Frequently Asked Questions (FAQs)

  1. What are the common approaches for multiomics integration?
  2. When should I use OmicsAnalyst?
  3. What are the main features of OmicsAnalyst?
  4. Which species does OmicsAnalyst support?
  5. What are some common use cases of OmicsAnalyst?
  6. Which browsers are supported by OmicsAnalyst?
  7. What if WebGL is supported but disabled on my browser?
  8. How many data points can be visualized?
  9. Is the data that I uploaded kept confidential?
  1. What are the common approaches for multiomics integration?

    There are two main types of approaches for multiomics integration

    • Knowledge - driven integration: this type of integration is based on prior knowledge to link key features in different omics. For instance, the KEGG metabolic network is often used to connect key genes, proteins or metabolites obtained from different omics layers to help identify the "activated biological processes". This type of analysis can be expanded to include other molecular interaction networks such as protein-protein interactions, TF-gene-miRNA interactions etc. We have developed OmicsNet and miRNet to support multi-omics integration based on comprehensive, high-quality molecular networks.

      The knowledge-based integration is mainly limited to model organisms where comprehensive knowledgebase exists. In addition, it is biased to existing knowledge with limited capacity for discovering novel relationships. This is where the data & model driven integration aims to address.

    • Data & model – driven integration: this type of integration applies various statistical models or machine learning algorithms to detect key features and patterns that co-vary across omics layers. In general, this type of integration is not confined to existing knowledge and is more suitable for novel discoveries.

      A key limitation of this type of integration is that there are no consensus approaches and a wide variety of methods have been developed over the past decade. Each method carries its own model assumption (or bias) and pitfalls. Properly using different methods and interpreting their results become the main challenges to researchers in the field. This is one of the main motivations driving the development of OmicsAnalyst.

  2. When should I use OmicsAnalyst?

    OmicsAnalyst has been developed as a general purpose platform to support common tasks in Data & model – driven integration of multi-omics data. In particular, it helps answer these common questions:

    1. What are the key features that are closely correlated within and across omics layers (correlation analysis)?
    2. Which samples share similar coordinated patterns of change across omics layers (clustering analysis)?
    3. What are the main shared the co-variance of the data, and the key features underlying the co-variance (dimension reduction or projection analysis)?

  3. What are the main features of OmicsAnalyst?

    OmicsAnalyst was designed to provide an intuitive means for clinicians and bench scientists to work directly with big omics data. It achieves this by integrating multivaritate statistics, density-based clustering, and 3D visual analytics in a user-friendly web-based platform to allow users to interact and discover patterns within their large datasets from their personal computer. It offers data processing and QA/QC prior to three main visual analytics systems:

    1. Interactive scatter plot displaying simultaneously feature and sample space in 3D space.
    2. Dual-heatmap viewer to visually compare expression patterns of two omics datasets.
    3. 2D/3D network viewer to visualize correlations and associations between features.

    All of our visual analytics systems are coupled with extensive clustering analysis and flexible differential analysis.

  4. Which species does OmicsAnalyst support?

    OmicsAnalyst has annotation files for human and mouse data, however data from any species can be processed and analyzed by leaving the "Specify Organism" drop-down menu as "---- Not Specified ----". All tools in OmicsAnalyst will work except for the targeted enrichment analysis, since this requires IDs to match to the pathway libraries. Since enrichment analysis is typically the final step in an analysis pipeline, the lack of annotation should not have a large impact.

  5. What are some common use cases of OmicsAnalyst?

    OmicsAnalyst is very flexible and can be used to answer many different questions in omics and multi-omics data analysis. Below are some common questions that OmicsAnalyst can address.

    1. Explore inherent trends and patterns in multi-omics data and whether samples cluster according to biological condition
    2. -Heatmap viewer, mbPCA, Procrustes

    3. …and identify correlated features between two datasets.
    4. -Correlation network using rCCA, PLS, DIABLO, univariate and partial correlation.

    5. …and identify potential biomarker features
    6. -DIABLO, MCIA, differential analysis

    7. Identify clusters from dimensionally reduced sample space and/or expression heatmap
    8. -K-Means, Peakcluster, Hierarchical

  6. Which browsers are supported by OmicsAnalyst?

    The 3D visualization system was developed based on the Web Graphics Library or WebGL technology. WebGL is the standard 3D graphics API for the web. It allows developers to harness the full power of the computer’s 3D rendering hardware from within the browser using JavaScript. Before WebGL, developers had to rely on plugins or native applications and ask their users to download and install custom software in order to deliver a hardware-accelerated 3D experience.

    WebGL is supported by most major modern browsers that support HTML5. We have tested OmicsNet in several major browsers (see below). Our empirical testings have shown that Google Chrome usually gives the best performance for the same computer:

    Name Version Note
    Google Chrome 50+ ★★★★★
    Mozilla Firefox 47+ ★★★★☆
    Apple Safari 10.1+ ★★★☆☆
    Microsoft Edge 12+ ★★★☆☆

  7. What if WebGL is supported but disabled on my browser?

    Chrome

    First, enable hardware acceleration:

    • Go to chrome://settings
    • Click the + Show advanced settings button
    • In the System section, ensure the Use hardware acceleration when available checkbox is checked (you'll need to relaunch Chrome for any changes to take effect)

    Then enable WebGL:

    • Type chrome://flags in the browser and press Enter
    • Ensure that Disable WebGL is not activated (you will need to relaunch Chrome for any changes to take effect)
    • Here you will have to change Default to Enabled in the drop down.

    • [Try this if above doesn't work] Enable - Override software rendering list

    For more information, see: Chrome Help: WebGL and 3D graphics.

    Firefox

    First, enable WebGL:

    • Type about:config in the browser address bar and press enter
    • Search for webgl.disabled
    • Ensure that its value is false (any changes take effect immediately without relaunching Firefox)

    Then inspect the status of WebGL:

    • Go to about:support
    • Inspect the WebGL Renderer row in the Graphics table:

    If your graphics card/drivers are blacklisted, you can override the blacklist. Warning: this is not recommended! (see blacklists note below). To override the blacklist:

    • Go to about:config
    • Search for webgl.force-enabled
    • Set it to true

    Safari

    • Go to Safari's Preferences
    • Select the Security tab
    • Make sure to check theAllow WebGL checkbox
    Source: https://superuser.com/questions/836832/how-can-i-enable-webgl-in-my-browser
  8. How many data points can be visualized?

    The visualization is limited by the performance of users' computers and screen resolutions. Too many data points will result in greater latency in manipulating the plot. Based on empirical tests and practical utilities, we recommend to keep the total data points to be less than 5000 - it is rare that the sample size will be larger than this number. For very large data, please make sure you have a decent computer equipped with a high performing graphics card.

  9. Is the data that I uploaded kept confidential?

    Yes. The data files you upload for analysis as well as any analysis results, are not downloaded or examined in any way by the administrators, unless required for system maintenance and troubleshooting. All files will be deleted automatically after 72 hours, and no archives or backups are kept. You are advised to download your results immediately after performing an analysis.

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