Species Barretts Relationships
Last updated: 2022-08-15
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Knit directory:
esoph-micro-cancer-workflow/
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Histology (+/- Barretts)
#root function
root<-function(x){
x <- ifelse(x < 0, 0, x)
x**(0.25)
}
#inverse root function
invroot<-function(x){
x**(4)
}
DIM <- c(6, 4)
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number),
Barretts = ifelse(`Barretts.`=="Y",1,0)) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Barretts)
dat <- dat.rna.s %>%
dplyr::mutate(Barretts = ifelse(Barrett.s.Esophagus.Reported=="Yes",1,0)) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Barretts)
analysis.dat <- full_join(analysis.dat, dat)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Barretts")dat <- dat.wgs.s %>%
dplyr::mutate(Barretts = ifelse(Barrett.s.Esophagus.Reported=="Yes",1,0)) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Barretts)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(
pres = ifelse(Abundance > 0, 1, 0),
Abund = Abundance*100,
Tumor = ifelse(tumor==1, "Tumor", "No Tumor"),
Barretts = ifelse(Barretts == 1, "Yes", "No")
)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Barretts")p <- analysis.dat %>%
filter(!is.na(Barretts), source=="16s")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 120 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_NCI_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 137 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_NCI_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 127 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(source=="rna", !is.na(Barretts))%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 749 rows containing non-finite values (stat_ydensity).Warning: Removed 828 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_tcga_rna_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 749 rows containing non-finite values (stat_ydensity).Warning: Removed 822 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_tcga_rna_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 749 rows containing non-finite values (stat_ydensity).Warning: Removed 818 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(source=="wgs", !is.na(Barretts))%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 112 rows containing non-finite values (stat_ydensity).Warning: Removed 321 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_tcga_wgs_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 112 rows containing non-finite values (stat_ydensity).Warning: Removed 304 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_tcga_wgs_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 112 rows containing non-finite values (stat_ydensity).Warning: Removed 337 rows containing missing values (geom_point).Subset by Bacterium
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number),
Barretts = ifelse(`Barretts.`=="Y",1,0)) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Barretts)
dat <- dat.rna.s %>%
dplyr::mutate(Barretts = ifelse(Barrett.s.Esophagus.Reported=="Yes",1,0)) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Barretts)
analysis.dat <- full_join(analysis.dat, dat)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Barretts")dat <- dat.wgs.s %>%
dplyr::mutate(Barretts = ifelse(Barrett.s.Esophagus.Reported=="Yes",1,0)) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Barretts)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(
pres = ifelse(Abundance > 0, 1, 0),
Abund = Abundance*100,
Tumor = ifelse(tumor==1, "Tumor", "No Tumor"),
Barretts = ifelse(Barretts == 1, "Yes", "No")
)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Barretts")p <- analysis.dat %>%
filter(!is.na(Barretts), source=="16s", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 44 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_NCI_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 40 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_NCI_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 45 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="rna", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 112 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_tcga_rna_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 109 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_tcga_rna_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).
Removed 109 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="wgs", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 42 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_tcga_wgs_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 30 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_tcga_wgs_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 36 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="16s", OTU == "Prevotella melaninogenica" | OTU =="Prevotella spp.")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 22 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_NCI_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_NCI_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 25 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="rna", OTU == "Prevotella melaninogenica")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 109 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_tcga_rna_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 111 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_tcga_rna_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 109 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="wgs", OTU == "Prevotella melaninogenica")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 31 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_tcga_wgs_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 33 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_tcga_wgs_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).
Removed 33 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="16s", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 70 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_NCI_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 67 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_NCI_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 65 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="rna", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 134 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_tcga_rna_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 132 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_tcga_rna_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 125 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="wgs", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 52 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_tcga_wgs_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 42 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_tcga_wgs_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 49 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="16s", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 3 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_NCI_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 4 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_NCI_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 6 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="rna", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 111 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_tcga_rna_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 110 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_tcga_rna_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 111 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Barretts), source=="wgs", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Barretts, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Barretts", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 45 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2C_tcga_wgs_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 41 rows containing missing values (geom_point).ggsave("output/supplemental_figure2C_tcga_wgs_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 45 rows containing missing values (geom_point).Sex
#root function
root<-function(x){
x <- ifelse(x < 0, 0, x)
x**(0.25)
}
#inverse root function
invroot<-function(x){
x**(4)
}
DIM <- c(6, 4)
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number),
Gender = ifelse(gender=="M","Male","Female")) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Gender)
dat <- dat.rna.s %>%
dplyr::mutate(Gender = ifelse(Gender=="male","Male","Female")) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Gender)
analysis.dat <- full_join(analysis.dat, dat)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Gender")dat <- dat.wgs.s %>%
dplyr::mutate(Gender = ifelse(Gender=="male","Male","Female")) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Gender)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(
pres = ifelse(Abundance > 0, 1, 0),
Abund = Abundance*100,
Tumor = ifelse(tumor==1, "Tumor", "No Tumor")
)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Gender")p <- analysis.dat %>%
filter(!is.na(Gender), source=="16s")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 122 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_NCI_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 137 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_NCI_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 129 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(source=="rna", !is.na(Gender))%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 749 rows containing non-finite values (stat_ydensity).Warning: Removed 817 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_tcga_rna_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 749 rows containing non-finite values (stat_ydensity).Warning: Removed 840 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_tcga_rna_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 749 rows containing non-finite values (stat_ydensity).Warning: Removed 823 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(source=="wgs", !is.na(Gender))%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 112 rows containing non-finite values (stat_ydensity).Warning: Removed 322 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_tcga_wgs_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 112 rows containing non-finite values (stat_ydensity).Warning: Removed 305 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_tcga_wgs_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 112 rows containing non-finite values (stat_ydensity).Warning: Removed 314 rows containing missing values (geom_point).Subset by Bacterium
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number),
Gender = ifelse(gender=="M","Male","Female")) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Gender)
dat <- dat.rna.s %>%
dplyr::mutate(Gender = ifelse(Gender=="male","Male","Female")) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Gender)
analysis.dat <- full_join(analysis.dat, dat)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Gender")dat <- dat.wgs.s %>%
dplyr::mutate(Gender = ifelse(Gender=="male","Male","Female")) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Gender)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(
pres = ifelse(Abundance > 0, 1, 0),
Abund = Abundance*100,
Tumor = ifelse(tumor==1, "Tumor", "No Tumor")
)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Gender")p <- analysis.dat %>%
filter(!is.na(Gender), source=="16s", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 39 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_NCI_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 39 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_NCI_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 39 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="rna", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 112 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_tcga_rna_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 111 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_tcga_rna_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 110 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="wgs", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 42 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_tcga_wgs_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 34 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_tcga_wgs_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 41 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="16s", OTU == "Prevotella melaninogenica" | OTU =="Prevotella spp.")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 26 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_NCI_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 22 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_NCI_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 19 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="rna", OTU == "Prevotella melaninogenica")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 111 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_tcga_rna_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 110 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_tcga_rna_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).
Removed 110 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="wgs", OTU == "Prevotella melaninogenica")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 29 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_tcga_wgs_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 32 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_tcga_wgs_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 36 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="16s", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 62 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_NCI_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 63 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_NCI_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 65 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="rna", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 123 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_tcga_rna_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 134 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_tcga_rna_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 129 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="wgs", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 41 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_tcga_wgs_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 48 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_tcga_wgs_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).
Removed 48 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="16s", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 5 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_NCI_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 5 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_NCI_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 2 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="rna", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 108 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_tcga_rna_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 111 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_tcga_rna_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 107 rows containing non-finite values (stat_ydensity).Warning: Removed 109 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Gender), source=="wgs", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Gender, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Gender", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 49 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2D_tcga_wgs_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 46 rows containing missing values (geom_point).ggsave("output/supplemental_figure2D_tcga_wgs_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 45 rows containing missing values (geom_point).Race
#root function
root<-function(x){
x <- ifelse(x < 0, 0, x)
x**(0.25)
}
#inverse root function
invroot<-function(x){
x**(4)
}
DIM <- c(6, 4)
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number)) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Race)
dat <- dat.rna.s %>%
dplyr::mutate(Race = race) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Race)
analysis.dat <- full_join(analysis.dat, dat)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Race")dat <- dat.wgs.s %>%
dplyr::mutate(Race = race) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Race)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(
pres = ifelse(Abundance > 0, 1, 0),
Abund = Abundance*100,
Tumor = ifelse(tumor==1, "Tumor", "No Tumor")
)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Race")analysis.dat$Race[analysis.dat$Race == "asian"] <- "Asian"
analysis.dat$Race[analysis.dat$Race == "B"] <- "Black"
analysis.dat$Race[analysis.dat$Race == "black or african american"] <- "Black"
analysis.dat$Race[analysis.dat$Race == "H"] <- "Hispanic"
analysis.dat$Race[analysis.dat$Race == "O"] <- "Other"
analysis.dat$Race[analysis.dat$Race == "W"] <- "White"
analysis.dat$Race[analysis.dat$Race == "white"] <- "White"
p <- analysis.dat %>%
filter(!is.na(Race), source=="16s")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 132 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_NCI_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 141 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_NCI_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 136 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(source=="rna", !is.na(Race))%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 651 rows containing non-finite values (stat_ydensity).Warning: Removed 714 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_tcga_rna_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 651 rows containing non-finite values (stat_ydensity).Warning: Removed 712 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_tcga_rna_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 651 rows containing non-finite values (stat_ydensity).Warning: Removed 725 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(source=="wgs", !is.na(Race))%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 112 rows containing non-finite values (stat_ydensity).Warning: Removed 305 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_tcga_wgs_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 112 rows containing non-finite values (stat_ydensity).Warning: Removed 309 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_tcga_wgs_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 112 rows containing non-finite values (stat_ydensity).Warning: Removed 316 rows containing missing values (geom_point).Subset by Bacterium
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number)) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Race)
dat <- dat.rna.s %>%
dplyr::mutate(Race = race) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Race)
analysis.dat <- full_join(analysis.dat, dat)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Race")dat <- dat.wgs.s %>%
dplyr::mutate(Race = race) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, Race)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(
pres = ifelse(Abundance > 0, 1, 0),
Abund = Abundance*100,
Tumor = ifelse(tumor==1, "Tumor", "No Tumor")
)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"Race")analysis.dat$Race[analysis.dat$Race == "asian"] <- "Asian"
analysis.dat$Race[analysis.dat$Race == "B"] <- "Black"
analysis.dat$Race[analysis.dat$Race == "black or african american"] <- "Black"
analysis.dat$Race[analysis.dat$Race == "H"] <- "Hispanic"
analysis.dat$Race[analysis.dat$Race == "O"] <- "Other"
analysis.dat$Race[analysis.dat$Race == "W"] <- "White"
analysis.dat$Race[analysis.dat$Race == "white"] <- "White"
p <- analysis.dat %>%
filter(!is.na(Race), source=="16s", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 46 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_NCI_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 36 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_NCI_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 44 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="rna", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 96 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_tcga_rna_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 96 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_tcga_rna_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 96 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="wgs", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 37 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_tcga_wgs_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 39 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_tcga_wgs_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 38 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="16s", OTU == "Prevotella melaninogenica" | OTU =="Prevotella spp.")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 21 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_NCI_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 21 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_NCI_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 25 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="rna", OTU == "Prevotella melaninogenica")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 98 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_tcga_rna_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 97 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_tcga_rna_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 97 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="wgs", OTU == "Prevotella melaninogenica")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 36 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_tcga_wgs_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 35 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_tcga_wgs_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 34 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="16s", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 59 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_NCI_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 59 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_NCI_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 69 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="rna", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 108 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_tcga_rna_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 113 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_tcga_rna_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 114 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="wgs", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 47 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_tcga_wgs_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 52 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_tcga_wgs_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 46 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="16s", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 2 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_NCI_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 1 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_NCI_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 6 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="rna", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 99 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_tcga_rna_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 97 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_tcga_rna_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 93 rows containing non-finite values (stat_ydensity).Warning: Groups with fewer than two data points have been dropped.Warning: Removed 99 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Race), source=="wgs", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Race, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Race", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 48 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2E_tcga_wgs_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 51 rows containing missing values (geom_point).ggsave("output/supplemental_figure2E_tcga_wgs_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 50 rows containing missing values (geom_point).Stage
#root function
root<-function(x){
x <- ifelse(x < 0, 0, x)
x**(0.25)
}
#inverse root function
invroot<-function(x){
x**(4)
}
DIM <- c(6, 4)
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number)) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, tumor.stage)
dat <- dat.rna.s %>%
dplyr::mutate(Race = race) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, tumor.stage)
analysis.dat <- full_join(analysis.dat, dat)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"tumor.stage")dat <- dat.wgs.s %>%
dplyr::mutate(Race = race) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, tumor.stage)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(
pres = ifelse(Abundance > 0, 1, 0),
Abund = Abundance*100,
Tumor = ifelse(tumor==1, "Tumor", "No Tumor"),
Tumor_Stage = tumor.stage
)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"tumor.stage")#analysis.dat$Tumor_Stage[analysis.dat$Tumor_Stage == "0"] <- ""
p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="16s")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 127 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_NCI_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 126 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_NCI_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 129 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(source=="rna", !is.na(Tumor_Stage))%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 658 rows containing non-finite values (stat_ydensity).Warning: Removed 728 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_tcga_rna_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 658 rows containing non-finite values (stat_ydensity).Warning: Removed 730 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_tcga_rna_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 658 rows containing non-finite values (stat_ydensity).Warning: Removed 734 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(source=="wgs", !is.na(Tumor_Stage))%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 98 rows containing non-finite values (stat_ydensity).Warning: Removed 276 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_tcga_wgs_combined.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 98 rows containing non-finite values (stat_ydensity).Warning: Removed 282 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_tcga_wgs_combined.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 98 rows containing non-finite values (stat_ydensity).Warning: Removed 295 rows containing missing values (geom_point).Subset by Bacterium
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number)) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, tumor.stage)
dat <- dat.rna.s %>%
dplyr::mutate(Race = race) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, tumor.stage)
analysis.dat <- full_join(analysis.dat, dat)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"tumor.stage")dat <- dat.wgs.s %>%
dplyr::mutate(Race = race) %>%
dplyr::select(OTU, sample_type, tumor, Abundance, ID, source, tumor.stage)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(
pres = ifelse(Abundance > 0, 1, 0),
Abund = Abundance*100,
Tumor = ifelse(tumor==1, "Tumor", "No Tumor"),
Tumor_Stage = tumor.stage
)Joining, by = c("OTU", "sample_type", "tumor", "Abundance", "ID", "source",
"tumor.stage")#analysis.dat$Tumor_Stage[analysis.dat$Tumor_Stage == "0"] <- ""
p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="16s", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Groups with fewer than two data points have been dropped.Warning: Removed 46 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_NCI_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Groups with fewer than two data points have been dropped.
Removed 46 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_NCI_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Groups with fewer than two data points have been dropped.Warning: Removed 40 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="rna", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 94 rows containing non-finite values (stat_ydensity).Warning: Removed 97 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_tcga_rna_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 94 rows containing non-finite values (stat_ydensity).
Removed 97 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_tcga_rna_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 94 rows containing non-finite values (stat_ydensity).Warning: Removed 96 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="wgs", OTU == "Fusobacterium nucleatum")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 38 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_tcga_wgs_fuso.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 35 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_tcga_wgs_fuso.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 36 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="16s", OTU == "Prevotella melaninogenica" | OTU =="Prevotella spp.")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Groups with fewer than two data points have been dropped.Warning: Removed 20 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_NCI_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Groups with fewer than two data points have been dropped.Warning: Removed 18 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_NCI_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Groups with fewer than two data points have been dropped.Warning: Removed 14 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="rna", OTU == "Prevotella melaninogenica")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 94 rows containing non-finite values (stat_ydensity).Warning: Removed 97 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_tcga_rna_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 94 rows containing non-finite values (stat_ydensity).Warning: Removed 96 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_tcga_rna_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 94 rows containing non-finite values (stat_ydensity).Warning: Removed 97 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="wgs", OTU == "Prevotella melaninogenica")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 31 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_tcga_wgs_prevo.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 29 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_tcga_wgs_prevo.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 34 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="16s", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Groups with fewer than two data points have been dropped.Warning: Removed 66 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_NCI_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Groups with fewer than two data points have been dropped.Warning: Removed 57 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_NCI_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Groups with fewer than two data points have been dropped.Warning: Removed 66 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="rna", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 94 rows containing non-finite values (stat_ydensity).Warning: Removed 112 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_tcga_rna_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 94 rows containing non-finite values (stat_ydensity).Warning: Removed 118 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_tcga_rna_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 94 rows containing non-finite values (stat_ydensity).Warning: Removed 103 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="wgs", OTU == "Campylobacter concisus")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 39 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_tcga_wgs_campy.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 41 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_tcga_wgs_campy.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 48 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="16s", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Groups with fewer than two data points have been dropped.Warning: Removed 6 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_NCI_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Groups with fewer than two data points have been dropped.Warning: Removed 4 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_NCI_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Groups with fewer than two data points have been dropped.
Removed 4 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="rna", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 94 rows containing non-finite values (stat_ydensity).Warning: Removed 97 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_tcga_rna_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 94 rows containing non-finite values (stat_ydensity).Warning: Removed 96 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_tcga_rna_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 94 rows containing non-finite values (stat_ydensity).
Removed 96 rows containing missing values (geom_point).p <- analysis.dat %>%
filter(!is.na(Tumor_Stage), source=="wgs", OTU == "Streptococcus sanguinis")%>%
ggplot(aes(x=Tumor_Stage, y=Abund))+
geom_violin(scale="width", adjust=1)+
geom_jitter(alpha=0.5, width = 0.25)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
) +
labs(x="Tumor_Stage", y="Relative Abundance (%)")+
theme_classic()
pWarning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 45 rows containing missing values (geom_point).
ggsave("output/supplemental_figure2F_tcga_wgs_strepto.pdf", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 42 rows containing missing values (geom_point).ggsave("output/supplemental_figure2F_tcga_wgs_strepto.png", p, units = "in", width = DIM[1], height = DIM[2])Warning: Removed 14 rows containing non-finite values (stat_ydensity).Warning: Removed 45 rows containing missing values (geom_point).
sessionInfo()R version 4.2.0 (2022-04-22 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_1.1.1 dendextend_1.16.0 ggdendro_0.1.23 reshape2_1.4.4
[5] car_3.1-0 carData_3.0-5 gvlma_1.0.0.3 patchwork_1.1.1
[9] viridis_0.6.2 viridisLite_0.4.0 gridExtra_2.3 xtable_1.8-4
[13] kableExtra_1.3.4 MASS_7.3-56 data.table_1.14.2 readxl_1.4.0
[17] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
[21] readr_2.1.2 tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6
[25] tidyverse_1.3.2 lmerTest_3.1-3 lme4_1.1-30 Matrix_1.4-1
[29] vegan_2.6-2 lattice_0.20-45 permute_0.9-7 phyloseq_1.40.0
[33] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] googledrive_2.0.0 minqa_1.2.4 colorspace_2.0-3
[4] ellipsis_0.3.2 rprojroot_2.0.3 XVector_0.36.0
[7] fs_1.5.2 rstudioapi_0.13 farver_2.1.1
[10] fansi_1.0.3 lubridate_1.8.0 xml2_1.3.3
[13] codetools_0.2-18 splines_4.2.0 cachem_1.0.6
[16] knitr_1.39 ade4_1.7-19 jsonlite_1.8.0
[19] nloptr_2.0.3 broom_1.0.0 cluster_2.1.3
[22] dbplyr_2.2.1 BiocManager_1.30.18 compiler_4.2.0
[25] httr_1.4.3 backports_1.4.1 assertthat_0.2.1
[28] fastmap_1.1.0 gargle_1.2.0 cli_3.3.0
[31] later_1.3.0 htmltools_0.5.2 tools_4.2.0
[34] igraph_1.3.4 gtable_0.3.0 glue_1.6.2
[37] GenomeInfoDbData_1.2.8 Rcpp_1.0.8.3 Biobase_2.56.0
[40] cellranger_1.1.0 jquerylib_0.1.4 vctrs_0.4.1
[43] Biostrings_2.64.0 rhdf5filters_1.8.0 multtest_2.52.0
[46] svglite_2.1.0 ape_5.6-2 nlme_3.1-157
[49] iterators_1.0.14 xfun_0.31 ps_1.7.0
[52] rvest_1.0.2 lifecycle_1.0.1 googlesheets4_1.0.0
[55] getPass_0.2-2 zlibbioc_1.42.0 scales_1.2.0
[58] hms_1.1.1 promises_1.2.0.1 parallel_4.2.0
[61] biomformat_1.24.0 rhdf5_2.40.0 yaml_2.3.5
[64] sass_0.4.2 stringi_1.7.6 highr_0.9
[67] S4Vectors_0.34.0 foreach_1.5.2 BiocGenerics_0.42.0
[70] boot_1.3-28 GenomeInfoDb_1.32.2 systemfonts_1.0.4
[73] rlang_1.0.2 pkgconfig_2.0.3 bitops_1.0-7
[76] evaluate_0.15 Rhdf5lib_1.18.2 processx_3.7.0
[79] tidyselect_1.1.2 plyr_1.8.7 magrittr_2.0.3
[82] R6_2.5.1 IRanges_2.30.0 generics_0.1.3
[85] DBI_1.1.3 withr_2.5.0 pillar_1.8.0
[88] haven_2.5.0 whisker_0.4 mgcv_1.8-40
[91] abind_1.4-5 survival_3.3-1 RCurl_1.98-1.8
[94] modelr_0.1.8 crayon_1.5.1 utf8_1.2.2
[97] tzdb_0.3.0 rmarkdown_2.14 grid_4.2.0
[100] callr_3.7.1 git2r_0.30.1 webshot_0.5.3
[103] reprex_2.0.1 digest_0.6.29 httpuv_1.6.5
[106] numDeriv_2016.8-1.1 stats4_4.2.0 munsell_0.5.0
[109] bslib_0.4.0