Statistical Applications in Genetics and Molecular Biology最新文献_第6页

Inference of finite mixture models and the effect of binning 有限混合模型的推理与分形效应

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-07-26 DOI: 10.1515/sagmb-2018-0035

Eva-Maria Geissen, J. Hasenauer, N. Radde

引用次数: 1

A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus. 一种新的个体化药物重新定位方法预测1型糖尿病个体化候选药物。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-07-09 DOI: 10.1515/sagmb-2018-0052

Hong Zheng

{"title":"A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus.","authors":"Hong Zheng","doi":"10.1515/sagmb-2018-0052","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0052","url":null,"abstract":"The existence of high cost-consuming and high rate of drug failures suggests the promotion of drug repositioning in drug discovery. Existing drug repositioning techniques mainly focus on discovering candidate drugs for a kind of disease, and are not suitable for predicting candidate drugs for an individual sample. Type 1 diabetes mellitus (T1DM) is a disorder of glucose homeostasis caused by autoimmune destruction of the pancreatic β-cell. Here, we present a novel single sample drug repositioning approach for predicting personalized candidate drugs for T1DM. Our method is based on the observation of drug-disease associations by measuring the similarities of individualized pathway aberrance induced by disease and various drugs using a Kolmogorov-Smirnov weighted Enrichment Score algorithm. Using this method, we predicted several underlying candidate drugs for T1DM. Some of them have been reported for the treatment of diabetes mellitus, and some with a current indication to treat other diseases might be repurposed to treat T1DM. This study conducts drug discovery via detecting the functional connections among disease and drug action, on a personalized or customized basis. Our framework provides a rational way for systematic personalized drug discovery of complex diseases and contributes to the future application of custom therapeutic decisions.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37407908","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 penalized regression approach for DNA copy number study using the sequencing data. 使用测序数据进行DNA拷贝数研究的惩罚回归方法。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-05-30 DOI: 10.1515/sagmb-2018-0001

Jaeeun Lee, Jie Chen

{"title":"A penalized regression approach for DNA copy number study using the sequencing data.","authors":"Jaeeun Lee, Jie Chen","doi":"10.1515/sagmb-2018-0001","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0001","url":null,"abstract":"Modeling the high-throughput next generation sequencing (NGS) data, resulting from experiments with the goal of profiling tumor and control samples for the study of DNA copy number variants (CNVs), remains to be a challenge in various ways. In this application work, we provide an efficient method for detecting multiple CNVs using NGS reads ratio data. This method is based on a multiple statistical change-points model with the penalized regression approach, 1d fused LASSO, that is designed for ordered data in a one-dimensional structure. In addition, since the path algorithm traces the solution as a function of a tuning parameter, the number and locations of potential CNV region boundaries can be estimated simultaneously in an efficient way. For tuning parameter selection, we then propose a new modified Bayesian information criterion, called JMIC, and compare the proposed JMIC with three different Bayes information criteria used in the literature. Simulation results have shown the better performance of JMIC for tuning parameter selection, in comparison with the other three criterion. We applied our approach to the sequencing data of reads ratio between the breast tumor cell lines HCC1954 and its matched normal cell line BL 1954 and the results are in-line with those discovered in the literature.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37289302","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

Truncated rank correlation (TRC) as a robust measure of test-retest reliability in mass spectrometry data. 截断秩相关(TRC)作为质谱数据测试-重测信度的可靠度量。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-05-30 DOI: 10.1515/sagmb-2018-0056

Johan Lim, Donghyeon Yu, Hsun-Chih Kuo, Hyungwon Choi, Scott Walmsley

{"title":"Truncated rank correlation (TRC) as a robust measure of test-retest reliability in mass spectrometry data.","authors":"Johan Lim, Donghyeon Yu, Hsun-Chih Kuo, Hyungwon Choi, Scott Walmsley","doi":"10.1515/sagmb-2018-0056","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0056","url":null,"abstract":"In mass spectrometry (MS) experiments, more than thousands of peaks are detected in the space of mass-to-charge ratio and chromatographic retention time, each associated with an abundance measurement. However, a large proportion of the peaks consists of experimental noise and low abundance compounds are typically masked by noise peaks, compromising the quality of the data. In this paper, we propose a new measure of similarity between a pair of MS experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. A comprehensive numerical study suggests that TRC outperforms traditional sample correlation and Kendall's τ. We apply TRC to measuring test-retest reliability of two MS experiments, including biological replicate analysis of the metabolome in HEK293 cells and metabolomic profiling of benign prostate hyperplasia (BPH) patients. An R package trc of the proposed TRC and related functions is available at https://sites.google.com/site/dhyeonyu/software.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37289303","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

Reproducibility of biomarker identifications from mass spectrometry proteomic data in cancer studies. 癌症研究中质谱蛋白质组学数据中生物标志物鉴定的可重复性。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-05-11 DOI: 10.1515/sagmb-2018-0039

Yulan Liang, Adam Kelemen, Arpad Kelemen

{"title":"Reproducibility of biomarker identifications from mass spectrometry proteomic data in cancer studies.","authors":"Yulan Liang, Adam Kelemen, Arpad Kelemen","doi":"10.1515/sagmb-2018-0039","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0039","url":null,"abstract":"Reproducibility of disease signatures and clinical biomarkers in multi-omics disease analysis has been a key challenge due to a multitude of factors. The heterogeneity of the limited sample, various biological factors such as environmental confounders, and the inherent experimental and technical noises, compounded with the inadequacy of statistical tools, can lead to the misinterpretation of results, and subsequently very different biology. In this paper, we investigate the biomarker reproducibility issues, potentially caused by differences of statistical methods with varied distribution assumptions or marker selection criteria using Mass Spectrometry proteomic ovarian tumor data. We examine the relationship between effect sizes, p values, Cauchy p values, False Discovery Rate p values, and the rank fractions of identified proteins out of thousands in the limited heterogeneous sample. We compared the markers identified from statistical single features selection approaches with machine learning wrapper methods. The results reveal marked differences when selecting the protein markers from varied methods with potential selection biases and false discoveries, which may be due to the small effects, different distribution assumptions, and p value type criteria versus prediction accuracies. The alternative solutions and other related issues are discussed in supporting the reproducibility of findings for clinical actionable outcomes.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37230336","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

Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions. 结合基因表达数据和先验知识，利用结构限制通过贝叶斯网络推断基因调控网络。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-05-01 DOI: 10.1515/sagmb-2018-0042

Luis M de Campos, Andrés Cano, Javier G Castellano, Serafín Moral

{"title":"Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions.","authors":"Luis M de Campos, Andrés Cano, Javier G Castellano, Serafín Moral","doi":"10.1515/sagmb-2018-0042","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0042","url":null,"abstract":"Gene Regulatory Networks (GRNs) are known as the most adequate instrument to provide a clear insight and understanding of the cellular systems. One of the most successful techniques to reconstruct GRNs using gene expression data is Bayesian networks (BN) which have proven to be an ideal approach for heterogeneous data integration in the learning process. Nevertheless, the incorporation of prior knowledge has been achieved by using prior beliefs or by using networks as a starting point in the search process. In this work, the utilization of different kinds of structural restrictions within algorithms for learning BNs from gene expression data is considered. These restrictions will codify prior knowledge, in such a way that a BN should satisfy them. Therefore, one aim of this work is to make a detailed review on the use of prior knowledge and gene expression data to inferring GRNs from BNs, but the major purpose in this paper is to research whether the structural learning algorithms for BNs from expression data can achieve better outcomes exploiting this prior knowledge with the use of structural restrictions. In the experimental study, it is shown that this new way to incorporate prior knowledge leads us to achieve better reverse-engineered networks.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37199702","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: 6

Data-adaptive multi-locus association testing in subjects with arbitrary genealogical relationships. 任意家谱关系的数据自适应多位点关联检验。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-04-08 DOI: 10.1515/sagmb-2018-0030

Gail Gong, Wei Wang, Chih-Lin Hsieh, David J Van Den Berg, Christopher Haiman, Ingrid Oakley-Girvan, Alice S Whittemore

{"title":"Data-adaptive multi-locus association testing in subjects with arbitrary genealogical relationships.","authors":"Gail Gong, Wei Wang, Chih-Lin Hsieh, David J Van Den Berg, Christopher Haiman, Ingrid Oakley-Girvan, Alice S Whittemore","doi":"10.1515/sagmb-2018-0030","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0030","url":null,"abstract":"Genome-wide sequencing enables evaluation of associations between traits and combinations of variants in genes and pathways. But such evaluation requires multi-locus association tests with good power, regardless of the variant and trait characteristics. And since analyzing families may yield more power than analyzing unrelated individuals, we need multi-locus tests applicable to both related and unrelated individuals. Here we describe such tests, and we introduce SKAT-X, a new test statistic that uses genome-wide data obtained from related or unrelated subjects to optimize power for the specific data at hand. Simulations show that: a) SKAT-X performs well regardless of variant and trait characteristics; and b) for binary traits, analyzing affected relatives brings more power than analyzing unrelated individuals, consistent with previous findings for single-locus tests. We illustrate the methods by application to rare unclassified missense variants in the tumor suppressor gene BRCA2, as applied to combined data from prostate cancer families and unrelated prostate cancer cases and controls in the Multi-ethnic Cohort (MEC). The methods can be implemented using open-source code for public use as the R-package GATARS (Genetic Association Tests for Arbitrarily Related Subjects) <https://gailg.github.io/gatars/>.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37127926","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: 1

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