MEDFL5155 – Introduction to statistics and bioinformatics for the analysis of large-scale biological data

The course considers methods integral to data analysis in modern molecular medical research. As such it is relevant to all PhD students and researchers who need to analyze large-scale molecular data themselves, as well as those who need to interpret results and understand publications in the molecular life sciences.

High-throughput techniques are becoming increasingly more prevalent in research in life sciences and the clinic. However, to make effective use of the resulting large datasets it is necessary to understand and critically apply advanced computational and statistical methods as well as be able to apply good practices in programming and data analysis. We will describe guidelines for good practice such as the FAIR data principles and introduce the statistical concepts behind typical data analysis tasks for large-scale biological data, including the following topics:

a) high-throughput screening of cancer microbiome data,

b) single-cell RNA-seq analysis,

c) large-scale protein sequence analysis.

The above topics have been chosen to demonstrate the importance of carefully considering several issues that can bias conclusions, such as contamination and normalization. Both supervised (classification) and unsupervised (clustering, visualization) methods will be considered as central steps in the data analysis pipeline.

To encourage continued learning after the course, we will also provide an overview of available web-based courses and exercises.

Knowledge:

• Learn important statistical and bioinformatics concepts for analysing molecular data, including good practices in programming and data analysis.
• Have knowledge of the specific statistical challenges associated with the analysis of high-throughput biological data.
• Know important molecular databases and relevant statistics/ bioinformatics software tools.
• Understand some of the challenges you will face when trying to apply this knowledge to the analysis of real datasets.

Skills:

• Be able to identify the data analysis problem and match the appropriate type of statistical method and corresponding software.
• Perform basic analyses of high-throughput biological data using R, Bioconductor and python.
• Be able to understand and critically evaluate the data analysis procedures in publications in molecular biology/ molecular medicine.

The course is reserved for students at the Medical Student Research Programme at the Faculty of Medicine and the Faculty of Dentistry, UiO.

Students sign up for classes and exam to this course in StudentWeb.

Students will upon registration receive an immediate reply in StudentWeb as to whether a seat at this course is granted or not.

Students should have passed the exam in an introductory course in statistics (for example MEDFL5130, MEDFL5130E).

Students should also have working knowledge and practical experience in analysing data with the statistical programming language R and preferably also the python language. Basic familiarity with the Unix shell is also required, for example by having completed a software carpentry workshop.

It is recommended that students have a basic understanding of molecular biology, at least roughly corresponding to 5-10 university study points in molecular biology or similar. Students would have completed an introductory course in R could for example complete an introductory online courseor follow a software carpentry course at UiO.

The teaching will be organized as an intensive course over six days.

There will be lectures coupled with hands-on practicals and example data analyses in the computer labs as well as group project work.

Students will need to allow for sufficient time in advance for course preparations, which include some required reading, as well as after the course for the home exam.

You have to participate in at least 80 % of the teaching to be allowed to take the exam. Attendance will be registered.