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Plot1.R
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Plot1.R
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library(tidyverse)
library(FactoMineR)
#####
specified_mirna_COAD<-subset( Mirna2, Cancer=="COAD" & oncomirna %in% c("hsa-miR-324-3p", "hsa-miR-671-3p", "hsa-miR-139-3p",
"hsa-miR-328-3p", "hsa-miR-652-3p", "hsa-miR-193a-5p", "hsa-miR-574-3p",
"hsa-miR-532-3p", "hsa-miR-628-5p","hsa-miR-136-5p", "hsa-miR-29a-5p",
"hsa-miR-126-5p","hsa-miR-29b-3p",
"hsa-miR-142-3p", "hsa-miR-125a-5p", "hsa-miR-197-3p","hsa-miR-378a-3p",
"hsa-miR-21-5p", "hsa-miR-126-3p","hsa-miR-144-5p"))
COAD1 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-125a-5p")
COAD2 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-126-3p")
COAD3 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-126-5p")
COAD4 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-136-5p")
COAD5 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-139-3p")
COAD6 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-142-3p")
COAD7 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-144-5p")
COAD8 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-193a-5p")
COAD9 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-197-3p")
COAD10 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-21-5p")
COAD11 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-29a-5p")
COAD12 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-29b-3p")
COAD13 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-324-3p")
COAD14 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-328-3p")
COAD15 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-378a-3p")
COAD16 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-532-3p")
COAD17 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-574-3p")
COAD18 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-628-5p")
COAD19 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-652-3p")
COAD20 <- subset(specified_mirna_COAD, oncomirna == "hsa-miR-671-3p")
Men2_extracted <- Men2 %>%
distinct(lab_id, Cancer, .keep_all = TRUE) %>%
select(lab_id, Cancer, Residence_Traffic)
Traffic_COAD <- Men2_extracted |> filter(Cancer == "COAD")
data_covariates_COAD <- data.frame(ID = COAD1$lab_id,
PM_10_Annual_Average = COAD1$PM10_Annual_Average,
PCR = COAD1$PCR,
Age = COAD1$Age,
BMI = COAD1$BMI,
Alcohol = COAD1$Alcohol,
Smoking = COAD1$Smoking,
Workingstatus = COAD1$Workingstatus,
Antidepressant = COAD1$Antidepressant,
Homocysteine = COAD1$Homocysteine,
Coffee = COAD1$Coffee,
Residence_Traffic = Traffic_COAD$Residence_Traffic,
Life_style = COAD1$Physical_Activity_New,
Education = COAD1$Primary_School,
Passive_Smoke_New = COAD1$Passive_Smoke_New)
dim(data_covariates_COAD)
datamirna_list_COAD <- list(COAD1,COAD2,COAD3,COAD4,COAD5,COAD6,COAD7,COAD8,COAD9,COAD10,
COAD11,COAD12,COAD13,COAD14,COAD15,COAD16,COAD17,COAD18, COAD19, COAD20)
length(datamirna_list_COAD)
datafinal_COAD <- data_covariates_COAD
for (k in 1:20) {
tempdataset_COAD <- datamirna_list_COAD[[k]]
response_dftemp_COAD <- data.frame(ID = tempdataset_COAD$lab_id,
log2_rq = tempdataset_COAD$log2_rq )
datafinal_COAD <- datafinal_COAD %>%
left_join(response_dftemp_COAD, by = "ID")
}
colnames(datafinal_COAD)
colnames(datafinal_COAD)[16:35] <- paste(rep(c("hsa-miR-324-3p", "hsa-miR-671-3p", "hsa-miR-139-3p",
"hsa-miR-328-3p", "hsa-miR-652-3p", "hsa-miR-193a-5p", "hsa-miR-574-3p",
"hsa-miR-532-3p", "hsa-miR-628-5p","hsa-miR-136-5p", "hsa-miR-29a-5p",
"hsa-miR-126-5p","hsa-miR-29b-3p",
"hsa-miR-142-3p", "hsa-miR-125a-5p", "hsa-miR-197-3p","hsa-miR-378a-3p",
"hsa-miR-21-5p", "hsa-miR-126-3p","hsa-miR-144-5p")),1:20,sep="")
colnames(datafinal_COAD)
#####
#LUAD
specified_mirna_LUAD<-subset( Mirna2, Cancer=="LUAD" & oncomirna %in% c("hsa-let-7a-5p", "hsa-miR-30d-5p", "hsa-miR-139-3p",
"hsa-miR-133a-3p", "hsa-miR-30a-3p", "hsa-miR-140-3p", "hsa-let-7c-5p",
"hsa-miR-184", "hsa-miR-139-5p","hsa-miR-338-5p", "hsa-miR-21-5p",
"hsa-miR-708-5p","hsa-miR-9-5p","hsa-miR-212-3p",
"hsa-miR-210-3p", "hsa-miR-7-1-3p", "hsa-miR-20a-3p","hsa-miR-301a-3p",
"hsa-miR-128-3p", "hsa-miR-625-5p","hsa-miR-130b-3p",
"hsa-miR-590-5p", "hsa-miR-151a-5p"))
#Subsetting columns of oncomirna
LUAD1 <- subset(specified_mirna_LUAD, oncomirna == "hsa-let-7a-5p",drop = FALSE)
LUAD2 <- subset(specified_mirna_LUAD, oncomirna == "hsa-let-7c-5p",drop = FALSE)
LUAD3 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-128-3p",drop = FALSE)
LUAD4 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-130b-3p",drop = FALSE)
LUAD5 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-133a-3p",drop = FALSE)
LUAD6 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-139-3p",drop = FALSE)
LUAD7 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-139-5p",drop = FALSE)
LUAD8 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-140-3p",drop = FALSE)
LUAD9 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-151a-5p",drop = FALSE)
LUAD10 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-184",drop = FALSE)
LUAD11 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-20a-3p",drop = FALSE)
LUAD12 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-210-3p",drop = FALSE)
LUAD13 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-212-3p",drop = FALSE)
LUAD14 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-21-5p",drop = FALSE)
LUAD15 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-301a-3p",drop = FALSE)
LUAD16 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-30a-3p",drop = FALSE)
LUAD17 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-30d-5p",drop = FALSE)
LUAD18 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-338-5p",drop = FALSE)
LUAD19 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-590-5p",drop = FALSE)
LUAD20 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-625-5p",drop = FALSE)
LUAD21 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-708-5p",drop = FALSE)
LUAD22 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-7-1-3p",drop = FALSE)
LUAD23 <- subset(specified_mirna_LUAD, oncomirna == "hsa-miR-9-5p",drop = FALSE)
Traffic_LUAD <- Men2_extracted |> filter(Cancer == "LUAD")
# Example data frame with IDs
data_covariates_LUAD <- data.frame(ID = LUAD1$lab_id,
PM_10_Annual_Average = LUAD1$PM10_Annual_Average,
PCR = LUAD1$PCR,
Age = LUAD1$Age,
BMI = LUAD1$BMI,
Alcohol = LUAD1$Alcohol,
Smoking = LUAD1$Smoking,
Workingstatus = LUAD1$Workingstatus,
Antidepressant = LUAD1$Antidepressant,
Homocysteine = LUAD1$Homocysteine,
Coffee = LUAD1$Coffee,
Residence_Traffic = Traffic_LUAD$Residence_Traffic,
Life_style = LUAD1$Physical_Activity_New,
Education = LUAD1$Primary_School,
Passive_Smoke_New = LUAD1$Passive_Smoke_New)
dim(data_covariates_LUAD)
datamirna_list_LUAD <- list(LUAD1,LUAD2,LUAD3,LUAD4,LUAD5,LUAD6,LUAD7,LUAD8,LUAD9,LUAD10,
LUAD11,LUAD12,LUAD13,LUAD14,LUAD15,LUAD16,LUAD17,LUAD18,LUAD19,LUAD20,LUAD21,LUAD22,LUAD23)
length(datamirna_list_LUAD)
datafinal_LUAD <- data_covariates_LUAD
for (k in 1:23) {
tempdataset_LUAD <- datamirna_list_LUAD[[k]]
response_dftemp_LUAD <- data.frame(ID = tempdataset_LUAD$lab_id,
log2_rq = tempdataset_LUAD$log2_rq )
datafinal_LUAD <- datafinal_LUAD %>%
left_join(response_dftemp_LUAD, by = "ID")
}
colnames(datafinal_LUAD)
colnames(datafinal_LUAD)[16:38] <- paste(c("hsa-let-7a-5p", "hsa-miR-30d-5p", "hsa-miR-139-3p",
"hsa-miR-133a-3p", "hsa-miR-30a-3p", "hsa-miR-140-3p", "hsa-let-7c-5p",
"hsa-miR-184", "hsa-miR-139-5p","hsa-miR-338-5p", "hsa-miR-21-5p",
"hsa-miR-708-5p","hsa-miR-9-5p","hsa-miR-212-3p",
"hsa-miR-210-3p", "hsa-miR-7-1-3p", "hsa-miR-20a-3p","hsa-miR-301a-3p",
"hsa-miR-128-3p", "hsa-miR-625-5p","hsa-miR-130b-3p",
"hsa-miR-590-5p", "hsa-miR-151a-5p"))
colnames(datafinal_LUAD)
#####
#PRAD
specified_mirna_PRAD<-subset( Mirna2, Cancer=="PRAD" & oncomirna %in% c("hsa-miR-342-3p", "hsa-miR-143-3p", "hsa-miR-17-5p",
"hsa-miR-708-5p", "hsa-miR-148a-3p", "hsa-miR-423-5p", "hsa-miR-7-1-3p",
"hsa-miR-20a-3p", "hsa-miR-769-5p","hsa-miR-142-3p", "hsa-miR-151a-5p",
"hsa-miR-21-5p","hsa-miR-92a-3p","hsa-miR-30d-5p",
"hsa-miR-103a-3p", "hsa-miR-20b-5p", "hsa-miR-375","hsa-miR-126-3p",
"hsa-miR-93-3p", "hsa-miR-140-5p","hsa-miR-93-5p"))
PRAD1 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-103a-3p",drop = FALSE)
PRAD2 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-126-3p",drop = FALSE)
PRAD3 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-140-5p",drop = FALSE)
PRAD4 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-142-3p",drop = FALSE)
PRAD5 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-143-3p",drop = FALSE)
PRAD6 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-148a-3p",drop = FALSE)
PRAD7 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-151a-5p",drop = FALSE)
PRAD8 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-17-5p",drop = FALSE)
PRAD9 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-20a-3p",drop = FALSE)
PRAD10 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-20b-5p",drop = FALSE)
PRAD11 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-21-5p",drop = FALSE)
PRAD12 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-30d-5p",drop = FALSE)
PRAD13 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-342-3p",drop = FALSE)
PRAD14 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-375",drop = FALSE)
PRAD15 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-423-5p",drop = FALSE)
PRAD16 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-708-5p",drop = FALSE)
PRAD17 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-7-1-3p",drop = FALSE)
PRAD18 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-769-5p",drop = FALSE)
PRAD19 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-92a-3p",drop = FALSE)
PRAD20 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-93-3p",drop = FALSE)
PRAD21 <- subset(specified_mirna_PRAD, oncomirna == "hsa-miR-93-5p",drop = FALSE)
Traffic_PRAD <- Men2_extracted |> filter(Cancer == "PRAD")
data_covariates_PRAD <- data.frame(ID = PRAD1$lab_id,
PM_10_Annual_Average = PRAD1$PM10_Annual_Average,
PCR = PRAD1$PCR,
Age = PRAD1$Age,
BMI = PRAD1$BMI,
Alcohol = PRAD1$Alcohol,
Smoking = PRAD1$Smoking,
Workingstatus = PRAD1$Workingstatus,
Antidepressant = PRAD1$Antidepressant,
Homocysteine = PRAD1$Homocysteine,
Coffee = PRAD1$Coffee,
Residence_Traffic = Traffic_PRAD$Residence_Traffic,
Life_style = PRAD1$Physical_Activity_New,
Education = PRAD1$Primary_School,
Passive_Smoke_New = PRAD1$Passive_Smoke_New)
dim(data_covariates_PRAD)
datamirna_list_PRAD <- list(PRAD1,PRAD2,PRAD3,PRAD4,PRAD5,PRAD6,PRAD7,PRAD8,PRAD9,PRAD10,
PRAD11,PRAD12,PRAD13,PRAD14,PRAD15,PRAD16,PRAD17,
PRAD18,PRAD19, PRAD20, PRAD21)
length(datamirna_list_PRAD)
datafinal_PRAD <- data_covariates_PRAD
for (k in 1:21) {
tempdataset_PRAD <- datamirna_list_PRAD[[k]]
response_dftemp_PRAD <- data.frame(ID = tempdataset_PRAD$lab_id,
log2_rq = tempdataset_PRAD$log2_rq )
datafinal_PRAD <- datafinal_PRAD %>%
left_join(response_dftemp_PRAD, by = "ID")
}
colnames(datafinal_PRAD)
colnames(datafinal_PRAD)[16:36] <- paste(c("hsa-miR-342-3p", "hsa-miR-143-3p", "hsa-miR-17-5p",
"hsa-miR-708-5p", "hsa-miR-148a-3p", "hsa-miR-423-5p", "hsa-miR-7-1-3p",
"hsa-miR-20a-3p", "hsa-miR-769-5p","hsa-miR-142-3p", "hsa-miR-151a-5p",
"hsa-miR-21-5p","hsa-miR-92a-3p","hsa-miR-30d-5p",
"hsa-miR-103a-3p", "hsa-miR-20b-5p", "hsa-miR-375","hsa-miR-126-3p",
"hsa-miR-93-3p", "hsa-miR-140-5p","hsa-miR-93-5p"))
colnames(datafinal_PRAD)
#####
#Women: LUAD
women_LUAD<-subset(women_data, Cancer=="LUAD" & oncomirna %in% c("hsa-miR-210-3p", "hsa-miR-151a-5p", "hsa-miR-21-5p",
"hsa-miR-130b-3p", "hsa-miR-590-5p", "hsa-miR-625-5p", "hsa-miR-20a-3p",
"hsa-miR-301a-3p", "hsa-miR-128-3p","hsa-miR-26b-3p", "hsa-miR-708-5p",
"hsa-let-7a-5p","hsa-miR-133a-3p","hsa-miR-184",
"hsa-miR-338-5p", "hsa-miR-30d-5p", "hsa-let-7c-5p","hsa-miR-139-5p",
"hsa-miR-7-1-3p", "hsa-miR-212-3p","hsa-miR-9-5p","hsa-miR-139-3p",
"hsa-miR-30a-3p", "hsa-miR-140-3p"))
W_LUAD1 <- subset(women_LUAD, oncomirna == "hsa-let-7a-5p",drop = FALSE)
W_LUAD2 <- subset(women_LUAD, oncomirna == "hsa-let-7c-5p",drop = FALSE)
W_LUAD3 <- subset(women_LUAD, oncomirna == "hsa-miR-128-3p",drop = FALSE)
W_LUAD4 <- subset(women_LUAD, oncomirna == "hsa-miR-130b-3p",drop = FALSE)
W_LUAD5 <- subset(women_LUAD, oncomirna == "hsa-miR-133a-3p",drop = FALSE)
W_LUAD6 <- subset(women_LUAD, oncomirna == "hsa-miR-139-3p",drop = FALSE)
W_LUAD7 <- subset(women_LUAD, oncomirna == "hsa-miR-139-5p",drop = FALSE)
W_LUAD8 <- subset(women_LUAD, oncomirna == "hsa-miR-140-3p",drop = FALSE)
W_LUAD9 <- subset(women_LUAD, oncomirna == "hsa-miR-151a-5p",drop = FALSE)
W_LUAD10 <- subset(women_LUAD, oncomirna == "hsa-miR-184",drop = FALSE)
W_LUAD11 <- subset(women_LUAD, oncomirna == "hsa-miR-20a-3p",drop = FALSE)
W_LUAD12 <- subset(women_LUAD, oncomirna == "hsa-miR-210-3p",drop = FALSE)
W_LUAD13 <- subset(women_LUAD, oncomirna == "hsa-miR-212-3p",drop = FALSE)
W_LUAD14 <- subset(women_LUAD, oncomirna == "hsa-miR-21-5p",drop = FALSE)
W_LUAD15 <- subset(women_LUAD, oncomirna == "hsa-miR-26b-3p",drop = FALSE)
W_LUAD16 <- subset(women_LUAD, oncomirna == "hsa-miR-301a-3p",drop = FALSE)
W_LUAD17 <- subset(women_LUAD, oncomirna == "hsa-miR-30a-3p",drop = FALSE)
W_LUAD18 <- subset(women_LUAD, oncomirna == "hsa-miR-30d-5p",drop = FALSE)
W_LUAD19 <- subset(women_LUAD, oncomirna == "hsa-miR-338-5p",drop = FALSE)
W_LUAD20 <- subset(women_LUAD, oncomirna == "hsa-miR-590-5p",drop = FALSE)
W_LUAD21 <- subset(women_LUAD, oncomirna == "hsa-miR-625-5p",drop = FALSE)
W_LUAD22 <- subset(women_LUAD, oncomirna == "hsa-miR-708-5p",drop = FALSE)
W_LUAD23 <- subset(women_LUAD, oncomirna == "hsa-miR-7-1-3p",drop = FALSE)
W_LUAD24 <- subset(women_LUAD, oncomirna == "hsa-miR-9-5p",drop = FALSE)
Women2_extracted <- women_data %>%
distinct(lab_id, Cancer, .keep_all = TRUE) %>%
select(lab_id, Cancer, Residence_Traffic)
Traffic_LUAD_women <- Women2_extracted |> filter(Cancer == "LUAD")
data_covariates_W_LUAD <- data.frame(ID = W_LUAD1$lab_id,
PM_10_Annual_Average = W_LUAD1$PM10_Annual_Average,
PCR = W_LUAD1$PCR,
Age = W_LUAD1$age,
BMI = W_LUAD1$bmi,
Alcohol = W_LUAD1$Alcohol,
Workingstatus = W_LUAD1$Working_Status,
Antidepressant = W_LUAD1$Antidepressent,
Homocysteine = W_LUAD1$Homocysteine,
Menopause = W_LUAD1$Menopause,
No_Pregnancy = W_LUAD1$No_Pregency,
Oral_Contraceptives = W_LUAD1$Oral_Contraceptives,
Smoking = W_LUAD1$Smoking_New,
Coffee = W_LUAD1$Coffee,
Residence_Traffic = Traffic_LUAD_women$Residence_Traffic,
Life_style = W_LUAD1$Life_style,
Primary_School = W_LUAD1$Prima,
Passive_Smoke_New = W_LUAD1$Passive_Smoke_New)
dim(data_covariates_W_LUAD)
length(unique(data_covariates_W_LUAD$ID))
datamirna_list_W_LUAD <- list(W_LUAD1,W_LUAD2,W_LUAD3,W_LUAD4,W_LUAD5,W_LUAD6,W_LUAD7,W_LUAD8,W_LUAD9,W_LUAD10,
W_LUAD11,W_LUAD12,W_LUAD13,W_LUAD14,W_LUAD15,W_LUAD16,W_LUAD17,W_LUAD18,W_LUAD19,
W_LUAD20,W_LUAD21,W_LUAD22,W_LUAD23, W_LUAD24)
length(datamirna_list_W_LUAD)
datafinal_W_LUAD <- data_covariates_W_LUAD
for (k in 1:24) {
tempdataset_W_LUAD <- datamirna_list_W_LUAD[[k]]
response_dftemp_W_LUAD <- data.frame(ID = tempdataset_W_LUAD$lab_id,
log2_rq = tempdataset_W_LUAD$log2_rq )
datafinal_W_LUAD <- datafinal_W_LUAD %>%
left_join(response_dftemp_W_LUAD, by = "ID")
}
colnames(datafinal_W_LUAD)
colnames(datafinal_W_LUAD)[21:44] <- paste(rep("log2_rq",24),1:24,sep="")
colnames(datafinal_W_LUAD)
#####
#FAMD
str(datafinal_COAD)
datafinal_COAD$Smoking <- factor(datafinal_COAD$Smoking)
datafinal_COAD$Workingstatus <- factor(datafinal_COAD$Workingstatus)
datafinal_COAD$Antidepressant <- factor(datafinal_COAD$Antidepressant)
datafinal_COAD[,13:15] <- lapply(datafinal_COAD[,13:15], factor)
tmp <- na.omit(datafinal_COAD)
tmp2 <- datafinal_COAD[,-1]
tmp <- tmp |> select(-ID)
prova1 <- FAMD(tmp, sup.var = 15:34)
FAMD(tmp2)
library(readr)
library(cluster)
library(Rtsne)
data <- read_delim("data/data_MANCOAD.csv",
delim = ";", escape_double = FALSE, trim_ws = TRUE)
data$Antidepressant <- ifelse(data$Antidepressant == "si","Antidepressant_sì","Antidepressant_no")
tmp3 <- rep("",nrow(data))
tmp3[data$Smoking == "si"] <- "Fumatore"
tmp3[data$Smoking == "ex"] <- "ex_Fumatore"
tmp3[data$Smoking == "no"] <- "Non_Fumatore"
data$Smoking <- factor(tmp3, levels = c("Non_Fumatore","ex_Fumatore","Fumatore"))
data$Passive_Smoke <- ifelse(data$Passive_Smoke == "si","Fumo_passivo_sì","Fumo_passivo_no")
tmp2 <- na.omit(data)
tmp2 <- tmp2 |> select(-lab_id)
prova1 <- FAMD(tmp2, sup.var = 1:20, axes = c(2,3))
fviz_famd_var(prova1, geom = c("point"),
alpha.var = 0.5,
axes = c(1,2))
tmp2[,c(24,25,29,30,31,32)] <- lapply(tmp2[,c(24,25,29,30,31,32)], factor)
gower_dist <- daisy(tmp2,
metric = "gower",
type = list(logratio = c(21:23,26:27,33:34)))
gower_mat <- as.matrix(gower_dist)
tmp2[
which(gower_mat == min(gower_mat[gower_mat != min(gower_mat)]),
arr.ind = TRUE)[1, ], ]
sil_width <- c(NA)
for(i in 2:10){
pam_fit <- pam(gower_dist,
diss = TRUE,
k = i)
sil_width[i] <- pam_fit$silinfo$avg.width
}
plot(1:10, sil_width,
xlab = "Number of clusters",
ylab = "Silhouette Width")
lines(1:10, sil_width)
pam_fit <- pam(gower_dist, diss = TRUE, k = 3)
pam_results <- tmp2 %>%
mutate(cluster = pam_fit$clustering) %>%
group_by(cluster) %>%
do(the_summary = summary(.))
pam_results$the_summary
tsne_obj <- Rtsne(gower_dist, is_distance = TRUE)
tsne_data <- tsne_obj$Y %>%
data.frame() %>%
setNames(c("X", "Y")) %>%
mutate(cluster = factor(pam_fit$clustering))
ggplot(aes(x = X, y = Y), data = tsne_data) +
geom_point(aes(color = cluster))
tmp3 <- cbind(tmp2,pam_fit$clustering)
tmp3 <- tmp3 |> rename(Cluster = `pam_fit$clustering`)
clust1 <- tmp3 |> filter(Cluster == 1)
clust2 <- tmp3 |> filter(Cluster == 2)
clust3 <- tmp3 |> filter(Cluster == 3)
clust1_miRNA <- clust1 |> select(starts_with("hsa-"), Traffic_no)
clust1_miRNA_long <- pivot_longer(clust1_miRNA, cols = 1:20, names_to = "miRNA")
clust1_miRNA_long$Traffic_no <- factor(clust1_miRNA_long$Traffic_no, levels = c("mild","moderate","heavy"))
ggplot(clust1_miRNA_long, aes(x = miRNA, y = value)) +
geom_boxplot() +
facet_wrap(~Traffic_no)
clust1_miRNA_long$group <- interaction(clust1_miRNA_long$miRNA, clust1_miRNA_long$Traffic_no)
ggplot(clust1_miRNA_long, aes(x = miRNA, y = value, fill = Traffic_no)) +
geom_boxplot(position = "dodge") +
xlab("miRNA") +
ylab("Expression Value") +
ggtitle("Boxplot of miRNA Expression by Traffic_no") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))