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\begin{document}

\title{mrbin}
\author{Matthias S. Klein}
\date{Version 1.9.0}
\maketitle
\tableofcontents
\pagebreak

\section{Introduction}

This package is a collection of functions for processing and analyzing
metabolomics data.\\
The namesake function mrbin() uses spectral binning to convert 1D or 2D Nuclear
Magnetic Resonance (NMR) data into a matrix of values
suitable for further data analysis and performs basic processing steps in a
reproducible way. Negative values, a common issue in NMR data, are replaced by
positive values. All used parameters are stored in a readable text file and can
be restored from that file to enable exact reproduction of the data at a later
time.\\
The atnv algorithm for replacing negative values in NMR data sets can be
employed using atnv().\\
NMR plotting functions are found in mrplot().\\
Artificial Neural Network features can be analyzed using Feature Impact
Assessment (FIA) using the function fia().\\

\section{mrbin: Magnetic Resonance Binning, Integration and Normalization}

The main NMR binning functions of this package are controlled via the mrbin() function.
Results returned include the final bin list and a set
of used parameters.\\



\subsection{Prerequisites and Considerations}

To use this package, you will need NMR data in the Bruker file format
accessible on your computer. Please make sure your data is Fourier transformed,
phase corrected, baseline corrected, and correctly referenced. The data has to
be stored in folders according to standard Bruker folders, that means
foldername/1/pdata/1 etc. Experiment numbers and processing numbers can be
freely chosen.\\

This package has been tested for 1D $^1$H NOESY and 2D $^1$H-$^{13}$C HSQC spectra.\\

Before starting mrbin, take a look at your NMR data, for example in Bruker
Topspin, and decide on the following parameters (you will be able to set the values of
these parameters for your data during running mrbin):\\
\begin{itemize}
  \item Bin area: Area where signals are observed in your data set
  \item Bin width: Should match roughly the width of a singlet peak in your data set. Given in ppm.
  \item Bin height (only 2D): Should match roughly the height of a singlet peak in your data set. Given in ppm.
  \item Solvent area: Area to exclude to remove solvent artifacts
  \item Additional areas to be removed: Any other area containing artifacts, such as streaks surrounding strong peaks.
\end{itemize}

mrbin will also show you preview plots for these parameters during the run.\\


\subsection{Running mrbin in Interactive Mode}

You can start mrbin using the following code:\\

<<>>=
library(mrbin)
@
<<eval=FALSE>>=
results<-mrbin()
@

This will start a series of questions that will guide you through the parameters to be used.\\

mrbin() returns an (invisible) object of type mrbin, containing the following variables:

\begin{itemize}
  \item bins: A matrix containing bin data for all samples, Depending on the option you chose, the data will be cleaned up and scaled.
  \item parameters: A list containing all parameters used to create the bin matrix.
  \item metadata: A list containing metadata, if provided.
  \item transformations: A character vector containing information on the data transformation and scaling that has been performed, for example reference scaling, PQN, atnv, log transform, etc.
  \item changeLog: A data.frame containing information on documented changes that were made to the data, including time stamps.
  \item changeValues: A list containing control values, enabling verifying changes by checkmrbin(results)
\end{itemize}

Please be aware that the generated bin values are calculated as the average
value
of all data points within each bin, multiplied by the area of the bin. This
avoids the issue of different numbers of data points within a bin in different
spectra, and corrects for bins that are different in their size. Therefore,
the bin intensities can be viewed as pseudo-integrals of the contained signals.\\

Several files may be written to the chosen directory:

\begin{itemize}
  \item A .Rdata file containing the generated mrbin data object
  \item A .pdf file containing quality control plots of the raw binned data
  \item A .pdf file containing quality control plots of the processed binned data
  \item A .pdf file containing preview plots of the chosen signal-to-noise ratios (SNR) of selected spectra
  \item A .txt file containing all parameters and potential warning messages from the mrbin run. This file can be reloaded to mrbin to recreate a dataset for reproducibility
\end{itemize}

The generated quality control plots show four panels:

\begin{itemize}
  \item Previews of a few selected spectra, overlaid with the areas of the bins
    generated (in green) to make sure all peaks of interest are covered
  \item Boxplots of bin intensities per bin (feature) to aid in spotting data quality issues
  \item Boxplots of bin intensities per sample to aid in spotting data quality issues
  \item A PCA plot of the generated bin data to aid in spotting data quality issues
\end{itemize}

The preview plots of the signal-to-noise ratios (SNR) of selected spectra
are provided to help chose an SNR level matching the used data. Please note
that the visual representation of SNR levels is approximate, as
noise removal is performed on the binned data, but not on the raw spectra. To
achieve a satisfactory preview of SNR levels, each spectrum is scaled in a way
so its peak heights are equivalent to the respective bin value. The median of these
ratios is used as a scaling factor for the whole spectrum.\\



\subsection{Setting Parameters at the Command Line}
Parameters can also be submitted at the command line. With the default setting
of silent=FALSE, the user will be guided through the user input
questionnaire to view, and make adjustments to, the parameters submitted at the
command line.\\

When using silent=TRUE, this will
set up all parameters and run all steps without asking for further user input.
This should only be used after carefully reviewing all parameters in interactive
mode, as data issues might be missed.\\

The following example provides parameters for analyzing a 1D data set.\\

<<eval=FALSE>>=
results<-mrbin(parameters=list(dimension="1D",
             binwidth1D=.02,
             referenceScaling="Yes",
             removeSolvent="Yes",
             solventRegion=c(4.95,4.65),
             noiseRemoval="Yes",
             signal_to_noise1D=35,
             noiseThreshold=0.75,
             PQNScaling="No",
             fixNegatives="No",
             logTrafo="No",
             useAsNames="Spectrum titles",
             NMRfolders=c(system.file("extdata/1/10/pdata/10",package="mrbin"),
                          system.file("extdata/2/10/pdata/10",package="mrbin"),
                          system.file("extdata/3/10/pdata/10",package="mrbin"))
     ))
@

<<fig=TRUE,pdf=FALSE,png=TRUE,echo=FALSE>>=
results<-mrbin(silent=TRUE,#this suppresses all interactive prompts
     setDefault=FALSE,
     parameters=list(verbose=TRUE,
             dimension="1D",
             binwidth1D=.02,
             referenceScaling="Yes",
             removeSolvent="Yes",
             solventRegion=c(4.95,4.65),
             noiseRemoval="Yes",
             signal_to_noise1D=35,
             noiseThreshold=0.75,
             PQNScaling="No",
             fixNegatives="No",
             logTrafo="No",
			 useAsNames="Spectrum titles",
             NMRfolders=c(system.file("extdata/1/10/pdata/10",package="mrbin"),
                          system.file("extdata/2/10/pdata/10",package="mrbin"),
                          system.file("extdata/3/10/pdata/10",package="mrbin"))
     ))
@

~\\

Approximate signal-to-noise ratios (SNR) will be shown for up to three selected
spectra:\\

<<fig=TRUE,echo=FALSE,hide=FALSE, pdf=FALSE, png=TRUE>>=
setNoiseLevels(results,plotOnly=TRUE)
@

~\\

In the example shown above, spectral data from the mrbin package is used. To
use your own data, change the folder names to the full names of the folders 
containing the files "1r" (or "2rr" for 2D data) as follows:

<<eval=FALSE>>=
results<-mrbin(parameters=list(
   NMRfolders=c("C:/Bruker/TopSpin3.6.1/data/guest/nmr/sample_1/10/pdata/10",
             "C:/Bruker/TopSpin3.6.1/data/guest/nmr/sample_2/10/pdata/10",
             "C:/Bruker/TopSpin3.6.1/data/guest/nmr/sample_3/10/pdata/10")
   ))
@

\subsection{Processing of mrbin Objects}

All processing algorithms, such as noise removal, scaling, etc., can be
performed within mrbin(). However, it is also possible to run these (optional)
steps later. For this, you should select "No" in mrbin() when asked about
noise removal, PQN scaling, atnv, and log transform. Please note that scaling
to the reference substance happens at the raw spectrum level and needs to be
done during running mrbin(); it cannot be performed later. Please consider that,
apart from noise removal, most of the possible processing methods might not be
appropriate for your specific dataset and/or data analysis plan. Therefore, you
will need to make a decision based on your specific situation.  Here is a short
overview of implemented commands, which should be performed in the order shown
below:\\

Set signal-to-noise ratios (SNR) interactively:

<<eval=FALSE>>=
results<-setNoiseLevels(results)
@

To remove noise signals using the newly defined SNR levels, use removeNoise().
Noise levels are individually checked for each bin of each spectrum. Expected
noise levels
are calculated for each bin of each spectrum, based on the estimated noise level
of the respective spectrum, corrected for the area of each bin and
the number of data points within this bin (inverse square root relationship).
Noise removal needs to be performed
before any other scaling or transformation that changes bin values.

<<eval=FALSE>>=
results<-removeNoise(results)
@

Remove negative values by atnv scaling (see below for details):

<<eval=FALSE>>=
results<-atnv(results)
@

Correction for individual dilutions. Use if sample volumes or sample weights
were not identical for all samples. The bin value of a sample will be multiplied
by the respective dilution factor:

<<eval=FALSE>>=
results<-setDilutionFactors(results)
results<-dilutionCorrection(results)
@

Probabilistic Quotient Normalization (PQN) scaling, this is similar, but better,
than scaling to the total sum. Use for urine or tissue extracts:

<<eval=FALSE>>=
results<-PQNScaling(results)
@

Logarithm transform to make data more normal. Data will no longer be linearly
correlated to molecular concentration but rather represent fold changes. This
might only work after atnv transform due to negative values:

<<eval=FALSE>>=
results<-logTrafo(results)
@

Scaling to unit variance. This is rarely necessary, but might be advantageous,
e.g. for use in artificial neural networks. Data will no longer be linearly
correlated to molecular concentration:

<<eval=FALSE>>=
results<-unitVarianceScaling(results)
@

Plot results:

<<eval=FALSE>>=
plotResults(results)
@

Removing a spectrum from the dataset. This should not be done after noise
removal, PQN scaling, unit variance scaling, or atnv, as these methods employ
data from all samples in the dataset. Only removing a sample before invoking
any of these methods will ensure reproducible data transformations.

<<eval=FALSE>>=
results<-removeSpectrum(results)
@


\subsection{Adding Metadata to mrbin Objects}
You can add or edit metadata, including metabolite identities, for an mrbin
object as follows:

<<eval=FALSE>>=
results<-metadatamrbin(results)
@

If you choose "edit" for any of the categories, an editor window may open. After
editing text, close this window and select "Save changes" to save your edits.
Make sure to keep the opening and closing quotation marks.\\

Alternatively, you can edit metadata fields in the command line:

<<>>=
results<-metadatamrbin(results,metadata=list(
  projectTitle="Test project",
  factors=factor(c("Control","Control","Treatment"))))
@

If you choose to define treatment groups in this step, you can plot a PCA
with color coded group information as follows:

<<fig=TRUE, pdf=FALSE, png=TRUE>>=
plotPCA(results)
@

The results can be annotated with potential metabolite identities using the command
metadatamrbin(), or in the command line as follows. Left, right, top, and bottom
borders of the metabolite signal need to provided as a matrix, along with metabolite 
names. The provided data will then be used to annotate the
bins of the dataset. Bins might be assigned to multiple metabolites if they
cover the areas of several potential signals.

<<>>=
#Annotate the dataset with signal identities
metaboliteIdentities<-matrix(c(1.346,1.324,21,23,
                              4.12,4.1,70.8578,71.653,
                              3.052,3.043,30.5,33.5,
                              4.066,4.059,57,59.5,
                              5.7,6.0,0,150),
                   ncol=4,byrow=TRUE)
rownames(metaboliteIdentities)<-c("Lactate","Lactate","Creatinine",
	"Creatinine","Urea")
results<-annotatemrbin(results,metaboliteIdentities=metaboliteIdentities)
@

The loadings plot of the PCA can be displayed as follows:

<<fig=TRUE, pdf=FALSE, png=TRUE>>=
plotPCA(results,loadings=TRUE,annotate=TRUE)
@


\subsection{Editing mrbin Objects}
As mrbin objects use a documented change log, ideally you would use only
functions from the mrbin package to change them (e.g. log transform), as these
are documenting changes. If you
wish to perform other edits, please do so using the command editmrbin(), as in
the following example:

<<eval=FALSE>>=
results<-editmrbin(
  results,
  bins=results$bins,#omit this line if no changes are made to bins
  parameters=list(noiseThreshold=0.75),
  metadata=list(projectTitle="Test project"),
  comment="Changed title and noise parameters",
  transformations="Scaling"#If you change bins, provide a short explanation
  )
@

To see the change log, use:

<<eval=FALSE>>=
results$changeLog
@

To see a summary of scalings and transformations, and check for undocumented
edits to the data, use:

<<echo=TRUE,hide=FALSE,results=tex>>=
checkmrbin(results)
@


\subsection{Example: 2D Data}

The following example provides parameters for analyzing a 2D data set.\\

<<echo=TRUE,eval=FALSE>>=
results2D<-mrbin(setDefault=TRUE,parameters=list(dimension="2D",
               binwidth2D=0.3,
               binheight=4,
               removeSolvent="Yes",
               solventRegion=c(4.95,4.65),
               noiseRemoval="Yes",
               signal_to_noise2D=5,
               noiseThreshold=0.75,
               PQNScaling="No",
               fixNegatives="No",
               logTrafo="No",
			   useAsNames="Spectrum titles",
               NMRfolders=c(system.file("extdata/1/12/pdata/10",package="mrbin"),
                       system.file("extdata/2/12/pdata/10",package="mrbin"),
                       system.file("extdata/3/12/pdata/10",package="mrbin"))
               ))
@
<<fig=TRUE, pdf=FALSE, png=TRUE,echo=FALSE>>=
results2D<-mrbin(silent=TRUE,#this suppresses all interactive prompts
     setDefault=TRUE,
     parameters=list(verbose=TRUE,
               dimension="2D",
               binwidth2D=0.3,
               binheight=4,
               removeSolvent="Yes",
               solventRegion=c(4.95,4.65),
               noiseRemoval="Yes",
               signal_to_noise2D=5,
               noiseThreshold=0.75,
               PQNScaling="No",
               fixNegatives="No",
               logTrafo="No",
			   useAsNames="Spectrum titles",
               NMRfolders=c(system.file("extdata/1/12/pdata/10",package="mrbin"),
                       system.file("extdata/2/12/pdata/10",package="mrbin"),
                       system.file("extdata/3/12/pdata/10",package="mrbin"))
               ))
@

~\\
The approximate signal-to-noise levels are plotted 
for up to three selected spectra. This plot will also be saved as a pdf file.\\

<<fig=TRUE,echo=FALSE,hide=FALSE, pdf=FALSE, png=TRUE>>=
setNoiseLevels(results2D,plotOnly=TRUE)
@

~\\

The binned NMR data can be found in results\$bins. This numeric matrix contains
bins in columns and samples in rows, and can be directly used for further data
analysis.\\

The binned data can also be loaded from the hard drive later (if an output file
was specified):

<<eval=FALSE>>=
load("C:/Users/User/Documents/mrbin.Rdata")
@

Column names include left and right borders or each bin separated by commas. For
2D data, column names include left, right, top, and bottom border of each bin.\\

You can edit metadata fields in the command line:

<<>>=
results2D<-metadatamrbin(results2D,metadata=list(
  projectTitle="Test project",
  factors=factor(c("Control","Control","Treatment"))))
@

If you define treatment groups in this step, you can plot a PCA
with color coded group information as follows:

<<fig=TRUE, pdf=FALSE, png=TRUE>>=
plotPCA(results2D)
@

The results can be annotated with potential metabolite identities using the command
metadatamrbin(), or in the command line as follows:

<<>>=
#Annotate the dataset with signal identities
metaboliteIdentities<-matrix(c(1.346,1.324,21,23,
                              4.12,4.1,70.8578,71.653,
                              3.052,3.043,30.5,33.5,
                              4.066,4.059,57,59.5,
                              5.7,6.0,0,150),
                   ncol=4,byrow=TRUE)
rownames(metaboliteIdentities)<-c("Lactate","Lactate","Creatinine",
	"Creatinine","Urea")
results2D<-annotatemrbin(results2D,metaboliteIdentities=metaboliteIdentities)
@

The loadings plot of the PCA can be displayed as follows:

<<fig=TRUE, pdf=FALSE, png=TRUE>>=
plotPCA(results2D,loadings=TRUE,annotate=TRUE)
@


\subsection{Example: Lipid Data Analysis}

Analyzing lipid NMR signals can be accomplished using custom bin lists rather
than a regular grid of bins. This can be done as in the following code:\\

<<eval=FALSE>>=
results <- mrbin(parameters=list(dimension="1D",binMethod="Custom bin list",
 specialBinList=matrix(c(5.45,5.2,0,160,
                         2.9,2.74,0,160,
                         2.14,1.93,0,160,
                         1.41,1.2,0,160,
                         0.94,0.8,0,160,
                         2.44,2.2,0,160,
                         4.325,4.26,0,160
                         ),ncol=4,byrow=TRUE,dimnames=list(c(
                         "-CH=CH- Methene",
                         "=CH-CH2-CH= Diallylic",
                         "-CH2-CH=CH- Allylic",
                         "-CH2- Methylene",
                         "-CH3 Methyl",
                         "COO-CH2-CH2- Methylene_to_carboxyl",
                         "Glycerol"
                         ),NULL)),
 referenceScaling="Yes",reference1D=c(0.03,-0.03),removeSolvent="No",
 noiseRemoval="No",PQNScaling="No",fixNegatives="Yes",logTrafo="No",
 NMRfolders=c(system.file("extdata/1/10/pdata/10",package="mrbin"),
              system.file("extdata/2/10/pdata/10",package="mrbin"),
              system.file("extdata/3/10/pdata/10",package="mrbin"))
 ))
@

When using custom bin lists, each spectral data point may be part of multiple
bins. For rectangular bin lists, each data point will only be counted once. The
lipid signal areas used in this example are based on Klein et al, 2011. Please
note that the data used in this example does not include lipid signals but
signals of small molecules.\\

\subsection{Recreating Data and Parameters}
In order to create reproducible results, mrbin will save the used parameters to
a text file. Please keep this file. You may want to share this file in a data
repository when publishing your findings.\\

While it is fine to view the parameter text file in a text editor, please do not
change its contents, as this may break its formatting.\\

In order to recreate a previous data set, or to reload previously used
parameters, use:

<<eval=FALSE>>=
results<-mrbin()
@

and select ""Reload from file" when asked "Set parameters or use existing
parameters?". This will restore all parameters that were previously used. If the
file was created using an older version of mrbin, this may cause inconsistencies.
Missing parameters will be added using standard parameters. Ideally, download the
older mrbin version at CRAN and use the old version to recreate the
data in an exact way.\\

Please be aware that bins will have to be recalculated in this case, so the original NMR
spectra will have to be present to do this.\\

\subsection{mrbin Workflow}

The sequence of data processing is as follows:\\

\begin{itemize}
  \item Gathering initial parameters from user
  \item Creating a set with coordinates of each bin
  \item Optional: Removing solvent region
  \item Optional: Removing additional regions
  \item Optional: Cropping of HSQC spectra to the region along the diagonal
  \item Optional: Merging regions containing peaks with unstable positions such as citric acid
  \item Reading NMR spectral data
  \item Optional: Scaling to reference region
  \item Binning
  \item Optional: Removal of bins containing only zeros due to solvent removal (zero trimming)
  \item Quality control plot, including PCA
  \item Optional: Removal of bins containing mostly noise
  \item Optional: Correction for dilution due to differing sample volume or weight
  \item Optional: PQN transform
  \item Optional: Replacement of negative values (atnv transform)
  \item Optional: Log transform
  \item Optional: Scaling to unit variance
  \item Quality control plot, including PCA
  \item Returning final bin data and parameters as an mrbin object
\end{itemize}

\section{atnv: Affine Transformation of Negative Values}

The function atnv replaces (column-wise) negative values by a small positive
number. The number is calculated as an affine transformation to the range of
the lowest positive number to 0,01*the lowest positive number (of this
column). Ranks stay unchanged. Positive numbers are not altered.\\

If sample-wise noise levels are available, the median noise level of samples
with negative values is calculated and replaces the lowest positive number in
case it is smaller. If no noise data is available, the 1\% percentile of all
positive values in the data set is used as an estimate.\\

It is recommended to use this function AFTER noise removal and other data
clean-up methods, as it may alter (reduce) the noise level of the binned data.
If no NMR data and noise levels are provided as arguments, the function will
use NMR data and noise levels from the global variables mrbin.env\$bins and
mrbin.env\$mrbinTMP.\\

To use own (user provided) data:\\

<<eval=FALSE>>=
atnv(NMRdataMatrix,noiseLevelVector)
@

To use current mrbin data from the internal memory, use atnv()) without
parameters. This requires data loaded
using mrbin(). This is usually not necessary as it is included in the mrbin work
flow.\\


\section{mrplot: NMR Plotting}

The mrbin package contains basic plotting commands for 1D and 2D NMR data.\\

Most convenient is using the mrplot function, which is menu-based:

<<eval=FALSE>>=
mrplot()
@

In the mrplot menu, spectra can be added or removed, the view can be zoomed in 
or out, and intensities can be changed. If multiple spectra are added to a plot, 
they are shown overlaid. If both 1D and 2D spectra are added,
both are shown in region-matched plots, e.g. for metabolite identification
purposes.\\

Other options are as follows:

It is possible and convenient to highlight peaks of interest, this will show a 
gray line including the chemical shift value in ppm. If two peaks are highlighted, 
the distance is shown in Hz. This can be used to determine coupling constants.\\

You can also provide a list of mrbin bin names for highlighting. For example, this 
could be the names of significant bins from a statistical analysis. The areas 
of these bins will be shown in green. This can be used to aid in identifying 
unknown signals.\\

The areas of known metabolites can be shown using a peak identification file, or a 
matrix containing the same information.\\

In case of using a file, provide the full 
file path for a .csv file containing such a matrix, the first columns containing 
metabolite names, and the first row being a header. Each row belongs to one unique 
metabolite signal (left, right, top, bottom borders). Row names are metabolite 
names.\\

In case of a matrix, this should be a 4-column matrix where each row belongs to 
one unique metabolite signal (left, 
right, top, bottom borders). Row names are metabolite names.\\

Areas of known metabolites are shown in the plot as red shaded boxes. This 
is useful for identification of unknown peaks.\\

Parameters and folder names can passed to mrplot on the command line, as follows:\\

<<fig=TRUE, pdf=FALSE, png=TRUE>>=
metaboliteIdentities<-matrix(c(1.346,1.324,21,23,
                              4.12,4.1,70.8578,71.653,
                              3.052,3.043,30.5,33.5,
                              4.066,4.059,57,59.5,
                              2.582,2.479,46,49,
                              2.737,2.634,46,49),
                   ncol=4,byrow=TRUE)
rownames(metaboliteIdentities)<-c("Lactate","Lactate","Creatinine","Creatinine",
        "Citrate","Citrate")
mrplot(folders=c(system.file("extdata/1/12/pdata/10",package="mrbin"),
                 system.file("extdata/1/10/pdata/10",package="mrbin"),
                 system.file("extdata/2/10/pdata/10",package="mrbin"),
                 system.file("extdata/3/10/pdata/10",package="mrbin")),
       dimensions=c("2D","1D","1D","1D"),
	   zoom=c(2.8,2.4,20,60),
       highlight=c(2.564,2.537),
       binlist=c("2.725,2.675","2.575,2.525"),
       annotate=TRUE,metaboliteIdentities=metaboliteIdentities,
       plotTitle="Significant Bins",intensity1D=24,hideMenu=TRUE)
@

\section{Heatmap Plots}
Heatmaps are a convenient way to visualize data of significant bins. You can create 
heatmaps from mrbin objects and a list of significant bins as follows:\\

<<fig=TRUE, pdf=FALSE, png=TRUE>>=
# First create NMR bin data, then plot some differential bins.
results<-mrbin(silent=TRUE,setDefault=TRUE,parameters=list(verbose=FALSE,
                dimension="1D",binwidth1D=0.01,PCA="No",
				showSpectrumPreview="No",
                signal_to_noise1D=25,noiseThreshold=0.75,
				useAsNames="Spectrum titles",
                NMRfolders=c(
                system.file("extdata/1/10/pdata/10",package="mrbin"),
                system.file("extdata/2/10/pdata/10",package="mrbin"),
                system.file("extdata/3/10/pdata/10",package="mrbin"))
                ))
metadata<-c(0,0,1)
#Find significant signals
pvalues<-rep(NA,ncol(results$bins))
names(pvalues)<-colnames(results$bins)
for(i in 1:ncol(results$bins)){
	model<-stats::lm(intensity~treatment, 
     data=data.frame(intensity=results$bins[,i],treatment=metadata))
	pvalues[i]<-stats::anova(model)$"Pr(>F)"[1]
}
significantBins<-names(sort(pvalues)[1:30]) 
#Annotate the dataset with signal identities
metaboliteIdentities<-matrix(c(1.346,1.324,21,23,
                              4.12,4.1,70.8578,71.653,
                              3.052,3.043,30.5,33.5,
                              4.066,4.059,57,59.5,
                              5.7,6.0,0,150),
                   ncol=4,byrow=TRUE)
rownames(metaboliteIdentities)<-c("Lactate","Lactate","Creatinine",
	"Creatinine","Urea")
results<-annotatemrbin(results,metaboliteIdentities=metaboliteIdentities)
mrheatmap(results=results,
    binlist=significantBins,annotate=TRUE,
    main="Significant signals",closeDevice=FALSE)
@


\section{Basic Plotting Commands}

Instead of using the convenient mrplot() function, basic NMR plotting commands 
can be accessed from the command line. In most cases, it is preferable to use the 
mrplot command as detailed above.\\

To use the basic plotting commands, you need to first load one NMR spectrum:

<<eval=FALSE>>=
readBruker(dimension="1D",
  folder=system.file("extdata/1/10/pdata/10",package="mrbin"))
plotNMR()
@

The system.file command loads example data from the mrbin package. To use your
own spectra, please adjust as follows:\\

<<eval=FALSE>>=
addToPlot(dimension="1D",
  folder="C:/Bruker/TopSpin3.6.1/data/guest/nmr/sample_1/12/pdata/10")
plotNMR()
@


For 2D data, this code reads as follows:

<<eval=FALSE>>=
addToPlot(dimension="2D",
  folder=system.file("extdata/1/12/pdata/10",package="mrbin"))
plotNMR()
@

There are multiple commands for editing the plot:

<<eval=FALSE>>=
zoom(left=4.6, right=2, top=10, bottom=150) #Exact zoom
zoomIn() #Zoom in
zoomOut() #Zoom out
intPlus() #Increase intensity
intMin() #Decrease intensity
left() #Move spectrum to the left
right() #Move spectrum to the right
@

For 2D data, you can additionally use the following commands:

<<eval=FALSE>>=
contMin() #Decrease minimum contour level (show more small peaks)
contPlus() #Increase minimum contour level (remove small peaks)
up() #Move spectrum up
down() #Move spectrum down
@


\section{fia: Feature Impact Assessment of Artificial Neural Networks}
The function fia() finds features that can change the outcomes of a model's
prediction. For example, fia=1.00 means single compound found in all, or 100
percent of samples. fia=2.45 would indicate that this compound is found in
pairs in all but 45 percent of tested samples.\\
You will need a trained Artificial Neural Network (ANN) model available in R to
use fia(), e.g. using packages keras and/or tensorflow. A function named
predict needs
to be present for fia() to work. If the function name of the prediction function
is different, the function name has to be provided in the parameter
functionNamePredict. Please make sure to have loaded all required packages
before starting fia().\\

As an example, we will use a logit model instead of ANN, as ANN training would
require additional programs to be installed.

<<eval=FALSE>>=
#First, define group membership and create the example feature data
group<-factor(c(rep("Group1",4),rep("Group2",5)))
names(group)<-paste("Sample",1:9,sep="")
dataset<-data.frame(
    Feature1=c(5.1,5.0,6.0,2.9,4.8,4.6,4.9,3.8,5.1),
    Feature2=c(2.6,4.0,3.2,1.2,3.1,2.1,4.5,6.1,1.3),
    Feature3=c(3.1,6.1,5.8,5.1,3.8,6.1,3.4,4.0,4.4),
    Feature4=c(5.3,5.2,3.1,2.7,3.2,2.8,5.9,5.8,3.1),
    Feature5=c(3.2,4.4,4.8,4.9,6.0,3.6,6.1,3.9,3.5),
    Feature6=c(6.8,6.7,7.2,7.0,7.3,7.1,7.2,6.9,6.8)
)
rownames(dataset)<-names(group)
#train the logit model
mod<-glm(group~Feature1+Feature2+Feature3+Feature4+Feature5+Feature6,
   data=data.frame(group=group,dataset),family="binomial")
fiaresults<-fia(model=mod,dataSet=dataset,factors=group,
  parameterNameData="newdata",firstLevel=0,type="response")
fiaresults$scores
@

\section{Known Issues}

\subsection{Linux tcl Issues}
In rare cases, the following error (or similar) might occur while navigating 
pop-up selections in mrbin or mrplot: 

tcl grab failed: window not viewable.\\

This issue seems to be related to tcl handling of select.list pop-up windows.
It can potentially be avoided by avoiding double-clicking inside the pop-up 
windows, instead selecting the desired option with a single click and then clicking 
OK. To stop the error message from occurring, try to close all plot windows 
and pop-up windows. It might be helpful to create a new plot window, e.g. by 
example(mrbin), and then closing the new plot window.
To avoid pop-up windows altogether, use mrbin(graphics=FALSE). 

\subsection{Pop-up Windows Missing on Apple Computers, RStudio}
In some cases, running mrbin on Apple computers and/or within RStudio will not
generate pop-up windows. To enable pop-up windows, it might be helpful to install
the newest version of xquartz from https://www.xquartz.org.\\

\subsection{Pop-Up Windows}
mrbin is set up to ask for user input through pop-up windows. This requires
graphics support. If graphics support is not enabled on your system, user 
input will be asked through command line prompts, which offers the full functionality.\\

\subsection{Firewall Warnings}
mrbin will try to use the socket approach for parallel computing. This requires
establishing network connections to the local cluster, which might
trigger the firewall. It is safe to unblock these connections.\\

\subsection{Spectra are Missing}
If a Bruker spectrum is not shown during browsing, please make sure a file
named title is present in the PROCNO folder of that spectrum. You
can create a title file by opening the spectrum in Bruker Topspin, selecting
the Title tab, entering a title and clicking the disk symbol for saving.\\


\section{License}

This project is licensed under GPL-3.0.\\

\section{Citation}
If you are using mrbin in a publication, please cite the following manuscript:\\

Klein, M.S. (2021): Affine Transformation of Negative Values for NMR Metabolomics
Using the mrbin R Package. J. Proteome Res. 20(2):1397-1404,
DOI: 10.1021/acs.jproteome.0c00684\\


\section{References}

\textbf{Klein MS}, Dorn C, Saugspier M, Hellerbrand C, Oefner PJ \& Gronwald W
(2011): Discrimination of Steatosis and NASH in Mice Using Nuclear Magnetic
Resonance Spectroscopy. \textit{Metabolomics} 7:237-246\\

\end{document}
