Statistical Applications in Genetics and Molecular Biology最新文献_第10页

Tests for comparison of multiple endpoints with application to omics data. 用组学数据应用多个端点的比较测试。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-01-30 DOI: 10.1515/sagmb-2017-0033

Marco Marozzi

引用次数: 2

Non-parametric estimation of population size changes from the site frequency spectrum 基于站点频谱的人口规模变化的非参数估计

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-04-07 DOI: 10.1101/125351

B. L. Waltoft, A. Hobolth

{"title":"Non-parametric estimation of population size changes from the site frequency spectrum","authors":"B. L. Waltoft, A. Hobolth","doi":"10.1101/125351","DOIUrl":"https://doi.org/10.1101/125351","url":null,"abstract":"Abstract Changes in population size is a useful quantity for understanding the evolutionary history of a species. Genetic variation within a species can be summarized by the site frequency spectrum (SFS). For a sample of size n, the SFS is a vector of length n − 1 where entry i is the number of sites where the mutant base appears i times and the ancestral base appears n − i times. We present a new method, CubSFS, for estimating the changes in population size of a panmictic population from an observed SFS. First, we provide a straightforward proof for the expression of the expected site frequency spectrum depending only on the population size. Our derivation is based on an eigenvalue decomposition of the instantaneous coalescent rate matrix. Second, we solve the inverse problem of determining the changes in population size from an observed SFS. Our solution is based on a cubic spline for the population size. The cubic spline is determined by minimizing the weighted average of two terms, namely (i) the goodness of fit to the observed SFS, and (ii) a penalty term based on the smoothness of the changes. The weight is determined by cross-validation. The new method is validated on simulated demographic histories and applied on unfolded and folded SFS from 26 different human populations from the 1000 Genomes Project.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"17 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47621345","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: 7

Polyunphased: an extension to polytomous outcomes of the Unphased package for family-based genetic association analysis. Polyunphased：基于家庭的遗传关联分析的Unphased软件包的多元体结果的扩展

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-03-01 DOI: 10.1515/sagmb-2016-0035

Alexandre Bureau, Jordie Croteau

{"title":"Polyunphased: an extension to polytomous outcomes of the Unphased package for family-based genetic association analysis.","authors":"Alexandre Bureau, Jordie Croteau","doi":"10.1515/sagmb-2016-0035","DOIUrl":"10.1515/sagmb-2016-0035","url":null,"abstract":"<p><p>Polytomous phenotypes arise when a disease has multiple subtypes or when two dichotomous phenotypes are analyzed simultaneously. Few software programs offer the option to analyze such phenotypes in family studies, and none implements conditional polytomous logistic regression for within-family analysis robust to population stratification. We introduce Polyunphased, an extension to polytomous phenotypes of the Unphased package, a flexible software tool for genetic association analysis in nuclear families. Like Unphased, Polyunphased is written in C++ and runs from the command line or from a Java graphical user interface. Most Unphased options remain available in Polyunphased, including those handling missing parental genotypes while preserving robustness to population stratification, and the modelling options. Simulation studies confirmed the expected statistical behaviour of the maximum likelihood estimates of the association parameters of the conditional logistic regression model when the corresponding association parameters in the parental term of the likelihood function are set to 0, but revealed convergence problems when estimating these parental association parameters separately. The former approach is thus recommended with polytomous phenotypes.</p>","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"16 1","pages":"75-81"},"PeriodicalIF":0.9,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5576982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42874518","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: 0

Generalized partial linear varying multi-index coefficient model for gene-environment interactions 基因-环境相互作用的广义偏线性变多指标系数模型

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-03-01 DOI: 10.1515/sagmb-2016-0045

Xu Liu, Bin Gao, Yuehua Cui

{"title":"Generalized partial linear varying multi-index coefficient model for gene-environment interactions","authors":"Xu Liu, Bin Gao, Yuehua Cui","doi":"10.1515/sagmb-2016-0045","DOIUrl":"https://doi.org/10.1515/sagmb-2016-0045","url":null,"abstract":"Abstract Epidemiological studies have suggested the joint effect of simultaneous exposures to multiple environments on disease risk. However, how environmental mixtures as a whole jointly modify genetic effect on disease risk is still largely unknown. Given the importance of gene-environment (G×E) interactions on many complex diseases, rigorously assessing the interaction effect between genes and environmental mixtures as a whole could shed novel insights into the etiology of complex diseases. For this purpose, we propose a generalized partial linear varying multi-index coefficient model (GPLVMICM) to capture the genetic effect on disease risk modulated by multiple environments as a whole. GPLVMICM is semiparametric in nature which allows different index loading parameters in different index functions. We estimate the parametric parameters by a profile procedure, and the nonparametric index functions by a B-spline backfitted kernel method. Under some regularity conditions, the proposed parametric and nonparametric estimators are shown to be consistent and asymptotically normal. We propose a generalized likelihood ratio (GLR) test to rigorously assess the linearity of the interaction effect between multiple environments and a gene, while apply a parametric likelihood test to detect linear G×E interaction effect. The finite sample performance of the proposed method is examined through simulation studies and is further illustrated through a real data analysis.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"16 1","pages":"59 - 74"},"PeriodicalIF":0.9,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2016-0045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44875724","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: 2

Statistical models and computational algorithms for discovering relationships in microbiome data 发现微生物组数据关系的统计模型和计算算法

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-01-01 DOI: 10.1515/sagmb-2015-0096

M. Shaikh, J. Beyene

引用次数: 0

Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data 基于广义线性模型的RNA-seq数据差分表达分析样本量计算

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2016-12-01 DOI: 10.1515/sagmb-2016-0008

Chung-I Li, Y. Shyr

{"title":"Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data","authors":"Chung-I Li, Y. Shyr","doi":"10.1515/sagmb-2016-0008","DOIUrl":"https://doi.org/10.1515/sagmb-2016-0008","url":null,"abstract":"Abstract As RNA-seq rapidly develops and costs continually decrease, the quantity and frequency of samples being sequenced will grow exponentially. With proteomic investigations becoming more multivariate and quantitative, determining a study’s optimal sample size is now a vital step in experimental design. Current methods for calculating a study’s required sample size are mostly based on the hypothesis testing framework, which assumes each gene count can be modeled through Poisson or negative binomial distributions; however, these methods are limited when it comes to accommodating covariates. To address this limitation, we propose an estimating procedure based on the generalized linear model. This easy-to-use method constructs a representative exemplary dataset and estimates the conditional power, all without requiring complicated mathematical approximations or formulas. Even more attractive, the downstream analysis can be performed with current R/Bioconductor packages. To demonstrate the practicability and efficiency of this method, we apply it to three real-world studies, and introduce our on-line calculator developed to determine the optimal sample size for a RNA-seq study.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"30 1","pages":"491 - 505"},"PeriodicalIF":0.9,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2016-0008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67003125","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: 7

A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments 高通量测序或蛋白质组学实验中特征子集相关计数数据的模拟框架

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2016-10-01 DOI: 10.1515/sagmb-2015-0082

Jochen Kruppa, F. Kramer, T. Beissbarth, K. Jung

{"title":"A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments","authors":"Jochen Kruppa, F. Kramer, T. Beissbarth, K. Jung","doi":"10.1515/sagmb-2015-0082","DOIUrl":"https://doi.org/10.1515/sagmb-2015-0082","url":null,"abstract":"Abstract As part of the data processing of high-throughput-sequencing experiments count data are produced representing the amount of reads that map to specific genomic regions. Count data also arise in mass spectrometric experiments for the detection of protein-protein interactions. For evaluating new computational methods for the analysis of sequencing count data or spectral count data from proteomics experiments artificial count data is thus required. Although, some methods for the generation of artificial sequencing count data have been proposed, all of them simulate single sequencing runs, omitting thus the correlation structure between the individual genomic features, or they are limited to specific structures. We propose to draw correlated data from the multivariate normal distribution and round these continuous data in order to obtain discrete counts. In our approach, the required distribution parameters can either be constructed in different ways or estimated from real count data. Because rounding affects the correlation structure we evaluate the use of shrinkage estimators that have already been used in the context of artificial expression data from DNA microarrays. Our approach turned out to be useful for the simulation of counts for defined subsets of features such as individual pathways or GO categories.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"15 1","pages":"401 - 414"},"PeriodicalIF":0.9,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2015-0082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67003073","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

Accounting for isotopic clustering in Fourier transform mass spectrometry data analysis for clinical diagnostic studies 用于临床诊断研究的傅里叶变换质谱数据分析中的同位素聚类

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2016-10-01 DOI: 10.1515/sagmb-2016-0005

A. Kakourou, W. Vach, S. Nicolardi, Y. V. D. van der Burgt, B. Mertens

{"title":"Accounting for isotopic clustering in Fourier transform mass spectrometry data analysis for clinical diagnostic studies","authors":"A. Kakourou, W. Vach, S. Nicolardi, Y. V. D. van der Burgt, B. Mertens","doi":"10.1515/sagmb-2016-0005","DOIUrl":"https://doi.org/10.1515/sagmb-2016-0005","url":null,"abstract":"Abstract Mass spectrometry based clinical proteomics has emerged as a powerful tool for high-throughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"15 1","pages":"415 - 430"},"PeriodicalIF":0.9,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2016-0005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67003117","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

Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks 阶乘高斯图形模型的模型选择及其在动态调节网络中的应用

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2016-06-01 DOI: 10.1515/sagmb-2014-0075

V. Vinciotti, L. Augugliaro, A. Abbruzzo, E. Wit

{"title":"Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks","authors":"V. Vinciotti, L. Augugliaro, A. Abbruzzo, E. Wit","doi":"10.1515/sagmb-2014-0075","DOIUrl":"https://doi.org/10.1515/sagmb-2014-0075","url":null,"abstract":"Abstract Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l1-penalized maximum likelihood, imposing a further constraint on the absolute value of its entries, which results in sparse networks. Selecting the optimal sparsity level is a major challenge for this type of approaches. In this paper, we evaluate the performance of a number of model selection criteria for fGGMs by means of two simulated regulatory networks from realistic biological processes. The analysis reveals a good performance of fGGMs in comparison with other methods for inferring dynamic networks and of the KLCV criterion in particular for model selection. Finally, we present an application on a high-resolution time-course microarray data from the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis. The methodology described in this paper is implemented in the R package sglasso, freely available at CRAN, http://CRAN.R-project.org/package=sglasso.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"15 1","pages":"193 - 212"},"PeriodicalIF":0.9,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2014-0075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67002224","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: 19

Testing differentially expressed genes in dose-response studies and with ordinal phenotypes 在剂量反应研究和顺序表型中检测差异表达基因

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2016-06-01 DOI: 10.1515/sagmb-2015-0091

E. Sweeney, C. Crainiceanu, J. Gertheiss

{"title":"Testing differentially expressed genes in dose-response studies and with ordinal phenotypes","authors":"E. Sweeney, C. Crainiceanu, J. Gertheiss","doi":"10.1515/sagmb-2015-0091","DOIUrl":"https://doi.org/10.1515/sagmb-2015-0091","url":null,"abstract":"Abstract When testing for differentially expressed genes between more than two groups, the groups are often defined by dose levels in dose-response experiments or ordinal phenotypes, such as disease stages. We discuss the potential of a new approach that uses the levels’ ordering without making any structural assumptions, such as monotonicity, by testing for zero variance components in a mixed models framework. Since the mixed effects model approach borrows strength across doses/levels, the test proposed can also be applied when the number of dose levels/phenotypes is large and/or the number of subjects per group is small. We illustrate the new test in simulation studies and on several publicly available datasets and compare it to alternative testing procedures. All tests considered are implemented in R and are publicly available. The new approach offers a very fast and powerful way to test for differentially expressed genes between ordered groups without making restrictive assumptions with respect to the true relationship between factor levels and response.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"55 1","pages":"213 - 235"},"PeriodicalIF":0.9,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2015-0091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67003076","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