Statistical Applications in Genetics and Molecular Biology最新文献_第2页

A global test of hybrid ancestry from genome-scale data. 从基因组尺度数据对杂交血统进行全球测试。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2024-02-19 eCollection Date: 2024-01-01 DOI: 10.1515/sagmb-2022-0061

Md Rejuan Haque, Laura Kubatko

{"title":"A global test of hybrid ancestry from genome-scale data.","authors":"Md Rejuan Haque, Laura Kubatko","doi":"10.1515/sagmb-2022-0061","DOIUrl":"10.1515/sagmb-2022-0061","url":null,"abstract":"Methods based on the multi-species coalescent have been widely used in phylogenetic tree estimation using genome-scale DNA sequence data to understand the underlying evolutionary relationship between the sampled species. Evolutionary processes such as hybridization, which creates new species through interbreeding between two different species, necessitate inferring a species network instead of a species tree. A species tree is strictly bifurcating and thus fails to incorporate hybridization events which require an internal node of degree three. Hence, it is crucial to decide whether a tree or network analysis should be performed given a DNA sequence data set, a decision that is based on the presence of hybrid species in the sampled species. Although many methods have been proposed for hybridization detection, it is rare to find a technique that does so globally while considering a data generation mechanism that allows both hybridization and incomplete lineage sorting. In this paper, we consider hybridization and coalescence in a unified framework and propose a new test that can detect whether there are any hybrid species in a set of species of arbitrary size. Based on this global test of hybridization, one can decide whether a tree or network analysis is appropriate for a given data set.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"23 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139747669","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

Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes. 利用基因表达、miRNA、甲基化和拷贝数变异对乳腺癌亚型进行整合通路分析。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2024-02-19 eCollection Date: 2024-01-01 DOI: 10.1515/sagmb-2019-0050

Henry Linder, Yuping Zhang, Yunqi Wang, Zhengqing Ouyang

引用次数: 0

Bayesian LASSO for population stratification correction in rare haplotype association studies. 贝叶斯 LASSO 用于稀有单倍型关联研究中的人群分层校正。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2024-01-19 eCollection Date: 2024-01-01 DOI: 10.1515/sagmb-2022-0034

Zilu Liu, Asuman Seda Turkmen, Shili Lin

{"title":"Bayesian LASSO for population stratification correction in rare haplotype association studies.","authors":"Zilu Liu, Asuman Seda Turkmen, Shili Lin","doi":"10.1515/sagmb-2022-0034","DOIUrl":"10.1515/sagmb-2022-0034","url":null,"abstract":"Population stratification (PS) is one major source of confounding in both single nucleotide polymorphism (SNP) and haplotype association studies. To address PS, principal component regression (PCR) and linear mixed model (LMM) are the current standards for SNP associations, which are also commonly borrowed for haplotype studies. However, the underfitting and overfitting problems introduced by PCR and LMM, respectively, have yet to be addressed. Furthermore, there have been only a few theoretical approaches proposed to address PS specifically for haplotypes. In this paper, we propose a new method under the Bayesian LASSO framework, QBLstrat, to account for PS in identifying rare and common haplotypes associated with a continuous trait of interest. QBLstrat utilizes a large number of principal components (PCs) with appropriate priors to sufficiently correct for PS, while shrinking the estimates of unassociated haplotypes and PCs. We compare the performance of QBLstrat with the Bayesian counterparts of PCR and LMM and a current method, haplo.stats. Extensive simulation studies and real data analyses show that QBLstrat is superior in controlling false positives while maintaining competitive power for identifying true positives under PS.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"23 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10794901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139486664","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

Mediation analysis method review of high throughput data. 高通量数据的中介分析方法综述。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2023-11-29 eCollection Date: 2023-01-01 DOI: 10.1515/sagmb-2023-0031

Qiang Han, Yu Wang, Na Sun, Jiadong Chu, Wei Hu, Yueping Shen

{"title":"Mediation analysis method review of high throughput data.","authors":"Qiang Han, Yu Wang, Na Sun, Jiadong Chu, Wei Hu, Yueping Shen","doi":"10.1515/sagmb-2023-0031","DOIUrl":"10.1515/sagmb-2023-0031","url":null,"abstract":"High-throughput technologies have made high-dimensional settings increasingly common, providing opportunities for the development of high-dimensional mediation methods. We aimed to provide useful guidance for researchers using high-dimensional mediation analysis and ideas for biostatisticians to develop it by summarizing and discussing recent advances in high-dimensional mediation analysis. The method still faces many challenges when extended single and multiple mediation analyses to high-dimensional settings. The development of high-dimensional mediation methods attempts to address these issues, such as screening true mediators, estimating mediation effects by variable selection, reducing the mediation dimension to resolve correlations between variables, and utilizing composite null hypothesis testing to test them. Although these problems regarding high-dimensional mediation have been solved to some extent, some challenges remain. First, the correlation between mediators are rarely considered when the variables are selected for mediation. Second, downscaling without incorporating prior biological knowledge makes the results difficult to interpret. In addition, a method of sensitivity analysis for the strict sequential ignorability assumption in high-dimensional mediation analysis is still lacking. An analyst needs to consider the applicability of each method when utilizing them, while a biostatistician could consider extensions and improvements in the methodology.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"22 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138452936","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

Patterns of differential expression by association in omic data using a new measure based on ensemble learning. 基于集成学习的组学数据关联差分表达模式研究。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2023-11-23 eCollection Date: 2023-01-01 DOI: 10.1515/sagmb-2023-0009

Jorge M Arevalillo, Raquel Martin-Arevalillo

{"title":"Patterns of differential expression by association in omic data using a new measure based on ensemble learning.","authors":"Jorge M Arevalillo, Raquel Martin-Arevalillo","doi":"10.1515/sagmb-2023-0009","DOIUrl":"10.1515/sagmb-2023-0009","url":null,"abstract":"The ongoing development of high-throughput technologies is allowing the simultaneous monitoring of the expression levels for hundreds or thousands of biological inputs with the proliferation of what has been coined as omic data sources. One relevant issue when analyzing such data sources is concerned with the detection of differential expression across two experimental conditions, clinical status or two classes of a biological outcome. While a great deal of univariate data analysis approaches have been developed to address the issue, strategies for assessing interaction patterns of differential expression are scarce in the literature and have been limited to ad hoc solutions. This paper contributes to the problem by exploiting the facilities of an ensemble learning algorithm like random forests to propose a measure that assesses the differential expression explained by the interaction of the omic variables so subtle biological patterns may be uncovered as a result. The out of bag error rate, which is an estimate of the predictive accuracy of a random forests classifier, is used as a by-product to propose a new measure that assesses interaction patterns of differential expression. Its performance is studied in synthetic scenarios and it is also applied to real studies on SARS-CoV-2 and colon cancer data where it uncovers associations that remain undetected by other methods. Our proposal is aimed at providing a novel approach that may help the experts in biomedical and life sciences to unravel insightful interaction patterns that may decipher the molecular mechanisms underlying biological and clinical outcomes.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"22 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138292231","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

Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization. 肿瘤微环境的综合调节和代谢网络对治疗目标的优先排序。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2023-11-21 eCollection Date: 2023-01-01 DOI: 10.1515/sagmb-2022-0054

Tiange Shi, Han Yu, Rachael Hageman Blair

{"title":"Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization.","authors":"Tiange Shi, Han Yu, Rachael Hageman Blair","doi":"10.1515/sagmb-2022-0054","DOIUrl":"10.1515/sagmb-2022-0054","url":null,"abstract":"Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"22 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138292230","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

Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization. 使用非负矩阵因子分解对“组学数据”进行综合亚型分析的随机奇异值分解。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2023-11-09 eCollection Date: 2023-01-01 DOI: 10.1515/sagmb-2022-0047

Yonghui Ni, Jianghua He, Prabhakar Chalise

{"title":"Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization.","authors":"Yonghui Ni, Jianghua He, Prabhakar Chalise","doi":"10.1515/sagmb-2022-0047","DOIUrl":"10.1515/sagmb-2022-0047","url":null,"abstract":"Integration of multiple 'omics datasets for differentiating cancer subtypes is a powerful technic that leverages the consistent and complementary information across multi-omics data. Matrix factorization is a common technique used in integrative clustering for identifying latent subtype structure across multi-omics data. High dimensionality of the omics data and long computation time have been common challenges of clustering methods. In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization: intNMF-rsvd. The method utilizes RSVD to reduce the dimensionality by projecting the data into eigen vector space with user specified lower rank. Then, clustering analysis is carried out by estimating common basis matrix across the projected multi-omics datasets. The performance of the proposed method was assessed using the simulated datasets and compared with six state-of-the-art integrative clustering methods using real-life datasets from The Cancer Genome Atlas Study. intNMF-rsvd was found working efficiently and competitively as compared to standard intNMF and other multi-omics clustering methods. Most importantly, intNMF-rsvd can handle large number of features and significantly reduce the computation time. The identified subtypes can be utilized for further clinical association studies to understand the etiology of the disease.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"22 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71488028","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

CAT PETR: a graphical user interface for differential analysis of phosphorylation and expression data. CAT PETR：用于磷酸化和表达数据差异分析的图形用户界面。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2023-08-21 eCollection Date: 2023-01-01 DOI: 10.1515/sagmb-2023-0017

Keegan Flanagan, Steven Pelech, Yossef Av-Gay, Khanh Dao Duc

引用次数: 0

Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets. 提高基于回归的高维数据集聚类法的准确性和内部一致性。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2023-07-25 eCollection Date: 2023-01-01 DOI: 10.1515/sagmb-2022-0031

Bo Zhang, Jianghua He, Jinxiang Hu, Prabhakar Chalise, Devin C Koestler

{"title":"Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets.","authors":"Bo Zhang, Jianghua He, Jinxiang Hu, Prabhakar Chalise, Devin C Koestler","doi":"10.1515/sagmb-2022-0031","DOIUrl":"10.1515/sagmb-2022-0031","url":null,"abstract":"Component-wise Sparse Mixture Regression (CSMR) is a recently proposed regression-based clustering method that shows promise in detecting heterogeneous relationships between molecular markers and a continuous phenotype of interest. However, CSMR can yield inconsistent results when applied to high-dimensional molecular data, which we hypothesize is in part due to inherent limitations associated with the feature selection method used in the CSMR algorithm. To assess this hypothesis, we explored whether substituting different regularized regression methods (i.e. Lasso, Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), Minmax Convex Penalty (MCP), and Adaptive-Lasso) within the CSMR framework can improve the clustering accuracy and internal consistency (IC) of CSMR in high-dimensional settings. We calculated the true positive rate (TPR), true negative rate (TNR), IC and clustering accuracy of our proposed modifications, benchmarked against the existing CSMR algorithm, using an extensive set of simulation studies and real biological datasets. Our results demonstrated that substituting Adaptive-Lasso within the existing feature selection method used in CSMR led to significantly improved IC and clustering accuracy, with strong performance even in high-dimensional scenarios. In conclusion, our modifications of the CSMR method resulted in improved clustering performance and may thus serve as viable alternatives for the regression-based clustering of high-dimensional datasets.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"22 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10891458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10227703","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 fast and efficient approach for gene-based association studies of ordinal phenotypes. 序型基因关联研究的一种快速有效的方法。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2023-01-01 DOI: 10.1515/sagmb-2021-0068

Nanxing Li, Lili Chen, Yajing Zhou, Qianran Wei

{"title":"A fast and efficient approach for gene-based association studies of ordinal phenotypes.","authors":"Nanxing Li, Lili Chen, Yajing Zhou, Qianran Wei","doi":"10.1515/sagmb-2021-0068","DOIUrl":"https://doi.org/10.1515/sagmb-2021-0068","url":null,"abstract":"Many human disease conditions need to be measured by ordinal phenotypes, so analysis of ordinal phenotypes is valuable in genome-wide association studies (GWAS). However, existing association methods for dichotomous or quantitative phenotypes are not appropriate to ordinal phenotypes. Therefore, based on an aggregated Cauchy association test, we propose a fast and efficient association method to test the association between genetic variants and an ordinal phenotype. To enrich association signals of rare variants, we first use the burden method to aggregate rare variants. Then we respectively test the significance of the aggregated rare variants and other common variants. Finally, the combination of transformed variant-level P values is taken as test statistic, that approximately follows Cauchy distribution under the null hypothesis. Extensive simulation studies and analysis of GAW19 show that our proposed method is powerful and computationally fast as a gene-based method. Especially, in the presence of an extremely low proportion of causal variants in a gene, our method has better performance.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"22 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10825661","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