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Using a 10x Genomics single-cell cancer dataset, we will demonstrate the core capabilities of BBrowserX Pro. The session will cover clustering, cell type prediction, marker gene identification, and differential expression analysis. We will also explore advanced features for investigating cell-to-cell communication and cancer cell prediction, showing how these tools can support deeper biological insights from single-cell datasets.

In this webinar, we will show how Log Points can be used in Qlucore Omics Explorer to save important stages during an analysis. Log Points help you keep track of different analysis steps, return to previous analysis states, and document how your analysis develops. We will demonstrate how to use Log Points to document analysis workflows, compare different approaches, and keep track of important findings.

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.

Using a 10x Genomics spatial omics cancer dataset, we will explore the functionality of SpatialX, focusing on the integration of H&E images and single-cell expression data. The session will cover cell type prediction, identification of regions of interest such as tumors, analysis of data in the vicinity of these regions, and cell-to-cell communication.

Multi-omics studies often involve integrating RNA-seq, metabolomics, and lipidomics data to uncover relationships across different biological layers. In this webinar, we will demonstrate how Qlucore Omics Explorer supports an interactive workflow for exploring and analyzing multi-omics datasets through visualization and statistical analysis, without the need for coding. Using practical examples, we will show how to investigate correlations between genes, metabolites, and lipids, identify meaningful patterns through interactive filtering and statistical testing, and integrate different omics data types to explore relationships across datasets. The focus is on moving efficiently from data exploration to biological insight in an intuitive visual environment. This webinar is suitable for scientists and researchers working with RNA-seq, metabolomics, lipidomics, and other multi-omics data who are interested in interactive and accessible data analysis approaches.