# Plotting Css95

#### Caroline Ring

#### 2017-07-14

This vignette contains the code to plot Css95 values vs. independent MC (Supplemental Figures).

    library('data.table')
    library('ggplot2')
    library('httk')
    library('reshape2')
    #>
    #> Attaching package: 'reshape2'
    #> The following objects are masked from 'package:data.table':
    #>
    #>     dcast, melt

First, we set up a function to read in the data files and do some reshaping.

    data_read <- function(model, poormetab, fup.censor, chemlist){
    httk.dat <- readRDS(paste0('data/',
                          paste('allchems', 'indepMC',
                                'poormetab', poormetab,
                                'fup.censor', fup.censor,
                                model, "FuptoFub", sep='_'),
                          '.Rdata'))

    httk.LCL <- melt(httk.dat[,
                                           c('chemcas',
                                             grep(x=names(httk.dat),
                                                  pattern='LCLcss',
                                                  value=TRUE)),
                                           with=FALSE],
                             measure.vars=grep(x=names(httk.dat),
                                               pattern='LCLcss',
                                               value=TRUE),
                             variable.name='pctile',
                             value.name='LCL')
    httk.LCL[, pctile:=as.numeric(gsub(x=pctile,
                                               pattern='LCLcss',
                                               replacement=''))]
    httk.UCL <- melt(httk.dat[,
                              c('chemcas',
                                grep(x=names(httk.dat),
                                     pattern='UCLcss',
                                     value=TRUE)),
                              with=FALSE],
                     measure.vars=grep(x=names(httk.dat),
                                       pattern='UCLcss',
                                       value=TRUE),
                     variable.name='pctile',
                     value.name='UCL')
    httk.UCL[, pctile:=as.numeric(gsub(x=pctile,
                                       pattern='UCLcss',
                                       replacement=''))]

    httk.cssval <- melt(httk.dat[,
                                           c('chemcas',
                                             grep(x=names(httk.dat),
                                                  pattern='(?<!CL)css\\d{1}',
                                                  perl=TRUE,
                                                  value=TRUE)),
                                           with=FALSE],
                             measure.vars=grep(x=names(httk.dat),
                                               pattern='(?<!CL)css\\d{1}',
                                               perl=TRUE,
                                               value=TRUE),
                             variable.name='pctile',
                             value.name='css')
    httk.cssval[, pctile:=as.numeric(gsub(x=pctile,
                                               pattern='css',
                                               replacement=''))]
    httk.tmp <- merge(httk.cssval,
                           httk.LCL,
                           by=c('chemcas',
                                'pctile'))
    httk.tmp <- merge(httk.tmp,
                      httk.UCL,
                      by=c('chemcas',
                           'pctile'))
    httk.tmp[, method:='independentMC']
    httk.tmp[, ExpoCast.group:='none']

    ExpoCast.groups<-list("Total",
                          "Age.6.11",
                          "Age.12.19",
                          "Age.20.65",
                          "Age.GT65",
                          "BMIgt30",
                          "BMIle30",
                          "Females",
                          "Males",
                          "ReproAgeFemale",
                          "Age.20.50.nonobese")

    tmpfun <- function(grp, popmethod, poormetab, fup.censor){
      tmp.dt <- readRDS(paste0('data/',
                                  paste('allchems', grp, popmethod,
                                        'poormetab', poormetab,
                                        'fup.censor', fup.censor,
                                        model, "FuptoFub", sep='_'),
                                  '.Rdata'))
      tmp.dt[, ExpoCast.group:=grp]
      return(tmp.dt)

    }
    httkpop.dat <- rbindlist(lapply(ExpoCast.groups, tmpfun, popmethod='dr',
                                    poormetab=poormetab,
                                    fup.censor=fup.censor))

    httkpop.LCL <- melt(httkpop.dat[,
                                        c('chemcas',
                                          grep(x=names(httkpop.dat),
                                               pattern='LCLcss',
                                               value=TRUE),
                                          'ExpoCast.group'),
                                        with=FALSE],
                          measure.vars=grep(x=names(httkpop.dat),
                                            pattern='LCLcss',
                                            value=TRUE),
                          variable.name='pctile',
                          value.name='LCL')
    httkpop.LCL[, pctile:=as.numeric(gsub(x=pctile,
                                            pattern='LCLcss',
                                            replacement=''))]

    httkpop.UCL <- melt(httkpop.dat[,
                                   c('chemcas',
                                     grep(x=names(httkpop.dat),
                                          pattern='UCLcss',
                                          value=TRUE),
                                     'ExpoCast.group'),
                                   with=FALSE],
                          measure.vars=grep(x=names(httkpop.dat),
                                            pattern='UCLcss',
                                            value=TRUE),
                          variable.name='pctile',
                          value.name='UCL')
    httkpop.UCL[, pctile:=as.numeric(gsub(x=pctile,
                                            pattern='UCLcss',
                                            replacement=''))]

    httkpop.cssval <- melt(httkpop.dat[,
                                           c('chemcas',
                                             grep(x=names(httkpop.dat),
                                                  pattern='(?<!CL)css\\d{1}',
                                                  perl=TRUE,
                                                  value=TRUE),
                                             'ExpoCast.group'),
                                           with=FALSE],
                             measure.vars=grep(x=names(httkpop.dat),
                                               pattern='(?<!CL)css\\d{1}',
                                               perl=TRUE,
                                               value=TRUE),
                             variable.name='pctile',
                             value.name='css')
    httkpop.cssval[, pctile:=as.numeric(gsub(x=pctile,
                                               pattern='css',
                                               replacement=''))]
    httkpop.tmp <- merge(httkpop.cssval,
                           httkpop.LCL,
                           by=c('chemcas',
                                'ExpoCast.group',
                                'pctile'))
    httkpop.tmp <- merge(httkpop.tmp,
                           httkpop.UCL,
                           by=c('chemcas',
                                'ExpoCast.group',
                                'pctile'))
    httkpop.tmp[, method:='dr']

    m.dat <- rbindlist(list(httkpop.tmp,
                   httk.tmp),
                   use.names=TRUE)
    m.dat <- merge(m.dat, chemlist, by='chemcas') #Add compound name and MW columns
    m.dat[, poormetab:=poormetab]
    m.dat[, fup.censor:=fup.censor]

    paramfun <- switch(model,
                       '3compartmentss'='parameterize_steadystate',
                       '3compartment'='parameterize_3comp',
                       'pbtk'='parameterize_pbtk',
                       '1compartment'='parameterize_1comp')
    paramfunargs <- '(chem.cas=chemcas, species="Human", default.to.human=TRUE)'
    if (model!='1compartment'){
    p.dt <- chemlist[, eval(parse(text=paste(paramfun,
                                               paramfunargs))),
                       by=chemcas]
    }

    if (model=='1compartment'){ #get metabolism by doing steady-state parameterization
      p.dt <- chemlist[, eval(parse(text=paste('parameterize_steadystate',
                                                paramfunargs))),
                        by=chemcas]
    }

    metabname <- switch(model,
                        '3compartmentss'='Clint',
                        '3compartment'='Clmetabolismc',
                        'pbtk'='Clmetabolismc',
                        '1compartment'='Clint')

    m.dat<-merge(m.dat,
          p.dt[, c('chemcas',
                   metabname,
                   'Funbound.plasma'),
               with=FALSE],
          by='chemcas')

    setnames(m.dat,
             c(metabname,
                      'Funbound.plasma'),
             c('metab',
               'fup'))

    m.dat[, fup.lod:=factor(ifelse(fup<=0.01, TRUE, FALSE),
                            levels=c(TRUE, FALSE),
                            labels=c('below LOD',
                                     'above LOD'))]
    m.dat[, metab.zero:=factor(ifelse(metab==0, TRUE, FALSE),
                               levels=c(TRUE, FALSE),
                               labels=c('zero',
                                        'nonzero'))]

    return(m.dat)
    }

Then we read in the data.

    model <- '3compartmentss'
    css.method <- 'analytic'
    use.seed <- TRUE
    pmlist <- c(TRUE, FALSE)
    fclist <- c(TRUE, FALSE)
    pmfc <- expand.grid(poormetab=pmlist,fup.censor=fclist)
    chemlist <- as.data.table(get_cheminfo(info=c('CAS', 'Compound', 'MW'),
                                             species='Human',
                                             model=model,
                                             exclude.fub.zero=FALSE))
      setnames(chemlist, 'CAS', 'chemcas')

      data.read.list <- mapply(data_read,
                               fup.censor=pmfc$fup.censor,
                               poormetab=pmfc$poormetab,
                               MoreArgs=list(model='3compartmentss',
                                             chemlist=chemlist),
                               SIMPLIFY=FALSE)
      m.dat <- rbindlist(data.read.list)

We then need to cast m.dat so we can compare the Css percentiles directly.

    methodvect <- c('dr', 'independentMC')
    pctilevect <- c(5, 50, 95)
    #First: cast the data table into appropriate form
    tmp.css<-dcast.data.table(m.dat[method %in% methodvect &
                                      pctile %in% pctilevect],
                              Compound+chemcas+ExpoCast.group+pctile+fup.lod+
                                metab.zero+fup.censor+poormetab+metab+fup~method,
                              value.var=c('css'))
    setnames(tmp.css, methodvect,
            paste(methodvect,
                  'css',
                  sep='.'))
    tmp.LCL <- dcast.data.table(m.dat[method %in% methodvect &
                                        pctile %in% pctilevect],
                                Compound+chemcas+ExpoCast.group+pctile+
                                  fup.lod+metab.zero+fup.censor+poormetab+metab+fup~method,
                                value.var=c('LCL'))
    setnames(tmp.LCL, methodvect,
             paste(methodvect,
                   'LCL',
                   sep='.'))
    tmp.UCL <- dcast.data.table(m.dat[method %in% methodvect &
                                        pctile %in% pctilevect],
                                Compound+chemcas+ExpoCast.group+pctile+
                                  fup.lod+metab.zero+fup.censor+poormetab+metab+fup~method,
                                value.var=c('UCL'))
    setnames(tmp.UCL, methodvect,
             paste(methodvect,
                   'UCL',
                   sep='.'))
    tmp <- merge(tmp.css,
                 tmp.LCL,
                 by=c('Compound',
                      'chemcas',
                      'ExpoCast.group',
                      'pctile',
                      'fup.lod',
                      'metab.zero',
                      'poormetab',
                      'fup.censor',
                      'metab',
                      'fup'))
    tmp <- merge(tmp,
                 tmp.UCL,
                 by=c('Compound',
                      'chemcas',
                      'ExpoCast.group',
                      'pctile',
                      'fup.lod',
                      'metab.zero',
                      'poormetab',
                      'fup.censor',
                      'metab',
                      'fup'))

And we need to handle the independent MC values properly.

     xonlynames<-c('dr.css',
                                                               'dr.UCL',
                                                               'dr.LCL')
                                                  yonlynames<-c('independentMC.css',
                                                               'independentMC.LCL',
                                                               'independentMC.UCL')
    #Handle the independent MC numbers properly
    tmp.httk <- tmp[ExpoCast.group=='none',]
    tmp[, (yonlynames):=NULL, with=FALSE]
    tmp.httkpop <- tmp[ExpoCast.group!='none',]
    tmp.httk[, (xonlynames):=NULL, with=FALSE]
    tmp.httk[, ExpoCast.group:=NULL]
    tmp.merge <- merge(tmp.httkpop,
                       tmp.httk,
                       by=c('Compound',
                            'chemcas',
                            'pctile',
                            'fup.lod',
                            'metab.zero',
                            'poormetab',
                            'fup.censor',
                            'metab',
                            'fup'))
    tmp <- copy(tmp.merge)

    ExpoCast.groups<-list("Total",
                          "Age.6.11",
                          "Age.12.19",
                          "Age.20.65",
                          "Age.GT65",
                          "BMIgt30",
                          "BMIle30",
                          "Females",
                          "Males",
                          "ReproAgeFemale",
                          "Age.20.50.nonobese")
     tmp[, poormetab:=factor(poormetab,
                              levels=c(TRUE, FALSE),
                              labels=c('included', 'excluded'))]
      setnames(tmp, 'fup.censor', 'Fub') #rename this column for better labeling
      tmp[, Fub:=factor(Fub,
                        levels=c(TRUE, FALSE),
                        labels=c('<lod censored', '<lod=lod/2'))]
      tmp[, metab.zero:=factor(metab.zero, levels=c('nonzero', 'zero'))]
      tmp[, fup.lod:=factor(fup.lod)]
      tmp[, metab.zero.fup.lod:=factor(paste('CLint',
                                             paste0(metab.zero, ','),
                                             'Fub',
                                             fup.lod))]
      tmp[, ExpoCast.group:=factor(ExpoCast.group,
                               levels=ExpoCast.groups)]

Now, do the plotting by subsets of demographic groups.

     subset.list <- list(age=c('Total',
                                'Age.6.11',
                                'Age.12.19',
                                'Age.20.65',
                                'Age.GT65'),
                          bmi=c('Total',
                                'BMIle30',
                                'BMIgt30'),
                          sex=c('Total',
                                'Males',
                                'Females',
                                'ReproAgeFemale'))

    axis.text.size <- 1.2
    axis.title.size <- 1.2
    legend.text.size <- 1.2
    point.size <- 1.5
    legend.title.size <- 1.2
    strip.text.size <- 1.2
    for (pct in c(5,50,95)){
        for (i in seq_along(subset.list)){

           p <- ggplot(data=tmp[pctile==pct &
                                          ExpoCast.group %in% subset.list[[i]],]) +
      geom_point(aes(x=independentMC.css,
                     y=dr.css,
                     color=ExpoCast.group),
                 alpha=0.3,
                 size=point.size) +
      scale_x_log10() +
      scale_y_log10() +
      geom_abline(slope=1,intercept=0)+
      facet_grid(poormetab~Fub, labeller=labeller(poormetab=function(x) paste("CLint: PMs", x), Fub=label_both)) +
      scale_color_brewer(type='qual', palette=2) +
      labs(x=bquote(paste(C[ss]^.(pct), '(uM) using independent MC')),
           y=bquote(paste(C[ss]^.(pct), '(uM) using HTTK-Pop')),
           color='Demographic') +
      guides(color=guide_legend(override.aes=list(size=4,alpha=1)))+
      theme_bw() +
      theme(strip.background=element_blank(),
            legend.key=element_blank(),
            legend.title=element_text(size=rel(legend.title.size)),
            axis.text.x=element_text(size=rel(axis.text.size)),
            axis.text.y=element_text(size=rel(axis.text.size)),
            axis.title.x=element_text(size=rel(axis.title.size)),
            axis.title.y=element_text(size=rel(axis.title.size)),
            legend.text=element_text(size=rel(legend.text.size)),
            strip.text=element_text(size=rel(strip.text.size)))

      ggsave(plot=p,
             filename=paste0('pdf_figures/',
                                            'cssplot_',
                                            names(subset.list)[i],
                                            '_drvsindep_',
                                            model,
                                            '_',
                                            pct,
                             "FuptoFub",
                                            '.pdf'),
             width=11,
             height=8.5)
      print(p)
        }
      }