Statistics in Biosciences最新文献_第9页

A Statistical Method for Association Analysis of Cell Type Compositions. 细胞类型组成关联分析的统计方法。

IF 1

Statistics in Biosciences Pub Date : 2021-12-01 Epub Date: 2021-09-15 DOI: 10.1007/s12561-020-09293-0

Licai Huang, Paul Little, Jeroen R Huyghe, Qian Shi, Tabitha A Harrison, Greg Yothers, Thomas J George, Ulrike Peters, Andrew T Chan, Polly A Newcomb, Wei Sun

{"title":"A Statistical Method for Association Analysis of Cell Type Compositions.","authors":"Licai Huang, Paul Little, Jeroen R Huyghe, Qian Shi, Tabitha A Harrison, Greg Yothers, Thomas J George, Ulrike Peters, Andrew T Chan, Polly A Newcomb, Wei Sun","doi":"10.1007/s12561-020-09293-0","DOIUrl":"10.1007/s12561-020-09293-0","url":null,"abstract":"Gene expression data are often collected from tissue samples that are composed of multiple cell types. Studies of cell type composition based on gene expression data from tissue samples have recently attracted increasing research interest and led to new method development for cell type composition estimation. This new information on cell type composition can be associated with individual characteristics (e.g., genetic variants) or clinical outcomes (e.g., survival time). Such association analysis can be conducted for each cell type separately followed by multiple testing correction. An alternative approach is to evaluate this association using the composition of all the cell types, thus aggregating association signals across cell types. A key challenge of this approach is to account for the dependence across cell types. We propose a new method to quantify the distances between cell types while accounting for their dependencies, and use this information for association analysis. We demonstrate our method in two applied examples: to assess the association between immune cell type composition in tumor samples of colorectal cancer patients versus survival time and SNP genotypes. We found immune cell composition has prognostic value, and our distance metric leads to more accurate survival time prediction than other distance metrics that ignore cell type dependencies. In addition, survival time-associated SNPs are enriched among the SNPs associated with immune cell composition.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"13 3","pages":"373-385"},"PeriodicalIF":1.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s12561-020-09293-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10319800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intergenerational Associations Between Maternal Diet and Childhood Adiposity: A Bayesian Regularized Mediation Analysis. 母亲饮食与儿童肥胖的代际关联：贝叶斯正则中介分析

IF 0.4

Statistics in Biosciences Pub Date : 2021-12-01 Epub Date: 2021-03-21 DOI: 10.1007/s12561-021-09305-7

Yu-Bo Wang, Cuilin Zhang, Zhen Chen

{"title":"Intergenerational Associations Between Maternal Diet and Childhood Adiposity: A Bayesian Regularized Mediation Analysis.","authors":"Yu-Bo Wang, Cuilin Zhang, Zhen Chen","doi":"10.1007/s12561-021-09305-7","DOIUrl":"10.1007/s12561-021-09305-7","url":null,"abstract":"Growing evidence supports a positive association between childhood obesity and chronic diseases in later life. It is also suggested that childhood obesity is more prevalent for children born from pregnancies complicated by metabolic disorders such as gestational diabetes, and can be related to maternal dietary factors during gestation. Extending conventional analyses that report only the marginal associations within non-causal mediation frameworks, we present mediation analysis in the case of multiple exposures and multiple mediators using a regularized two-stage approach. By placing shrinkage priors on each parameter relating to direct and indirect effects, a parsimonious model can be obtained, and consequently, the most relevant pathways will be selected to inform the development of efficient prevention programs. We apply this method to data from the Danish site of the Diabetes & Women's Health Study, Danish National Birth Cohort (DNBC), and find 6 significant maternal risk factors either directly or indirectly affecting childhood body mass index <math><mi>z</mi></math> score at age 7. Simulations with data-generating mechanisms similar to the DNBC data demonstrate good performance of the proposed model.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"13 1","pages":"524-542"},"PeriodicalIF":0.4,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49468922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 Unified Decision Framework for Phase I Dose-Finding Designs 一期剂量寻找设计的统一决策框架

IF 1

Statistics in Biosciences Pub Date : 2021-11-24 DOI: 10.1007/s12561-023-09379-5

Yunshan Duan, Shijie Yuan, Yuan Ji, Peter Mueller

引用次数: 0

Epistasis Detection via the Joint Cumulant 通过联合积存量检测上溢

IF 1

Statistics in Biosciences Pub Date : 2021-11-12 DOI: 10.1007/s12561-022-09336-8

Randall Reese, G. Fu, Geran Zhao, Xiaotian Dai, Xiaotian Li, K. Chiu

引用次数: 0

A Power Prior Approach for Leveraging External Longitudinal and Competing Risks Survival Data Within the Joint Modeling Framework 在联合建模框架内利用外部纵向和竞争风险生存数据的幂优先方法

IF 1

Statistics in Biosciences Pub Date : 2021-11-06 DOI: 10.1007/s12561-021-09330-6

Md. Tuhin Sheikh, Ming-Hui Chen, J. Gelfond, J. Ibrahim

引用次数: 0

Efficacy-Driven Dose Finding with Toxicity Control in Phase I Oncology Studies 在I期肿瘤研究中，疗效驱动的剂量发现和毒性控制

IF 1

Statistics in Biosciences Pub Date : 2021-10-23 DOI: 10.1007/s12561-021-09327-1

Qingyang Liu, J. Geng, F. Fleischer, Q. Deng

引用次数: 0

Simultaneous Learning the Dimension and Parameter of a Statistical Model with Big Data 利用大数据同时学习统计模型的维数和参数

IF 1

Statistics in Biosciences Pub Date : 2021-10-15 DOI: 10.1007/s12561-021-09324-4

Long Wang, Fangzheng Xie, Yanxun Xu

引用次数: 0

A Weighted Sample Framework to Incorporate External Calculators for Risk Modeling 将外部计算器纳入风险建模的加权样本框架

IF 1

Statistics in Biosciences Pub Date : 2021-10-08 DOI: 10.1007/s12561-021-09325-3

D. Ghosh, M. Sabel

引用次数: 0

Leveraging Natural History Data in One- and Two-Arm Hierarchical Bayesian Studies of Rare Disease Progression 利用自然史数据进行罕见病进展的单臂和双臂层次贝叶斯研究

IF 1

Statistics in Biosciences Pub Date : 2021-10-01 DOI: 10.1007/s12561-021-09323-5

A. Monseur, B. Carlin, B. Boulanger, A. Seferian, L. Servais, Chris Freitag, L. Thielemans, Teresa Elena Virginie Ulrike Andrea Adele James J. Basil Gidaro Gargaun Chê Schara Gangfuß D’Amico Dowling , T. Gidaro, E. Gargaun, V. Chê, U. Schara, A. Gangfuss, A. D’Amico, J. Dowling, B. Darras, A. Daron, Arturo E. Hernandez, C. de Lattre, J. Arnal, Michèle Mayer, J. Cuisset, C. Vuillerot, S. Fontaine, R. Bellance, V. Biancalana, A. Buj-Bello, J. Hogrel, H. Landy, K. Amburgey, B. Andres, E. Bertini, R. Cardaş, S. Denis, Dominique Duchêne, V. Latournerie, Nacera Reguiba, E. Tsuchiya, C. Wallgren‐Pettersson

引用次数: 1

Flexible Conditional Borrowing Approaches for Leveraging Historical Data in the Bayesian Design of Superiority Trials 在优势试验的贝叶斯设计中利用历史数据的灵活条件借用方法

IF 1

Statistics in Biosciences Pub Date : 2021-09-18 DOI: 10.1007/s12561-021-09321-7

Weiying Yuan, Ming-Hui Chen, J. Zhong

引用次数: 1