arXiv subjects

Manuela Zucknick

Publications and source records attributed to Manuela Zucknick.

13 recordsLinked to original sources

arXiv2026

PliableBVS: A flexible Bayesian variable selection method for modeling interactions with mandatory modifying variables

High-dimensional interaction models are useful for studying, for example, how a large set of variables of interest, such as gene expression or other omics features, interact with a smaller set of modifying variables, such as clinical covariates. In this context, the pliable lasso has recently been proposed as an efficient method for screening large numbers of potential interaction terms under an asymmetric weak hierarchical constraint. In this work, we extend this framework by introducing PliableBVS, a Bayesian variable selection approach that preserves the hierarchical structure of the pliable lasso while inducing sparsity through spike-and-slab priors. The proposed model combines the continuous shrinkage effect of Bayesian lasso with a hierarchical spike-and-slab prior formulation that has two layers of decision variables: one governing the inclusion of main effects and another controlling the inclusion of interaction effects which is conditional on the inclusion of the corresponding main effects. This structure enables simultaneous selection of high-dimensional main and interaction effects within a coherent probabilistic framework. In simulation studies the proposed method outperforms the original pliable lasso in identifying active main and interaction effects, reducing false discoveries, and improving prediction accuracy in most scenarios. Applications with data from a labor onset study and a preeclampsia study demonstrate that PliableBVS selects biologically meaningful features and interactions.

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arXiv2025

Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis

Single-cell technologies provide an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated tumor microenvironment, and the produced high-dimensional omics data should also augment existing survival modeling approaches for identifying tumor cell type-specific genes predictive of cancer patient survival. However, there is no statistical model to integrate multiscale data including individual-level survival data, multicellular-level cell composition data and cellular-level single-cell omics covariates. We propose a class of Bayesian generalized promotion time cure models (GPTCMs) for the multiscale data integration to identify cell-type-specific genes and improve cancer prognosis. We demonstrate with simulations in both low- and high-dimensional settings that the proposed Bayesian GPTCMs are able to identify cell-type-associated covariates and improve survival prediction.

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arXiv2025

Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification

An important goal in cancer research is the survival prognosis of a patient based on a minimal panel of genomic and molecular markers such as genes or proteins. Purely data-driven models without any biological knowledge can produce non-interpretable results. We propose a penalized semiparametric Bayesian Cox model with graph-structured selection priors for sparse identification of multi-omics features by making use of a biologically meaningful graph via a Markov random field (MRF) prior to capturing known relationships between multi-omics features. Since the fixed graph in the MRF prior is for the prior probability distribution, it is not a hard constraint to determine variable selection, so the proposed model can verify known information and has the potential to identify new and novel biomarkers for drawing new biological knowledge. Our simulation results show that the proposed Bayesian Cox model with graph-based prior knowledge results in more trustable and stable variable selection and non-inferior survival prediction, compared to methods modeling the covariates independently without any prior knowledge. The results also indicate that the performance of the proposed model is robust to a partially correct graph in the MRF prior, meaning that in a real setting where not all the true network information between covariates is known, the graph can still be useful. The proposed model is applied to the primary invasive breast cancer patients data in The Cancer Genome Atlas project.

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arXiv2024

Integrating Multiple Data Sources with Interactions in Multi-Omics Using Cooperative Learning

Modeling with multi-omics data presents multiple challenges such as the high-dimensionality of the problem ($p \gg n$), the presence of interactions between features, and the need for integration between multiple data sources. We establish an interaction model that allows for the inclusion of multiple sources of data from the integration of two existing methods, pliable lasso and cooperative learning. The integrated model is tested both on simulation studies and on real multi-omics datasets for predicting labor onset and cancer treatment response. The results show that the model is effective in modeling multi-source data in various scenarios where interactions are present, both in terms of prediction performance and selection of relevant variables.

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arXiv2024

Tutorial on survival modeling with applications to omics data

Motivation: Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics data sets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes. Results: We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are are especially useful for high-dimensional data, but the framework is applicable more generally. Availability and implementation: A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics/survomics.html.

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arXiv2023

An ADMM approach for multi-response regression with overlapping groups and interaction effects

In this paper, we consider the regularized multi-response regression problem where there exists some structural relation within the responses and also between the covariates and a set of modifying variables. To handle this problem, we propose MADMMplasso, a novel regularized regression method. This method is able to find covariates and their corresponding interactions, with some joint association with multiple related responses. We allow the interaction term between covariate and modifying variable to be included in a (weak) asymmetrical hierarchical manner by first considering whether the corresponding covariate main term is in the model. For parameter estimation, we develop an ADMM algorithm that allows us to implement the overlapping groups in a simple way. The results from the simulations and analysis of a pharmacogenomic screen data set show that the proposed method has an advantage in handling correlated responses and interaction effects, both with respect to prediction and variable selection performance.

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arXiv2023

Multivariate Bayesian structured variable selection for pharmacogenomic studies

Precision cancer medicine aims to determine the optimal treatment for each patient. In-vitro cancer drug sensitivity screens combined with multi-omics characterization of the cancer cells have become an important tool to achieve this aim. Analyzing such pharmacogenomic studies requires flexible and efficient joint statistical models for associating drug sensitivity with high-dimensional multi-omics data. We propose a multivariate Bayesian structured variable selection model for sparse identification of omics features associated with multiple correlated drug responses. Since many anti-cancer drugs are designed for specific molecular targets, our approach makes use of known structure between responses and predictors, e.g. molecular pathways and related omics features targeted by specific drugs, via a Markov random field (MRF) prior for the latent indicator variables of the coefficients in sparse seemingly unrelated regression. The structure information included in the MRF prior can improve the model performance, i.e. variable selection and response prediction, compared to other common priors. In addition, we employ random effects to capture heterogeneity between cancer types in a pan-cancer setting. The proposed approach is validated by simulation studies and applied to the Genomics of Drug Sensitivity in Cancer data, which includes pharmacological profiling and multi-omics characterization of a large set of heterogeneous cell lines.

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arXiv2021

BayesSUR: An R package for high-dimensional multivariate Bayesian variable and covariance selection in linear regression

In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with high-dimensional genomic and other omics data, a problem that can be studied with high-dimensional multi-response regression, where the response variables are potentially highly correlated. To this purpose, we recently introduced several multivariate Bayesian variable and covariance selection models, e.g., Bayesian estimation methods for sparse seemingly unrelated regression for variable and covariance selection. Several variable selection priors have been implemented in this context, in particular the hotspot detection prior for latent variable inclusion indicators, which results in sparse variable selection for associations between predictors and multiple phenotypes. We also propose an alternative, which uses a Markov random field (MRF) prior for incorporating prior knowledge about the dependence structure of the inclusion indicators. Inference of Bayesian seemingly unrelated regression (SUR) by Markov chain Monte Carlo methods is made computationally feasible by factorisation of the covariance matrix amongst the response variables. In this paper we present BayesSUR, an R package, which allows the user to easily specify and run a range of different Bayesian SUR models, which have been implemented in C++ for computational efficiency. The R package allows the specification of the models in a modular way, where the user chooses the priors for variable selection and for covariance selection separately. We demonstrate the performance of sparse SUR models with the hotspot prior and spike-and-slab MRF prior on synthetic and real data sets representing eQTL or mQTL studies and in vitro anti-cancer drug screening studies as examples for typical applications.

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arXiv2020

Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression

Important objectives in cancer research are the prediction of a patient's risk based on molecular measurements such as gene expression data and the identification of new prognostic biomarkers (e.g. genes). In clinical practice, this is often challenging because patient cohorts are typically small and can be heterogeneous. In classical subgroup analysis, a separate prediction model is fitted using only the data of one specific cohort. However, this can lead to a loss of power when the sample size is small. Simple pooling of all cohorts, on the other hand, can lead to biased results, especially when the cohorts are heterogeneous. For this situation, we propose a new Bayesian approach suitable for continuous molecular measurements and survival outcome that identifies the important predictors and provides a separate risk prediction model for each cohort. It allows sharing information between cohorts to increase power by assuming a graph linking predictors within and across different cohorts. The graph helps to identify pathways of functionally related genes and genes that are simultaneously prognostic in different cohorts. Results demonstrate that our proposed approach is superior to the standard approaches in terms of prediction performance and increased power in variable selection when the sample size is small.

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arXiv2020

Structured penalized regression for drug sensitivity prediction

Large-scale {\it in vitro} drug sensitivity screens are an important tool in personalized oncology to predict the effectiveness of potential cancer drugs. The prediction of the sensitivity of cancer cell lines to a panel of drugs is a multivariate regression problem with high-dimensional heterogeneous multi-omics data as input data and with potentially strong correlations between the outcome variables which represent the sensitivity to the different drugs. We propose a joint penalized regression approach with structured penalty terms which allow us to utilize the correlation structure between drugs with group-lasso-type penalties and at the same time address the heterogeneity between omics data sources by introducing data-source-specific penalty factors to penalize different data sources differently. By combining integrative penalty factors (IPF) with tree-guided group lasso, we create the IPF-tree-lasso method. We present a unified framework to transform more general IPF-type methods to the original penalized method. Because the structured penalty terms have multiple parameters, we demonstrate how the interval-search Efficient Parameter Selection via Global Optimization (EPSGO) algorithm can be used to optimize multiple penalty parameters efficiently. Simulation studies show that IPF-tree-lasso can improve the prediction performance compared to other lasso-type methods, in particular for heterogenous data sources. Finally, we employ the new methods to analyse data from the Genomics of Drug Sensitivity in Cancer project.

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arXiv2019

A Bayesian approach to study synergistic interaction effects in in-vitro drug combination experiments

In cancer translational research, increasing effort is devoted to the study of the combined effect of two drugs when they are administered simultaneously. In this paper, we introduce a new approach to estimate the part of the effect of the two drugs due to the interaction of the compounds, i.e. which is due to synergistic or antagonistic effects of the two drugs, compared to a reference value representing the condition when the combined compounds do not interact, called zero-interaction. We describe an in-vitro cell viability experiment as a random experiment, by interpreting cell viability as the probability of a cell in the experiment to be viable after treatment, and including information related to different exposure conditions. We propose a flexible Bayesian spline regression framework for modelling the viability surface of two drugs combined as a function of the concentrations. Since the proposed approach is based on a statistical model, it allows to include replicates of the experiments, to evaluate the uncertainty of the estimates, and to perform prediction. We test the model fit and prediction performance on a simulation study, and on an ovarian cancer cell dataset. Posterior estimates of the zero-interaction level and of the synergy term, obtained via adaptive MCMC algorithms, are used to compute interpretable measures of efficacy of the combined experiment, including relative volume under the surface (rVUS) measures to summarise the zero-interaction and synergy terms and a bi-variate alternative to the well-known EC50 measure.

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arXiv2019

Composite local low-rank structure in learning drug sensitivity

The molecular characterization of tumor samples by multiple omics data sets of different types or modalities (e.g. gene expression, mutation, CpG methylation) has become an invaluable source of information for assessing the expected performance of individual drugs and their combinations. Merging the relevant information from the omics data modalities provides the statistical basis for determining suitable therapies for specific cancer patients. Different data modalities may each have their specific structures that need to be taken into account during inference. In this paper, we assume that each omics data modality has a low-rank structure with only few relevant features that affect the prediction and we propose to use a composite local nuclear norm penalization for learning drug sensitivity. Numerical results show that the composite low-rank structure can improve the prediction performance compared to using a global low-rank approach or elastic net regression.

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arXiv2014

MCMC algorithms for Bayesian variable selection in the logistic regression model for large-scale genomic applications

In large-scale genomic applications vast numbers of molecular features are scanned in order to find a small number of candidates which are linked to a particular disease or phenotype. This is a variable selection problem in the "large p, small n" paradigm where many more variables than samples are available. Additionally, a complex dependence structure is often observed among the markers/genes due to their joint involvement in biological processes and pathways. Bayesian variable selection methods that introduce sparseness through additional priors on the model size are well suited to the problem. However, the model space is very large and standard Markov chain Monte Carlo (MCMC) algorithms such as a Gibbs sampler sweeping over all p variables in each iteration are often computationally infeasible. We propose to employ the dependence structure in the data to decide which variables should always be updated together and which are nearly conditionally independent and hence do not need to be considered together. Here, we focus on binary classification applications. We follow the implementation of the Bayesian probit regression model by Albert and Chib (1993) and the Bayesian logistic regression model by Holmes and Held (2006) which both lead to marginal Gaussian distributions. We in- vestigate several MCMC samplers using the dependence structure in different ways. The mixing and convergence performances of the resulting Markov chains are evaluated and compared to standard samplers in two simulation studies and in an application to a real gene expression data set.

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