Variational Bayesian learning for Gaussian mixture HMM in seizure prediction based on long term EEG of epileptic rats

Abstract Background Copy number alterations (CNAs), due to their large impact on the genome, have been an important contributing factor to oncogenesis and metastasis. Detecting genomic alterations from the shallow-sequencing data of a low-purity tumor sample remains a challenging task. Results We introduce Accucopy, a method to infer total copy numbers (TCNs) and allele-specific copy numbers (ASCNs) from challenging low-purity and low-coverage tumor samples. Accucopy adopts many robust statistical techniques such as kernel smoothing of coverage differentiation information to discern signals from noise and combines ideas from time-series analysis and the signal-processing field to derive a range of estimates for the period in a histogram of coverage differentiation information. Statistical learning models such as the tiered Gaussian mixture model, the expectation–maximization algorithm, and sparse Bayesian learning were customized and built into the model. Accucopy is implemented in C++ /Rust, packaged in a docker image, and supports non-human samples, more at http://www.yfish.org/software/. Conclusions We describe Accucopy, a method that can predict both TCNs and ASCNs from low-coverage low-purity tumor sequencing data. Through comparative analyses in both simulated and real-sequencing samples, we demonstrate that Accucopy is more accurate than Sclust, ABSOLUTE, and Sequenza.

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Stochastic complexities of general mixture models in variational Bayesian learning

Neural Networks ◽

10.1016/j.neunet.2006.05.030 ◽

2007 ◽

Vol 20 (2) ◽

pp. 210-219 ◽

Cited By ~ 12

Author(s):

Kazuho Watanabe ◽

Sumio Watanabe

Keyword(s):

Mixture Models ◽

Bayesian Learning ◽

Variational Bayesian

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Blind separation of nonlinear mixtures by variational Bayesian learning

Digital Signal Processing ◽

10.1016/j.dsp.2007.02.009 ◽

2007 ◽

Vol 17 (5) ◽

pp. 914-934 ◽

Cited By ~ 17

Author(s):

Antti Honkela ◽

Harri Valpola ◽

Alexander Ilin ◽

Juha Karhunen

Keyword(s):

Bayesian Learning ◽

Blind Separation ◽

Variational Bayesian ◽

Nonlinear Mixtures

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Reconciling differences in stratospheric ozone composites

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-12269-2017 ◽

2017 ◽

Vol 17 (20) ◽

pp. 12269-12302 ◽

Cited By ~ 22

Author(s):

William T. Ball ◽

Justin Alsing ◽

Daniel J. Mortlock ◽

Eugene V. Rozanov ◽

Fiona Tummon ◽

...

Keyword(s):

Time Series ◽

Likelihood Function ◽

Stratospheric Ozone ◽

Gaussian Mixture ◽

Robust Estimate ◽

Mixture Density ◽

Source Data ◽

Ozone Trends ◽

Decadal Trend

Abstract. Observations of stratospheric ozone from multiple instruments now span three decades; combining these into composite datasets allows long-term ozone trends to be estimated. Recently, several ozone composites have been published, but trends disagree by latitude and altitude, even between composites built upon the same instrument data. We confirm that the main causes of differences in decadal trend estimates lie in (i) steps in the composite time series when the instrument source data changes and (ii) artificial sub-decadal trends in the underlying instrument data. These artefacts introduce features that can alias with regressors in multiple linear regression (MLR) analysis; both can lead to inaccurate trend estimates. Here, we aim to remove these artefacts using Bayesian methods to infer the underlying ozone time series from a set of composites by building a joint-likelihood function using a Gaussian-mixture density to model outliers introduced by data artefacts, together with a data-driven prior on ozone variability that incorporates knowledge of problems during instrument operation. We apply this Bayesian self-calibration approach to stratospheric ozone in 10° bands from 60° S to 60° N and from 46 to 1 hPa (∼ 21–48 km) for 1985–2012. There are two main outcomes: (i) we independently identify and confirm many of the data problems previously identified, but which remain unaccounted for in existing composites; (ii) we construct an ozone composite, with uncertainties, that is free from most of these problems – we call this the BAyeSian Integrated and Consolidated (BASIC) composite. To analyse the new BASIC composite, we use dynamical linear modelling (DLM), which provides a more robust estimate of long-term changes through Bayesian inference than MLR. BASIC and DLM, together, provide a step forward in improving estimates of decadal trends. Our results indicate a significant recovery of ozone since 1998 in the upper stratosphere, of both northern and southern midlatitudes, in all four composites analysed, and particularly in the BASIC composite. The BASIC results also show no hemispheric difference in the recovery at midlatitudes, in contrast to an apparent feature that is present, but not consistent, in the four composites. Our overall conclusion is that it is possible to effectively combine different ozone composites and account for artefacts and drifts, and that this leads to a clear and significant result that upper stratospheric ozone levels have increased since 1998, following an earlier decline.

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