivolution.snps %>%
filter(sample == "C3A1" | sample == "C1C1")
Invivolution.snps <- Invivolution.snps %>%
anti_join(SNPs.referencias, by = c("POS", "TYPE", "REF", "ALT")) %>% view()
Invivolution.snps %>%
group_by(sample,LOCUS_TAG) %>%
summarise(N=n())
## # A tibble: 85 Ć 3
## # Groups: sample [21]
## sample LOCUS_TAG N
## <chr> <chr> <int>
## 1 C1A1 <NA> 1
## 2 C1B1 <NA> 1
## 3 C2C1 <NA> 1
## 4 C3B1 LGONEGAH_03709 1
## 5 C4C1 LGONEGAH_03644 3
## 6 C4C1 LGONEGAH_03677 1
## 7 C4C1 LGONEGAH_03709 1
## 8 C4C1 LGONEGAH_03826 1
## 9 C5.1C1 <NA> 1
## 10 C5.2C1 <NA> 1
## # ā¦ with 75 more rows
Invivolution.snps.count = Invivolution.snps %>%
group_by(sample, TYPE, organism) %>%
summarise(NumberSNPs = n())
# Number snps per strain
Invivolution.snps.count %>%
filter(!is.na(TYPE)) %>%
ggplot() +
geom_col(aes(sample, NumberSNPs, color = organism, fill = organism)) +
facet_wrap(~organism, scales="free_x","free_y")+
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
# TYPE snps per strain
Invivolution.snps.count %>% left_join(barcodes) %>%
filter(!is.na(TYPE)) %>%
ggplot() +
geom_col(aes(TYPE, NumberSNPs, color = pulsotype, fill = pulsotype)) +
facet_wrap(~sample) +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
# POS snps per strain
# Invivolution.snps %>% group_by(sample,POS) %>% summarise(N=n()) %>%
# ggplot() +
# geom_col(aes(POS, N)) +
# facet_wrap(~sample) +
# theme_bw() +
# theme(
# panel.background = element_blank(),
# panel.grid = element_blank(),
# aspect.ratio = 1,
# #legend.position = "none",
# strip.background = element_blank(),
# axis.text.x = element_text(angle = 45, hjust = 1)
# )
# Invivolution.snps %>%
# ggplot(aes(x = sample, y = GENE, color = sample, fill = sample)) +
# geom_tile() +
# facet_wrap(~organism,scales="free_x") +
# theme_bw() +
# theme(
# panel.background = element_blank(),
# panel.grid = element_blank(),
# aspect.ratio = 1,
# #legend.position = "none",
# strip.background = element_blank(),
# axis.text.x = element_text(angle = 45, hjust = 1)
# )
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# SNIPPY CON LOS READS LIMPIOS CALIDAD 20
# Colis contra C3A1 como referencia Y Klebsiellas contra C1C1 como referencia
Ec.snps <- read.delim2("~/MEGA/Tesis/In_vivolution/Bioinfo/Snippy/tabla_all_SNPS_Ec_20.txt")
Kp.snps <- read.delim2("~/MEGA/Tesis/In_vivolution/Bioinfo/Snippy/tabla_all_SNPS_Kp_20.txt")
Invivolution.snps.20 = Ec.snps %>% rbind(Kp.snps) %>%
separate(filename.CHROM,
into = c("fastq", "contig"),
sep = ",") %>%
separate(fastq, into = c("fastq", "cosa"), sep = "_snps") %>%
select(-cosa) %>%
filter(fastq != "filename")
Invivolution.snps.20 = Invivolution.snps.20 %>% full_join(barcodes)
# Limpiamos los SNPs de las referencias contra ellas mismas
SNPs.referencias.20 <- Invivolution.snps.20 %>%
filter(sample == "C3A1" | sample == "C1C1")
Invivolution.snps.20 <- Invivolution.snps.20 %>%
anti_join(SNPs.referencias.20, by = c("POS", "TYPE", "REF", "ALT"))
Invivolution.snps.20 %>%
group_by(sample,LOCUS_TAG) %>%
summarise(N=n())
## # A tibble: 141 Ć 3
## # Groups: sample [21]
## sample LOCUS_TAG N
## <chr> <chr> <int>
## 1 C1A1 "" 9
## 2 C1A1 "DNEPDMAL_04732" 1
## 3 C1A1 "DNEPDMAL_04743" 1
## 4 C1B1 "" 8
## 5 C1B1 "DNEPDMAL_04732" 1
## 6 C1B1 "DNEPDMAL_04743" 1
## 7 C2C1 "" 10
## 8 C2C1 "DNEPDMAL_00059" 1
## 9 C2C1 "DNEPDMAL_00565" 1
## 10 C2C1 "DNEPDMAL_01973" 1
## # ā¦ with 131 more rows
Invivolution.snps.20 <- Invivolution.snps.20 %>% mutate( species = ifelse(grepl("coli", organism), yes = "Ec",
no = ifelse(grepl("pneumoniae", organism), yes = "Kp",
no = NA)))
Invivolution.snps.count.20 = Invivolution.snps.20 %>%
group_by(sample, TYPE, organism) %>%
summarise(NumberSNPs = n())
# Number snps per strain
Invivolution.snps.count.20 %>%
filter(!is.na(TYPE)) %>%
ggplot() +
geom_col(aes(sample, NumberSNPs, color = organism, fill = organism)) +
facet_wrap(~organism, scales="free_x","free_y")+
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
# TYPE snps per strain
Invivolution.snps.count.20 %>% left_join(barcodes) %>%
filter(!is.na(TYPE)) %>%
ggplot() +
geom_col(aes(TYPE, NumberSNPs, color = sample, fill = sample)) +
facet_wrap(~organism) +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
# POS snps per strain
Invivolution.snps.20 %>%
ggplot(aes(x = sample, y = GENE, color = organism, fill = organism)) +
geom_tile() +
facet_wrap(~species,scales="free_x") +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
# Tabla SNPs entre C3A1, C3B1, C4 y P1
table.1 = Invivolution.snps.20 %>%
filter(species == "Ec") %>%
filter(sample == "C3B1" | sample == "C4C1" | sample == "P1C1") %>% view()
write.table(table.1, file="~/MEGA/Tesis/In_vivolution/Bioinfo/SNPs_C3_C4_P1",
sep="\t", quote = F, row.names = T, col.names = T)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# RESULTADOS CON ENSAMBLADOS CERRADOS POR NANOPORE
## GENES DE RESISTENCIA
data <- read_excel("~/MEGA/Tesis/In_vivolution/Bioinfo/invivolution.xlsx", skip =)
barcodes <- read.delim2("~/MEGA/Tesis/In_vivolution/Bioinfo/strains_metadata.tsv")
# plasmidfinder.nanopore <- plasmidfinder.nanopore %>%
# select(strain, Plasmid, Contig) %>%
# mutate(Plasmid = ifelse(Plasmid == "IncFIB(K)", yes = "IncFIB(K)/IncFII(K)", no = ifelse(Plasmid == "IncFII(K)", yes = "IncFIB(K)/IncFII(K)", no = Plasmid))) %>%
# unique()
# AquĆ solo estĆ”n cerrados los plĆ”smidos
atbresistance.nanopore <- read.delim2("~/MEGA/Tesis/In_vivolution/Bioinfo/tabla_abricate_nanopore.txt")
atbresistance.nanopore$filename..FILE = str_remove(string = atbresistance.nanopore$filename..FILE, "_abricate_plsdb.tsv,/mnt/lustre/scratch/home/csanchez/Invivolution/pilon")
atbresistance.nanopore$filename..FILE = str_remove(string = atbresistance.nanopore$filename..FILE, "_abricate_resfinder.tsv,/mnt/lustre/scratch/home/csanchez/Invivolution/pilon/")
atbresistance.nanopore$filename..FILE = str_remove(string = atbresistance.nanopore$filename..FILE, "_pilon_round4.fasta")
atbresistance.nanopore <- atbresistance.nanopore %>%
filter(SEQUENCE != "SEQUENCE") %>%
separate(filename..FILE, into = c("nanopore_barcode", "cosa"), sep = "barcode") %>%
mutate(nanopore_barcode = cosa) %>%
select(-cosa)
atbresistance.nanopore$nanopore_barcode = str_remove(string = atbresistance.nanopore$nanopore_barcode, "/")
atbresistance.nanopore$SEQUENCE = str_remove(string = atbresistance.nanopore$SEQUENCE, "_pilon_pilon_pilon_pilon")
atbresistance.nanopore$nanopore_barcode = as.numeric(atbresistance.nanopore$nanopore_barcode)
atbresistance.nanopore <- atbresistance.nanopore %>%
mutate(nanopore_barcode = nanopore_barcode) %>% left_join(barcodes, by = "nanopore_barcode")
atbresistance.nanopore <- atbresistance.nanopore %>% filter(DATABASE != "plsdb") %>% mutate(Contig = SEQUENCE)
#PLASMIDOS
plsdb.nanopore <- atbresistance.nanopore %>%
filter(DATABASE == "plsdb")
plasmidfinder.nanopore <- read.delim2("~/MEGA/Tesis/In_vivolution/Bioinfo/tabla_plasmidfinder_nanopore.txt")
plasmidfinder.nanopore$filename.Database = str_remove(string = plasmidfinder.nanopore$filename.Database, "_results_tab.tsv,enterobacteriales")
plasmidfinder.nanopore <- plasmidfinder.nanopore %>%
filter(Plasmid != "Plasmid") %>%
separate(filename.Database, into = c("cosa", "nanopore_barcode"), sep = ("barcode")) %>%
select(-cosa, -Note)
plasmidfinder.nanopore$Contig = str_remove(string = plasmidfinder.nanopore$Contig, "_pilon_pilon_pilon_pilon")
plasmidfinder.nanopore$nanopore_barcode = as.numeric(plasmidfinder.nanopore$nanopore_barcode)
plasmidfinder.nanopore <- plasmidfinder.nanopore %>%
right_join(barcodes, by = "nanopore_barcode")
TOTAL.nanopore <- plasmidfinder.nanopore %>% left_join (atbresistance.nanopore, by=c("sample", "Contig")) %>% left_join(barcodes)
atbresistance.nanopore %>%
ggplot(aes(x = sample, y = GENE, color = organism, fill = organism)) +
geom_tile() +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
# Heatmap genes de resistencia
adj <- atbresistance.nanopore %>% select(sample, organism) %>% distinct()
adj <- column_to_rownames(adj, var = 'sample')
zz <- atbresistance.nanopore %>% select(GENE, sample)
zz$value <- 1
zz_matrix <- zz %>% distinct() %>% pivot_wider(names_from = GENE,
values_from = value,
values_fill = 0) %>% column_to_rownames(var = "sample")
f2 <- pheatmap::pheatmap(zz_matrix,
clustering_method = "ward.D2",
annotation_row = adj)
# Heatmap x plasmido
adj <- plasmidfinder.nanopore %>% select(sample) %>% distinct()
adj <- column_to_rownames(adj, var = 'sample')
zz <- plasmidfinder.nanopore %>% select(Plasmid, sample) %>% filter(Plasmid != "NA")
zz$value <- 1
zz_matrix <- zz %>% distinct() %>% pivot_wider(names_from = Plasmid,
values_from = value,
values_fill = 0) %>% column_to_rownames(var = "sample")
pheatmap::pheatmap(zz_matrix, clustering_method = "ward.D2")
# Heatmap genes x plasmido
# adj <- plasmidfinder.nanopore %>% select(sample, organism) %>% distinct()
# adj <- column_to_rownames(adj, var = 'sample')
#
# zz <- plasmidfinder.nanopore %>% select(GENE, Plasmid)
#
# zz$value <- 1
#
# zz_matrix <-
# zz %>% distinct() %>% pivot_wider(names_from = GENE,
# values_from = value,
# values_fill = 0) %>% column_to_rownames(var = "Plasmid")
#
# pheatmap::pheatmap(zz_matrix)
TOTAL.nanopore %>%
filter(!is.na(GENE)) %>%
ggplot(aes(x = Plasmid, y = GENE, color = organism, fill = organism)) +
geom_point() +
# facet_wrap(~Plasmid)+
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
# Geom_TILE plasmidos por cepas
TOTAL.nanopore %>% filter(!is.na(Plasmid)) %>%
ggplot(aes(x = sample, y = Plasmid, color = organism, fill = organism)) +
geom_tile() +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF")) +
scale_color_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF"))
TOTAL.nanopore %>% filter(!is.na(Plasmid)) %>%
ggplot(aes(x = sample, y = Plasmid, color = organism, fill = organism)) +
geom_point(size = 5, stroke = 4) +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF")) +
scale_color_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF"))
# PLOT Gen resistencia y plasmid por Contig
f1 <- TOTAL.nanopore %>% filter(!is.na(Plasmid)) %>%
ggplot(aes(x = Contig, y = Plasmid, color = cepa, fill = cepa)) +
geom_tile() +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
# scale_fill_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF")) +
# scale_color_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF"))
f2 <- TOTAL.nanopore %>% filter(!is.na(Plasmid)) %>% filter(!is.na(PRODUCT)) %>%
ggplot(aes(x = Contig, y = PRODUCT, color = cepa, fill = cepa)) +
geom_tile() +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
# scale_fill_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF")) +
# scale_color_manual(values=c("#B9C0DA", "#998DA0", "#C4DACF"))
ggarrange(f1, f2)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# ANALISIS SNIPPY CON LOS CONTIGS DE LOS GENOMAS CERRADOS
# Leemos la tabla del snippy para las cepas con el plƔsmido
Invivolution.snps <- read.delim2("~/MEGA/Tesis/In_vivolution/Ensamblados/Snippy/compilation_tabs/tabla_all_SNPS_variantesInvivolution.txt")
Invivolution.snps = Invivolution.snps %>%
separate(filename.CHROM,
into = c("strain", "contig"),
sep = ",") %>%
separate(strain, into = c("strain", "cosa"), sep = "_") %>%
select(-cosa) %>%
filter(strain != "filename")
Invivolution.snps = Invivolution.snps %>% full_join(data)
Invivolution.snps <- Invivolution.snps %>% separate(EFFECT, c("TYPE_EFFECT", "COSA"), sep ="_")
Invivolution.snps.count = Invivolution.snps %>%
group_by(strain, TYPE_EFFECT, species) %>%
summarise(NumberSNPs = n())
# Number snps per strain
Invivolution.snps.count %>%
filter(!is.na(TYPE_EFFECT)) %>%
ggplot() +
geom_col(aes(strain, NumberSNPs, color = species, fill = species)) +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
# TYPE snps per strain
Invivolution.snps.count %>%
filter(!is.na(TYPE_EFFECT)) %>%
ggplot() +
geom_col(aes(TYPE_EFFECT, NumberSNPs, color = strain, fill = strain)) +
facet_wrap(~strain) +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
# Genes de resistencia presentes en el chr
atbresistance <- rename(atbresistance, sample = strain)
genes <- atbresistance %>%
select(GENE, sample, tratamiento)
genes.plasmid <- atbresistance.nanopore %>%
select(GENE, sample, organism)
genes.chr <- anti_join(genes, genes.plasmid) %>% filter(GENE != "GENE")
genes.chr %>%
ggplot(aes(x = sample, y = GENE,color = sample, fill = sample)) +
geom_tile() +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# BLAST C2C1 vs C4C1
# Hacemos un BLAST para ver si el INC-L de C2C1 es el pOXA48 pero sin el gen de OXA-48
# Length = length contig
BLAST.C2vsC4 = read.delim(file = "~/MEGA/Tesis/In_vivolution/Bioinfo/Blast/C2C1vsC4C1_nanopore.blast", header = TRUE, sep = "", dec = ",")
BLAST.C5.2vsC4 = read.delim(file = "~/MEGA/Tesis/In_vivolution/Bioinfo/Blast/C5.2vsC4.blast", header = TRUE, sep = "", dec = ",")
# SNIPPY del pOXA-48 de las cepas C2C1 y C5.2C2 contra el pOXA-48 de C4C1
pOXA48.snps <-read.delim2("~/MEGA/Tesis/In_vivolution/Snippy/tabla_all_SNPS_pOXA48.txt")
pOXA48.snps = pOXA48.snps %>%
separate(filename.CHROM,
into = c("strain", "contig"),
sep = ",") %>%
separate(strain, into = c("strain", "cosa"), sep = "_") %>%
select(-cosa) %>%
filter(strain != "filename")
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# Antibiogramas
Antibiogramas <- read.delim2("~/MEGA/Tesis/In_vivolution/Bioinfo/Antibiogramas.txt")
Antibiogramas <- Antibiogramas %>% pivot_longer(names_to = "Antibiotic", values_to = "value", cols = c(FOS50:COL10)) %>% mutate(value=as.numeric(value))
Ec <- Antibiogramas %>% filter(Antibiotic == "AMC30" | Antibiotic == "CHL30" | Antibiotic == "CTX30") %>% filter(Strain == "C3A1"| Strain == "C3A1 BLEE" | Strain == "C3B1"| Strain == "C3B1BLEE" | Strain == "C4C1" | Strain == "C4 SIN BLEE" | Strain == "P1" | Strain == "P1 + OXA48" | Strain == "P8" | Strain == " P8 BLEE clon 1") %>% filter(Species =="E. coli") %>%
ggplot()+
geom_col(aes(x = Strain, y = value, color = Species, fill = Species)) +
facet_wrap(~Antibiotic, scales="free_x","free_y") +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
Ec + scale_fill_manual(values=c("#d1cfe2ff")) +
scale_color_manual(values=c("#d1cfe2ff"))
Kp <- Antibiogramas %>% filter(Species =="K. pneumoniae") %>% filter(Antibiotic == "AMC30" | Antibiotic == "CHL30" | Antibiotic == "CTX30") %>% filter(Strain == "C1B1" | Strain == " C1B1 pOXA-48 clon 1" | Strain == "C5.1C1" | Strain == " C5.1C1 pOXA48") %>%
ggplot()+
geom_col(aes(x = Strain, y = value, color = Species, fill = Species)) +
facet_wrap(~Antibiotic, scales="free_x","free_y") +
theme_bw() +
theme(
panel.background = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1))
Kp + scale_fill_manual(values=c("#7ec4cfff")) +
scale_color_manual(values=c("#7ec4cfff"))
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# CHORD DIAGRAM - TODAS LAS CEPAS
##Vamos a hacer un chord diagram de los datos de plasmidfinder con los contigs de illumina
# install.packages("circlize")
library(circlize)
library(viridisLite)
# Create an adjacency matrix:
# data <- plasmidfinder %>%
# pivot_wider(names_from=strain, values_from= strain) %>% select(-Contig)
#
# data <- data %>%
# mutate(IncFI_IncFII = data$`IncFIB(K)/IncFII(K)`) %>% select(-`IncFIB(K)/IncFII(K)`) %>%
# mutate(IncL = ifelse(is.na(IncL), yes= 0, no= 1 )) %>%
# mutate(IncFI_IncFII = ifelse(is.na(IncFI_IncFII), yes= 0, no= 1 )) %>%
# mutate(`Col(MG828)` = ifelse(is.na(`Col(MG828)`), yes= 0, no= 1 )) %>%
# mutate(IncQ1 = ifelse(is.na(IncQ1), yes= 0, no= 1 )) %>%
# mutate(`IncFIB(AP001918)` = ifelse(is.na(`IncFIB(AP001918)`), yes= 0, no= 1 )) %>%
# mutate(Col156 = ifelse(is.na(Col156), yes= 0, no= 1 )) %>%
# mutate(pXuzhou21 = ifelse(is.na(pXuzhou21), yes= 0, no= 1 )) %>%
# mutate(IncFII = ifelse(is.na(IncFII), yes= 0, no= 1 ))
# Guardar para cambiar en excel los nombres
# write.table(data, file="~/MEGA/Tesis/In_vivolution/Bioinfo/data.txt",
# sep="\t", quote = F, row.names = T, col.names = T)
data <-read.delim2("~/MEGA/Tesis/In_vivolution/Bioinfo/data.txt")
data1= as.matrix(data %>% select(-strain))
rownames(data1) = data$strain
mycolor <- viridis(10, alpha = 1, begin = 0, end = 1, option = "D")
mycolor <- mycolor[sample(1:10)]
# Make the circular plot
chordDiagram(data1, annotationTrack = c("name", "grid"), transparency = 0.5)
#saving the plot (high definition)
dev.copy(jpeg,'plot.png', width=8, height=8, units="in", res=500)
## jpeg
## 3
dev.off()
## png
## 2
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# CURVAS DE CRECIMIENTO
library(tidyverse)
library(readxl)
library(lattice)
library(deSolve)
library(growthrates)
library(caTools)
library(gridExtra)
library(flux)
library(ggplot2)
library(ggrepel)
library(ggsci)
library(dplyr)
library(tidyr)
#La carpeta dĆ³nde estĆ”n las curvas
path_to_txt= "~/MEGA/Tesis/In_vivolution/curve_data//"
#donde guardar las vmax
path_to_Growthrates="~/MEGA/Tesis/In_vivolution/Growth_curves/"
#La carpeta dĆ³nde guardaremos el output
path_to_output="~/MEGA/Tesis/In_vivolution/Growth_curves/"
#La piedra de rosetta!
Rosetta <- read.delim("~/MEGA/Tesis/In_vivolution/Rosetta")
#Leer todos los archivos de la carpeta "path_to_txt"
file.list <- list.files(path =path_to_txt, full.names = F)
df.list <- lapply(paste0(path_to_txt, file.list),
function(x)read.delim(x, header=T, nrows=133, dec=","))
#No me acuerdo que habia que poner en (0, (133*10)-10, 10)
attr(df.list, "names") <- file.list
df <- bind_rows(df.list, .id = "id") %>%
mutate(Time=rep(seq(0, (133*10)-10, 10),length(file.list)))
df_test<-df %>%
#select -> para seleccionar columnas, en este caso - Optical density es para deseleccionar la TĀŖ
select( -`Optical.Density.600`) %>%
#mutate -> para cambiar la columna plate por id
mutate(Plate=id) %>%
select(-id) %>%
#gather(data, key, value) -> data is the dataframe you are working with; key is the name of the key column to create; value is the name of the value column to create.
gather(-Time,-Plate, key = Well, value = OD )
#Ahora tenemos una tabla (df_test) con la OD en funciĆ³n de la placa, los pocillos y el tiempo. La guardamos con el cĆ³digo de abajo
# write.table(df_test, file=paste0(path_to_output, "Curvas_en_formato_R"))
#Ahora juntamos la tabla df_test con la tabla Rosetta con la funcion left_join
curve_data <- df_test %>%
left_join(Rosetta %>% mutate(Plate=as.character(Plate),
Well=as.character(Well)))
#Filtramos en la tabla curve_data los blancos para quitarlos y ponemos la OD como una variable numƩrica
curve_data <- curve_data %>%
filter(Replicate != "blanco") %>%
mutate(OD=as.numeric(OD))
#
if (file.exists(paste0(path_to_Growthrates, "GrowthRates_results"))){
Growthrate_results<-read.table((paste0(path_to_Growthrates, "GrowthRates_results")), header=T)
} else{
#all_easylinear ->Determine maximum growth rates from log-linear part of the growth curve for a series of experiments
manysplits<- all_easylinear(OD~Time | Strain + Morfotipo + Replicate + Plate,
data=curve_data)
# write.table(results(manysplits), paste0(path_to_Growthrates, "GrowthRates_results"))
Growthrate_results<-results(manysplits)
}
data_analysed<- curve_data %>% group_by(Strain, Morfotipo, Replicate, Plate) %>%
group_modify(~ as.data.frame(flux::auc(.x$Time, .x$OD))) %>%
mutate(AUC=`flux::auc(.x$Time, .x$OD)`) %>%
select(-`flux::auc(.x$Time, .x$OD)`)%>%
ungroup()
data_analysed_2<- data_analysed %>%
left_join(curve_data %>%
group_by(Strain, Morfotipo, Replicate, Plate) %>%
summarise(ODmax=max(OD, na.rm = T)))
data<-data_analysed_2 %>%
mutate(Replicate=as.numeric(Replicate),
Plate=as.numeric(Plate)) %>%
left_join(Growthrate_results) %>%
left_join(Rosetta %>% mutate(Replicate=as.numeric(Replicate),
Plate=as.numeric(Plate))) %>%
mutate(Species=ifelse(Pulsotype %in% c("A1", "A"), "Kpn","Eco")) %>%
mutate(Plasmid=ifelse(pOXA48=="yes",
ifelse(pBlee=="yes", "pBlee+pOXA48", "pOXA48"),
ifelse(pBlee=="yes", "pBlee",
ifelse(Inc.l=="yes", "INC-L", "Plasmid-free")))
)
curve_data <- curve_data %>%
mutate(Replicate=as.numeric(Replicate),
Plate=as.numeric(Plate)) %>%
left_join(data %>% select(c("Strain", "Replicate", "Plate", "Plasmid", "Well", "Species")))
data %>%
# unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=AUC, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="AUC")
data %>%
unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=mumax, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="Vmax")
data %>%
ggplot(aes(y=lag, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="Lag")
plot(data$AUC, data$ODmax)
# Curvas por plasmido
curve_data %>%
unite(Strain, Strain, Morfotipo) %>%
# filter(Strain == "C3B_B" |Strain == "C3A_A" | Strain =="C4_A" | Strain =="P4_C" | Strain =="P8_A"| Strain =="C6A_A"| Strain =="C6B_B") %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ungroup() %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
facet_grid(~Plasmid)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# Curvas por especie
curve_data %>%
unite(Strain, Strain, Morfotipo) %>%
# filter(Strain == "C3B_B" |Strain == "C3A_A" | Strain =="C4_A" | Strain =="P4_C" | Strain =="P8_A"| Strain =="C6A_A"| Strain =="C6B_B") %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ungroup() %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
facet_grid(~Species)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# CorrelaciĆ³n entre llevar pOXA y pBLEE y crecer mejor?
data_analysed_2$Replicate <- as.numeric(data_analysed_2$Replicate)
data_analysed_2$Plate <- as.numeric(data_analysed_2$Plate)
analisis_corrrelacion <- left_join(data_analysed_2, curve_data)
# cor.test(analisis_corrrelacion$AUC, analisis_corrrelacion$pOXA48)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# CURVAS DE CRECIMIENTO - CONJUGACIONES
#La carpeta dĆ³nde estĆ”n las curvas
path_to_txt= "~/MEGA/Tesis/In_vivolution/curve_data_total_conj/"
#donde guardar las vmax
path_to_Growthrates="~/MEGA/Tesis/In_vivolution/Growth_curves/"
#La carpeta dĆ³nde guardaremos el output
path_to_output="~/MEGA/Tesis/In_vivolution/Growth_curves/"
#La piedra de rosetta!
Rosetta <- read.delim("~/MEGA/Tesis/In_vivolution/Rosetta_conj_total")
#Leer todos los archivos de la carpeta "path_to_txt"
file.list <- list.files(path =path_to_txt, full.names = F)
df.list <- lapply(paste0(path_to_txt, file.list),
function(x)read.delim(x, header=T, nrows=133, dec=","))
#No me acuerdo que habia que poner en (0, (133*10)-10, 10)
attr(df.list, "names") <- file.list
df <- bind_rows(df.list, .id = "id") %>%
mutate(Time=rep(seq(0, (133*10)-10, 10),length(file.list)))
df_test<-df %>%
#select -> para seleccionar columnas, en este caso - Optical density es para deseleccionar la TĀŖ
select( -`Optical.Density.600`) %>%
#mutate -> para cambiar la columna plate por id
mutate(Plate=id) %>%
select(-id) %>%
#gather(data, key, value) -> data is the dataframe you are working with; key is the name of the key column to create; value is the name of the value column to create.
gather(-Time,-Plate, key = Well, value = OD )
#Ahora tenemos una tabla (df_test) con la OD en funciĆ³n de la placa, los pocillos y el tiempo. La guardamos con el cĆ³digo de abajo
# write.table(df_test, file=paste0(path_to_output, "Curvas_en_formato_R"))
#Ahora juntamos la tabla df_test con la tabla Rosetta con la funcion left_join
curve_data <- df_test %>%
left_join(Rosetta %>% mutate(Plate=as.character(Plate),
Well=as.character(Well)))
#Filtramos en la tabla curve_data los blancos para quitarlos y ponemos la OD como una variable numƩrica
curve_data <- curve_data %>%
filter(Replicate != "blanco") %>%
mutate(OD=as.numeric(OD))
#
if (file.exists(paste0(path_to_Growthrates, "GrowthRates_results"))){
Growthrate_results<-read.table((paste0(path_to_Growthrates, "GrowthRates_results")), header=T)
} else{
#all_easylinear ->Determine maximum growth rates from log-linear part of the growth curve for a series of experiments
manysplits<- all_easylinear(OD~Time | Strain + Morfotipo + Replicate + Plate,
data=curve_data)
# write.table(results(manysplits), paste0(path_to_Growthrates, "GrowthRates_results"))
Growthrate_results<-results(manysplits)
}
data_analysed<- curve_data %>% group_by(Strain, Morfotipo, Replicate, Plate) %>%
group_modify(~ as.data.frame(flux::auc(.x$Time, .x$OD))) %>%
mutate(AUC=`flux::auc(.x$Time, .x$OD)`) %>%
select(-`flux::auc(.x$Time, .x$OD)`)%>%
ungroup()
data_analysed_2<- data_analysed %>%
left_join(curve_data %>%
group_by(Strain, Morfotipo, Replicate, Plate) %>%
summarise(ODmax=max(OD, na.rm = T)))
data<-data_analysed_2 %>%
mutate(Replicate=as.numeric(Replicate),
Plate=as.numeric(Plate)) %>%
left_join(Growthrate_results) %>%
left_join(Rosetta %>% mutate(Replicate=as.numeric(Replicate),
Plate=as.numeric(Plate))) %>%
mutate(Species=ifelse(Pulsotype %in% c("A1", "A"), "Kpn","Eco")) %>%
mutate(Plasmid=ifelse(pOXA48=="yes",
ifelse(pBlee=="yes", "pBlee+pOXA48", "pOXA48"),
ifelse(pBlee=="yes", "pBlee",
ifelse(Inc.l=="yes", "INC-L", "Plasmid-free")))
)
curve_data <- curve_data %>%
mutate(Replicate=as.numeric(Replicate),
Plate=as.numeric(Plate)) %>%
left_join(data %>% select(c("Strain", "Replicate", "Plate", "Plasmid", "Well", "Species")))
# CONJUGACIONES DEL DĆA 5/07/22
# AUC Conjugaciones 5/07/22
data %>%
filter(Plate==1) %>%
# unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=AUC, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="AUC")
# Vmax Conjugaciones 5/07/22
data %>%
filter(Plate==1) %>%
unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=mumax, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="Vmax")
# LAG Conjugaciones 5/07/22
data %>%
filter(Plate==1) %>%
ggplot(aes(y=lag, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="Lag")
# Curvas Conjugaciones 5/07/22
curve_data %>%
filter(Plate==1) %>%
unite(Strain, Strain, Morfotipo) %>%
# filter(Strain == "C3B_B" |Strain == "C3A_A" | Strain =="C4_A" | Strain =="P4_C" | Strain =="P8_A"| Strain =="C6A_A"| Strain =="C6B_B") %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ungroup() %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
# facet_grid(~Strain)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# Curvas Conjugaciones 5/07/22 por plƔsmido
curve_data %>%
filter(Plate==1) %>%
unite(Strain, Strain, Morfotipo) %>%
# filter(Strain == "C3B_B" |Strain == "C3A_A" | Strain =="C4_A" | Strain =="P4_C" | Strain =="P8_A"| Strain =="C6A_A"| Strain =="C6B_B") %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ungroup() %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
facet_grid(~Plasmid)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# CONJUGACIONES DEL DĆA 21/10/22
# AUC Conjugaciones 21/10/22
data %>%
filter(Plate==2) %>%
# unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=AUC, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="AUC")
# Vmax Conjugaciones 21/10/22
data %>%
filter(Plate==2) %>%
unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=mumax, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="Vmax")
# LAG Conjugaciones 21/10/22
data %>%
filter(Plate==2) %>%
ggplot(aes(y=lag, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="Lag")
# Curvas Conjugaciones 21/10/22
curve_data %>%
filter(Plate==2) %>%
unite(Strain, Strain, Morfotipo) %>%
# filter(Strain == "C3B_B" |Strain == "C3A_A" | Strain =="C4_A" | Strain =="P4_C" | Strain =="P8_A"| Strain =="C6A_A"| Strain =="C6B_B") %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ungroup() %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
facet_grid(~Species)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# Curvas Conjugaciones 21/10/22 por plƔsmido
curve_data %>%
filter(Plate==2) %>%
unite(Strain, Strain, Morfotipo) %>%
# filter(Strain == "C3B_B" |Strain == "C3A_A" | Strain =="C4_A" | Strain =="P4_C" | Strain =="P8_A"| Strain =="C6A_A"| Strain =="C6B_B") %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ungroup() %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
facet_grid(~Plasmid~Species)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# CONJUGACIONES DEL DĆA 7/10/22
# AUC Conjugaciones 7/10/22
data %>%
filter(Plate==3) %>%
unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=AUC, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="AUC")
# Vmax Conjugaciones 7/10/22
data %>%
filter(Plate==3) %>%
unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=mumax, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="Vmax")
# LAG Conjugaciones 7/10/22
data %>%
filter(Plate==3) %>%
unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=lag, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="Lag")
# Curvas Conjugaciones 7/10/22
curve_data %>%
filter(Plate==3) %>%
unite(Strain, Strain, Morfotipo) %>%
# filter(Strain == "C3B_B" |Strain == "C3A_A" | Strain =="C4_A" | Strain =="P4_C" | Strain =="P8_A"| Strain =="C6A_A"| Strain =="C6B_B") %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ungroup() %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
# facet_grid(~Strain)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# Curvas Conjugaciones 7/10/22 por plƔsmido
curve_data %>%
filter(Plate==3) %>%
unite(Strain, Strain, Morfotipo) %>%
# filter(Strain == "C3B_B" |Strain == "C3A_A" | Strain =="C4_A" | Strain =="P4_C" | Strain =="P8_A"| Strain =="C6A_A"| Strain =="C6B_B") %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ungroup() %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
facet_grid(~Plasmid)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# CONJUGACIONES DEL DĆA 2/11/22
# AUC Conjugaciones 2/11/22
data %>%
filter(Plate==4) %>%
unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=AUC, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="AUC")
# Vmax Conjugaciones 2/11/22
data %>%
filter(Plate==4) %>%
unite(Strain, Strain, Morfotipo) %>%
ggplot(aes(y=mumax, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="Vmax")
# LAG Conjugaciones 2/11/22
data %>%
filter(Plate==4) %>%
ggplot(aes(y=lag, x=Strain, color=Plasmid, fill=Plasmid)) +
geom_jitter(alpha=.4, width=.1)+
geom_boxplot(alpha=.2)+
facet_wrap(~Species, scale="free_x")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1,
#legend.position = "none",
strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))+
labs(title="Lag")
# Curvas Conjugaciones 2/11/22
curve_data %>%
filter(Plate==4) %>%
unite(Strain, Strain, Morfotipo) %>%
# filter(Strain == "C3B_B" |Strain == "C3A_A" | Strain =="C4_A" | Strain =="P4_C" | Strain =="P8_A"| Strain =="C6A_A"| Strain =="C6B_B") %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ungroup() %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
facet_grid(~Species)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# Curvas Conjugaciones 2/11/22 por plƔsmido
curve_data %>%
filter(Plate==4) %>%
unite(Strain, Strain, Morfotipo) %>%
# filter(Strain == "C3B_B" |Strain == "C3A_A" | Strain =="C4_A" | Strain =="P4_C" | Strain =="P8_A"| Strain =="C6A_A"| Strain =="C6B_B") %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ungroup() %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
facet_grid(~Plasmid~Species)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# Curvas B3914
curve_data %>% filter(Plate==2) %>% filter(Strain != "B3914 BLEE" ) %>% filter(Strain != "B3914 pOXA48" ) %>% filter(Strain != "B3914 BLEE pOXA48" ) %>%
filter(Strain != "blanco") %>%
unite(Strain, Strain, Morfotipo) %>%
group_by(Time, Strain, Species, Plasmid) %>%
summarise(media=mean(OD), STDEV=sd(OD)) %>%
ggplot()+
geom_ribbon(aes(ymin=media-STDEV, ymax=media+STDEV, x=Time, fill=Strain), alpha=.2)+
geom_line(aes(y=media, x=Time, color=Strain))+
facet_grid(~Species)+
xlab("Time (min)")+
ylab("OD (600nm)")+
theme_bw()+
theme(panel.background = element_blank(), panel.grid = element_blank(), aspect.ratio = 1,
# legend.position = "none",
strip.background = element_blank())
# CorrelaciĆ³n entre llevar pOXA y pBLEE y crecer mejor?
data_analysed_2$Replicate <- as.numeric(data_analysed_2$Replicate)
data_analysed_2$Plate <- as.numeric(data_analysed_2$Plate)
analisis_corrrelacion <- left_join(data_analysed_2, curve_data)
# cor.test(analisis_corrrelacion$AUC, analisis_corrrelacion$pOXA48)