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Many users get RNA-seq data in the form of a raw count matrix, either directly in an excel/csv/ txt file, or through aligned BAM file import. You can import the data into Qlucore Omics Explorer, and then select the normalization method you want to apply, and the tests you want to utilize. In this webinar we will both have a look at VST normalization and the use of DESeq2 statistical tests (Wald and LRT), as well as TMM/TPM/FPKM normalization and standard statistics (T-test/ANOVA etc).

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

Free online hands-on training. Learn how to easily analyze your experiment data yourself. This is an introduction to how you can explore and analyze experimental data using a highly visual and interactive tool in several hands-on exercises. You will do exercises on visualization of data in PCA plots and Heatmaps, run a statistical test to find discriminating variables, and learn how to explore a dataset to find interesting clusters. You do not need any previous experience with the program. No programming is needed. Course material and a training license will be sent out prior to the course.

Extend your knowledge of powerful statistical methods and integrated tools such as the GSEA workbench (for pathway analysis) and the Biomarker Workbench to achieve faster and more efficient data analysis. In the training we will do a multi-omics (exercise transcriptomics and proteomics) exercise using correlation analysis. We will also have a look at how you can import and visualize single cell data from 10X Genomics CellRanger data files. This training is for those who have previous experience with Qlucore Omics Explorer. You may have attended our basic training or have similar experience.

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.

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.