Omics Dashboard

The Pathway Tools Omics Dashboard is a tool for visualizing omics data. It 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.).

See the Omics Dashboard Help document for more information, or choose one of the links in the examples table below to see the Omics Dashboard in action on an example dataset.

See the Omics Dashboard without omics data to see the full set of systems, subsystems, and the genes that comprise them for the currently selected organism.

Please cite the Omics Dashboard as Paley et al. (2017) "The Omics Dashboard for interactive exploration of gene-expression data", Nucleic Acids Research 45:12113-24.

Invoke the Omics Dashboard

SingleOmics Datasets

Choose one of the following options to specify omics data for the Omics Dashboard:
Your most recently uploaded omics dataset
There is no recently uploaded omics dataset in the current session.

Upload a tab-delimited file

MultiOmics Datasets

Supply two or three related datasets (e.g. transcriptomics and metabolomics, or transcriptomics, proteomics and metabolomics) to view them together on the Omics Dashboard. If three datasets are supplied, two of them will share a y-axis, so you may find it useful to normalize the data before submission.
Number of datasets:


Dataset 1
Dataset 2
Dataset 3

Dataset 1
Dataset 2
Dataset 3


Performance Note: While uploading a large dataset to the dashboard may take some time to process initially, if you are experiencing ongoing performance issues while interacting with the dashboard, we recommend you try one of the following:

  • We have found that performance tends to be best using an up-to-date version of the Chrome browser, as compared to Firefox or Safari. If you are not currently using Chrome, you may wish to switch browsers.
  • If your dataset contains a large number of data columns (e.g. timepoints), try loading or displaying only a subset of them to reduce the amount of data that needs to be processed.
  • If your dataset contains a set of replicate groups, with multiple data columns per group, try preprocessing your data in the spreadsheet and upload columns containing group averages only.

Omics Dashboard Examples

Data Type Organism Example Description Display on Dashboard Link to Data
None Currently selected organism This display shows the organization of the dashboard for the current organism (the set of systems and subsystems available), but without any data loaded. Display on Dashboard
Transcriptomics Escherichia coli This RNA-Seq time series dataset depicting the anaerobic to aerobic transition in E. coli, is derived from GSE71562, von Wulffen et al, PMID 27384956, and has been normalized using the TPM approach. The dataset has been filtered to include only genes w/ fold-change > +/- 2 and p-value < .05. Display on Dashboard Link to Data
Transcriptomics Escherichia coli This is a more complete version of the above dataset. It includes data for every gene, for all three replicates, including columns of significance values. It therefore can be used to demonstrate the replicate averaging and enrichment analysis capabilities of the dashboard. Due to the large size of this dataset, it can be expected to take much longer to load into the dashboard than the filtered dataset above. Display on Dashboard Link to Data
Metabolomics Homo sapiens This dataset is derived from dataset ST000061 in the Metabolomics Workbench data repository, and contains metabolome profiling results of subcutaneous vs. visceral adipose tissue in 60 human individuals with colon cancer. Data from the 60 individuals was averaged to produce the values in this dataset. Display on Dashboard Link to Data
Select a local file from your computer containing a table of data (tab delimited). The first column of that file must contain names (or Pathway Tools unique identifiers) of genes, proteins, reactions, compounds, or a mix of these. The following column(s) of that file typically contains experimental numerical values.
The numerical values in the data file might be considered absolute or relative. If you select absolute, all negative values in your data file will be skipped.
The numerical data in your file might need to be interpreted by the ratio of columns. If this is the case, select "Ratio of two data columns". Then specify the denominator data column(s).
  • 0-centered scale: implies that the numerical data of your file can contain positive and negative values. The value 0 is considered to be the center of the numerical values provided in your data file. Data in log ratio format are 0-centered.
  • 1-centered scale: implies that any negative or zero values in your data file should be skipped. Moreover, the data is centered around the value 1. For example, the value 0.1 is considered to be at the same distance to 1 as the value 10. So, a logarithm of base 10 is applied to your data before the linear coloring mapping is applied.
If you wish to include a denominator column for a ratio calculation, you can enter either a single column number (in which case the same data column will be used as the denominator for all timepoints), or one column number for each numerator column number.

The first column of your data file must contain names or identifiers, and not numerical data. Using this selector you specify what the names are.
For a single experiment time step: enter a single column number in the box. This column corresponds to the data to use from the data file.

For a time series or multiple conditions: enter a list of column numbers (each column number corresponding to a single timepoint), separated by spaces, a range of integers (e.g., 2-4), or both. Alternatively, use the wildcard * to specify all columns from 1 to the last column, or N-* to specify all columns from column N to the last column (e.g. 5-*). The number of columns in the file is determined by the number of columns in the first non-comment line in the file.

Important note: The first column of your file contains names and/or identifiers and is column number 0. The first potential numerical data column is column number 1.