In broadinstitute/ImagingAMARETTO: ImagingAMARETTO: tools for interpreting multi-omics networks for relevance to clinical outcomes and radiographic and histopathology imaging-derived biomarkers

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library(knitr)
# You need this code to conduct the magic dependences attaching...
DT::datatable(matrix())

for(parameter in parameters){
  #p_survival<-matrix(nrow=AMARETTOresults$NrModules, ncol=11)
    moduleNr <- paste0("Module_",i)
    sample_size<-nrow(annotation%>%select(!!parameter) %>% drop_na())

    cat(sprintf("\n\n## %s\n\n", gsub("_|\\."," ",parameter)))
    cat("\n\n")

    cat("Phenotypic association was calculated for ",parameter, " on ",sample_size," patients.<br>")
    test<-typelist[parameter]

    if (test == "cathegorical" && sample_size<30 ){
      annotation[,parameter]<-as.factor(annotation[,parameter])
      if (nlevels(annotation[,parameter])==2){
        cat("A wilcox test was performed for ",parameter,".\n")
        cat("\n\n#### Test Results \n\n")
        testresults<-wilcox.test(annotation[,moduleNr]~annotation[,parameter], conf.int = TRUE)

        cat("pvalue: ",testresults$p.value,"\n\n")
        cat("confidence interval: ",testresults$conf.int,"\n\n")
        cat("W statistics: ",testresults$statistic,"\n\n")

        print(ggplot(annotation %>% drop_na(!!parameter),aes(x=get(parameter), y=get(moduleNr), fill=get(parameter)))+
              geom_boxplot()+
              geom_jitter(color="lightgray")+
              theme_classic()+
              theme(legend.position = "none")+
              labs(x=parameter,y=sub("_"," ",moduleNr),caption=paste0("p=",round(testresults$p.value,4))))

      } else if (nlevels(annotation[,parameter])>2){
        cat("A Kruskal-Wallis Rank sum test was performed for ",parameter,".\n")
        cat("\n\n#### Test Results \n\n")
        testresults <- kruskal.test(annotation[,i]~annotation[,parameter])

        cat("pvalue: ",testresults$p.value,"\n\n")
        cat("KW Chi-squared statistics: ",testresults$statistic,"\n\n")

        print(ggplot(annotation %>% drop_na(!!parameter),aes(x=get(parameter), y=get(moduleNr), fill=get(parameter)))+
              geom_boxplot()+
              geom_jitter(color="lightgray")+
              theme_classic()+
              theme(legend.position = "none",axis.text.x = element_text(angle=90))+
              labs(x=parameter,y=sub("_"," ",moduleNr)))
      } else {
        stop(paste0(parameter, " has less than two levels."))
      }
    } else if (test == "cathegorical" && sample_size>=30){
      annotation[,parameter]<-as.factor(annotation[,parameter])
      if (nlevels(annotation[,parameter])==2){
        cat("A t-test was performed for ",parameter,".\n")
        cat("\n\n#### Test Results \n\n")
        testresults<-t.test(annotation[,moduleNr]~annotation[,parameter])

        cat("pvalue: ",testresults$p.value,"\n\n")
        cat("confidence interval: ",testresults$conf.int,"\n\n")
        cat("t statistics: ",testresults$statistic,"\n\n")

        print(ggplot(annotation %>% drop_na(!!parameter),aes(x=get(parameter), y=get(moduleNr), fill=get(parameter)))+
              geom_boxplot()+
              theme_classic()+
              theme(legend.position = "none")+
              labs(x=parameter,y=sub("_"," ",moduleNr)))

      } else if (nlevels(annotation[,parameter])>2){
        cat("An ANOVA test is performed for ",parameter, ". The levels are: ",paste0(levels(annotation[,parameter]),collapse=", "),".\n")
        cat("\n\n#### Test Results \n\n")
        lmod<-lm(annotation[,moduleNr]~annotation[,parameter])
        testresults<-aov(annotation[,moduleNr]~annotation[,parameter])

        cat("pvalue: ",unlist(summary(testresults)[[1]][5])[1],"\n\n")
        cat("F statistics: ",unlist(summary(testresults)[[1]][4])[1],"\n\n")

        print(ggplot(annotation %>% drop_na(!!parameter),aes(x=get(parameter), y=get(moduleNr), fill=get(parameter)))+
                    geom_boxplot()+
                    theme_classic()+
                    theme(legend.position = "none",axis.text.x = element_text(angle=90))+
                    labs(x="",y=sub("_"," ",moduleNr)))
        cat("<br><br>")
        print(ggplot(rownames_to_column(as.data.frame(TukeyHSD(testresults)$`annotation[, parameter]`),"comparison"))+
          geom_pointrange(aes(x=comparison,y=diff,ymin=lwr,ymax=upr))+
          geom_hline(yintercept = 0, color="gray",linetype="longdash")+
          theme_classic()+
          coord_flip()+
          labs(y="Differences between two groups",x="Group comparisons")+
          theme(axis.line.y = element_blank(),axis.ticks.y = element_blank()))
        cat("<br><br>")
        cat("Result table one by one analysis: \n\n")
        print(as.data.frame(TukeyHSD(testresults)$`annotation[, parameter]`) %>% mutate(Groups=rownames(.),`p adj`= cell_spec(round(`p adj`,5), background = ifelse(`p adj` < 0.05, "green", "white"))) %>% select(Groups,`p adj`,diff,lwr,upr)%>%
                kable(escape = F,digits=3,col.names=c("Groups","Padj","Difference","CI Lower Boundary","CI Higher Boundary")) %>%
                kable_styling() %>%
                scroll_box(width = "100%", height = "200px"))
                 # colnames=c("Difference","CI Lower Boundary","CI Higher Boundary","Padj"))))
        cat("\n\n")

        } else {
        stop(paste0(parameter, " has only one level."))
      }
    } else if ( test=="ordinal" || (test == "continuous" && sample_size<30)){
      if (test == "continuous"){
        annotation[,parameter] <- as.numeric(annotation[,parameter])
        cat("A Spearman Correlation was performed for ",parameter,".\n")
        cat("\n\n#### Test Results \n\n")
      } else {
        annotation[,parameter]<-as.factor(annotation[,parameter])
        cat("A Spearman correlation was calculated. The order of the factor levels is: ",paste0(levels(annotation[,parameter]),collapse=", "),".\n")
        cat("\n\n#### Test Results \n\n")
        annotation[,parameter]<-as.numeric(annotation[,parameter])
      }

        testresults<-suppressWarnings(cor.test(annotation[,moduleNr],as.numeric(annotation[,parameter]), method="spearman", use="complete.obs"))

        cat("pvalue: ",testresults$p.value,"\n\n")
        cat("S statistics: ",testresults$statistic,"\n\n")

      if (test == "continuous"){ 
        print(ggplot(annotation %>% drop_na(!!parameter),aes(x=get(parameter), y=get(moduleNr), fill=get(parameter)))+
                  geom_point()+
                  theme_classic()+
                  theme(legend.position = "none")+
                  labs(y=sub("_"," ",moduleNr),x=" "))
      } else {
        print(ggplot(annotation %>% drop_na(!!parameter),aes(x=as.factor(get(parameter)), y=get(moduleNr), fill=get(parameter)))+
                  geom_boxplot()+
                  theme_classic()+
                  theme(legend.position = "none")+
                  labs(y=sub("_"," ",moduleNr),x=" "))
      }
    } else if ( test == "continuous" && sample_size>=30){
      cat("A Pearson Correlation was performed for ",parameter,".\n")
      cat("\n\n#### Test Results \n\n")
      annotation[,parameter]<-as.numeric(annotation[,parameter])
      testresults <- suppressWarnings(cor.test(annotation[,moduleNr],as.numeric(annotation[,parameter]), method="pearson", use="complete.obs"))

      cat("pvalue: ",testresults$p.value,"\n\n")
      cat("S statistics: ",testresults$statistic,"\n\n")

      print(ggplot(annotation %>% drop_na(!!parameter),aes(x=get(parameter), y=get(moduleNr), fill=get(parameter)))+
                  geom_point()+
                  geom_smooth(method="lm")+
                  theme_classic()+
                  theme(legend.position = "none")+
                  labs(y=sub("_"," ",moduleNr),x=" "))
    }
    cat("<br><br><br>")
} 

broadinstitute/ImagingAMARETTO documentation built on Dec. 3, 2019, 6:38 p.m.