datex <- read_excel("swedd_dm.xlsx")
datex <- read_excel("prodromal_dm.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df2 <- df_new1 %>%
select(contains('dopamine_metabolism_phenotype'), Time) %>%
dplyr::mutate(dopamine_metabolism = rowSums(dplyr::across(contains("dopamine_metabolism"))))
df3 <- df_new1 %>%
select(contains('neuron_survival_phenotype'), Time) %>%
dplyr::mutate(neuron_survival = rowSums(dplyr::across(contains("neuron_survival"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"prodromal_dm.csv")
datex <- read_excel("parkinsonism_dm.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df2 <- df_new1 %>%
select(contains('dopamine_metabolism_phenotype'), Time) %>%
dplyr::mutate(dopamine_metabolism = rowSums(dplyr::across(contains("dopamine_metabolism"))))
df3 <- df_new1 %>%
select(contains('neuron_survival_phenotype'), Time) %>%
dplyr::mutate(neuron_survival = rowSums(dplyr::across(contains("neuron_survival"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism_dm.csv")
datex <- read_excel("parkinsonism.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df2 <- df_new1 %>%
select(contains('dopamine_metabolism_phenotype'), Time) %>%
dplyr::mutate(dopamine_metabolism = rowSums(dplyr::across(contains("dopamine_metabolism"))))
df3 <- df_new1 %>%
select(contains('neuron_survival_phenotype'), Time) %>%
dplyr::mutate(neuron_survival = rowSums(dplyr::across(contains("neuron_survival"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism.csv")
library(ggplot2)
datex <- read_excel("parkinsonism.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df2 <- df_new1 %>%
select(contains('dopamine_metabolism_phenotype'), Time) %>%
dplyr::mutate(dopamine_metabolism = rowSums(dplyr::across(contains("dopamine_metabolism"))))
df3 <- df_new1 %>%
select(contains('neuron_survival_phenotype'), Time) %>%
dplyr::mutate(neuron_survival = rowSums(dplyr::across(contains("neuron_survival"))))
df4 <- df_new1 %>%
select(contains('axonal_transport_phenotype'), Time) %>%
dplyr::mutate(axonal_transport = rowSums(dplyr::across(contains("axonal_transportl"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism.csv")
datex <- read_excel("parkinsonism.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df2 <- df_new1 %>%
select(contains('dopamine_metabolism_phenotype'), Time) %>%
dplyr::mutate(dopamine_metabolism = rowSums(dplyr::across(contains("dopamine_metabolism"))))
df3 <- df_new1 %>%
select(contains('neuron_survival_phenotype'), Time) %>%
dplyr::mutate(neuron_survival = rowSums(dplyr::across(contains("neuron_survival"))))
df4 <- df_new1 %>%
select(contains('axonal_transport_phenotype'), Time) %>%
dplyr::mutate(axonal_transport = rowSums(dplyr::across(contains("axonal_transport"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism.csv")
datex <- read_excel("parkinsonism_dm.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df2 <- df_new1 %>%
select(contains('dopamine_metabolism_phenotype'), Time) %>%
dplyr::mutate(dopamine_metabolism = rowSums(dplyr::across(contains("dopamine_metabolism"))))
df3 <- df_new1 %>%
select(contains('neuron_survival_phenotype'), Time) %>%
dplyr::mutate(neuron_survival = rowSums(dplyr::across(contains("neuron_survival"))))
df4 <- df_new1 %>%
select(contains('axonal_transport_phenotype'), Time) %>%
dplyr::mutate(axonal_transport = rowSums(dplyr::across(contains("axonal_transport"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism_dm.csv")
datex <- read_excel("parkinsonism_dm.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df2 <- df_new1 %>%
select(contains('dopamine_metabolism_phenotype'), Time) %>%
dplyr::mutate(dopamine_metabolism = rowSums(dplyr::across(contains("dopamine_metabolism"))))
df3 <- df_new1 %>%
select(contains('neuron_survival_phenotype'), Time) %>%
dplyr::mutate(neuron_survival = rowSums(dplyr::across(contains("neuron_survival"))))
df4 <- df_new1 %>%
select(contains('axonal_transport_phenotype'), Time) %>%
dplyr::mutate(axonal_transport = rowSums(dplyr::across(contains("axonal_transport"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism_dm.csv")
setwd("C:/Users/T440s/Desktop/modelsCorrST/foxo3_ activity/Results/PBNsimulations/trajectories_probabilities")
datex <- read_excel("parkinsonism_dm.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('response_to_oxidative_stress_phenotype'), Time) %>%
dplyr::mutate(response_to_oxidative_stress = rowSums(dplyr::across(contains("response_to_oxidative_stress"))))
df2 <- df_new1 %>%
select(contains('Fission_Fusion_phenotype'), Time) %>%
dplyr::mutate(Fission_Fusion = rowSums(dplyr::across(contains("Fission_Fusion"))))
df3 <- df_new1 %>%
select(contains('autophagy_phenotype'), Time) %>%
dplyr::mutate(autophagy = rowSums(dplyr::across(contains("autophagy"))))
df4 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df5 <- df_new1 %>%
select(contains('apoptosis_phenotype'), Time) %>%
dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4,df5) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism_dm.csv")
datex <- read_excel("parkinsonism.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('response_to_oxidative_stress_phenotype'), Time) %>%
dplyr::mutate(response_to_oxidative_stress = rowSums(dplyr::across(contains("response_to_oxidative_stress"))))
df2 <- df_new1 %>%
select(contains('Fission_Fusion_phenotype'), Time) %>%
dplyr::mutate(Fission_Fusion = rowSums(dplyr::across(contains("Fission_Fusion"))))
df3 <- df_new1 %>%
select(contains('autophagy_phenotype'), Time) %>%
dplyr::mutate(autophagy = rowSums(dplyr::across(contains("autophagy"))))
df4 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df5 <- df_new1 %>%
select(contains('apoptosis_phenotype'), Time) %>%
dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4,df5) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism.csv")
datex <- read_excel("prodromal_dm.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('response_to_oxidative_stress_phenotype'), Time) %>%
dplyr::mutate(response_to_oxidative_stress = rowSums(dplyr::across(contains("response_to_oxidative_stress"))))
df2 <- df_new1 %>%
select(contains('Fission_Fusion_phenotype'), Time) %>%
dplyr::mutate(Fission_Fusion = rowSums(dplyr::across(contains("Fission_Fusion"))))
df3 <- df_new1 %>%
select(contains('autophagy_phenotype'), Time) %>%
dplyr::mutate(autophagy = rowSums(dplyr::across(contains("autophagy"))))
df4 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df5 <- df_new1 %>%
select(contains('apoptosis_phenotype'), Time) %>%
dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4,df5) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"prodromal_dm.csv")
datex <- read_excel("swedd_dm.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('response_to_oxidative_stress_phenotype'), Time) %>%
dplyr::mutate(response_to_oxidative_stress = rowSums(dplyr::across(contains("response_to_oxidative_stress"))))
df2 <- df_new1 %>%
select(contains('Fission_Fusion_phenotype'), Time) %>%
dplyr::mutate(Fission_Fusion = rowSums(dplyr::across(contains("Fission_Fusion"))))
df3 <- df_new1 %>%
select(contains('autophagy_phenotype'), Time) %>%
dplyr::mutate(autophagy = rowSums(dplyr::across(contains("autophagy"))))
df4 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df5 <- df_new1 %>%
select(contains('apoptosis_phenotype'), Time) %>%
dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4,df5) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"swedd_dm.csv")
datex <- read_excel("swedd.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('response_to_oxidative_stress_phenotype'), Time) %>%
dplyr::mutate(response_to_oxidative_stress = rowSums(dplyr::across(contains("response_to_oxidative_stress"))))
df2 <- df_new1 %>%
select(contains('Fission_Fusion_phenotype'), Time) %>%
dplyr::mutate(Fission_Fusion = rowSums(dplyr::across(contains("Fission_Fusion"))))
df3 <- df_new1 %>%
select(contains('autophagy_phenotype'), Time) %>%
dplyr::mutate(autophagy = rowSums(dplyr::across(contains("autophagy"))))
df4 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df5 <- df_new1 %>%
select(contains('apoptosis_phenotype'), Time) %>%
dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4,df5) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"swedd.csv")
datex <- read_excel("prodromal.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('response_to_oxidative_stress_phenotype'), Time) %>%
dplyr::mutate(response_to_oxidative_stress = rowSums(dplyr::across(contains("response_to_oxidative_stress"))))
df2 <- df_new1 %>%
select(contains('Fission_Fusion_phenotype'), Time) %>%
dplyr::mutate(Fission_Fusion = rowSums(dplyr::across(contains("Fission_Fusion"))))
df3 <- df_new1 %>%
select(contains('autophagy_phenotype'), Time) %>%
dplyr::mutate(autophagy = rowSums(dplyr::across(contains("autophagy"))))
df4 <- df_new1 %>%
select(contains('mitochondrial_biogenesis_phenotype'), Time) %>%
dplyr::mutate(mitochondrial_biogenesis = rowSums(dplyr::across(contains("mitochondrial_biogenesis"))))
df5 <- df_new1 %>%
select(contains('apoptosis_phenotype'), Time) %>%
dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4,df5) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"prodromal.csv")
setwd("C:/Users/T440s/Desktop/modelsCorrST/mtor")
setwd("C:/Users/T440s/Desktop/modelsCorrST/mtor/Results/PBN/trajectories_prob")
library(readxl)
library(tidyverse)
library(tidyselect)
library(dplyr)
library(sjPlot)
library(ggplot2)
datex <- read_excel("prodromal.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('glycolysis_phenotype'), Time) %>%
dplyr::mutate(glycolysis = rowSums(dplyr::across(contains("glycolysis"))))
df2 <- df_new1 %>%
select(contains('RHEB_lysosome'), Time) %>%
dplyr::mutate(RHEB_lysosome = rowSums(dplyr::across(contains("RHEB_lysosome"))))
df3 <- df_new1 %>%
select(contains('Akt_pathway_phenotype'), Time) %>%
dplyr::mutate(Akt = rowSums(dplyr::across(contains("Akt"))))
df4 <- df_new1 %>%
select(contains('catabolism_phenotype'), Time) %>%
dplyr::mutate(catabolism= rowSums(dplyr::across(contains("catabolism"))))
#df5 <- df_new1 %>%
# select(contains('apoptosis_phenotype'), Time) %>%
# dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"prodromal.csv")
datex <- read_excel("prodromal_dm.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('glycolysis_phenotype'), Time) %>%
dplyr::mutate(glycolysis = rowSums(dplyr::across(contains("glycolysis"))))
df2 <- df_new1 %>%
select(contains('RHEB_lysosome'), Time) %>%
dplyr::mutate(RHEB_lysosome = rowSums(dplyr::across(contains("RHEB_lysosome"))))
df3 <- df_new1 %>%
select(contains('Akt_pathway_phenotype'), Time) %>%
dplyr::mutate(Akt = rowSums(dplyr::across(contains("Akt"))))
df4 <- df_new1 %>%
select(contains('catabolism_phenotype'), Time) %>%
dplyr::mutate(catabolism= rowSums(dplyr::across(contains("catabolism"))))
#df5 <- df_new1 %>%
# select(contains('apoptosis_phenotype'), Time) %>%
# dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"prodromal_dm.csv")
datex <- read_excel("swedd_dm.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('glycolysis_phenotype'), Time) %>%
dplyr::mutate(glycolysis = rowSums(dplyr::across(contains("glycolysis"))))
df2 <- df_new1 %>%
select(contains('RHEB_lysosome'), Time) %>%
dplyr::mutate(RHEB_lysosome = rowSums(dplyr::across(contains("RHEB_lysosome"))))
df3 <- df_new1 %>%
select(contains('Akt_pathway_phenotype'), Time) %>%
dplyr::mutate(Akt = rowSums(dplyr::across(contains("Akt"))))
df4 <- df_new1 %>%
select(contains('catabolism_phenotype'), Time) %>%
dplyr::mutate(catabolism= rowSums(dplyr::across(contains("catabolism"))))
#df5 <- df_new1 %>%
# select(contains('apoptosis_phenotype'), Time) %>%
# dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"swedd_dm.csv")
datex <- read_excel("parkinsonism_dm.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('glycolysis_phenotype'), Time) %>%
dplyr::mutate(glycolysis = rowSums(dplyr::across(contains("glycolysis"))))
df2 <- df_new1 %>%
select(contains('RHEB_lysosome'), Time) %>%
dplyr::mutate(RHEB_lysosome = rowSums(dplyr::across(contains("RHEB_lysosome"))))
df3 <- df_new1 %>%
select(contains('Akt_pathway_phenotype'), Time) %>%
dplyr::mutate(Akt = rowSums(dplyr::across(contains("Akt"))))
df4 <- df_new1 %>%
select(contains('catabolism_phenotype'), Time) %>%
dplyr::mutate(catabolism= rowSums(dplyr::across(contains("catabolism"))))
#df5 <- df_new1 %>%
# select(contains('apoptosis_phenotype'), Time) %>%
# dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism_dm.csv")
datex <- read_excel("parkinsonism.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('glycolysis_phenotype'), Time) %>%
dplyr::mutate(glycolysis = rowSums(dplyr::across(contains("glycolysis"))))
df2 <- df_new1 %>%
select(contains('RHEB_lysosome'), Time) %>%
dplyr::mutate(RHEB_lysosome = rowSums(dplyr::across(contains("RHEB_lysosome"))))
df3 <- df_new1 %>%
select(contains('Akt_pathway_phenotype'), Time) %>%
dplyr::mutate(Akt = rowSums(dplyr::across(contains("Akt"))))
df4 <- df_new1 %>%
select(contains('catabolism_phenotype'), Time) %>%
dplyr::mutate(catabolism= rowSums(dplyr::across(contains("catabolism"))))
#df5 <- df_new1 %>%
# select(contains('apoptosis_phenotype'), Time) %>%
# dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism.csv")
datex <- read_excel("prodromal.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('glycolysis_phenotype'), Time) %>%
dplyr::mutate(glycolysis = rowSums(dplyr::across(contains("glycolysis"))))
df2 <- df_new1 %>%
select(contains('RHEB_lysosome'), Time) %>%
dplyr::mutate(RHEB_lysosome = rowSums(dplyr::across(contains("RHEB_lysosome"))))
df3 <- df_new1 %>%
select(contains('Akt_pathway_phenotype'), Time) %>%
dplyr::mutate(Akt = rowSums(dplyr::across(contains("Akt"))))
df4 <- df_new1 %>%
select(contains('catabolism_phenotype'), Time) %>%
dplyr::mutate(catabolism= rowSums(dplyr::across(contains("catabolism"))))
#df5 <- df_new1 %>%
# select(contains('apoptosis_phenotype'), Time) %>%
# dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"prodromal.csv")
datex <- read_excel("swedd.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('glycolysis_phenotype'), Time) %>%
dplyr::mutate(glycolysis = rowSums(dplyr::across(contains("glycolysis"))))
df2 <- df_new1 %>%
select(contains('RHEB_lysosome'), Time) %>%
dplyr::mutate(RHEB_lysosome = rowSums(dplyr::across(contains("RHEB_lysosome"))))
df3 <- df_new1 %>%
select(contains('Akt_pathway_phenotype'), Time) %>%
dplyr::mutate(Akt = rowSums(dplyr::across(contains("Akt"))))
df4 <- df_new1 %>%
select(contains('catabolism_phenotype'), Time) %>%
dplyr::mutate(catabolism= rowSums(dplyr::across(contains("catabolism"))))
#df5 <- df_new1 %>%
# select(contains('apoptosis_phenotype'), Time) %>%
# dplyr::mutate(apoptosis = rowSums(dplyr::across(contains("apoptosis"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"swedd.csv")
setwd("C:/Users/T440s/Desktop/modelsCorrST/wnt_pi3k akt/endpoints_prob/tRAJ_PROB")
datex <- read_excel("swedd.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('TFEB_phosphorylated'), Time) %>%
dplyr::mutate(TFEB_phosphorylated = rowSums(dplyr::across(contains("TFEB_phosphorylated"))))
df2 <- df_new1 %>%
select(contains('insulin_resistence'), Time) %>%
dplyr::mutate(insulin_resistence = rowSums(dplyr::across(contains("insulin_resistence"))))
df3 <- df_new1 %>%
select(contains('TFEB_SNCA_complex'), Time) %>%
dplyr::mutate(TFEB_SNCA_complex = rowSums(dplyr::across(contains("TFEB_SNCA_complex"))))
df4 <- df_new1 %>%
select(contains('TFEB_complex'), Time) %>%
dplyr::mutate(TFEB_complex= rowSums(dplyr::across(contains("TFEB_complex"))))
df5 <- df_new1 %>%
select(contains('neuron_death_phenotype'), Time) %>%
dplyr::mutate(neuron_death = rowSums(dplyr::across(contains("neuron_death_phenotype"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4,df5) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"swedd.csv")
datex <- read_excel("prodromal.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('TFEB_phosphorylated'), Time) %>%
dplyr::mutate(TFEB_phosphorylated = rowSums(dplyr::across(contains("TFEB_phosphorylated"))))
df2 <- df_new1 %>%
select(contains('insulin_resistence'), Time) %>%
dplyr::mutate(insulin_resistence = rowSums(dplyr::across(contains("insulin_resistence"))))
df3 <- df_new1 %>%
select(contains('TFEB_SNCA_complex'), Time) %>%
dplyr::mutate(TFEB_SNCA_complex = rowSums(dplyr::across(contains("TFEB_SNCA_complex"))))
df4 <- df_new1 %>%
select(contains('TFEB_complex'), Time) %>%
dplyr::mutate(TFEB_complex= rowSums(dplyr::across(contains("TFEB_complex"))))
df5 <- df_new1 %>%
select(contains('neuron_death_phenotype'), Time) %>%
dplyr::mutate(neuron_death = rowSums(dplyr::across(contains("neuron_death_phenotype"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4,df5) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"prodromal.csv")
datex <- read_excel("parkinsonism.xlsx")
df<- data.frame(datex)
df_new1 <- df %>% select(-contains("ErrProb"))
#df_new %>% dplyr::mutate(new = rowSums(dplyr::across(contains("PRKN_ubiquitinated"))))
df1 <- df_new1 %>%
select(contains('TFEB_phosphorylated'), Time) %>%
dplyr::mutate(TFEB_phosphorylated = rowSums(dplyr::across(contains("TFEB_phosphorylated"))))
df2 <- df_new1 %>%
select(contains('insulin_resistence'), Time) %>%
dplyr::mutate(insulin_resistence = rowSums(dplyr::across(contains("insulin_resistence"))))
df3 <- df_new1 %>%
select(contains('TFEB_SNCA_complex'), Time) %>%
dplyr::mutate(TFEB_SNCA_complex = rowSums(dplyr::across(contains("TFEB_SNCA_complex"))))
df4 <- df_new1 %>%
select(contains('TFEB_complex'), Time) %>%
dplyr::mutate(TFEB_complex= rowSums(dplyr::across(contains("TFEB_complex"))))
df5 <- df_new1 %>%
select(contains('neuron_death_phenotype'), Time) %>%
dplyr::mutate(neuron_death = rowSums(dplyr::across(contains("neuron_death_phenotype"))))
#Join three dataframes based on id column by performing inner join
x1 <- list(df1,df2,df3,df4,df5) %>% reduce(inner_join, by='Time')
data <- x1 %>% select(-contains("Prob"))
write.csv(data,"parkinsonism.csv")
source("C:/Users/T440s/Desktop/modelsCorrST - Copy/PRKN/extraxt specific node PROBABILITIES.R")
