Statistical Applications in Genetics and Molecular Biology最新文献_第7页

LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data. LCox:使用纵向基因表达数据选择与生存结果相关的基因的工具。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-02-13 DOI: 10.1515/sagmb-2017-0060

Jiehuan Sun, Jose D Herazo-Maya, Jane-Ling Wang, Naftali Kaminski, Hongyu Zhao

引用次数: 0

Meta-analytic framework for modeling genetic coexpression dynamics. 遗传共表达动态建模的元分析框架。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-02-09 DOI: 10.1515/sagmb-2017-0052

Tyler G Kinzy, Timothy K Starr, George C Tseng, Yen-Yi Ho

{"title":"Meta-analytic framework for modeling genetic coexpression dynamics.","authors":"Tyler G Kinzy, Timothy K Starr, George C Tseng, Yen-Yi Ho","doi":"10.1515/sagmb-2017-0052","DOIUrl":"10.1515/sagmb-2017-0052","url":null,"abstract":"Methods for exploring genetic interactions have been developed in an attempt to move beyond single gene analyses. Because biological molecules frequently participate in different processes under various cellular conditions, investigating the changes in gene coexpression patterns under various biological conditions could reveal important regulatory mechanisms. One of the methods for capturing gene coexpression dynamics, named liquid association (LA), quantifies the relationship where the coexpression between two genes is modulated by a third \"coordinator\" gene. This LA measure offers a natural framework for studying gene coexpression changes and has been applied increasingly to study regulatory networks among genes. With a wealth of publicly available gene expression data, there is a need to develop a meta-analytic framework for LA analysis. In this paper, we incorporated mixed effects when modeling correlation to account for between-studies heterogeneity. For statistical inference about LA, we developed a Markov chain Monte Carlo (MCMC) estimation procedure through a Bayesian hierarchical framework. We evaluated the proposed methods in a set of simulations and illustrated their use in two collections of experimental data sets. The first data set combined 10 pancreatic ductal adenocarcinoma gene expression studies to determine the role of possible coordinator gene USP9X in the Hippo pathway. The second experimental data set consisted of 907 gene expression microarray Escherichia coli experiments from multiple studies publicly available through the Many Microbe Microarray Database website (http://m3d.bu.edu/) and examined genes that coexpress with serA in the presence of coordinator gene Lrp.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36944546","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

Sliced inverse regression for integrative multi-omics data analysis. 切片逆回归整合多组学数据分析。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-01-26 DOI: 10.1515/sagmb-2018-0028

Yashita Jain, Shanshan Ding, Jing Qiu

{"title":"Sliced inverse regression for integrative multi-omics data analysis.","authors":"Yashita Jain, Shanshan Ding, Jing Qiu","doi":"10.1515/sagmb-2018-0028","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0028","url":null,"abstract":"Advancement in next-generation sequencing, transcriptomics, proteomics and other high-throughput technologies has enabled simultaneous measurement of multiple types of genomic data for cancer samples. These data together may reveal new biological insights as compared to analyzing one single genome type data. This study proposes a novel use of supervised dimension reduction method, called sliced inverse regression, to multi-omics data analysis to improve prediction over a single data type analysis. The study further proposes an integrative sliced inverse regression method (integrative SIR) for simultaneous analysis of multiple omics data types of cancer samples, including MiRNA, MRNA and proteomics, to achieve integrative dimension reduction and to further improve prediction performance. Numerical results show that integrative analysis of multi-omics data is beneficial as compared to single data source analysis, and more importantly, that supervised dimension reduction methods possess advantages in integrative data analysis in terms of classification and prediction as compared to unsupervised dimension reduction methods.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36901134","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: 1

A powerful test for ordinal trait genetic association analysis. 序性状遗传关联分析的有力检验。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-01-26 DOI: 10.1515/sagmb-2017-0066

Yuan Xue, Jinjuan Wang, Juan Ding, Sanguo Zhang, Qizhai Li

引用次数: 2

Sample size calculations for the differential expression analysis of RNA-seq data using a negative binomial regression model. 使用负二项回归模型计算RNA-seq数据差异表达分析的样本量。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-01-22 DOI: 10.1515/sagmb-2018-0021

Xiaohong Li, Dongfeng Wu, Nigel G F Cooper, Shesh N Rai

{"title":"Sample size calculations for the differential expression analysis of RNA-seq data using a negative binomial regression model.","authors":"Xiaohong Li, Dongfeng Wu, Nigel G F Cooper, Shesh N Rai","doi":"10.1515/sagmb-2018-0021","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0021","url":null,"abstract":"High throughput RNA sequencing (RNA-seq) technology is increasingly used in disease-related biomarker studies. A negative binomial distribution has become the popular choice for modeling read counts of genes in RNA-seq data due to over-dispersed read counts. In this study, we propose two explicit sample size calculation methods for RNA-seq data using a negative binomial regression model. To derive these new sample size formulas, the common dispersion parameter and the size factor as an offset via a natural logarithm link function are incorporated. A two-sided Wald test statistic derived from the coefficient parameter is used for testing a single gene at a nominal significance level 0.05 and multiple genes at a false discovery rate 0.05. The variance for the Wald test is computed from the variance-covariance matrix with the parameters estimated from the maximum likelihood estimates under the unrestricted and constrained scenarios. The performance and a side-by-side comparison of our new formulas with three existing methods with a Wald test, a likelihood ratio test or an exact test are evaluated via simulation studies. Since other methods are much computationally extensive, we recommend our M1 method for quick and direct estimation of sample sizes in an experimental design. Finally, we illustrate sample sizes estimation using an existing breast cancer RNA-seq data.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36885647","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

MLML2R: an R package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions. MLML2R:一个R包DNA甲基化和羟甲基化比例的最大似然估计。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-01-17 DOI: 10.1515/sagmb-2018-0031

Samara F Kiihl, Maria Jose Martinez-Garrido, Arce Domingo-Relloso, Jose Bermudez, Maria Tellez-Plaza

{"title":"MLML2R: an R package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions.","authors":"Samara F Kiihl, Maria Jose Martinez-Garrido, Arce Domingo-Relloso, Jose Bermudez, Maria Tellez-Plaza","doi":"10.1515/sagmb-2018-0031","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0031","url":null,"abstract":"Accurately measuring epigenetic marks such as 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) at the single-nucleotide level, requires combining data from DNA processing methods including traditional (BS), oxidative (oxBS) or Tet-Assisted (TAB) bisulfite conversion. We introduce the R package MLML2R, which provides maximum likelihood estimates (MLE) of 5-mC and 5-hmC proportions. While all other available R packages provide 5-mC and 5-hmC MLEs only for the oxBS+BS combination, MLML2R also provides MLE for TAB combinations. For combinations of any two of the methods, we derived the pool-adjacent-violators algorithm (PAVA) exact constrained MLE in analytical form. For the three methods combination, we implemented both the iterative method by Qu et al. [Qu, J., M. Zhou, Q. Song, E. E. Hong and A. D. Smith (2013): \"Mlml: consistent simultaneous estimates of dna methylation and hydroxymethylation,\" Bioinformatics, 29, 2645-2646.], and also a novel non iterative approximation using Lagrange multipliers. The newly proposed non iterative solutions greatly decrease computational time, common bottlenecks when processing high-throughput data. The MLML2R package is flexible as it takes as input both, preprocessed intensities from Infinium Methylation arrays and counts from Next Generation Sequencing technologies. The MLML2R package is freely available at https://CRAN.R-project.org/package=MLML2R.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36872982","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: 10

An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data 从Hi-C数据中鉴定远距离染色体相互作用的经验贝叶斯方法

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-12-17 DOI: 10.1101/497776

Qi Zhang, Zheng Xu, Yutong Lai

{"title":"An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data","authors":"Qi Zhang, Zheng Xu, Yutong Lai","doi":"10.1101/497776","DOIUrl":"https://doi.org/10.1101/497776","url":null,"abstract":"Abstract Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the “true” interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"20 1","pages":"1 - 15"},"PeriodicalIF":0.9,"publicationDate":"2018-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44544987","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

False discovery control for penalized variable selections with high-dimensional covariates. 具有高维协变量的惩罚性变量选择的错误发现控制。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-12-15 DOI: 10.1515/sagmb-2018-0038

Kevin He, Xiang Zhou, Hui Jiang, Xiaoquan Wen, Yi Li

引用次数: 0

A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS). 在全基因组关联研究中调整人群分层的实用方法：主成分和倾向分数 (PCAPS)。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-12-04 DOI: 10.1515/sagmb-2017-0054

Huaqing Zhao, Nandita Mitra, Peter A Kanetsky, Katherine L Nathanson, Timothy R Rebbeck

{"title":"A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS).","authors":"Huaqing Zhao, Nandita Mitra, Peter A Kanetsky, Katherine L Nathanson, Timothy R Rebbeck","doi":"10.1515/sagmb-2017-0054","DOIUrl":"10.1515/sagmb-2017-0054","url":null,"abstract":"Genome-wide association studies (GWAS) are susceptible to bias due to population stratification (PS). The most widely used method to correct bias due to PS is principal components (PCs) analysis (PCA), but there is no objective method to guide which PCs to include as covariates. Often, the ten PCs with the highest eigenvalues are included to adjust for PS. This selection is arbitrary, and patterns of local linkage disequilibrium may affect PCA corrections. To address these limitations, we estimate genomic propensity scores based on all statistically significant PCs selected by the Tracy-Widom (TW) statistic. We compare a principal components and propensity scores (PCAPS) approach to PCA and EMMAX using simulated GWAS data under no, moderate, and severe PS. PCAPS reduced spurious genetic associations regardless of the degree of PS, resulting in odds ratio (OR) estimates closer to the true OR. We illustrate our PCAPS method using GWAS data from a study of testicular germ cell tumors. PCAPS provided a more conservative adjustment than PCA. Advantages of the PCAPS approach include reduction of bias compared to PCA, consistent selection of propensity scores to adjust for PS, the potential ability to handle outliers, and ease of implementation using existing software packages.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"17 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6475581/pdf/nihms-1022442.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36745351","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

A novel method to accurately calculate statistical significance of local similarity analysis for high-throughput time series. 一种精确计算高通量时间序列局部相似度统计显著性的新方法。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-11-17 DOI: 10.1515/sagmb-2018-0019

Fang Zhang, Ang Shan, Yihui Luan

{"title":"A novel method to accurately calculate statistical significance of local similarity analysis for high-throughput time series.","authors":"Fang Zhang, Ang Shan, Yihui Luan","doi":"10.1515/sagmb-2018-0019","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0019","url":null,"abstract":"In recent years, a large number of time series microbial community data has been produced in molecular biological studies, especially in metagenomics. Among the statistical methods for time series, local similarity analysis is used in a wide range of environments to capture potential local and time-shifted associations that cannot be distinguished by traditional correlation analysis. Initially, the permutation test is popularly applied to obtain the statistical significance of local similarity analysis. More recently, a theoretical method has also been developed to achieve this aim. However, all these methods require the assumption that the time series are independent and identically distributed. In this paper, we propose a new approach based on moving block bootstrap to approximate the statistical significance of local similarity scores for dependent time series. Simulations show that our method can control the type I error rate reasonably, while theoretical approximation and the permutation test perform less well. Finally, our method is applied to human and marine microbial community datasets, indicating that it can identify potential relationship among operational taxonomic units (OTUs) and significantly decrease the rate of false positives.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"17 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36739757","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