# Mass Data Integrations Featured by &MineXp;

TIC/XIC chromatogram mass spectrum drift spectrum

Analyzing mass spectrometric data (with or without drift data) usually involves performing various data integrations in sequence. We saw earlier that upon loading a mass spectrometry data file, the first data visualization that becomes available is the TIC chromatogram. Then, the user might ask to show the MS run data set table view (see ). Thus, the TIC chromatogram and the table view are both starting points for the mass spectrometric data exploration. Once a number of mass data integrations have been performed, new integrations will be available in any direction, by selecting the integration type in the plot widget (see ).

Regarding the parallel execution of mass spectral integrations, please see .

## General Behaviour of Plot Widgets

There are patterns, in the way mass spectral data integrations can be configured, that are common to all plot widgets. These common patterns are described in the next sections. First, however, the Processing Flow concept needs to be described, as it serves as the basis for the integration configurations.

### Processing Flow Entities Document all Integrations

Each time an integration is performed, &MineXp; stores processing information data that permits to characterize the integration for later internal reuse. Any integration, as it is performed, documents itself to the Processing Flow. For example, one integration might be from TIC chromatogram to mass spectrum, another integration can be from mass spectrum to drift spectrum. These kinds of integration are documented as Processing Types . For each integration, there is thus a source and a destination. But this is not sufficient to document precisely the integration: there must be other informational data along with the integration type. When integrating from TIC chromatogram to mass spectrum, the user might select only a region of interest in the chromatogram. That is another kind of information that is documented: the source range (in this case a retention time range). Likewise, when integrating from a mass spectrum to a drift spectrum, the origin is a &mz; range and the destination is a drift spectrum. Other data are stored in the processing information data, like the fragmentation specification—if any has been set— and the &mz; integration parameters, in case the integration produces a mass spectrum.

Together, all the processing information data are stored in what are called Processing Flow instances. A processing flow instance can contain a default &msn; fragmentation specification and/or default &mz; integration parameters. It can contain any number of Processing Step instances that in turn can contain any number of Processing Spec instances. Like for the processing flow, each processing step can contain a &msn; fragmentation specification and/or &mz; integration parameters.

How is the processing flow documented ? When a mass spectrometry data file is loaded from disk, &MineXp; iterates in all the mass spectra of the MS run data set and computes a TIC chromatogram. That TIC chromatogram is displayed in the TIC/XIC chromatograms window. At this point, no &msn; fragmentation specification is set and no &mz; integration parameters are set. When the user starts exploring the data, their first integration will determine how the processing flow will be filled-in with details about that integration.

If an integration is performed from the TIC chromatogram to a mass spectrum, then &mz; integration parameters will necessarily be used. Either the user has configured these parameters ( ) and they will be used to perform the mass spectral combinations, or default values will be used (these default values are crafted from the statistical analysis of the whole MS run data set). The processing step that is created to document this specific integration will thus document the &mz; integration parameters that have been used for the mass spectra combination. That processing step is added to the processing flow that documents the whole integration process. The plot that is created in the Mass spectra window will have, associated to it, that processing flow. Think of the Processing Flow as a pedigree ID card that each plot has associated to it. That pedigree gets incremented with new processing steps each time an integration is chained to the previous integrations.

The original feature of the Processing Flow concept is that, because each plot has its own processing flow, when a new integration is performed starting from that plot, then all the preceding steps of the processing flow are replayed and the new step is added to the processing flow. This has the beneficial effect of virtually restricting the scope of the initial data set into a much smaller data set all along the various integrations that occur during a data exploration session. When the scope reduces, the computing times reduce accordingly. However, the initial data set is not reduced in memory, only the scope of it is reduced. This is much different from other software in which mass data are effectively pruned off the initial data set with the drawback that data must be reloaded each time an integration must start from the initial data set.

### Setting the &msn; Fragmentation Parameters

At any moment is it possible to set the &msn; integration parameters by selecting the Open MS fragmentation dialog menu item from the plot widget main menu ( ).

Setting the &msn; fragmentation parameters

The &msn; fragmentation parameters might be set at any time from the plot widget's main menu. The criteria are of three kinds, as described in detail in the text.

The dialog window that is displayed is depicted in . The available settings are explained below:

- MS level: MS level that is targeted by the integration. For example, from a TIC chromatogram, setting MS level to 2 would perform an integration only accounting for mass spectra acquired for MS fragmentations of MS level 2, that is, for MS/MS spectra.

- Precursor &mz; values: SPACE or RETURN separated list of &mz; values. The precursor ions' &mz; values are taken into account for the integration. When iterating in the mass spectra acquired in the MS run data set, each spectrum will be checked for the existence of an ion selection list. If any of the &mz; values entered in this dialog window are found, the spectrum is accounted for; otherwise it is dismissed. A precursor &mz; value is the &mz; value of an ion that was selected for fragmentation in a precursor spectrum acquired right before the acquisition of the fragmentation spectrum.

  There is a great probability that entering &mz; values in the text widget above will fail to provide any result without some value tolerance set. Indeed, the matches are performed strictly using the entered values and the values recorded by the mass spectrometer, which might have 10 decimals! It is thus necessary to enter a tolerance value for the &mz; value matches. In the figure, the tolerance is set to 0.05 Dalton. Other units are available: resolution power and ppm.

- Precursor spectra indices: SPACE or RETURN separated list of integer values. The precursor spectra indices are taken into account for the integration. When iterating in the mass spectra acquired in the MS run data set, each spectrum will be checked for the existence of precursor spectra indices. If the precursor spectra indices entered in this dialog window are found, the spectrum is accounted for; otherwise it is dismissed. A precursor spectrum index is the number of the spectrum in the MS run data set that contained an ion that was selected for further fragmentation.

Note that in the dialog window above, none of the three criteria is essential. Setting the MS level to 0 disactivates that parameter as a valid criterion for filtering spectra during the integration. In this case, only &msn; fragmentation specifications used in preceding integrations are taken into account (that is, fragmentation specifications present in the Processing Flow). If one wants to reset the &msn; fragmentation specification to a lower MS level than that of the last preceding integration, the only way to achieve this is to go back to the previous plot where the processing flow contained processing steps of the right &msn; fragmentation level values. Indeed, it is not possible to reset the &msn; fragmentation level in a plot by setting the MS level to 0 because that plot already has a processing flow containing pocessing steps configured with a non modifiable MS level.

There is a shortcut to the &msn; fragmentation MS level in each composite plot widget, as shown below, where the spin box widget on top of the right hand side column of allows one to set the MS level.

Shortcut to the fragmentation MS level setting

If the only fragmentation setting to be modified is the MS level, then, instead of opening the dialog window described above, it is possible to set the required MS level right into the spin box widget at the top of the right hand side column of buttons in the composite plot widget.

### Setting the &mz; Integration Parameters

The &mz; integration parameters are only compulsorily used in case an integration will produce a mass spectrum or a color map involving &mz; data in any one of the two dimensions. Indeed, these parameters are required to configure the way mass spectra are combined together into a single product mass spectrum.

An integration to a mass spectrum occurs when the user &rmb;-selects a range in a given plot while the &integrate-to-mz-check-button; checkbutton is checked. Integrations to a mass spectrum can be elicited from any plot.

Very rarely can one perform an integration to a mass spectrum without having to first configure the way the &mz; values are binned into the combination spectrum. The determination of the best &mz; integration parameters is a process that might be required each time a new data set comes from an instrument from which to data have been obtained before. Indeed, there is a vast number of different mass spectral data acquisition/storage modes that depend on the vendors or on the mass spectrometer models. In general, this diversity of mass spectral data files

Even if they are obtained by conversion of raw vendor files to mzML files using ProteoWizard 's msconvert tool.

creates difficulties in the mass spectra integrations, as shown below in .

Unusable combination spectrum without binning

The mass data used to compute this combination spectrum originate from a Lumos Orbitrap analyzer. The visible signal should have been three peaks belonging to a 2-charged ion isotopic cluster. Each peak of the isotopic cluster is artifactually made of numerous data points because the data from the Orbitrap analyzer have 10 decimal digits. Without binning, the mass spectrum is almost unusable.

In this combination mass spectrum, computed without binning from Lumos Orbitrap-originating data, three peaks that belong to an isotopic cluster appear as made of a number of sub-peaks. This is due to the fact that the number of decimals for the &mz; values in the data file is so high that a single isotopic peak appears as a set of peaks. The signal in this mass spectrum is totally useless. This is a perfect illustration of the necessity of data binning of the &mz; values of the mass spectra to be combined.

The m/z integration parameters window

Integrations to any plot that involves &mz; as one of its dimension (be that a mass spectrum or a color map) can be configured to ensure the best results, depending on the kind of mass data. Proper binning configuration is key to getting best results.

&MineXp; provides a number of ways to configure mass spectral combinations such that the obtained mass spectrum is usable. The &mz; integration parameters that might be set are described in the following sections. The window depicted in can be displayed from any plot widget menu using the Open the &mz; integration params dialog menu item (see ). The following parameters can be set:

- No binning : no binning is carried-over during the mass spectral integration. In this case, all the &mz; values encountered in all the mass spectra combined into the final combination spectrum are going to be used for the combination and will be encountered in the result spectrum. This is illustrated in .

  There are instruments that produce perfectly binned mass spectra. In this case, using No binning is certainly the best option. For example, the files acquired from the Synapt 2 HDMS mass spectrometer sold by Waters are perfectly binned.

- Data-based binning: binning is performed using bins already set in the mass spectrum into which the combination is to be performed. This kind of situation only arises when the user has pushed down the pin in one of the composite plot widgets and also has specified that the combination must be performed in the pinned plot widget.

- Arbitrary binning: binning is requested and the size of the bins might be defined using either dalton, resolving power or ppm units.

  In the case of resolution binning, the bins are computed by compounding the &mz; value to the inverse of the resolving power. In the case of ppm (part-per-million) binning, the bins are computed by compounding the &mz; value to the inverse of one million.

- Decimal places: when performing the combination, each &mz; value needs to be restricted to that number of decimal places. This setting may in some situations improve the signal or accelerate the speed of the combination for very large datasets.

  If Decimal places is let to the default value -1, that datum will be determined experimentally by looking into the bins size. Typically, if the bins size is, for example, 0.05, then, the decimal places will be determined to be 2 (number of 0 decimals after the decimal point, plus one). If the bins size is 0.003, the decimal places will be determined to be 3.

- Remove 0-value data points: remove all the &mzi; pairs having a naught i value.

When all the parameters have been set, click onto Apply or key in CTRL RETURN . At this point the next integrations started from the plot widget in which these &mz; integration parameters were set will be performed using the settings. These parameters will propagate to all the descendant widgets. It will be possible, at any moment and in any widget, to modify the parameters again.

If the check box \guilabel{Set as default} is checked, the system records these parameters and makes them the default ones for any coming integration.

### Step-by-step Determination of the Best &mz; Integration Parameters

This section provides guidelines on the best way to determine the &mz; integration parameters that will result in nicely shaped mass peaks in the combination mass spectrum. There might be situations where there is no need to set the &mz; integration parameters, typically if the mass data have been properly binned by the software running the mass spectral data acquisition (Waters and Agilent systems often provide nicely binned mass spectra).

However, in a vast majority of the cases, proper setting of the &mz; integration parameters will be essential to achieve a combination of mass spectra resulting in a useful mass spectrum. In particular, it is important to grasp that the binning might need to be dynamically set while combining &mzi; pairs along the &mz; axis. This dynamic binning is configured by choosing either res or ppm (for resolving power or part-per-million, respectively; bins have varying sizes throughout of the whole &mz; range).

When dealing with a mass spectrometry data set from an instrument that is unknown, the first step is to perform an integration to a mass spectrum without binning. In this situation, all the distinct peaks observed in all the mass spectra being combined together are going to be stored in the combination spectrum. When the data come from an instrument that does not produce nicely binned mass spectra, the resulting combination mass spectrum might be highly spiky, with spurious data points interspersed between seemingly correct data points. This is typically visible in the mass spectrum shown in .

Determination of the best &mz; integration parameters for integration to mass spectra

Zoomed in view of a protein mass spectrum.

The different plots in the figure show the mass spectral data combined for the same acquisition data and for the same retention time range. From top to bottom:

- No binning . Visibly, each peak that is part of the isotopic cluster is made of a number of peaks that appear to be superfluous.

- Bins res: 30000, bin size divisor: 1. This resolving power was used as a starting value for exploring the data because the apparent resolving power was found to be around that value in . Remarkably, the bin size divisor value of 1 cannot permit to shape the isotopic cluster peaks properly.

- Bins res: 30000, bin size divisor: 5. Dividing the bin size determined above for resolving power of 30000 starts to give meaninful results: the peaks of the isotopologues start to be shaped. They are still a bit rough.

- Bins res: 30000, bin size divisor: 6. Using value 6 instead of 5 refines the isotopologue peak shapes. However, there is still an artifactual shoulder on the left of the isotopologue peaks.

- Bins res: 40000, bin size divisor: 5. That is the right combination: resolving power of 40000, with bin size divisor of 5. The charge determination by deconvolution of the isotopic cluster yields the right charge of the polymer: +36.

Estimating the resolving power of a mass spectral acquisition

Dragging the mouse cursor between the extremities of an isotopic cluster peak, at half of its height, provides a rough estimation of the resolving power of the instrument.

Determination of the ion charge by using the isotopic cluster

One evidence that the setting for this integration is correct is that it is now possible to use the inter-peak deconvolution method for the determination of the ion charge (36+), which confirms the result of a previous deconvolution based on the charge state envelope.

One important concept to grasp is that the bin size used for the mass spectra combinations will actually determine the final resolution of the mass spectrum (or color map). For example, imagine that the setting is the following: Arbitrary binning with bin size of 1~Da; in that case the mass spectrum or the &mz;-based color map will be very rough. Instead, setting the bin size to 0.05 will provide a much better spectrum (or color map). Of course, going down with the size of the bins will increase almost linearly the computation time required to go through them all while adding signal from the various spectra that are being combined. This is exemplified in .

Effects of the bin size on the color map involving one &mz; dimension

The three different color map regions were computed in the Arbitrary binning mode with bin sizes 1~Da, 0.1~Da and 0.01~Da (top to bottom).

Because the computation time increases inversely with the size of the bins, it is recommended to start a color map-based data exploration session with an integration over the whole data set but with large bins (typically 1~Da) that will provide enough sight on the data. Then, when the feature of interest has been located on the color map to a given region, reduce the bin size to increase the resolution of the color map and integrate from that same region to a new color map.

### Removing 0-intensity &mz; data points is useful

Mass spectrum data points with an intensity value equal to 0 might arise either from the initial data, as read from the file, or from the binning process, as detailed below.

When bins are prepared, prior to starting a mass spectral combination operation, they all correspond to a &mz; value associated to a 0-value intensity. Once the bins have been prepared, the combination starts and, while iterating through all the data points of all the spectra, fills-in the bins. If, however, a bin is never filled (it ends up never updated during the combination), that bin will have an intensity of 0. Bins having a 0-intensity value have a bad effect on the result mass spectrum. Removing them by setting the Remove 0-value data points parameter proves beneficial, as shown in . When the 0-intensity data points are not removed (upper spectrum), the signal is deteriorated by spurious inverted spikes. Removal of the 0-intensity data points, cleans the trace perfectly.

Removing 0-intensity data points

When arbitrary binning is performed, residual 0-intensity data points might survive in the combination spectrum, which deteriorates the resulting mass spectrum. Removing these data points from the combined mass spectrum cleans the trace.

### Savitzky-Golay filtering of any kind of data

The Savitzky-Golay filtering method is widely known for its effectiveness in removing noise from mass spectral data. It is possible to apply that filter to any plot by selecting the corresponding menus as described at . The Savitzky-Golay parameters can be set by selecting the Configure Savitzky-Golay smoothing filter menu as shown on .

Menus to configure and run the Savitzky-Golay filter to smooth traces

The Savitzky-Golay algorithm is a powerful filter that allows one to smooth any graph. The first menu enables the configuration of the filter and the second menu actually applies the filter to the trace.

The Savitzky-Golay filter requires careful configuration. The configuration provided by default is shown in .

Configuration of the Savitzky-Golay filter to smooth traces

The different parameters used to configure the Savitzky-Golay algorithm are described in the text.

- nL: specifies the number of data points to the left of the point being filtered;

- nR: specifies the number of data points to the right of the point being filtered. The total number of points in the window that is considered for the regression is thus nL + nR + 1.

- m: specifies the order of the polynomial to use in the regression analysis leading to the Savitzky-Golay coefficients (typically between 2 and 6);

- lD: specifies the order of the derivative to extract from the Savitzky-Golay smoothing algorithm (for regular smoothing, use value 0);

### Centroid Extraction from Mass Spectra

A general overview of the centroid extraction→deconvolution→analyte identification workflow is illustrated at .

The low mass deconvolution algorithm can be tightly configured using a large set of parameter settings that are detailed below and that are accessible via the application preferences window shown in and detailed below.

The centroidation of a mass spectrum is also called peak picking. This process is aimed at converting a profile-mode spectrum, that has its peaks properly shaped in Gaussian-like form, into a set of &mz; values that recapitulate the initial peaks.

The configuration of the centroid extraction process is performed in the application preferences window, under the Centroidation section, as shown in . There are two aspects in that configuration: the signal-to-noise estimator needs to be configured for the peak picker to be able to actually pick peaks. The peak picker needs also to be configured.

One aspect that is very important in peak picking is the determination of what is considered to be noise and what is considered to be actual signal. The signal-to-noise estimation is an optional task that is only carried out when the Signal to noise ratio is set to a value greater than 0. The signal-to-noise estimator needs a number of parameters to be set up (in the Configuration of the signal to noise estimator group box widget). One important configuration bit is related to the determination of what intensity value is considered to be the maximum intensity value over the whole mass spectrum. There are multiple ways to define that maximum intensity value, as described in .

- Manual setting: this is the simplest method to define that maximum intensity value, by having the user setting it up.

- Standard deviation factor: the software first makes a statistical analysis of the intensity values throughout the mass spectrum and any data point that has an intensity over the standard deviation compounded by this factor is discarded.

- Max percentile: any data point that has an intensity over the intensity of the data point representing the top 5 percent of the intensity value space of the mass spectrum is discarded.

Other configuration bits are the Rolling window size (m/z) which specifies the &mz; range considered when computing the local signal-to-noise ratio. The rolling window is moved data point by data point over the whole mass spectrum. Each mass spectrum data point has thus a corresponding signal-to-noise ratio value.The Intensity histogram bins configuration bit is the number of bins in the histogram of the intensities in the mass spectrum. This parameter defines the granularity with which the signal-to-noise ratio is computed for each mass spectrum data point. The Mininum required elements configuration bit is the number of data points that are required to be found inside the rolling window.

Configuration of the centroidation of mass spectra

The peak picker needs the following bits of configuration. The Min. intensity configures the signal intensity below which the centroids are not extracted. This is useful for highly noisy traces or when one knows that the signal of interest is above a determinat value. The Signal to noise ratio value determines the ratio below which the peak centroid is not extracted. Setting this value to 0.0 will disable the signal to noise process step. Finally, one last bit of configuration for the centroid extraction is the Allowed missing peaks value that defines the number of &mz; values that can be found to be absent from the rolling window mentioned above. This parameter provides some leeway when dealing with sparse mass spectra.

The menu to start the centroid extraction is described in . The figure show the mass spectrum for which centroidation is asked. When the centroidation is actually performed, the centroids are plotted in the form of a bar graph, colored in blue (see .

Menu to be used to extract centroids from a trace

Centroid extraction from a profile mode mass spectrum.

When the centroid extraction process is terminated, the centroids are plotted as a bar graph. These centroid data points can be exported to a file or a clipboard exactly the same way as for the normal trace graphs (see ).

The successfully extracted centroids are displayed in the form of a bar plot

The centroids are plotted as a bar graph that can be exported to the clipboard or to a file exactly like for any trace mass spectrum.

Upon successful extraction of centroids, these are output to the console window.

## Various Mass Spectral Data Integrations

Now that the configuration patterns common to all plot widgets have been described, the various mass spectral data integrations can be reviewed.

All the integration operations are performed using the right mouse mutton (&rmb;). This setting allows to easily distinguish all these integration operations from all the &lmb;-based visualization operations ( ).

- Integrations to a mass spectrum This kind of operation is triggered upon &rmb;-click-dragging the mouse over the region of interest after having selected the &integrate-to-mz-check-button; button on the right button column of the widget. &MineXp; integrates all the spectra that have been acquired between the start marker and the end marker as set during the mouse drag movement. A new mass spectrum is then plotted in the Mass spectra window.

  Integrating mass data from a TIC chromatogram to a mass spectrum

  The integration is defined to be a [TIC chromatogram to mass spectrum] integration because the plot widget is in the TIC/XIC chromatograms window and the MZ push button is checked.

  As seen on the figure above, the region defined by the &rmb;-click-dragging operation is delimited by arrows, a green marker at the start and a red marker at the end. The arrows at the ends of the horizontal line allow one to make the difference between an integration selection and a simple distance-measuring selection (detailed later).

- Integrations to a drift spectrum This kind of operation is similar to the one described above, unless for the &integrate-to-dt-check-button; button that is now checked. The new plot is added to the Drift spectra window.

- Integrations to TIC intensity This operation involves &rmb;-selecting a plot region of interest encompassing the mass spectral feature for which the intensity is to be integrated while the &integrate-to-tic-intensity-check-button; is checked. The obtained numerical result is displayed in the status bar below the plot widget and in the Console window.

  Integrating mass data from a mass spectrum to a single TIC intensity value

  The integration is defined to be a Mass spectrum to TIC intensity integration because the plot widget is in the Mass spectra window and the INT push button is checked. The TIC intensity value is printed in the status bar.

- The same mechanics is at work in the other plot widget windows. For example, to trigger the integration of any kind of plot to an intensity = f(dt,&mz;) color map, simply check the &integrate-to-dt-mz-check-button; button and drag the mouse over the plot region of interest.

### Considerations on the Diversity of Mass Data Contents

Loading data from mass data files in mzML format does not guarantee that the data will be of the same kind when they originate from different mass spectrometers. For example, data from Orbitrap mass spectrometers have the following characteristics:

- All spectra do not start at the same &mz; value;

- All spectra do not have the same number of data points (they do not have the same size);

- A large number of data points might have 0 values (intensity at a given &mz; value is 0);

- The &mz; delta between two consecutive &mz; values is not constant, and this is the major difficulty for data integration to a mass spectrum.

This is the output of the statistical analysis of the data loaded from a Lumos Orbitrap-originating file:

            Spectral data set statistics:
            Total number of spectra: 6203
            Average of spectrum size: 391.311946
            StdDev of spectrum size: 168.062934
            Mininum m/z value: 400.007111
            Average of first m/z value: 401.448935
            StdDev of first m/z value: 1.590049
            Maximum m/z value: 1999.928589
            Average of last m/z value: 1901.852315
            StdDev of last m/z value: 45.864131
            Minimum m/z shift: -0.344452
            Maximum m/z shift: 0.000000
            Average of m/z shift: 1.097372
            StdDev of m/z shift: 1.590049
            Smallest Delta of m/z (step): 0.006195
            Average of smallest Delta of m/z (step): 0.023757
            StdDev of smallest Delta of m/z (step): 0.013179
            Greatest Delta of m/z (step): 405.356934
            Average of greatest Delta of m/z (step): 163.112057
            StdDev of greatest Delta of m/z (step): 75.947334

As mentioned earlier, the most interesting bit of information is in the line reproduced below:

    Smallest Delta of m/z (step): 0.006195

That 0.0062 value somehow gives an indication of the definition of the spectrum, that is, the smallest distance possible between two consecutive points in the &mz;-axis.

In general, the fact that the spectra of an acquisition do not all have the same &mz; vector as the &mz;-axis is a great difficulty for mass spectral integration because it requires setting up binning prior to performing the mass spectral combination. That binning is nothing else than crafting a &mz; value vector able to receive the intensities of all the &mz; data points in the spectra to be combined. These concepts are developed in the next paragraph.

### Statistical Analysis of Mass Data

At the end of the data file loading, &MineXp; performs a rudimentary statistical analysis of the data. The main datum of interest is the smallest m/z step that is observed in the whole set of mass data loaded from disk (the mass spectrum list, that can hold mass spectra in the thousands). For each mass spectrum in the list, the smallest m/z delta between any two consecutive data points is recorded. Then, the smallest ever m/z delta value is sought amidst all the recorded values. Intuitively, that smallest &mz; delta value provides an idea of the resolution power of the instrument that generated the mass spectra. Interestingly, this is not the proper value to configure binning. The best value is the median value of the smallest &mz; delta values encountered over all the mass spectra of the data file. It is the value that is suggested by default to arbitrarily construct the bins during an integration to a mass spectrum, as described in (Arbitrary binning value with bin size unit dalton).

## Integrations Originating in Color Map Plots

There are two different kinds of plots: the trace plots and the color map plots. TIC chromatograms, mass spectra and drift spectra are trace plots. Color map plots plot intensity values as colors for pairs of data, like mass spectra as a function of retention times or drift times, or retention times as a function of drift times.

When an integration starts from a trace plot, the integration region selection (with a right-button mouse drag operation) of a given plot range (over the X axis) can be performed without drawing a rectangle over the region of interest. Indeed, only the X axis is the criterion for the selection of the region of interest. When an integration starts from a color map plot, the second dimension (the Y axis) has a meaning for the selection of the region of interest. In color map plots, the selection region has to be bi-dimensional, that is, not a horizontal line, but a real rectangle over both the X and the Y axes.

By default, the rectangle that is drawn upon mouse dragging is a real rectangle, that is, a four-square-corner polygon. There are a number of situations, however, where the user might want to select a non-rectangle region, like if the rectangle were skewed. This is illustrated in

Drawing a skewed selection rectangle is a two-step process. One first draws the base of the rectangle, as usual, with a horizontal-only mouse drag movement (see ). When the base of the rectangle has the right width, the user presses the S keyboard key. Pressing that keyboard key triggers the definition of the width of the skewed selection rectangle, that gets stored in memory.

Integrations from a color map plot using a skewed selection rectangle (first step)

The first step in the definition of a skewed selection rectangle is to right-button-drag the mouse along the X axis in order to draw a selection line that has the final skewed rectangle width. Once the width is properly defined, the user presses the S keyboard key, which stores the rectangle width in memory.

When the mouse drag operation draws the actual two-dimensional polygon (by dragging the mouse cursor also along the Y axis), the user maintains the Alt keyboard key pressed. Maintaing that key pressed has the effect of switching the drawing of a conventional four-square-corner selection rectangle into a skewed selection rectangle (see ).

Integrations from a color map plot using a skewed selection rectangle (last step)

The last step in the definition of a skewed selection rectangle is to drag the right mouse button over a bi-dimensional space (that is, also along the Y axis) while maintaining the Alt modifier key pressed. The rectangle that is drawn is now skewed, in contrast to a normal square-corner rectangle.

When the mouse right-button is released, the integration computation is started that involves checking if the points that are processed are indeed contained inside the skewed selection rectangle. The result is shown in .

Integrations from a color map plot using a skewed selection rectangle (first step)

The result of a skewed selection rectangle-based integration is inherently of the shape of the selection.

## Noise Reduction in Color Map Plots

The signal obtained in ion mobility–mass spectrometry experiments may be of a very wide dynamics, which hamper proper visualization of details. One solution is to run a Z axis scale (the axis holding intensity values) conversion from linear scale to log10 scale (see ). While this has the benefit of highlighting details, it also has the drawback of potently enhancing noise. As of version 8.1.0, &MineXp; provides means to signal-to-noise ratio tweaking as detailed below.

There are two different approaches to signal-to-noise ratio enhancement:

- Low pass-filter Z axis data: The value that is entered in the spin box widget is a percentage of the maximum intensity over the whole map. The function first computes a threshold using the formula threshold = percentage * max_int / 100. All the cells having an intensity value above that threshold have their value set to that threshold value. All other cells are unchanged. This function is used to reduce the dynamic range of the map and potentially enhance features of interest.

- High pass-filter Z axis data: The value that is entered in the spin box widget is a percentage of the maximum intensity over the whole map. The function first computes a threshold using the formula threshold = percentage * max_int / 100. All the cells having an intensity value below that threshold have their value set to that threshold value. All other cells are unchanged.

  This function is typically used for removing noise. Indeed, by setting the percentage value to 5, for example, all the cells having a pretty low intensity value (that is, below 5 % of the maximum intensity value in the whole map data) will be set to that value. An example is provided in .

Example of signal-to-noise ratio enhancement

The top left vignette shows the colormap as it is computed. The top right vignette show that colormap after a log10 recomputation has been performed. The remaining vignettes correspond to the application of a high pass filter with percentage values increasing from 10 to 40 in 10 % increments.

## Low Intensity Signal Removal in Traces

Any trace (that is, any plot that is not a color map) can be noisy. That is particularly true for mass spectra coming from non-Orbitrap analyzers. If one can tell what is the level of the noise, it is possible to remove all the peaks that are below that level. The process is complex because it needs to take into account ever-more frequent high resolution mass spectrometry data that shows resolved isotopic cluster for which certains peaks might be below the threshold while others are above threshold. Of course, if any peak of an isotopic cluster is above threshold, then the whole isotopic cluster, no matter how it might comprise below-threshold peaks, should be retained.

At the time of this writing, the low intensity signal removal filter is working best for samples containing large analytes, where the isotopic cluster is most approaching an overall Gaussian shape. It is considered that this feature is still experimental as it still has a number of rough edges. The step are described in the following figures (see ).

Example of low intensity signal removal (starting trace)

This is a segment of the initial mass spectrum with some isotopic clusters having part of their peaks above the 50000 threshold and some others that are entirely below that threshold.

Example of low intensity signal removal (configuration)

The menu item shows the Low intensity threshold removal menu item. The line edit widget allows one to enter the threshold below which signal is to be removed from the trace. Once the proper value has been entered, the user clicks the menu text. The program runs the filter and produces a new trace that is displayed along with the original trace.

Example of low intensity signal removal (creation of a new trace)

The new trace is displayed in the same composite plot widget. The user needs to zoom in enough to find it. Here, it is clearly visible at the location of the cursor near the baseline. It appears as a straight line under the mass peaks that were filtered out. In order to better visualize the new trace, select it and duplicate it using the duplication icon button at the top of the functional button on the right hand side of the composite plot widget.

Example of low intensity signal removal (display of the new trace)

As is visible in the image, a great amount of signal present in the original mass spectrum (top plot) was removed from the trace (bottom plot). Note that the line connecting the isotopic cluster is faint where mass peaks were removed, but it is actually there. Note some remaining artifacts right after the very first isotopic cluster in the filtered trace. This is experimental work on progress.

## Chained Integrations

The user, in the process of exploring the data, will inevitably chain integrations to pinpoint a specific feature of interest. For example, let's say that the user is exploring ion mobility mass spectrometry data.

Thanks to Dr Valérie Gabelica (IECB, Bordeaux, France) for permission to use her data set.

After having loaded the data file, the TIC chromatogram is computed and displayed. From there, it is possible to perform a TIC chromatogram to an intensity = f(dt,&mz;) color map integration (see .)

Example of chained integrations, color map where data exploration may start

There starts the exploration. The user sees that there are a number of species having discrete drift times at the &mz; ratio around 1220 (lower region of the colormap). They thus integrate to a single drift spectrum that horizontal lower region of the colormap. The obtained drift spectrum is shown at the left hand side of .

Because there are five drift peaks in the drift spectrum, the user performs as many individual mass data integrations to a mass spectrum, from left to right. The obtained mass spectra are all shown in the window next to the Drift spectra window of the same figure.

Multiple chained integrations

Most interestingly, the various drift regions are integrated to almost identical &mz; values in their respective mass spectrum. In order to know when the various molecular species eluted in the chromatogram, the user performs for each mass spectrum an integration to a XIC chromatogram. The XIC chromatograms are shown on the window next to the Mass spectra window. Visibly, each molecular species was eluting from the chromatography column at discrete retention times (this was clearly not a true chromatography but instead an infusion during which instrument parameters were changed to modify the mobility properties of the ubiquitin molecule).

At this point, it is interesting to confirm that each XIC chromatogram contains exactly the molecular species that were responsible for the appearance of the purple bands on the int = f(dt, mz) initial color map. The different XIC chromatograms are thus integrated to a color map and the results are shown below the initial color map (less one color map that did not fit in the window). Visibly, the extracted color maps reconstitute the initial pattern visible at the top of the Drift spec / mass spec color maps window.

The dataset used for was kind courtesy of Dr Valérie Gabelica and correspond to a work entitled Optimizing Native Ion Mobility Q-TOF in Helium and Nitrogen for Very Fragile Noncovalent Structures published in JASMS with DOI: 10.1007/s13361-018-2029-4.