Core, Tox and Metabolomics Analyses
What does it mean to "Run an Analysis"?
All analyses in IPA return the following information:
1. The relevant functions associated with the uploaded data.
2. Affected signaling and metabolic pathways associated with the uploaded data.
3. Networks of interactions among the uploaded data as well as unstudied molecules
4. Contextual Data analysis
Analysis Type
Description
Identifiers Supported
Order of Results in Analysis Summary
Molecules added from the Ingenuity Knowledge Base
Available for Comparison Analysis
Core
Rapid assessment of the signaling and metabolic pathways, molecular networks, and biological processes that are most significantly perturbed in the dataset of interest.
Gene/Protein
Top Networks
Top Biofunctions
Top Canonical Pathways
Top Molecules
Top My Lists
Top Pathways
Top Tox Lists
Top Tox Functions
Genes (automatic)
Endogenous Chemicals (if selected)
Yes
Tox
Assess toxicity and safety of compounds.
Understand the relevant toxicity phenotypes and clinical pathology endpoints associated with a dataset.
Gene/Protein
Top Networks
Top Tox Functions
Top Tox Lists
Top Canonical Pathways
Top Molecules
Top My Lists
Top Pathways
Top Biofunctions
Genes (automatic)
Endogenous Chemicals (if selected)
Yes
Metabolomics
Overcomes the metabolomics data analysis challenge by providing the critical context necessary to gain biological insight into cell physiology and metabolism from metabolite data.
Chemical
Top Networks
Top Biofunctions
Top Canonical Pathways
Top Molecules
Top My Lists
Top Pathways
Top Tox Lists
Top Tox Functions
Endogenous Chemicals (automatic)
Genes (if selected)
Yes
How to Create an Analysis
1. To start a Core Analysis, Click File, New, Core Analysis. To start a Metabolomics Analysis, Click File, New, Metabolomics Analysis. To start a Tox Analysis, click File, New, Tox Analysis.
2. Select the dataset you wish to analyze. You may either upload a new dataset from your computer or select a previously uploaded dataset.
Note: For more information on dataset upload,
Click Here.
3. For a new dataset, specify the format, identifier type, and observations details, such as the expression value type. Click the Save & Create Analysis button. For an existing dataset, skip to Step 5.
4. Choose a project from the dropdown menu or create a New Project in which to store your dataset file, and click the Save button.
5. On the Create Analysis page, you can specify a variety of parameters to apply to your analysis.
Analysis Type
User's Dataset is chosen for reference set:
Ingenuity Knowledge Base Chosen for Reference Set:
Default Ingenuity Knowledge Base Used for Analysis
Core
if the dataset has >90,000 identifiers
if the dataset has <90,000 identifiers
Genes only
Tox
if the dataset has >90,000 identifiers
if the dataset has <90,000 identifiers
Genes only
Metabolomics
if the dataset has > 9,000 identifiers
if the dataset has <9,000 identifiers
Endogenous chemicals only
Network Analysis: Choose the types of interactions (direct and/or indirect) you would like to include in your network analysis. Click here for more information.
For Core and Tox Analyses, you can specify whether you want endogenous chemicals (metabolites) to be included in your networks. When endogenous chemicals are included, the network analysis will also include relevant specific metabolic reactions involving input/focus chemical or enzyme (group) molecules.
For Metabolomics Analyses, you can specify whether you want Genes also included in your networks. For example, running an analysis with a list of metabolites will return networks that indicate reactions that convert between the metabolites, enzymes that catalyze those reactions, and other regulators.
Network Size: The network size can be customized to 35, 70, or 140 nodes per network. (Resetting this paramater will change the networks generated not only in size, but also in the actual molecules that are included within the networks.)
Number of Networks: The number of networks generated can also be customized. IPA can display 10, 25, or 50 networks.
Nodes Per Network
Number of Networks
35
10, 25 (default), 50
70
10 (default), 25
140
10 (default), 25
Optional Analyses:
My Pathways: IPA will run an analysis against any custom pathways you have saved and approved. Click here for more information.
My List Analysis: IPA will run an analysis against any custom Lists you have saved. Click here for more information.
6. To specify other pre-analysis filters, click on the respective tabs.
7. If you have more than one observation in your dataset file, you have the option to run all of the observations or a subset of them. To specify which observations to run an analysis on, click the Edit button.
8. Specify an Expression Value Cutoff. This cutoff value determines which molecules are included in the analysis. For example, you might only be interested in molecules with a p-value of 0.05 or less; here, you would enter 0.05 into the expression value cutoff field.
In situations where you have multiple expression value types associated with molecules, a Expression Value Cutoff may be specified for each. When using multiple expression value types, IPA uses an "AND" function for calculating Network Eligible and Functions/ Pathways Eligible molecules. To become a Network Eligible or Functions/ Pathway Eligible molecule, an identifier must meet ALL cutoff criteria that has been set.
If you are interested in analyzing all molecules in your dataset, we suggest setting the cutoff value to include all molecules. For example, if you are using fold-change and wish to analyze all molecules in your dataset, set the cutoff value to 1.0. For p-value, a cutoff of 1.0 includes all molecules in the dataset.
Focus On: This parameter allows you to limit your analysis to Up-regulated, Down-regulated, or Up- and Down-regulated molecules. Use the pull-down menu to select.
Advanced Settings: The advanced settings allows you more options to customize your analysis. To set your advanced settings, click the Advanced Settings button.
Resolve Duplicates: If a dataset contains duplicate identifiers for the same molecule, the identifier with the highest Expression Value (lowest when the Expression Value is a p-value), by default, is used in the analysis. When there are multiple Expression Value types, you can determine which type should be used to resolve duplicates. In the absence of Expression Values, the first instance of the molecule is used in the analysis.
You can also select how the application should resolve duplicate identifiers in your dataset file. Select the Expression Value type and the preferred value (maximum, average, median or minimum) to be used in the analysis if the dataset contains duplicate identifiers for the same molecule. For example, if you choose Fold Change and Maximum for this parameter, then the identifier with the highest fold change value (absolute value) will be used for the analysis. For Ratio and Fold Change expression values, Minimum and Maximum refer to the lowest and highest magnitudes, respectively (absolute values), and the application uses Maximum as the default setting. For p-values, the application defaults to the Minimum setting.
Color Nodes
You can also select the preferred Expression Value type to use for node coloring. Node colors signify the up or downregulation of Network Eligible molecules in Network Explorer. The default view displays upregulated nodes as red and downregulated nodes as green. Please note that if the Expression Value type is a p-value or Intensity, then the expression values are always positive (p-values: between 0 and 1; Intensity: between 0 and ?), so all nodes will have a single base color (default color: red).
Recalculate: Use the Recalculate button after setting your analysis parameters to see the number of Eligible molecules you have for your analysis. For networks, we suggest that for the best results, you should have <800. This ensures that IPA is not analyzing noise in your dataset. If you have >800 molecules eligible for network generation, try increasing the stringency of your cutoff values.
We suggest that before running the analysis, you review the mapped molecules.
9. Run the Analysis. Once you are satisfied with your analysis parameters, click the Run Analysis button. You will automatically be taken back to the Project Manager window. In the Project Manager window, an analysis will be tagged with the icon if it is still running. Once it has completed, the clock will disappear and the tag will appear simply as and the analysis name will be in bold type. NOTE: The analysis status will refresh automatically, or you can click the Refresh button. Running an analysis may take up to 15 min to complete, depending on the size of the dataset. In addition, when the analysis is complete, you will receive an email informing you.
10. To open your complete Core Analysis, double-click the file name in the Project Manager. Once open, the Analysis Summary page appears. This page contains the most statistically significant results from the analysis.
11. If you have multiple datasets that represent multiple timepoints or dosage treatments, use Comparison Analysis to understand which biological processes and/or diseases are relevant to each timepoint or dose. To start a Comparison Analysis, Click File, New, Core comparison Analysis.
12. Select and add the Core Analysis results to the Analyses to Compare box by clicking the Add button.
13. Click View Comparison to display the results of the Comparison Analysis. Click Save & Exit to save your results to your Project Manager.
