One of Qlucore Omics Explorer's cornerstones is the possibility to analyze data using a flexible and easy to use general linear statistical model.
•Qlucore Omics Explore
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
•Qlucore Omics Explore
• Shorten analysis time
• Add more creativity to your research
• Perform statistical filtering
• Quickly and easily analyze data
• Delivers immediate results
Qlucore Omics Explorer is a next-generation bioinformatics software program that delivers immediate results. Combine visualization with powerful statistical methods in the easy to use program to obtain new results faster.A busy day in a scientist’s life includes many different tasks, data analysis being one of them.
Qlucore Omics Explorer is designed to support a user at all stages of his/her data analysis, from checking the quality of data in a pre-series to reporting final results with stunning visual plots.
At the heart of Qlucore Omics Explorer is the fast and generic calculation engine which generates plots and visualizations faster than any other tool. This enables a user to secure full interactivity and make his/her analysis fast, easy and productive.
The fact that the engine is generic allows anyone to work with many types of data, some examples include: flow cytometry, proteomics, DNA methylation, RNA-seq, array.Any user can work not only with his/her own data but also with the data of others, either through downloading directly from sources as Gene Expression Omnibus (GEO) or by using the Wizard and importing data sets from co-workers.
The combination of powerful statistics and instant visualization can generate exciting results. One of Qlucore Omics Explorer's cornerstones is the possibility to analyze data using a flexible and easy to use general linear statistical model. The statistical linear model also supports the handling of eliminated factors, which means that you can remove for instance batch effects and handle paired tests.
The Build classifier and classify functionality enables both the option to easy build classifiers based on various models such as Support Vector Machines (SVM), Random Trees (RT) and kNN and to classify new samples absed on the selected model.