1
|
Smirnov P, Smith I, Safikhani Z, Ba-Alawi W, Khodakarami F, Lin E, Yu Y, Martin S, Ortmann J, Aittokallio T, Hafner M, Haibe-Kains B. Evaluation of statistical approaches for association testing in noisy drug screening data. BMC Bioinformatics 2022; 23:188. [PMID: 35585485 PMCID: PMC9118710 DOI: 10.1186/s12859-022-04693-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 04/15/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. RESULTS To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. CONCLUSIONS We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.
Collapse
Affiliation(s)
- Petr Smirnov
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | - Ian Smith
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | - Zhaleh Safikhani
- Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | - Wail Ba-Alawi
- Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | | | - Eva Lin
- Department of Discovery Oncology, Genentech Inc., South San Francisco, USA
| | - Yihong Yu
- Department of Discovery Oncology, Genentech Inc., South San Francisco, USA
| | - Scott Martin
- Department of Discovery Oncology, Genentech Inc., South San Francisco, USA
| | - Janosch Ortmann
- Département d'analytique, opérations et technologies de l'information, École des sciences de la gestion, Université du Québec à Montréal, Montréal, Canada
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.,Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Marc Hafner
- Department of Oncology Bioinformatics, Genentech Inc., South San Francisco, USA
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, Canada. .,Princess Margaret Cancer Center, University Health Network, Toronto, Canada. .,Vector Institute, Toronto, Canada.
| |
Collapse
|
2
|
Mary D, Roquain E. Semi-supervised multiple testing. Electron J Stat 2022. [DOI: 10.1214/22-ejs2050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- David Mary
- Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, Bd de l’Observatoire, CS 34229, 06304, Nice cedex 4, France
| | - Etienne Roquain
- Laboratoire de Probabilités, Statistique et Modélisation, Sorbonne Université, Université de Paris & CNRS, 4, place Jussieu, 75005 Paris, France
| |
Collapse
|
3
|
Li F, Wei RB, Wang Y, Su TY, Li P, Huang MJ, Chen XM. Nomogram prediction model for renal anaemia in IgA nephropathy patients. Open Med (Wars) 2021; 16:718-727. [PMID: 34013043 PMCID: PMC8111477 DOI: 10.1515/med-2021-0284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 03/18/2021] [Accepted: 04/07/2021] [Indexed: 12/29/2022] Open
Abstract
In this study, we focused on the influencing factors of renal anaemia in patients with IgA nephropathy and constructed a nomogram model. We divided 462 patients with IgA nephropathy diagnosed by renal biopsy into anaemic and non-anaemic groups. Then, the influencing factors of renal anaemia in patients with IgA nephropathy were analysed by least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression, and a nomogram model for predicting renal anaemia was established. Eventually, nine variables were obtained, which are easy to apply clinically. The areas under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve reached 0.835 and 0.676, respectively, and the C-index reached 0.848. The calibration plot showed that the model had good discrimination, accuracy, and diagnostic efficacy. In addition, the C-index of the model following internal validation reached 0.823. Decision curve analysis suggested that the model had a certain degree of clinical significance. This new nomogram model of renal anaemia combines the basic information, laboratory findings, and renal biopsy results of patients with IgA nephropathy, providing important guidance for predicting and clinically intervening in renal anaemia.
Collapse
Affiliation(s)
- Fei Li
- School of Medicine, Nankai University, No. 94 Weijin Road, Nankai District, Tianjin 300073, People's Republic of China
| | - Ri-Bao Wei
- School of Medicine, Nankai University, No. 94 Weijin Road, Nankai District, Tianjin 300073, People's Republic of China.,Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| | - Yang Wang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| | - Ting-Yu Su
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| | - Ping Li
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| | - Meng-Jie Huang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| | - Xiang-Mei Chen
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, No. 28 Fuxing Road, Haidian District, Beijing 100853, People's Republic of China
| |
Collapse
|
4
|
Hahn G. On the expected runtime of multiple testing algorithms with bounded error. Stat Probab Lett 2020. [DOI: 10.1016/j.spl.2020.108844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
5
|
Hayes SA, Pandiri AR, Ton TVT, Hong HHL, Clayton NP, Shockley KR, Peddada SD, Gerrish K, Wyde M, Sills RC, Hoenerhoff MJ. Renal Cell Carcinomas in Vinylidene Chloride-exposed Male B6C3F1 Mice Are Characterized by Oxidative Stress and TP53 Pathway Dysregulation. Toxicol Pathol 2016; 44:71-87. [PMID: 26682919 PMCID: PMC4752433 DOI: 10.1177/0192623315610820] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Vinylidene chloride (VDC) has been widely used in the production of plastics and flame retardants. Exposure of B6C3F1 mice to VDC in the 2-year National Toxicology Program carcinogenicity bioassay resulted in a dose-dependent increases in renal cell hyperplasia, renal cell adenoma, and renal cell carcinomas (RCCs). Among those differentially expressed genes from controls and RCC of VDC-exposed mice, there was an overrepresentation of genes from pathways associated with chronic xenobiotic and oxidative stress as well as c-Myc overexpression and dysregulation of TP53 cell cycle checkpoint and DNA damage repair pathways in RCC. Trend analysis comparing RCC, VDC-exposed kidney, and chamber control kidney showed a conservation of pathway dysregulation in terms of overrepresentation of xenobiotic and oxidative stress, and DNA damage and cell cycle checkpoint pathways in both VDC-exposed kidney and RCC, suggesting that these mechanisms play a role in the pathogenesis of RCC in VDC-exposed mice.
Collapse
Affiliation(s)
- Schantel A Hayes
- Charles River Laboratories, Pathology Associates, Research Triangle Park, North Carolina, USA
| | - Arun R Pandiri
- Experimental Pathology Laboratories Inc., Research Triangle Park, North Carolina, USA Cellular and Molecular Pathology Branch, Division of the National Toxicologic Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Thai-vu T Ton
- Cellular and Molecular Pathology Branch, Division of the National Toxicologic Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Hue-Hua L Hong
- Cellular and Molecular Pathology Branch, Division of the National Toxicologic Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Natasha P Clayton
- Cellular and Molecular Pathology Branch, Division of the National Toxicologic Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Keith R Shockley
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Shyamal D Peddada
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Kevin Gerrish
- Microarray Core, Toxicology and Pharmacology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Michael Wyde
- Toxicology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Robert C Sills
- Cellular and Molecular Pathology Branch, Division of the National Toxicologic Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Mark J Hoenerhoff
- Cellular and Molecular Pathology Branch, Division of the National Toxicologic Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| |
Collapse
|
6
|
Gandy A, Hahn G. MMCTest-A Safe Algorithm for Implementing Multiple Monte Carlo Tests. Scand Stat Theory Appl 2014. [DOI: 10.1111/sjos.12085] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Axel Gandy
- Department of Mathematics; Imperial College London
| | - Georg Hahn
- Department of Mathematics; Imperial College London
| |
Collapse
|
7
|
Xiao J, Zhu W, Guo J. Large-scale multiple testing in genome-wide association studies via region-specific hidden Markov models. BMC Bioinformatics 2013; 14:282. [PMID: 24067069 PMCID: PMC3850654 DOI: 10.1186/1471-2105-14-282] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2013] [Accepted: 09/20/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Identifying genetic variants associated with complex human diseases is a great challenge in genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) arising from genetic background are often dependent. The existing methods, i.e., local index of significance (LIS) and pooled local index of significance (PLIS), were both proposed for modeling SNP dependence and assumed that the whole chromosome follows a hidden Markov model (HMM). However, the fact that SNP data are often collected from separate heterogeneous regions of a single chromosome encourages different chromosomal regions to follow different HMMs. In this research, we developed a data-driven penalized criterion combined with a dynamic programming algorithm to find change points that divide the whole chromosome into more homogeneous regions. Furthermore, we extended PLIS to analyze the dependent tests obtained from multiple chromosomes with different regions for GWAS. RESULTS The simulation results show that our new criterion can improve the performance of the model selection procedure and that our region-specific PLIS (RSPLIS) method is better than PLIS at detecting disease-associated SNPs when there are multiple change points along a chromosome. Our method has been used to analyze the Daly study, and compared with PLIS, RSPLIS yielded results that more accurately detected disease-associated SNPs. CONCLUSIONS The genomic rankings based on our method differ from the rankings based on PLIS. Specifically, for the detection of genetic variants with weak effect sizes, the RSPLIS method was able to rank them more efficiently and with greater power.
Collapse
Affiliation(s)
- Jian Xiao
- Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China
| | - Wensheng Zhu
- Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China
| | - Jianhua Guo
- Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China
| |
Collapse
|
8
|
Vélez JI, Chandrasekharappa SC, Henao E, Martinez AF, Harper U, Jones M, Solomon BD, Lopez L, Garcia G, Aguirre-Acevedo DC, Acosta-Baena N, Correa JC, Lopera-Gómez CM, Jaramillo-Elorza MC, Rivera D, Kosik KS, Schork NJ, Swanson JM, Lopera F, Arcos-Burgos M. Pooling/bootstrap-based GWAS (pbGWAS) identifies new loci modifying the age of onset in PSEN1 p.Glu280Ala Alzheimer's disease. Mol Psychiatry 2013; 18:568-75. [PMID: 22710270 PMCID: PMC3596442 DOI: 10.1038/mp.2012.81] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The literature on GWAS (genome-wide association studies) data suggests that very large sample sizes (for example, 50,000 cases and 50,000 controls) may be required to detect significant associations of genomic regions for complex disorders such as Alzheimer's disease (AD). Because of the challenges of obtaining such large cohorts, we describe here a novel sequential strategy that combines pooling of DNA and bootstrapping (pbGWAS) in order to significantly increase the statistical power and exponentially reduce expenses. We applied this method to a very homogeneous sample of patients belonging to a unique and clinically well-characterized multigenerational pedigree with one of the most severe forms of early onset AD, carrying the PSEN1 p.Glu280Ala mutation (often referred to as E280A mutation), which originated as a consequence of a founder effect. In this cohort, we identified novel loci genome-wide significantly associated as modifiers of the age of onset of AD (CD44, rs187116, P=1.29 × 10⁻¹²; NPHP1, rs10173717, P=1.74 × 10⁻¹²; CADPS2, rs3757536, P=1.54 × 10⁻¹⁰; GREM2, rs12129547, P=1.69 × 10⁻¹³, among others) as well as other loci known to be associated with AD. Regions identified by pbGWAS were confirmed by subsequent individual genotyping. The pbGWAS methodology and the genes it targeted could provide important insights in determining the genetic causes of AD and other complex conditions.
Collapse
Affiliation(s)
- J I Vélez
- Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - S C Chandrasekharappa
- Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - E Henao
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - A F Martinez
- Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - U Harper
- Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - M Jones
- Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - B D Solomon
- Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - L Lopez
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - G Garcia
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - D C Aguirre-Acevedo
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - N Acosta-Baena
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - J C Correa
- Escuela de Estadística, Universidad Nacional de Colombia, Sede Medellín, Medellín, Colombia
| | - C M Lopera-Gómez
- Escuela de Estadística, Universidad Nacional de Colombia, Sede Medellín, Medellín, Colombia
| | - M C Jaramillo-Elorza
- Escuela de Estadística, Universidad Nacional de Colombia, Sede Medellín, Medellín, Colombia
| | - D Rivera
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - K S Kosik
- Neuroscience Research Institute, University of California at Santa Barbara, Santa Barbara, CA, USA
| | - N J Schork
- Department of Biostatistics and Bioinformatics, The Scripps Research Institute, La Jolla, CA, USA
| | - J M Swanson
- Department of Psychiatry, Florida International University, Miami, FL, USA,Child Development Center, University of California at Irvine, Irvine, CA, USA
| | - F Lopera
- Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - M Arcos-Burgos
- Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA,Grupo de Neurociencias de Antioquia, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia,Translational Genomics Group, Department of Translational Medicine, John Curtin School of Medical Research, ANU College of Medicine, Biology & Environment, The Australian National University, Canberra, ACT, Australia,Translational Genomics Group, ANU College of Medicine, Biology & Environment, John Curtin School of Medical Research, The Australian National University, Building 131 Garran Road, Canberra, ACT 0200, Australia. E-mail:
| |
Collapse
|
9
|
Jiang H, Salzman J. Statistical properties of an early stopping rule for resampling-based multiple testing. Biometrika 2012; 99:973-980. [PMID: 23843675 DOI: 10.1093/biomet/ass051] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Resampling-based methods for multiple hypothesis testing often lead to long run times when the number of tests is large. This paper presents a simple rule that substantially reduces computation by allowing resampling to terminate early on a subset of tests. We prove that the method has a low probability of obtaining a set of rejected hypotheses different from those rejected without early stopping, and obtain error bounds for multiple hypothesis testing. Simulation shows that our approach saves more computation than other available procedures.
Collapse
Affiliation(s)
- Hui Jiang
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, Michigan 48109, U.S.A. ,
| | | |
Collapse
|
10
|
Pattengale ND, Alipour M, Bininda-Emonds ORP, Moret BME, Stamatakis A. How many bootstrap replicates are necessary? J Comput Biol 2010; 17:337-54. [PMID: 20377449 DOI: 10.1089/cmb.2009.0179] [Citation(s) in RCA: 584] [Impact Index Per Article: 41.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Phylogenetic bootstrapping (BS) is a standard technique for inferring confidence values on phylogenetic trees that is based on reconstructing many trees from minor variations of the input data, trees called replicates. BS is used with all phylogenetic reconstruction approaches, but we focus here on one of the most popular, maximum likelihood (ML). Because ML inference is so computationally demanding, it has proved too expensive to date to assess the impact of the number of replicates used in BS on the relative accuracy of the support values. For the same reason, a rather small number (typically 100) of BS replicates are computed in real-world studies. Stamatakis et al. recently introduced a BS algorithm that is 1 to 2 orders of magnitude faster than previous techniques, while yielding qualitatively comparable support values, making an experimental study possible. In this article, we propose stopping criteria--that is, thresholds computed at runtime to determine when enough replicates have been generated--and we report on the first large-scale experimental study to assess the effect of the number of replicates on the quality of support values, including the performance of our proposed criteria. We run our tests on 17 diverse real-world DNA--single-gene as well as multi-gene--datasets, which include 125-2,554 taxa. We find that our stopping criteria typically stop computations after 100-500 replicates (although the most conservative criterion may continue for several thousand replicates) while producing support values that correlate at better than 99.5% with the reference values on the best ML trees. Significantly, we also find that the stopping criteria can recommend very different numbers of replicates for different datasets of comparable sizes. Our results are thus twofold: (i) they give the first experimental assessment of the effect of the number of BS replicates on the quality of support values returned through BS, and (ii) they validate our proposals for stopping criteria. Practitioners will no longer have to enter a guess nor worry about the quality of support values; moreover, with most counts of replicates in the 100-500 range, robust BS under ML inference becomes computationally practical for most datasets. The complete test suite is available at http://lcbb.epfl.ch/BS.tar.bz2, and BS with our stopping criteria is included in the latest release of RAxML v7.2.5, available at http://wwwkramer.in.tum.de/exelixis/software.html.
Collapse
Affiliation(s)
- Nicholas D Pattengale
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico 87123, USA.
| | | | | | | | | |
Collapse
|
11
|
Peddada SD, Harris SF, Davidov O. Analysis of Correlated Gene Expression Data on Ordered Categories. JOURNAL OF THE INDIAN SOCIETY OF AGRICULTURAL STATISTICS. INDIAN SOCIETY OF AGRICULTURAL STATISTICS 2010; 64:45-60. [PMID: 21998487 PMCID: PMC3190572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
A bootstrap based methodology is introduced for analyzing repeated measures/longitudinal microarray gene expression data over ordered categories. The proposed non-parametric procedure uses order-restricted inference to compare gene expressions among ordered experimental conditions. The null distribution for determining significance is derived by suitably bootstrapping the residuals. The procedure addresses two potential sources of correlation in the data, namely, (a) correlations among genes within a chip ("intra-chip" correlation), and (b) correlation within subject due to repeated/longitudinal measurements ("temporal" correlation). To make the procedure computationally efficient, the adaptive bootstrap methodology of Guo and Peddada (2008) is implemented such that the resulting procedure controls the false discovery rate (FDR) at the desired nominal level.
Collapse
Affiliation(s)
| | | | - Ori Davidov
- Department of Statistics, University of Haifa, Haifa, Israel
| |
Collapse
|
12
|
Wei Z, Sun W, Wang K, Hakonarson H. Multiple testing in genome-wide association studies via hidden Markov models. ACTA ACUST UNITED AC 2009; 25:2802-8. [PMID: 19654115 DOI: 10.1093/bioinformatics/btp476] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION Genome-wide association studies (GWAS) interrogate common genetic variation across the entire human genome in an unbiased manner and hold promise in identifying genetic variants with moderate or weak effect sizes. However, conventional testing procedures, which are mostly P-value based, ignore the dependency and therefore suffer from loss of efficiency. The goal of this article is to exploit the dependency information among adjacent single nucleotide polymorphisms (SNPs) to improve the screening efficiency in GWAS. RESULTS We propose to model the linear block dependency in the SNP data using hidden Markov models (HMMs). A compound decision-theoretic framework for testing HMM-dependent hypotheses is developed. We propose a powerful data-driven procedure [pooled local index of significance (PLIS)] that controls the false discovery rate (FDR) at the nominal level. PLIS is shown to be optimal in the sense that it has the smallest false negative rate (FNR) among all valid FDR procedures. By re-ranking significance for all SNPs with dependency considered, PLIS gains higher power than conventional P-value based methods. Simulation results demonstrate that PLIS dominates conventional FDR procedures in detecting disease-associated SNPs. Our method is applied to analysis of the SNP data from a GWAS of type 1 diabetes. Compared with the Benjamini-Hochberg (BH) procedure, PLIS yields more accurate results and has better reproducibility of findings. CONCLUSION The genomic rankings based on our procedure are substantially different from the rankings based on the P-values. By integrating information from adjacent locations, the PLIS rankings benefit from the increased signal-to-noise ratio, hence our procedure often has higher statistical power and better reproducibility. It provides a promising direction in large-scale GWAS. AVAILABILITY An R package PLIS has been developed to implement the PLIS procedure. Source codes are available upon request and will be available on CRAN (http://cran.r-project.org/). CONTACT zhiwei@njit.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Zhi Wei
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA.
| | | | | | | |
Collapse
|
13
|
|