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ICC.Diver analysis.R
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ICC.Diver analysis.R
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# Package load
library(tidyverse)
library(survminer)
library(haven)
library(ggcorrplot)
library(pander)
library(psych)
library(corrgram)
# Data preparation
dat <- read_sav("/Volumes/MacintoshD/iCCA/ICC_paper/iCCA_SCFs/OS.ICC.sav")
dat2 <- read_sav("/Volumes/MacintoshD/iCCA/ICC_paper/iCCA_SCFs/ICC.Final analysis.sav")
head(dat2)
attach(dat2)
# Correlation analysis with psych package
vars <- cbind (SBD, CLC, DPM, Rep.HGP, Size.cm, MVD.mm, Portal.tract, AVD.mm, u.AVD.mm, OS.m, DFS.m)
a1 <- pairs.panels(vars, stars = TRUE,col.stars = "red", cex.cor = 0.8, cor=TRUE, show.legend=TRUE, colors = TRUE, adjust="BH", method = "pearson", alpha = .05)
cocol<- colorRampPalette(c("#6D9EC1", "white", "#E46726"))
corPlot(vars, stars=TRUE, cex =1, keep.par=FALSE, upper = TRUE, scale = TRUE, pval = TRUE, adjust="BH")
mycorrelations <- psych::corr.test(vars, use = "pairwise",method="pearson",adjust="BH",
alpha=.05,ci=TRUE,minlength=5)
mycorrelations$r %>%
pander()
mycorrelations$p %>%
pander()
write.csv(mycorrelations$p, file="iCCA_corre-p-values.csv")
library("PerformanceAnalytics")
chart.Correlation(vars, histogram=TRUE, pch=19, col = "blue")
library(RColorBrewer)
library(relaimpo)
model1 = lm(OS.m ~ BD + Pus.HGP + Size.cm + MVD.mm + AVD.mm + Portal.tract)
metrics = calc.relimp (model1, type = c("lmg"))
metrics
attach(dat)
# * p<0.05, ** p<0.01, *** p<0.001
# Diagnosis5a in survirval analysis = Diagnosis5WHO in others
# Evaluate each component in 5% increaments
# Definition: HCC-like = >95% (100 %) hepatoid pattern
# Bile ductular (cholangiolocarcinoma >80% CLC pattern = WHO 2019)
# Small bile duct > SBD component < 5 %, in this study actually SBD = >95% (100 %) SBD pattern
# Mixied with DPM => DPM pattern >=5%
# Mixed SBD-BD: 5% =< BD =< 80%
# iCCA with CLC >= 5% CLC (ductular) pattern
# DFS: Disease free survival: The period from operation to first recurence or die of disease with either comimng first
# Cases no recurence and alive at last followup were censored for DFS
# OS: Overal survival: Time from operation till death regardless of etiology
# Cases alive at last followup were censored for OS
# Recur = original data
# Recur2 = Original data + death of disease - stage 4 cases or case die within 1 month
# Recur 3 = Original data + death of disease including stage 4
# reported Recur2 in paper.
# Cases with stage IV were excluded from DFS analysis
# Cases died within one months from operation were excluded from OS analysis
# Interpretation of the results:
os5 <- survfit(Surv(OSY, Death) ~ Diagnosis5, data = dat)
os5
os5a <- survfit(Surv(OSY, Death) ~ Diagnosis5a, data = dat)
os5a
os.SOTH <- survfit(Surv(OSY, Death) ~ Diagnosis.SOTH, data = dat)
os.SOTH
os.D100 <- survfit(Surv(OSY, Death) ~ DiagnosisD100, data = dat)
os.D100
os.D3compo <- survfit(Surv(OSY, Death) ~ D3compo, data = dat)
os.D3compo
osst <- survfit(Surv(OSM, Death) ~ St.AJCC2018, data = dat)
osst
osIDH1 <- survfit(Surv(OSY, Death) ~ IDH1.status, data = dat)
osIDH <- survfit(Surv(OSY, Death) ~ IDHmutation, data = dat)
fds5 <- survfit(Surv(FDS.Y, Recur2) ~ Diagnosis5, data = dat)
fds5
fds5a <- survfit(Surv(FDS.Y, Recur2) ~ Diagnosis5a, data = dat)
fds5a
fds5e4 <- survfit(Surv(FDS.Me4, Recur2) ~ Diagnosis5, data = dat)
fds5e4
fdsSOTH <- survfit(Surv(FDS.Y, Recur2) ~ Diagnosis.SOTH, data = dat)
fdsSOTH
fdsD3compo <- survfit(Surv(FDS.Y, Recur2) ~ D3compo, data = dat)
fdsD3compo
ggsurvplot(os5,
pval = TRUE, conf.int = F,
risk.table = T, # Add risk table
# Change risk table color by groups
linetype = "strata", # Change line type by groups
break.time.by = 1, # Specify median survival
legend.title = "Histological subtypes",
xlab = "Years since operation",
ylab = "Probability of Survival",
xlim = c(0, 15),
main = "Survival curve",
font.main = 14,
font.x = c(13, "bold"),
font.y = c(13, "bold"),
font.tickslab = 10,
# legend.labs = c("Ductular type", "Mixed type with DPM", "Mixed type", "Small bile duct type"),
legend = "right",
palette = "lancet") # Change ggplot2 theme
ggsurvplot(os5a,
pval = TRUE, conf.int = F,
risk.table = T, # Add risk table
# Change risk table color by groups
linetype = "strata", # Change line type by groups
break.time.by = 1, # Specify median survival
legend.title = "Histological subtypes",
xlab = "Years since operation",
ylab = "Probability of Survival",
xlim = c(0, 15),
main = "Survival curve",
font.main = 14,
font.x = c(13, "bold"),
font.y = c(13, "bold"),
font.tickslab = 10,
# legend.labs = c("Ductular type", "Mixed type with DPM", "Mixed type", "Small bile duct type"),
legend = "right",
palette = "lancet") # Change ggplot2 theme
ggsurvplot(os.SOTH,
pval = T, conf.int = F,
risk.table = F, # Add risk table
# Change risk table color by groups
linetype = "strata", # Change line type by groups
break.time.by = 1, # Specify median survival
legend.title = "Histological subtypes",
xlab = "Years since operation",
ylab = "Probability of Survival",
xlim = c(0, 15),
main = "Survival curve",
font.main = 14,
font.x = c(13, "bold"),
font.y = c(13, "bold"),
# legend.labs = c("Ductular type", "Mixed type with DPM", "Mixed type", "Small bile duct type"),
legend = "right",
palette = "lancet") # Change ggplot2 theme
ggsurvplot(fds5,
pval = T, conf.int = F,
risk.table = F, # Add risk table
# Change risk table color by groups
linetype = "strata",# Change line type by groups
break.time.by = 1, # Specify median survival
legend.title = "Histological subtypes",
xlim = c(0, 10),
xlab = "Years since operation",
ylab = "Probability of Disease-free survival",
main = "Survival curve",
font.main = 14,
font.x = c(13, "bold"),
font.y = c(13, "bold"),
font.tickslab = 10,
legend.labs = levels(dat$Diagnosis5),
legend = "right",
palette = "lancet") # Change ggplot2 theme
ggsurvplot(fds5a,
pval = T, conf.int = F,
risk.table = F, # Add risk table
# Change risk table color by groups
linetype = "strata",# Change line type by groups
break.time.by = 1, # Specify median survival
legend.title = "Histological subtypes",
xlim = c(0, 10),
xlab = "Years since operation",
ylab = "Probability of Disease-free survival",
main = "Survival curve",
font.main = 14,
font.x = c(13, "bold"),
font.y = c(13, "bold"),
font.tickslab = 10,
legend.labs = levels(dat$Diagnosis5),
legend = "right",
palette = "lancet") # Change ggplot2 theme
summary(fds5e4)$table
ggsurvplot(fdsSOTH,
pval = T, conf.int = F,
risk.table = F, # Add risk table
# Change risk table color by groups
linetype = "strata",# Change line type by groups
break.time.by = 1, # Specify median survival
legend.title = "Histological subtypes",
xlim = c(0, 10),
ylab = "Probability of Disease-free survival",
xlab = "Years since operation",
main = "Survival curve",
font.main = 14,
font.x = c(13, "bold"),
font.y = c(13, "bold"),
font.tickslab = 10,
legend.labs = levels(dat$Diagnosis5),
legend = "right",
palette = "lancet") # Change ggplot2 theme
ggsurvplot(osIDH1,
pval = T, conf.int = F,
risk.table = F, # Add risk table
# Change risk table color by groups
linetype = "strata", # Change line type by groups
break.time.by = 1, # Specify median survival
legend.title = "Histological subtypes",
xlab = "Years since operation",
ylab = "Probability of Survival",
xlim = c(0, 15),
main = "Survival curve",
font.main = 14,
font.x = c(13, "bold"),
font.y = c(13, "bold"),
# legend.labs = c("Ductular type", "Mixed type with DPM", "Mixed type", "Small bile duct type"),
legend = "right",
palette = "lancet") # Change ggplot2 theme
ggsurvplot(osIDH,
pval = T, conf.int = F,
risk.table = T, # Add risk table
# Change risk table color by groups
linetype = "strata", # Change line type by groups
break.time.by = 1, # Specify median survival
legend.title = "Histological subtypes",
xlab = "Years since operation",
ylab = "Probability of Survival",
xlim = c(0, 15),
main = "Survival curve",
font.main = 14,
font.x = c(13, "bold"),
font.y = c(13, "bold"),
# legend.labs = c("Ductular type", "Mixed type with DPM", "Mixed type", "Small bile duct type"),
legend = "right",
palette = "lancet") # Change ggplot2 theme
ggsurvplot(os.D3compo,
pval = T, conf.int = F,
risk.table = F, # Add risk table
# Change risk table color by groups
linetype = "strata", # Change line type by groups
break.time.by = 1, # Specify median survival
legend.title = "Histological subtypes",
xlab = "Years since operation",
ylab = "Probability of Survival",
xlim = c(0, 15),
main = "Survival curve",
font.main = 14,
font.x = c(13, "bold"),
font.y = c(13, "bold"),
# legend.labs = c("Ductular type", "Mixed type with DPM", "Mixed type", "Small bile duct type"),
legend = "right",
palette = "lancet") # Change ggplot2 theme
ggsurvplot(fdsD3compo,
pval = T, conf.int = F,
risk.table = F, # Add risk table
# Change risk table color by groups
linetype = "strata", # Change line type by groups
break.time.by = 1, # Specify median survival
legend.title = "Histological subtypes",
xlab = "Years since operation",
ylab = "Probability of Survival",
xlim = c(0, 15),
main = "Survival curve",
font.main = 14,
font.x = c(13, "bold"),
font.y = c(13, "bold"),
# legend.labs = c("Ductular type", "Mixed type with DPM", "Mixed type", "Small bile duct type"),
legend = "right",
palette = "lancet") # Change ggplot2 theme
# Cox regression models
head(dat)
surv_diffa <- survdiff(Surv(OSM, Death) ~ Diagnosis5a , data = dat)
surv_diffa
surv_diffSOTH <- survdiff(Surv(OSM, Death) ~ Diagnosis.SOTH , data = dat)
surv_diffSOTH
res.cox5a <- coxph(Surv(OSM, Death==1) ~ Diagnosis5a, data = dat)
res.cox5a
summary(res.cox5a)
res.coxT <- coxph(Surv(OSM, Death) ~ T.AJCC18Group, data = dat)
res.coxT
summary(res.coxT)
res.coxst <- coxph(Surv(OSM, Death) ~ St.AJCC2018, data = dat)
res.coxst
summary(res.coxst)
res.coxstgroup <- coxph(Surv(OSM, Death) ~ Stagegroup, data = dat)
res.coxstgroup
summary(res.coxstgroup)
res.coxB.ductalspread <- coxph(Surv(OSM, Death==1) ~ B.ductalspread, data = dat)
res.coxB.ductalspread
summary(res.coxB.ductalspread)
res.coxVascularinvasion <- coxph(Surv(OSM, Death==1) ~ Vascularinvasion, data = dat)
res.coxVascularinvasion
summary(res.coxVascularinvasion)
res.coxNeuralinvasion <- coxph(Surv(OSM, Death==1) ~ Neuralinvasion, data = dat)
res.coxNeuralinvasion
summary(res.coxNeuralinvasion)
res.coxIntrahepaticMets <- coxph(Surv(OSY, Death==1) ~ IntrahepaticMets, data = dat)
res.coxIntrahepaticMets
summary(res.coxIntrahepaticMets)
res.coxF <- coxph(Surv(OSM, Death==1) ~ F.stage, data = dat)
res.coxF
summary(res.coxF)
res.coxno.TumorSt.Mt <- coxph(Surv(OSM, Death==1) ~ no.TumorSt.Mt, data = dat)
res.coxno.TumorSt.Mt
summary(res.coxno.TumorSt.Mt)
res.coxSenosaS <- coxph(Surv(OSM, Death==1) ~ SenosaS, data = dat)
res.coxSenosaS
summary(res.coxSenosaS)
res.coxN<- coxph(Surv(OSM, Death==1) ~ N, data = dat)
res.coxN
summary(res.coxN)
res.coxT.diff<- coxph(Surv(OSY, Death==1) ~ T.diffgroup, data = dat)
res.coxT.diff
summary(res.coxT.diff)
res.coxAgegroup60<- coxph(Surv(OSM, Death==1) ~ Agegroup60, data = dat)
res.coxAgegroup60
summary(res.coxAgegroup60)
res.coxSizegroup<- coxph(Surv(OSM, Death==1) ~ Sizegroup, data = dat)
res.coxSizegroup
summary(res.coxSizegroup)
res.coxF.stage<- coxph(Surv(OSM, Death==1) ~ F.stage, data = dat)
res.coxF.stage
summary(res.coxF.stage)
res.coxGrow<- coxph(Surv(OSM, Death==1) ~ Growthpattern, data = dat)
res.coxGrow
summary(res.coxGrow)
res.coxp53.Status50 <- coxph(Surv(OSM, Death==1) ~ p53.Status50, data = dat)
res.coxp53.Status50
summary(res.coxp53.Status50 )
res.coxT2 <- coxph(Surv(OSM, Death) ~ T.AJCC18Group2, data = dat)
res.coxT2
summary(res.coxT2)
res.coxgender <- coxph(Surv(OSM, Death) ~ Gender , data = dat)
res.coxgender
summary(res.coxgender)
res.coxhepatoid <- coxph(Surv(OSM, Death) ~ Hepatoid , data = dat)
res.coxhepatoid
summary(res.coxhepatoid)
# Multiple analysis OS
res.coxOsmul <- coxph(Surv(OSY, Death==1) ~ Diagnosis5a + T.AJCC18Group2 + T.diff + IntrahepaticMets + no.TumorSt.Mt, data = dat)
res.coxOsmul
summary(res.coxOsmul)
res.coxOsmulSOTH <- coxph(Surv(OSY, Death==1) ~ Diagnosis5a + T.AJCC18Group2 + T.diff + IntrahepaticMets, data = dat)
res.coxOsmulSOTH
summary(res.coxOsmulSOTH)
# Disease free survival
res.coxfdD5a <- coxph(Surv(FDS.M, Recur2) ~ Diagnosis5a, data = dat)
res.coxfdD5a
summary(res.coxfdD5a)
res.coxfdstg <- coxph(Surv(FDS.M, Recur2) ~ Stagegroup, data = dat)
res.coxfdstg
summary(res.coxfdstg)
res.coxfn <- coxph(Surv(FDS.M, Recur2) ~ N, data = dat)
res.coxfn
summary(res.coxfn)
res.coxfim <- coxph(Surv(FDS.M, Recur2) ~ IntrahepaticMets, data = dat)
res.coxfim
summary(res.coxfim)
res.coxfstg <- coxph(Surv(FDS.M, Recur2) ~ Stage.AJCC2018, data = dat)
res.coxfstg
res.coxfst <- coxph(Surv(FDS.M, Recur2) ~ St.AJCC2018, data = dat)
res.coxfst
summary(res.coxst)
res.coxfvs <- coxph(Surv(FDS.Y, Recur2==1) ~ Vascularinvasion, data = dat)
res.coxfvs
summary(res.coxfvs)
res.coxfneu <- coxph(Surv(FDS.Y, Recur2) ~ Neuralinvasion, data = dat)
res.coxfneu
summary(res.coxfneu)
res.coxfgro <- coxph(Surv(FDS.M, Recur2) ~ Growthpattern , data = dat)
res.coxfgro
summary(res.coxfgro)
res.coxftdif <- coxph(Surv(FDS.M, Recur2) ~ T.diff , data = dat)
res.coxftdif
summary(res.coxftdif)
res.coxfdT <- coxph(Surv(FDS.Y, Recur2==1) ~ T.AJCC18Group, data = dat)
res.coxfdT
summary(res.coxfdT)
res.coxfdB.ductalspread <- coxph(Surv(FDS.Y, Recur2==1) ~ B.ductalspread, data = dat)
res.coxfdB.ductalspread
summary(res.coxfdB.ductalspread)
res.coxfdmul <- coxph(Surv(FDS.M, Recur2) ~ Diagnosis5a + T.AJCC18Group2 + T.diff + Neuralinvasion + IntrahepaticMets + Growthcode, data = dat)
res.coxfdmul
summary(res.coxfdmul)
res.coxD.SOTH <- coxph(Surv(OSY, Death==1) ~ Diagnosis.SOTH , data = dat)
res.coxD.SOTH
summary(res.coxD.SOTH)
res.coxD.SOTHm <- coxph(Surv(OSY, Death==1) ~ Diagnosis.SOTH + IntrahepaticMets + T.diff + T.AJCC18Group2, data = dat)
res.coxD.SOTHm
summary(res.coxD.SOTHm)
res.coxfdmul <- coxph(Surv(FDS.M, Recur2==1) ~ Diagnosis5a + T.AJCC18Group2 + T.diff + IntrahepaticMets + N, data = dat)
res.coxfdmul
summary (res.coxfdmul)
res.size <- coxph(Surv(OSM, Death==1) ~ Sizegroup2, data = dat)
res.size
res.agegroup60 <- coxph(Surv(OSM, Death==1) ~ Agegroup60, data = dat)
res.agegroup60
summary (res.agegroup60)
res.agegroup65 <- coxph(Surv(OSM, Death==1) ~ Agegroup65, data = dat)
res.agegroup65
res.Fstage <- coxph(Surv(FDS.Y, Recur2==1) ~ F.stage, data = dat)
res.Fstage
summary(res.Fstage)
res.coxfno.TumorSt.Mt <- coxph(Surv(FDS.M, Recur2==1) ~ no.TumorSt.Mt, data = dat)
res.coxfno.TumorSt.Mt
summary (res.coxfno.TumorSt.Mt)
res.coxfdT2 <- coxph(Surv(FDS.Y, Recur2) ~ T.AJCC18Group2, data = dat)
res.coxfdT2
summary(res.coxfdT2)
res.coxfdgender <- coxph(Surv(FDS.Y, Recur2) ~ Gender , data = dat)
res.coxfdgender
summary(res.coxfdgender)
res.fdagegroup60 <- coxph(Surv(FDS.Y, Recur2==1) ~ Agegroup60, data = dat)
res.fdagegroup60
summary (res.fdagegroup60)
res.coxfdhepatoid <- coxph(Surv(FDS.Y, Death) ~ Hepatoid , data = dat)
res.coxfdhepatoid
summary(res.coxfdhepatoid)
# Vascular analysis
library(readxl)
vas <- read_excel("/Volumes/Macintosh.D/ICC.paper/ICC with stem cell feature/Vascular.xlsx",
col_types = c("numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"text", "numeric", "numeric",
"numeric","numeric"))
# Note Diangosis5 = Diagnosis5a WHO 2019
View(vas)
head(vas)
attach(vas)
library(tidyverse)
library(ggplot2)
library(RColorBrewer)
library("ggpubr")
library(ggsignif)
display.brewer.all(colorblindFriendly = TRUE)
ordered(vas$Diagnosis.5,
levels = c("sbd", "mixed", "mdpm", "clc", "HCC-like"))
dat3 <- gather(vas, "CD34.bl.mm", "CD34.IM.mm", "Cd34.tc.mm", "MDV.CD34mm", "CD34.TC.p.mm", key = "location", value = "CD34")
dat4 <- gather(vas, "Ki67.im", "Ki67.tc", key = "Ki.location", value = "Ki67")
dat5 <- gather(vas, "AVD.lb.mm", "AVD.im.mm", "u.ADV.im.mm", "ADV.TC.mm", "u.ADV.TC.mm", "AVD.mm", "u.AVD.mm", key = "adv.location", value = "adv")
dat6 <- gather(vas, "AVD.lb.mm", "AVD.im.mm", "u.ADV.im.mm", "ADV.TC.mm", "u.ADV.TC.mm", "AVD.mm", "u.AVD.mm", key = "HGPs.location", value = "HGPs")
GCOLOR <- c("#82d142", "darkorange1", "deepskyblue", "blueviolet", "#247f79")
GCOLOR2 <- c("#6605f4", "#5500f9", "#34d1f0", "#a4f4f3", "#fbfea3")
g34 <- ggplot(dat3, mapping = aes(x = location, y = CD34, fill = Diagnosis.5), ylim(0, 300))
cd34plot <- g34 + geom_boxplot(outlier.shape = NA) +
scale_fill_manual (values = GCOLOR) +
geom_point(position=position_jitterdodge(jitter.width=0.02, dodge.width = 0.8), size = 2) +
scale_color_manual (values = GCOLOR) +
theme_classic() + theme(legend.position = "top")+
stat_compare_means(aes(group = Diagnosis.5), label = "p.format") +
scale_x_discrete(limits=c("CD34.bl.mm", "MDV.CD34mm", "CD34.IM.mm", "Cd34.tc.mm")) +
labs(title="Vascular density",x="Locations", y = "Number of vessels/mm2") +
geom_signif(comparisons = list(c("CD34.bl.mm", "MDV.CD34mm"),
c("CD34.IM.mm", "Cd34.tc.mm"))) +
theme( plot.title=element_text(size = 16, face = "bold"),
text=element_text(size = 14),
axis.text.x=element_text(colour="black", size = 12),
axis.text.y=element_text(colour="black", size = 12))
cd34plot
g67 <- ggplot(dat4, mapping = aes(x = Ki.location, y = Ki67, fill = Diagnosis.5), ylim(0, 150))
ki67plot <- g67 + geom_boxplot(outlier.size = 0.8) +
geom_point(position=position_jitterdodge(jitter.width=0.02, dodge.width = 0.8), size = 2,
aes(fill= Diagnosis.5)) +
scale_fill_manual (values = GCOLOR) +
scale_colour_manual(values = GCOLOR) +
theme_classic() + theme(legend.position = "top") +
stat_compare_means(aes(group = Diagnosis.5), label = "p.format") +
scale_x_discrete(limits=c("Ki67.im", "Ki67.tc")) +
labs(title="Ki67 proliferation index",x="Locations", y = "Percentage of tumor cells possitive for Ki67") +
geom_signif(comparisons = list(c("Ki67.im", "Ki67.tc"))) +
geom_signif(y_position = c(120, 120, 120)) +
theme( plot.title=element_text(size = 14, face = "bold"),
text=element_text(size = 12),
axis.text.x=element_text(colour="black", size = 12),
axis.text.y=element_text(colour="black", size = 12))
ki67plot
gadv <- ggplot(dat5, mapping = aes(x = adv.location, y = adv, fill = Diagnosis.5), ylim(0, 300))
advplot <- gadv + geom_boxplot(outlier.size = 0.8) +
scale_fill_manual (values = GCOLOR) +
geom_point(position=position_jitterdodge(jitter.width=0.02, dodge.width = 0.8), size = 1.5,
) + theme_classic() +
theme(legend.position = "top") +
stat_compare_means(aes(group = Diagnosis.5), label = "p.format") +
scale_x_discrete(limits=c("AVD.lb.mm", "AVD.mm", "AVD.im.mm", "ADV.TC.mm","u.ADV.im.mm", "u.ADV.TC.mm")) +
labs(title="Arterial density",x="Locations", y = "Number of arteries/mm2") +
geom_signif(comparisons = list(c("AVD.lb.mm", "AVD.mm"),
c("AVD.im.mm", "ADV.TC.mm"),
c("u.ADV.im.mm", "u.ADV.TC.mm"))) +
geom_signif(y_position = c(40, 40, 40)) +
theme( plot.title=element_text(size = 20, face = "bold"),
text=element_text(size = 16),
axis.text.x=element_text(colour="black", size = 12),
axis.text.y=element_text(colour="black", size = 12))
advplot
dat3$Fibro <- transmute(dat3, Fibro = as.character(dat3$Fibrosis))
g34f <- ggplot(dat3, mapping = aes(x = location, y = CD34, color = Fibro$Fibro))
cd34fplot <- g34f + geom_boxplot(outlier.shape = NA) +
geom_point(position=position_jitterdodge(jitter.width=0.02, dodge.width = 0.8), size = 1,
aes(shape = Fibro$Fibro)) +
theme_classic() + theme(legend.position = "top")+
stat_compare_means(aes(group = Fibro$Fibro), label = "p.format") +
scale_x_discrete(limits=c("CD34.bl.mm", "MDV.CD34mm", "CD34.IM.mm", "Cd34.tc.mm")) +
labs(title="Vascular density",x="Locations", y = "Number of vessels/mm2") +
geom_signif(comparisons = list(c("CD34.bl.mm", "MDV.CD34mm"),
c("CD34.IM.mm", "Cd34.tc.mm")))
cd34fplot
dat5$Fibro <- transmute(dat5, Fibro = as.character(dat5$Fibrosis))
gadvf <- ggplot(dat5, mapping = aes(x = adv.location, y = adv, color = Fibro$Fibro))
advfplot <- gadvf + geom_boxplot(outlier.size = 0.8) +
geom_point(position=position_jitterdodge(jitter.width=0.02, dodge.width = 0.8), size = 0.8,
aes(shape= Fibro$Fibro)) + theme_classic() +
theme(legend.position = "top") +
stat_compare_means(aes(group = Fibro$Fibro), label = "p.format") +
scale_x_discrete(limits=c("AVD.lb.mm", "AVD.mm", "u.AVD.mm")) +
labs(title="Arterial density",x="Locations", y = "Number of arteries/mm2") +
geom_signif(comparisons = list(c("AVD.lb.mm", "AVD.mm"))) +
geom_signif(y_position = c(40, 40, 40)) +
theme( plot.title=element_text(size = 20),
text=element_text(size = 16),
axis.text.x=element_text(colour="black", size = 14),
axis.text.y=element_text(colour="black", size = 14))
advfplot
wilcox.test(vas$CD34.IM, vas$Cd34.tc,
p.adjust.method = "BH")
wilcox.test(vas$CD34.bl, vas$MDV.CD34,
p.adjust.method = "BH")
wilcox.test(dat3$Ki67.im, dat3$Ki67.tc,
p.adjust.method = "BH")
wilcox.test(vas$ADV.TC.mm, vas$AVD.im.mm,
p.adjust.method = "BH")
wilcox.test(vas$AVD.lb.mm, vas$AVD.mm,
p.adjust.method = "BH")
library(haven)
PGC1a <- read_sav("Documents/PGC1a.sav")
View(PGC1a)
head(PGC1a)
osPGC1a <- survfit(Surv(OSM, Death) ~ PGC1a.status, data = PGC1a)
osPGC1a
dfsPGC1a <- survfit(Surv(DFSM, Recurence) ~ PGC1a.status, data = PGC1a)
dfsPGC1a
ggsurvplot(osPGC1a,
pval = T, conf.int = F,
risk.table = T, # Add risk table
# Change risk table color by groups
linetype = "strata", # Change line type by groups
break.time.by = 12, # Specify median survival
legend.title = "PGC1a",
legend.labs = levels(PGC1a$PGC1a.status),
xlab = "Time (month)",
ylab = "Overall survival",
legend = "right",
palette = "lancet") # Change ggplot2 theme
ggsurvplot(dfsPGC1a,
pval = T, conf.int = F,
risk.table = T, # Add risk table
# Change risk table color by groups
linetype = "strata", # Change line type by groups
break.time.by = 12, # Specify median survival
legend.title = "PGC1a",
legend.labs = levels(PGC1a$PGC1a.status),
xlab = "Time (month)",
ylab = "Probability of Disease-free survival",
legend = "right",
palette = "lancet") # Change ggplot2 theme