Joanna Ficek, M.Sc. in Biostatistics
"Success is not final, failure is not fatal; it is the courage to continue that counts.” W. Churchill
- joanna.ficek@ inf.ethz.ch
My interest lies in data integration, especially of clinical and multiomics data, as well as developing and applying new statistical methods to improve disease diagnostics and treatment.
I studied Mathematics (B.Sc.) at the Jagiellonian University in Krakow, Poland with academic exchange at the University of Bath, UK. I further obtained the M.Sc. in Biostatistics from the Ludwig-Maximilians-Universität in Munich, Germany with the fellowship from DAAD. My Master’s thesis project under supervision of Prof. Dr. Sonja Greven (LMU Munich) and Prof. Dr. Nikolaus Umlauf (University of Innsbruck) was focused on boosting joint models, a state-of-the-art statistical modelling approach for complex data structures in the medical domain. Parallel to and between the degrees I gathered experience in working with clinical data (Galen Ortopedia, Poland), analysing genomic data (Institute of Pharmacology, Polish Academy of Sciences) as well as in statistical consulting (StaBLab LMU, Germany). In October 2018 I joined the Rätsch lab as a PhD student and since then I have been involved in exciting interdisciplinary projects focused on cancer research.
Abstract Dynamic assessment of mortality risk in the intensive care unit (ICU) can be used to stratify patients, inform about treatment effectiveness or serve as part of an early-warning system. Static risk scoring systems, such as APACHE or SAPS, have recently been supplemented with data-driven approaches that track the dynamic mortality risk over time. Recent works have focused on enhancing the information delivered to clinicians even further by producing full survival distributions instead of point predictions or fixed horizon risks. In this work, we propose a non-parametric ensemble model, Weighted Resolution Survival Ensemble (WRSE), tailored to estimate such dynamic individual survival distributions. Inspired by the simplicity and robustness of ensemble methods, the proposed approach combines a set of binary classifiers spaced according to a decay function reflecting the relevance of short-term mortality predictions. Models and baselines are evaluated under weighted calibration and discrimination metrics for individual survival distributions which closely reflect the utility of a model in ICU practice. We show competitive results with state-of-the-art probabilistic models, while greatly reducing training time by factors of 2-9x.
Authors Jonathan Heitz, Joanna Ficek, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Matthias Hüser
Submitted arXiv Preprints
Abstract Abstract Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively.
Authors Stefan G. Stark, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane Chevrier, Franziska Singer, Tumor Profiler Consortium, Gunnar Rätsch, Kjong-Van Lehmann
Abstract Recent technological advances allow profiling of tumor samples to an unparalleled level with respect to molecular and spatial composition as well as treatment response. We describe a prospective, observational clinical study performed within the Tumor Profiler (TuPro) Consortium that aims to show the extent to which such comprehensive information leads to advanced mechanistic insights of a patient9s tumor, enables prognostic and predictive biomarker discovery, and has the potential to support clinical decision making. For this study of melanoma, ovarian carcinoma, and acute myeloid leukemia tumors, in addition to the emerging standard diagnostic approaches of targeted NGS panel sequencing and digital pathology, we perform extensive characterization using the following exploratory technologies: single-cell genomics and transcriptomics, proteotyping, CyTOF, imaging CyTOF, pharmacoscopy, and 4i drug response profiling (4i DRP). In this work, we outline the aims of the TuPro study and present preliminary results on the feasibility of using these technologies in clinical practice showcasing the power of an integrative multi-modal and functional approach for understanding a tumor9s underlying biology and for clinical decision support.
Authors Anja Irmisch, Ximena Bonilla, Stephane Chevrier, Kjong-Van Lehmann, Franziska Singer, Nora Toussaint, Cinzia Esposito, Julien Mena, Emanuela S Milani, Ruben Casanova, Daniel J Stekhoven, Rebekka Wegmann, Francis Jacob, Bettina Sobottka, Sandra Goetze, Jack Kuipers, Jacobo Sarabia del Castillo, Michael Prummer, Mustafa Tuncel, Ulrike Menzel, Andrea Jacobs, Stefanie Engler, Sujana Sivapatham, Anja Frei, Rene Holtackers, Gabriele Gut, Joanna Ficek, Reinhard Dummer, Rudolf Aebersold, Marina Bacac, Niko Beerenwinkel, Christian Beisel, Bernd Bodenmiller, Viktor H Koelzer, Holger Moch, Lucas Pelkmans, Berend Snijder, Markus Tolnay, Bernd Wollscheid, Gunnar Raetsch, Mitchell P Levesque, Tumor Profiler Consortium