Statistics for Bioinformatics and eScience (StatBI/E)

Course content

The course is based on a set of concrete cases that will take the participants through the following content.

 

  • Standard discrete and continuous distributions, descriptive methods, the frequency and Bayesian interpretations, conditioning, independence, and selected probability results.
  • Simulation.
  • Mean, variance, estimators, two-sample comparisons, multiple testing.
  • Maximum likelihood and least squares estimation.
  • Standard errors and confidence intervals.
  • Bootstrapping.
  • Correlation, linear, non-linear, logistic and Poisson regression.
  • Dimensionality reduction, model selection and model validation.
  • The statistical programming language R.
  • Models for neuron activity, gene expression, database searches, motif and word occurrences, internet traffic, diagnostic tests etc.

 

Education

MSc Programme in Bioinformatics

Learning outcome

Knowledge:

The basic concepts in mathematical statistics, such as;

  • Probability distributions
  • Standard errors and confidence intervals
  • Maximum likelihood and least squares estimation
  • Bootstrapping
  • Hypothesis testing and p-values
  • Linear, non-linear, logistic and Poisson regression


Skills:

  • Master practical implementation in R.
  • Use computer simulations for computations with probability distributions, including bootstrapping.
  • Compute uncertainty measures, such as standard errors and confidence intervals, for estimated parameters.
  • Compute predictions based on regression models taking into account the uncertainty of the predictions.
  • Assess a fitted distribution using descriptive methods.
  • Use general purpose methods, such as the method of least squares and maximum likelihood, to fit probability distributions to empirical data.
  • Summarize empirical data and compute relevant descriptive statistics for discrete and continuous probability distributions.


Competences:

  • Formulate scientific questions in statistical terms.
  • Interpret and report the conclusions of a practical data analysis.
  • Assess the fit of a regression model based on diagnostic quantities and plots.
  • Investigate scientific questions that are formulated in terms of comparisons of distributions or parameters by statistical methods.
  • Investigate scientific questions regarding association in terms of linear, non-linear, logistic and Poisson regression models.

5 hours of lectures and 3 hours of exercises per week. 7 weeks of classes.

MSc students and BSc students in their 3rd year with MatIntro or an equivalent course.

ECTS
7,5 ECTS
Type of assessment
Written assignment, 30 hours
Take-home assignment.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
One internal examiner
Criteria for exam assessment

In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 35
  • Practical exercises
  • 21
  • Preparation
  • 90
  • Project work
  • 30
  • Exam
  • 30
  • English
  • 206