arXiv subjects

Alexandra Jauhiainen

Publications and source records attributed to Alexandra Jauhiainen.

3 recordsLinked to original sources

arXiv2026

UQSA -- An R-Package for Uncertainty Quantification and Sensitivity Analysis for Biochemical Reaction Network Models

Biochemical reaction models describing subcellular processes generally come with a large uncertainty. To be able to account for this during the modeling process, we have developed the R-package UQSA, performing uncertainty quantification and sensitivity analysis in an integrated fashion. UQSA is designed for fast sampling of complicated multi-dimensional parameter distributions, using efficient Markov chain Monte Carlo (MCMC) sampling techniques and Vine-copulas to model complicated joint distributions. We perform MCMC sampling both from stochastic and deterministic models, in either likelihood-free or likelihood-based settings. In the likelihood-free case, we use Approximate Bayesian Computation (ABC), while for likelihood-based sampling we provide different algorithms, including the fast geometry-informed algorithm SMMALA (Simplified Manifold Metropolis-Adjusted Langevin Algorithm). The uncertainty quantification can be followed by a variance decomposition-based global sensitivity analysis. We are aiming for biochemical models, but UQSA can be used for any type of reaction networks. VineCopula: The use of Vine-copulas allows us to describe, evaluate, and sample from complicated parameter distributions, as well as adding new datasets in a sequential manner without redoing the previous parameter fit. The code is written in R, with C as a back-end to improve speed. We use the SBtab table format for Systems Biology projects for the model description as well as the experimental data. An event system allows the user to model complicated transient input, common within, e.g., neuroscience. UQSA has an extensive documentation with several examples describing different types of models and data. The code has been tested on up to 2000 cores on several nodes on a computing cluster, but we also include smaller examples that can be run on a laptop. Source code: https://github.com/icpm-kth/uqsa

q-bio.QM↗

arXiv2017

ROPE: high-dimensional network modeling with robust control of edge FDR

Network modeling has become increasingly popular for analyzing genomic data, to aid in the interpretation and discovery of possible mechanistic components and therapeutic targets. However, genomic-scale networks are high-dimensional models and are usually estimated from a relatively small number of samples. Therefore, their usefulness is hampered by estimation instability. In addition, the complexity of the models is controlled by one or more penalization (tuning) parameters where small changes to these can lead to vastly different networks, thus making interpretation of models difficult. This necessitates the development of techniques to produce robust network models accompanied by estimation quality assessments. We introduce Resampling of Penalized Estimates (ROPE): a novel statistical method for robust network modeling. The method utilizes resampling-based network estimation and integrates results from several levels of penalization through a constrained, over-dispersed beta-binomial mixture model. ROPE provides robust False Discovery Rate (FDR) control of network estimates and each edge is assigned a measure of validity, the q-value, corresponding to the FDR-level for which the edge would be included in the network model. We apply ROPE to several simulated data sets as well as genomic data from The Cancer Genome Atlas. We show that ROPE outperforms state-of-the-art methods in terms of FDR control and robust performance across data sets. We illustrate how to use ROPE to make a principled model selection for which genomic associations to study further. ROPE is available as an R package on CRAN.

stat.CO↗

arXiv2013

Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles

Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g. wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network.

stat.ML↗