Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/38718
Title: BTR: training asynchronous Boolean models using single-cell expression data.
Authors: Lim, C. Y.
Wang, H.
Woodhouse, S.
Piterman, Nir
Wernisch, L.
Fisher, J.
Göttgens, B.
First Published: 6-Sep-2016
Publisher: BioMed Central
Citation: BMC Bioinformatics, 2016 17:355
Abstract: BACKGROUND: Rapid technological innovation for the generation of single-cell genomics data presents new challenges and opportunities for bioinformatics analysis. One such area lies in the development of new ways to train gene regulatory networks. The use of single-cell expression profiling technique allows the profiling of the expression states of hundreds of cells, but these expression states are typically noisier due to the presence of technical artefacts such as drop-outs. While many algorithms exist to infer a gene regulatory network, very few of them are able to harness the extra expression states present in single-cell expression data without getting adversely affected by the substantial technical noise present. RESULTS: Here we introduce BTR, an algorithm for training asynchronous Boolean models with single-cell expression data using a novel Boolean state space scoring function. BTR is capable of refining existing Boolean models and reconstructing new Boolean models by improving the match between model prediction and expression data. We demonstrate that the Boolean scoring function performed favourably against the BIC scoring function for Bayesian networks. In addition, we show that BTR outperforms many other network inference algorithms in both bulk and single-cell synthetic expression data. Lastly, we introduce two case studies, in which we use BTR to improve published Boolean models in order to generate potentially new biological insights. CONCLUSIONS: BTR provides a novel way to refine or reconstruct Boolean models using single-cell expression data. Boolean model is particularly useful for network reconstruction using single-cell data because it is more robust to the effect of drop-outs. In addition, BTR does not assume any relationship in the expression states among cells, it is useful for reconstructing a gene regulatory network with as few assumptions as possible. Given the simplicity of Boolean models and the rapid adoption of single-cell genomics by biologists, BTR has the potential to make an impact across many fields of biomedical research.
DOI Link: 10.1186/s12859-016-1235-y
ISSN: 1471-2105
eISSN: 1471-2105
Links: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1235-y
http://hdl.handle.net/2381/38718
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Description: The haematopoietic data, which include two Boolean models [38, 39] and the two datasets [10] are included in the BTR package, and are also available in their respective publications. BTR is available as an R package on CRAN and also on Github [https://github.com/cheeyeelim/btr] [49]. All data and scripts that are used to generate results in this paper are available either as part of the BTR package or at [https://github.com/cheeyeelim/btr_resultscripts] [50].
Appears in Collections:Published Articles, Dept. of Computer Science

Files in This Item:
File Description SizeFormat 
BTR: training asynchronous Boolean models using single-cell expression data.pdfPublished (publisher PDF)3.74 MBAdobe PDFView/Open


Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.