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###### AUTOMATIZE READING RESULTS ########

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## INPUT : 

#         1) studyType : name of the IMPACT directory where the results/data are saved

#         2) method : Method implemented to be analysed (LASSO,RF,GBM,CF)

#         3) LT : is the data left truncated : TRUE v FALSE

## OUTPUT :

#         For each of the methods to be analyzed will give 

#         1) CI distribution bar plot

#         2) An influence plot of top hit genes

#         3) Kaplan meier plot based on 4 risk groups

#         4) Proportion of mutated genes in each of the 4 risk groups, per top hit gene

getResults <- function(studyType,method,geneList){

  load("./Study/Lung/results/Lung_LASSO.Rdata")

  data <- read.csv("./Study/Lung/data/LungReadyStudy.csv",header = TRUE, row.names = 1)

  ## determine if left truncated

  LT = T

  MD = 12

  time.type = "Months"

  ### LASSO ANALYSIS ###

  #try(setwd("./results/"))

  if("LASSO" %in% method) {

    Variables <- colnames(data)

    allCoefs <- as.data.frame(matrix(nrow=length(LASSO),ncol=length(Variables)))

    colnames(allCoefs) <- Variables

    for(x in 1:length(LASSO)){

      coefsValues <- LASSO[[x]]$fit[,1]

      allCoefs[x,match(names(coefsValues),colnames(allCoefs))] <- as.numeric(coefsValues)

    }

    allCoefs[is.na(allCoefs)] <- 0

    meanCoefs <- apply(allCoefs,2,function(x){mean(x,na.rm = TRUE)})

    selectFreq <- apply(allCoefs,2,function(x){

      length(which(x!=0))/length(x)

    })

    ## get mu freq

    data.temp <- data

    MutationFrequency <- apply(data.temp,2,function(x){

      sum(x)/length(x)

    })

    resultsAll <- as.data.frame(cbind(meanCoefs,selectFreq,MutationFrequency))

    colnames(resultsAll) <- c("MeanCoefficient","SelectionFrequency","MutationFrequency")

    rownames(resultsAll) <- names(meanCoefs)

    resultsAll <- resultsAll[complete.cases(resultsAll),]

    resultsAll$GeneName <- rownames(resultsAll)

    resultsAll$MutationFrequency2 <- cut(resultsAll$MutationFrequency, c(0,0.10,0.20,0.40))

    if(length(geneList)!=0){

      m <- resultsAll[match(geneList,rownames(resultsAll)), ]

      a <- list(

        x = m$MeanCoefficient,

        y = m$SelectionFrequency,

        text = rownames(m),

        xref = "x",

        yref = "y",

        showarrow = TRUE,

        arrowhead = 7,

        ax = 20,

        ay = -40

      )

      selectInflPlot <- plot_ly(data = resultsAll, x = ~MeanCoefficient, y = ~SelectionFrequency,

                                text = ~paste('Gene :',GeneName,

                                              '</br> Hazard Ratio :',round(exp(MeanCoefficient),digits=2)),

                                mode = "markers",size = ~MutationFrequency,color = ~MutationFrequency) %>% 

        layout(title ="Volcano Plot",annotations = a)

    }

    else{

      selectInflPlot <- plot_ly(data = resultsAll, x = ~MeanCoefficient, y = ~SelectionFrequency,

                                text = ~paste('Gene :',GeneName,

                                              '</br> Hazard Ratio :',round(exp(MeanCoefficient),digits=2)),

                                mode = "markers",size = ~MutationFrequency,color = ~MutationFrequency) %>% 

        layout(title ="Volcano Plot")

    }

  }

  # return(list("ciSummary" = CI.BP,"inflPlot" = influencePlot,"topHits" = topHits,"average.risk"=average.risk,"data.out"= data,

  #             "selectInflPlot" = selectInflPlot,"MethodUsed" = method,"RiskRefit"=refit.risk,"ClinRefitTable"=ClinRefitTable,

  #             "RiskHistogram"=RiskHistogram,"LassoFits"=allCoefs,"ClinRefit"=refit.risk.clin,"time.type"=time.type,"MD"=MD,

  #             "RiskScoreSummary"=as.data.frame(t(summary.RiskScore))))

 return(list("selectInflPlot" = selectInflPlot))

}

#test <- getResults(studyType="Lung",method="LASSO",CNV=FALSE,OnlyCNV= FALSE)

# FirstRun <- getResults(studyType="Lung",method="LASSO",CNV=FALSE,OnlyCNV= FALSE,geneList = NULL)

# save(FirstRun,file="FirstRun.Rdata")