Descriptive summary of source datafile
Plotting means by timepoint: KP biomarkers
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = TRP_nM))
gg.idline <- gg.base + geom_line(aes(color = TRP_nM, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = five_HT_nM))
gg.idline <- gg.base + geom_line(aes(color = five_HT_nM, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = KYN_nM))
gg.idline <- gg.base + geom_line(aes(color = KYN_nM, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = three_HK_nM))
gg.idline <- gg.base + geom_line(aes(color = three_HK_nM, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = KYNA_nM))
gg.idline <- gg.base + geom_line(aes(color = KYNA_nM, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = PIC_nM))
gg.idline <- gg.base + geom_line(aes(color = PIC_nM, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = Quin_nM))
gg.idline <- gg.base + geom_line(aes(color = Quin_nM, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = AA_nM))
gg.idline <- gg.base + geom_line(aes(color = AA_nM, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = KYN_TRP_ratio))
gg.idline <- gg.base + geom_line(aes(color = KYN_TRP_ratio, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = KYN_SER_ratio))
gg.idline <- gg.base + geom_line(aes(color = KYN_SER_ratio, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = QUIN_PIC_ratio))
gg.idline <- gg.base + geom_line(aes(color = QUIN_PIC_ratio, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = QUIN_KYNA_ratio))
gg.idline <- gg.base + geom_line(aes(color = QUIN_KYNA_ratio, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = threeHK_KYN_ratio))
gg.idline <- gg.base + geom_line(aes(color = threeHK_KYN_ratio, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = threeHK_KYNA_ratio))
gg.idline <- gg.base + geom_line(aes(color = threeHK_KYNA_ratio, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
# gg.base + geom_line(aes(color = Remission, group = patientno))+ geom_point()+
# stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
Plotting means by timepoint: inflammatory markers
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL1B_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = IL1B_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL1B_pg_mL_LLOD))
gg.idline <- gg.base + geom_line(aes(color = IL1B_pg_mL_LLOD, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL2_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = IL2_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL2_pg_mL_LLOD))
gg.idline <- gg.base + geom_line(aes(color = IL2_pg_mL_LLOD, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL4_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = IL4_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL4_pg_mL_LLOD))
gg.idline <- gg.base + geom_line(aes(color = IL4_pg_mL_LLOD, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL6_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = IL6_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL8_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = IL8_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL10_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = IL10_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL10_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = IL10_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL12p70_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = IL12p70_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL12p70_pg_mL_LLOD))
gg.idline <- gg.base + geom_line(aes(color = IL12p70_pg_mL_LLOD, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL13_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = IL13_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IL13_pg_mL_LLOD))
gg.idline <- gg.base + geom_line(aes(color = IL13_pg_mL_LLOD, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = TNFa_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = TNFa_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = IFNy_pg_mL))
gg.idline <- gg.base + geom_line(aes(color = IFNy_pg_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = CRP_ng_mL))
gg.idline <- gg.base + geom_line(aes(color = CRP_ng_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
Plotting means by timepoint: vascular endothelial biomarkers
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = NIC_nM))
gg.idline <- gg.base + geom_line(aes(color = NIC_nM, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = NIC_nM_LLOD))
gg.idline <- gg.base + geom_line(aes(color = NIC_nM_LLOD, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = NTA_nM))
gg.idline <- gg.base + geom_line(aes(color = NTA_nM, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = SAA_ng_mL))
gg.idline <- gg.base + geom_line(aes(color = SAA_ng_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = VCAM_1_ng_mL))
gg.idline <- gg.base + geom_line(aes(color = VCAM_1_ng_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
gg.base <- ggplot(Biok_vert_noNA, aes(x = infusionno, y = ICAM_1_ng_mL))
gg.idline <- gg.base + geom_line(aes(color = ICAM_1_ng_mL, group = Remission))
gg.idline + facet_wrap( ~ Remission, labeller = label_both) + geom_point()
gg.base + stat_summary(aes(group = Remission, color = Remission), geom = "line", fun.y = mean, size = 3)
PCA - Scree plot
pca_df<-Biok_vert_df %>% dplyr::select(all_of(vars_demo),
all_of(vars_tx),
all_of(vars_cx),
all_of(vars_KP) ,
all_of(vars_inflam),
all_of(vars_vasc)) %>%
dplyr::select(-patientno, -age, -sex, -BMI, -race, -Site_Location, -infusionno, -Sample_ID, -Batch_Number, -Blood_Draw_Event, -Remission) %>%
arrange(Remission_new)
#PCA function
row.names(pca_df) <- paste(pca_df$Remission_new, row.names(pca_df), sep="_")
pca_df$Remission_new <- NULL
bx_pca_scaled <- data.frame(t(na.omit(t(pca_df))))
pca_sample <- prcomp(bx_pca_scaled, retx=TRUE, center = TRUE, scale. = TRUE)
#SCREE PLOT
var_explained = pca_sample$sdev^2 / sum(pca_sample$sdev^2)
qplot(c(1:31), var_explained) +
geom_line() +
xlab("Principal Component") +
ylab("Variance Explained") +
ggtitle("Scree Plot of total sample") +
ylim(0, 0.2)
PCA by Remission status
#PCA visualization
pca_sample_remission <- c(rep("Non-remitter (N=157)", 157), rep("Remitter (N=60)", 60), rep("(missing, N=1)",1))
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,2),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,3),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA2 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,3),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA3 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA4 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA5 vs. all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(5,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(7,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA6 and 7 vs. all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(7,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_remission) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
PCA by sex
#PCA visualization
pca_sample_sex <- c(rep("Female (N=136)", 136), rep("Male (N=82)", 82))
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,2),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,3),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA2 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,3),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA3 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA4 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA5 vs. all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(5,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(7,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA6 and 7 vs. all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(7,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_sex) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
PCA by infusion number
pca_sample_infusionno <- c(rep("Baseline", 73), rep("1st infusion", 72), rep("3rd infusion", 73))
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,2),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,3),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA2 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,3),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA3 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA4 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA5 vs. all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(5,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(7,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA6 and 7 vs. all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(7,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_infusionno) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
PCA by site location
pca_sample_Site_Location <- c(rep("Johns Hopkins", 22),
rep("University of Michigan", 69),
rep("Mayo Clinic", 114),
rep("Pine Rest/MSU", 13))
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,2),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,3),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA2 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,3),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA3 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA4 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA5 vs. all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(5,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(7,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA6 and 7 vs. all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(7,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Site_Location) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
PCA by batch
pca_sample_Batch <- c(rep("Batch #1", 35),
rep("Batch #2", 37),
rep("Batch #3", 37),
rep("Batch #4", 36),
rep("Batch #5", 36),
rep("Batch #6", 37))
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,2),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,3),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(1,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA2 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,3),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(2,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA3 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,4),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(3,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA4 vs all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,5),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,6),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(4,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA5 vs. all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(5,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(7,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
#PCA6 and 7 vs. all
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,7),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(6,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()
ggbiplot::ggbiplot(pca_sample,
ellipse=TRUE,
choices=c(7,8),
obs.scale = 1,
var.scale = 1,
var.axes=FALSE,
labels=rownames(Biok_vert_df),
groups=pca_sample_Batch) +
ggtitle("Principle component analysis (Biok sample N=218)")+
theme_light()