Manuela Zucknick
Professor in Biostatistics, Director of the Oslo Centre for Biostatistics and Epidemiology
Research Interests
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About
Professor Manuela Zucknick is a faculty member in the Department of Biostatistics at the University of Oslo, Norway. She serves as the Director of the Oslo Centre for Biostatistics and Epidemiology. Her research encompasses various areas including multivariate Bayesian methods in pharmacogenomics, survival modeling in omics data, and statistical analysis of RNA-Sequencing. She has also contributed to studies on maternal health impacts on fetal outcomes and developed tools for drug combination screen analysis.
Positions (2)
Articles (13)
Multivariate Bayesian structured variable selection for pharmacogenomic studies
Cancer drug sensitivity screens combined with multi-omics characterisation of the cancer cells have become an important tool to determine the optimal treatment for each patient. We propose a multivariate Bayesian structured variable selection model for sparse identification of multi-omics features associated with multiple correlated drug responses. Our model uses known structure between drugs and their targeted genes via a Markov random field (MRF) prior in sparse seemingly unrelated regression. The use of MRF prior can improve the model performance compared to other common priors. The proposed model is applied to the Genomics of Drug Sensitivity in Cancer data.
Year:
2023
Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data
Motivation Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes. Results We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method’s potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data. Availability and implementation The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.
Year:
2023
Collaborators (11)
Ellen Gabrielsen Hjelle
Associate Professor
OsloMet – Oslo Metropolitan University
Thomas Fleischer
-
simon rayner
Professor
University of Oslo
Tero Aittokallio
University of Helsinki
Jorrit M Enserink
Oslo University Hospital
Arvind Y.M. Sundaram
University of Oslo
Gabriele Kitzmüller
Associate professor, PHD
UiT Norges arktiske universitet - Campus Narvik
Geir K Sandve
University of Oslo
Eivind Hovig
Professor/ Head, Center of Bioinformatics
University of Oslo
Theresa L. Powell
-
Marieke Lydia Kuijjer
University of Oslo

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