X-Student Research Group - Understanding circadian clock function in human cancers using data science


You must attend the organizational meeting on October 10 or contact me, if you cannot, in order to register for this course.

Aim of the Course

This is a research-based learning course, in which Bachelor and Master students can get first-hand experience engaging in a current open research project (here to understand circadian rhythms in different human tumors). I will guide each of you through your chosen cancer dataset to answer the central question “are there circadian rhythms in in-vivo human tumors?". Specific questions and analyses to run will be decided by you (the students) in the course. In the end, we as a team, will gain new insights into human cancers by comparing and reflecting on our results collaboratively, while not knowing what we will learn from each analysis (i.e., doing research). Finally, we will aim to assemble our results for publication in a scientific journal.

Course Description

Near 24h biological rhythms, called circadian rhythms, are essential to human health and their disruption is associated with several diseases. Measuring rhythms in a human under health and disease requires measurements at regular time intervals over one or more cycles known as a timeseries. But there are practical and ethical barriers to repeatedly sampling most internal human tissues.

To quantify rhythms in internal tissues, we developed a new machine learning algorithm (called COFE) for de novo profiling of clock-regulated entities in human tissues from population biosamples. In this Research Group, students will apply COFE to transcriptomic data from their chosen cancer tissue from the public The Cancer Genome Atlas to get an unprecedented first look at clock function in different tumors. Students will learn to process data, statistically test hypotheses and contrast insights against the literature. They will thus get a practical introduction to computational biology and data science in the biomedical domain.

Time & Location

  • Winter Semester (WiSe) 2023/2024 (19 October, 2023 - 11 February, 2024)
  • Organizational meeting: 10 October 2023, 10:00-11:00 Room 207 Haus 20, Philippstr. 13 (Campus Nord)[Email me (bharath.ananthasubramaniam@hu-berlin.de) for if you cannot make it¯]
  • Online programming course: 17 October 2023 – 31 December 2023
  • Tentative lectures times: Tuesdays 10:00-12:00, from 28 November, 2023 (Times can be adjusted at first meeting)
  • Block course End of January or February, 2024 (Decided at first meeting)
  • Meeting location: Institute for Theoretical Biology, SR302, Phillipstr. 13, Haus 20 (Campus Nord), 10115 Berlin-Mitte


  1. Online/in-person lectures on circadian rhythms, cancer and high-throughput data analysis.
  2. Online course to improve programming for data science in R or Python.
  3. One-week block course to apply COFE to chosen tumor data and perform follow-up analyses.
  4. Short report (max. 5 pages) outlining the analysis performed and results obtained.

Participation Requirements

  • Students interested in this seminar need to be have some basic biology background (or at least strong interest)
  • Ability of do basic programming in R or Python is mandatory (there will be only time to advance your skills).
  • Prior interest or knowledge in cancer or human physiology is valuable but not required.
  • This course is suitable for advanced Bachelor and Master students
  • This course is open to students of Humboldt-Universität, Freie Universität, Technische Universität and Charité Universitätsmedizin (we will register this course via the Berlin University Alliance).