New VisuMap Release: More fun with scripting

We have just released VisuMap version 3.5.882. Worth mentioning in this release are two enhancements to the scripting interface. The first enhancement is the auto-complete editing. We have added script analyzer to the editor so that it will automatically suggest class members with documentation when it notices that user is about to add code to access class methods or properties. The following shows a screen short of the auto-complete editing:

The second enhancement to the scripting interface is the so-called property spinor: within an editor window the user can now double click a property value to link the property to the mouse wheel. When the mouse-wheel is linked to a particular value in a script and when the user spins the mouse wheel, the property spinor will automatically change the linked value and execute the whole script. With this feature VisuMap provides a very generic control mechanism to probe different settings, so that user can see immediately the consequence of changed settings.

The following is short video to demonstrate the usage of property spinor:

Ensemble Clustering with VisuMap

A common question by data clustering is: How stable is the clustering results? It often happens that some data points are classified by an algorithm to a common cluster, but when you run the algorithm again on the same data with slightly different initialization conditions these data points might be classified into different clusters. This phenom seems to be a consequence of the nondeterministic behavior of the algorithm, but it is rather a manifestation of the clustering structure among the data. From certain point of view, these unstable data points tells us actually more interesting information about data than those stable data points.


One way to answer the stability question is using the boosting method (also called ensemble clustering) where we aggregate the results of multiple runs of a clustering algorithm. One of the simplest aggregation method is as following: we classify two points to a common cluster (i.e. assign the same color to two points in a map) if all runs classified the two points to a common cluster. 


To visualize the ensemble clustering let us consider a sample as by the following picture. Th picture shows how the k-Mean algorithm clustered a data set in three different runs (i.e. with different random initialization.) When we look closely at these three maps, we can notice, as marked in the circles, that the boundary between two clusters varies among the three maps. This means that data points in this region are unstable. But each individual one of the three maps does not tell us this information; and even with all three maps displayed together, it is rather hard to find these unstable points by visual comparisons.

Now, when we aggregate the three maps with the simple aggregation method mentioned above, we get a  map as show in the following picture. We can recognize easily that, as circled in 3 regions, that there are 3 unstable regions: data points in these regions have different colors than those major clusters. We also notice here easily that there are some stable boundaries between some clusters.

Although this kind of ensemble clustering is fairly simple to implement with the scripting interface of VisuMap, it lacked a simple friendly user interface for this service. The new release of VisuMap,  the version 3.5.881, resolved this problem with a new utility, called cluster manager, that offers the services to capture, store and explore clustering results. And especially, the cluster manager makes it very straightforward to do ensemble clustering. As illustrated in the following screenshot, the user just need to select the clustering results, called named coloring, then choose an appropriate context menu to compose the aggregated clustering:

VisuMap for Flow Cytometry

I have just uploaded a 10 minutes video that demonstrates the basic steps to use VisuMap to explore flow cytometry data:

Notice that VisuMap version 3.5.881 is needed to perform demonstrated tasks. The sample datasets, i.e. FCS files, used in this video are downloaded from the web site FowRepository.

New VisuMap release with multi-band spectrum view

I have just released VisuMap 3.5.879. Worth mentioning in this release is the spectrum band view to visualize large tables. The spectrum band view displays a table as multiple bands of spectrums with one spectrum for each data row or data column.  The spectrum band view is relatively simple to implement and to understand, it is amazingly effective to capture the value distribution of variables and to compare large number of variables. The following picture shows a scenario how VisuMap goes about exploring a large data table with the spectrum band view:

In this example, the data set  is a 4000x1700 number table. Each of the 4000 rows represents a compound with about 1700 physical and chemical features. On the top left side the data set is displayed in a PCA map with one dot for each compound (5 clusters are discernible.)  On the top right side, the data set is displayed as a heat map with one line for each data row. We have selected about 60 features from the heat map and shown them in a spectrum band view at the lower right side. The spectrum band shows each feature as vertical spectrum. We see that most features are more or less Gaussian distributed; some features are discrete as their spectrum comprise only isolated bars.

The spectrum band also helps to answer a frequently asked question in data analysis, namely: if the value of  one feature is in certain range, how will the values of other features be distributed? We can do this very simply with the spectrum map as illustrated in the following picture:

In above picture we have displayed 8 features in a spectrum band with 8 horizontal spectrums. We have selected with the mouse point a small section in the first feature, the spectrum band view immediately shows the density curve (i.e. the distribution) of the other features of selected objects.

Furthermore, with the spectrum band view we can record a series of selections, then repeatedly replay the selection in animation as shown in the following video clip: