Bayesian Clustering Algorithm Research Articles

Differential permeability of rivers to raccoon gene flow corresponds to rabies incidence in Ontario, Canada

The correlation of landscape features with genetic discontinuities reveals barriers to dispersal that can contribute to understanding present and future spread of wildlife diseases. This knowledge can then be used for targeting control efforts. The impact of natural barriers on raccoon dispersal was assessed through genetic analysis of samples from two regions, Niagara (N = 666) and St. Lawrence (N = 802). These areas are transected by major rivers and are at the northern front of a raccoon rabies epizootic. Genetic clusters were identified in each region using Bayesian clustering algorithms. In the Niagara region, two clusters were identified corresponding to either side of the Niagara River. For the St. Lawrence region, spatially congruent clusters were not identified, despite the presence of the intervening St. Lawrence River. These genetic data are consistent with raccoon rabies incidence data where rabies has been detected across the St. Lawrence River in Ontario while no cases have been detected in Ontario across the Niagara River. This is despite expectations of rabies incidence in Niagara before the St. Lawrence based on the progression of rabies from New York. The results from the two regions suggest different permeabilities to raccoons between New York and Ontario that may be attributed to the rivers. However, other factors have also been explored that could contribute to this difference between these study sites including the shape of the landscape and resource distribution.

Bayesian Inference of Functional Connectivity and Network Structure From Spikes

Current multielectrode techniques enable the simultaneous recording of spikes from hundreds of neurons. To study neural plasticity and network structure it is desirable to infer the underlying functional connectivity between the recorded neurons. Functional connectivity is defined by a large number of parameters, which characterize how each neuron influences the other neurons. A Bayesian approach that combines information from the recorded spikes (likelihood) with prior beliefs about functional connectivity (prior) can improve inference of these parameters and reduce overfitting. Recent studies have used likelihood functions based on the statistics of point-processes and a prior that captures the sparseness of neural connections. Here we include a prior that captures the empirical finding that interactions tend to vary smoothly in time. We show that this method can successfully infer connectivity patterns in simulated data and apply the algorithm to spike data recorded from primary motor (M1) and premotor (PMd) cortices of a monkey. Finally, we present a new approach to studying structure in inferred connections based on a Bayesian clustering algorithm. Groups of neurons in M1 and PMd show common patterns of input and output that may correspond to functional assemblies.

Population substructure in Finland and Sweden revealed by the use of spatial coordinates and a small number of unlinked autosomal SNPs

BackgroundDespite several thousands of years of close contacts, there are genetic differences between the neighbouring countries of Finland and Sweden. Within Finland, signs of an east-west duality have been observed, whereas the population structure within Sweden has been suggested to be more subtle. With a fine-scale substructure like this, inferring the cluster membership of individuals requires a large number of markers. However, some studies have suggested that this number could be reduced if the individual spatial coordinates are taken into account in the analysis.ResultsWe genotyped 34 unlinked autosomal single nucleotide polymorphisms (SNPs), originally designed for zygosity testing, from 2044 samples from Sweden and 657 samples from Finland, and 30 short tandem repeats (STRs) from 465 Finnish samples. We saw significant population structure within Finland but not between the countries or within Sweden, and isolation by distance within Finland and between the countries. In Sweden, we found a deficit of heterozygotes that we could explain by simulation studies to be due to both a small non-random genotyping error and hidden substructure caused by immigration. Geneland, a model-based Bayesian clustering algorithm, clustered the individuals into groups that corresponded to Sweden and Eastern and Western Finland when spatial coordinates were used, whereas in the absence of spatial information, only one cluster was inferred.ConclusionWe show that the power to cluster individuals based on their genetic similarity is increased when including information about the spatial coordinates. We also demonstrate the importance of estimating the size and effect of genotyping error in population genetics in order to strengthen the validity of the results.

Boosting Predictions by Calibration of Traffic Model and Learning of Indicators' Distributions

In this paper, we discuss a two-step scheme using measurements that are taken on a real network. The two steps are complementary and aim to enhance the precision and the quality of a radio network planning tool. We have, in a first step, calibrated the tool by means of live traffic data and/or measurements that are taken on the air interface of a real network and are processed to calculate the traffic values on each cell. We show that the availability of real data is highly valuable since it provides a more detailed view of the network behavior and performance. In a second step, we have proposed a novel algorithm based on a fuzzy Bayesian framework to ameliorate the generalization of a distribution learning system. The learning system aims to correct the predictions of the planning tool and uses the information contained in the simulations as well as the knowledge of the measurements to learn a relation function. The fuzzy Bayesian clustering algorithm is a preprocessing technique that divides the whole learning space into subspaces, where the capacity of the learning system to predict <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unobserved</i> configurations (generalization) is better performed.

High genetic differentiation with no evidence of hybridisation between four limpet species (&lt;i&gt;Patella&lt;/i&gt; spp.) revealed by allozyme loci

The occurrence of hybridisation between limpet species of the genus Patella has always been a contentious issue. Although a previous allozyme study reported high differentiation and no hybridisation between Patella vulgata Linnaeus, 1758, Patella depressa Pennant, 1777 and Patella ulyssiponensis Gmelin, 1791 along English shores, the recent finding of an mtDNA haplotype of P. depressa in a P. vulgata individual raised new doubts on this issue. To further study the possibility of hybridisation between limpet species and their level of genetic differentiation, ten allozyme loci were screened using starch gel electrophoresis for P. ulyssiponensis, P. depressa, P. vulgata and Patella rustica Linnaeus, 1758, from the Atlantic coast of the Iberian Peninsula. Our results show high differentiation between species, which could be clearly separated into different clusters with a Bayesian clustering algorithm. No significant signs of hybridisation were detected between any of the four species. Thus, the hypothesis of hybridisation between P. vulgata and P. depressa across their sympatric distribution is not supported. Two sympatric clusters were recovered within P. vulgata that could be related to Hardy-Weinberg disequilibrium found in locus MPI. Finally, due to the high level of intraspecific variability, the studied loci are interesting tools for the analysis of population structure and stock identification.

Disentangling the role of hybridization in the evolution of the endangered Arizona cliffrose (Purshia subintegra; Rosaceae): a molecular and morphological analysis

Hybridization may threaten the conservation status of rare species through genetic assimilation and may confound the ability to distinguish among taxa. We studied these issues in an endangered shrub, Purshia subintegra (Rosaceae), known from four populations growing on limestone outcrops in central Arizona (USA). Using amplified fragment length polymorphisms (AFLP) and the Bayesian clustering algorithm implemented in STRUCTURE, we identified three distinct genetic lineages among Arizona Purshia subintegra and P. stansburiana. An initial split divided San Carlos Basin P. subintegra (considered P. pinkavae by Schaack) from northern P. stansburiana populations (FST = 0.394). A subsequent split separated northern P. stansburiana from two P. subintegra populations at Horseshoe Lake and Burro Creek (FST = 0.207), which comprised a nearly perfect admixture of the two lineages identified in the initial analysis. In the Verde River Valley P. subintegra is sympatric with P. stansburiana and exhibited an average 27% P. stansburiana genes for 5 of 6 stands analyzed, indicating ongoing hybridization and backcrossing with P. subintegra. Individuals carrying >90% P. subintegra markers are identifiable 68% of the time based on morphology, with leaf lobing, leaf size, and leaf length acting as the most reliable indicators of taxonomic status. However, the genetic and morphological distance correlation among individuals was low (r = 0.17, P = 0.0002), indicating that morphology cannot always accurately predict genetic admixture or taxonomy. Overall, our study confirmed the genetic distinctiveness of the San Carlos Basin population, an ancient natural hybrid origin of P. subintegra, and the presence of a hybrid swarm in the Verde Valley, whose conservation value may lie in its heightened genetic diversity.

Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study

AbstractOn the basis of simulated data, this study compares the relative performances of the Bayesian clustering computer programs structure, geneland, geneclust and a new program named tess. While these four programs can detect population genetic structure from multilocus genotypes, only the last three ones include simultaneous analysis from geographical data. The programs are compared with respect to their abilities to infer the number of populations, to estimate membership probabilities, and to detect genetic discontinuities and clinal variation. The results suggest that combining analyses using tess and structure offers a convenient way to address inference of spatial population structure.

Argentine population genetic structure: Large variance in Amerindian contribution

Argentine population genetic structure was examined using a set of 78 ancestry informative markers (AIMs) to assess the contributions of European, Amerindian, and African ancestry in 94 individuals members of this population. Using the Bayesian clustering algorithm STRUCTURE, the mean European contribution was 78%, the Amerindian contribution was 19.4%, and the African contribution was 2.5%. Similar results were found using weighted least mean square method: European, 80.2%; Amerindian, 18.1%; and African, 1.7%. Consistent with previous studies the current results showed very few individuals (four of 94) with greater than 10% African admixture. Notably, when individual admixture was examined, the Amerindian and European admixture showed a very large variance and individual Amerindian contribution ranged from 1.5 to 84.5% in the 94 individual Argentine subjects. These results indicate that admixture must be considered when clinical epidemiology or case control genetic analyses are studied in this population. Moreover, the current study provides a set of informative SNPs that can be used to ascertain or control for this potentially hidden stratification. In addition, the large variance in admixture proportions in individual Argentine subjects shown by this study suggests that this population is appropriate for future admixture mapping studies.

Bayesian clustering using hidden Markov random fields in spatial population genetics.

We introduce a new Bayesian clustering algorithm for studying population structure using individually geo-referenced multilocus data sets. The algorithm is based on the concept of hidden Markov random field, which models the spatial dependencies at the cluster membership level. We argue that (i) a Markov chain Monte Carlo procedure can implement the algorithm efficiently, (ii) it can detect significant geographical discontinuities in allele frequencies and regulate the number of clusters, (iii) it can check whether the clusters obtained without the use of spatial priors are robust to the hypothesis of discontinuous geographical variation in allele frequencies, and (iv) it can reduce the number of loci required to obtain accurate assignments. We illustrate and discuss the implementation issues with the Scandinavian brown bear and the human CEPH diversity panel data set.

Microsatellite variation and differentiation in African and non‐African populations of Drosophila simulans

Drosophila simulans originated in sub-Saharan Africa or Madagascar and colonized the rest of the world after the last glaciation about 10 000 years ago. Consistent with this demographic history, sub-Saharan African populations have been shown to harbour higher levels of microsatellite and sequence variation than cosmopolitan populations. Nevertheless, only limited information is available on the population structure of D. simulans. Here, we analysed X-linked and autosomal microsatellite loci in four sub-Saharan African, one North African, one Israeli, and two European D. simulans populations. Bayesian clustering algorithms combined the North African, Israeli, and European populations into a single cosmopolitan group. The four sub-Saharan populations were split into two separate groups. Pairwise F(ST) analysis, however, indicated significant population differentiation between all eight populations surveyed. A significant signal for population reduction in cosmopolitan populations was found only for X-linked loci.

A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes

Malaria represents one of the major worldwide challenges to public health. A recent breakthrough in the study of the disease follows the annotation of the genome of the malaria parasite Plasmodium falciparum and the mosquito vector (an organism that spreads an infectious disease)Anopheles. Of particular interest is the molecular biology underlying the immune response system of Anopheles, which actively fights against Plasmodium infection. This article reports a statistical analysis of gene expression time profiles from mosquitoes that have been infected with a bacterial agent. Specifically, we introduce a Bayesian model-based hierarchical clustering algorithm for curve data to investigate mechanisms of regulation in the genes concerned; that is, we aim to cluster genes having similar expression profiles. Genes displaying similar, interesting profiles can then be highlighted for further investigation by the experimenter. We show how our approach reveals structure within the data not captured by other approaches. One of the most pertinent features of the data is the sample size, which records the expression levels of 2,771 genes at 6 time points. Additionally, the time points are unequally spaced, and there is expected nonstationary behavior in the gene profiles. We demonstrate our approach to be readily implementable under these conditions, and highlight some crucial computational savings that can be made in the context of a fully Bayesian analysis.

Inference of ancestry: constructing hierarchical reference populations and assigning unknown individuals

The ability to infer personal genetic ancestry is being increasingly utilised in certain medical and forensic situations. Herein, the unsupervised Bayesian clustering algorithms structure, is employed to analyse 377 autosomal short tandem repeats typed on 1,056 individuals from the Centre d'Etude du Polymorphisme Humain Human Diversity Panel. Individuals of known geographical origin were hierarchically classified into a framework of increasingly homogeneous clusters to serve as reference populations into which individuals of unknown ancestry can be assigned. The groupings were characterised by the geographical affinities of cluster members and the accuracy of these procedures was verified using several genetic indices. Fine-scale substructure was detectable beyond the broad population level classifications that previously have been explored in this dataset. Metrics indicated that within certain lines, the strongest structuring signals were detected at the leaves of the hierarchy where lineage-specific groupings were identified. The accuracy of unknown assignment was assessed at each level of the hierarchy using a 'leave one out' strategy in which each individual was stripped of cluster membership and then re-assigned using the supervised Bayesian clustering algorithm implemented in GeneClass2. Although most clusters at all levels of resolution experienced highly accurate assignment, a decline was observed in the finer levels due to the mixed membership characteristics of some individuals. The parameters defined by this study allowed for assignment of unknown individuals to genetically defined clusters with measured likelihood. Shared ancestry data can then be inferred for the unknown individual.

Examination of ancestry and ethnic affiliation using highly informative diallelic DNA markers: application to diverse and admixed populations and implications for clinical epidemiology and forensic medicine

We and others have identified several hundred ancestry informative markers (AIMs) with large allele frequency differences between different major ancestral groups. For this study, a panel of 199 widely distributed AIMs was used to examine a diverse set of 796 DNA samples including self-identified European Americans, West Africans, East Asians, Amerindians, African Americans, Mexicans, Mexican Americans, Puerto Ricans and South Asians. Analysis using a Bayesian clustering algorithm (STRUCTURE) showed grouping of individuals with similar ethnic identity without any identifier other than the AIMs genotyping and showed admixture proportions that clearly distinguished different individuals of mixed ancestry. Additional analyses showed that, for the majority of samples, the predicted ethnic identity corresponded with the self-identified ethnicity at high probability (P > 0.99). Overall, the study demonstrates that AIMs can provide a useful adjunct to forensic medicine, pharmacogenomics and disease studies in which major ancestry or ethnic affiliation might be linked to specific outcomes.

Application of SNPs for assessing biodiversity and phylogeny among yeast strains

We examined the efficacy of single-nucleotide polymorphism (SNP) markers for the assessment of the phylogeny and biodiversity of Saccharomyces strains. Each of 32 Saccharomyces cerevisiae strains was genotyped at 30 SNP loci discovered by sequence alignment of the S. cerevisiae laboratory strain SK1 to the database sequence of strain S288c. In total, 10 SNPs were selected from each of the following three categories: promoter regions, nonsynonymous and synonymous sites (in open reading frames). The strains in this study included 11 haploid laboratory strains used for genetic studies and 21 diploids. Three non-cerevisiae species of Saccharomyces (sensu stricto) were used as an out-group. A Bayesian clustering-algorithm, Structure, effectively identified four different strain groups: laboratory, wine, other diploids and the non-cerevisiae species. Analysing haploid and diploid strains together caused problems for phylogeny reconstruction, but not for the clustering produced by Structure. The ascertainment bias introduced by the SNP discovery method caused difficulty in the phylogenetic analysis; alternative options are proposed. A smaller data set, comprising only the nine most polymorphic loci, was sufficient to obtain most features of the results.

Limited genetic exchanges between populations of an insect pest living on uncultivated and related cultivated host plants

Habitats in agroecosystems are ephemeral, and are characterized by frequent disturbances forcing pest species to successively colonize various hosts belonging either to the cultivated or to the uncultivated part of the agricultural landscape. The role of wild habitats as reservoirs or refuges for the aphid Sitobion avenae that colonize cultivated fields was assessed by investigating the genetic structure of populations collected on both cereal crops (wheat, barley and oat) and uncultivated hosts (Yorkshire fog, cocksfoot, bulbous oatgrass and tall oatgrass) in western France. Classical genetic analyses and Bayesian clustering algorithms indicate that genetic differentiation is high between populations collected on uncultivated hosts and on crops, revealing a relatively limited gene flow between the uncultivated margins and the cultivated part of the agroecosystem. A closer genetic relatedness was observed between populations living on plants belonging to the same tribe (Triticeae, Poeae and Aveneae tribes) where aphid genotypes appeared not to be specialized on a single host, but rather using a group of related plant species. Causes of this ecological differentiation and its implications for integrated pest management of S. avenae as cereals pest are discussed.

하이브리드 분류기법을 이용한 위성영상의 분류에 관한 연구

본 논문에서는 위성영상의 분류에 대한 성능 개선을 위하여 ISODATA 클러스터링, 퍼지 C-Means 알고리즘, 베이시안 최대우도 분류기법을 통합한 하이브리드 분류기법을 제안하였다. 본 연구에서는 분석자에 의하여 분류항목별 학습 데이터를 선정한 후 이를 ISODATA 클러스터링을 이용하여 각각의 분류항목별로 분광특징에 따라 학습 데이터를 세분화하여 새로운 학습 데이터를 선정하였다. 새롭게 선정된 학습 데이터를 이용하여 퍼지 C-Means 알고리즘을 이용하여 분류를 수행하고 그 결과를 베이시안 최대우도 분류기의 사전확률로 적용하여 분류를 수행하였다. 그 결과 분석자가 선정한 분류항목별 훈련데이터의 분광적인 특징에 관계없이 분류를 수행할 수 있었으며 위성영상의 분류의 성능을 개선할 수 있었다. 제안된 기법은 Landsat TM 위성영상을 이용하여 그 적용성을 시험하였다. This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel's membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method Is applied to a Landsat TM satellite image for the verifying test.