Algorithm for droplet digital PCR data classification
Droplet digital PCR (ddPCR) is a recently introduced technology that enables absolute quantification of molecules, such as DNA, with very high sensitivity. This method holds great promise in research and clinics, and is applied in our laboratory to detect and monitor cancers. ddPCR experiments generate fluorescence amplitude data. As yet, the tools for downstream analysis of this data are limited and compromise the reproducibility of the results.
Project goals and challenges
This master project will focus on implementing an algorithm for classifying ddPCR data as positive or negative, based on the intensity of fluorescence signals. A good classification is critical to ensure the precise quantification of molecules we are interested in, and for succeeding in improving the clinical management of cancer patients. An in-house solution has recently been generated but there are many unmet needs in the full development of the solution. The main challenges will be to identify these needs, in collaboration with the bioinformaticians and biologists in the team, implement the solutions in an R package and provide an accompanying graphical user interface (GUI), suitable for non-R users. If successful, the software will be used in routine for ddPCR data analysis.
This master project is offered as a long master project. Main supervisor will be Marine Jeanmougin (email firstname.lastname@example.org) and co-supervisors will be Guro E. Lind and Rolf Skotheim. The work will include time at the Radiumhospitalet, Oslo University Hospital, in the Molecular Oncology department. Our group has a broad range of expertise, from molecular biology to bioinformatics and statistics.