An Introduction to Data Exploration, Experimental Design, and Biomarker Expression Analysis using JMP Software Tools

Through the use of real world examples and the JMP, JMP Pro, and JMP Genomics software, we will cover best practices used in both industry and academia today to visually explore data, plan biological experiments, detect differential expression patterns, find signals in next-generation sequencing data and easily discover statistically appropriate biomarker profiles and patterns.

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

• Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
• Please be aware that this is a non-charged event.
• Further details regarding eligibility criteria are available here

This course is suitable for novice genomic analysts and assumes no prior programming experience or experience in JMP, JMP Pro, or JMP Genomics software.

Number of sessions: 1

Bioinformatics, Genomics, Omics, Data quality management, Data visualisation, Genomics, Omics, Statistical modelling, Biomarkers Pathway or network analysis

After this course you should be able to:

• Prepare, clean, and subset genomic data appropriately
• Select an appropriate statistical test for detection of differentially expressed biomarkers
• Interpret the results from the statistical analysis of expression data, including volcano plots, waterfall plots, Venn diagrams, dendrograms and parallel plots
• Find examples and help for JMP and JMP Genomics

During this course you will learn about:

• Bring data into JMP and JMP Genomics
• Prepare, clean, and subset genomic data appropriately (common practices and why and how
• Select an appropriate statistical test for detection of differentially expressed biomarkers including when and how to use t-tests, multiple comparisons corrections, ANOVA, mixed models, survival models, and predictive models
• Interpret the results from the statistical analysis of expression data, including volcano plots, waterfall plots, Venn diagrams, dendrograms and parallel plots
• Find examples and help for JMP and JMP Genomics

Presentations and demonstrations

0.5

Once per year