After successfully setting up and running a viSNE analysis, the results will need to be interpreted and analyzed. This article outlines methods and considerations for analyzing viSNE results. Click the links below to jump to the relevant section:
- Understand and Review viSNE Results
- Color the viSNE Map by Channel
- Assess viSNE Map Quality
- Color the viSNE Map by Additional Overlaid Variables: Manual Gates, Disease Groups, etc.
- Partition and Label the viSNE Map into Cellular Populations
A key workflow for understanding and analyzing a viSNE map is to color it by channel. Use the dot plots colored by channel functionality to color each event in the viSNE map according to its intensity on a channel within the dataset. The patterns that emerge show why dots in the map are nearby each other, or which markers make them similar to each other. In cell phenotyping, you'll see events close together on the viSNE map based on their phenotypes. In sample heterogeneity analyses, you'll see samples close together on the viSNE map based on their similarity across multiple biomarkers. Use the dot plots colored by channel functionality in the Working Illustration to quickly and easily make a figure such as the one below, where viSNE maps across different samples and channels can be visualized simultaneously to understand the dataset:
(click to expand - dot plots colored by channel within the Working Illustration. Relevant settings are highlighted in red and green. Green settings indicate the key components of making a figure that rotates through a variety of channels. The Panel/Channel Values option as the coloring channel defers control of this parameter to the Channels Figure Dimension Box where multiple selections can be made.)
Before attempting to proceed with an analysis of a viSNE map, it is necessary to understand if the settings used in the run resulted in a viSNE map with sufficient quality to warrant analysis. Regardless of the quality of the data that were passed through viSNE, it's possible that the settings used for the analysis did not allow for a good result. The most typical reason for this is a lack of iterations when working with viSNE runs with larger numbers of events (as a rough example, in abundance of 400,000 events). If coloring the viSNE map by channel results in overlapping and poorly formed islands of events that don't separate the expression of a single marker into distinct locations on the map, another run should be attempted with more iterations to improve the clarity of the results. In addition, changing the perplexity may improve the separation of events in the viSNE map.
(viSNE maps colored by channel comparing a poorly converged viSNE map to a nicely converged viSNE map. Each row is a single sample colored by four different markers as indicated. The two samples are not related and are from different viSNE analyses. They are only shown together for purposes of comparison and reference for the idea of poor versus good convergence.)
Colored overlay dot plots are really helpful in interpreting viSNE results in two different contexts. First, they are useful for comparing the quality and efficiency of traditional gating schemes with viSNE results. Second, they are useful for exploring the relationship between cell populations or biomarkers displayed on the viSNE map and other meta-data variables like disease status, treatment status, or response group. Either of these goals can be achieved using colored overlay dot plots.
Color Overlay of Manually Gated Populations
The workflow comparing viSNE results to manual gates has been demonstrated many times in the literature and can quickly show four important results:
- Populations that were not captured by traditional gates and thus were uncategorized
- Manual gates that are capturing populations that they should not be capturing
- Manual gates that are not capturing all the cells that they should be capturing
- Populations that appear to correlate nicely between the viSNE map and traditional gates
The workflow is summarized in the visual below where the cells falling into manual gates are colored on the viSNE map:
(viSNE map with cells colored by which manual gate they belong to)
The next image shows an example highlighting the four situations outlined above. The image is a dataset gated manually by a researcher and then subsequently run through viSNE and all manually gated populations expressed as colors on the viSNE map. Anything colored as dark blue did not fall into a manual gate. Any other color corresponds to a different manual gate:
(Situation 1 is seen indicated by a discrete viSNE population with no gate color. Situation 2 (top) is seen by a discrete viSNE population that has the same color as a different discrete viSNE population, showing that a sub-population exists that was not captured by manual gating. Situation 2 (bottom) is also demonstrated by a population that is mostly uncategorized except for small numbers of events captured by various manually gated populations. Situation 3 is shown in the bottom left with a population that seems contiguous in the viSNE map but is only partially captured by manual gates, leading to the spotty coloring. Situation 4 is shown as the small population in the top right that appears categorized nicely both in the viSNE map and by manual gates, since there is very little dark blue (ungated) and no other colors simultaneously present)
Color Overlay of Other Variables
Colored overlay dots can also be used to visualize differences between groups of samples:
(dots colored by their disease tissue type show that the ~800 RNA transcripts used to separate these samples on the viSNE map group them into distinct islands that correlate with their disease type)
Note that the examples shown above describe overlay of discrete or categorical variables. Overlay of continuous variables that were not used for clustering is also helpful for exploring relationships in your data quickly, and can be achieved with the color by channel workflow described above.
viSNE takes data and arranges it into an organized map according to the expression profile of each observation within the dataset. After this point it is up to the researcher to decide whether and how to partition and label the map for biological context. A typical approach to this workflow is to manually gate the viSNE map. A simple strategy for manually gating the viSNE map is to use dot plots colored by channel (see above) and the natural separations of the viSNE map to draw gates. Zooming the gating interface will also help with this process. For regions that are difficult to gate because of a lack of clear separation between more continuous phenotypes, other plot types such as uncolored contour plots and contour plots colored by density can be used to help reveal density trends in the viSNE map that can guide gate placement. Black dot plots can be used to increase contrast for small populations that might go overlooked in other contexts:
(different plot types such as contour plots colored by density, black dot plots, and uncolored contour plots can help with gating. Density visualizations assist with dissection of phenotypic subsets that are biologically unique but in a more continuous distribution. Arrow indicates one large subset with more continuous smaller nested subsets, as is often seen in T and B cell biology, for example)
(example of a viSNE map that has been gated)
While it is a typical workflow, manually gating the viSNE map is a somewhat labor-intensive and non-scalable process. It can be challenging (just as in traditional sequential gating) to draw gates in places where there are subtle differences between adjacent populations. Furthermore, gates drawn on one viSNE map aren't portable to future analyses because of the stochastic nature of viSNE. Researchers interested in accelerating viSNE-based workflows should understand options for automated methods for categorizing viSNE maps.
Concept Overview - Clustering a viSNE Map
A number of methods have been described in the literature whereby manual gating of the viSNE map is replaced with clustering to categorize populations or groups. Regardless of the exact implementation, the general logic of this approach builds on similar principles that a human might use to categorize the viSNE map with manual gates. In particular, the geography of the tSNE map and patterns of density are used to assign similarly distributed observations to groups (i.e., clusters). An important distinction of this clustering workflow is that it clusters only on the tSNE parameters that provide the coordinates for the viSNE map. In contrast, typical clustering workflows cluster based on a larger number of channels in the dataset. One example methodology for clustering the viSNE map can be visualized as follows:
(a methodological example of one way to cluster a viSNE map. Left: a viSNE map is displayed as a contour plot colored by density to show patterns of event distribution. Center: peaks are defined using a density estimation algorithm to decide on population centers. Right: an assignment algorithm operates on the data to assign cells to clusters based on proximity to established peaks)
Methods Available for Clustering a viSNE Map
Various methods for the automatic categorization of viSNE maps have been described and used in the literature. Some examples include DensVM, ClusterX, and ACCENSE. DensVM, ClusterX, and ACCENSE are not available in Cytobank. SPADE, however, can easily be used to cluster a viSNE map within Cytobank in order to automatically categorize viSNE populations. To learn more, read the article: how to run SPADE on viSNE.
After identifying populations or groups from a viSNE map, it's often useful to summarize the expression of all channels for each of these populations. This gives you a quick summary of the variety of phenotypes or groups present on a viSNE map in a condensed, easy-to-interpret space:
(methodological example of representing populations identified in a viSNE map as a heatmap with their component expression on each channel within the dataset)
Remember that proper scaling of channel values is an important concept when working with heatmaps. Read about how to configure a heatmap of populations versus many channels and also how to scale it properly.
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