Prof. Dr. Tim Conrad
Prof. Dr. Gitta Kutyniok
Prof. Dr. Christof Schütte
Freie Universität Berlin
/ Technische Universität Berlin
/ Konrad-Zuse-Zentrum für Informationstechnik Berlin
In living organisms, biological cells transition from one state to another. This happens
during normal cell development (e.g. aging) or is triggered by events, such as diseases.
The time-ordered set of state changes is called a trajectory. Identifying these cell
trajectories is a crucial part in bio-medical research to understand changes on a gene
and molecular level. It allows to derive biological insights such as disease mechanisms
and can lead to new biomedical discoveries and to advances in health-care. With
the advent of single cell experiments such as Drop-Seq or inDrop, individual gene
expression profiles of thousands of cells can be measured in a single experiment.
These large data-sets allow to determine a cell's state based on its gene activity (cell
expression profiles, CEPs), which can be expressed as a large feature vector representing its location in some large state space. The main problem with these experiments is that the actual time-information is lost, and needs to be recovered. The state-of-the art solution is to introduce the concept of pseudo-time in
which the cells are ordered by CEP similarity.
To find robust and biological meaningful trajectories based on CEPs, two main tasks have to be performed: (1) A CEP-based metric has to be learned to define pair-wise distances between CEPs. (2) Given this metric, similar CEP groups and transition paths between those groups should be identified and analysed.