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Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data

Thalia E. Chan, Ananth V. Pallaseni, Ann C. Babtie, Kirsten R. McEwen, Michael P.H. Stumpf
doi: https://doi.org/10.1101/264853
Thalia E. Chan
1Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
†T.E.C.(Author One) and A.V.P. (Author Two) contributed equally to this work.
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Ananth V. Pallaseni
1Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
†T.E.C.(Author One) and A.V.P. (Author Two) contributed equally to this work.
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Ann C. Babtie
1Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
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Kirsten R. McEwen
1Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
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Michael P.H. Stumpf
1Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
2MRC London Institute of Medical Sciences, Hammersmith Campus, Imperial College London, London W12 0NN, UK
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Abstract

Gene expression is controlled by networks of transcription factors and regulators, but the structure of these networks is as yet poorly understood and is thus inferred from data. Recent work has shown the efficacy of information theoretical approaches for network reconstruction from single cell transcriptomic data. Such methods use information to estimate dependence between every pair of genes in the dataset, then edges are inferred between top-scoring pairs. Dependence, however, does not indicate significance, and the definition of “top-scoring” is often arbitrary and a priori related to expected network size. This makes comparing networks across datasets difficult, because networks of a similar size are not necessarily similarly accurate. We present a method for performing formal hypothesis tests on putative network edges derived from information theory, bringing together empirical Bayes and work on theoretical null distributions for information measures. Thresholding based on empirical Bayes allows us to control network accuracy according to how we intend to use the network. Using single cell data from mouse pluripotent stem cells, we recover known interactions and suggest several new interactions for experimental validation (using a stringent threshold) and discover high-level interactions between sub-networks (using a more relaxed threshold). Furthermore, our method allows for the inclusion of prior information. We use in-silico data to show that even relatively poor quality prior information can increase the accuracy of a network, and demonstrate that the accuracy of networks inferred from single cell data can sometimes be improved by priors from population-level ChIP-Seq and qPCR data.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 13, 2018.
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Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data
Thalia E. Chan, Ananth V. Pallaseni, Ann C. Babtie, Kirsten R. McEwen, Michael P.H. Stumpf
bioRxiv 264853; doi: https://doi.org/10.1101/264853
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Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data
Thalia E. Chan, Ananth V. Pallaseni, Ann C. Babtie, Kirsten R. McEwen, Michael P.H. Stumpf
bioRxiv 264853; doi: https://doi.org/10.1101/264853

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