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MF9155
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KARAKTERFORDELING · H 2025
15 kandidater
Kilde: Felles studentsystem (FS) via Universitetet i Oslo, slik den var publisert for høsten 2026. Universitetet i Oslo er ikke ansvarlig for innholdet på denne siden.
UiO sin egen emnebeskrivelse — læringsutbytte, innhold og vurderingsform.
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:
Skills:
Students should have passed the exam in an introductory course in statistics (for example MF9130, MF9130E).
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.
Students should have passed the exam in an introductory course in statistics (e.g. MF9130). They should also have some experience with the statistical programming language R and have basic familiarity with the Unix shell, for example by having completed a software carpentry workshop.
To gain sufficient experience with R, students could for example complete an introductory online course or follow a software carpentry course at UiO.
Applicants admitted to a PhD programme at UiO sign up for classes and exam to this course in StudentWeb.
Applicants who are not admitted to a PhD programme at UiO must apply for a right to study before they can sign up for classes and exam to this course. See information here: How to apply for a right to study and admission to elective PhD courses in medicine and health sciences.
Applicants will upon registration receive an immediate reply in StudentWeb as to whether a seat at this course is granted or not.
Maximum number of participants is 40
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.
Kilde: Felles studentsystem (FS) via Universitetet i Oslo, slik den var publisert for høsten 2026. Universitetet i Oslo er ikke ansvarlig for innholdet på denne siden.
Hva burde andre vite før de tar dette? Anonymt, ingen innlogging.
Basert på hvor kandidatene til eksamen var registrert (DBH).
Emner som tas i de samme studieprogrammene som MF9155.
| Semester | Kandidater | Stryk |
|---|---|---|
| Høst 2025 | 15 | 0 % |
| Høst 2024 | 11 | 0 % |
| Høst 2023 | 21 | 0 % |
| Høst 2022 | 20 | 0 % |
| Høst 2021 | 21 | 0 % |
| Høst 2020 | 28 | 10,7 % |
| Høst 2019 | 22 | 0 % |
| Høst 2018 | 19 | 0 % |
| Høst 2017 | 24 | 0 % |
| Høst 2016 | 12 | 0 % |