One of the cornerstones for quick and efficient data analysis is a flexible and easy to use statistical framework that can be combined with visualizations and plots to give valuable insights. Qlucore Omics Explorer is fast and include a broad scope of analysis options.

Finding interesting variables in your data


A wide range of statistical methods and metrics

  • two group comparison (t-test, Welch or Mann-Whitney) 
  • paired t-test  
  • multi group comparison (ANOVA or Kruskal-Wallis) 
  • two-way ANOVA 
  • linear regression 
  • quadratic regression 
  • rank regression
  • fold change
  • difference(log Fold change)
  • Tukey
  • Kaplan-Meier 

Batch correction is supported by the eliminated factors functionality.

Expand to additional methods with the Open API to R.

Biomarker discovery analysis

The Biomarker Workbench is optimized for experiments and studies in the areas of drug development and biomarker discovery. Easily set up a suite of different statistical tests to run in batch mode with the objective of selecting effective compounds or other relevant signals. Add post hoc analysis including Tukey and Fold change.

Inspect the results in tables and directly use the structured outcome for the following analysis steps. The Response variable option is tailored for biomarker discovery and assists in finding correlation between sample annotations – an excellent way to focus on key annotations when working with large amounts of clinical data.

Survival analysis

Survival analysis is supported with integrated statistical methods such as Hazard ratio calculations as well as visualizations in Kaplan-Meier plots.

Genome analysis

With the NGS module and the NGS filters more options are available. Select which regions of the genome to analyze in the Genome browser, dynamically select if the regions should be restricted by read coverage, specific regions or if variants should be present or not.

Cluster analysis

Clustering is supported in several ways; either in a semi-supervised mode using PCA or t-SNE or UMAP with Projection score and variance filtering or in an unsupervised mode using kmeans++. Hierarchical clustering is combined with the heatmap plot.

Classification and machine learning

The Build classifier and Classify machine learning functionality enables both the option to easily build classifiers based on models such as Boosted trees, Support Vector Machines (SVM), Random Trees (RT) and kNN and to classify new samples.

Pathway analysis

To compare and enhance your findings use the integrated GSEA Workbench which is set-up using only a few mouse-clicks. It does not get easier. Easy conversion of gene ids from different species to human orthologs is supported.

Keep track of the calculations

The Status panel will continuously show exactly what calculations that have been applied to your data. The lof functionality further enable the user to store a logpoint with the current program state including all plots and all settings. The logpoint can later be restored.

Scripted workflows

Templates is the functionality used for scripting workflows in the program. You can use built-in Templates as a quick start to standard analyses or write your own templates to simplify repetitive tasks. Templates is also the tool for integration of the program into tool chains.

Read about all features in Qlucore Omics Explorer