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Free online hands-on training with hands-on exercises. Learn how to easily analyze your experiment data yourself. You will do exercises on data import, 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 using UMAP and PCA plots. No previous experience and no programming needed.

We look at data import, normalization, and the visualization and statistical tests. We will present VST normalization and the use of DESeq2 statistical tests (Wald and LRT), as well as visualization in Volcano, PCA and UMAP plots.

Free online hands-on training with hands-on exercises. Learn how to easily analyze your experiment data yourself. You will do exercises on data import, 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 using UMAP and PCA plots. No previous experience and no programming needed.

In the webinar we will introduce the Qlucore Biomarker Workbench, a very efficient way to examine your dataset using your sample annotations, to better understand where you have potentially interesting information. We will also look at how you can use the Biomarker Workbench to find correlations between sample annotations and between datasets.

In this webinar, we will guide you through the steps required to import and analyze Olink® data, from smaller to large protein panels. We will look at the Qlucore Import Wizard when data is organized as a matrix or as a vector (such as NPX Map).

Discover how BioTuring makes single-cell and spatial omics analysis more interactive and accessible. In this webinar, we will introduce the BioTuring platform, demonstrate how to explore single-cell datasets, use curated public data for cell-type annotation, and present spatial analysis.

You can import Proteomics data, as an example from LC-MS, using the Qlucore Import Wizard. The tool has support to handle empty cells, prefiltering, transformation (offset data or apply a threshold), log transformation, and to perform Quantile Normalization. You can also select to remove control samples and variables, and to collapse data on both the sample side and the variable side to remove replicates. You can then use the inbuilt statistical tests and plots available in Qlucore Omics Explorer.