Biosystems Data Analysis

Seven different data analysis methods are discussed in this course. For each of these methods the students will be provided knowledge on how and when to use the multivariate method for analyzing complex sets of multivariate data in biological and chemical systems.

Date: Jan 5-30, 2015

Target audience: Master students

Program: In the analysis of biochemical systems, many measurements are performed, leading to complex multivariate data sets. The tendency is to measure more and more of just a few samples. Multivariate data analysis methods have to be used to explore such sets. This course covers a broad range of multivariate data analysis methods, for e.g. exploration, calibration, clustering and classification. The latter is especially important in biomarker discovery. Design of experiments and ANOVA for multivariate data is also discussed. The course starts with an introduction on how the data is measured and the properties of the data.
After the course:

  • Students know where the data comes from.
  • Students understand the relationship between the data and the biological system that is measured.
  • Students understand how the data analysis methods work.
  • Students are able to apply the methods on new data, and they are able to interpret the results.
  • Students comprehend the pitfalls of multivariate data and validation strategies to prevent overfit.
  • Students are able to critically review data analysis applications in which the above mentioned methods have been used.
  • Students are able to select the most appropriate method for a given biological question.
  • Students are able to define a relevant biological research goal that can be achieved from a given data set.

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