Book a webinar/training

In this webinar we will demonstrate the Biomarker Workbench, a very efficient way to examine the vast amount of annotations in combination data for Biomarker Discovery or Exploratory Data Analysis. We will show how to easily set-up of a suite of different statistical tests to run on all annotations in batch mode with the objective to identify effective drug candidates, treatments or other relevant signals in your dataset. We will also look at how you can use Response Variables to find correlations between sample annotations.

Multi-omics data analysis approaches are becoming increasingly popular, providing a better understanding of the system under study. In this webinar, we will combine transcriptomics and proteomics data and demonstrate how you can quickly identify correlations between genes and proteins. The results will be visualized in several different plots.

Would you like to start using publicly available omics datasets and biological networks in your research? Isn’t it a smart way to boost your hypothesis generation and testing when you can access relevant datasets / biological networks and start with the analysis? Come join us for a live demo on accessing and handling TCGA datasets, finding and utilizing annotated gene sets in MSigDB for pathway analysis and visualizing biological networks and signaling pathways in NDEx integrated with Cytoscape. Everything will be done live in TCGA, MSigDB, Qlucore Omics Explorer and NDEx.

During the session we will show Qlucore Insights through a step-by-step live demonstration of the complete process, from data import to a ready report. Both gene expression based subtyping as well as gene fusion analysis will be demonstrated.

In this webinar we will demonstrate the Biomarker Workbench, a very efficient way to examine the vast amount of annotations in combination data for Biomarker Discovery or Exploratory Data Analysis. We will show how to easily set-up of a suite of different statistical tests to run on all annotations in batch mode with the objective to identify effective drug candidates, treatments or other relevant signals in your dataset. We will also look at how you can use Response Variables to find correlations between sample annotations.

Free online hands-on training. Learn how to easily analyze your experiment data yourself. This is an introduction to how you canbefore and analyze experiment data using a highly visual and interactive tool in several hands-on exercises. You do not need any previous experience with the program. Course material and a training license will be sent out prior to the course.

How to use Gene Set Enrichment Analysis (GSEA) to analyze datasets using functional information such as pathways in Qlucore Omics Explorer. The GSEA workbench is fully integrated into Qlucore Omics Explorer and easy to use and with fast ranking of provided pathways (lists of genes), making it ideal for biologists.

Qlucore Insights imports standard RNA-seq data and enables subtype classification of BCP-ALL (leukemia) samples. With the latest classifier model for BCP-ALL up to 7% more samples are assigned to the correct class. In addition is unbiased gene fusion analysis. Data is presented in an easy to read report supported by tailored visualizations. In this webinar, we will perform a live demo of the software to show how to get from data to report.

The webinar will cover the reasons why Qlucore uses transcriptomic data for classification, a description of our new lung cancer classifier for Qlucore Insights, and a live demonstration of Qlucore Insights. In this webinar, we will perform a live demo of the software to show how to get from data to report.