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In the new version of Qlucore Omics Explorer, 3.10, an optimized DESeq2 family of methods and statistics is supported. A broad range of statistical tests including Wald and Likelihood ratio test as well as VST normalization and independent filtering are available. This gives you full freedom to analyze count-based data such as RNA-seq faster than ever before. The DESeq2 functionality is easy to use and is set-up with only a few keystrokes. What’s more, calculations are 15 times faster as compared to R. This enables more in-depth analysis in a shorter time-frame. Get more and better answers from your data!

In the new version of Qlucore Omics Explorer, 3.10, an optimized DESeq2 family of methods and statistics is supported. A broad range of statistical tests including Wald and Likelihood ratio test as well as VST normalization and independent filtering are available. This gives you full freedom to analyze count-based data such as RNA-seq faster than ever before. The DESeq2 functionality is easy to use and is set-up with only a few keystrokes. What’s more, calculations are 15 times faster as compared to R. This enables more in-depth analysis in a shorter time-frame. Get more and better answers from your data!

During the session we will show Qlucore Insights through a step-by-step live demonstration of the complete process, from data import to a ready report. Both gene expression based subtyping as well as gene fusion analysis will be demonstrated.

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.

In the new version of Qlucore Omics Explorer, 3.10, an optimized DESeq2 family of methods and statistics is supported. A broad range of statistical tests including Wald and Likelihood ratio test as well as VST normalization and independent filtering are available. This gives you full freedom to analyze count-based data such as RNA-seq faster than ever before. The DESeq2 functionality is easy to use and is set-up with only a few keystrokes. What’s more, calculations are 15 times faster as compared to R. This enables more in-depth analysis in a shorter time-frame. Get more and better answers from your data!

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).

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).

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.

Extend your knowledge of powerful statistical methods and integrated tools such as the GSEA workbench (for pathway analysis) and the Biomarker Workbench to achieve faster and more efficient data analysis. In the training we will do a multi-omics (exercise transcriptomics and proteomics) exercise using correlation analysis. We will also have a look at how you can import and visualize single cell data from 10X Genomics CellRanger data files. This training is for those who have previous experience with Qlucore Omics Explorer. You may have attended our basic training or have similar experience.

Multi-omics data analysis approaches are becoming increasingly popular, providing a better understanding of the system under study. In this webinar, we will combine transcriptomics and proteomics data and demonstrate how you can quickly identify correlations between genes and proteins. The results will be visualized in several different plots.