In computational-metabolomics/qcrms: Creates quality control output from XCMS output

require(knitr, quietly=TRUE)
#knitr::opts_chunk$set(echo = TRUE)
#opts_knit$set(root.dir = projectdir)

 

 

 

Data processing summary

print(kable(QCreportObject$projectHeader))

 

 

 

XCMS peak picking summary:

print(kable(QCreportObject$peakPickingParams))

\newpage

QCreportObject$plots$ticplot_1

r figs("S1")

\newpage

print(QCreportObject$plots$ticplot_2)

r figs("S2")

\newpage

print(QCreportObject$plots$ticplot_3)

r figs("S3")

\newpage

print(QCreportObject$plots$ticplot_4)

r figs("S4")

\newpage

print(QCreportObject$plots$ticplot_5)

r figs("size_vs_peaknr")  

 

r tbls("S1")

kable (QCreportObject$tables$corrMatrix, digits=10)

\newpage

print (QCreportObject$plots$PCAallSamples)

r figs("S5")

if (!is.null(QCreportObject$QC_label)){
  print(QCreportObject$plots$PCAQCsamples)
}

r figs("S6")

\newpage

if (!is.null(QCreportObject$QC_label)){
  print (QCreportObject$plots$PCAQCleading)
}

r figs("S7")

\newpage

if (!is.null(QCreportObject$QC_label)){
  print (QCreportObject$plots$PCAallQCleading)
}

r figs("S8")

\newpage r tbls("RT_RSD")

#if (!is.null(QCreportObject$QC_label)){
  #pander::pandoc.table(QCreportObject$tables$RT_rsd, round=c(rep(2,6),0), style="rmarkdown")
  #xtable::xtable(QCreportObject$tables$RT_rsd, digits=c(0,rep(2,6),0))
  kable (QCreportObject$tables$RT_rsd, digits=2)
#}

    r tbls("RT_MAD")

#if (!is.null(QCreportObject$QC_label)){
  kable (QCreportObject$tables$RT_mad, digits=2)
#}

#if (!is.null(QCreportObject$QC_label)){
  print (QCreportObject$plots$MAD_rt)
#}

r figs("RT_MAD")

\newpage

r tbls("peak_width")

#if (!is.null(QCreportObject$QC_label)){
  kable (QCreportObject$tables$peak_width, digits=2)
#}

#if (!is.null(QCreportObject$QC_label)){
  print(QCreportObject$plots$peak_width)
  #gridExtra::grid.arrange(ncol=1, QCreportObject$plots$peak_width_all, QCreportObject$plots$peak_width)
#}

r figs("peak_width")

\newpage

if (!is.null(QCreportObject$plots$EICs)){
  QCreportObject$plots$EICs[[2]] <- QCreportObject$plots$EICs[[2]] + theme(legend.position = "none")
  QCreportObject$plots$EICs[[3]] <- QCreportObject$plots$EICs[[3]] + theme(legend.position = "none")
  grid.arrange (grobs=QCreportObject$plots$EICs, layout_matrix=matrix(c(1,2,1,3), nrow=2, ncol=2))
}

r figs("S9")

\newpage

r tbls("mz_precision")

#if (!is.null(QCreportObject$QC_label)){
  kable (QCreportObject$tables$mz_median, digits=2)
#}

#if (!is.null(QCreportObject$QC_label)){
  #gridExtra::grid.arrange(ncol=1, QCreportObject$plots$mz_all, QCreportObject$plots$mz_median)
  print (QCreportObject$plots$mz_median)
#}

r figs("mz_precision")

\newpage

grid.arrange(QCreportObject$plots$MVplot1, QCreportObject$plots$MVplot2,                                          QCreportObject$plots$MVplot3,   QCreportObject$plots$MVplot4, 
             ncol=2, nrow=2)

r figs("S10")

\newpage

print(QCreportObject$plots$RSDplot1)

r figs("S11")  

 

r tbls("S2")

kable (QCreportObject$tables$RSDtable1, digits=1)

\newpage

print (QCreportObject$plots$RSDplot2)

r figs("S12")

\newpage

if (!is.null(QCreportObject$QC_label)){
  print (QCreportObject$plots$QCplot1)
}

r figs("S13")

\newpage

if (!is.null(QCreportObject$QC_label)){
  print (QCreportObject$plots$QCplot2)
}

r figs("S14")

\newpage

r tbls("filtering")

if (!is.null(QCreportObject$QC_label)){
  kable (QCreportObject$filtering$table, digits=1)
}

List of samples removed by missing value filter:

if (!is.null(QCreportObject$QC_label)){
  print(QCreportObject$filtering$samples_removed)
}

Optimised glog lamba values:

if (!is.null(QCreportObject$QC_label)){
  kable(data.frame(data=c("Filttred", "Filtered, S/B corrected"), 
    "glog lambda"=c(QCreportObject$filtering$glog_lambda_filtered, 
      QCreportObject$filtering$glog_lambda_filtered_SB),
    check.names=FALSE))
}

\newpage

if (!is.null(QCreportObject$QC_label)){ 
  print(QCreportObject$plots$SBPCAbefore)
}
if (!is.null(QCreportObject$plots$plots_per_batch_pca)){
  print(QCreportObject$plots$plots_per_batch_pca)
}

r figs("S15")

\newpage

if (!is.null(QCreportObject$QC_label)) {
  print(QCreportObject$plots$SBPCAbeforeQC)
}

r figs("S16")

\newpage

if (!is.null(QCreportObject$QC_label)){
  print (QCreportObject$plots$SBPCAfter)
}

r figs("S17")

\newpage

if (!is.null(QCreportObject$QC_label)){
  print (QCreportObject$plots$SBPCAfterQC)
}

r figs("S18")

\newpage

if (!is.null(QCreportObject$QC_label)){
  print (QCreportObject$plots$SBRSDbefore)
}

r figs("S19")

\newpage

if (!is.null(QCreportObject$plots$plots_per_batch_qc_rsd)){
  print (QCreportObject$plots$plots_per_batch_qc_rsd)
}

r if (!is.null(QCreportObject$plots$plots_per_batch_qc_rsd)) figs ("S19a")

\newpage

if (!is.null(QCreportObject$QC_label)){

  print (QCreportObject$plots$SBRSDafter)
}

r figs("S20")

\newpage r tbls("S3")

if (!is.null(QCreportObject$QC_label)){
  kable (QCreportObject$tables$SBtableBefore, digits=1)
}

 

 

r tbls("S4")

if (!is.null(QCreportObject$QC_label)){
  kable (QCreportObject$tables$SBtableAfter, digits=1)
}

\newpage r tbls("S5")  

kable (QCreportObject$samp.sum, catption="Summary of the sample metadata of the analytical batch")

computational-metabolomics/qcrms documentation built on Jan. 18, 2021, 1:46 a.m.