scholarly journals Variational inference using approximate likelihood under the coalescent with recombination

2021 ◽  
pp. gr.273631.120
Author(s):  
Xinhao Liu ◽  
Huw A Ogilvie ◽  
Luay Nakhleh
Keyword(s):  
Simulated Data ◽  
Variational Inference ◽  
Divide And Conquer ◽  
Data Sets ◽  
Transition Rates ◽  
Data Set ◽  
Population Sizes ◽  
Novel Method ◽  
Approximate Likelihood ◽  
Promising Avenue

Coalescent methods are proven and powerful tools for population genetics, phylogenetics, epidemiology, and other fields. A promising avenue for the analysis of large genomic alignments, which are increasingly common, are coalescent hidden Markov model (coalHMM) methods, but these methods have lacked general usability and flexibility. We introduce a novel method for automatically learning a coalHMM and inferring the posterior distributions of evolutionary parameters using black-box variational inference, with the transition rates between local genealogies derived empirically by simulation. This derivation enables our method to work directly with three or four taxa and through a divide-and-conquer approach with more taxa. Using a simulated data set resembling a human-chimp-gorilla scenario, we show that our method has comparable or better accuracy to previous coalHMM methods. Both species divergence times and population sizes were accurately inferred. The method also infers local genealogies and we report on their accuracy. Furthermore, we discuss a potential direction for scaling the method to larger data sets through a divide-and-conquer approach. This accuracy means our method is useful now, and by deriving transition rates by simulation it is flexible enough to enable future implementations of all kinds of population models.

scholarly journals Variational Inference Using Approximate Likelihood Under the Coalescent With Recombination

2020 ◽  
Author(s):  
Xinhao Liu ◽  
Huw A. Ogilvie ◽  
Luay Nakhleh
Keyword(s):  
Simulated Data ◽  
Variational Inference ◽  
Divide And Conquer ◽  
Data Sets ◽  
Transition Rates ◽  
Data Set ◽  
Population Sizes ◽  
Novel Method ◽  
Approximate Likelihood ◽  
Promising Avenue

AbstractCoalescent methods are proven and powerful tools for population genetics, phylogenetics, epidemiology, and other fields. A promising avenue for the analysis of large genomic alignments, which are increasingly common, are coalescent hidden Markov model (coalHMM) methods, but these methods have lacked general usability and flexibility. We introduce a novel method for automatically learning a coalHMM and inferring the posterior distributions of evolutionary parameters using black-box variational inference, with the transition rates between local genealogies derived empirically by simulation. This derivation enables our method to work directly with three or four taxa and through a divide-and-conquer approach with more taxa. Using a simulated data set resembling a human-chimp-gorilla scenario, we show that our method has comparable or better accuracy to previous coalHMM methods. Both species divergence times and population sizes were accurately inferred. The method also infers local genealogies and we report on their accuracy. Furthermore, we illustrate how to scale the method to larger data sets through a divide-and-conquer approach. This accuracy means our method is useful now, and by deriving transition rates by simulation it is flexible enough to enable future implementations of all kinds of population models.

scholarly journals Efficient detection of repeating sites to accelerate phylogenetic likelihood calculations

2016 ◽  
Author(s):  
Kassian Kobert ◽  
Alexandros Stamatakis ◽  
Tomáš Flouri
Keyword(s):  
Evolutionary Biology ◽  
Likelihood Function ◽  
Simulated Data ◽  
Evolutionary Model ◽  
Identical Result ◽  
Model Parameters ◽  
Data Sets ◽  
Efficient Detection ◽  
Novel Method ◽  
Computational Bottleneck

The phylogenetic likelihood function is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory saving attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 10-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the phylogenetic likelihood function currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation.

scholarly journals Using target enrichment sequencing to study the higher-level phylogeny of the largest lichen-forming fungi family: Parmeliaceae (Ascomycota)

IMA Fungus ◽  
2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Felix Grewe ◽  
Claudio Ametrano ◽  
Todd J. Widhelm ◽  
Steven Leavitt ◽  
Isabel Distefano ◽  
...  
Keyword(s):  
Data Sets ◽  
Target Enrichment ◽  
Data Set ◽  
Reduced Genome ◽  
Genome Data ◽  
Worldwide Distribution ◽  
Phylogenetic Studies ◽  
Genome Scale ◽  
Scale Data ◽  
Promising Avenue

AbstractParmeliaceae is the largest family of lichen-forming fungi with a worldwide distribution. We used a target enrichment data set and a qualitative selection method for 250 out of 350 genes to infer the phylogeny of the major clades in this family including 81 taxa, with both subfamilies and all seven major clades previously recognized in the subfamily Parmelioideae. The reduced genome-scale data set was analyzed using concatenated-based Bayesian inference and two different Maximum Likelihood analyses, and a coalescent-based species tree method. The resulting topology was strongly supported with the majority of nodes being fully supported in all three concatenated-based analyses. The two subfamilies and each of the seven major clades in Parmelioideae were strongly supported as monophyletic. In addition, most backbone relationships in the topology were recovered with high nodal support. The genus Parmotrema was found to be polyphyletic and consequently, it is suggested to accept the genus Crespoa to accommodate the species previously placed in Parmotrema subgen. Crespoa. This study demonstrates the power of reduced genome-scale data sets to resolve phylogenetic relationships with high support. Due to lower costs, target enrichment methods provide a promising avenue for phylogenetic studies including larger taxonomic/specimen sampling than whole genome data would allow.

scholarly journals Feature Scaling via Second-Order Cone Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Zhizheng Liang
Keyword(s):  
Simulated Data ◽  
Performance Measure ◽  
Second Order ◽  
Data Sets ◽  
Scaling Factors ◽  
Data Set ◽  
Cone Programming ◽  
Second Order Cone Programming ◽  
Second Order Cone ◽  
Feature Scaling

Feature scaling has attracted considerable attention during the past several decades because of its important role in feature selection. In this paper, a novel algorithm for learning scaling factors of features is proposed. It first assigns a nonnegative scaling factor to each feature of data and then adopts a generalized performance measure to learn the optimal scaling factors. It is of interest to note that the proposed model can be transformed into a convex optimization problem: second-order cone programming (SOCP). Thus the scaling factors of features in our method are globally optimal in some sense. Several experiments on simulated data, UCI data sets, and the gene data set are conducted to demonstrate that the proposed method is more effective than previous methods.

A Growth Model for Multilevel Ordinal Data

2005 ◽  
Vol 30 (4) ◽  
pp. 369-396 ◽  
Author(s):  
Eisuke Segawa
Keyword(s):  
Latent Variable ◽  
Ordinal Data ◽  
Linear Models ◽  
Growth Models ◽  
Simulated Data ◽  
Real Data ◽  
Analytic Structure ◽  
Data Sets ◽  
Data Set ◽  
Time Points

Multi-indicator growth models were formulated as special three-level hierarchical generalized linear models to analyze growth of a trait latent variable measured by ordinal items. Items are nested within a time-point, and time-points are nested within subject. These models are special because they include factor analytic structure. This model can analyze not only data with item- and time-level missing observations, but also data with time points freely specified over subjects. Furthermore, features useful for longitudinal analyses, “autoregressive error degree one” structure for the trait residuals and estimated time-scores, were included. The approach is Bayesian with Markov Chain and Monte Carlo, and the model is implemented in WinBUGS. They are illustrated with two simulated data sets and one real data set with planned missing items within a scale.

Classification with Local Clustering in Imbalanced Data Sets

2011 ◽  
Vol 219-220 ◽  
pp. 151-155 ◽  
Author(s):  
Hua Ji ◽  
Hua Xiang Zhang
Keyword(s):  
Data Distribution ◽  
Imbalanced Data ◽  
Support Vector ◽  
Data Sets ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Local Clustering ◽  
Rare Class ◽  
Novel Method ◽  
The Cost

In many real-world domains, learning from imbalanced data sets is always confronted. Since the skewed class distribution brings the challenge for traditional classifiers because of much lower classification accuracy on rare classes, we propose the novel method on classification with local clustering based on the data distribution of the imbalanced data sets to solve this problem. At first, we divide the whole data set into several data groups based on the data distribution. Then we perform local clustering within each group both on the normal class and the disjointed rare class. For rare class, the subsequent over-sampling is employed according to the different rates. At last, we apply support vector machines (SVMS) for classification, by means of the traditional tactic of the cost matrix to enhance the classification accuracies. The experimental results on several UCI data sets show that this method can produces much higher prediction accuracies on the rare class than state-of-art methods.

scholarly journals A Novel Method to Detect Bias in Short Read NGS Data

2017 ◽  
Vol 14 (3) ◽  
Author(s):  
Jamie Alnasir ◽  
Hugh P. Shanahan
Keyword(s):  
Biological Significance ◽  
Gc Content ◽  
Next Generation Sequencing Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Data Set ◽  
Short Read ◽  
Novel Method ◽  
Type Data ◽  
Ngs Data

AbstractDetecting sources of bias in transcriptomic data is essential to determine signals of Biological significance. We outline a novel method to detect sequence specific bias in short read Next Generation Sequencing data. This is based on determining intra-exon correlations between specific motifs. This requires a mild assumption that short reads sampled from specific regions from the same exon will be correlated with each other. This has been implemented on Apache Spark and used to analyse two D. melanogaster eye-antennal disc data sets generated at the same laboratory. The wild type data set in drosophila indicates a variation due to motif GC content that is more significant than that found due to exon GC content. The software is available online and could be applied for cross-experiment transcriptome data analysis in eukaryotes.

scholarly journals MSIGNET: a Metropolis sampling-based method for global optimal significant network identification

2018 ◽  
Author(s):  
Xi Chen ◽  
Jianhua Xuan
Keyword(s):  
Cancer Recurrence ◽  
Simulated Data ◽  
Superior Performance ◽  
Biological Knowledge ◽  
Specific Gene ◽  
Data Sets ◽  
Data Set ◽  
Novel Approach ◽  
Network Identification ◽  
Global Optimal

AbstractIn this paper, we propose a novel approach namely MSIGNET to identify subnetworks with significantly expressed genes by integrating context specific gene expression and protein-protein interaction (PPI) data. Specifically, we integrate differential expression of each gene and mutual information of gene pairs in a Bayesian framework and use Metropolis sampling to identify functional interactions. During the sampling process, a conditional probability is calculated given a randomly selected gene to control the network state transition. Our method provides global statistics of all genes and their interactions, and finally achieves a global optimal sub-network. We apply MSIGNET to simulated data and have demonstrated its superior performance over comparable network identification tools. Using a validated Parkinson data set we show that the network identified using MSIGNET is consistent to previously reported results but provides more biology meaningful interpretation of Parkinson’s disease. Finally, to study networks related to ovarian cancer recurrence, we investigate two patient data sets. Identified networks from independent data sets show functional consistence. And those common genes and interactions are well supported by current biological knowledge.

scholarly journals A Simulation Study on Increasing Capture Periods in Bayesian Closed Population Capture-Recapture Models with Heterogeneity

2020 ◽  
Vol 18 (1) ◽  
pp. 2-23
Author(s):  
Ross M. Gosky ◽  
Joel Sanqui
Keyword(s):  
Population Size ◽  
Simulation Study ◽  
Simulated Data ◽  
Population Models ◽  
Data Set ◽  
Closed Population ◽  
Population Sizes ◽  
Capture Recapture

Capture-Recapture models are useful in estimating unknown population sizes. A common modeling challenge for closed population models involves modeling unequal animal catchability in each capture period, referred to as animal heterogeneity. Inference about population size N is dependent on the assumed distribution of animal capture probabilities in the population, and that different models can fit a data set equally well but provide contradictory inferences about N. Three common Bayesian Capture-Recapture heterogeneity models are studied with simulated data to study the prevalence of contradictory inferences is in different population sizes with relatively low capture probabilities, specifically at different numbers of capture periods in the study.

scholarly journals Comparison of silhouette-based reallocation methods for vegetation classification

2019 ◽  
Author(s):  
Attila Lengyel ◽  
David W. Roberts ◽  
Zoltán Botta-Dukát
Keyword(s):  
Simulated Data ◽  
Primary Objective ◽  
Vegetation Classification ◽  
Data Sets ◽  
Data Set ◽  
Number Of Clusters ◽  
Silhouette Width ◽  
Diagnostic Species ◽  
Order Of Magnitude ◽  
Initial Classification

AbstractAimsTo introduce REMOS, a new iterative reallocation method (with two variants) for vegetation classification, and to compare its performance with OPTSIL. We test (1) how effectively REMOS and OPTSIL maximize mean silhouette width and minimize the number of negative silhouette widths when run on classifications with different structure; (2) how these three methods differ in runtime with different sample sizes; and (3) if classifications by the three reallocation methods differ in the number of diagnostic species, a surrogate for interpretability.Study areaSimulation; example data sets from grasslands in Hungary and forests in Wyoming and Utah, USA.MethodsWe classified random subsets of simulated data with the flexible-beta algorithm for different values of beta. These classifications were subsequently optimized by REMOS and OPTSIL and compared for mean silhouette widths and proportion of negative silhouette widths. Then, we classified three vegetation data sets of different sizes from two to ten clusters, optimized them with the reallocation methods, and compared their runtimes, mean silhouette widths, numbers of negative silhouette widths, and the number of diagnostic species.ResultsIn terms of mean silhouette width, OPTSIL performed the best when the initial classifications already had high mean silhouette width. REMOS algorithms had slightly lower mean silhouette width than what was maximally achievable with OPTSIL but their efficiency was consistent across different initial classifications; thus REMOS was significantly superior to OPTSIL when the initial classification had low mean silhouette width. REMOS resulted in zero or a negligible number of negative silhouette widths across all classifications. OPTSIL performed similarly when the initial classification was effective but could not reach as low proportion of misclassified objects when the initial classification was inefficient. REMOS algorithms were typically more than an order of magnitude faster to calculate than OPTSIL. There was no clear difference between REMOS and OPTSIL in the number of diagnostic species.ConclusionsREMOS algorithms may be preferable to OPTSIL when (1) the primary objective is to reduce or eliminate negative silhouette widths in a classification, (2) the initial classification has low mean silhouette width, or (3) when the time efficiency of the algorithm is important because of the size of the data set or the high number of clusters.

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