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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.

The webinar will cover a live walk through of Qlucore Insights and its model for classification of Bladder tumors. Before the demonstration we will give a short introduction to Qlucore Insights and the theoretical underpinnings of the classification. You will get to know how to use the tool and how it can support risk assessment of tumors through expression profiling and gene fusion detection. We hope to see you there.

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).

This webinar is an introduction to Qlucore Diagnostics BCP-ALL. This is a CE-marked IVDR software offering RNA-seq based cancer diagnostics. It includes both gene expression subtype classification of B-cell leukemias and gene fusion analysis support.

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.

Gene set enrichment analysis (GSEA) is an excellent method to quickly identify sets of genes such as pathways and other lists of variables that are enriched in a data set. The program provides a flexible framework for using the built in GSEA Workbench and it allows the user to easily select publicly available sets of variables or manually curated pathways, for instance from the Molecular Signature Database. This webinar gives you a short introduction on how to find relevant pathways for your genes and other variables of interest.