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s01_clinical_data_process.R
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### clinical information
### Liurong
### 2023-09-18
##install.packages("readr")
library("readr")
setwd("D:/analysis_lab/pharmacomicrobiomics/1_ICB_metagenome_SV/pipeline/4_R/00.rawData")
source("D:/analysis_lab/pharmacomicrobiomics/1_ICB_metagenome_SV/pipeline/4_R/functions.R")
###################################################################################
### PRJNA397906
clin_397906<-read.csv("clinical/PRJNA397906_clinical.csv",header=T)
id_397906<-read.csv("clinical/PRJNA397906_id.csv",header=T)
colnames(clin_397906)[1]<-"Patient"
clin_397906_whole<-merge(clin_397906,id_397906,by.x="Patient",by.y="Patient")
names(clin_397906_whole)[names(clin_397906_whole) =="Run"] <- "id"
names(clin_397906_whole)[names(clin_397906_whole) =="Age"] <- "age"
names(clin_397906_whole)[names(clin_397906_whole) =="RECIST.Category"] <- "recist"
names(clin_397906_whole)[names(clin_397906_whole) =="Change.in.Tumor.Size...."] <- "recist_ratio"
nr<-nrow(clin_397906_whole)
for(i in 1:nr){
if(is.na(clin_397906_whole$Sex[i])==T){clin_397906_whole$gender[i]=NA}
if(is.na(clin_397906_whole$Sex[i])==F){
if (clin_397906_whole$Sex[i]=="F"){clin_397906_whole$gender[i]="female"}
else if (clin_397906_whole$Sex[i]=="M"){clin_397906_whole$gender[i]="male"}
}
}
nr<-nrow(clin_397906_whole)
for(i in 1:nr){
if(is.na(clin_397906_whole$recist[i])==T){clin_397906_whole$response_code[i]=NA}
if(is.na(clin_397906_whole$recist[i])==F){
if (clin_397906_whole$recist[i]=="Response"){clin_397906_whole$response_code[i]=1}
else if (clin_397906_whole$recist[i]=="Progression"){clin_397906_whole$response_code[i]=0}
else if (clin_397906_whole$recist[i]=="Stable"){clin_397906_whole$response_code[i]=0}
}
}
clin_397906_whole$os<-NA
clin_397906_whole$os_event<-NA
clin_397906_whole$pfs<-NA
clin_397906_whole$pfs_event<-NA
clin_397906_whole$dataset<-"PRJNA397906"
clin_397906_whole$cancer_type<-"melanoma"
clin_397906_whole$LDH<-NA
clin_397906_whole$BMI<-NA
clin_397906_whole$pfs_12_months<-NA
clin_397906_whole$pfs_6_months<-NA
clin_397906_whole$irAEs<-NA
clin_397906_whole$irAEs_grade<-NA
clin_397906_whole$study<-"FrankelAE_2017"
clin_397906_Count<-read.csv("read_number/PRJNA397906.csv",header=T)
colnames(clin_397906_Count)<-c("id","read_count")
clin_397906_whole<-merge(clin_397906_whole,clin_397906_Count,by.x="id",by.y="id")
clin_397906_sub<-clin_397906_whole[,c("id","age","gender","os","os_event","pfs","pfs_event","drug","recist",
"study","dataset","cancer_type","recist_ratio","LDH","BMI","response_code","target","read_count","pfs_6_months",
"pfs_12_months","irAEs","irAEs_grade")]
################################################################################################################
### PRJNA399742
clin_399742<-read.csv("clinical/SRP116709_399742/human_16S.sampleinfo.csv",header=T)
id_399742<-read.csv("clinical/PRJNA399742_Run_table.csv",header=T)
colnames(clin_399742)[1]<-"subject_id"
clin_399742_whole<-merge(clin_399742,id_399742,by.x="subject_id",by.y="subject_id")
names(clin_399742_whole)[names(clin_399742_whole) =="Run"] <- "id"
names(clin_399742_whole)[names(clin_399742_whole) =="Response"] <- "response"
names(clin_399742_whole)[names(clin_399742_whole) =="BORR"] <- "recist"
names(clin_399742_whole)[names(clin_399742_whole) =="RECIST."] <- "recist_ratio"
nr<-nrow(clin_399742_whole)
for(i in 1:nr){
if(is.na(clin_399742_whole$recist[i])==T){clin_399742_whole$response_code[i]=NA}
if(is.na(clin_399742_whole$recist[i])==F){
if (clin_399742_whole$recist[i]=="Complete Response"){clin_399742_whole$response_code[i]=1}
else if (clin_399742_whole$recist[i]=="Partial Response"){clin_399742_whole$response_code[i]=1}
else if (clin_399742_whole$recist[i]=="Progressive Disease"){clin_399742_whole$response_code[i]=0}
else if (clin_399742_whole$recist[i]=="Stable Disease"){clin_399742_whole$response_code[i]=0}
}
}
clin_399742_whole$os<-NA
clin_399742_whole$os_event<-NA
clin_399742_whole$pfs<-NA
clin_399742_whole$pfs_event<-NA
clin_399742_whole$dataset<-"PRJNA399742"
clin_399742_whole$study<-"MatsonV_2018"
clin_399742_whole$cancer_type<-"melanoma"
clin_399742_whole$age<-NA
clin_399742_whole$drug<-NA
clin_399742_whole$gender<-NA
clin_399742_whole$LDH<-NA
clin_399742_whole$BMI<-NA
clin_399742_whole$pfs_12_months<-NA
clin_399742_whole$pfs_6_months<-NA
clin_399742_whole$irAEs<-NA
clin_399742_whole$irAEs_grade<-NA
clin_399742_Count<-read.csv("read_number/PRJNA399742.csv",header=T)
colnames(clin_399742_Count)<-c("id","read_count")
clin_399742_whole<-merge(clin_399742_whole,clin_399742_Count,by.x="id",by.y="id")
clin_399742_sub<-clin_399742_whole[,c("id","age","gender","os","os_event","pfs","pfs_event","drug","recist",
"study","dataset","cancer_type","recist_ratio","LDH","BMI","response_code","target","read_count","pfs_6_months",
"pfs_12_months","irAEs","irAEs_grade")]
####################################################################################################
## PRJNA541981
clin_541981<-read.csv("clinical/PRJNA541981_clinical.csv",header=T)
names(clin_541981)[names(clin_541981) =="AGE"] <- "age"
names(clin_541981)[names(clin_541981) =="dead"] <- "os_event"
names(clin_541981)[names(clin_541981) =="any_prog"] <- "pfs_event"
names(clin_541981)[names(clin_541981) =="immunotherapy"] <- "target"
names(clin_541981)[names(clin_541981) =="sex"] <- "gender"
clin_541981$os<-clin_541981$os_months/12
clin_541981$pfs<-clin_541981$pfs_months/12
nr<-nrow(clin_541981)
for(i in 1:nr){
if(is.na(clin_541981$pfs[i])==T | is.na(clin_541981$pfs_event[i])==T){clin_541981$pfs_6_months[i]=NA}
if(is.na(clin_541981$pfs[i])==F & is.na(clin_541981$pfs_event[i])==F){
if (clin_541981$pfs[i]>=0.5){clin_541981$pfs_6_months[i]=1}
else if (clin_541981$pfs[i]<0.5 & clin_541981$pfs_event[i]==1){clin_541981$pfs_6_months[i]=0}
else if (clin_541981$pfs[i]<0.5 & clin_541981$pfs_event[i]==0){clin_541981$pfs_6_months[i]=NA}
}
}
nr<-nrow(clin_541981)
for(i in 1:nr){
if(is.na(clin_541981$pfs[i])==T | is.na(clin_541981$pfs_event[i])==T){clin_541981$pfs_12_months[i]=NA}
if(is.na(clin_541981$pfs[i])==F & is.na(clin_541981$pfs_event[i])==F){
if (clin_541981$pfs[i]>=1){clin_541981$pfs_12_months[i]=1}
else if (clin_541981$pfs[i]<1 & clin_541981$pfs_event[i]==1){clin_541981$pfs_12_months[i]=0}
else if (clin_541981$pfs[i]<1 & clin_541981$pfs_event[i]==0){clin_541981$pfs_12_months[i]=NA}
}
}
clin_541981$dataset<-"PRJNA541981"
clin_541981$study<-"PetersBA_2019"
clin_541981$cancer_type<-"melanoma"
clin_541981$drug<-NA
clin_541981$recist_ratio<-NA
clin_541981$recist<-NA
clin_541981$response_code<-NA
clin_541981$irAEs<-NA
clin_541981$irAEs_grade<-NA
clin_541981_Count<-read.csv("read_number/PRJNA541981.csv",header=T)
colnames(clin_541981_Count)<-c("id","read_count")
clin_541981<-merge(clin_541981,clin_541981_Count,by.x="id",by.y="id")
clin_541981<-subset(clin_541981,time=="Baseline",drop=T)
clin_541981_sub<-clin_541981[,c("id","age","gender","os","os_event","pfs","pfs_event","drug","recist",
"study","dataset","cancer_type","recist_ratio","LDH","BMI","response_code","target","read_count","pfs_6_months",
"pfs_12_months","irAEs","irAEs_grade")]
## write.csv(clin_541981_sub,"clinical_541981.csv")
########################################################################################################
## PRJEB22863
clin_22863<-read.csv("clinical/PRJEB22863_clinical.csv",header=T)
id_22863<-read.csv("clinical/PRJEB22863_SraRunInfo.csv",header=T)
colnames(clin_22863)[1]<-"BioSample"
clin_22863_whole<-merge(id_22863,clin_22863,by.x="BioSample",by.y="BioSample")
names(clin_22863_whole)[names(clin_22863_whole) =="Run"] <- "id"
names(clin_22863_whole)[names(clin_22863_whole) =="host_age"] <- "age"
names(clin_22863_whole)[names(clin_22863_whole) =="host_disease_status"] <- "cancer_type"
names(clin_22863_whole)[names(clin_22863_whole) =="host_sex"] <- "gender"
names(clin_22863_whole)[names(clin_22863_whole) =="response"] <- "recist"
nr<-nrow(clin_22863_whole)
for(i in 1:nr){
if(is.na(clin_22863_whole$recist[i])==T){clin_22863_whole$response_code[i]=NA}
if(is.na(clin_22863_whole$recist[i])==F){
if (clin_22863_whole$recist[i]=="Partialresponse"){clin_22863_whole$response_code[i]=1}
else if (clin_22863_whole$recist[i]=="Dead"){clin_22863_whole$response_code[i]=0}
else if (clin_22863_whole$recist[i]=="Progression"){clin_22863_whole$response_code[i]=0}
else if (clin_22863_whole$recist[i]=="Stable"){clin_22863_whole$response_code[i]=0}
}
}
nr<-nrow(clin_22863_whole)
for(i in 1:nr){
if(is.na(clin_22863_whole$PFS_6_month[i])==T){clin_22863_whole$pfs_6_months[i]=NA}
if(is.na(clin_22863_whole$PFS_6_month[i])==F){
if (clin_22863_whole$PFS_6_month[i]=="Above"){clin_22863_whole$pfs_6_months[i]=1}
else if (clin_22863_whole$PFS_6_month[i]=="Below"){clin_22863_whole$pfs_6_months[i]=0}
else if (clin_22863_whole$PFS_6_month[i]=="Notdetermined"){clin_22863_whole$pfs_6_months[i]=NA}
}
}
clin_22863_whole$dataset<-"PRJEB22863"
clin_22863_whole$study<-"RoutyB_2018"
clin_22863_whole$drug<-NA
clin_22863_whole$recist_ratio<-NA
clin_22863_whole$recist<-NA
clin_22863_whole$LDH<-NA
clin_22863_whole$BMI<-NA
clin_22863_whole$os<-NA
clin_22863_whole$os_event<-NA
clin_22863_whole$pfs<-NA
clin_22863_whole$pfs_event<-NA
clin_22863_whole$pfs_12_months<-NA
clin_22863_whole$irAEs<-NA
clin_22863_whole$irAEs_grade<-NA
clin_22863_Count<-read.csv("read_number/PRJEB22863.csv",header=T)
colnames(clin_22863_Count)<-c("id","read_count")
clin_22863_whole<-merge(clin_22863_whole,clin_22863_Count,by.x="id",by.y="id")
clin_22863_sub<-clin_22863_whole[,c("id","age","gender","os","os_event","pfs","pfs_event","drug","recist",
"study","dataset","cancer_type","recist_ratio","LDH","BMI","response_code","target","read_count","pfs_6_months",
"pfs_12_months","irAEs","irAEs_grade")]
write.csv(clin_22863_whole,"clinical/PRJNA22863_clinical_whole.csv",row.names=F)
###########################################################################################################
### PRJNA770295
clin_770295<-read.csv("clinical/PRJNA770295_clinical.csv",header=T)
names(clin_770295)[names(clin_770295) =="Run"] <- "id"
names(clin_770295)[names(clin_770295) =="Age"] <- "age"
names(clin_770295)[names(clin_770295) =="Sex"] <- "gender"
clin_770295$pfs<-clin_770295$pfs_d/365
clin_770295$pfs_event<-clin_770295$pfsevent
clin_770295$response_code<-clin_770295$response
nr<-nrow(clin_770295)
for(i in 1:nr){
if(is.na(clin_770295$pfs[i])==T | is.na(clin_770295$pfs_event[i])==T){clin_770295$pfs_6_months[i]=NA}
if(is.na(clin_770295$pfs[i])==F & is.na(clin_770295$pfs_event[i])==F){
if (clin_770295$pfs[i]>=0.5){clin_770295$pfs_6_months[i]=1}
else if (clin_770295$pfs[i]<0.5 & clin_770295$pfs_event[i]==1){clin_770295$pfs_6_months[i]=0}
else if (clin_770295$pfs[i]<0.5 & clin_770295$pfs_event[i]==0){clin_770295$pfs_6_months[i]=NA}
}
}
nr<-nrow(clin_770295)
for(i in 1:nr){
if(is.na(clin_770295$pfs[i])==T | is.na(clin_770295$pfs_event[i])==T){clin_770295$pfs_12_months[i]=NA}
if(is.na(clin_770295$pfs[i])==F & is.na(clin_770295$pfs_event[i])==F){
if (clin_770295$pfs[i]>=1){clin_770295$pfs_12_months[i]=1}
else if (clin_770295$pfs[i]<1 & clin_770295$pfs_event[i]==1){clin_770295$pfs_12_months[i]=0}
else if (clin_770295$pfs[i]<1 & clin_770295$pfs_event[i]==0){clin_770295$pfs_12_months[i]=NA}
}
}
clin_770295$dataset<-"PRJNA770295"
clin_770295$study<-"SpencerCN_2021"
clin_770295$drug<-NA
clin_770295$recist_ratio<-NA
clin_770295$recist<-NA
clin_770295$os<-NA
clin_770295$os_event<-NA
clin_770295$cancer_type<-"melanoma"
clin_770295$irAEs<-NA
clin_770295$irAEs_grade<-NA
clin_770295_Count<-read.csv("read_number/PRJNA770295.csv",header=T)
colnames(clin_770295_Count)<-c("id","read_count")
clin_770295<-merge(clin_770295,clin_770295_Count,by.x="id",by.y="id")
clin_770295<-subset(clin_770295,treatment!="other systemic",drop=T)
clin_770295_sub<-clin_770295[,c("id","age","gender","os","os_event","pfs","pfs_event","drug","recist",
"study","dataset","cancer_type","recist_ratio","LDH","BMI","response_code","target","read_count","pfs_6_months",
"pfs_12_months","irAEs","irAEs_grade")]
###########################################################################################################
### PRJNA751792
clin_751792<-read.csv("clinical/PRJNA751792_clinical.csv",header=T)
id_751792<-read.csv("clinical/PRJNA751792_id_convert_sra.csv",header=T)
colnames(id_751792)<-c("id","SampleID")
clin_751792<-merge(id_751792,clin_751792,by.x="SampleID",by.y="SampleID")
##names(clin_751792)[names(clin_751792) =="Age"] <- "age"
nr<-nrow(clin_751792)
for(i in 1:nr){
if(is.na(clin_751792$sex[i])==T){clin_751792$gender[i]=NA}
if(is.na(clin_751792$sex[i])==F){
if (clin_751792$sex[i]=="Femme"){clin_751792$gender[i]="female"}
else if (clin_751792$sex[i]=="Homme"){clin_751792$gender[i]="male"}
}
}
nr<-nrow(clin_751792)
for(i in 1:nr){
if(is.na(clin_751792$Best.response[i])==T){clin_751792$response_code[i]=NA}
if(is.na(clin_751792$Best.response[i])==F){
if (clin_751792$Best.response[i]=="CR"){clin_751792$response_code[i]=1}
else if (clin_751792$Best.response[i]=="RP"){clin_751792$response_code[i]=1}
else if (clin_751792$Best.response[i]=="PD"){clin_751792$response_code[i]=0}
else if (clin_751792$Best.response[i]=="SD"){clin_751792$response_code[i]=0}
}
}
clin_751792$os<-clin_751792$OS_time/12
clin_751792$os_event<-clin_751792$OS_evt
clin_751792$study<-"DerosaL_2022"
clin_751792$dataset<-"PRJNA751792"
clin_751792$drug<-NA
clin_751792$recist_ratio<-NA
clin_751792$recist<-NA
clin_751792$BMI<-NA
clin_751792$pfs<-NA
clin_751792$pfs_event<-NA
clin_751792$cancer_type<-"NSCLC"
clin_751792$LDH<-NA
clin_751792$target<-"PD-1"
clin_751792$pfs_12_months<-NA
clin_751792$pfs_6_months<-NA
clin_751792$irAEs<-NA
clin_751792$irAEs_grade<-NA
clin_751792_Count<-read.csv("read_number/PRJNA751792.csv",header=T)
colnames(clin_751792_Count)<-c("id","read_count")
clin_751792<-merge(clin_751792,clin_751792_Count,by.x="id",by.y="id")
clin_751792_sub<-clin_751792[,c("id","age","gender","os","os_event","pfs","pfs_event","drug","recist",
"study","dataset","cancer_type","recist_ratio","LDH","BMI","response_code","target","read_count","pfs_6_months",
"pfs_12_months","irAEs","irAEs_grade")]
###############################################################################################
## PRJNA762360
clin_762360<-read.csv("clinical/PRJNA762360_clinical.csv",header=T)
clin_762360<-subset(clin_762360,Cohort=="Pittsburgh_Early",drop=T)
##clin_762360<-subset(clin_762360,timepoint_days<=120,drop=T)
names(clin_762360)[names(clin_762360) =="Run"] <- "id"
names(clin_762360)[names(clin_762360) =="AGE"] <- "age"
names(clin_762360)[names(clin_762360) =="Clin_Response_3_mth"] <- "response"
names(clin_762360)[names(clin_762360) =="Death_Event"] <- "os_event"
names(clin_762360)[names(clin_762360) =="Progression_Event"] <- "pfs_event"
names(clin_762360)[names(clin_762360) =="Pre_Treatment_Disease_Stage"] <- "stage"
names(clin_762360)[names(clin_762360) =="Immunotherapy_Drug"] <- "drug"
names(clin_762360)[names(clin_762360) =="sex"] <- "gender"
nr<-nrow(clin_762360)
for(i in 1:nr){
if(is.na(clin_762360$response[i])==T){clin_762360$response_code[i]=NA}
if(is.na(clin_762360$response[i])==F){
if (clin_762360$response[i]=="Responder"){clin_762360$response_code[i]=1}
else if (clin_762360$response[i]=="Non_Responder"){clin_762360$response_code[i]=0}
}
}
clin_762360$os<-clin_762360$overall_survival_days/365
clin_762360$pfs<-clin_762360$PFS_days/365
nr<-nrow(clin_762360)
for(i in 1:nr){
if(is.na(clin_762360$pfs[i])==T | is.na(clin_762360$pfs_event[i])==T){clin_762360$pfs_6_months[i]=NA}
if(is.na(clin_762360$pfs[i])==F & is.na(clin_762360$pfs_event[i])==F){
if (clin_762360$pfs[i]>=0.5){clin_762360$pfs_6_months[i]=1}
else if (clin_762360$pfs[i]<0.5 & clin_762360$pfs_event[i]==1){clin_762360$pfs_6_months[i]=0}
else if (clin_762360$pfs[i]<0.5 & clin_762360$pfs_event[i]==0){clin_762360$pfs_6_months[i]=NA}
}
}
nr<-nrow(clin_762360)
for(i in 1:nr){
if(is.na(clin_762360$pfs[i])==T | is.na(clin_762360$pfs_event[i])==T){clin_762360$pfs_12_months[i]=NA}
if(is.na(clin_762360$pfs[i])==F & is.na(clin_762360$pfs_event[i])==F){
if (clin_762360$pfs[i]>=1){clin_762360$pfs_12_months[i]=1}
else if (clin_762360$pfs[i]<1 & clin_762360$pfs_event[i]==1){clin_762360$pfs_12_months[i]=0}
else if (clin_762360$pfs[i]<1 & clin_762360$pfs_event[i]==0){clin_762360$pfs_12_months[i]=NA}
}
}
clin_762360$study<-"McCullochJA_2022"
clin_762360$dataset<-"PRJNA762360"
clin_762360$cancer_type<-"melanoma"
clin_762360$drug<-NA
clin_762360$recist_ratio<-NA
clin_762360$recist<-NA
clin_762360_Count<-read.csv("read_number/PRJNA762360.csv",header=T)
colnames(clin_762360_Count)<-c("id","read_count")
clin_762360<-merge(clin_762360,clin_762360_Count,by.x="id",by.y="id")
table(clin_762360$Most_Severe_IrAE)
nr<-nrow(clin_762360)
for(i in 1:nr){
if(is.na(clin_762360$Most_Severe_IrAE[i])==T){clin_762360$irAEs[i]=NA}
if(is.na(clin_762360$Most_Severe_IrAE[i])==F){
if (clin_762360$Most_Severe_IrAE[i]=="Grades_1-2"){clin_762360$irAEs[i]=1
clin_762360$irAEs_grade[i]="G1-G2" }
else if (clin_762360$Most_Severe_IrAE[i]=="Grades_3-4"){clin_762360$irAEs[i]=1
clin_762360$irAEs_grade[i]="G3-G4"}
else if (clin_762360$Most_Severe_IrAE[i]=="No_AE_reported"){clin_762360$irAEs[i]=0
clin_762360$irAEs_grade[i]=NA}
}
}
clin_762360_sub<-clin_762360[,c("id","age","gender","os","os_event","pfs","pfs_event","drug","recist",
"study","dataset","cancer_type","recist_ratio","LDH","BMI","response_code","target","read_count","pfs_6_months",
"pfs_12_months","irAEs","irAEs_grade")]
###############################################################################################
## PRJEB43119
clin_43119<-read.csv("clinical/PRJEB43119_clinical.csv",header=T)
names(clin_43119)[names(clin_43119) =="Run"] <- "id"
names(clin_43119)[names(clin_43119) =="age"] <- "age_bin"
nr<-nrow(clin_43119)
for(i in 1:nr){
if(is.na(clin_43119$ORR[i])==T){clin_43119$response_code[i]=NA}
if(is.na(clin_43119$ORR[i])==F){
if (clin_43119$ORR[i]=="CR"){clin_43119$response_code[i]=1}
if (clin_43119$ORR[i]=="PR"){clin_43119$response_code[i]=1}
else if (clin_43119$ORR[i]=="SD"){clin_43119$response_code[i]=0}
else if (clin_43119$ORR[i]=="PD"){clin_43119$response_code[i]=0}
}
}
nr<-nrow(clin_43119)
for(i in 1:nr){
if(is.na(clin_43119$PFS12[i])==T){clin_43119$pfs_12_months[i]=NA}
if(is.na(clin_43119$PFS12[i])==F){
if (clin_43119$PFS12[i]=="yes"){clin_43119$pfs_12_months[i]=1}
else if (clin_43119$PFS12[i]=="no"){clin_43119$pfs_12_months[i]=0}
}
}
clin_43119$study<-"LeeKA_2022"
clin_43119$dataset<-"PRJEB43119"
clin_43119$cancer_type<-"melanoma"
clin_43119$drug<-NA
clin_43119$recist_ratio<-NA
clin_43119$recist<-NA
clin_43119$age<-NA
clin_43119$os<-NA
clin_43119$os_event<-NA
clin_43119$pfs<-NA
clin_43119$pfs_event<-NA
clin_43119$LDH<-NA
clin_43119$target<-NA
clin_43119$pfs_6_months<-NA
clin_43119$irAEs<-NA
clin_43119$irAEs_grade<-NA
clin_43119_Count<-read.csv("read_number/PRJEB43119.csv",header=T)
colnames(clin_43119_Count)<-c("id","read_count")
clin_43119<-merge(clin_43119,clin_43119_Count,by.x="id",by.y="id")
clin_43119_sub<-clin_43119[,c("id","age","gender","os","os_event","pfs","pfs_event","drug","recist",
"study","dataset","cancer_type","recist_ratio","LDH","BMI","response_code","target","read_count","pfs_6_months",
"pfs_12_months","irAEs","irAEs_grade","age_bin")]
#############################################################################################################
### 合并临床信息 (399742除了response，缺乏基本的临床资料, clin_541981_sub 样本量小于30
clinical_sub<-rbind(clin_770295_sub,clin_22863_sub,clin_762360_sub,
clin_397906_sub,clin_751792_sub,clin_541981_sub)
min(clinical_sub$age,na.rm=T)
nr<-nrow(clinical_sub)
for(i in 1:nr){
if(is.na(clinical_sub$age[i])==T){clinical_sub$age_bin[i]=NA}
else if (is.na(clinical_sub$age[i])==F){
if (clinical_sub$age[i]>=20 & clinical_sub$age[i]<25){clinical_sub$age_bin[i]="20-25"}
else if (clinical_sub$age[i]>=25 & clinical_sub$age[i]<30){clinical_sub$age_bin[i]="25-30"}
else if (clinical_sub$age[i]>=30 & clinical_sub$age[i]<35){clinical_sub$age_bin[i]="30-35"}
else if (clinical_sub$age[i]>=35 & clinical_sub$age[i]<40){clinical_sub$age_bin[i]="35-40"}
else if (clinical_sub$age[i]>=40 & clinical_sub$age[i]<45){clinical_sub$age_bin[i]="40-45"}
else if (clinical_sub$age[i]>=45 & clinical_sub$age[i]<50){clinical_sub$age_bin[i]="45-50"}
else if (clinical_sub$age[i]>=50 & clinical_sub$age[i]<55){clinical_sub$age_bin[i]="50-55"}
else if (clinical_sub$age[i]>=55 & clinical_sub$age[i]<60){clinical_sub$age_bin[i]="55-60"}
else if (clinical_sub$age[i]>=60 & clinical_sub$age[i]<65){clinical_sub$age_bin[i]="60-65"}
else if (clinical_sub$age[i]>=65 & clinical_sub$age[i]<70){clinical_sub$age_bin[i]="65-70"}
else if (clinical_sub$age[i]>=70 & clinical_sub$age[i]<75){clinical_sub$age_bin[i]="70-75"}
else if (clinical_sub$age[i]>=75 & clinical_sub$age[i]<80){clinical_sub$age_bin[i]="75-80"}
else if (clinical_sub$age[i]>=80 & clinical_sub$age[i]<85){clinical_sub$age_bin[i]="80-85"}
else if (clinical_sub$age[i]>=85 & clinical_sub$age[i]<90){clinical_sub$age_bin[i]="85-90"}
else if (clinical_sub$age[i]>=90 & clinical_sub$age[i]<95){clinical_sub$age_bin[i]="90-95"}
}
}
clinical_whole<-rbind(clinical_sub,clin_43119_sub)
table(clinical_whole$dataset,clinical_whole$os_event)
median(clin_751792_sub$os) ### 0.90
median(clin_762360_sub$os) ### 2.47
nr<-nrow(clinical_whole)
for(i in 1:nr){
if(is.na(clinical_whole$os[i])==T){clinical_whole$os_median[i]=NA}
else if (is.na(clinical_whole$os_event[i])==T){clinical_whole$os_median[i]=NA}
else if (is.na(clinical_whole$os[i])==F & is.na(clinical_whole$os_event[i])==F){
if(clinical_whole$dataset[i]=="PRJNA751792"){
if (clinical_whole$os[i]>=0.89){clinical_whole$os_median[i]=1}
else if (clinical_whole$os[i]<0.89 & clinical_whole$os_event[i]==1){clinical_whole$os_median[i]=0}
else if (clinical_whole$os[i]<0.89 & clinical_whole$os_event[i]==0){clinical_whole$os_median[i]=NA}
}
if(clinical_whole$dataset[i]=="PRJNA762360"){
if (clinical_whole$os[i]>=2.47){clinical_whole$os_median[i]=1}
else if (clinical_whole$os[i]<2.47 & clinical_whole$os_event[i]==1){clinical_whole$os_median[i]=0}
else if (clinical_whole$os[i]<2.47 & clinical_whole$os_event[i]==0){clinical_whole$os_median[i]=NA}
}
}
}
write.csv(clinical_whole,"clinical/clinical_whole.csv",row.names=F)
table(clinical_whole$study)
clinical_whole_reads<-subset(clinical_whole,read_count>1000000,drop=T)
clinical_whole_reads<-subset(clinical_whole_reads,is.na(age_bin)==F,drop=T)
nrow(subset(clinical_whole,read_count<=1000000,drop=T))
nrow(subset(clinical_whole,is.na(gender)==T,drop=T))
nrow(subset(clinical_whole,is.na(age_bin)==T,drop=T))
clinical_whole_reads$cohort<-as.factor(clinical_whole_reads$study) %>% as.numeric
clinical_whole_reads$cohort <- clinical_whole_reads$cohort-1
#clinical_whole_reads$cancer<-as.factor(clinical_whole_reads$cancer_type) %>% as.numeric
#clinical_whole_reads$cancer <- clinical_whole_reads$cancer-1
clinical_whole_reads$age_bins<-as.factor(clinical_whole_reads$age_bin) %>% as.numeric
table(clinical_whole_reads$age_bins)
clinical_whole_reads$gender_num<-as.factor(clinical_whole_reads$gender) %>% as.numeric-1
write.csv(clinical_whole_reads,"clinical/clinical_whole_reads.csv",row.names=F)
write.table(clinical_whole_reads, "D:/analysis_lab/pharmacomicrobiomics/1_ICB_metagenome_SV/pipeline/4_R/01.cleanData/phen/Clinical_basic.tsv",sep = '\t')
##########################################
## response
clinical_whole_reads<-read.csv("clinical/clinical_whole_reads.csv",header=T)
nrow(subset(clinical_whole_reads,is.na(response_code)==T,drop=T))
clin_resp<-subset(clinical_whole_reads,is.na(response_code)==F,drop=T)
table(clin_resp$cancer_type)
table(clin_resp$study)
melanoma<-subset(clin_resp,cancer_type=="melanoma",drop=T)
table(melanoma$study)
NSCLC<-subset(clin_resp,cancer_type=="NSCLC",drop=T)
table(NSCLC$study)
RCC<-subset(clin_resp,cancer_type=="RCC",drop=T)
table(RCC$study)
clin_pfs<-subset(clinical_whole_reads,is.na(pfs_12_months)==F,drop=T)
table(clin_pfs$cancer_type)
nrow(subset(clinical_whole_reads,is.na(pfs_12_months)==T,drop=T))
melanoma<-subset(clin_pfs,cancer_type=="melanoma",drop=T)
table(melanoma$study)
nrow(subset(clinical_whole_reads,is.na(os)==T,drop=T))
nrow(subset(clinical_whole_reads,is.na(os_event)==T,drop=T))
clin_os<-subset(clinical_whole_reads,is.na(os)==F & is.na(os_event)==F ,drop=T)
table(clin_os$cancer_type)
melanoma<-subset(clin_os,cancer_type=="melanoma",drop=T)
table(melanoma$study)
NSCLC<-subset(clin_os,cancer_type=="NSCLC",drop=T)
table(NSCLC$study)
clin_irAEs<-subset(clinical_whole_reads,is.na(irAEs)==F,drop=T)
table(clin_irAEs$cancer_type)
melanoma<-subset(clin_irAEs,cancer_type=="melanoma",drop=T)
table(melanoma$study)
#############################################################################
#### table 1B(basic characteristics)
source("D:/analysis_lab/pharmacomicrobiomics/1_ICB_metagenome_SV/pipeline/4_R/functions.R")
clinical<-read.csv("clinical/clinical_whole_reads.csv",header=T)
table(clinical$drug)
table(clinical$target)
table(clinical$recist)
table(clinical$cancer_type)
table(clinical$study)
table(clinical$recist,clinical$cancer_type)
characteristic<-function(datasets){
#clin<-subset(clinical,study=="TCGA_PAAD",drop=T)
clin<-subset(clinical,study==datasets,drop=T)
nr<-nrow(clin)
print(nr)
clin$agebin50<-0
for(i in 1:nr){
if(is.na(clin$age[i])==T){clin$agebin50[i]="NA"}
else if(clin$age[i]<50){clin$agebin50[i]="<50"}
else if(clin$age[i]>=50){clin$agebin50[i]=">=50"}
}
attach(clin)
print("age")
print(mean(clin$age,na.rm=T))
print(sd(clin$age,na.rm=T))
#print("agebin")
#print(table(age_bin,useNA="always"))
#table(age_bin,useNA="always")/nr
print("agebin50")
print(table(agebin50,useNA="always"))
table(agebin50,useNA="always")/nr
print("gender")
print(table(gender,useNA="always"))
table(gender,useNA="always")/nr
print("os_event")
print(table(os_event,useNA="always"))
print("os")
print(mean(clin$os,na.rm=T))
print(sd(clin$os,na.rm=T))
print("pfs_12_months")
print(table(pfs_12_months,useNA="always"))
print("target")
print(table(target,useNA="always"))
table(target,useNA="always")/nr
print("response_recist")
print(table(response_code,useNA="always"))
table(response_code,useNA="always")/nr
print("irAEs")
print(table(irAEs,useNA="always"))
table(irAEs,useNA="always")/nr
print("irAEs_grade")
print(table(irAEs_grade,useNA="always"))
table(irAEs_grade,useNA="always")/nr
}
table(clinical$study)
characteristic("FrankelAE_2017")
characteristic("McCullochJA_2022")
characteristic("RoutyB_2018")
characteristic("SpencerCN_2021")
characteristic("DerosaL_2022")
characteristic("LeeKA_2022")
characteristic("PetersBA_2019")