Li, S, Lopez Garcia, M orcid.org/0000-0003-3833-8595 and Cutillo, L (2022) Simulation-based Evaluation of the Reliability of Bayesian Hierarchical Models for sc-RNAseq Data. In: 2021 20th International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS). 20th International Conference on Ubiquitous Computing and Communications (IUCC-2021), 20-22 Dec 2021, Online. IEEE ISBN 978-1-6654-6668-4
Abstract
A Bayesian hierarchical model (BHM) is typically formulated specifying the data model, the parameters model and the prior distributions. The posterior inference of a BHM depends both on the model specification and on the computation algorithm used. The most straightforward way to test the reliability of a BHM inference is to compare the posterior distributions with the ground truth value of the model parameters, when available. However, when dealing with experimental data, the true value of the underlying parameters is typically unknown. In these situations, numerical experiments
based on synthetic datasets generated from the model itself offer a natural approach to check model performance and posterior estimates. Surprisingly, validation of BHMs with high-dimensional parameter spaces and non-Gaussian distributions, is unexplored. In this paper, we show how to test the reliability of a BHM. We introduce a change in the model assumptions to allow for prior contamination, and develop a simulation based evaluation framework to assess the reliability of the inference of a given BHM. We illustrate our approach on a specific BHM used for the analysis of Single-cell Sequencing Data (BASiCS).
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Bayesian Hierarchical Model; Parameter Calibration; Simulation-based Calibration; Single-cell Sequencing Data |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Mar 2022 10:51 |
Last Modified: | 04 Mar 2022 10:51 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00063 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184223 |