GUIANA VS NO-TREAT
1 Diversity index
1.1 Shapiro test
# diver_gral <- diver_gral %>%
# pivot_longer(cols=chao_gral:dominance.simpson_REL, names_to = "cosa", values_to = "value") %>%
# separate(cosa, into=c("index", "database2"), sep="_")
diver_gral <- diver_gral %>%
pivot_longer(cols=chao_gral:dominance.simpson_BACBiocide, names_to = "cosa", values_to = "value") %>%
separate(cosa, into=c("index", "database2"), sep="_")
shapiro <- diver_gral %>%
group_by(database2, index, Treatment) %>%
do(tidy(shapiro.test(.$value)))
datatable(shapiro, rownames = FALSE, filter="top", options = list(pageLength = 30, scrollX=T, scrollY=T) )1.2 Wilcox Test
wilcox <- diver_gral %>%
group_by(database2, index) %>%
do(tidy(wilcox.test(.$value~.$Treatment)))
datatable(wilcox, rownames = FALSE, filter="top", options = list(pageLength = 30, scrollX=T, scrollY=T) )student <- diver_gral %>%
group_by(database2, index) %>%
do(tidy(t.test(.$value~.$Treatment)))
datatable(student, rownames = FALSE, filter="top", options = list(pageLength = 30, scrollX=T, scrollY=T) )1.3 Shannon Index
1.4 Chao index
diver_gral %>% filter(index== "chao") %>%
ggplot(aes(x=Treatment, y = value,fill=Treatment)) +
geom_violin()+
geom_boxplot(width=.2) +
facet_wrap(.~database2)+
ggtitle ("chao index") +xlab("Cohort origin") +ylab("chao Index")+
theme_classic()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1, legend.position = "none",strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))
1.5 Dominance simpson
diver_gral %>% filter(index== "dominance.simpson") %>%
ggplot(aes(x=Treatment, y = value,fill=Treatment)) +
geom_violin()+
geom_boxplot(width=.2) +
facet_wrap(.~database2)+
ggtitle ("dominance simpson diversity") +xlab("Cohort origin") +ylab("simpson Index")+
theme_classic()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1, legend.position = "none",strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))
1.6 Eveness simpson
diver_gral %>% filter(index== "evenness.simpson") %>%
ggplot(aes(x=Treatment, y = value,fill=Treatment)) +
geom_violin()+
geom_boxplot(width=.2) +
facet_wrap(.~database2)+
ggtitle ("eveness simpson diversity") +xlab("Cohort origin") +ylab("simpson Index")+
theme_classic()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1, legend.position = "none",strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))
2 PCA
2.1 PCA gral by Origin
as.data.frame(PCA$x[,1:2]) %>% rownames_to_column("label") %>% inner_join(Tabla_inicial) %>%
select(label,PC1,PC2,origin) %>% distinct() %>% ggplot(aes(x = PC1, y = PC2, color = origin)) + geom_point() +
ggtitle ("Principal Component Analysis") +xlab("PC1 69.08%") +ylab("PC2 14.57%")
2.2 PCA ABR by origin
as.data.frame(PCA_ABR$x[,1:2]) %>% rownames_to_column("label") %>% inner_join(Tabla_inicial) %>%
select(label,PC1,PC2,origin) %>% distinct() %>% ggplot(aes(x = PC1, y = PC2, color = origin)) + geom_point() +
ggtitle ("Principal Component Analysis") +xlab("PC1 53.75%") +ylab("PC2 13.22%")
2.3 PCA BAC by origin
as.data.frame(PCA_BAC$x[,1:2]) %>% rownames_to_column("label") %>% inner_join(Tabla_inicial) %>%
select(label,PC1,PC2,origin) %>% distinct() %>% ggplot(aes(x = PC1, y = PC2, color = origin)) + geom_point() +
ggtitle ("Principal Component Analysis") +xlab("PC1 93.44%") +ylab("PC2 2.19%")
2.4 PCA REL by origin
as.data.frame(PCA_REL$x[,1:2]) %>% rownames_to_column("label") %>% inner_join(Tabla_inicial) %>%
select(label,PC1,PC2,origin) %>% distinct() %>% ggplot(aes(x = PC1, y = PC2, color = origin)) + geom_point() +
ggtitle ("Principal Component Analysis") +xlab("PC1 81.85%") +ylab("PC2 6.86%")
2.5 PCA BAC Metals by origin
as.data.frame(PCA_BAC_Metal$x[,1:2]) %>% rownames_to_column("label") %>% inner_join(Tabla_inicial) %>%
select(label,PC1,PC2,origin) %>% distinct() %>% ggplot(aes(x = PC1, y = PC2, color = origin)) + geom_point() +
ggtitle ("Principal Component Analysis") +xlab("PC1 94.65%") +ylab("PC2 2.17%")
2.6 PCA BAC Biocide by origin
as.data.frame(PCA_BAC_Biocide$x[,1:2]) %>% rownames_to_column("label") %>% inner_join(Tabla_inicial) %>%
select(label,PC1,PC2,origin) %>% distinct() %>% ggplot(aes(x = PC1, y = PC2, color = origin)) + geom_point() +
ggtitle ("Principal Component Analysis") +xlab("PC1 95.28%") +ylab("PC2 2.8%")
3 UMAP
HE5, HE152
Guayana_tsne_spearman <- as.matrix(Guayana_tsne_spearman)
Guayana_tsne_spearman_REL <- as.matrix(Guayana_tsne_spearman_REL)
Guayana_tsne_spearman_ABR <- as.matrix(Guayana_tsne_spearman_ABR)
Guayana_tsne_spearman_BAC <- as.matrix(Guayana_tsne_spearman_BAC)
Guayana_tsne_spearman_BAC_Metal = get_dist(Guayana_spread_BAC_Metal, method = "spearman")
Guayana_tsne_spearman_BAC_Biocide = get_dist(Guayana_spread_BAC_Biocide, method = "spearman")
umap_plot <- umap(Guayana_tsne_spearman, input="dist")
umap_plot_REL <- umap(Guayana_tsne_spearman_REL)
umap_plot_ABR <- umap(Guayana_tsne_spearman_ABR)
umap_plot_BAC <- umap(Guayana_tsne_spearman_BAC)
umap_plot_BAC_Metal <- umap(Guayana_spread_BAC_Metal)
umap_plot_BAC_Biocide <- umap(Guayana_spread_BAC_Biocide)
umap_plot <- as.data.frame(umap_plot$layout) %>% rownames_to_column("label") %>% inner_join(., Tabla_inicial)
umap_plot_REL <- as.data.frame(umap_plot_REL$layout) %>% rownames_to_column("label") %>% inner_join(., Tabla_inicial)
umap_plot_ABR <- as.data.frame(umap_plot_ABR$layout) %>% rownames_to_column("label") %>% inner_join(., Tabla_inicial)
umap_plot_BAC <- as.data.frame(umap_plot_BAC$layout) %>% rownames_to_column("label") %>% inner_join(., Tabla_inicial)
umap_plot_BAC_Metal <- as.data.frame(umap_plot_BAC_Metal$layout) %>% rownames_to_column("label") %>% inner_join(., Tabla_inicial)
umap_plot_BAC_Biocide <- as.data.frame(umap_plot_BAC_Biocide$layout) %>% rownames_to_column("label") %>% inner_join(., Tabla_inicial)3.1 UMAP general by origin
3.2 UMAP ABR by origin
umap_plot_ABR %>%
select(label,V1,V2,origin) %>% distinct() %>% ggplot(aes(x = V1, y =V2, color = origin)) + geom_point()+
ggtitle ("UMAP ABR")+
theme_classic()+
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))
3.3 UMAP BAC by origin
umap_plot_BAC %>%
select(label,V1,V2,origin) %>% distinct() %>% ggplot(aes(x = V1, y =V2, color = origin)) + geom_point()+
ggtitle ("UMAP BAC")+
theme_classic()+
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))
3.4 UMAP REL by origin
umap_plot_REL %>%
select(label,V1,V2,origin) %>% distinct() %>% ggplot(aes(x = V1, y =V2, color = origin)) + geom_point()+
ggtitle ("UMAP REL")+
theme_classic()+
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))
3.5 UMAP BAC Metal by origin
umap_plot_BAC_Metal %>%
select(label,V1,V2,origin) %>% distinct() %>% ggplot(aes(x = V1, y =V2, color = origin)) + geom_point()+
ggtitle ("UMAP BAC Metal")+
theme_classic()+
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))
3.6 UMAP BAC Biocide by origin
umap_plot_BAC_Biocide %>%
select(label,V1,V2,origin) %>% distinct() %>% ggplot(aes(x = V1, y =V2, color = origin)) + geom_point()+
ggtitle ("UMAP BAC biocide")+
theme_classic()+
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))
4 T-SNE
4.1 T-SNE general by origin
ggplot(tsne_spearman_30_plot) + geom_point(aes(x=x,y=y,color=origin)) + ggtitle("T-SNE")
4.2 T-SNE ABR by origin
ggplot(tsne_spearman_30_plot_ABR) + geom_point(aes(x=x,y=y,color=origin)) + ggtitle("T-SNE ABR")
4.3 T-SNE BAC by origin
ggplot(tsne_spearman_30_plot_BAC) + geom_point(aes(x=x,y=y,color=origin)) + ggtitle("T-SNE BAC")
4.4 T-SNE REL by origin
ggplot(tsne_spearman_30_plot_REL) + geom_point(aes(x=x,y=y,color=origin)) + ggtitle("T-SNE REL")
5 Adonis
5.1 Adonis all
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = Guayana_spread_t[, 1:918] ~ origin, data = Guayana_spread_t, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## origin 1 1.7207 0.09418 12.685 0.001 ***
## Residual 122 16.5498 0.90582
## Total 123 18.2705 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.2 Adonis ABR
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = Guayana_spread_ABR_t[, 1:342] ~ origin, data = Guayana_spread_ABR_t, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## origin 1 2.0269 0.16245 23.662 0.001 ***
## Residual 122 10.4503 0.83755
## Total 123 12.4771 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.3 Adonis BAC
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = Guayana_spread_BAC_t[, 1:232] ~ origin, data = Guayana_spread_BAC_t, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## origin 1 0.484 0.01395 1.726 0.115
## Residual 122 34.213 0.98605
## Total 123 34.697 1.000005.4 Adonis REL
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = Guayana_spread_REL_t[, 1:344] ~ origin, data = Guayana_spread_REL_t, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## origin 1 3.7312 0.1342 18.91 0.001 ***
## Residual 122 24.0716 0.8658
## Total 123 27.8028 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.5 Adonis BAC metal
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = Guayana_spread_BAC_Metal_t[, 1:144] ~ origin, data = Guayana_spread_REL_t, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## origin 1 0.581 0.01657 2.055 0.068 .
## Residual 122 34.494 0.98343
## Total 123 35.075 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.6 Adonis BAC biocide
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = Guayana_spread_BAC_Biocide_t[, 1:109] ~ origin, data = Guayana_spread_REL_t, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## origin 1 0.291 0.00848 1.0438 0.335
## Residual 122 33.993 0.99152
## Total 123 34.284 1.000006 Different genes stats
6.1 Quantity of distinct genes by origin
6.2 Mean of distinct genes by origin per person
6.3 Proportion unique genes
6.4 Proportion unique genes per person
6.5 Total unique genes per person
6.6 genes per million
6.7 Rarefaction
6.8 Abundance vs Depth
7 Tables of Genes
7.1 Common genes ABR
7.2 not in common genes ABR
7.3 Common genes BAC
7.4 not in common genes BAC
7.5 Common genes REL
7.6 not in common genes REL
7.7 Common genes all
7.8 not in common genes
8 Core genes
8.1 Core genes all
tabla_core <- Tabla_inicial %>% filter(Treatment!="Treat")%>% group_by(template) %>% summarise(TotalFreq = n())%>% ungroup() %>% filter(TotalFreq == 124)
datatable(tabla_core, rownames = FALSE, filter="top", options = list(pageLength = 30, scrollX=T, scrollY=T) )8.2 Core genes europe
tabla_core_eur <- Tabla_inicial %>% filter(Treatment=="NO-Treat") %>% group_by(template) %>% summarise(TotalFreq = n())%>% ungroup() %>% filter(TotalFreq == 29)
datatable(tabla_core_eur, rownames = FALSE, filter="top", options = list(pageLength = 30, scrollX=T, scrollY=T) )8.3 Core genes guayana
tabla_core_guayana <- Tabla_inicial %>% filter(Treatment=="guayana") %>% group_by(template) %>% summarise(TotalFreq = n())%>% ungroup() %>% filter(TotalFreq == 95)
datatable(tabla_core_guayana, rownames = FALSE, filter="top", options = list(pageLength = 30, scrollX=T, scrollY=T) )9 Summary of genes
9.1 summary of gene distribution
ABR_common_genes <- tabla_para_teresa_ABR %>% group_by(Label) %>% summarise(number = n())
BAC_common_genes <- tabla_para_teresa_BAC %>% group_by(Label) %>% summarise(number = n())
REL_common_genes <- tabla_para_teresa_REL %>% group_by(Label) %>% summarise(number = n())
ABR_notcommon_genes <- anti_tabla_para_teresa_ABR %>% group_by(Label) %>% summarise(number = n())
BAC_notcommon_genes <- anti_tabla_para_teresa_BAC %>% group_by(Label) %>% summarise(number = n())
REL_notcommon_genes <- anti_tabla_para_teresa_REL %>% group_by(Label) %>% summarise(number = n())
Tipo <- c('ABR','BAC','REL')
aaEur <- c(109,39,76)
All <- c(155,167,182)
Gui <- c(79,26,86)
mix_num_genes <- data.frame( Tipo, aaEur,All,Gui)
mix_num_genes %>% pivot_longer(cols=aaEur:Gui, names_to = "cosa", values_to = "value")%>%
ggplot(aes(x = cosa, y = value, fill = Tipo))+geom_col(position = "dodge")+
theme_classic()+
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))
# Tipo <- c('ABR','BAC','REL')
# aaEur <- c(10,5,2)
# All <- c(10,2,0)
# Gui <- c(14,2,8)
# core_num_genes <- data.frame( Tipo, aaEur,All,Gui)
#
# core_num_genes %>% pivot_longer(cols=aaEur:Gui, names_to = "cosa", values_to = "value")%>%
# ggplot(aes(x = cosa, y = value, fill = Tipo))+geom_col(position = "dodge")+
# theme_classic()+
# 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))9.2 Core genes
10 Hist of Genes
10.1 hist all genes
10.2 hist all genes europe_t0
10.3 hist all genes guayana
10.4 hist ABR
10.5 hist BAC
10.6 hist REL
10.7 hist ABR Guayana
10.8 hist BAC Guayana
10.9 hist REL Guayana
10.10 hist ABR Europe
10.11 hist BAC Europe
10.12 hist REL Europe
11 Resistance genes diversity in population
11.1 ABR diversity
11.2 REL diversity
11.3 BAC diversity
12 Resistance genes diversity in cohort and origin
12.1 ABR diversity in origin
12.2 REL diversity in origin
12.3 BAC diversity in origin
13 Total freq of Resistance genes
13.1 ABR freq by origin
13.2 depth of BAC by origin
13.3 REL freq by origin
14 Total depth of Resistance genes
14.1 depth of ABR by origin
14.2 depth of BAC by origin
14.3 depth of REL by origin
15 CHI-SQUARE PERCENTAGES
#Amerindians <- Tabla_inicial %>%select(origin) %>% filter(origin == "guayana")
tabla_america <- Tabla_inicial %>% filter(origin == "guayana") %>% group_by(label) %>% distinct(template,label, .keep_all = TRUE) %>% ungroup %>%
group_by(template) %>% summarize(suma = n())
tabla_europa <- Tabla_inicial %>% filter(Treatment == 'NO-Treat' ) %>% group_by(label) %>% distinct(template,label, .keep_all = TRUE) %>% ungroup %>%
group_by(template) %>% summarize(suma = n())
tabla_chi <- full_join(tabla_europa, tabla_america, by= "template")
colnames(tabla_chi) <- c("template","europe_P", "america_P" )
tabla_chi[is.na(tabla_chi)] <- 0
tabla_chi_filt <- tabla_chi %>% mutate(europe_N = 29 - europe_P ) %>% mutate(america_N = 95 - america_P )#%>% filter(europe_P > 2 | america_P > 9 )
chi_pvalue <- tabla_chi_filt %>%
rowwise() %>%
mutate(
p.value = fisher.test(matrix(c(europe_P, europe_N, america_P, america_N), nrow = 2))$p.value,
odds_ratio = fisher.test(matrix(c(europe_P, europe_N, america_P, america_N), nrow = 2))$estimate,
)
colnames(chi_pvalue) <- c("template","europe_P","america_P","europe_N","america_N","p_value", "odds_ratio")
chi_pvalue <- as.data.frame(chi_pvalue)
chi_pvalue.adj <-chi_pvalue %>% mutate(padj= p.adjust(chi_pvalue$p_value, method = "BH")) #%>% filter(padj<0.05)
chi_pvalue.adj <- chi_pvalue.adj %>% mutate(america_odds = europe_P*america_N) %>% mutate(europe_odds = america_P*europe_N)
chi_pvalue.adj <- chi_pvalue.adj %>% mutate(result1= america_odds/europe_odds) %>% mutate(result2 = europe_odds/america_odds)
chi_pvalue.adj <- chi_pvalue.adj %>% mutate(porcentaje_eur=europe_P/(europe_P+europe_N)) %>% mutate(porcentaje_amer=america_P/(america_P+america_N))
chi_pvalue.adj[is.na(chi_pvalue.adj)] <- 0
chi_pvalue.adj$odds_ratio <- ifelse(chi_pvalue.adj$odds_ratio < 1, -1/chi_pvalue.adj$odds_ratio, chi_pvalue.adj$odds_ratio )
chi_pvalue.adj$direction <- ifelse(chi_pvalue.adj$odds_ratio < 0, 'america', 'europe')
chi_pvalue.adj.plot <- chi_pvalue.adj
chi_pvalue.adj.plot$fisher <- ifelse(chi_pvalue.adj.plot$padj<0.05, "yes", "no" )
chi_pvalue.adj.plot$label <- ifelse(chi_pvalue.adj.plot$padj<0.000000000000001, chi_pvalue.adj.plot$template, "" )
chi_pvalue.adj.plot %>% ggplot(aes(porcentaje_eur, porcentaje_amer, color= fisher))+geom_point()+
geom_text_repel(aes(label=label),hjust = 0, nudge_x = 0.05, max.overlaps = 99)+
theme_classic()+
theme(panel.background = element_blank(), panel.grid = element_blank(),
aspect.ratio = 1, legend.position = "none",strip.background = element_blank(),
axis.text.x = element_text(angle = 45, hjust=1))
chi_pvalue.adj <-chi_pvalue.adj %>% filter(padj<0.05)
chi_pvalue.adj$database = "ABR"
chi_pvalue.adj$database[grepl("^BAC",chi_pvalue.adj$template)] = "BAC"
chi_pvalue.adj$database[grepl("^MOB",chi_pvalue.adj$template)] = "REL"
# chi_pvalue_rel <- chi_pvalue.adj %>% filter(str_detect(template, "^MOB"))
# chi_pvalue_rel_short <- chi_pvalue.adj %>% filter(str_detect(template, "^MOB"))
# chi_pvalue_rel_short$template <- sub( "_.*" ," ", chi_pvalue_rel_short$template)
chi_pvalue_rel <- chi_pvalue.adj %>%filter(database == "REl")
chi_pvalue_rel_short <- chi_pvalue.adj %>%filter(database == "REL")
chi_pvalue_rel_short$template <- sub( "_.*" ," ", chi_pvalue_rel_short$template)
chi_pvalue_abr <- chi_pvalue.adj %>% filter(database == "ABR")
chi_pvalue_abr_short <- chi_pvalue.adj %>%filter(database == "ABR")
#no se puede simplificar sin perder info
# chi_pvalue_abr_short$template <- sub( ":.*" ," ", chi_pvalue_abr_short$template)
# chi_pvalue_abr_short$template <- sub( "(_[^_]+)_.*" ,"\\1", chi_pvalue_abr_short$template)
# chi_pvalue_abr_short$template = sapply(chi_pvalue_abr_short$template , function(x) paste(strsplit(x, "_")[[1]][1:2], collapse = '_'))
chi_pvalue_bac <- chi_pvalue.adj %>% filter(database == "BAC")
# chi_pvalue_bac_short <- chi_pvalue.adj %>% filter(database == "BAC") %>% left_join(Bac_met_db, by = 'template')
# chi_pvalue_bac_short$template <- sub( "\\|+" ,"-", chi_pvalue_bac_short$template)
# chi_pvalue_bac_short$template <- sub( "(\\|[^\\|]+)\\|.*" ,"", chi_pvalue_bac_short$template)15.1 ABR Percentages by padj 2 colors
chi_pvalue_abr_short_longzzz <- chi_pvalue_abr_short %>%
select(template,porcentaje_eur,porcentaje_amer) %>%
melt( id.vars = 'template')
chi_pvalue_abr_short_longzzz$value <- ifelse(chi_pvalue_abr_short_longzzz$variable == 'porcentaje_eur', chi_pvalue_abr_short_longzzz$value*-1, chi_pvalue_abr_short_longzzz$value)
joinedabr <- full_join(chi_pvalue_abr_short_longzzz, chi_pvalue_abr_short, by='template')
joinedabrz <- joinedabr %>% arrange(desc(value))
americcc <- joinedabrz %>% filter(variable == 'porcentaje_amer')
europpp <- joinedabrz %>% filter(variable == 'porcentaje_eur')
ggplot() +
geom_col(data=americcc, aes(reorder(template, -padj), value, fill=padj)) +
scale_fill_viridis_c(option = "D") +
new_scale_fill() +
geom_col(data= europpp, aes(reorder(template, -padj), value, fill=padj)) +
scale_fill_viridis_c(option = "C")+
coord_flip()
15.2 BAC Percentages by padj 2 colors
chi_pvalue_BAC_short_longzzz <- chi_pvalue_bac %>%
select(template,porcentaje_eur,porcentaje_amer) %>%
melt( id.vars = 'template')
chi_pvalue_BAC_short_longzzz$value <- ifelse(chi_pvalue_BAC_short_longzzz$variable == 'porcentaje_eur', chi_pvalue_BAC_short_longzzz$value*-1, chi_pvalue_BAC_short_longzzz$value)
chi_pvalue_rel_short <- chi_pvalue_rel_short %>% arrange(desc(odds_ratio))
joinedBAC <- full_join(chi_pvalue_BAC_short_longzzz, chi_pvalue_bac, by='template')
joinedBACz <- joinedBAC %>% arrange(desc(value))
americcc <- joinedBACz %>% filter(variable == 'porcentaje_amer')
europpp <- joinedBACz %>% filter(variable == 'porcentaje_eur')
ggplot() +
geom_col(data=americcc, aes(reorder(template, -padj), value, fill=padj)) +
scale_fill_viridis_c(option = "D") +
new_scale_fill() +
geom_col(data= europpp, aes(reorder(template, -padj), value, fill=padj)) +
scale_fill_viridis_c(option = "C")+
coord_flip()
15.3 REL Percentages by padj 2 colors
chi_pvalue_REL_short_longzzz <- chi_pvalue_rel_short %>%
select(template,porcentaje_eur,porcentaje_amer) %>%
melt( id.vars = 'template')
chi_pvalue_REL_short_longzzz$value <- ifelse(chi_pvalue_REL_short_longzzz$variable == 'porcentaje_eur', chi_pvalue_REL_short_longzzz$value*-1, chi_pvalue_REL_short_longzzz$value)
chi_pvalue_rel_short <- chi_pvalue_rel_short %>% arrange(desc(odds_ratio))
joinedREL <- full_join(chi_pvalue_REL_short_longzzz, chi_pvalue_rel_short, by='template')
joinedRELz <- joinedREL %>% arrange(desc(value))
americcc <- joinedRELz %>% filter(variable == 'porcentaje_amer')
europpp <- joinedRELz %>% filter(variable == 'porcentaje_eur')
ggplot() +
geom_col(data=americcc, aes(reorder(template, -padj), value, fill=padj)) +
scale_fill_viridis_c(option = "D") +
new_scale_fill() +
geom_col(data= europpp, aes(reorder(template, -padj), value, fill=padj)) +
scale_fill_viridis_c(option = "C")+
coord_flip()
16 Lefse
16.1 Lefse general
16.2 Lefse ABR
plotLDA(res_origin_ABR_LEFSE,group=c("europa","guayana"),lda=3,pvalue=0.05)
16.3 Lefse BAC
plotLDA(res_origin_BAC_LEFSE,group=c("europa","guayana"),lda=4,pvalue=0.05)
16.4 Lefse REL
plotLDA(res_origin_REL_LEFSE,group=c("europa","guayana"),lda=5,pvalue=0.05)
17 HEATMAPS
17.1 ALL Heatmap
pheatmap(log(heat_padj+1), clustering_method = "ward.D2")
17.2 ABR Heatmap
pheatmap(log(heat_padj_abr+1), clustering_method = "ward.D2")
Heatmap of the Antimicrobial resistance genes. The genes that appear on the y axis and the individuals of the x axis were previously selected From an ANOVA test with p-value lower than 0,05 in. Red indicates the highest value of appearance of the gene in the population and blue the contrary.
17.3 REL Heatmap
pheatmap(log(heat_padj_rel+1), clustering_method = "ward.D2") 
Heatmap of the Relaxases resistance genes. The genes that appear on the y axis and the individuals of the x axis were previously selected From an ANOVA test with p-value lower than 0,05 in. Red indicates the highest value of appearance of the gene in the population and blue the contrary.
17.4 BAC Heatmap
# pheatmap(log(theat_padj_bac+1), clustering_method = "ward.D2")
pheatmap(log(heat_padj_bac+1), clustering_method = "ward.D2")
Heatmap of the Biocides and metals resistance genes. The genes that appear on the y axis and the individuals of the x axis were previously selected From an ANOVA test with p-value lower than 0,05 in. Red indicates the highest value of appearance of the gene in the population and blue the contrary.
17.5 ALL Heatmap ALL
pheatmap(log(heat_padj+1), clustering_method = "ward.D2")
17.6 ABR Heatmap ALL
pheatmap(log(heat_padj_abr+1), clustering_method = "ward.D2")
17.7 ALL Heatmap LEFSE
pheatmap(log(heat_padj+1), clustering_method = "ward.D2")
17.8 ABR Heatmap LEFSE
pheatmap(log(heat_padj+1), clustering_method = "ward.D2")
17.9 BAC Heatmap LEFSE
pheatmap(log(heat_padj+1), clustering_method = "ward.D2")
## REL Heatmap LEFSE
pheatmap(log(heat_padj+1), clustering_method = "ward.D2")