1.5.1.1. Peptide templates

The PTM Dashboard Template can be used to deep query across different analyses for post translational modifications (PTMs). The PTM Dashboard Template contains the following visualizations:

• Pivot table for Relative Glycosylation Short Name: This converts the glycan subunit name from the glycopeptide identification to short name nomenclature. For example: HexNAc(4)Hex(3)Fuc(1) to G0F. This will then calculate the relative % of each Glycan that is identified.
• Stacked bar chart for Stacked Bar Chart Glycan Distribution: This is the same as the pivot table described above but presented in a stacked bar chart.
• Pivot table for Relative Fucosylation %: This will categorize glycopeptides into a-Fucose (where the Glycan does not contain a Fucose) and Fucose (where the identified Glycan does contain a Fucose). The relative % between these two categories is then calculated.
• Bar chart for Relative Fucosylation %.: This is the same as the pivot table described above but presented in a bar chart.
• Pivot table for % Modification (Multiple Mods per Peptide): This table is the same as the pivot table used in Byos reports to quantitate the relative levels of modified and unmodified peptides. Normalization occurs at the peptide level. By default, the unmodified (or Wild Type) peptides are hidden using a filter. This filter may be removed by editing the visualization and going to the Filter and Sort tab. The filter is applied as a Show/Hide filter and can be removed simply by deleting the filter.
• Line chart for % Modification (Multiple Mods per Peptide) Trend for Indicator Peptides: This allows the user to monitor a trend of defined peptides and is useful for monitoring trends over time of a modification; for example, % oxidation between samples. This uses the same normalization calculation as the previous pivot table. A user may define their own peptides and modifications by using the filters. An Include/Exclude filter may be used for the peptide sequence, as normalization occurs at the peptide level. However, it is important to remember to define specific modification information using a Show/Hide filter, so it will not affect the normalization calculation.
• Pivot Table for Average XIC Ratio Percent of Modifications per Peptide: This will show the XIC ratio percent of modified peptides per sample.

The PTM Oxidation Dashboard Template can be used to deep query across different analyses for oxidation as a post translational modification (PTM).

This template contains the following Visualizations:

• Line chart for % Oxidation Modification (Multiple Mods per Peptide) Trend for Indicator Peptides: This allows the user to monitor a trend of defined peptides and is useful for monitoring oxidation trends over time. This uses the same normalization calculation as the pivot tables in the Dashboard. A user may define their own peptides and modifications by using the filters, and the template is filtered for a specific peptide as an example, which may be changed after Dashboard creation. An Include/Exclude filter may be used for the peptide sequence, as normalization occurs at the peptide level. However, it is important to remember to define specific modification information using a Show/Hide filter, so it will not affect the normalization calculation.
• Pivot table for % Oxidation Modification (Multiple Mods per Peptide): This table is similar to the pivot table used in Byos reports and is used to quantitate the relative levels of modified and unmodified peptides. Normalization occurs at the peptide level. By default, the unmodified (or Wild Type) peptides are hidden using a filter. This filter may be removed by editing the visualization and going to the Filter and Sort tab. The filter is applied as a Show/Hide filter and can be removed simply by deleting the filter.
• Pivot table for Average XIC Ratio Percentage of Oxidations per Peptide: This pivot table is used to calculate the average XIC ratio across all charge states for a modified peptide for each sample.

The System Suitability Dashboard Template can be used for the suitability assessment of LC-MS/MS systems or sample preparation. The following visualizations can be useful for monitoring LC-MS/MS system performance:

• Most Recent ppm: This visualization is presented as a gauge on the Dashboard, where a user can define the range in which a ppm is acceptable (by default this is green) and red where the value falls outside of the defined range. Settings for the gauge visualization may be configured under the Visualization Settings tab when editing the visualization. By default, this visualization is monitoring the most recent project file or a specific peptide, defined by its sequence. The peptide sequence to be monitored and the number of n most recent project to be queried may be defined in the Filter and Sort tab when editing the visualization.
• Line Chart for Average ppm trend: This chart will display the average ppm of all peptides within a given sample file over time (average ppm vs Created On Date).
• Line Chart for Retention Time Trend: This shows the average intensity of an XIC peak apex vs Created On Date for a single peptide. A user may define a custom peptide and/or instrument in the Include/Exclude filters when editing the visualization.

There are also visualizations within the System Suitability Template that will allow a user to monitor parameters related to sample preparation, such as:

• Pivot Table to monitor over and under alkylation of peptides: Peptides are categorized into those that have been under alkylated, those that have been over alkylated, and all other peptides. The filters applied only include those peptides that are under- or over-alkylated. Categorization is dependent upon the modification names provided in the Byos project. Over alkylated peptides must contain the name “overalkylation” and under alkylated peptides must contain the name “(de) carboxymethyl” in their modification names. Here, the values are determined by averaging the XIC Ratio Percent.
• Bar chart to monitor alkylation levels: This is the same as the pivot table described above but presented in a bar chart.
• Pivot Table to calculate the relative % of missed cleavages: This will calculate the relative levels of miss cleaved peptides. This visualization will only populate for those analyses that have their enzyme defined.
• Bar chart displaying the relative % of miss cleaved peptides: This is the same as the pivot table described above but presented in a bar chart.
• Data Grid for Digestion Specificity : This will calculate the average level of non-specific cleavages.
• Bar Chart for Digestion Specificity: This is the same as the pivot table described above but presented in a bar chart.

The MAM Dashboard Template can be used to deep query results across different peptide analysis projects for MAM studies. The MAM Dashboard Template contains the following Visualizations:

• Line chart – Monitoring of CQA Peptide Levels This displays the relative % modification of peptides that have the label CQA (Critical Quality Attributes) and is useful for monitoring trends over time of those peptides. For example, a user would be able to monitor the relative % oxidated peptides across samples. This uses a similar Normalization calculation as used in the default Byos PTM Report to calculate relative % of different peptide species (% Mod (Multi Mods per Peptide)). There is a Show/Hide filter applied to only display those peptides which were set to have the label CQA within the BYOS project. An Include/Exclude filter may be used for the peptide sequence, as Normalization occurs at the peptide level. However, it is important to remember to only use a Show/Hide filter on the following data fields: Modifications Summary List, and Label Names, so it will not affect the normalization calculation.
• Data Grid – Fold Change from Reference for All Peptides XIC AUCs (Filtered >=4 Fold Change from Reference): This data grid will display the XIC AUC of peptides and the fold change of that XIC AUC in samples that have been classified as Non-Reference Sample Type vs a sample that has been given the Sample Type Reference within a Byos project. There is a default Show/Hide filter applied to only display those peptides that have a fold change of >= 4 in Non-Reference data type samples compared to a Reference data type sample. This value can be changed by a user in the Filter & Sort tab when editing the visualization. All Non-Reference samples are compared to a single Reference, which is important to keep in mind when querying multiple projects. At this time, the Visualization will only support one sample defined with the Sample Type Reference from a Byos Project.
• Pivot Table – Fold Change from Unknown XIC AUCs (Filtered >=4 Fold Change from Reference): This data grid will display the XIC AUC of unknown but detected peaks and fold change of that XIC AUC in samples that have been classified as a Non-Reference Sample Type vs a sample that has been given the Sample Type Reference within a Byos project. This is calculated for unknown XICs that were found as a result of running the feature finder algorithm in Byos. These unknown XICs are not linked with any peptide identification, so this visualization will allow a user to monitor levels of XICs that have not previously been identified. There is a default Show/Hide filter applied to only display those unknown XICs that have a fold change of >= 4 in Non-Reference Sample Type samples compared to a Reference Sample Type sample. This value can be changed by a user in the Filter & Sort tab when editing the visualization. All Non-Reference samples are compared to a single Reference. At this time the visualization will only support one sample defined with the Sample Type Reference from a Byos Project. If queried Byos projects do not contain any of these unknown XICs (for example, if feature finder had not been implemented for that project), the visualization will be empty.
• Pivot table for Relative Glycosylation Short Name: This converts the glycan subunit name from the glycopeptide identification to a short name nomenclature. For example: HexNAc(4)Hex(3)Fuc(1) to G0F. This will then calculate the relative % of each Glycan that is identified.
• Stacked bar chart for Stacked Bar Chart Glycan Distribution: This is the same as the pivot table described above but presented in a stacked bar chart.
• Bar Chart – Fold Change from Unknown Peaks: This bar chart displays the fold change of all unknown XIC AUCs for Non-Reference samples vs a Reference sample and is grouped based upon MS Alias Name. There is a visual indicator (dotted constant red line) set to a value of 4, to enable a user to readily view samples that contain unknown peaks where the value of XIC AUC has increased >= 4 fold from the reference sample. This setting can be changed in the Constant Lines section of the Visualization Settings tab when customizing the visualization.
• CQA and Peptide Labels % All Modifications (Multiple Mods per Peptide): This table is the same as the pivot table used in Byos reports used to quantitate the relative levels of modified and unmodified peptides and also uses the same Normalization calculation as described in the Visualization below. By default, there is a Show/Hide filter to only display the relative % of those peptides which were set to have the label CQA within the BYOS project. In addition, there is a column to display Peptide Label. The Label is concatenated from the Protein Alias Name, and the start/end peptide number.
• Pivot table for % Modification (Multiple Mods per Peptide): This table is the same as the pivot table used in Byos reports used to quantitate the relative levels of modified and unmodified peptides. Normalization occurs at the peptide level. By default, the unmodified (or Wild Type) peptides are hidden using a filter. This filter may be removed by editing the Visualization and going to the Filter and Sort tab. The filter is applied as a Show/Hide filter and can be removed simply by deleting the filter.
• % Deamidation - (Multiple Mods per Peptide): This table is the same as the pivot table explained above, except there is a Show/Hide filter applied to only display deamidated peptides.
• % Oxidation - (Multiple Mods per Peptide): This table is the same as the pivot table explained above, except there is a Show/Hide filter applied to only display oxidated peptides.
• Line chart Trend of Sum of Deamidation XIC AUC per Residue: This line chart monitors the trend of XIC levels for residues such as Q and N that have been deamidated. Each data point represents the sum of the XICs for peptides that contain that residue that has been deamidated. This allows the user to monitor a trend of instrument response dependent level of total deamidations on their proteins across different samples (as we are reporting an XIC value not a relative quantitative value).
• Average XIC Ratio of Modifications per Region: This pivot table is similar to the pivot table % Mod by ModName across Samples in the default PTM template used in Byos reports, which is used to report the average XIC ratio for different modifications at different protein positions, across samples. In this Visualization, these values are displayed alongside the protein region, as defined via Protein Annotation in Byos (such as CDR regions of mAbs). There is an Include/Exclude filter applied to only display those XIC ratio values that related to parts of the protein sequence that have a designated Protein Annotation.
• Bar chart of % Oxidation per Sample and Region: This Bar Chart displays the relative% of oxidated peptides per Sample, per Region (where there may be differing CDR locations such as CDR-L1, CDR-H3 etc). By default, it will only display oxidated peptides that are within a sequence position that is covered by a Protein Annotation within Byos (there is a Show/Hide filter to only display peptides that are oxidated and contain a Protein Annotation). This Bar Chart uses the same Normalization calculation as in the other relative peptide quantitation Visualizations
• in tabular form.

The HCP Dashboard template facilitates the querying of projects focused on discovery, analysis, and monitoring of Host Cell Proteins (HCPs) by mass spectrometry and contains the following Visualizations:

• Relative Protein Abundance - Top 3 Peptides per Protein: This pivot table shows the relative protein abundance within a sample based on top 3 peptides for each named protein.
• XIC AUC - Top 3 Peptides per Protein: This pivot table shows the XIC AUC values of the top 3 peptides for each named protein across samples and replicates.
• Relative Protein Abundance - All Peptides per Protein: This pivot table shows the relative protein abundance based on all detected peptides for each named protein across samples and replicates.
• XIC AUC - All Peptides per Protein: This pivot table shows the XIC AUC for all detected peptides for each named protein.
• Bar Chart - HCPs per Sample (All Peptides): This bar chart shows the relative levels of HCPs per sample based on all peptides detected by protein.
• Error Bar Chart per Sample - HCPs per Sample (All Peptides): This bar chart shows the average relative levels of HCPs per sample for all peptides with error bars conveying variation amongst replicates.

The Multi-Protein Quant Dashboard template for HCPs. The Multi-Protein Quant Dashboard template for HCPs contains the following Visualizations:

• Pivot Table - XIC AUC Protein Abundance - With Peptides: This pivot table provides a tabular representation of the XIC AUC levels of the top 3 peptides per protein.
• Pivot Table - Total XIC Area Summed per Protein: This pivot table provides a tabular representation of the summed XIC AUC levels per protein.
• Bar Chart - Total XIC Area Summed per Protein with Standard Deviation Error Bars: This bar chart is to visualize proteins based on the summed XIC AUC levels using Protein Accession Numbers as X-axis value, hiding the most abundant protein to better visualize the lower level host cell contaminants.
• Pivot Table - Relative Protein Abundance - With Peptides: This pivot table provides a tabular representation of the relative levels of top 3 peptide per protein displayed relative to the most abundant peptide.
• Pivot Table - Relative Protein Abundance: This pivot table provides a tabular representation of the relative protein levels based on summed XIC area and relative to the most abundant protein.
• Bar Chart - Relative Protein Abundance: This bar chart is to visualize the relative protein levels based on XIC quantitation and relative to the most abundant protein
• Stacked Bar Chart - Relative Protein Abundance: This bar chart is to visualize the relative protein levels based on XIC quantitation and normalized to total protein abundance.
• Pivot Table - Protein ppm Concentration – Relative to mAb: This Pivot Table provides the average protein ppm concentration based upon the mAb across different replicates and conditions.
• Stacked Bar Chart – Protein ppm Concentration: This bar chart is used to visualize the relation outlined above.

1.5.1.2. Intact templates

The Intact Protein Dashboard Template facilitates queries across different analyses for Intact Proteoform analysis. The Intact template contains the following Visualizations:

• Pivot Table for All Mass Intensity: This pivot table is used to display the mass intensity for each mass peak. This includes all mass peaks, both identified and non-identified. Grouping is by sample and peak number.
• Pivot Table for Expected Mass Intensity: This pivot table is used to display the mass intensity for identified mass peaks only. Grouping is by sample and peak number.
• Pivot Table for Expected Mass Relative % Modification: Normalization occurs at the protein level and is grouped by sample and peak number.
• Bar Chart – Relative % Modification grouped by Sample Name/Peak Number/Protein (Expected Mass): This is the same as the pivot table described above but presented in a bar chart.
• Pivot Table for Expected mass – mass accuracy (ppm): This pivot table is used to display the mass accuracy in ppm for identified mass peaks. Grouping is by sample and peak number.
• Line Chart Expected mass – mass accuracy (ppm): A line chart displaying the same information as described in the pivot table above.

The ADC Dashboard Template facilitates queries across different analyses for Antibody Drug Conjugate (ADC) and Drug to Antibody Ratio (DAR) analysis. The ADC template contains the following Visualizations:

• Bar Chart – DAR per Sample: This bar chart is used to display the calculated DAR per sample (and is grouped by sample name).
• Bar Chart – Relative % Drug Count per Sample: This bar chart is used to display the relative % of conjugated mAb/ADC species distinguished by the number of drugs conjugated to the antibody. Normalization occurs at the protein level and calculates the relative % based on mass intensities per different delta mass species for the same protein (in this case, n-drug). Bars are grouped by sample and peak number.
• Pivot Table for Expected Mass Intensity: This pivot table is used to display the mass intensity for identified mass peaks only. Grouping is by sample and peak number.
• Pivot Table for Relative % Drug Count and DAR: This pivot table is used to display the relative % of ADC species (specified in the Visualization as modifications determined by delta mass) and calculated DAR value. Normalization occurs at the protein level and is grouped by sample and peak number.
• Pivot Table for Expected mass – mass accuracy (ppm): This pivot table is used to display the mass accuracy in ppm for identified mass peaks. Grouping is by sample and peak number.
• Line Chart Expected mass – mass accuracy (ppm): A line chart displaying the same information as described in the pivot table above.
• Pivot Table for Expected Mass Relative Intensity: This pivot table is used to display the relative % of matched masses per delta masses. Normalization occurs at the protein level and is grouped by sample and peak number.
• Pivot Table for All Mass Intensity: This pivot table is used to display the mass intensity for each deconvoluted mass peak. This includes all mass peaks, both identified and unidentified. Grouping is by sample and peak number.

The Intact Oligonucleotide template facilitates the querying of results across different analyses for Intact Oligonucleotide analysis and contains the following Visualizations:

• All Oligo Mass Intensity: This pivot table displays the mass intensity for all deconvoluted masses per sample.
• Expected Oligo Mass Intensity: This pivot table displays the mass intensity for all expected and identified oligonucleotide species per sample.
• Expected Oligo Mass – Relative Intensity %: This pivot table displays the relative intensity% for all identified expected oligonucleotide species per sample.
• Expected Oligo Mass – Mass Accuracy (ppm): This pivot table displays the mass accuracy in ppm of all identified expected oligonucleotides per samples.
• Bar Chart - Expected Mass Relative Intensity % Across Sample: This bar chart displays the mass accuracy in ppm of all identified expected oligonucleotides per sample.
• Bar Chart - Expected Mass Relative Intensity % per Oligo Candidate: This bar chart displays the mass accuracy in ppm of all identified expected masses per sample.
• Line Chart Expected Oligo - Mono Mass Accuracy (ppm): This line chart displays the monoisotopic mass accuracy per sample.

1.5.1.3. Chromatogram templates

The Chromatogram Comparison template contains the following Visualizations:

• Line Chart Displaying Norm Peak Area for each Peak across Multiple Samples: This line chart is to visualize the normed peak area trends for the same peak across different samples for monitoring purposes
• Bar Chart - Relative Trace Peak Area Grouped by Peaks Comment: This bar chart is to visualize the relative normalized trace peak area values within a group of peaks of interest when identified to belong to a group by a Peak Comment.
• Pivot Table - Trace Peak Area Grouped by Peaks Comment: This Pivot table is a tabular representation of trace peak area values for peaks of interest when identified to belong to a group by a Peak Comment.
• Bar Chart Average Apex Time (minutes) per Peak across Samples (Error bar +/- 3 Std. Dev): This line chart is to visualize apex time consistency and standard deviations across samples within a comparison study.
• Scatter Plot Average Apex time per peak across samples (Error bar +/- 2 Std. Dev) for monitored peaks: This scatter plot is to visualize the same apex time monitoring for a selected few peaks of interest with 2*standard deviation as error bar.
• Bar Chart - Normalized Peak Area % All Peaks: This bar chart is to visualize the normed area values for peaks across samples to allow observation of obvious deviations.
• Bar Chart Average Normed Are% per Peak across Samples (Error bar +/- 1 Std. Dev): This bar chart is to visualize average normed area with standard deviation for peaks across samples.
• Line Chart Average Normed Area% per Peak per Sample filtered for monitored peaks: This line chart is to visualize average normed area for peaks across samples to present visual differences between samples.
• Pivot Table – Normed Area% - All Peaks: This pivot table is a tabular representation of normed area% values of all peaks across samples.

The Released Glycan template can facilitate the querying of projects focused on the characterization of glycans and contains the following Visualizations:

• Annotations by Peak# - Normed Area % - based Relative Glycan Quantitation: This pivot table displays relative glycan levels  per sample glycans shown by chromatographic peak within the table.
• Annotations by Glycan - Normed Area % - based Relative Glycan Quantitation This pivot table displays relative glycan levels by glycan short name.
• Glycan Area Bar Chart - Normed Area % Grouped Across Samples: This bar chart shows normed area% for glycans grouped across samples.
• Peak Area Bar Chart - Normed Area % Across Samples: This bar chart shows normed area% for glycans grouped across peaks and  samples.
• Bar Chart - Normed Area % for Each Glycan: This bar chart shows normed area% per glycan as resulted in the released glycan analysis.
• Line Chart - Normed Area % for Each Glycan Peak across Samples: This line chart shows normed area% for each glycan peak across samples.

1.5.1.4. Combined templates

The Multi-level Glycosylation Dashboard Template uses the Combined Analysis data source, which ingests data from both Intact and Peptide projects. This template can be used for mapping glycosylation levels and distribution using both Intact and Peptide assay results within one Dashboard.

• Pivot Table for Expected Mass Relative % Glycosylation (Intact Protein Analysis): This pivot table is used to quantitate the relative levels of Glycans on proteins from intact analysis.
• Stacked Bar Chart showing the Relative Levels of Glycans per Sample and Protein: This is the same as the pivot table described above but presented in a stacked bar chart.
• Pivot Table for Relative Glycosylation Distribution of Glycopeptides: This converts the glycan subunit name from the glycopeptide identification to short name nomenclature. For example: HexNAc(4)Hex(3)Fuc(1) to G0F. This will then calculate the relative % of each Glycan that is identified.
• Stacked Bar Chart Glycan Distribution (Glycopeptide Quantitation): This is the same as the pivot table described above but presented in a bar chart.
• Stacked Bar Chart - Glycan Distribution (Released Glycan Analysis): This bar chart shows the relative% of different glycans for samples processed in Released Glycan analysis projects.
• Glycan Distribution (Released Glycan Analysis): This pivot table shows the same information

The Biophysical data correlation template uses the Biophysical Analysis data source, which ingests data from both Intact and Peptide projects that are correlated with Biophysical data. This template can be used for comparing results from Biophysical analyses such as ICIEF, CE-SDS, and SEC to Byos Intact and Byos Peptide results, respectively.

• Pivot Table - iciEF - Comparing Relative % of Trace Peak Area across Biophysical Data Samples: This pivot table provides a tabular representation of relative % trace peak area across samples (using icIEF Biophysical data).
• Pivot Table - SEC - Comparing Relative % of Trace Peak Area across Biophysical Data Samples: This pivot table provides a tabular representation of relative % trace peak area across samples (using SEC Biophysical data).
• Pivot Table (icIEF vs Peptide MS) - Comparing % Deamidation of a Specific Peptide at Different Residue Positions vs % Acidic Peak Area: This pivot table is used to visualize % deamidation for the same peptide at different residue positions and their relationship with % acidic peak area coming from an icIEF analysis of the same sample.
• Pivot Table (Peptide MS) - Relative % Modification at Protein Position across Samples): This pivot table provides a tabular representation of relative % modifications at specific positions across different samples (using Peptide data).
• Line Chart (icIEF vs Peptide MS)- Comparing % Deamidation of a Specific Peptide at Different Residue Positions vs % Acidic Peak Area: This line chart is used to visualize % deamidation for the same peptide at different residue positions and their relationship with % acidic peak area coming from an icIEF analysis of the same sample.
• Bar Chart - Comparing % Deamidation of a Specific Peptide at Different Residue Positions vs % Acidic Peak Area: The same comparison as the above Visualization using a bar chart.
• Line Chart - Comparing % Modification of All Modified Peptides at Different Residue Positions vs % Acidic Peak Area: This line chart compares % modification (not limited to deamidation) for the same peptide at different residue positions to the % acidic peak from an icIEF analysis of the same sample.
• Pivot Table - Intact MS relative Intensity Expected mass: This pivot table shows the Intact relative intensity for expected masses.
• Pivot Table - Intact MS relative Intensity - All masses: This pivot table shows the Intact relative intensity for all masses, including those that are not named.
• Line Chart (icIEF vs Intact MS)- Comparing Relative Levels of Expected Intact Species vs % Acidic Peak Area: This line chart compares relative levels of expected species identified using Intact MS with the % acidic peak area obtained when running icIEF analysis on the same sample.
• Line Chart (icIEF vs Intact MS) - Comparing Relative Levels of Expected Intact Species vs % Basic Peak Area: This line chart compares relative levels of expected species identified using Intact MS with the % basic peak area obtained when running icIEF analysis on the same sample.
• Line chart - (SEC vs Intact MS) LMWS 2 peak vs Intact mass relative intensity: This line chart compares the Intact mass relative intensity obtained using Intact MS to the LMWS peak area obtained when running SEC analysis on the same sample.
• Line chart - (SEC vs Intact MS) LMWS 2 peak area vs Intact mass relative intensity <5%: This line chart compares the Intact mass relative intensity with values under 5% obtained using Intact MS to the LMWS peak area obtained when running SEC analysis on the same sample.
• Bar chart (SEC vs Intact MS) - SEC HMWS 1 peak vs expected Intact mass relative intensity: This bar chart compares the expected Intact mass relative intensity obtained using Intact MS to the HMWS peak obtained when running SEC analysis on the same sample.
• Stacked bar chart (SEC vs Intact MS) - SEC Main peak Relative Levels vs relative normalized levels of Intact Expected Species: This bar chart depicts SEC main peak relative levels vs the normalized levels of Intact Expected Species obtained using Intact MS.

Once the desired template is selected, the Add Resource dialog prompts the user to enter a name and file location for the new Dashboard. The new Dashboard will be populated with settings copied from the template, and from there the user can modify their copy of the template as desired.

1.6.2.1. Basic Data Settings

• Group By Fields (X axis): Field(s) to be represented on the X-axis. The user has the option to select multiple fields. Fields can be selected directly from the dropdown, or the user can type within the box to filter through the available fields to find a specific field. Users can select both Data Source Fields and Metadata fields. In this case, the field Data.MS Alias Name has been selected, and the user has the option to search for and add additional fields.

• Series: Field to group for separate series in a chart. In the example below, multiple fields have been added to be included in the series grouping.

If multiple fields have been added, they are clustered within the Visualization to represent groups combining values from each field as shown below:

• Y Axis Value: Field(s) to be represented on the Y-axis. Requires an aggregation function.
  • Aggregation: Available aggregation options will depend upon the data type selected e.g., Data.Apex Posit time has all aggregations available, while Data.Modifications Name List is only a string field and only has count available.

Aggregation Types

X-axis and Y-axis labels are automatically populated within the Visualization once a field has been selected and applied. If the field(s) is (are) removed and another added, the user will have to manually update the X-axis or Y-axis label values under the Group By Axis and Value Axis tabs.

Note: If you start by selecting an aggregation function, your data fields will be filtered based upon the selected function; for instance, if you select a numeric aggregation e.g., average, text fields such as “Sample name” will not appear.

• Display as relative to/Groups to normalize on: These settings support the following normalization calculations:
• Min (where min is 100%)
• Max (where max is 100%)
• Sum (where sum is 100%)

Options for Groups to Normalize By include:

• Series
• Group By

Note that these settings only pertain to Line Charts, Bar Charts, and Scatter Plots (excluding Stacked Bar Charts).

• Error Bars: Error bar values can be added for Min/Max, Std. Dev, 2 Std. Dev, and 3 Std. Dev. All values are + or - of the aggregation selected, with the exception of min/max. When a user selects a +/- Min/Max the error bar will function as a range (low value is the min and the highest value is the max). If Variance is used as the aggregation error bars will not be present. Note that SDTEV.P and VAR.P are used when determining error values.

1.6.2.2. Basic Display Settings

Basic Display Settings for Line and Bar Chart Visualizations

As of release v5.10, users are directed to Select Visualization Type within the basic Display settings. In addition, the fields used to build the Visualization itself, including Group By Fields, Series, and Y Axis value, are all specified within the Display settings. Available fields are based upon the fields selected within the Data Settings.

Users can also error bars within the Display settings for Line Charts.

Error bars

1.6.2.3. Additional Display Settings

1.6.3.1. Basic Data and Display Settings

Basic Data and Display Settings are the same for both Line Charts and Bar Charts. See Basic Data Settings and Basic Display Settings above for more information. Any exceptions have been outlined below.

Within the General settings, Error Bars do not apply to Stacked Bar Charts.

Note that if a Chart-type Visualization is created (including Line Charts and Scatter Plots) and then switched to another Chart-type Visualization, all applicable settings should remain in place.

1.6.3.2. Display Settings

Refer to Display Settings for Line Charts and Scatter Plots as the settings for Bar Charts are the same, with two exceptions. Under Display > General for Bar Charts, Bar Normalization provides the ability to normalize values based upon each group-by value, with the following options: Fraction, Percent, or None (default).

There is one additional tab available under Display Settings for Bar Charts called Series. The Series tab (applicable to the Bar Chart and Stacked Bar Chart only) provides options for highlighting series elements.

Note: Color and opacity options do not apply to Stacked Bar Charts.

1.6.4.1. Basic Data Settings

• Data Fields: Select data fields which can be added to the Visualization and configured within the Display settings
• Aggregation: Numeric value to aggregate and display as value

1.6.4.2. Basic Display Settings

Basic Display Settings for Pivot Table Visualizations

As of release v5.10, users are directed to Select Visualization Type within the basic Display settings. In addition, the fields used to build the Visualization itself, including the rows, columns, and data aggregated and displayed within the Pivot Table, are specified within the Basic Display Settings. Available fields are based upon the fields selected within the Data Settings.

1.6.4.3. Additional Display Settings

1.6.5.1. Basic Data Settings

• Data Fields: Data fields that can be selected in Display Settings to be included in the Data Grid
• Aggregations:
  • Aggregation: Numeric value to aggregate and display as value
  • Error bar: Error bar values can be added for Min/Max, Std. Dev, 2 Std. Dev, and 3 Std. Dev. All values are + or - of the aggregation selected, with the exception of min/max. When a user selects a +/- Min/Max the error bar will function as a range (low value is the min and the highest value is the max). If Variance is used as the aggregation error bars will not be present. Note that SDTEV.P and VAR.P are used when determining error values.

1.6.5.2. Basic Display Settings

Basic Display Settings for Data Grid Visualizations

As of release v5.10, users are directed to Select Visualization Type within the basic Display setting. For Data Grid visualizations, users only need to specify a list of fields to add to the Data Grid. Available fields are based upon the fields selected within the Data Settings.

1.6.5.3. Additional Display Settings

1.6.6.1. Basic Data Settings

• Data Fields: Data fields that can be selected in the Display Settings to be included in the Gauge Visualization
• Aggregations:
  • Aggregation: Numeric value to aggregate and display as value

1.6.6.2. Basic Display Settings

Basic Display Settings for Gauge Visualizations

As of release v5.10, users are directed to Select Visualization Type within the basic Display settings. In addition, users need to specify the Y Axis value to be used in the Gauge. Available fields are based upon the fields selected within the Data Settings.

1.6.6.3. Additional Display Settings

1.6.7.1. Basic Data Settings

Basic Data Settings for Pie Charts

• Data Fields: Data fields that can be selected in Display Settings to be included in the Pie Chart
• Aggregation:
  • Aggregation: Numeric value to aggregate and display as value
  • Error bar: Error bar values can be added for Min/Max, Std. Dev, 2 Std. Dev, and 3 Std. Dev. All values are + or - of the aggregation selected, with the exception of min/max. When a user selects a +/- Min/Max the error bar will function as a range (low value is the min and the highest value is the max). If Variance is used as the aggregation error bars will not be present. Note that SDTEV.P and VAR.P are used when determining error values.

Note: Manually zooming in and out of the Visualization using ctrl + mouse wheel may cause rendering issues with the Visualization. If this occurs, clicking Apply should reset the Visualization.

1.6.7.2. Basic Display Settings

Basic Display Settings for Pie Chart Visualizations

As of release v5.10, users are directed to Select Visualization Type within the basic Display settings. In addition, the fields used to build the Visualization itself are added here, including the labels used for different categories with a single pie chart, the numerical to aggregate for chart categories, and Series specification for the generation of multiple pie charts within a single Visualization.

1.6.7.3. Additional Display Settings

1.6.8.1. Global Filters

Global Filters are data filter settings that are applied across all Visualizations. Data from the selected Data Source will be filtered by these global settings before being passed to the Visualizations. Users can only view any Global Filters that have been applied, as the option to edit has been grayed out. For more information on applying Global Filters, see Global Filters .

1.6.8.2. Visualization Filters

Users can add or edit filters within a single Visualization by clicking the Visualization filters tab.

Visualization Filters tab

The user can also apply filters to specific Visualizations from the Dashboard by clicking the Filter button in the header of a Visualization. This will open the same Visualization Filters page as seen in the Filter&Sort tab when editing a Visualization.

To define a new filtering condition using either filter option, click Add Condition. This will generate a row wherein you can determine which field should be filtered (from within a dropdown), the condition it must satisfy, and a user-entered value. The user is given the option to Include or Exclude results that satisfy either All or Any specified conditions (provided more than one filter has been applied).

Include/Exclude Filters provides filtering of the data that is to be included or excluded in the calculations involved in the analysis; Show/Hide Filters provides filtering of the data that is to be shown in the analysis. If data is hidden based upon defined conditions in the Show/Hide Filters option, it will still be used to make calculations, but it will not be visualized.

The user also has the option to create multiple conditions within a Group.

The user must ensure that a filter is defined with a value if it is added and that it is not left blank. If the filter is no longer needed, the user can remove the filter by clicking the x next to the row of interest.

In the above example, the group must “Include results that satisfy All of the following [conditions]”. In this case, if three different conditions are defined, they must all be met by a data point for it to be included in the Visualization.

To delete a group of condition(s), click Delete group.

The Filters icon on a Visualization will show the number of conditions applied. The Visualization below currently has two filters applied.

After performing a Deep Query search and generating a specific plot, the user can apply filters to an attribute of the data from the Visualization and use these filters to do the following:

1. Include/Exclude specific attributes (If Excluded, data is neither considered in calculations nor included in the Visualization).

   

No filter applied

Exclusion filter applied

2. Show/Hide specific attributes: In this case, query results are filtered out from the Visualization, but this data is still used to perform the visualized calculations.

Additionally, the user can perform a combination of Include/Exclude and Show/Hide filters.

It is possible for too many filters to be applied or the conditions for the filters to be too narrow, which can result in the warning shown below. In this case, the user will need to try removing filters or adjust conditions (such as Any/All) to ensure that data has not been completely filtered out.

No Data warning

1.6.8.3. Sort

Sort orders data according to sort rules determined by the user.

Users can also order filtered data within a Visualization using the Sort function.

A new Sort rule can be added to the Visualization by selecting a field from the dropdown. Multiple sort rules can be added at a time. Data fields can be sorted by ascending or descending order (selected by clicking on the ASC/DSC button to the right of the field name).

1.6.8.4. Metadata field parsing

Users can parse individual fields from “Data.Samples Custom Fields” to use in querying, filtering, visualization, and calculations. A plus sign icon displayed next to this field indicates the ability to parse.

Users can parse metadata out of Samples Custom Fields

Clicking on this icon will open a dialog box. Within this dialog box, users can designate the property (the metadata value to parse out, based on which are available in the field) and type (Text, Date, Numeric).

Metadata parsing dialog

The following example shows the mapping between parsed fields and fields available within Data.Samples Custom Fields and their subsequent parsing as individual fields as the last two columns in the table.

Result of parsing out individual metadata fields

Once a field has been created, it will remain present as a field option in the dropdown for that Visualization.

1.6.8.5. Field aggregations

When applicable, users can select an aggregation type for a specific numerical list field. Aggregation options include Min, Max, Average, and Sum.

Adding an aggregation to a field

Once an aggregation is selected, it is listed next to the field in brackets.

Fields with added aggregations within a Data Grid

1.6.8.6. Calculation Presets

The Calculation Presets tab provides the user with the ability to build calculated fields based upon Calculation Type.

When a user creates and opens a new Visualization, they will have the option to add a new Calculation Preset.

Figure 87: Normalization Calculation Preset options

The user will be prompted to provide a preset option from the Calculation Type dropdown list. Currently, users can use this tool to build a custom normalized field.

The inputs available are dependent upon the Calculation Type selected. The user will be required to enter values for each field. Users can consult the tooltips for more information on how to populate each field.

The Normalization Total to 100% calculation type provides a template for normalization calculations, which involve dividing a value across the aggregation of that value across a defined window/partition.

Advanced SQL code used to build the preset is updated in real time and can be viewed by the user underneath the Advanced option. Users can click the copy button in the corner to copy the SQL code to the clipboard.

Figure 88: Advanced options can be used to preview SQL code

Some templates contain Visualizations that include calculation presets by default. For example, the Stacked Bar Chart Glycan Distribution Visualization in the PTM template contains the Relative Glycan Quant preset.

Figure 86: Relative Glycan Quant preset

The resultant Relative Glycan Quant field is used to build the Visualization provided.

1.6.8.7. Derived Fields

Derived Fields are variables that are created from one or more existing data fields that exist in a single data source or across data sources. User-created Derived Fields can be used in local Filters, as x or y-axis Values within Visualizations, or within Background Alerts.

Figure 97:Derived Fields Tab

The SQL field names dropdown contains a searchable list of fields and their associated SQL name. Double-clicking the field of interest will populate the SQL text box.

Alternatively, the user can manually enter a SQL field into the text box themselves if an applicable field is known.

There are only certain functions available to users. If a user types in a function that does not exist, they are met with a warning:

Additionally, if a user specifies a number outside of the allowed number of arguments they are warned :

Label denotes the name of the generated derived field. The user can change the Label for any derived field. The user can also provide an optional Description.

The Data Type dropdown enables the user to specify the expected data type they wish to generate using SQL commands, with the options of Date, Numeric, and Text (which includes alphanumeric values). The default data type is numeric.

Once all required fields are filled in, clicking Create will add the user-created derived field to the Visualization (as an option under the X and Y-field dropdowns)

Once the derived field is created, it can be found within the available Data Fields within Data Settings and can be added to become available in Display Settings.

If the user wishes to make changes to a Derived Field they created, they can click the Derived Field of interest and either Update to make changes or Revert, which will undo the last change that was made after clicking Update.

To create another Derived Field, click Add new Derived Field. Users can create multiple Derived Fields.

1.6.8.8. Background Alerts

Backgrounds alerts can be set up in Deep Query that will notify the user if a value has fulfilled a condition (e.g., Obs > 5) previously assigned. To add a background alert, click . Background alert filters operate on top of the Global, Include/Exclude, and Show/Hide filters associated with the Visualization.

The user must provide a name to the alert and has the option to add a description. To assign a condition that must be fulfilled to trigger the background alert, click Add Condition.

In this example, an alert will be triggered for data that satisfies the following condition: For the field “Data.Mass Err PPM”, the value must exceed the value of 20. Users can use both Data Source fields and Derived Fields when building conditions for Background Alerts.

Alerts will not incorporate documents that the user does not have access to, and users lacking the correct roles (Contributor or Advanced Viewer) do not have permission to create a background alert.

A Background Alert can be set to Active or Inactive, as shown in the above figure. If an alert is set to Inactive, the alert will not be run at all.

The user can also define how frequently an alert is sent out if conditions are met for a Background Alert. If Daily, the user will receive an alert at 0:00 in their own time zone if the alert conditions have been met within a 24 hour period. If Hourly, the user will receive an alert every hour if the alert conditions have been met. Alerts will only notify a user if new data has been added since the alert was last run. For example, a daily alert run yesterday will only trigger for data that has been added since the previous alert that also meets the specified alert criteria.

The user can create multiple Groups of conditions for an alert filter. This enables the user to set up alerts that are triggered by results that satisfy any of the conditions listed in the group, rather than just one criterion. Multiple conditions can be created per group and multiple groups per designated alert.

Note: Alerts will not be fully saved unless changes are published to the Dashboard.

A message will be sent to the user’s email if the alert is triggered. The user will also receive a notification within the web client.

Background alert notification email

To delete an alert, the user can click the red x icon on the Alert title.

If the user has not saved the alert yet, they will be prompted with a warning dialog.

Once an alert has been saved, the user will receive a dialog warning that they are about to delete an existing alert.

If an alert in a project created in v5.0 contains an unsupported field or has any other issues, the user will receive an email with a link to the affected Dashboard and the background alert will be disabled.

Currently, there can only be one alert per Visualization at a time.

If the user is not the owner of a Background Alert, they are only capable of deleting it if they delete the Dashboard or Visualization it is tied to. All alerts tied to a Visualization/Dashboard are deleted when the Visualization/Dashboard is deleted. These alerts cannot be recovered even if the Visualization/Dashboard is restored. Any alert without a valid Visualization or Dashboard will be deleted automatically if it is run.

1.6.8.9. Transformations

If a user has selected either a Data Grid or Pivot Table Visualization, a plus sign icon will appear within the Visualization Builder. Clicking on this shows a list of Transformations which the user can apply to their data.

Transformations

Once a Transformation has been added, it will be visible within the Visualization Builder.

Transformation within Visualization Builder

Users can choose from Normalization, Ranking, Filter, Aggregate and Reduce, and Grouped Aggregate options. Each option has its own information that the user must populate. To perform a transformation on data, that data field must be included in the Visualization. Numeric derived fields will also be available. The way in which a grouping occurs for a transformation may be controlled by the user by utilizing the partitioning function, in much the same way as can be performed for the function “Normalization within Calculation Presets” under the Data Settings tab. The types of Transformation possible are outlined below:

• Grouped Aggregate: This transformation will allow a user to customize a group of data and apply an aggregation to that group. In the example below, if a user wished to sum the data field “Intensity” of each Delta Mass per Protein Name (within each Sample Name), they could select the aggregation type “Sum” and determine grouping by defining the fields to partition by Sample Name and Protein Name. For transformations, only data/meta data fields present in the Visualization may be used.

  The figure below shows the original Pivot Table displaying the intensity of each Delta Mass:

The figure below shows the result of the above Transformation settings:

• Aggregate and Reduce: This transformation allows the user to apply a grouped aggregation in the same way as the Grouped Aggregate function, but will also reduce the data by collapsing the data field that is aggregated into a single cell (in the example below, into one row) so that the aggregated value will only appear once. In cases where multiple data fields are partitioned (in the example below, it is partitioning against Sample Name and Protein Name), it will include all the non-partitioned field results into the same cell. Here, all the values for Delta Mass Name are displayed in the same cell for each row, while Protein Name is used to partition. The aggregation applied here is the same as previously described in the Grouped Aggregate section above. The main difference between the two transformation functions is that Grouped Aggregate may report the aggregate multiple times, which could potentially impact any further calculations.

• Ranking: This transformation allows the user to rank numeric values within the tables. There is an option to assign the value of 1 to correspond to the highest value (Ascending Order) vs 1 to represent the lowest numeric value (Descending Order). It is possible to choose which values to partition the ranking over. In the example below, the Ranking partitioning is based upon the data fields Sample Name and Protein Name. In this case the Ranking is based upon the numeric values within each Protein Name, within each Sample. If a user wanted to Rank the entire column, they would only partition by Sample Name.

• Normalization: There is an option within Transformations to perform Normalization. This is in addition to the options within Calculation Presets on the Data Settings tab. An option is provided in the Transformations tab so that a user may perform Normalizations on data that has previously been calculated using other Transformation Types. For example, a user may wish to Normalize on data that has previously been aggregated within certain groupings using either the Grouped Aggregate or Aggregate and Reduce transformation. In the figure below, there is a Normalization to the highest numeric value, and all other Normalized values are calculated relative to the highest (Max) value. As the order of operation for the transformations is based upon the sequence order in which they are set up on the transformations tab, this would mean that the Normalization (and hence determination of the highest value to use to Normalize against), is based upon the aggregated value as determined here by the Aggregate and Reduce transformation. In this example, the aggregation (summing) was applied by summing the intensity values within each Protein Name Group, per Sample Name (the same example used in the Aggregate and Reduced section above). The Normalization transformation was then applied on the entire column (in this case the partition was only applied to Sample Name) after the aggregation step, as this is the order in which they appear on the tab. The same calculated result would be obtained by using the Grouped Aggregate Transformation, however, the value itself will be repeated within the cells within the group that is aggregated. For this particular Normalization type, the calculated value would not be impacted, however, for the Normalization type “Sum” the repeating numeric display would affect the final calculated %. In cases like this, it is recommended to use Aggregate and Reduce.

• Filter: As with Normalization, the Filter function that is available under Data Settings is also available as part of the Transformations, so that it may be applied sequentially within, or after, other transformation calculations. In the following example a user wants to only display the Intensity values for the 3 most intense Delta Masses within each Protein, per Sample Name.

  The first operation would be to Rank each of the intensities within each Protein Name Group. This may be carried out as described earlier under the Ranking section. However, in this case, as the user wishes to then add a filter, the Transformation itself must be saved as a Transformation Field, so that it will be available as an option when applying the Filtering Transformation. In this example, the custom Transformation name that was given is “Rank”. When saved as a transformation field, it is only available for further transformation calculations, and not as an option within the Visualization itself.

  When the user adds the next Transformation in the order of operations, they are able to select a filter to only display those intensities that were in the top 3 ranked.

  

Example with ranking applied

The Filter transformation step shown below is then applied after the Ranking transformation.

Filter to be applied along with Ranking transformation

Visualization with Filter and Ranking applied

Note: Unlike Visualization filters, Transformation filters will apply in the sequence that they appear in the transformations. They will act as a filter to anything at a given step at the sequence and won't impact anything before that step. Since transformations happen after the data settings step, the transformations filters apply after the major settings for global, include/exclude, show-hide filters and before any previous transformations have been applied.

1.6.8.10. Advanced Functions

If a user has selected either a Line Chart or Scatter Plot Visualization, an Advanced Function option is available for user selection in the dropdown of the Visualization Builder:

Advanced Functions option in dropdown

Currently, the only Advanced Function available is Linear Regression. Upon adding the Linear Regression Advanced Function, the user must add the Independent and Dependent Variables from the fields provided in the dropdown.

Linear Regression Advanced Function

Adding Linear Regression will calculate the equation for the line of best fit, the Pearson Correlation, and the R2 value. Display of these values within the Visualization can be enabled from General > Display settings.

1.6.12.1. Ways to associate data

Mass spectrometry (MS) data must be associated with Biophysical data to be ingested. Data can be associated in one of two ways:

1. Sample Names are identical – if the value of Sample Name matches for both sets of data, data will be ingested and correlated in the Biophysical data source (Users can assure this by reviewing and editing the sample name in the Samples Table within the data analysis project)
2. A metadata value, sample_code, exists for the Biophysical data (saved with the project at the file level within Byosphere) and can be also found within the Sample Name of the MS data. Example:

• The Biophysical data file, for instance, a .peaks file (a Byosphere generated file format to include previously exported 3^rd party chromatographic software results), has the following metadata value, which was parsed from a .ars report file containing metadata:

  

• The MS raw data file, processed within a Byos Intact project, has the following associated sample name within the projects samples table and is corresponding to the same sample run on both instruments:

  

In this case, the user does not need to alter their sample naming conventions for either sample, which would be undesirable when there are company or laboratory standards of naming. Instead, the user must simply provide an additional metadata value in the Biophysical data that correlates with a unique identifier also present in the established MS sample name.

1.6.12.2. Using the Inspection Tool

Like with all other data sources, users can view the files present within their Biophysical Dashboard by clicking the Visualization Inspection icon. However, due to the complexity of the Biophysical data source, there are additional fields present to accommodate identification of all contributing data points.

Visualization Inspection fields for Biophysical Data