Qlucore tools allow researchers to perform advanced visualization, exploration and statistical analysis of omics data with the help of an intuitive GUI. Targets of interest can be further explored in terms of biological insight using GO and GSEA. Unmatched speed, immediate visual feedback, continuous visualization, and synchronized views significantly shorten both data-to-result and query-to-discovery times.
By combining right annotations with statistical methods, data selection tools, and the eliminated factors function, a very broad range of different experiment designs can be analyzed with exceptional productivity. This solution draws upon both innovative and classical approaches, fueled by best-in-class industrial and academic research.
Qlucore Omics Explorer helps you advance your research by:
- boosting the speed of your analysis at least by 50%;
- generating new ideas, hypotheses, and giving you a new prospective on your data, and questions you ask of it;
- helping recognize significant insight that is specific to biological process, disease, or function, as well as assumption-free exploration;
- keeping your projects on track with simple QC checks on every step;
- providing publication ready graphics, and intermediate results for collaboration.
Qlucore Omics Explorer is used by big commercial companies as well as major research organizations and Universities across Europe and US. (e.g., Boehringer Ingelheim, Roche Diagnostics, AstraZeneca, DFCI, BWH, Harvard, MD Anderson, MSKCC, MedImmune, Novo Nordisk, etc.).
In this session:
- Intro into Qlucore 3.3 – new major release
- Tips and tricks
- Configuring your data for specific experiment design (sample groups for hypoesis testing)
- Exploration – looking for new sample or variable clusters (visualization and statistical tests)
- Accounting for covariates like batch effect
- Many configurations for heatmaps and PCA plots
- Working with synchronized plots, concept of biplots
- Working with variable lists, create your own lists
- Creating and selecting data subsets
- Comparing different datasets using variable lists of interest
- Approaches to integrating/merging different datasets
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