R/make_RelAmounts.R

In danielbraas/MetabFUN: This is a collection of functions that I use for metabolomics data analysis

#' This function produces a data frame with relative metabolite amounts.
#' @author Daniel Braas
#' @param DF The input data. This should be a data frame with Name, Iso, Condition, Exp, Value, Used_ID, KEGG.ID, Nr.C, Rt and Formula columns.
#' @return A data frame with relative amount information both averaged as well as averaged and normalized to the first condition.
#' @export

make_RelAmounts <- function(DF){
  if (exists('Title')==F) stop('Title not specified')
  data4 <- DF %>%
    select(Name, Condition, Iso, KEGG.ID, Exp, Value) %>%
    mutate(Exp = factor(Exp, levels = unique(Exp))) %>%
    group_by(Name, Condition, Exp) %>%
    mutate(Amount=sum(Value, na.rm=T)) %>%
    ungroup() %>%
    filter(Iso=='M0') %>%
    select(Name, Condition, KEGG.ID, Exp, Amount) %>%
    spread(Exp, Amount)

  ATP_ADP=try(cbind(Name="ADP/ATP",data4[1:length(levels(data4$Condition)),2], 'KEGG.ID'=NA,
                    data4[data4$Name=="ADP",4:length(data4)]/data4[data4$Name=="ATP",4:length(data4)]),
              silent=T)
  if (exists('ATP_ADP')==T & class(ATP_ADP) != 'try-error') data4 <- rbind(data4, ATP_ADP)
  ATP_AMP=try(cbind(Name="AMP/ATP",data4[1:length(levels(data4$Condition)),2], 'KEGG.ID'=NA,
                    data4[data4$Name=="AMP",4:length(data4)]/data4[data4$Name=="ATP",4:length(data4)]),
              silent = T)
  if (exists('ATP_AMP')==T & class(ATP_AMP) != 'try-error') data4 <- rbind(data4, ATP_AMP)
  GSH_GSSG=try(cbind(Name="GSH/GSSG",data4[1:length(levels(data4$Condition)),2], 'KEGG.ID'=NA,
                     data4[data4$Name=="GSH",4:length(data4)]/data4[data4$Name=="GSSG",4:length(data4)]),
               silent = T)
  if (exists('GSH_GSSG')==T & class(GSH_GSSG) != 'try-error') data4 <- rbind(data4, GSH_GSSG)
  Creatine_PCreatine=try(cbind(Name="Creatine/P-Creatine",data4[1:length(levels(data4$Condition)),2], 'KEGG.ID'=NA,
                               data4[data4$Name=="Creatine",4:length(data4)]/data4[data4$Name=="P-Creatine",4:length(data4)]),
                         silent = T)
  if (exists('Creatine_PCreatine')==T & class(Creatine_PCreatine) != 'try-error') data4 <- rbind(data4, Creatine_PCreatine)

  data4 <- data4 %>%
    gather(Exp, Amount, -Name, -Condition,-KEGG.ID)
  data4$Amount <- suppressMessages(mapvalues(data4$Amount, c('Inf','NaN'), c(NA,NA)))
  data4$Amount[data4$Amount==0] <- NA
  test1 <- split(data4, data4[c('Name','Condition')])
  NA_function <- function(x) if (sum(is.na(x)) < length(x)) return(x=x) else return(x=rep(0, length(x)))
  new.Value <- as.vector(sapply(test1, function(x) NA_function(x$Amount)))

  data4 <- suppressWarnings(data4 %>%
    arrange(Condition, Name) %>%
    mutate(Amount=new.Value) %>%
    group_by(Name, Condition) %>%
    mutate(Av=mean(Amount, na.rm=T),
           Std=sd(Amount, na.rm=T),
           CV=sd(Amount, na.rm=T)/mean(Amount, na.rm=T)) %>%
    ungroup())

  data8=split(data4, data4[,1])
  ANOVA=suppressWarnings(sapply(data8, function(x) anova(aov(x$Amount~x$Condition))$Pr[1]))
  ANOVA=rep(ANOVA,1,each=length(levels(data4$Condition)))

  data4 <- data4 %>%
    select(Name, KEGG.ID, Condition, Exp, Amount, Av, Std, CV) %>%
    spread(Exp, Amount) %>%
    arrange(Name, Condition) %>%
    cbind('ANOVA'=ANOVA, 'Sig'=NA) %>%
    group_by(Name) %>%
    mutate(Norm_Av=Av/Av[1],
           Norm_Std=Std/Av[1]) %>%
    ungroup()

  for (i in 1:nrow(data4)){
    if (is.na(data4$ANOVA[i])==T) data4$Sig[i]=""
    else if (data4$ANOVA[i] <= 0.001) data4$Sig[i]="***"
    else if (data4$ANOVA[i] <= 0.01) data4$Sig[i]="**"
    else if (data4$ANOVA[i] <= 0.05) data4$Sig[i]="*"
    else data4$Sig[i]=""
  }
  write.csv(data4, file=paste0(Title,"-Amounts.csv"), row.names=FALSE)
  save(data4, file='RelAmounts.rdata')
  return(data4)
}