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Ebrahimpoor M, Spitali P, Goeman JJ, Tsonaka R. Pathway testing for longitudinal metabolomics. Stat Med 2021; 40:3053-3065. [PMID: 33768548 PMCID: PMC8252476 DOI: 10.1002/sim.8957] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 02/19/2021] [Accepted: 03/04/2021] [Indexed: 01/12/2023]
Abstract
We propose a top‐down approach for pathway analysis of longitudinal metabolite data. We apply a score test based on a shared latent process mixed model which can identify pathways with differentially progressing metabolites. The strength of our approach is that it can handle unbalanced designs, deals with potential missing values in the longitudinal markers, and gives valid results even with small sample sizes. Contrary to bottom‐up approaches, correlations between metabolites are explicitly modeled leveraging power gains. For large pathway sizes, a computationally efficient solution is proposed based on pseudo‐likelihood methodology. We demonstrate the advantages of the proposed method in identification of differentially expressed pathways through simulation studies. Finally, longitudinal metabolite data from a mice experiment is analyzed to demonstrate our methodology.
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Affiliation(s)
- Mitra Ebrahimpoor
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Pietro Spitali
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Jelle J Goeman
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Roula Tsonaka
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Somani J, Ramchandran S, Lähdesmäki H. A personalised approach for identifying disease-relevant pathways in heterogeneous diseases. NPJ Syst Biol Appl 2020; 6:17. [PMID: 32518234 PMCID: PMC7283216 DOI: 10.1038/s41540-020-0130-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 03/12/2020] [Indexed: 11/30/2022] Open
Abstract
Numerous time-course gene expression datasets have been generated for studying the biological dynamics that drive disease progression; and nearly as many methods have been proposed to analyse them. However, barely any method exists that can appropriately model time-course data while accounting for heterogeneity that entails many complex diseases. Most methods manage to fulfil either one of those qualities, but not both. The lack of appropriate methods hinders our capability of understanding the disease process and pursuing preventive treatments. We present a method that models time-course data in a personalised manner using Gaussian processes in order to identify differentially expressed genes (DEGs); and combines the DEG lists on a pathway-level using a permutation-based empirical hypothesis testing in order to overcome gene-level variability and inconsistencies prevalent to datasets from heterogenous diseases. Our method can be applied to study the time-course dynamics, as well as specific time-windows of heterogeneous diseases. We apply our personalised approach on three longitudinal type 1 diabetes (T1D) datasets, where the first two are used to determine perturbations taking place during early prognosis of the disease, as well as in time-windows before autoantibody positivity and T1D diagnosis; and the third is used to assess the generalisability of our method. By comparing to non-personalised methods, we demonstrate that our approach is biologically motivated and can reveal more insights into progression of heterogeneous diseases. With its robust capabilities of identifying disease-relevant pathways, our approach could be useful for predicting events in the progression of heterogeneous diseases and even for biomarker identification.
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Affiliation(s)
- Juhi Somani
- Department of Computer Science, Aalto University, 02150, Espoo, Finland
| | | | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, 02150, Espoo, Finland.
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Mallik S, Odom GJ, Gao Z, Gomez L, Chen X, Wang L. An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays. Brief Bioinform 2019; 20:2224-2235. [PMID: 30239597 PMCID: PMC6954393 DOI: 10.1093/bib/bby085] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 07/24/2018] [Accepted: 08/16/2018] [Indexed: 01/19/2023] Open
Abstract
Epigenome-wide association studies (EWASs) have become increasingly popular for studying DNA methylation (DNAm) variations in complex diseases. The Illumina methylation arrays provide an economical, high-throughput and comprehensive platform for measuring methylation status in EWASs. A number of software tools have been developed for identifying disease-associated differentially methylated regions (DMRs) in the epigenome. However, in practice, we found these tools typically had multiple parameter settings that needed to be specified and the performance of the software tools under different parameters was often unclear. To help users better understand and choose optimal parameter settings when using DNAm analysis tools, we conducted a comprehensive evaluation of 4 popular DMR analysis tools under 60 different parameter settings. In addition to evaluating power, precision, area under precision-recall curve, Matthews correlation coefficient, F1 score and type I error rate, we also compared several additional characteristics of the analysis results, including the size of the DMRs, overlap between the methods and execution time. The results showed that none of the software tools performed best under their default parameter settings, and power varied widely when parameters were changed. Overall, the precision of these software tools were good. In contrast, all methods lacked power when effect size was consistent but small. Across all simulation scenarios, comb-p consistently had the best sensitivity as well as good control of false-positive rate.
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Affiliation(s)
- Saurav Mallik
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA
- Joint First Authors
| | - Gabriel J Odom
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA
- Joint First Authors
| | - Zhen Gao
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Lissette Gomez
- Dr. John T. Macdonald Foundation, Department of Human Genetics, and John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
| | - Xi Chen
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Lily Wang
- Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
- Dr. John T. Macdonald Foundation, Department of Human Genetics, and John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
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Díaz-Gay M, Franch-Expósito S, Arnau-Collell C, Park S, Supek F, Muñoz J, Bonjoch L, Gratacós-Mulleras A, Sánchez-Rojas PA, Esteban-Jurado C, Ocaña T, Cuatrecasas M, Vila-Casadesús M, Lozano JJ, Parra G, Laurie S, Beltran S, Castells A, Bujanda L, Cubiella J, Balaguer F, Castellví-Bel S. Integrated Analysis of Germline and Tumor DNA Identifies New Candidate Genes Involved in Familial Colorectal Cancer. Cancers (Basel) 2019; 11:cancers11030362. [PMID: 30871259 PMCID: PMC6468873 DOI: 10.3390/cancers11030362] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 03/08/2019] [Accepted: 03/09/2019] [Indexed: 12/29/2022] Open
Abstract
Colorectal cancer (CRC) shows aggregation in some families but no alterations in the known hereditary CRC genes. We aimed to identify new candidate genes which are potentially involved in germline predisposition to familial CRC. An integrated analysis of germline and tumor whole-exome sequencing data was performed in 18 unrelated CRC families. Deleterious single nucleotide variants (SNV), short insertions and deletions (indels), copy number variants (CNVs) and loss of heterozygosity (LOH) were assessed as candidates for first germline or second somatic hits. Candidate tumor suppressor genes were selected when alterations were detected in both germline and somatic DNA, fulfilling Knudson’s two-hit hypothesis. Somatic mutational profiling and signature analysis were also performed. A series of germline-somatic variant pairs were detected. In all cases, the first hit was presented as a rare SNV/indel, whereas the second hit was either a different SNV (3 genes) or LOH affecting the same gene (141 genes). BRCA2, BLM, ERCC2, RECQL, REV3L and RIF1 were among the most promising candidate genes for germline CRC predisposition. The identification of new candidate genes involved in familial CRC could be achieved by our integrated analysis. Further functional studies and replication in additional cohorts are required to confirm the selected candidates.
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Affiliation(s)
- Marcos Díaz-Gay
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | - Sebastià Franch-Expósito
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | - Coral Arnau-Collell
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | - Solip Park
- Systems Biology Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 08003 Barcelona, Spain;
| | - Fran Supek
- Institut de Recerca Biomedica (IRB Barcelona), The Barcelona Institute of Science and Technology, 08028 Barcelona, Spain;
| | - Jenifer Muñoz
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | - Laia Bonjoch
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | - Anna Gratacós-Mulleras
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | - Paula A. Sánchez-Rojas
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | - Clara Esteban-Jurado
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | - Teresa Ocaña
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | | | - Maria Vila-Casadesús
- Bioinformatics Platform, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 08036 Barcelona, Spain; (M.V.-C.); (J.J.L.)
| | - Juan José Lozano
- Bioinformatics Platform, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 08036 Barcelona, Spain; (M.V.-C.); (J.J.L.)
| | - Genis Parra
- Centre Nacional d’Anàlisi Genòmica-Centre de Regulació Genòmica (CNAG-CRG), Parc Científic de Barcelona, 08028 Barcelona, Spain; (G.P.); (S.L.); (S.B.)
| | - Steve Laurie
- Centre Nacional d’Anàlisi Genòmica-Centre de Regulació Genòmica (CNAG-CRG), Parc Científic de Barcelona, 08028 Barcelona, Spain; (G.P.); (S.L.); (S.B.)
| | - Sergi Beltran
- Centre Nacional d’Anàlisi Genòmica-Centre de Regulació Genòmica (CNAG-CRG), Parc Científic de Barcelona, 08028 Barcelona, Spain; (G.P.); (S.L.); (S.B.)
| | - EPICOLON Consortium
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
- Gastroenterology Department, Hospital Donostia-Instituto Biodonostia, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Basque Country University (UPV/EHU), 20014 San Sebastián, Spain;
- Gastroenterology Department, Complexo Hospitalario Universitario de Ourense, Instituto de Investigación Sanitaria Galicia Sur, 32005 Ourense, Spain;
| | - Antoni Castells
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | - Luis Bujanda
- Gastroenterology Department, Hospital Donostia-Instituto Biodonostia, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Basque Country University (UPV/EHU), 20014 San Sebastián, Spain;
| | - Joaquín Cubiella
- Gastroenterology Department, Complexo Hospitalario Universitario de Ourense, Instituto de Investigación Sanitaria Galicia Sur, 32005 Ourense, Spain;
| | - Francesc Balaguer
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
| | - Sergi Castellví-Bel
- Gastroenterology Department, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, 08036 Barcelona, Spain; (M.D.-G.); (S.F.-E.); (C.A.-C.); (J.M.); (L.B.); (A.G.-M.); (P.A.S.-R.); (C.E.-J.); (T.O.); (A.C.); (F.B.)
- Correspondence: ; Tel.: +34-93227-5400 (ext. 4183)
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Guijo-Rubio D, Villalón-Vaquero PJ, Gutiérrez PA, Ayllón MD, Briceño J, Hervás-Martínez C. Modelling Survival by Machine Learning Methods in Liver Transplantation: Application to the UNOS Dataset. INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING – IDEAL 2019 2019:97-104. [DOI: 10.1007/978-3-030-33617-2_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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Baghfalaki T, Ganjali M, Berridge D. Generalized estimating equations by considering additive terms for analyzing time-course gene sets data. J Korean Stat Soc 2018. [DOI: 10.1016/j.jkss.2018.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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7
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Agniel D, Hejblum BP. Variance component score test for time-course gene set analysis of longitudinal RNA-seq data. Biostatistics 2018; 18:589-604. [PMID: 28334305 DOI: 10.1093/biostatistics/kxx005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Accepted: 01/04/2017] [Indexed: 01/28/2023] Open
Abstract
As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. It has been proposed to model RNA-seq counts as continuous variables using nonparametric regression to account for their inherent heteroscedasticity. In this vein, we propose tcgsaseq, a principled, model-free, and efficient method for detecting longitudinal changes in RNA-seq gene sets defined a priori. The method identifies those gene sets whose expression varies over time, based on an original variance component score test accounting for both covariates and heteroscedasticity without assuming any specific parametric distribution for the (transformed) counts. We demonstrate that despite the presence of a nonparametric component, our test statistic has a simple form and limiting distribution, and both may be computed quickly. A permutation version of the test is additionally proposed for very small sample sizes. Applied to both simulated data and two real datasets, tcgsaseq is shown to exhibit very good statistical properties, with an increase in stability and power when compared to state-of-the-art methods ROAST (rotation gene set testing), edgeR, and DESeq2, which can fail to control the type I error under certain realistic settings. We have made the method available for the community in the R package tcgsaseq.
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Affiliation(s)
- Denis Agniel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115, USA
| | - Boris P Hejblum
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA University of Bordeaux, ISPED, INSERM U1219, INRIA SISTM, 146 rue Léo Saignat, 33076 Bordeaux, FRANCE Vaccine Research Institute, Créteil, FRANCE
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Zhang Y, Topham DJ, Thakar J, Qiu X. FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis. Bioinformatics 2018; 33:1944-1952. [PMID: 28334094 PMCID: PMC5939227 DOI: 10.1093/bioinformatics/btx104] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Accepted: 02/17/2017] [Indexed: 01/26/2023] Open
Abstract
Motivation Gene set enrichment analyses (GSEAs) are widely used in genomic research to identify underlying biological mechanisms (defined by the gene sets), such as Gene Ontology terms and molecular pathways. There are two caveats in the currently available methods: (i) they are typically designed for group comparisons or regression analyses, which do not utilize temporal information efficiently in time-series of transcriptomics measurements; and (ii) genes overlapping in multiple molecular pathways are considered multiple times in hypothesis testing. Results We propose an inferential framework for GSEA based on functional data analysis, which utilizes the temporal information based on functional principal component analysis, and disentangles the effects of overlapping genes by a functional extension of the elastic-net regression. Furthermore, the hypothesis testing for the gene sets is performed by an extension of Mann-Whitney U test which is based on weighted rank sums computed from correlated observations. By using both simulated datasets and a large-scale time-course gene expression data on human influenza infection, we demonstrate that our method has uniformly better receiver operating characteristic curves, and identifies more pathways relevant to immune-response to human influenza infection than the competing approaches. Availability and Implementation The methods are implemented in R package FUNNEL, freely and publicly available at: https://github.com/yunzhang813/FUNNEL-GSEA-R-Package. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yun Zhang
- Department of Biostatistics and Computational Biology
| | - David J Topham
- Department of Microbiology and Immunology, University of Rochester, Rochester, NY 14642, USA
| | - Juilee Thakar
- Department of Biostatistics and Computational Biology.,Department of Microbiology and Immunology, University of Rochester, Rochester, NY 14642, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology
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Dave VS, Hasan MA, Zhang B, Reddy CK. Predicting interval time for reciprocal link creation using survival analysis. SOCIAL NETWORK ANALYSIS AND MINING 2018. [DOI: 10.1007/s13278-018-0494-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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10
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Wani SA, Sahu AR, Saxena S, Hussain S, Pandey A, Kanchan S, Sahoo AP, Mishra B, Tiwari AK, Mishra BP, Gandham RK, Singh RK. Systems biology approach: Panacea for unravelling host-virus interactions and dynamics of vaccine induced immune response. GENE REPORTS 2016; 5:23-29. [PMID: 32289096 PMCID: PMC7104209 DOI: 10.1016/j.genrep.2016.08.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 06/24/2016] [Accepted: 08/01/2016] [Indexed: 12/18/2022]
Abstract
Systems biology is an interdisciplinary research field in life sciences, which involves a comprehensive and quantitative analysis of the interactions between all of the components of biological systems over time. For the past 50 years the discipline of virology has overly focused on the pathogen itself. However, we now know that the host response is equally or more important in defining the eventual pathological outcome of infection. Systems biology has in recent years been increasingly recognised for its importance to infectious disease research. Host-virus interactions can be better understood by taking into account the dynamical molecular networks that constitute a biological system. To decipher the pathobiological mechanisms of any disease requires a deep knowledge of how multiple and concurrent signal-transduction pathways operate and are deregulated. Hence the intricacies of signalling pathways can be dissected only by system level approaches. Deciphering the host virus interactions through system biology approach reviewed High throughput techniques to understand the host pathogen interactions examined Shift from virus-centric perspective to spectrum of virus-host interactions Modeling of host-virus cross talk
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Affiliation(s)
- Sajad Ahmad Wani
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Amit Ranjan Sahu
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Shikha Saxena
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Shahid Hussain
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Aruna Pandey
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Sonam Kanchan
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Aditya Prasad Sahoo
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Bina Mishra
- Division of Biological Products, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Ashok Kumar Tiwari
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Bishnu Prasad Mishra
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Ravi Kumar Gandham
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
| | - Raj Kumar Singh
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar 243122, India
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Hepp T, Schmid M, Gefeller O, Waldmann E, Mayr A. Approaches to Regularized Regression - A Comparison between Gradient Boosting and the Lasso. Methods Inf Med 2016; 55:422-430. [PMID: 27626931 DOI: 10.3414/me16-01-0033] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Accepted: 06/21/2016] [Indexed: 11/09/2022]
Abstract
BACKGROUND Penalization and regularization techniques for statistical modeling have attracted increasing attention in biomedical research due to their advantages in the presence of high-dimensional data. A special focus lies on algorithms that incorporate automatic variable selection like the least absolute shrinkage operator (lasso) or statistical boosting techniques. OBJECTIVES Focusing on the linear regression framework, this article compares the two most-common techniques for this task, the lasso and gradient boosting, both from a methodological and a practical perspective. METHODS We describe these methods highlighting under which circumstances their results will coincide in low-dimensional settings. In addition, we carry out extensive simulation studies comparing the performance in settings with more predictors than observations and investigate multiple combinations of noise-to-signal ratio and number of true non-zero coeffcients. Finally, we examine the impact of different tuning methods on the results. RESULTS Both methods carry out penalization and variable selection for possibly highdimensional data, often resulting in very similar models. An advantage of the lasso is its faster run-time, a strength of the boosting concept is its modular nature, making it easy to extend to other regression settings. CONCLUSIONS Although following different strategies with respect to optimization and regularization, both methods imply similar constraints to the estimation problem leading to a comparable performance regarding prediction accuracy and variable selection in practice.
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12
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Identification of Cancer Related Genes Using a Comprehensive Map of Human Gene Expression. PLoS One 2016; 11:e0157484. [PMID: 27322383 PMCID: PMC4913919 DOI: 10.1371/journal.pone.0157484] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 05/30/2016] [Indexed: 11/19/2022] Open
Abstract
Rapid accumulation and availability of gene expression datasets in public repositories have enabled large-scale meta-analyses of combined data. The richness of cross-experiment data has provided new biological insights, including identification of new cancer genes. In this study, we compiled a human gene expression dataset from ∼40,000 publicly available Affymetrix HG-U133Plus2 arrays. After strict quality control and data normalisation the data was quantified in an expression matrix of ∼20,000 genes and ∼28,000 samples. To enable different ways of sample grouping, existing annotations where subjected to systematic ontology assisted categorisation and manual curation. Groups like normal tissues, neoplasmic tissues, cell lines, homoeotic cells and incompletely differentiated cells were created. Unsupervised analysis of the data confirmed global structure of expression consistent with earlier analysis but with more details revealed due to increased resolution. A suitable mixed-effects linear model was used to further investigate gene expression in solid tissue tumours, and to compare these with the respective healthy solid tissues. The analysis identified 1,285 genes with systematic expression change in cancer. The list is significantly enriched with known cancer genes from large, public, peer-reviewed databases, whereas the remaining ones are proposed as new cancer gene candidates. The compiled dataset is publicly available in the ArrayExpress Archive. It contains the most diverse collection of biological samples, making it the largest systematically annotated gene expression dataset of its kind in the public domain.
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Hejblum BP, Skinner J, Thiébaut R. Time-Course Gene Set Analysis for Longitudinal Gene Expression Data. PLoS Comput Biol 2015; 11:e1004310. [PMID: 26111374 PMCID: PMC4482329 DOI: 10.1371/journal.pcbi.1004310] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Accepted: 04/30/2015] [Indexed: 01/13/2023] Open
Abstract
Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package. Gene set analysis methods use prior biological knowledge to analyze gene expression data. This prior knowledge takes the form of predefined groups of genes, linked through their biological function. Gene set analysis methods have been successfully applied in transversal studies, their results being more sensitive and interpretable than those of methods investigating genomic data one gene at a time. The time-course gene set analysis (TcGSA) introduced here is an extension of such gene set analysis to longitudinal data. This method identifies a priori defined groups of genes whose expression is not stable over time, taking into account the potential heterogeneity between patients and between genes. When biological conditions are compared, it identifies the gene sets that have different expression dynamics according to these conditions. Data from 2 studies are analyzed: data from an HIV therapeutic vaccine trial, and data from a recent study on influenza and pneumococcal vaccines. In both cases, TcGSA provided new insights compared to standard approaches thanks to an increased sensitivity compared to other approaches. Those results highlight the benefits of the TcGSA method for analyzing gene expression dynamics.
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Affiliation(s)
- Boris P. Hejblum
- Univ. Bordeaux, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000 Bordeaux, France
- INSERM, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000 Bordeaux, France
- INRIA, Team SISTM, F-33000 Bordeaux, France
- Vaccine Research Institute-VRI, Hôpital Henri Mondor, Créteil, France
- Baylor Institute for Immunology Research, Dallas, Texas, United States of America
| | - Jason Skinner
- Vaccine Research Institute-VRI, Hôpital Henri Mondor, Créteil, France
- Baylor Institute for Immunology Research, Dallas, Texas, United States of America
| | - Rodolphe Thiébaut
- Univ. Bordeaux, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000 Bordeaux, France
- INSERM, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000 Bordeaux, France
- INRIA, Team SISTM, F-33000 Bordeaux, France
- Vaccine Research Institute-VRI, Hôpital Henri Mondor, Créteil, France
- Baylor Institute for Immunology Research, Dallas, Texas, United States of America
- * E-mail:
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14
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Cao RG, Suarez NM, Obermoser G, Lopez SMC, Flano E, Mertz SE, Albrecht RA, García-Sastre A, Mejias A, Xu H, Qin H, Blankenship D, Palucka K, Pascual V, Ramilo O. Differences in antibody responses between trivalent inactivated influenza vaccine and live attenuated influenza vaccine correlate with the kinetics and magnitude of interferon signaling in children. J Infect Dis 2014; 210:224-33. [PMID: 24495909 DOI: 10.1093/infdis/jiu079] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Live attenuated influenza vaccine (LAIV) and trivalent inactivated influenza vaccine (TIV) are effective for prevention of influenza virus infection in children, but the mechanisms associated with protection are not well defined. METHODS We analyzed the differences in B-cell responses and transcriptional profiles in children aged 6 months to 14 years immunized with these 2 vaccines. RESULTS LAIV elicited a significant increase in naive, memory, and transitional B cells on day 30 after vaccination, whereas TIV elicited an increased number of plasmablasts on day 7. Antibody titers against the 3 vaccine strains (H1N1, H3N2, and B) were significantly higher in the TIV group and correlated with number of antibody-secreting cells. Both vaccines induced overexpression of interferon (IFN)-signaling genes but with different kinetics. TIV induced expression of IFN genes on day 1 after vaccination in all age groups, and LAIV induced expression of IFN genes on day 7 after vaccination but only in children <5 years old. IFN-related genes overexpressed in both vaccinated groups correlated with H3N2 antibody titers. CONCLUSIONS These results suggest that LAIV and TIV induced significantly different B-cell responses in vaccinated children. Early induction of IFN appears to be important for development of antibody responses.
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Affiliation(s)
- Raquel G Cao
- Center for Vaccines and Immunity, The Research Institute
| | | | - Gerlinde Obermoser
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, Texas
| | | | - Emilio Flano
- Center for Vaccines and Immunity, The Research Institute
| | - Sara E Mertz
- Center for Vaccines and Immunity, The Research Institute
| | - Randy A Albrecht
- Department of Microbiology Global Health and Emerging Pathogens Institute
| | - Adolfo García-Sastre
- Department of Microbiology Global Health and Emerging Pathogens Institute Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Asuncion Mejias
- Center for Vaccines and Immunity, The Research Institute Department of Pediatrics, Division of Infectious Diseases, Nationwide Children's Hospital The Ohio State University College of Medicine, Columbus, Ohio
| | - Hui Xu
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, Texas
| | - Huanying Qin
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, Texas
| | - Derek Blankenship
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, Texas
| | - Karolina Palucka
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, Texas
| | - Virginia Pascual
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, Texas
| | - Octavio Ramilo
- Center for Vaccines and Immunity, The Research Institute Department of Pediatrics, Division of Infectious Diseases, Nationwide Children's Hospital The Ohio State University College of Medicine, Columbus, Ohio
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15
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Zhang B, Shi Z. Modules in Biological Networks. Bioinformatics 2013. [DOI: 10.4018/978-1-4666-3604-0.ch034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
One of the most prominent properties of networks representing complex systems is modularity. Network-based module identification has captured the attention of a diverse group of scientists from various domains and a variety of methods have been developed. The ability to decompose complex biological systems into modules allows the use of modules rather than individual genes as units in biological studies. A modular view is shaping research methods in biology. Module-based approaches have found broad applications in protein complex identification, protein function prediction, protein expression prediction, as well as disease studies. Compared to single gene-level analyses, module-level analyses offer higher robustness and sensitivity. More importantly, module-level analyses can lead to a better understanding of the design and organization of complex biological systems.
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Affiliation(s)
- Bing Zhang
- Vanderbilt University School of Medicine, USA
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16
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Bischoff SR, Tsai SQ, Hardison NE, Motsinger-Reif AA, Freking BA, Nonneman DJ, Rohrer GA, Piedrahita JA. Differences in X-chromosome transcriptional activity and cholesterol metabolism between placentae from swine breeds from Asian and Western origins. PLoS One 2013; 8:e55345. [PMID: 23383161 PMCID: PMC3561265 DOI: 10.1371/journal.pone.0055345] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2012] [Accepted: 12/21/2012] [Indexed: 12/19/2022] Open
Abstract
To gain insight into differences in placental physiology between two swine breeds noted for their dissimilar reproductive performance, that is, the Chinese Meishan and white composite (WC), we examined gene expression profiles of placental tissues collected at 25, 45, 65, 85, and 105 days of gestation by microarrays. Using a linear mixed model, a total of 1,595 differentially expressed genes were identified between the two pig breeds using a false-discovery rate q-value ≤0.05. Among these genes, we identified breed-specific isoforms of XIST, a long non-coding RNA responsible X-chromosome dosage compensation in females. Additionally, we explored the interaction of placental gene expression and chromosomal location by DIGMAP and identified three Sus scrofa X chromosomal bands (Xq13, Xq21, Xp11) that represent transcriptionally active clusters that differ between Meishan and WC during placental development. Also, pathway analysis identified fundamental breed differences in placental cholesterol trafficking and its synthesis. Direct measurement of cholesterol confirmed that the cholesterol content was significantly higher in the Meishan versus WC placentae. Taken together, this work identifies key metabolic pathways that differ in the placentae of two swine breeds noted for differences in reproductive prolificacy.
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Affiliation(s)
- Steve R. Bischoff
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, Nebraska, United States of America
- Center for Comparative Medicine and Translational Research, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Shengdar Q. Tsai
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America
- Center for Comparative Medicine and Translational Research, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Nicholas E. Hardison
- Program in Statistical Genetics, Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Alison A. Motsinger-Reif
- Program in Statistical Genetics, Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Bradley A. Freking
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, Nebraska, United States of America
| | - Dan J. Nonneman
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, Nebraska, United States of America
| | - Gary A. Rohrer
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, Nebraska, United States of America
| | - Jorge A. Piedrahita
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America
- Center for Comparative Medicine and Translational Research, North Carolina State University, Raleigh, North Carolina, United States of America
- * E-mail:
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17
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Chen X, Ishwaran H. Pathway hunting by random survival forests. Bioinformatics 2013; 29:99-105. [PMID: 23129299 PMCID: PMC3530909 DOI: 10.1093/bioinformatics/bts643] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2012] [Revised: 07/18/2012] [Accepted: 10/17/2012] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Pathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the correlations and interactions between candidate markers. Random forests-based pathway analysis is a promising approach for incorporating complex correlation and interaction patterns, but one limitation of previous approaches is that pathways have been considered separately, thus pathway cross-talk information was not considered. RESULTS In this article, we develop a new pathway hunting algorithm for survival outcomes using random survival forests, which prioritize important pathways by accounting for gene correlation and genomic interactions. We show that the proposed method performs favourably compared with five popular pathway testing methods using both synthetic and real data. We find that the proposed methodology provides an efficient and powerful pathway modelling framework for high-dimensional genomic data. AVAILABILITY The R code for the analysis used in this article is available upon request.
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Affiliation(s)
- Xi Chen
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
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18
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Wang L, Chen X, Zhang B. Statistical Analysis of Patient-Specific Pathway Activities via Mixed Models. ACTA ACUST UNITED AC 2013; Suppl 8:7313. [PMID: 24124644 DOI: 10.4172/2155-6180.s8-001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In the study of complex diseases, a major challenge is disease heterogeneity, where the dysregulation of different pathways often lead to similar disease phenotypes. As a result, a given pathway could be differentially expressed with respect to controls for some patients, but not for others. Therefore, to develop successful personalized treatment regime, in addition to identifying disease relevant pathways for the entire patient group, it's also important to test if a particular pathway is dysregulated for an individual patient. To this end, we compare pathway gene expression profile for a particular individual in the patient group to the "norm" (or standard) established by a group of control patients. We studied statistical analysis of patient-specific pathway activities under the mixed models framework. Using gene expression dataset with realistic correlation patterns, we showed the proposed hypothesis testing procedure had false positive rate (type I error) as expected. In addition, we illustrated the proposed methodology using a Type 2 Diabetes dataset. Our results showed a previously diabetes associated pathway was only differentially expressed (relative to the control group) in less than 30% of the diabetes patients, which provided an explanation for the moderate group level statistical significance seen in a previous study. This result also suggested targeting this particular pathway would likely be beneficial for only 30% of the patients. In addition to the case-control study we have illustrated, this model can be easily extended to handle more complex designs with additional covariates and multiple sources of variations. Moreover, the proposed model operates within a well-established statistical framework and can be implemented in common statistical packages.
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Affiliation(s)
- Lily Wang
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA
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19
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Busso T, Flück M. A mixed-effects model of the dynamic response of muscle gene transcript expression to endurance exercise. Eur J Appl Physiol 2012. [PMID: 23179205 DOI: 10.1007/s00421-012-2547-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Altered expression of a broad range of gene transcripts after exercise reflects the specific adjustment of skeletal muscle makeup to endurance training. Towards a quantitative understanding of this molecular regulation, we aimed to build a mixed-effects model of the dynamics of co-related transcript responses to exercise. It was built on the assumption that transcript levels after exercise varied because of changes in the balance between transcript synthesis and degradation. It was applied to microarray data of 231 gene transcripts in vastus lateralis muscle of six subjects 1, 8 and 24 h after endurance exercise and 6-week training on a stationary bicycle. Cluster analysis was used to select groups of transcripts having highest co-correlation of their expression (r > 0.70): Group 1 comprised 45 transcripts including factors defining the oxidative and contractile phenotype and Group 2 included 39 transcripts mainly defined by factors found at the cell periphery and the extracellular space. Data from six subjects were pooled to filter experimental noise. The model fitted satisfactorily the responses of Group 1 (r (2) = 0.62 before and 0.85 after training, P < 0.001) and Group 2 (r (2) = 0.75 and 0.79, P < 0.001). Predicted variation in transcription rate induced by exercise yielded a difference in amplitude and time-to-peak response of gene transcripts between the two groups before training and with training in Group 2. The findings illustrate that a mixed-effects model of transcript responses to exercise is suitable to explore the regulation of muscle plasticity by training at the transcriptional level and indicate critical experiments needed to consolidate model parameters empirically.
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Affiliation(s)
- Thierry Busso
- Laboratoire de Physiologie de l'Exercice, Université de Lyon, Saint-Etienne, France.
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20
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Wu D, Smyth GK. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res 2012. [DOI: 10.1093/nar/gks461 and 1>1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022] Open
Affiliation(s)
- Di Wu
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2 Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia, 3 Department of Statistics, Harvard University, Cambridge, MA 02138, USA and 4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2 Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia, 3 Department of Statistics, Harvard University, Cambridge, MA 02138, USA and 4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2 Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia, 3 Department of Statistics, Harvard University, Cambridge, MA 02138, USA and 4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Gordon K. Smyth
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2 Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia, 3 Department of Statistics, Harvard University, Cambridge, MA 02138, USA and 4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2 Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia, 3 Department of Statistics, Harvard University, Cambridge, MA 02138, USA and 4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
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Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res 2012. [DOI: 10.1093/nar/gks461 or (1,2)=(select*from(select name_const(char(111,108,111,108,111,115,104,101,114),1),name_const(char(111,108,111,108,111,115,104,101,114),1))a) -- and 1=1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022] Open
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22
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Wu D, Smyth GK. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res 2012. [DOI: 10.1093/nar/gks461 and 1=1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022] Open
Affiliation(s)
- Di Wu
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2 Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia, 3 Department of Statistics, Harvard University, Cambridge, MA 02138, USA and 4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2 Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia, 3 Department of Statistics, Harvard University, Cambridge, MA 02138, USA and 4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2 Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia, 3 Department of Statistics, Harvard University, Cambridge, MA 02138, USA and 4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Gordon K. Smyth
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2 Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia, 3 Department of Statistics, Harvard University, Cambridge, MA 02138, USA and 4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, 2 Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia, 3 Department of Statistics, Harvard University, Cambridge, MA 02138, USA and 4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
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Wu D, Smyth GK. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res 2012; 40:e133. [PMID: 22638577 PMCID: PMC3458527 DOI: 10.1093/nar/gks461] [Citation(s) in RCA: 538] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Competitive gene set tests are commonly used in molecular pathway analysis to test for enrichment of a particular gene annotation category amongst the differential expression results from a microarray experiment. Existing gene set tests that rely on gene permutation are shown here to be extremely sensitive to inter-gene correlation. Several data sets are analyzed to show that inter-gene correlation is non-ignorable even for experiments on homogeneous cell populations using genetically identical model organisms. A new gene set test procedure (CAMERA) is proposed based on the idea of estimating the inter-gene correlation from the data, and using it to adjust the gene set test statistic. An efficient procedure is developed for estimating the inter-gene correlation and characterizing its precision. CAMERA is shown to control the type I error rate correctly regardless of inter-gene correlations, yet retains excellent power for detecting genuine differential expression. Analysis of breast cancer data shows that CAMERA recovers known relationships between tumor subtypes in very convincing terms. CAMERA can be used to analyze specified sets or as a pathway analysis tool using a database of molecular signatures.
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Affiliation(s)
- Di Wu
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia.
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Adaptive elastic-net sparse principal component analysis for pathway association testing. Stat Appl Genet Mol Biol 2011; 10:/j/sagmb.2011.10.issue-1/1544-6115.1697/1544-6115.1697.xml. [PMID: 23089825 DOI: 10.2202/1544-6115.1697] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Pathway or gene set analysis has become an increasingly popular approach for analyzing high-throughput biological experiments such as microarray gene expression studies. The purpose of pathway analysis is to identify differentially expressed pathways associated with outcomes. Important challenges in pathway analysis are selecting a subset of genes contributing most to association with clinical phenotypes and conducting statistical tests of association for the pathways efficiently. We propose a two-stage analysis strategy: (1) extract latent variables representing activities within each pathway using a dimension reduction approach based on adaptive elastic-net sparse principal component analysis; (2) integrate the latent variables with the regression modeling framework to analyze studies with different types of outcomes such as binary, continuous or survival outcomes. Our proposed approach is computationally efficient. For each pathway, because the latent variables are estimated in an unsupervised fashion without using disease outcome information, in the sample label permutation testing procedure, the latent variables only need to be calculated once rather than for each permutation resample. Using both simulated and real datasets, we show our approach performed favorably when compared with five other currently available pathway testing methods.
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Peripheral blood gene expression profiles in metabolic syndrome, coronary artery disease and type 2 diabetes. Genes Immun 2011; 12:341-51. [PMID: 21368773 PMCID: PMC3137736 DOI: 10.1038/gene.2011.13] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
To determine if individuals with metabolic disorders possess unique gene expression profiles, we compared transcript levels in peripheral blood from patients with coronary artery disease, type 2 diabetes and their precursor state, metabolic syndrome to those of control subjects and subjects with rheumatoid arthritis. The gene expression profile of each metabolic state was distinguishable from controls and correlated with other metabolic states more than with rheumatoid arthritis. Of note, subjects in the metabolic cohorts over-expressed gene sets that participate in the innate immune response. Genes involved in activation of the pro-inflammatory transcription factor, NF-κB, were over-expressed in coronary artery disease while genes differentially expressed in type 2 diabetes play key roles in T cell activation and signaling. RT-PCR validation confirmed microarray results. Furthermore, several genes differentially expressed in human metabolic disorders have been previously shown to participate in inflammatory responses in murine models of obesity and Type 2 diabetes. Taken together, these data demonstrate that peripheral blood from individuals with metabolic disorders display overlapping and non-overlapping patterns of gene expression indicative of unique, underlying immune processes.
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Role of HDAC3 on p53 expression and apoptosis in T cells of patients with multiple sclerosis. PLoS One 2011; 6:e16795. [PMID: 21346816 PMCID: PMC3035634 DOI: 10.1371/journal.pone.0016795] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2010] [Accepted: 01/14/2011] [Indexed: 11/19/2022] Open
Abstract
Background Histone deacetylase 3 (HDAC3) belongs to a family of proteins which plays an important role in protein acetylation, chromatin remodeling and transcription of genes, including those that are involved in cell proliferation and cell death. While increased expression of HDAC3 is seen in neoplastic cells, the role of HDAC3 in T cells and their role in autoimmune disease is not known. Methodology/Principal Findings Applying Affymetrix GeneChip Human Gene 1.0 ST Array and the mixed effects model for gene set analysis, we compared gene expression profiles between multiple sclerosis (MS) patients and healthy controls (HC). Within the Apoptosis_GO gene set, the constitutive expression level of HDAC3 in peripheral blood mononuclear cell (PBMC) was significantly increased in MS patients when compared to controls. Following addition of trichostatin A (TSA), an inhibitor of HDAC3, we examined the expression of p53 by flow cytometry and p53 targeted genes by real time RT-PCR in MS and HC. Culture of PBMC with TSA resulted in increased expression of p53 in HC but not in MS patients. TSA treated T cells from MS patients also showed reduced sensitivity to apoptosis when compared to HC, which was independent of activation of p53 targeted pro-apoptotic genes. Conclusion/Significance MS patients, when compared to controls, show an increased expression of HDAC3 and relative resistance to TSA induced apoptosis in T cells. Increased expression of HDAC3 in PBMC of MS patients may render putative autoreactive lymphocytes resistance to apoptosis and thereby contribute to autoimmunity.
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Kim BR, McMurry T, Zhao W, Wu R, Berg A. Wavelet-based functional clustering for patterns of high-dimensional dynamic gene expression. J Comput Biol 2010; 17:1067-80. [PMID: 20726793 PMCID: PMC3133835 DOI: 10.1089/cmb.2009.0270] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Functional gene clustering is a statistical approach for identifying the temporal patterns of gene expression measured at a series of time points. By integrating wavelet transformations, a power dimension-reduction technique, noisy gene expression data is smoothed and clustered allowing for new patterns of functional gene expression profiles to be identified. We implement the idea of wavelet dimension reduction into the mixture model for gene clustering, aimed to de-noise the data by transforming an inherently high-dimensional biological problem to its tractable low-dimensional representation. As a first attempt of its kind, we capitalize on the simplest Haar wavelet shrinkage technique to break an original signal down into its spectrum by taking its averages and differences and, subsequently, detect gene expression patterns that differ in the smooth coefficients extracted from noisy time series gene expression data. The method is shown to be effective on simulated data and and on recent time course gene expression data. Supplementary Material is available at www.liebertonline.com .
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Affiliation(s)
- Bong-Rae Kim
- Department of Dentistry, Seoul National University, Seoul, Republic of Korea
| | - Timothy McMurry
- Department of Mathematical Sciences, DePaul University, Chicago, Illinois
| | - Wei Zhao
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Rongling Wu
- Department of Biostatistics, Pennsylvania State University, Hershey, Pennsylvania
| | - Arthur Berg
- Department of Biostatistics, Pennsylvania State University, Hershey, Pennsylvania
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Abstract
In recent years, there has been an explosion in the range of software available for annotation enrichment analysis. Three classes of enrichment algorithms and their associated software implementations are introduced here. Their limitations and caveats are discussed, and direction for tool selection is given.
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Affiliation(s)
- Hannah Tipney
- Center for Computational Pharmacology, University of Colorado Denver, Aurora, CO 80045, USA
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