What are the data formats accepted by OmicsAnalyst?

OmicsAnalyst accepts two or more feature abundance tables generated from common omics technologies such as transcriptomics, proteomics, metabolomics, and miRNA data. Features must be in rows and samples in columns (example below). Files must either be in .txt, .csv, or their compressed format (.zip).

Example Abundance File (containing metadata)

                                    #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
                                    ......
                                

Example Abundance File (metadata in a separate file below)

                                    #NAME               sample10    sample105   sample11    sample113
                                    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
                                    ......
                                

A metadata file (upload together with the above feature abundance file)

                                    #NAME          Condition   Gender
                                    sample10       Classical    M
                                    sample105      Classical    M
                                    sample11       Classical    F
                                    sample113      Classical    M
                                    ......
                                

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 names must be consistent across all files (i.e. omics abundance tables and metadata file).
• Data values should contain only numeric values. Empty cells or cells with NA values are considered missing values.
• If you include metadata in the feature abundance file, metadata or group label should directly below sample names indicated by "#CLASS". You can indicated multiple factors. For instance, the above TCGA example data sets contains two factors indicated by "#CLASS:Condition", "#CLASS:Hours", respectively.

How many data sets can be uploaded per analysis?

OmicsAnalyst accepts a maximum of five data sets for upload in any one analysis. Data sets are always available along the left-hand side and the selected data can be changed until the visual analytics step. Every method in OmicsAnalyst works with two data sets, and a few work with more. One issue is that even though the multi-variate statistics may work for more than two data sets, the visualization quickly becomes so complicated that it is not useful. For this reason, we have disabled greater than two data sets for the heatmap and correlation network even when the statistics allow it. Up to five data sets are allowed for the 3D scatterplot for the MCIA, CPCA, PLS, and DIABLO methods.

Which ID types does OmicsAnalyst support?

OmicsAnalyst supports the following ID types:

What is the rationale behind filtering gene expression data?

The purpose of filtering is to increase the statistical power of differential analysis be removing any features that are less likely to be informative. Please refer to the paper Independent filtering increases detection power for high-throughput experiments for detailed discussion and benchmark tests

Low variance filter: features whose values do not change across different samples, and thus have very low variance. Features are ranked by their variance from low to high, and you can exclude a certain percentile of features with the lowest variance by adjusting the "Variance filter" slider. The above referenced study has suggested that up to 50% genes can be removed based on their variance with improved results

Low abundance filter: features with very low abundance are not measured reliably and may not be biologically important. You can exclude features below a certain threshold (Absolute option) or exclude a certain percentile (Relative option) by adjusting the "Low abundance" slider. The above referenced study has suggested 10% genes can be removed based on their abundance with improved results

What is the purpose of the Comparisons step?

This step aims to identify statistically significant features for the current omics data. The statistical comparisons perform univariate statistics to identify features with statistically significant differences across experimental groups. These features are used in some of the downstream visual analytics, for example the correlation network is constructed from those signficant features. In addition, those features can also serve as baseline to guide visual exploration using heatmap and 3D scatterplots.

What does "Metadata of interest" mean?

It is possible to include multiple classes that describe the samples (for example, sex and age). OmicsAnalyst restricts statistical comparisons to only consider one experimental factor, thus users must choose the meta-data labels that they wish to perform statistical comparisons on.

What is the "Data Quality Check" page for?

The "Data Quality Check" page provides density and PCA plots of the uploaded 'omics data sets. The density plot allows you to compare overall distributions between 'omics types while the PCA plots highlight whether there is separation between experimental groups within each of the 'omics data. This page gives a chance to ensure that the data are comparable to each other prior to proceeding to more complicated multi-variate statistics. If they data look like they need further normalization, transformation, or scaling, go back to the data upload page to update. This step is important for proper integration. For example, the image below shows distributions of example data 1 (proteomics and metabolomics) before and after scaling. The data are much more comparable after scaling.

What are the differences between sample normalization, transformation, and scaling?

Sample normalization refers to the method by which duplicated feature IDs are dealt with. Normalization by sum is more appropriate for discrete data, for example raw RNA-seq reads counts, while normalization by median is more appropriate for continuous data, for example MS-based data like metabolomics or proteomics.

Transformation generally means converting the data to a log2-scale, while also accounting for systematic differences between samples. Some of the methods were originally developed for RNA-seq counts data, and the cross-sample normalization was included to account for differences in sequencing depth. However, the same methods can be used to account for sample-to-sample differences in other 'omics data.

Scaling is applied to the feature-dimension and refers to standardizing the data such that each feature has roughly the same distribution. This tends to better highlight differences across samples. It is an important step for multi-omics data since data measured on different platforms can have vastly different values. Scaling the data can make the 'omics types more comparable to each other, making it easier to identify consistent patterns across multiple data sets.

What are the different options for transforming my data?

Transformations can be applied on both the "Data Processing" page (prior to differential analysis), and on the "Quality Checking" page (prior to multi-omics integration and visual analytics), and the available options depend on whether the data are RNA-seq reads or continuous values. Continuous values could be MS-based data, for example proteomics, or RNA-seq data that was normalized prior to upload. In particular, transformations are disabled prior to differential analysis if the data are RNA-seq reads, since the algorithms for these data are designed for raw counts.

What is the objective of correlation network analysis?

The objective of this analysis is to understand relationships between key features across multiple 'omics datasets. This is achieved in two main steps. First, we select key features to define the network nodes. There are two main ways to do this: either we select features that vary significantly across experimental groups using the contrast statistics that were computed at the data upload step, or we perform multi-variate dimension reduction to find sets of features that are both highly connected within individual 'omics datasets and highly associated across 'omics datasets. For the dimension reduction methods, the top 20 features from each component and 'omics data are selected based on the loading scores. Next, we compute the pairwise similarity of selected features to construct the network.

What is the purpose of the feature selection step?

Networks that contain too many nodes and edges are overwhelming, and difficult to use to draw clear biological conclusions. Thus, the feature selection step aims to consider only the most interesting features, and then to construct a network to understand the relationships between them. There are two ways of doing this: univariate differential analysis and multi-variate dimension reduction.

How can I adjust the number of DEGs?

The number of statistically significant features is determined by the method and thresholds set during the data processing step. The results are displayed in the data set summary visible on the left-most panel. Clicking the icon beside the summary allows you to change the thresholds to either increase or decrease the number of DEGs. This can be done at any point up to the visual analytics step.

How are dimension reduction algorithms used to select features?

The dimension reduction algorithms find sets of multi-dimensional components that explain the maximum variance within individual 'omics data sets, and that are maximally related across 'omics data sets. Looking at the loading scores for each component reveals the features that contribute most towards these components. You can adjust how many features are selected:

The first slider specifies the number of top features of each 'omics type that are taken from each component, based on the ranked abs(loading scores). The second slider specifies the number of top components that features are selected from. Thus, in this case, twenty features from each 'omics are selected from the top three components: 20 features * 2 'omics types * 3 components = 120 features used for network construction.

What is a similarity matrix and how is it computed?

A similarity matrix has the same elements along the rows and columns, and each matrix entry contains a measurement of similarity between the two elements. This makes the matrix both square and symmetric, and the diagonal values all equal to 1. In OmicsAnalyst, the similarity measurement options are all different types of correlation. For the correlation network, the similarity matrix gives the similarity between all pairwise combinations of 'omics features across both data sets.

What are the key differences between Pearson, Spearman, and Kendall correlation?

All the correlation types are a standardized measure (between -1 and 1) of the strength and direction of the relationship between two variables. The three types differ in the type of relationship that they measure. Pearson, the most commonly used correlation, measures linear relationships between variables, and thus assumes that each variable is normally distributed. Spearman measures monotonic relationships. It is rank-based, and therefore does not assume normality or linearity and can do a better job at representing non-linear relationships than Pearson correlation. Kendall correlation is the more general than Pearson and Spearman correlation in that it does not assume normality, linearity, or monotonicity. It tends to perform better than Spearman if the sample size is small.

What is partial correlation?

Partial correlation simply means computing the correlation between two variables while controlling for, or holding equal, all other variables. Partial correlation can be better at detecting associations that represent true dependency, rather than spurious connections that arise from the multicollinearity typically present in 'omics datasets. In our experience, partial correlation tends to create a sparse network, with many fewer connections between nodes. This can be helpful in combating the "hairball effect" because it retains more nodes without an overwhelming number of edges. See the image below for an example: (more details ....)

Why are there different correlation filters for inter and intra-omics?

The correlations between 'omics features of the same type is usually categorically higher than correlations between 'omics features of different types, likely due to systematic differences in platforms and methods used to collect the data. Because of this, using a single threshold to prioritize which edges to display will result in the vast majority of edges being between features of the same 'omics type. OmicsAnalyst has two thresholds to allow users to relax the threshold for inter-omics connections to create more balanced networks.

When should I use the degree or betweenness filters?

The degree and betweenness filters allow you to reduce the size of the network based on its connectivity alone (see later FAQ for explanations of "degree" and "betweenness"). The key takeaway is that the degree filter tends to retain hub genes (genes with many connections to other genes), and the betweenness filter tends to retain genes that connect dense clusters of genes.

What do the colours and sizes of the nodes and edges signify?

The node size is always based on the degree, with nodes of a higher degree being larger. The colour scheme can be either 'omics type (different types given different colours), expression (colour based on -log10(p-value)) or plain (all nodes and edges grey).

By default, the edges are coloured based on the correlation sign, with positive correlations in red and negative correlations in green. The thickness is determined by the absolute value of the correlation, with thicker edges indicating stronger relationships.

How do I identify which nodes are important based on their position within the network?

A basic assumption is that changes in nodes that occupy key positions within a network will have a greater impact on the overall network structure than changes in relatively isolated positions. In graph theory, measures of centrality are used to identify the most important nodes. OmicsAnalyst provides two well-established node centrality measures - degree and betweenness. The degree of a node is the number of connections it has to other nodes. Nodes with a high degree act as hubs within the network. The betweenness of a node is the number of paths that pass through it when considering the pairwise shortest paths between all nodes in the network. A node that occurs between two dense clusters will have a high betweenness, even if it has a low degree. Note, you can sort the node table based on either degree or betweenness values by double clicking the corresponding column header.

What is a network module and how are they identified in OmicsAnalyst?

Modules are tightly clustered subnetworks with more internal connections than expected randomly in the whole network. They are considered as to be relatively independent components in a graph. Members within a module are likely to work collectively to perform a biological function. The biological functions of a module can be explored using enrichment analysis. The image below gives an example of detected modules highlighted within a network:

OmicsAnalyst currently offers three different approaches for module detection - the WalkTrap, InfoMap, and Label Propagation algorithms. The general idea behind the Walktrap Algorithm is that if you perform random walks on a graph, a higher number of walks are more likely to stay within a group of nodes that are highly connected to each other because there are only a few edges that lead outside of them. The Walktrap algorithm runs many short random walks and uses the results to detect small modules, and then merge separate smaller modules in a bottom-up manner. The InfoMap Algorithm is also based on random walks, which it uses to minimize the hierarchical map equation for different partitions of the network into modules. The Label Propagation Algorithm works by randomly assigning a unique label to every node. On each iteration, node labels are updated to match the one that the maximum of its neighbours has. The algorithm converges when each node has the same label as the majority of its neighbours.

NetworkAnalyst also integrates the gene expression values as edge weights during module searches. Weights are calculated as the square of the mean absolute log fold changes of the two adjacent nodes. Larger weights mean closer connections during random walks.

How do I interpret the p-value of a module?

The p-value of a module is based solely on network connectivity, and gives some indication of how significant the connections within a defined module are. Let's call the edges within a module "internal" and the edges connecting the nodes of a module with the rest of the graph "external". The null hypothesis of the test is that there is no difference between the number of "internal" and "external" connections to a given node in the module. The p-value of a given module is calculated using a Wilcoxon rank-sum test of the "internal" and "external" degrees. Users should also consider whether the modules are 'active' under the experimental conditions, by taking into account the number of seed proteins, their average fold changes, as well as the enriched functions displayed in the Module Explorer table.

What is the objective of cluster heatmap analysis?

The objective of this analysis is to understand relationships among samples, features and clusters across multiple 'omics datasets. First, cluster analysis is performed on the samples using methods that integrate information from all 'omics datasets. Next, interactive heatmaps (one for each dataset) are placed side-by-side to allow visual identification and subsequent enrichment analysis of features that correspond to either the detected clusters or the experimental groups. In addition, standard hierarchical clustering methods can be applied to both the features and samples, making this the most flexible of the three visual analytics tools in OmicsAnalyst.

When should I use the Free Exploration option?

If you are not familiar with heatmap, this should be your first choice. This interactive heatmap visual analytics allows you to explore an omics data of interest within the context of the 2nd omics data or juxtopose two omics datasets simultaneously. It is based on the traditional hierarchical clustering (Ward, average, single-linkage, complete linkage) for sample clustering and feature clustering for individual omics data. Users can perform clustering for currently selected data. However, since they are from the same samples (sharing sample names), the sample clustering will be synchronized between the two omics datasets.

What is multi-view clustering?

Multi-view clustering refers to clustering samples that have multiple data representations. This is the case for multi-omics data measured on the same samples, where each data type is a separate representation of the same samples. All multi-view methods supported by OmicsAnalyst have the same general approach, which involves: 1) computing a sample similarity matrix from each ‘omics type individually; 2) integrating the individual matrices together; 3) determining the optimal number of sample clusters in the integrated matrix; and then 4) detecting this number of clusters. One main advantage of multi-view clustering is that it tends to reduce edges that are due to random noise or platform-specific technical artifacts, as it is highly unlikely that exact same erroneous effects are present across multiple data sets.

What are the main differences between the multi-view clustering algorithms?

Spectrum is self-tuning spectral clustering method for multi-omics data. It combines the strengths of several other methods: an adaptive density-aware kernel is used to strengthen connections in the graph based on common nearest neighbours, a tensor product graph with diffision method is used to reduce noise while integrating different data sources, a generalized eigengap method is used to automatically determine the optimal number of sample clusters, and a spectral clustering method is used define sample clusters due to its speed on large graphs. OmicsAnalyst uses the multi-modality mode in the Spectrum R package, which finds the optimal number of clusters for both Gaussian and non-Gaussian distributed data. (more details ....)

Similarity Network Fusion (SNF) generates an integrated sample similarity matrix from multiple 'omics datasets by first computing similarity matrices for each dataset individually, and then fusing them together. Individual similarity matrices are computed using an exponential similarity kernel that scales the Euclidean distance between samples. These matrices are then fused together by an iterative approach that adjusts each matrix to make it more similar to the others. The SNF algorithm is iterated until the matrices converge. OmicsAnalyst then uses the clustering method from the Spectrum R package to define sample clusters in the SNF matrix (select the Spectrum option for more details). (more details ....)

Perturbation clustering methods implemented in the PINSPlus R package are founded on the idea that even truly homogeneous populations will have small differences in 'omics features due to measurement error and natural variability, and that clusters corresponding to true sub-population should be robust this variation. Their approach is to repeatedly add small amounts of noise to individual 'omics data, and then cluster the samples. The number of clusters that gives the most stable sample similarity matrix is retained, and the corresponding similarity matrices from each 'omics are integrated to find connections that are stable across multiple datasets. Finally, k-means clustering on the integrated matrix is used to group samples into the optimal number of clusters found in the previous step. This method is not suggested for datasets with large number of samples (>50). (more details ....)

How should I choose the number of clusters?

All of the clustering methods in OmicsAnalyst automatically detect a reasonable number of clusters, however this can be updated on the parameter tuning page for SNF and Spectrum. Several diagnostic plots are provided to help users decide whether they want to keep, increase, or decrease the number of clusters to detect for these options. For example, the image below shows the results from using SNF on the pregnancy example data.

The algorithm determined that two clusters optimize some measure of variance on the integrated proteomics and metabolomics sample-sample similarity matrix. PCA plots of the individual 'omics data sets show sample-cluster membership.

How do I visualize expression patterns of specific features in the heatmap viewer?

On the rightmost side of top menu bar, click on "Advanced Options". A dialog containing "Batch Selection" will be displayed. Enter the list of features (can be from both datasets) you wish to be visualized and click on "Submit". The focus view will be updated if there are matched features.

What is the objective of dimension reduction analysis?

The objective of this analysis is to perform dimension reduction, and then visually explore corresponding scores, loadings and biplots in interactive 3D scatter plots to understand the common trends and underlying patterns. The multivariate dimension reduction techniques are kind of like parallel versions of PCA, where we try to find sets of multi-dimensional components that both reduce redundant information within individual datasets, and are related to each other across datasets. These sets of components are related to each other through some global scores, which are the dimensions that we use to visualize the sample space. The different methods are mainly distinguished by the way that they optimize similarity of component sets across the 'omics datasets.

Which dimension reduction methods are implemented in OmicsAnalyst? What are their main differences?

OmicsAnalyst currently supports five commonly used methods including multiple co-inertia analysis (MCIA), consensus PCA (CPCA), projection to latent structures (PLS), Procrustes Analysis, and Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO). The key distinguishing features between the five dimension reduction algorithms are summarized in the table below:

Note: Symmetry refers to whether the order that the data sets are analyzed/uploaded will give the same (symmetric) or different (asymmetric) results; Orthogonality constraints refers to which components must be orthogonal to each other - individual only means components must only be orthogonal within the set computed for a single 'omics data set whereas individual and global means components must also be orthogonal within the shared co-variance space; Supervision refers to whether the sample takes the sample labels into consideration when computing the components.

What is the difference between dimension reduction analysis in the 3D scatter plot vs. network tracks?

The same methods are available for both visual analytics tracks, however for the network OmicsAnalyst uses the dimension reduction loading scores to select the top features, prior to constructing and visualizing the selected features in a network, while the dimension reduction results are directly visualized in the 3D scatter plot. The two tools offer complementary information on the data. We suggest first performing dimension reduction with the 3D scatter plot to see how well the reduced dimensions separate interesting groups of samples. Then, these reduced dimensions can be examined in more detail using the network to see which features they are composed of, and how these features relate to each other.

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.

What is a biplot, and how should I interpret data points in a biplot?

A biplot overlays a score plot and a loadings plot in a single graph. In the biplot view, points are the projected observations (samples); vectors (arrows) are the projected variables. If the data are well-approximated by the first three components, a biplot enables you to visualize high-dimensional data by using a 3D space. You can visualize both the patterns (sample distributions) and the features that driving such patterns.

• Points that are close to each other in the biplot represent observations with similar values.
• The cosine of the angle between a vector and an axis indicates the importance of the contribution of the corresponding variable to the principal component.
• The cosine of the angle between pairs of vectors indicates correlation between the corresponding variables. Highly correlated variables point in similar directions; uncorrelated variables are nearly perpendicular to each other.

How do I visualize the loading contribution of a custom set of features?

In the "Biplot in Score Space" dialog, select "Custom" under "Feature Selection" drop down menu. In the text area that appears, enter the list of features to be visualized as projected vectors on the sample space.

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, and can help to show separation between sample groups:

How do I display contour or confidence ellipsoid around selected meta-data groups?

There are two options, either to display encasing for individual group of nodes or overall encasing:

1. Individual meta-data group: under "Overall Pattern" data table, click on "Edit" button corresponding the row corresponding to the meta-data group of interest. In "Highlight Settings" dialog, "Boundary" drop down menu, select either "Ellipse" for confidence ellipsoids or "Contour" and click on update.
2. Overall meta-data: Under "Main View" menu, select "Confidence encasing (Score) option. In the newly opened "Encasing Options" dialog, click on "Submit" button to visualize encasing around each meta-data group.

How do I perform differential analysis test on selected meta-data groups or clusters in scatter viewer?

Under "Overall pattern" panel, select meta-data or clustering algorithm. Select meta-data or cluster groups of interest.

1. Pairwise comparison by selecting a pair of meta-data groups or clusters
2. Topology analysis to reveal network properties such as node betweenness, degrees, community detection and identifying shortest paths.

What is the purpose of the grey sphere that is in the middle of the loading plot?

The grey sphere serves a similar purpose to correlation circle in 2D visualization. It helps users to quickly identify features above a certain threshold (0.5 by default) and reduce visual occlusion. The threshold can be changed in "Advanced Options" dialog, "Sphere" tab.

How do I hide text display in the scatter plot viewer?

Click on "Advanced Options" and click on "Text Display" tab. Select "Hide" under display option and click on submit.