Statistical Applications in Genetics and Molecular Biology最新文献_第8页

A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate. 一个用于研究基因-模块共表达与连续协变量之间关系的多元线性模型。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-03-15 DOI: 10.1515/sagmb-2018-0008

Trishanta Padayachee, Tatsiana Khamiakova, Ziv Shkedy, Perttu Salo, Markus Perola, Tomasz Burzykowski

{"title":"A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate.","authors":"Trishanta Padayachee, Tatsiana Khamiakova, Ziv Shkedy, Perttu Salo, Markus Perola, Tomasz Burzykowski","doi":"10.1515/sagmb-2018-0008","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0008","url":null,"abstract":"A way to enhance our understanding of the development and progression of complex diseases is to investigate the influence of cellular environments on gene co-expression (i.e. gene-pair correlations). Often, changes in gene co-expression are investigated across two or more biological conditions defined by categorizing a continuous covariate. However, the selection of arbitrary cut-off points may have an influence on the results of an analysis. To address this issue, we use a general linear model (GLM) for correlated data to study the relationship between gene-module co-expression and a covariate like metabolite concentration. The GLM specifies the gene-pair correlations as a function of the continuous covariate. The use of the GLM allows for investigating different (linear and non-linear) patterns of co-expression. Furthermore, the modeling approach offers a formal framework for testing hypotheses about possible patterns of co-expression. In our paper, a simulation study is used to assess the performance of the GLM. The performance is compared with that of a previously proposed GLM that utilizes categorized covariates. The versatility of the model is illustrated by using a real-life example. We discuss the theoretical issues related to the construction of the test statistics and the computational challenges related to fitting of the proposed model.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37234437","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

netprioR: a probabilistic model for integrative hit prioritisation of genetic screens. netprioR:遗传筛选综合命中优先级的概率模型。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-03-06 DOI: 10.1515/sagmb-2018-0033

Fabian Schmich, Jack Kuipers, Gunter Merdes, Niko Beerenwinkel

{"title":"netprioR: a probabilistic model for integrative hit prioritisation of genetic screens.","authors":"Fabian Schmich, Jack Kuipers, Gunter Merdes, Niko Beerenwinkel","doi":"10.1515/sagmb-2018-0033","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0033","url":null,"abstract":"In the post-genomic era of big data in biology, computational approaches to integrate multiple heterogeneous data sets become increasingly important. Despite the availability of large amounts of omics data, the prioritisation of genes relevant for a specific functional pathway based on genetic screening experiments, remains a challenging task. Here, we introduce netprioR, a probabilistic generative model for semi-supervised integrative prioritisation of hit genes. The model integrates multiple network data sets representing gene-gene similarities and prior knowledge about gene functions from the literature with gene-based covariates, such as phenotypes measured in genetic perturbation screens, for example, by RNA interference or CRISPR/Cas9. We evaluate netprioR on simulated data and show that the model outperforms current state-of-the-art methods in many scenarios and is on par otherwise. In an application to real biological data, we integrate 22 network data sets, 1784 prior knowledge class labels and 3840 RNA interference phenotypes in order to prioritise novel regulators of Notch signalling in Drosophila melanogaster. The biological relevance of our predictions is evaluated using in silico and in vivo experiments. An efficient implementation of netprioR is available as an R package at http://bioconductor.org/packages/netprioR.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37204570","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

Discrete Wavelet Packet Transform Based Discriminant Analysis for Whole Genome Sequences. 基于离散小波包变换的全基因组序列判别分析。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-02-15 DOI: 10.1515/sagmb-2018-0045

Hsin-Hsiung Huang, Senthil Balaji Girimurugan

引用次数: 1

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