Identifying subgroups and structures

To identify subgroups or strucutres in experimental data can be very challenging. Qlucore Omics Explorer is tailored to assist with this task in multiple ways. You can choose from unsupervised methods (kmeans++ with silhouette plots, hiearchical clustering) as well as supervised methods such as filter aided Principal Component Analysis (PCA) and ISOMAP.

These methods are well known and established and can easily be applied to a data set. The problem is that they seldom solve the problem by themselves and different supporting actions (such as noise reduction and limitations) are required.

In Qlucore Omics Explorer we support this by applying all methods in a dynamic and interconnected approach. As an example, regardless of selected method you can start the analysis by filtering on variance to remove noise and you can limit the variables from a biological point of view by using different input lists. In most cases multiple approaches are required to find the most promising subgroups, and in Qlucore all plots are always updated instantly and you can test many configurations quickly.

To learn more about how you can identify subgroups in your data many resources are available, for example in the recorded webinar Finding clusters and structures in data.

Congratulations to the winners of our 10-year anniversary lottery:

  • Päivi Saavalainen, PhD, University researcher, University of Helsinki, Finland
  • Christoph Cichon PhD, Senior scientist, Department of Infectiology, Centre for Molecular Biology of Inflammation(ZMBE), Germany
  • Rathi Ryan, Bioinformatics Research Scientist, UNITY Biotechnology, USA

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