Statistical Applications in Genetics and Molecular Biology最新文献_第5页

Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset. 计数数据判别问题的贝叶斯方法及其在多位点短串联重复数据集上的应用。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-05-04 DOI: 10.1515/sagmb-2018-0044

Koji Tsukuda, Shuhei Mano, Toshimichi Yamamoto

{"title":"Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset.","authors":"Koji Tsukuda, Shuhei Mano, Toshimichi Yamamoto","doi":"10.1515/sagmb-2018-0044","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0044","url":null,"abstract":"Short Tandem Repeats (STRs) are a type of DNA polymorphism. This study considers discriminant analysis to determine the population of test individuals using an STR database containing the lengths of STRs observed at more than one locus. The discriminant method based on the Bayes factor is discussed and an improved method is proposed. The main issues are to develop a method that is relatively robust to sample size imbalance, identify a procedure to select loci, and treat the parameter in the prior distribution. A previous study achieved a classification accuracy of 0.748 for the g-mean (geometric mean of classification accuracies for two populations) and 0.867 for the AUC (area under the receiver operating characteristic curve). We improve the maximum values for the g-mean to 0.830 and the AUC to 0.935. Computer simulations indicate that the previous method is susceptible to sample size imbalance, whereas the proposed method is more robust while achieving almost identical classification accuracy. Furthermore, the results confirm that threshold adjustment is an effective countermeasure to sample size imbalance.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"19 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37896963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of supervised and sparse functional genomic pathways. 有监督和稀疏功能基因组通路的鉴定。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-02-29 DOI: 10.1515/sagmb-2018-0026

Fan Zhang, Jeffrey C Miecznikowski, David L Tritchler

引用次数: 4

Joint variable selection and network modeling for detecting eQTLs. eqtl检测的联合变量选择与网络建模。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-02-20 DOI: 10.1515/sagmb-2019-0032

Xuan Cao, Lili Ding, Tesfaye B Mersha

{"title":"Joint variable selection and network modeling for detecting eQTLs.","authors":"Xuan Cao, Lili Ding, Tesfaye B Mersha","doi":"10.1515/sagmb-2019-0032","DOIUrl":"https://doi.org/10.1515/sagmb-2019-0032","url":null,"abstract":"In this study, we conduct a comparison of three most recent statistical methods for joint variable selection and covariance estimation with application of detecting expression quantitative trait loci (eQTL) and gene network estimation, and introduce a new hierarchical Bayesian method to be included in the comparison. Unlike the traditional univariate regression approach in eQTL, all four methods correlate phenotypes and genotypes by multivariate regression models that incorporate the dependence information among phenotypes, and use Bayesian multiplicity adjustment to avoid multiple testing burdens raised by traditional multiple testing correction methods. We presented the performance of three methods (MSSL - Multivariate Spike and Slab Lasso, SSUR - Sparse Seemingly Unrelated Bayesian Regression, and OBFBF - Objective Bayes Fractional Bayes Factor), along with the proposed, JDAG (Joint estimation via a Gaussian Directed Acyclic Graph model) method through simulation experiments, and publicly available HapMap real data, taking asthma as an example. Compared with existing methods, JDAG identified networks with higher sensitivity and specificity under row-wise sparse settings. JDAG requires less execution in small-to-moderate dimensions, but is not currently applicable to high dimensional data. The eQTL analysis in asthma data showed a number of known gene regulations such as STARD3, IKZF3 and PGAP3, all reported in asthma studies. The code of the proposed method is freely available at GitHub (https://github.com/xuan-cao/Joint-estimation-for-eQTL).","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"19 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2019-0032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37660750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An extended model for phylogenetic maximum likelihood based on discrete morphological characters. 基于离散形态特征的系统发育最大似然扩展模型。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-02-20 DOI: 10.1515/sagmb-2019-0029

David A Spade

引用次数: 1

Sparse latent factor regression models for genome-wide and epigenome-wide association studies 全基因组和表观全基因组关联研究的稀疏潜在因子回归模型

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-02-07 DOI: 10.1101/2020.02.07.938381

B. Jumentier, Kévin Caye, B. Heude, J. Lepeule, O. François

{"title":"Sparse latent factor regression models for genome-wide and epigenome-wide association studies","authors":"B. Jumentier, Kévin Caye, B. Heude, J. Lepeule, O. François","doi":"10.1101/2020.02.07.938381","DOIUrl":"https://doi.org/10.1101/2020.02.07.938381","url":null,"abstract":"Abstract Association of phenotypes or exposures with genomic and epigenomic data faces important statistical challenges. One of these challenges is to account for variation due to unobserved confounding factors, such as individual ancestry or cell-type composition in tissues. This issue can be addressed with penalized latent factor regression models, where penalties are introduced to cope with high dimension in the data. If a relatively small proportion of genomic or epigenomic markers correlate with the variable of interest, sparsity penalties may help to capture the relevant associations, but the improvement over non-sparse approaches has not been fully evaluated yet. Here, we present least-squares algorithms that jointly estimate effect sizes and confounding factors in sparse latent factor regression models. In simulated data, sparse latent factor regression models generally achieved higher statistical performance than other sparse methods, including the least absolute shrinkage and selection operator and a Bayesian sparse linear mixed model. In generative model simulations, statistical performance was slightly lower (while being comparable) to non-sparse methods, but in simulations based on empirical data, sparse latent factor regression models were more robust to departure from the model than the non-sparse approaches. We applied sparse latent factor regression models to a genome-wide association study of a flowering trait for the plant Arabidopsis thaliana and to an epigenome-wide association study of smoking status in pregnant women. For both applications, sparse latent factor regression models facilitated the estimation of non-null effect sizes while overcoming multiple testing issues. The results were not only consistent with previous discoveries, but they also pinpointed new genes with functional annotations relevant to each application.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"21 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46182011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics. Dirichlet过程混合物中变量选择的快速近似推断，并在泛癌症蛋白质组学中的应用。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-12-12 DOI: 10.1515/sagmb-2018-0065

Oliver M Crook, Laurent Gatto, Paul D W Kirk

{"title":"Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics.","authors":"Oliver M Crook, Laurent Gatto, Paul D W Kirk","doi":"10.1515/sagmb-2018-0065","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0065","url":null,"abstract":"The Dirichlet Process (DP) mixture model has become a popular choice for model-based clustering, largely because it allows the number of clusters to be inferred. The sequential updating and greedy search (SUGS) algorithm (Wang & Dunson, 2011) was proposed as a fast method for performing approximate Bayesian inference in DP mixture models, by posing clustering as a Bayesian model selection (BMS) problem and avoiding the use of computationally costly Markov chain Monte Carlo methods. Here we consider how this approach may be extended to permit variable selection for clustering, and also demonstrate the benefits of Bayesian model averaging (BMA) in place of BMS. Through an array of simulation examples and well-studied examples from cancer transcriptomics, we show that our method performs competitively with the current state-of-the-art, while also offering computational benefits. We apply our approach to reverse-phase protein array (RPPA) data from The Cancer Genome Atlas (TCGA) in order to perform a pan-cancer proteomic characterisation of 5157 tumour samples. We have implemented our approach, together with the original SUGS algorithm, in an open-source R package named sugsvarsel, which accelerates analysis by performing intensive computations in C++ and provides automated parallel processing. The R package is freely available from: https://github.com/ococrook/sugsvarsel.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10481523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

AdaReg: data adaptive robust estimation in linear regression with application in GTEx gene expressions AdaReg：线性回归中的数据自适应稳健估计及其在GTEx基因表达中的应用

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-12-10 DOI: 10.1101/869362

Meng Wang, Lihua Jiang, M. Snyder

{"title":"AdaReg: data adaptive robust estimation in linear regression with application in GTEx gene expressions","authors":"Meng Wang, Lihua Jiang, M. Snyder","doi":"10.1101/869362","DOIUrl":"https://doi.org/10.1101/869362","url":null,"abstract":"Abstract The Genotype-Tissue Expression (GTEx) project provides a valuable resource of large-scale gene expressions across multiple tissue types. Under various technical noise and unknown or unmeasured factors, how to robustly estimate the major tissue effect becomes challenging. Moreover, different genes exhibit heterogeneous expressions across different tissue types. Therefore, we need a robust method which adapts to the heterogeneities of gene expressions to improve the estimation for the tissue effect. We followed the approach of the robust estimation based on γ-density-power-weight in the works of Fujisawa, H. and Eguchi, S. (2008). Robust parameter estimation with a small bias against heavy contamination. J. Multivariate Anal. 99: 2053–2081 and Windham, M.P. (1995). Robustifying model fitting. J. Roy. Stat. Soc. B: 599–609, where γ is the exponent of density weight which controls the balance between bias and variance. As far as we know, our work is the first to propose a procedure to tune the parameter γ to balance the bias-variance trade-off under the mixture models. We constructed a robust likelihood criterion based on weighted densities in the mixture model of Gaussian population distribution mixed with unknown outlier distribution, and developed a data-adaptive γ-selection procedure embedded into the robust estimation. We provided a heuristic analysis on the selection criterion and found that our practical selection trend under various γ’s in average performance has similar capability to capture minimizer γ as the inestimable mean squared error (MSE) trend from our simulation studies under a series of settings. Our data-adaptive robustifying procedure in the linear regression problem (AdaReg) showed a significant advantage in both simulation studies and real data application in estimating tissue effect of heart samples from the GTEx project, compared to the fixed γ procedure and other robust methods. At the end, the paper discussed some limitations on this method and future work.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"20 1","pages":"51 - 71"},"PeriodicalIF":0.9,"publicationDate":"2019-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41415775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Bayesian framework for identifying consistent patterns of microbial abundance between body sites. 用于识别身体部位之间微生物丰度一致模式的贝叶斯框架。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-11-08 DOI: 10.1515/sagmb-2019-0027

Richard Meier, Jeffrey A Thompson, Mei Chung, Naisi Zhao, Karl T Kelsey, Dominique S Michaud, Devin C Koestler

{"title":"A Bayesian framework for identifying consistent patterns of microbial abundance between body sites.","authors":"Richard Meier, Jeffrey A Thompson, Mei Chung, Naisi Zhao, Karl T Kelsey, Dominique S Michaud, Devin C Koestler","doi":"10.1515/sagmb-2019-0027","DOIUrl":"https://doi.org/10.1515/sagmb-2019-0027","url":null,"abstract":"Recent studies have found that the microbiome in both gut and mouth are associated with diseases of the gut, including cancer. If resident microbes could be found to exhibit consistent patterns between the mouth and gut, disease status could potentially be assessed non-invasively through profiling of oral samples. Currently, there exists no generally applicable method to test for such associations. Here we present a Bayesian framework to identify microbes that exhibit consistent patterns between body sites, with respect to a phenotypic variable. For a given operational taxonomic unit (OTU), a Bayesian regression model is used to obtain Markov-Chain Monte Carlo estimates of abundance among strata, calculate a correlation statistic, and conduct a formal test based on its posterior distribution. Extensive simulation studies demonstrate overall viability of the approach, and provide information on what factors affect its performance. Applying our method to a dataset containing oral and gut microbiome samples from 77 pancreatic cancer patients revealed several OTUs exhibiting consistent patterns between gut and mouth with respect to disease subtype. Our method is well powered for modest sample sizes and moderate strength of association and can be flexibly extended to other research settings using any currently established Bayesian analysis programs.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2019-0027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41180334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Bi-level feature selection in high dimensional AFT models with applications to a genomic study 高维AFT模型的双水平特征选择及其在基因组研究中的应用

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-09-17 DOI: 10.1515/sagmb-2019-0016

Hailin Huang, Jizi Shangguan, Peifeng Ruan, Hua Liang

引用次数: 2

Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions 单细胞rna测序表达数据的聚类方法:不同样本量和细胞组成的性能评估

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-08-14 DOI: 10.1515/sagmb-2019-0004

A. Suner

{"title":"Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions","authors":"A. Suner","doi":"10.1515/sagmb-2019-0004","DOIUrl":"https://doi.org/10.1515/sagmb-2019-0004","url":null,"abstract":"Abstract A number of specialized clustering methods have been developed so far for the accurate analysis of single-cell RNA-sequencing (scRNA-seq) expression data, and several reports have been published documenting the performance measures of these clustering methods under different conditions. However, to date, there are no available studies regarding the systematic evaluation of the performance measures of the clustering methods taking into consideration the sample size and cell composition of a given scRNA-seq dataset. Herein, a comprehensive performance evaluation study of 11 selected scRNA-seq clustering methods was performed using synthetic datasets with known sample sizes and number of subpopulations, as well as varying levels of transcriptome complexity. The results indicate that the overall performance of the clustering methods under study are highly dependent on the sample size and complexity of the scRNA-seq dataset. In most of the cases, better clustering performances were obtained as the number of cells in a given expression dataset was increased. The findings of this study also highlight the importance of sample size for the successful detection of rare cell subpopulations with an appropriate clustering tool.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2019-0004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48981400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4