Frequently Asked Questions (FAQs)

  1. When should I use OmicsAnalyst?
  2. What are the main features of OmicsAnalyst?
  3. What are the data formats accepted by OmicsAnalyst?
  4. Which browsers are supported by OmicsAnalyst?
  5. What if WebGL is supported but disabled on my browser?
  6. Which dimensionality methods are implemented in OmicsAnalyst?
  7. What are some common use cases of OmicsAnalyst?
  8. Can I test whether two omics data are associated and how strong their association is?
  9. Which features are available in 3D scatter plot viewer?
  10. Which features are available in dual-heatmap viewer?
  11. Which features are available in network viewer?
  12. What is Procrustes Analysis?
  13. How do I interpret the Procrustes plot?
  14. How do I interpret the MCIA plot?
  15. How many data points can be visualized?
  16. How do I interpret "Contour" option used to highlight a group of nodes?
  1. When should I use OmicsAnalyst?

    A key underlying assumption of OmicsAnalyst is that discrete clusters are present in your omics data. OmicsAnalyst is designed to detect, visualize and analyze these clusters. A clear outcome of this approach is that OmicsAnalyst will partition your data into clusters, regardless of whether there are biologically meaningful groups present. Although this approach may not be suitable for all omics data, such knowledge is rarely known a priori. Therefore, we strongly recommend users to evaluate their omics data in an unbiased, data-driven manner to complement mainstream differential analysis and supervised methods.

    In addition, users can visualize and analyze the patterns/groups with regard to different metadata they provided. This function is independent of the clusters detected. For instance, users can directly visualize and compare any pre-defined groups within our 3D visualization system.

  2. 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 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.

  3. What are the data formats accepted by OmicsAnalyst?

      Omics Abundance Tables

      OmicsAnalyst accepts one or multiple omics abundance tables generated from high-throughput instruments such as metabolomics data, transcriptomics, proteomics and miRNA data. Gene and metabolite annotation from human and mouse is supported. Features must be in rows and samples in columns (example below). Files must either be in .txt, .csv, or .zip format.

      Example Abundance File

                                      #NAME               sample10    sample105   sample11    sample113
                                      #CLASS:Condition    Classical   Classical   Classical   Classical
                                      FSTL1               0.04085     1.09922     -0.45374    0.03402
                                      MMP2                1.76569     -0.50303    0.41764     1.25827
                                      BBOX1               0.9542      1.21379     0.95196     0.60273
                                      GCSH                0.59383     0.66385     -0.00448    0.47665
                                      EDN1                0.8455      -0.26195    -0.34848    0.08309
                                      CXCR4               0.1691      0.04824     0.47361     -0.55183
                                      SALL1               0.45267     0.97534     0.35652     1.32005
                                      MMP7                -1.47079    -1.29109    -1.40518    -1.08673
                                      C9orf45             -0.41859    -0.42783    -0.01162    -0.55135
                                      RTN1                0.08844     -1.07261    0.67485     -0.1453
                                      ZEB1                0.51088     -0.10636    -0.13515    0.04285
                                      SEMA4D              -0.46286    0.35512     -0.2117     -0.78839
                                      PIR                 -0.5913     0.83694     0.23133	-0.93257
                                      KIAA1199            -0.27161	0.13885     0.47737	0.81915
                                      SORL1               -0.27511	0.4553      0.49623	-0.4683
                                      ......
                                  

      Example data from a multi-omics (transcriptomics + miRNA) study of Breast Cancer from TCGA

      Notes about formatting your data files:

      • Sample and feature names must be unique and consist of a combination of common English letters, underscores and numbers for naming purpose. Latin/Greek letters are not supported.
      • Sample and feature names must be consistent across all files (i.e. omics abundance tables and metadata file).
      • Data values (read counts or proportions) should contain only numeric and positive values. Empty cells or cells with NA values will be replaced with zero.
      • Metadata is not permitted in the abundance tables.
  4. 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+ ★★★☆☆

  5. 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
  6. Which dimensionality methods are implemented in OmicsAnalyst?

    Algorithm Full Name Note
    MCIA Multiple Coinertial Analysis
    mbPCA Multi-block Principal Component Analysis
    PLS Partial Least Squares
    Procrustes Procrustes analysis
    DIABLO Data Integration Analysis for Biomarker discovery using Latent variable approaches for ‘Omics studies
    rCCA regularized Canonical Correlation Analysis
  7. 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

  8. How can I test whether two omics data are associated and how strong their association is?

    OmicsAnalyst offers Robust Maximum Association Between Data Sets using a high-performance R package ccaPP. The package tests the maximum association measures using several different mesures including Pearson, Spearman or Kendall. The signicance of maximum association estimates can be assessed via permutation tests.

  9. Which features are available in OmicsAnalyst scatter plot viewer?

    Our scatter plot viewer provides more than mere data visualization, it also provides analytics features allowing users to dissect their datasets.

    1. Clustering analysis on sample space to identify inherent data structure and patterns of samples: K-means, Peak cluster, Mean shift.
    2. Flexible comparative analysis to identify differentially abundant features between groups or clusters of interest.
    3. Targeted analysis to dissect meta-data group or cluster of interest.
    4. Enrichment analysis to interpret the DE features in the context of current knowledge.
  10. Which features are available in OmicsAnalyst heatmap viewer?

    Our dual-heatmap viewer facilitates the identification of coordinated expression changes.

    1. Clustering features and samples to identify inherent data structure and patterns of feature space using hierarchical clustering.
    2. Joint visualization of two heatmaps simultaneously to identify coordinated or contrasting expression patterns within each dataset
    3. Enrichment analysis to functionally interpret features of interest in the context of current knowledge.
  11. Which features are available in OmicsAnalyst network viewer?

    Our interactive network viewer displays feature relationships in the form of 2D or 3D ball-and-stick graphs.

    1. Various graph layout options to highlight different features from the network (i.e. visualize shortest paths, network modules, omics type)
    2. Topology analysis to reveal network properties such as node betweenness, degrees, community detection and identifying shortest paths.
    3. Enrichment analysis to interpret the DE features in the context of current knowledge.
  12. What is Procrustes Analysis?

    Procrustes analysis is the analysis of shapes. It takes as input two ordination matrices with corresponding points, and transforms one ordination by rotating, reflecting, scaling, and translating it to minimize the distances between corresponding points in the other ordination (maximizing fit between corresponding observations). In OmicsAnalyst, raw omics data is transformed into ordinations with PCA, which are then configured to minimize the sum of square deviations between corresponding points (samples).

  13. How do I interpret the Procrustes plot?

    The Procrustes plot provides a visual indication of match between two ordinations. Spheres represent samples and belong to either omics 1 or omics 2 depending on the color of the line connected to the sphere. The lines between two spheres represent the position of a sample in the second ordination to its position in the target ordination. Longer distances (lines) between the two spheres indicates poor match while short distances indicate good agreement between datasets.

  14. How do I interpret the MCIA plot?

    The MCIA plot shows the projection of two omics datasets into the same dimensional space. Shapes represent samples and identical samples are connected by a line to the center point, which represents the reference structure which maximizes the covariance derived from the MCIA synthetic analysis. The shorter the line, the better the correlation between samples obtained by different omics.

  15. 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 empircal 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.

  16. How do I interpret "Contour" option used to highlight a group of nodes?

    OmicsAnalyst uses kernel density estimation (KDE) from "ks" R package to estimate a probability density function of a random variable. The resulting probability cloud represents the density estimate containing 75% of all data points from the selected group.

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