The Pathway Tools Omics Dashboard


The Pathway Tools Omics Dashboard is a tool for visualizing omics data. It facilitates a rapid user survey of how all cellular systems are responding to a given stimulus. It enables the user to quickly find and understand the response of genes within one or more specific systems of interest, and to gauge the relative activity levels of different cellular systems. The dashboard also enables a user to compare the expression levels of a cellular system with those of its known regulators. To learn more about how to use the Omics Dashboard, watch the series of Omics Dashboard Webinar videos at the BioCyc website. To try out the Omics Dashboard on some sample datasets, see the Omics Dashboard page on the BioCyc website.

The dashboard consists of a set of panels, each representing a system of cellular function, e.g. Biosynthesis. For each panel, we show a graph depicting omics data for each of a set of subsystems, e.g. Amino Acid Biosynthesis and Carbohydrates Biosynthesis. Each panel has its own y-axis, so that omics data for the different subsystems within a panel can readily be compared with each other. Multiple timepoints or experimental conditions are plotted as separate data series within the graph. Clicking on the plot for a given subsystem brings up a detail panel, breaking that subsystem down further into its component subsystems. At the lowest level, the values along the x-axis correspond to the individual objects in the dataset (i.e. genes for gene expression data, metabolites for metabolomics data, etc.).

Panels whose x-axis labels are subsystems (e.g. pathways, pathway classes, other functional groupings) that combine data from multiple genes, reactions or metabolites are referred to as composite panels. Panels for which each x-axis label represents a single gene, metabolite, etc. are referred to as base panels. The two types of panel have different display styles and options.

The following illustration depicts the Carbohydrates Biosynthesis subsystem panel for a gene expression experiment containing two timepoints (values depicted here are log ratios relative to control):

  1. The large dot represents the average (mean) of all data values for objects (e.g. genes) belonging to that subsystem. In this case, the subsystem is the Gluconeogenesis pathway, and the data values are for the second timepoint. Genes that belong to the subsystem but which do not have values in the provided dataset are ignored and not included in the average. If the dataset consists of only a single timepoint, then the numeric value is also drawn on the chart, but this is omitted for legibility reasons when there are multiple timepoints or experimental conditions.
  2. Each of the small dots represents a data value for an individual gene within the subsystem. The line connecting them shows the full range of values for that subsystem. Different subsystems contain different numbers of genes, so the numbers of small dots will vary accordingly.
Mousing over the plot for a given subsystem shows a tooltip that lists the full name of the subsystem, along with the number of values for each timepoint and their average values.
This illustration is the result of clicking on the Glycogen and Starch Biosynthesis component of the previous panel. It shows a bar graph depicting the expression levels of each individual gene within the subsystem for the two timepoints.

Clicking on a gene name or one of its corresponding bars will navigate to the page for that gene. When the dashboard is invoked from a Pathway Tools web server such as the BioCyc website, this page will open in a new browser tab. When the dashboard is invoked from Pathway Tools operating in desktop mode, the gene will be shown in the main Pathway Tools Navigator window.

If the subsystem represents a metabolic pathway or collection of related metabolic pathways, the panel header will include a "Show Pathway" button to show the pathway diagram overlaid with omics data (if the initial color range for the omics data is not a good match for the diagram, you can change it using the "Update Color Scheme" command in the Options menu of the pathway diagram panel). The "Show Operons" button generates a diagram that shows how the genes in the subsystem are organized on the genome and, where available, their regulatory influences.

General Usage Instructions

Typical usage of the Omics Dashboard might entail the following steps:

Importing Data

The Omics Dashboard uses the same tab-delimited input file format as the Pathway Tools Omics Viewer. It is described, with examples, in the Pathway Tools Website User's Guide. The first column must contain object (e.g. gene or metabolite) names or identifiers (see userguide for acceptable options), and there must be one or more additional columns containing numeric data. Multiple data columns can represent time series datapoints, multiple replicates, and/or different sets of experimental conditions. As a practical matter, for performance and display reasons, it is best to keep the number of data columns under about 20. Because the dashboard can accommodate multiple types of data, you will be asked to provide some basic information about your datafile, such as the type of objects in the first column, which columns contain the numeric data of interest, and whether that data is absolute (e.g. counts, intensities) or relative (i.e. ratios or log ratios relative to control or some other condition), and if relative, whether the data is centered upon 0 or 1. Beyond those basic parameters, the dashboard does not impose any additional interpretation on the supplied numerical values, so it is important that any desired statistical analyses or corrections, such as normalization, significance analysis and/or filtering, be applied before the data is uploaded to the dashboard. If any data columns have corresponding columns containing significance values, those significance columns should be included in the list of uploaded columns like any other (after the upload, you will have a chance to associate each significance column with its corresponding data column).

In addition to traditional omics datasets that contain a single class of data (i.e. transcriptomics or metabolomics or proteomics), the Omics Dashboard also supports multi-omics data. For example, it can generate a display combining both transcriptomics and metabolomics data generated as part of the same set of experiments. The different classes of data are submitted as separate files or SmartTables, each with its own parameters as described above. Alternatively, a single file can be generated containing each component dataset along with a section containing the parameters for each dataset, and that file can be uploaded to the Omics Dashboard. Multi-omics datasets containing either two or three different component datasets can be accommodated. If two datasets are supplied, each will be displayed with its own y-axis (one on the left, the other on the right). If three datasets are supplied, then two of them must share a y-axis, so you may wish to normalize the data before upload. The Display Preferences panel lets you customize which axes to use for which datasets.

The Omics Dashboard is accessible both from a website powered by Pathway Tools (such as, and from Pathway Tools running as a desktop application. From a Pathway Tools website, use the Omics Dashboard command in the Analysis menu to access the data upload page. In lieu of a tab-delimited file, a SmartTable of similar format may also be used as an input data source (examples are provided). In the desktop application, use the Omics Viewer command in the Overviews menu, and select the Omics Dashboard as the target viewer.

Display Preferences

Global display preferences can be set using the Display Preferences panel at the right of the screen. Display preferences for an individual panel, which override the global defaults, can be set using the controls associated with each panel. For the top-level panels, these are displayed at the bottom of each panel. For pop-up panels, the controls are located in the panel header.

Show Sums/Counts

In addition to the average of data values within a subsystem, the dashboard can also show the sum of all data values within the subsystem. Because different subsystems have different numbers of genes, for RNAseq data, this sum can indicate the relative proportion of cell resources that are being invested in each subsystem. For other data, showing the sum of all data values can highlight cumulative trends that might be masked by otherwise small changes in averages.

This illustration shows the same panel as above, but with the "Show Sums" option enabled.
  1. As before, the large dot represents the average (mean) of all data values for genes belonging to that subsystem. However, because sums are now being shown, the vertical scale of the graph has been compressed.
  2. Each of the small dots continues to represent a data value for an individual gene within the subsystem, with a connecting line showing the range of values.
  3. The height of the colored bar corresponds to the sum of all data values for objects in the subsystem. Since these values are log ratios that can be either positive or negative, the sums can also be either positive or negative (for RNAseq data, the values would be expected to all be positive).


There are multiple options for sorting the various subsystems and data objects within a given panel. By default, composite panels (those whose components are subsystems) use a predefined ordering (which you can customize). Base panels default to sorting by map position when the data objects are genes, and otherwise are sorted alphabetically. The defaults for the two types of panels can be changed by selecting new defaults in the Display Preferences panel. The sorting option for any individual panel can be changed by clicking on the sort icon for that panel.

In addition to sorting alphabetically, by map position (where appropriate) or using the predefined ordering, plots within a panel can be sorted numerically. However, when there are multiple data series (e.g. timepoints), and multiple data values within a series (e.g. genes per subsystem), the question becomes how to determine which numeric value should be used for sorting. For composite panels, we support two ways to aggregate data values within a series: by taking the average (the mean, corresponding to the large dot), or by taking the sum (the height of the bar when sums are being shown). To aggregate across multiple data series, four options are available:

Since in every case one can sort in either ascending or descending order, that means that for composite panels with multiple data series, there are 4x2x2=16 possible numeric sorting options.


There are three possible ways to adjust the vertical scaling of a panel. The first is by clicking on the magnifying glass icons for a panel to increase or decrease its total height. There is a smallest possible height and a maximum possible height (the full height of the window). Depending on the size of your browser window, there may or may not also be one or more intermediate height settings.

For datasets in which all data values are absolute measurements (i.e. not ratios) and positive values (e.g. intensities, counts, concentrations), you can specify whether to use a linear or log10 scale for the y-axis (for relative datasets, the decision to use a log scale or not is predetermined and cannot be overridden). The default is set in the Display Preferences panel, but can be changed for any panel by clicking the log checkbox. Note that when using a log scale, the only y-axis labels shown will be powers of 10.

Finally, there may be cases in which one or more outlier values cause the entire vertical scale of a panel to become compressed to the point that it is difficult to distinguish differences in the portion of the scale where most of the data is. In that case, you can manually adjust the graph extrema to limit it to the desired extent, causing any values that fall outside those extrema to be omitted. To do this, either select the Change Graph Scale command from the Options menu for the panel, or click on one of the y-axis labels to bring up the relevant dialog. Use the slider to adjust the scale extent and click the Set button to update the panel accordingly. Use Reset to revert to the default scale.

From this dialog you may also set an exact numerical height and width for the graph, and adjust both horizontal and vertical scale that way.

The font sizes used for the panel axes can be adjusted using the font icons and to increase or decrease size respectively, or can be set globally in the Display Preferences panel. Note that if you set the font size too large, axis labels that don't fit may be truncated.

Data Column Selection and Grouping

Note on terminology: a data series can refer to either a single data column or a grouped set of replicate columns. We use the terms data column and data series more or less interchangeably in this document.

When there are multiple data series, the Data Column Selection panel allows you to selectively show only those you are interested in at the moment. It also allows you to edit the labels for each data series to change how they appear in the legend near the top of the screen, and to change the color associated with each data series. To change the order in which the data series are displayed, click and drag the corresponding labels.

Some datasets contain multiple replicates for each timepoint or experimental condition. Rather than showing each of these as a separate data series, you can opt to automatically group them together and average the values for each gene or metabolite in the replicate set. In the Series Grouping panel, specify how many total replicate groups should be generated. For example, if a 12-column dataset has data for 4 experimental conditions with 3 replicates each, you would specify 4 groups. At this time, for performance reasons, the maximum number of groups that can be created is 10. When you click OK, a table is generated that enables you to assign data columns to groups by clicking the appropriate radio button. The groups are initially labeled G1, G2, etc. but you can rename them. For data columns that do not belong to any group, select N/A. Each data column can be assigned to at most one group.

Creating or updating groups will cancel any previous selection made using the Data Column Selection panel, and that panel will be updated so that you can select which groups to show instead of which individual data series.

When the data is divided into replicate groups, base panels are no longer drawn as simple bar charts. Instead, the averages and values for the individual replicates are displayed as if the panel were a composite panel.

The ability to group multiple replicates is not available when displaying multi-omics data, so replicate data for multi-omics datasets will have to be combined and averaged before upload.

Data Filtering

Data can be filtered to highlight only those datapoints that are of particular significance. Any objects (genes, metabolites) that do not pass all filters for at least one visible data series are omitted from the display. Objects that do pass the filters for at least one series are included in the display for all data series, including any for which they do not pass the filters.

Three types of data filters are available:

Enrichment Analysis

Enrichment mode is designed to call attention to subsystems whose changes in gene or metabolite expression are statistically significant. Instead of showing the average and range of data values for each subsystem, enrichment mode displays computed statistical enrichment scores for each subsystem.

The enrichment score for a subsystem captures the degree to which a statistically significant number of genes or metabolites within that subsystem have significant expression values. The dashboard computes enrichment p-values using a Fisher-exact test, applies the specified multiple hypothesis correction, and then transforms each p-value to an enrichment score: -log10(p-value).

The following is an illustration of a portion of the Biosynthesis panel in enrichment mode:

  1. The thick colored bar represents the enrichment score (-log10(p-value)) for the entire subsystem, in this case Amino Acid Biosynthesis. This enrichment score is the numeric value printed near the top of the bar.
  2. If some component subsystem has a higher enrichment score than the subsystem as a whole, for example if Arginine Biosynthesis has a much higher enrichment score than Amino Acid Biosynthesis as a whole (because the enrichment score for Amino Acid Biosynthesis is diluted by other low-scoring amino acid pathways), then this is indicated using a thin vertical line, B, that extends past the top of the colored bar. The bar at the top of the line represents the greatest enrichment score of any component subsystem. This line suggests that you might want to drill down one level in this subsystem to investigate further.
  3. If no line extends past the top of a colored bar, then there is no component subsystem with a higher enrichment score than that of the subsystem as a whole. This situation tends to happen when the contributions towards significance are fairly evenly spread out amongst the component subsystems rather than being primarily confined to a specific subset.
For each data column (experiment, timepoint or replicate group) you wish to see an enrichment score for, you can specify a corresponding significance column that contains significance values that you have calculated for the data column. In the simplest case, this column may be the same as the data column itself, such as if you simply want to see which subsystems have an over-representation of outlier values. Alternatively, your data file may include one or more additional columns containing significance values (such as p-values derived from replicate analysis). These columns should already have been included in the file and designated as data columns when you uploaded it, but you will probably want to unselect them in the Data Column Selection panel when not in enrichment mode, so they will not be displayed as regular data series. If there is no significance column for a given data column, you can leave it unspecified -- that data column will be excluded from the enrichment analysis. Similarly, if the same significance column applies to multiple data columns, assign it to only one of them, as there is no point duplicating the analysis.

Once you have indicated which significance columns to use, you will need to specify a significance threshold -- the enrichment computation performed by the dashboard will include only those entities (e.g., genes) whose significance value exceeds the threshold in the direction you specify (p-values should be less than the specified threshold, but for other significance measures you might want them to be greater than the threshold).

Enrichment analysis is not available in multi-omics displays.

Visualizing Regulation

The header of most base panels includes a Show Operons button which generates a diagram showing the organization of the genes for that pathway or subsystem on the genome. When a database contains transcriptional regulatory data, that too will be included in the diagram.

For gene expression datasets, we can go further and see how the expression pattern of the transcription factors that regulate a pathway or subsystem correlates with the expression of the subsystem genes themselves. When such regulatory data is available, the Options menu for a base panel will include a Show/Filter Regulators command. This command will open two insets, both of which can be manually resized, repositioned and closed independently of each other.

The inset shown on top in these illustrations is a graph showing the expression levels for all of the transcription factor genes that regulate the Putrescine Degradation pathway. There is no way to tell from this diagram whether the transcription factors activate or inhibit gene expression -- that information is however included in the operon diagram described above.

The bottom inset is a set of checkboxes that allows you to filter by transcriptional regulator. If ArcA in this example, which you are told regulates 4 of the genes in the panel, were checked, then all but the 4 genes it regulates would be faded out so that only those 4 genes would be clearly visible, as in the second illustration at left. The inset showing the transcription factor genes would then similarly hide all genes that are not involved in the regulation of the 4 visible genes. Because one or more of the 4 genes is also regulated by Crp, this leaves both arcA and crp visible.

If multiple regulators are checked, select whether you wish to see only those genes that are regulated by all the checked regulators, or all genes regulated by any of the checked regulators. The regulators for a given gene are listed in the tooltip for that gene.

Customizing Panel Contents

Users can customize the dashboard to remove or reorder components of different panels, edit subsystem names, and add new panels or panel components, including custom lists of genes. If the user is running Pathway Tools in desktop mode, these customizations are saved persistently. When accessing the dashboard over the web such as from the BioCyc website, customizations persist only for the duration of the current session (but can be downloaded to the user's computer for later re-upload). In either case, you can specify whether a given customization should be applied to all datasets or only to those for the same organism. In general, applying or removing customizations necessitates a full dashboard page reload.

Deleting, Reordering and Editing Names of Subsystems

The Customize Panel command in the Options menu for any panel brings up a dialog like the one at left.

Each panel has a unique identifier or key, which usually but not always corresponds to the identifier for an object in the database, typically a pathway, pathway class, or GO term. These cannot be edited, but you can use this dialog to discover the key for some subsystem in case you want to add it to a different panel. Each panel or subsystem also has a full name and a short name. The dedicated panel for a subsystem displays its full name as the panel label at the top. When a subsystem is displayed as one component of its parent panel, the short name is displayed along the x-axis, and the full name is displayed in the corresponding tooltip (the top-level panels, which are never displayed as components of other panels, do not require short names). Both the short name and the full name, both for the panel and all its component subsystems, can be edited using this dialog.

To delete a subsystem from a panel, click the X in the box for that subsystem. To reorder the components of a panel, use the up and down arrows until the desired order is achieved. Note that any ordering changes you make will only be visible if the sort option for the panel is set to use the predefined order. There is currently no way to reorder any of the top-level panels. Top-level panes cannot be deleted, but they can be hidden using the Hide Panel command in the Options menu. Panels that are hidden in this way can be restored by clicking the corresponding Show button at the bottom of the screen.

Most base panels have their components computed automatically based on the contents of the database. The components of these panels cannot be edited.

Creating or Adding a New Subsystem

To add a new subsystem to any composite panel, use the Add New Custom Component command in the Options menu. To add a new top-level subsystem, use the command Add New Top-Level Panel in the Customize Contents control panel. Both commands will bring up a dialog like the one below.

There are 4 options for types of subsystems to add:
  • Pathway or Pathway Class: Start typing the name of any pathway or pathway class in the database, and select the desired pathway from the autocomplete menu. A base subsystem will be created (if it does not already exist) whose components are all the genes (or metabolites) of that pathway or class.
  • GO Term: Start typing a GO term identifier and select the desired term from the autocomplete menu. A base subsystem will be created (if it does not already exist) whose components are all genes that are annotated to that GO term or its child or part terms.
  • Custom List of Genes (pictured left): Enter names or identifiers of all the desired genes, and then click Search. The names of those genes that were found will be replaced by their internal object identifiers. Any genes that could not be found will be listed so that you can try again to identify them.
  • Composite: To create a composite subsystem, enter names or identifiers of component pathways, pathway classes, GO terms or other existing subsystems, and then click Search. The names of those components that were found will be replaced by their internal object identifiers. Any that could not be found will be listed so that you can try again to identify them.
In the first two cases, the key for the subsystem is predetermined. In the latter two cases, you must supply your own unique key. You should also supply a full name and short name for your subsystem.

Managing Your Customizations

You can manage your customizations using the various buttons in the Customize Contents control panel.

The View/Manage Customizations button shows you a list of all of your customizations, as in the example illustration, left. To remove a particular customization, click the X in its upper right corner.

To temporarily disable all your customizations without deleting them, and to view the dashboard as it would look in its uncustomized state, use the Show Default Display button.

To delete all your customizations and reload the uncustomized dashboard, use the Delete User Customizations button.

To download a record of your customizations to a file on your computer, use the Download Current Customizations to File button. To restore those customizations to a future session, use the Upload Previously Saved Customization File button.

If you do not have any current customizations, the only buttons you will see in this section are those to add a new panel or to upload a previously save customization file.


Any individual panel graph can be exported to a PNG image using the command Export to PNG Image in the Options menu. The exported image consists only of the graph itself, without the panel border and label, or any insets. The panel as displayed, with border, label and insets, can only be captured using a screen snapshot. For more attractive screen snapshots, you may wish to hide the controls associated with each toplevel panel. The Display Preferences control panel provides a checkbox for this purpose. The entire control panel accordion can also be hidden by clicking on the up-arrow icon in its lower right corner.

The source data for any panel graph can be displayed as a data table via the Show Data As Table command in the Options menu. This can then be downloaded to a csv file by clicking the download icon . Only source data values (and group averages, where replicate groups are created) are included in the table. There is currently no way to generate a table containing subsystem averages, sums or enrichment scores.

The pathway and operon images are PNG files that can be saved by right-clicking and using the appropriate browser command. Pathway diagrams can also be exported to a Pathway Collage using the command in the Options menu, where they can be edited and/or a higher resolution image can be generated.

When the dashboard is invoked from a Pathway Tools web server that has SmartTables enabled, such as BioCyc, the entire dataset can be exported to a SmartTable using the Save Dataset as SmartTable command in the Display Preferences control panel. A SmartTable generated in this fashion will "remember" some display settings, such as replicate groupings and default sort settings, so that if you later regenerate the dashboard display directly from that SmartTable, those settings will automatically be applied. If you wish to be able to do this, be sure not to edit the SmartTable by adding, deleting or reordering data columns after it has been generated.


The graphs that comprise the dashboard are implemented using the Google Charts API. The pop-up panels are implemented using jBox. Pathway images are generated from Pathway Tools pathway diagrams using Cytoscape.js.

If you use the Omics Dashboard in your work, we ask that you cite the following publication:

Suzanne Paley, Karen Parker, Aaron Spaulding, Jean-Francois Tomb, Paul O'Maille, and Peter Karp.
The Omics Dashboard for interactive exploration of gene-expression data,
Nucleic Acids Research 45:12113-24 doi:10.1093/nar/gkx910 (2017)

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