scholarly journals Statistical Inference From Stem Cell Barcoding Data Using Adaptive Approximate Bayesian Computation

2021 ◽  
Author(s):  
Siyi Chen ◽  
Katherine Y. King ◽  
Marek Kimmel
Keyword(s):  
Stem Cell ◽  
Dna Sequences ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Real Life ◽  
Lineage Tracing ◽  
Bayesian Computation ◽  
Multinomial Model ◽  
Hematopoietic Stem ◽  
Approximate Bayesian

Abstract Background: Barcodes that can be supplied to cells by transduction of a library of unique DNA sequences allow identification of heterogeneity in cell populations and lineage tracing applications. Estimation of the number of hematopoietic stem cell (HSC) clones is important since it also allows to approximate the number of hematopoietic stem cells from which the circulating blood cells descend. This problem is similar to the species problem, well-known to ecologists. However, an additional ”degree of freedom” exists, since different HSC generally give rise to clones with different growth rates. This adds credibility to sampling models based on different versions of Dirichlet-multinomial distributions. Results: We developed a truncated population approximate Bayesian computation (ABC) algorithm which is derived from sequential Monte Carlo ABC (SMC-ABC) and applied the method to the symmetric Dirichlet-multinomial model proposed by Zhang et al. (2005) and asymmetric Dirichlet-multinomial model we proposed. Methodology was tested using simulated and real-life data. Conclusions: Results suggest that flexibility of the asymmetric Dirichlet-multinomial helps to obtain insight into heterogeneity of proliferating cell systems such as HSC. Estimates based on experimental data approach the correct count of murine HSC.

scholarly journals Approximate Bayesian Computation for Discrete Spaces

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 312
Author(s):  
Ilze A. Auzina ◽  
Jakub M. Tomczak
Keyword(s):  
Approximate Bayesian Computation ◽  
Likelihood Function ◽  
Real Life ◽  
Random Variables ◽  
Bayesian Computation ◽  
Inference Problem ◽  
Markov Kernel ◽  
Inference Problems ◽  
Binary Neural Network ◽  
Approximate Bayesian

Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables, likelihood-free inference problems can be solved via Approximate Bayesian Computation (ABC). However, an optimal alternative for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose an adjusted population-based MCMC ABC method by re-defining the standard ABC parameters to discrete ones and by introducing a novel Markov kernel that is inspired by differential evolution. We first assess the proposed Markov kernel on a likelihood-based inference problem, namely discovering the underlying diseases based on a QMR-DTnetwork and, subsequently, the entire method on three likelihood-free inference problems: (i) the QMR-DT network with the unknown likelihood function, (ii) the learning binary neural network, and (iii) neural architecture search. The obtained results indicate the high potential of the proposed framework and the superiority of the new Markov kernel.

scholarly journals Testing hypotheses of diversification in Panamanian frogs and freshwater fishes using hierarchical approximate Bayesian computation with model averaging

2018 ◽  
Author(s):  
Justin C Bagley ◽  
Michael J Hickerson ◽  
Jerald B Johnson
Keyword(s):  
Freshwater Fish ◽  
Dna Sequences ◽  
Approximate Bayesian Computation ◽  
Bayes Factor ◽  
Fish Assemblages ◽  
Model Averaging ◽  
External Information ◽  
Bayesian Computation ◽  
Genetic Structuring ◽  
Approximate Bayesian

Most Neotropical frog and freshwater fish species sampled to date show phylogeographic breaks along the Pacific coast of the Isthmus of Panama, with lineages in Costa Rica and western Panama isolated from central Panama. We examine temporal patterns of diversification of taxa across this ‘western Panama isthmus’ (WPI) break to test hypotheses about the origin of species geographical distributions and genetic structuring in this region. We tested for synchronous diversification of four codistributed frog taxon-pairs and three fish taxon-pairs sharing the WPI break using hierarchical approximate Bayesian computation with model averaging based on mitochondrial DNA sequences. We also estimated lineage divergence times using full-Bayesian models. Several of our results supported synchronous divergences within the frog and freshwater fish assemblages; however, Bayes factor support was equivocal for or against synchronous or asynchronous diversification. Nevertheless, we infer that frog populations were likely isolated by one or multiple Pliocene–Pleistocene events more recently than predicted by previous models, while fish genetic diversity was structured by Pleistocene events. By integrating our results with external information from geology and elevational sea level modeling, we discuss the implications of our findings for understanding the biogeographical scenario of the diversification of Panamanian frogs and fishes. Consistent with the ‘Bermingham/Martin model’ (Mol. Ecol. 1998, 7: 499-517), we conclude that the regional fish assemblage was fractured by processes shaping isthmian landscapes during the Pleistocene glaciations, including drainage basin isolation during lowered sea levels.

scholarly journals Testing Hypotheses of Diversification in Panamanian Frogs and Freshwater Fishes Using Hierarchical Approximate Bayesian Computation with Model Averaging

Diversity ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 120 ◽  
Author(s):  
Justin Bagley ◽  
Michael Hickerson ◽  
Jerald Johnson
Keyword(s):  
Freshwater Fish ◽  
Dna Sequences ◽  
Approximate Bayesian Computation ◽  
Bayes Factor ◽  
Fish Assemblages ◽  
Model Averaging ◽  
External Information ◽  
Bayesian Computation ◽  
Genetic Structuring ◽  
Approximate Bayesian

Most Neotropical frog and freshwater fish species sampled to date show phylogeographic breaks along the Pacific coast of the Isthmus of Panama, with lineages in Costa Rica and western Panama isolated from central Panama. We examine temporal patterns of diversification of taxa across this ‘western Panama isthmus’ (WPI) break to test hypotheses about the origin of species geographical distributions and genetic structuring in this region. We tested for synchronous diversification of four codistributed frog taxon-pairs and three fish taxon-pairs sharing the WPI break using hierarchical approximate Bayesian computation with model averaging based on mitochondrial DNA sequences. We also estimated lineage divergence times using full-Bayesian models. Several of our results supported synchronous divergences within the frog and freshwater fish assemblages; however, Bayes factor support was equivocal for or against synchronous or asynchronous diversification. Nevertheless, we infer that frog populations were likely isolated by one or multiple Pliocene–Pleistocene events more recently than predicted by previous models, while fish genetic diversity was structured by Pleistocene events. By integrating our results with external information from geology and elevational sea level modeling, we discuss the implications of our findings for understanding the biogeographical scenario of the diversification of Panamanian frogs and fishes. Consistent with the ‘Bermingham/Martin model’ (Molecular Ecology 1998, 7, 499–517), we conclude that the regional fish assemblage was fractured by processes shaping isthmian landscapes during the Pleistocene glaciations, including drainage basin isolation during lowered sea levels.

scholarly journals GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

2020 ◽  
Vol 36 (10) ◽  
pp. 3286-3287 ◽  
Author(s):  
Evgeny Tankhilevich ◽  
Jonathan Ish-Horowicz ◽  
Tara Hameed ◽  
Elisabeth Roesch ◽  
Istvan Kleijn ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Computational Cost ◽  
Bayesian Computation ◽  
Parameter Inference ◽  
Likelihood Functions ◽  
Gaussian Process Emulation ◽  
Selection For ◽  
Approximate Bayesian

Abstract Motivation Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. Results We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using (i) standard rejection ABC or sequential Monte Carlo ABC or (ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost. Availability and implementation https://github.com/tanhevg/GpABC.jl.

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