Abstract
Single-cell sequencing technology holds the promise of unravelling cell heterogeneities hidden in ubiquitous bulk-level analyses. However, limitations of current experimental methods also pose new obstacles that prevent accurate conclusions from being drawn. To overcome this, researchers have developed computational methods which aim at extracting the biological signal of interest from the noisy observations. In this paper we focus on probabilistic models designed for this task. Particularly, we describe how variational inference constitutes a powerful inference mechanism for different sample sizes, and critically review two recent scRNA-seq models which use it.
Supported by the EU Horizon 2020 research and innovation program (grant No. 633974 – SOUND project), and the Portuguese Foundation for Science & Technology (FCT), through UID/EMS/50022/2019 (IDMEC,LAETA), UID/EEA/50008/2019 (IT), UID/CEC/50021/2019 (INESC-ID), PTDC/EMS-SIS/0642/2014, PTDC/CCI-CIF/29877/2017, PTDC/EEI-SII/1937/2014, IF/00653/2012, and by internal IT projects QBigData and RAPID.
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Notes
- 1.
For brevity, here we do not consider the sparse loadings of the original model. In our experiments the resulting performance did not change significantly.
- 2.
\(\alpha _{k1,2}\), \(\beta _{k1,2}\) and \(\pi _p\) are fixed hyperparameters which can be estimated in an Expectation-Maximization scheme. See the original paper for details.
- 3.
In these simplified descriptions we ignore the batch annotation observations, for brevity.
- 4.
\(l_{\mu }\) and \(l_{\sigma }^2\) are the observed log-library size mean and variance, respectively.
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Acknowledgements
The authors thank Ghislain Durif for the helpful discussions about pCMF.
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Ferreira, P.F., Carvalho, A.M., Vinga, S. (2020). Variational Inference in Probabilistic Single-cell RNA-seq Models. In: Raposo, M., Ribeiro, P., Sério, S., Staiano, A., Ciaramella, A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science(), vol 11925. Springer, Cham. https://doi.org/10.1007/978-3-030-34585-3_2
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