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### PREDICTING INCOMING PATIENT SURVIVAL ###

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### Method

## INPUT:

# 1) Set of mutated genes : All genes not inputed in string will be considered as wild type (from App).

# 2) LASSO runs : All refited (forced beta parameters) cox models

# 3) Set of covariates : All covariates available can be edited and added to the model

# 4) The character vector list of genes available (return colnames of data in GetResults function)

# 5) Refitted model with clinical covariates

## METHOD :

# Iterate through all the runs, using the coefficients found in the LASSO models generate a predicted 

# risk score for the incoming patient. Use the predicted risk score in a refitted coxph model with the 

# given clinical covariates.

## OUTPUT :

# 1) Survival curve for the patients inputed

# 2) Summary Table

### TEST

mutGenes <- c("alk","STK11","TP53")

clinical <- c("Age")

ClinRefit <- FirstRun$ClinRefit

time.type <- "Months"

MD <- 12

LassoFits <- as.matrix(FirstRun$LassoFits)

RiskScore <- FirstRun$average.risk

means.train <- means.train

predictIncomingPatient <- function(mutGenes,clinical,ClinRefit,time.type,MD,LassoFits,RiskScore,means.train){

  #if(length(mutGenes) == 0){stop("INPUT ERROR : Enter at least one gene name")}

  #library(plotly)

  ### Step 1 : Build the dataset ###

  ## Gene mutation data

  # create frame 

  mut <- as.data.frame(matrix(0L,nrow=1,ncol=ncol(LassoFits)))

  colnames(mut) <- colnames(LassoFits)

  rownames(mut) <- c("Patient")

  if(length(mutGenes) > 0){

    mutGenes <- gsub(" ","",mutGenes)}

  # Check available genes

  if(anyNA(match(mutGenes,colnames(mut)))){stop(paste("The mutated gene you have inputed :",

                                                      mutGenes[which(is.na((match(mutGenes,colnames(mut)))))],

                                                      "is not available at the moment") )}

  mut[,match(mutGenes,colnames(mut))] <- 1

  ## Mutation data complete

  ## Clinical 

  ### NEED A WHOLE PART FROM THE APP HERE TRANSFORMING TO VALUES IN THE DATA ###

  ## Create frame

  avClinNames <- names(ClinRefit$coefficients)[-length(names(ClinRefit$coefficients))]

  clin <- as.data.frame(matrix(0L,nrow=1,ncol=length(avClinNames)))

  colnames(clin) <- avClinNames 

  rownames(clin) <- "Patient"

  # match input from the app

  clin[,match(clinical,colnames(clin))] <- 1

  ### Part 2 : Get the predicted risk score

  LassoFits <- as.matrix(LassoFits)

  mut <- as.vector(mut)

  all.pred <- lapply(1:nrow(LassoFits),function(x){

    ### Subset to the coefs of that cv ###

    coefs <- LassoFits[x,LassoFits[x,] != 0]

    new.temp <- select(mut,names(coefs))

    ## substract mean mutation rate of TRAINING SET !!!###

    new.x <- new.temp - rep(means.train[[x]][match(names(coefs),names(means.train[[x]]))], each = nrow(new.temp))

    cal.risk.test <- drop(as.matrix(new.x) %*% coefs)

  })

  all.pred <- unlist(all.pred) #do.call("cbind",all.pred)

  Risk <- mean(all.pred) #apply(all.pred,1,mean)

  ori.risk.range <- range(RiskScore)

  RiskScore <- rescale(Risk, to = c(0, 10), from = ori.risk.range)

  # RiskScore.new <- mean(LassoFits%*%t(mut)) 

  # RiskScoreRange <- range(RiskScore)

  # RiskScore <- rescale(RiskScore.new, to = c(0, 10), from = RiskScoreRange)

  # RiskScore <- RiskScore[length(RiskScore)]

  ### part 3 : Refit with the given refitted clinical variable

  clin <- as.data.frame(cbind(clin,RiskScore))

  ### FIRST RUN

  ### GET CI ###

  survival.probs <- as.data.frame(matrix(nrow=(nrow(clin)+3),ncol=15))

  rownames(survival.probs) <- c(rownames(clin),"Lower","Upper","Time")

  surv.temp <- survfit(ClinRefit, newdata = clin)

  for(i in 1:ncol(survival.probs)){

    survival.probs[,i] <- c(as.numeric(summary(surv.temp, times = (i*3-3))$surv),

                            round(summary(surv.temp, times = (i*3-3))$lower,digits=2),

                            round(summary(surv.temp, times = (i*3-3))$upper,digits=2),

                            i*3-3)

  }

  a <- list(

    autotick = FALSE,

    dtick = 6,

    tickcolor = toRGB("black")

  )

  t.survival.probs <- as.data.frame(t(survival.probs))

  y <- list(

    title = "Survival Probability"

  )

  IndSurvKM <- plot_ly(t.survival.probs, x = ~Time, y = ~Patient, name = 'Estimated Survival', type = 'scatter',

                       mode = 'lines+markers',hoverinfo="hovertext",#color = ~Patient

                       hovertext = ~paste("Genetic Risk Score :",round(RiskScore,digits=3))

  ) %>% layout(yaxis = y,xaxis = ~a) %>%

    layout(shapes=list(type='line', x0= 3*MD, x1= 3*MD, y0=0,

                       y1=1, line=list(dash='dot', width=1,color = "red")),

           xaxis = list(title = paste0("Time (",time.type,")"), showgrid = TRUE)) %>%

    add_ribbons(data = t.survival.probs,

                ymin = ~Lower,

                ymax = ~Upper,

                line = list(color = 'rgba(7, 164, 181, 0.05)'),

                fillcolor = 'rgba(7, 164, 181, 0.2)',

                name = "Confidence Interval")

  ### MAKE TABLE SUMMARY

  survivalSummary <- as.data.frame(matrix(nrow=1,ncol=5))

  rownames(survivalSummary) <- "New patient"

  colnames(survivalSummary) <- c("MedianOS","95%CI","1Ysurvival","3Ysurvival","Predicted genetic risk")

  # for each group find closest value to median 

  if(time.type == "Months"){YR1 <- 1*12;YR3 <- 3*12}

  if(time.type == "Days"){YR1 <- 1*365;YR3 <- 3*365}

  Fit <- surv.temp 

  #Fit <- survfit(ClinRefit, newdata = clin)#, se.fit = T, conf.int = "log-log")

  med.index <- which.min(abs(Fit$surv-0.5))

  YR1.index <- which.min(abs(Fit$time-YR1))

  YR3.index <- which.min(abs(Fit$time-YR3))

  survivalSummary[1,] <- c(round(Fit$time[med.index],digits=2),paste0("(",round(Fit$time[which.min(abs(Fit$lower-0.5))],digits=2),",",

                                                                      round(Fit$time[which.min(abs(Fit$upper-0.5))],digits=2),")"),

                           round(Fit$surv[YR1.index],digits=2),round(Fit$surv[YR3.index],digits=2),

                           round(RiskScore,digits=3))

  return(list("IndSurvKM"=IndSurvKM,"IndPredTable"=survivalSummary,"RiskScore"=RiskScore))

  #}

}