Optimize your research with new features in latest software version

With Qlucore Omics Explorer 2.3 we have made analysis more visual so that it is easier for you to focus on the important aspects of your research data. In this newsletter we talk about different techniques for visualization and some of the new features in the new 2.3 version.

The two main plot types in Qlucore Omics Explorer for data analysis are PCA (Principal Component Analysis plots) and heatmap. These two types are augmented with line plots, scatter plots, box plots and histograms. You can now work simultaneously with many plot types; the graphic below shows an example where four different plot types are used to visualize how the software displays data.

The upgraded heatmap facility provides a wide range of new benefits. Visualization of data sets of more than 1000 variables is now possible. There are now even more ways to order the heatmap which enable new aspects of analysis and presentation. For example you can now order the variables according to a variable annotation, i.e. a list of variables, which could be the list of variables generated in a previous experiment. It's also possible to order the variables according to the fold change, making it easy to highlight the variables with highest effect. Another important aspect of the new heatmap functionality is the possibility to visualize several sample annotations in more detail. It is an excellent tool for highlighting common patterns between clinical variables and experiment data.

Reporting and sharing both intermediate and final results are crucial to an efficient research process. With Qlucore Omics Explorer 2.3 all plots are constantly updated and analysis is shown visually to the user. Reporting in the new version is made easier by flexible scatter plots allowing a number of various configurations, for example the new line plot facility makes it easy to highlight development over time, and the new box plot can instantly show how a variable influences a selection of data groups.