Computational Analytics Support Platform (CASP)

The Computational Analytics Support Platform description

Our mission is to help life science researchers understand complex chemical and biological systems. This is achieved via the application and development of advanced computer-based modelling tools. CASP’s focus is on the analysis of non-genomic related data from a wide array of technologies including, but not limited to, downstream omics (e.g., metabolomics, proteomics), spectroscopy and imaging. Our approach has multiple applications in areas including health, medicine, pharmaceuticals, agriculture and the food science industries.

What we do

At present, huge amounts of data are generated not only in genomics, but also by other modern high-throughput technologies such as ‘omics’ and sensor technologies. The ability to analyze and draw valid conclusions from complex data has become a bottleneck for many life science researchers.

CASP’s aim is to overcome this by becoming the bridge between data and life sciences. We provide researchers with modern data analytics tools within multivariate data analysis, statistics, AI/deep learning, machine learning, and design of experiments for the analysis of experimental data. Ultimately, we hope to provide the missing link that enables projects to go from data to completion. 

How we can help your project

Our main mission is to support researchers deliver high-quality results, by converting complex data into meaningful biological/chemical interpretations relating to their specific scientific question.

Our uniqueness arises from our expertise – we not only understand mathematics, statistics and modelling, but we have wide domain knowledge arising from active engagement in numerous projects in the ‘omics’ area and beyond. Therefore, we are able to ‘jump in’ to your project from day one and provide support every step of the way. This can mean starting from experimental design, quality control of generated data, basic and more in-depth analysis, through to interpretation of results, experimental validation and publishing.

Support and services we provide

We offer different kinds of support for all types of data and research questions:

• Packaged and customer-specific data analytics projects
• Personalised training - hands-on tutorials in the use of data processing and data analysis software for specific data types
• Consultations for data analysis support – long-term
• Extended data analytics support for UmU experimental platforms (SMC, ViSp)

Our services:

• Study design
  • Design of Experiments (DOE) / Statistical experimental design
• Data preprocessing, filtering, normalization, standardization and scaling
• Multivariate data analysis
  • Principal Components Analysis (PCA)
  • Partial Least Squares regression analysis (PLS)
  • Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)
  • Multiblock data analysis (O2PLS/OnPLS)
• Univariate analysis
  • Fold change analysis, T-test analysis, ANOVA, Correlation analysis, Volcano plots
• Additional services
  • Image analysis
  • Classification and feature selection
  • Enrichment analysis
  • Metabolic pathway analysis
  • Biological interpretation of data
  • Support with manuscript writing
  • AI/deep learning
  • Machine learning
  • Hybrid modelling
  • Generation of automated scripts

Availability and Access

To discuss your data analysis needs or enquire about our services, please fill in the contact request form provided in the contact section further down on the page. Alternatively, you can contact our Platform Manager directly, who will be happy to assist you and answer any questions.

CASP operates on a fee-for-service model. Pricing depends on the type and complexity of the analyses required. We offer package pricing and a range of fees for both academic and external users. Costs are determined after an initial consultation to assess project needs.

Staff

Johan Trygg, Director

Kate Bennett, Platform Manager

Hans Stenlund, Senior Data Scientist

David Nilsson, Senior Data Scientist

Contact

The platform has recently been launched within the framework of the Computational Life Science Cluster at Umeå University.