1
|
Jia J, Qu G, Jia P, Li D, Yao Y. The contest between artificial management and natural environment determines the adaptive strategies of leaf morphogenesis in Sabina chinensis. TREE PHYSIOLOGY 2024; 44:tpae060. [PMID: 38832722 DOI: 10.1093/treephys/tpae060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 05/05/2024] [Accepted: 06/01/2024] [Indexed: 06/05/2024]
Abstract
Sabina chinensis is a typically heteromorphic leaf evergreen tree worldwide with both ornamental and ecological value. However, the shaping mechanism of heteromorphic leaves of S. chinensis and its adaptability to environment are important factors determining its morphology. The morphological change of S. chinensis under different habitats (tree around) and treatments (light, pruning and nutrients) was investigated. Our findings suggested that the prickle leaves proportion was associated with low light intensity and soil nutrient scarcity. Stems and leaves are pruned together to form clusters of large prickle leaves, while only pruning leaves often form alternately growing small prickle leaves and scale leaves, and the length of the prickle leaves is between 0.5 cm and 1 cm. The gene expression of prickle leaves is higher than that of scale leaves under adverse environmental conditions, and the gene expression correlations between small prickle leaf and scale leaf were the highest. Homologous and heterologous mutants of gene structure in prickle leaves were larger than those in scale leaves. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway showed that phenylpropanone and flavonoid biosynthesis were common enrichment pathways, and that the enrichment genes were mainly related to metabolism, genetic information processing and organismal systems. Therefore, we concluded that the occurrence of the heteromorphic leaf phenomenon was related to the changes in photosynthesis, mechanical damage and nutrient supplementation. The organic matter in the S. chinensis prickle leaves was reduced under environmental stresses, and it will be allocated to the expression of prickle leaf or protective cuticles formation.
Collapse
Affiliation(s)
- Jing Jia
- School of Ecological and Environmental Sciences, East China Normal University, Dongchuan Road 500, Minhang district, Shanghai 200241, China
| | - Guojuan Qu
- School of Ecological and Environmental Sciences, East China Normal University, Dongchuan Road 500, Minhang district, Shanghai 200241, China
| | - Peng Jia
- National Marine Environmental Monitoring Center, Linghe Street 42, Shahekou district, Dalian 116023, China
| | - Dezhi Li
- School of Ecological and Environmental Sciences, East China Normal University, Dongchuan Road 500, Minhang district, Shanghai 200241, China
- Key Laboratory of Urbanization and Ecological Restoration of Shanghai, East China Normal University, Dongchuan Road 500, Minhang district, Shanghai 200241, China
- Institute of Eco-Chongming (IEC), Cuiniao Road 20, Chongming district, Shanghai 202162, China
- Technology Innovation Center for Land Spatial Eco-restoration in Metropolitan Area, Ministry of Natural Resources, Zhongshan Road 3633, Zhongbei district, Shanghai 200062, China
| | - Yifei Yao
- School of Ecological and Environmental Sciences, East China Normal University, Dongchuan Road 500, Minhang district, Shanghai 200241, China
| |
Collapse
|
2
|
Ou D, Wu Y. The prognostic and clinical significance of IFI44L aberrant downregulation in patients with oral squamous cell carcinoma. BMC Cancer 2021; 21:1327. [PMID: 34903206 PMCID: PMC8667451 DOI: 10.1186/s12885-021-09058-y] [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: 03/18/2021] [Accepted: 11/22/2021] [Indexed: 12/16/2022] Open
Abstract
Background It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. RankComp, an algorithm, could analyze the highly stable within-sample relative expression orderings (REOs) of gene pairs in a particular type of human normal tissue that are widely reversed in the cancer condition, thereby detecting DEGs for individual disease samples measured by a particular platform. Methods In the present study, Gene Expression Omnibus (GEO) Series (GSE) GSE75540, GSE138206 were downloaded from GEO, by analyzing DEGs in oral squamous cell carcinoma based on online datasets using the RankComp algorithm, using the Kaplan-Meier survival analysis and Cox regression analysis to survival analysis, Gene Set Enrichment Analysis (GSEA) to explore the potential molecular mechanisms underlying. Results We identified 6 reverse gene pairs with stable REOs. All the 12 genes in these 6 reverse gene pairs have been reported to be associated with cancers. Notably, lower Interferon Induced Protein 44 Like (IFI44L) expression was associated with poorer overall survival (OS) and Disease-free survival (DFS) in oral squamous cell carcinoma patients, and IFI44L expression showed satisfactory predictive efficiency by receiver operating characteristic (ROC) curve. Moreover, low IFI44L expression was identified as risk factors for oral squamous cell carcinoma patients’ OS. IFI44L downregulation would lead to the activation of the FRS-mediated FGFR1, FGFR3, and downstream signaling pathways, and might play a role in the PI3K-FGFR cascades. Conclusions Collectively, we identified 6 reverse gene pairs with stable REOs in oral squamous cell carcinoma, which might serve as gene signatures playing a role in the diagnosis in oral squamous cell carcinoma. Moreover, high expression of IFI44L, one of the DEGs in the 6 reverse gene pairs, might be associated with favorable prognosis in oral squamous cell carcinoma patients and serve as a tumor suppressor by acting on the FRS-mediated FGFR signaling. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-09058-y.
Collapse
Affiliation(s)
- Deming Ou
- Department of Stomatology, Panyu Central Hospital, Guangzhou, 511400, China.
| | - Ying Wu
- Department of Stomatology, Foshan Hospital of Traditional Chinese Medicine, Foshan, 528000, China
| |
Collapse
|
3
|
Imada EL, Sanchez DF, Dinalankara W, Vidotto T, Ebot EM, Tyekucheva S, Franco GR, Mucci LA, Loda M, Schaeffer EM, Lotan T, Marchionni L. Transcriptional landscape of PTEN loss in primary prostate cancer. BMC Cancer 2021; 21:856. [PMID: 34311724 PMCID: PMC8314517 DOI: 10.1186/s12885-021-08593-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/06/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND PTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. In this study, we highlight the transcriptional changes associated with PTEN loss in PCa. METHODS Using a meta-analysis approach, we leveraged two large PCa cohorts with experimentally validated PTEN and ERG status by Immunohistochemistry (IHC), to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. This signature was expanded to lncRNAs using the TCGA quantifications from the FC-R2 expression atlas. RESULTS The signatures indicate a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. Highlighting potential novel lncRNAs associated with PTEN loss and PCa progression. CONCLUSION We created a PCa specific signature of the transcriptional landscape of PTEN loss that comprises both the coding and an extensive non-coding counterpart, highlighting potential new players in PCa progression. We also show that contrary to what is observed in other cancers, PTEN loss in PCa leads to increased activation of the immune system. These findings can help the development of new biomarkers and help guide therapy choices.
Collapse
Affiliation(s)
- Eddie Luidy Imada
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Departamento de Bioquímica e Imunologia, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
| | | | - Wikum Dinalankara
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thiago Vidotto
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ericka M Ebot
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Svitlana Tyekucheva
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Gloria Regina Franco
- Departamento de Bioquímica e Imunologia, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Lorelei Ann Mucci
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Massimo Loda
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | | | - Tamara Lotan
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Luigi Marchionni
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
4
|
Zhang H, Li SJ, Zhang H, Yang ZY, Ren YQ, Xia LY, Liang Y. Meta-Analysis Based on Nonconvex Regularization. Sci Rep 2020; 10:5755. [PMID: 32238826 PMCID: PMC7113298 DOI: 10.1038/s41598-020-62473-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 03/06/2020] [Indexed: 01/10/2023] Open
Abstract
The widespread applications of high-throughput sequencing technology have produced a large number of publicly available gene expression datasets. However, due to the gene expression datasets have the characteristics of small sample size, high dimensionality and high noise, the application of biostatistics and machine learning methods to analyze gene expression data is a challenging task, such as the low reproducibility of important biomarkers in different studies. Meta-analysis is an effective approach to deal with these problems, but the current methods have some limitations. In this paper, we propose the meta-analysis based on three nonconvex regularization methods, which are L1/2 regularization (meta-Half), Minimax Concave Penalty regularization (meta-MCP) and Smoothly Clipped Absolute Deviation regularization (meta-SCAD). The three nonconvex regularization methods are effective approaches for variable selection developed in recent years. Through the hierarchical decomposition of coefficients, our methods not only maintain the flexibility of variable selection and improve the efficiency of selecting important biomarkers, but also summarize and synthesize scientific evidence from multiple studies to consider the relationship between different datasets. We give the efficient algorithms and the theoretical property for our methods. Furthermore, we apply our methods to the simulation data and three publicly available lung cancer gene expression datasets, and compare the performance with state-of-the-art methods. Our methods have good performance in simulation studies, and the analysis results on the three publicly available lung cancer gene expression datasets are clinically meaningful. Our methods can also be extended to other areas where datasets are heterogeneous.
Collapse
Affiliation(s)
- Hui Zhang
- Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, 999078, Macau
| | - Shou-Jiang Li
- Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, 999078, Macau
| | - Hai Zhang
- Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, 999078, Macau
- School of Mathematics, Northwest University, 710127, Xi'an, China
| | - Zi-Yi Yang
- Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, 999078, Macau
| | - Yan-Qiong Ren
- Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, 999078, Macau
| | - Liang-Yong Xia
- Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, 999078, Macau
| | - Yong Liang
- Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, 999078, Macau.
| |
Collapse
|
5
|
De Vito R, Bellio R, Trippa L, Parmigiani G. Multi-study factor analysis. Biometrics 2019; 75:337-346. [PMID: 30289163 DOI: 10.1111/biom.12974] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 08/23/2018] [Indexed: 12/13/2022]
Abstract
We introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate (1) common factors shared across multiple studies, and (2) study-specific factors. We develop an Expectation Conditional-Maximization algorithm for parameter estimates and we provide a procedure for choosing the numbers of common and specific factors. We present simulations for evaluating the performance of the method and we illustrate it by applying it to gene expression data in ovarian cancer. In both, we clarify the benefits of a joint analysis compared to the standard factor analysis. We have provided a tool to accelerate the pace at which we can combine unsupervised analysis across multiple studies, and understand the cross-study reproducibility of signal in multivariate data. An R package (MSFA), is implemented and is available on GitHub.
Collapse
Affiliation(s)
- Roberta De Vito
- Department of Computer Science, Princeton University, Princeton, New Jersey
| | - Ruggero Bellio
- Department of Economics and Statistics, University of Udine, Udine, Italy
| | - Lorenzo Trippa
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Giovanni Parmigiani
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| |
Collapse
|
6
|
Huo Z, Song C, Tseng G. BAYESIAN LATENT HIERARCHICAL MODEL FOR TRANSCRIPTOMIC META-ANALYSIS TO DETECT BIOMARKERS WITH CLUSTERED META-PATTERNS OF DIFFERENTIAL EXPRESSION SIGNALS. Ann Appl Stat 2019; 13:340-366. [PMID: 31007807 PMCID: PMC6472949 DOI: 10.1214/18-aoas1188] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Due to the rapid development of high-throughput experimental techniques and fast-dropping prices, many transcriptomic datasets have been generated and accumulated in the public domain. Meta-analysis combining multiple transcriptomic studies can increase the statistical power to detect disease-related biomarkers. In this paper, we introduce a Bayesian latent hierarchical model to perform transcriptomic meta-analysis. This method is capable of detecting genes that are differentially expressed (DE) in only a subset of the combined studies, and the latent variables help quantify homogeneous and heterogeneous differential expression signals across studies. A tight clustering algorithm is applied to detected biomarkers to capture differential meta-patterns that are informative to guide further biological investigation. Simulations and three examples, including a microarray dataset from metabolism-related knockout mice, an RNA-seq dataset from HIV transgenic rats, and cross-platform datasets from human breast cancer, are used to demonstrate the performance of the proposed method.
Collapse
Affiliation(s)
- Zhiguang Huo
- Department of Biostatistics University of Florida Gainesville, FL 32611
| | - Chi Song
- Division of Biostatistics College of Public Health The Ohio State University Columbus, OH 43210
| | - George Tseng
- Department of Biostatistics, Human Genetics and Computational Biology University of Pittsburgh Pittsburgh, PA 15261
| |
Collapse
|
7
|
Cai H, Li X, Li J, Liang Q, Zheng W, Guan Q, Guo Z, Wang X. Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings. Int J Biol Sci 2018; 14:892-900. [PMID: 29989020 PMCID: PMC6036750 DOI: 10.7150/ijbs.24548] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Accepted: 02/02/2018] [Indexed: 12/13/2022] Open
Abstract
It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. But the weakly differential expression signals between two phenotypes are hardly detectable with limited sample sizes. To solve this problem, many researchers tried to combine multiple independent datasets using meta-analysis or batch effect adjustment algorithms. However, these algorithms may distort true biological differences between two phenotypes and introduce unacceptable high false rates, as demonstrated in this study. These problems pose critical obstacles for analyzing the transcriptional data in The Cancer Genome Atlas where there are many small-scale batches of data. Previously, we developed RankComp to detect DEGs for individual disease samples through exploiting the incongruous relative expression orderings between two phenotypes and further improved it here to identify DEGs using multiple independent datasets. We demonstrated the improved RankComp can directly analyze integrated cross-site data to detect DEGs between two phenotypes without the need of batch effect adjustments. Its usage was illustrated in detecting weak differential expression signals of breast cancer drug-response data using combined datasets from multiple experiments.
Collapse
Affiliation(s)
- Hao Cai
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Xiangyu Li
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Jing Li
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Qirui Liang
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Weicheng Zheng
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China.,Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Qingzhou Guan
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Zheng Guo
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China.,Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou 350122, China.,Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Xianlong Wang
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| |
Collapse
|
8
|
Ma T, Liang F, Oesterreich S, Tseng GC. A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data. J Comput Biol 2017; 24:647-662. [PMID: 28541721 PMCID: PMC5510692 DOI: 10.1089/cmb.2017.0056] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
As the sequencing cost continued to drop in the past decade, RNA sequencing (RNA-seq) has replaced microarray to become the standard high-throughput experimental tool to analyze transcriptomic profile. As more and more datasets are generated and accumulated in the public domain, meta-analysis to combine multiple transcriptomic studies to increase statistical power has received increasing popularity. In this article, we propose a Bayesian hierarchical model to jointly integrate microarray and RNA-seq studies. Since systematic fold change differences across RNA-seq and microarray for detecting differentially expressed genes have been previously reported, we replicated this finding in several real datasets and showed that incorporation of a normalization procedure to account for the bias improves the detection accuracy and power. We compared our method with the popular two-stage Fisher's method using simulations and two real applications in a histological subtype (invasive lobular carcinoma) of breast cancer comparing PR+ versus PR- and early-stage versus late-stage patients. The result showed improved detection power and more significant and interpretable pathways enriched in the detected biomarkers from the proposed Bayesian model.
Collapse
Affiliation(s)
- Tianzhou Ma
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Faming Liang
- Department of Biostatistics, University of Florida, Gainesville, Florida
| | - Steffi Oesterreich
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Women's Cancer Research Center, Pittsburgh, Pennsylvania
| | - George C. Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Computational Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| |
Collapse
|
9
|
Ma T, Liang F, Tseng G. Biomarker detection and categorization in ribonucleic acid sequencing meta-analysis using Bayesian hierarchical models. J R Stat Soc Ser C Appl Stat 2016; 66:847-867. [PMID: 28785119 DOI: 10.1111/rssc.12199] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Meta-analysis combining multiple transcriptomic studies increases statistical power and accuracy in detecting differentially expressed genes. As the next-generation sequencing experiments become mature and affordable, increasing number of RNA-seq datasets are available in the public domain. The count-data based technology provides better experimental accuracy, reproducibility and ability to detect low-expressed genes. A naive approach to combine multiple RNA-seq studies is to apply differential analysis tools such as edgeR and DESeq to each study and then combine the summary statistics of p-values or effect sizes by conventional meta-analysis methods. Such a two-stage approach loses statistical power, especially for genes with short length or low expression abundance. In this paper, we propose a full Bayesian hierarchical model (namely, BayesMetaSeq) for RNA-seq meta-analysis by modelling count data, integrating information across genes and across studies, and modelling potentially heterogeneous differential signals across studies via latent variables. A Dirichlet process mixture (DPM) prior is further applied on the latent variables to provide categorization of detected biomarkers according to their differential expression patterns across studies, facilitating improved interpretation and biological hypothesis generation. Simulations and a real application on multi-brain-region HIV-1 transgenic rats demonstrate improved sensitivity, accuracy and biological findings of the proposed method.
Collapse
Affiliation(s)
- Tianzhou Ma
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261
| | - Faming Liang
- Department of Biostatistics, University of Florida, Gainesville, FL 32611
| | - George Tseng
- Department of Biostatistics (primary appointment), Department of Human Genetics, Department of Computational Biology, University of Pittsburgh, Pittsburgh, PA 15261
| |
Collapse
|
10
|
Bansal NK, Jiang H, Pradeep P. A Bayesian methodology for detecting targeted genes under two related experiments. Stat Med 2015; 34:3362-75. [PMID: 26112310 DOI: 10.1002/sim.6555] [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] [Received: 09/19/2014] [Revised: 03/25/2015] [Accepted: 05/24/2015] [Indexed: 11/10/2022]
Abstract
Many gene expression data are based on two experiments where the gene expressions of the targeted genes under both experiments are correlated. We consider problems in which objectives are to find genes that are simultaneously upregulated/downregulated under both experiments. A Bayesian methodology is proposed based on directional multiple hypotheses testing. We propose a false discovery rate specific to the problem under consideration, and construct a Bayes rule satisfying a false discovery rate criterion. The proposed method is compared with a traditional rule through simulation studies. We apply our methodology to two real examples involving microRNAs; where in one example the targeted genes are simultaneously downregulated under both experiments, and in the other the targeted genes are downregulated in one experiment and upregulated in the other experiment. We also discuss how the proposed methodology can be extended to more than two experiments.
Collapse
Affiliation(s)
- Naveen K Bansal
- Department of Mathematics, Statistics and Computer Science, Marquette University, Milwaukee, 53051, WI, U.S.A
| | - Hongmei Jiang
- Department of Statistics, Northwestern University, Evanston, 60208, IL, U.S.A
| | - Prachi Pradeep
- Department of Mathematics, Statistics and Computer Science, Marquette University, Milwaukee, 53051, WI, U.S.A
| |
Collapse
|
11
|
Wei Y. Integrative analyses of cancer data: a review from a statistical perspective. Cancer Inform 2015; 14:173-81. [PMID: 26041968 PMCID: PMC4435444 DOI: 10.4137/cin.s17303] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Revised: 02/01/2015] [Accepted: 02/09/2015] [Indexed: 12/17/2022] Open
Abstract
It has become increasingly common for large-scale public data repositories and clinical settings to have multiple types of data, including high-dimensional genomics, epigenomics, and proteomics data as well as survival data, measured simultaneously for the same group of biological samples, which provides unprecedented opportunities to understand cancer mechanisms from a more comprehensive scope and to develop new cancer therapies. Nevertheless, how to interpret a wealth of data into biologically and clinically meaningful information remains very challenging. In this paper, I review recent development in statistics for integrative analyses of cancer data. Topics will cover meta-analysis of homogeneous type of data across multiple studies, integrating multiple heterogeneous genomic data types, survival analysis with high-or ultrahigh-dimensional genomic profiles, and cross-data-type prediction where both predictors and responses are high-or ultrahigh-dimensional vectors. I compare existing statistical methods and comment on potential future research problems.
Collapse
Affiliation(s)
- Yingying Wei
- Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong
| |
Collapse
|
12
|
Expression analysis of all protease genes reveals cathepsin K to be overexpressed in glioblastoma. PLoS One 2014; 9:e111819. [PMID: 25356585 PMCID: PMC4214761 DOI: 10.1371/journal.pone.0111819] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 10/01/2014] [Indexed: 01/26/2023] Open
Abstract
Background Cancer genome and transcriptome analyses advanced our understanding of cancer biology. We performed transcriptome analysis of all known genes of peptidases also called proteases and their endogenous inhibitors in glioblastoma multiforme (GBM), which is one of the most aggressive and deadly types of brain cancers, where unbalanced proteolysis is associated with tumor progression. Methods Comparisons were performed between the transcriptomics of primary GBM tumors and unmatched non-malignant brain tissue, and between GBM cell lines (U87-MG and U373) and a control human astrocyte cell line (NHA). Publicly-available data sets and our own datasets were integrated and normalized using bioinformatics tools to reveal protease and protease inhibitor genes with deregulated expression in both malignant versus non-malignant tissues and cells. Results Of the 311 protease genes identified to be differentially expressed in both GBM tissues and cells, 5 genes were highly overexpressed, 2 genes coding for non-peptidase homologues transferrin receptor (TFRC) and G protein-coupled receptor 56 (GPR56), as well as 3 genes coding for the proteases endoplasmic reticulum aminopeptidase 2 (ERAP2), glutamine-fructose-6-phosphate transaminase 2 (GFPT2) and cathepsin K (CTSK), whereas one gene, that of the serine protease carboxypeptidase E (CPE) was strongly reduced in expression. Seventy five protease inhibitor genes were differentially expressed, of which 3 genes were highly overexpressed, the genes coding for stefin B (CSTB), peptidase inhibitor 3 (PI3 also named elafin) and CD74. Seven out of 8 genes (except CSTB) were validated using RT-qPCR in GBM cell lines. CTSK overexpression was validated using RT-qPCR in GBM tissues as well. Cathepsin K immunohistochemical staining and western blotting showed that only proteolytically inactive proforms of cathepsin K were overexpressed in GBM tissues and cells. Conclusions The presence of high levels of inactive proforms of cathepsin K in GBM tissues and cells indicate that in GBM the proteolytic/collagenolytic role is not its primary function but it plays rather a different yet unknown role.
Collapse
|
13
|
Wei Y, Tenzen T, Ji H. Joint analysis of differential gene expression in multiple studies using correlation motifs. Biostatistics 2014; 16:31-46. [PMID: 25143368 PMCID: PMC4263229 DOI: 10.1093/biostatistics/kxu038] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The standard methods for detecting differential gene expression are mostly designed for analyzing a single gene expression experiment. When data from multiple related gene expression studies are available, separately analyzing each study is not ideal as it may fail to detect important genes with consistent but relatively weak differential signals in multiple studies. Jointly modeling all data allows one to borrow information across studies to improve the analysis. However, a simple concordance model, in which each gene is assumed to be differential in either all studies or none of the studies, is incapable of handling genes with study-specific differential expression. In contrast, a model that naively enumerates and analyzes all possible differential patterns across studies can deal with study-specificity and allow information pooling, but the complexity of its parameter space grows exponentially as the number of studies increases. Here, we propose a correlation motif approach to address this dilemma. This approach searches for a small number of latent probability vectors called correlation motifs to capture the major correlation patterns among multiple studies. The motifs provide the basis for sharing information among studies and genes. The approach has flexibility to handle all possible study-specific differential patterns. It improves detection of differential expression and overcomes the barrier of exponential model complexity.
Collapse
Affiliation(s)
- Yingying Wei
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USADepartment of Statistics, The Chinese University of Hong Kong, Shatin NT, Hong Kong
| | - Toyoaki Tenzen
- Center for Regenerative Medicine, Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Hongkai Ji
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| |
Collapse
|
14
|
Tsiliki G, Vlachakis D, Kossida S. On integrating multi-experiment microarray data. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2014; 372:20130136. [PMID: 24751870 PMCID: PMC3996576 DOI: 10.1098/rsta.2013.0136] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
With the extensive use of microarray technology as a potential prognostic and diagnostic tool, the comparison and reproducibility of results obtained from the use of different platforms is of interest. The integration of those datasets can yield more informative results corresponding to numerous datasets and microarray platforms. We developed a novel integration technique for microarray gene-expression data derived by different studies for the purpose of a two-way Bayesian partition modelling which estimates co-expression profiles under subsets of genes and between biological samples or experimental conditions. The suggested methodology transforms disparate gene-expression data on a common probability scale to obtain inter-study-validated gene signatures. We evaluated the performance of our model using artificial data. Finally, we applied our model to six publicly available cancer gene-expression datasets and compared our results with well-known integrative microarray data methods. Our study shows that the suggested framework can relieve the limited sample size problem while reporting high accuracies by integrating multi-experiment data.
Collapse
Affiliation(s)
| | | | - Sophia Kossida
- Bioinformatics and Medical Informatics Group, Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou 115 27, Greece
| |
Collapse
|
15
|
Conlon EM, Postier BL, Methé BA, Nevin KP, Lovley DR. A Bayesian model for pooling gene expression studies that incorporates co-regulation information. PLoS One 2012; 7:e52137. [PMID: 23284902 PMCID: PMC3532429 DOI: 10.1371/journal.pone.0052137] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2012] [Accepted: 11/13/2012] [Indexed: 12/01/2022] Open
Abstract
Current Bayesian microarray models that pool multiple studies assume gene expression is independent of other genes. However, in prokaryotic organisms, genes are arranged in units that are co-regulated (called operons). Here, we introduce a new Bayesian model for pooling gene expression studies that incorporates operon information into the model. Our Bayesian model borrows information from other genes within the same operon to improve estimation of gene expression. The model produces the gene-specific posterior probability of differential expression, which is the basis for inference. We found in simulations and in biological studies that incorporating co-regulation information improves upon the independence model. We assume that each study contains two experimental conditions: a treatment and control. We note that there exist environmental conditions for which genes that are supposed to be transcribed together lose their operon structure, and that our model is best carried out for known operon structures.
Collapse
Affiliation(s)
- Erin M Conlon
- Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA, USA.
| | | | | | | | | |
Collapse
|
16
|
Li W. Volcano plots in analyzing differential expressions with mRNA microarrays. J Bioinform Comput Biol 2012; 10:1231003. [PMID: 23075208 DOI: 10.1142/s0219720012310038] [Citation(s) in RCA: 116] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
A volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log(10)(p-value) from the t-test). We review the basic and interactive use of the volcano plot and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide a unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility of applying volcano plots to other fields beyond microarray.
Collapse
Affiliation(s)
- Wentian Li
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, 350 Community Drive, NY 11030, USA.
| |
Collapse
|
17
|
Li Y, Ghosh D. Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data. Bioinformatics 2012; 28:807-14. [PMID: 22285559 DOI: 10.1093/bioinformatics/bts037] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
MOTIVATION There is now a large literature on statistical methods for the meta-analysis of genomic data from multiple studies. However, a crucial assumption for performing many of these analyses is that the data exhibit small between-study variation or that this heterogeneity can be sufficiently modelled probabilistically. RESULTS In this article, we propose 'assumption weighting', which exploits a weighted hypothesis testing framework proposed by Genovese et al. to incorporate tests of between-study variation into the meta-analysis context. This methodology is fast and computationally simple to implement. Several weighting schemes are considered and compared using simulation studies. In addition, we illustrate application of the proposed methodology using data from several high-profile stem cell gene expression datasets.
Collapse
Affiliation(s)
- Yihan Li
- Department of Statistics, Penn State University, University Park, PA 16802, USA
| | | |
Collapse
|
18
|
Tseng GC, Ghosh D, Feingold E. Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Res 2012; 40:3785-99. [PMID: 22262733 PMCID: PMC3351145 DOI: 10.1093/nar/gkr1265] [Citation(s) in RCA: 266] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.
Collapse
Affiliation(s)
- George C Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
| | | | | |
Collapse
|
19
|
Choi H, Pavelka N. When one and one gives more than two: challenges and opportunities of integrative omics. Front Genet 2012; 2:105. [PMID: 22303399 PMCID: PMC3262227 DOI: 10.3389/fgene.2011.00105] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2011] [Accepted: 12/21/2011] [Indexed: 12/24/2022] Open
Abstract
Since the dawn of the post-genomic era a myriad of novel high-throughput technologies have been developed that are capable of measuring thousands of biological molecules at once, giving rise to various “omics” platforms. These advances offer the unique opportunity to study how individual parts of a biological system work together to produce emerging phenotypes. Today, many research laboratories are moving toward applying multiple omics platforms to analyze the same biological samples. In addition, network information of interacting molecules is being incorporated more and more into the analysis and interpretation of these multiple omics datasets, which provides novel ways to integrate multiple layers of heterogeneous biological information into a single coherent picture. Here, we provide a perspective on how such recent “integrative omics” efforts are likely going to shift biological paradigms once again, and what challenges lie ahead.
Collapse
Affiliation(s)
- Hyungwon Choi
- Saw Swee Hock School of Public Health, National University of Singapore Singapore
| | | |
Collapse
|
20
|
Lawhon SD, Khare S, Rossetti CA, Everts RE, Galindo CL, Luciano SA, Figueiredo JF, Nunes JES, Gull T, Davidson GS, Drake KL, Garner HR, Lewin HA, Bäumler AJ, Adams LG. Role of SPI-1 secreted effectors in acute bovine response to Salmonella enterica Serovar Typhimurium: a systems biology analysis approach. PLoS One 2011; 6:e26869. [PMID: 22096503 PMCID: PMC3214023 DOI: 10.1371/journal.pone.0026869] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2011] [Accepted: 10/05/2011] [Indexed: 11/18/2022] Open
Abstract
Salmonella enterica Serovar Typhimurium (S. Typhimurium) causes enterocolitis with diarrhea and polymorphonuclear cell (PMN) influx into the intestinal mucosa in humans and calves. The Salmonella Type III Secretion System (T3SS) encoded at Pathogenicity Island I translocates Salmonella effector proteins SipA, SopA, SopB, SopD, and SopE2 into epithelial cells and is required for induction of diarrhea. These effector proteins act together to induce intestinal fluid secretion and transcription of C-X-C chemokines, recruiting PMNs to the infection site. While individual molecular interactions of the effectors with cultured host cells have been characterized, their combined role in intestinal fluid secretion and inflammation is less understood. We hypothesized that comparison of the bovine intestinal mucosal response to wild type Salmonella and a SipA, SopABDE2 effector mutant relative to uninfected bovine ileum would reveal heretofore unidentified diarrhea-associated host cellular pathways. To determine the coordinated effects of these virulence factors, a bovine ligated ileal loop model was used to measure responses to wild type S. Typhimurium (WT) and a ΔsipA, sopABDE2 mutant (MUT) across 12 hours of infection using a bovine microarray. Data were analyzed using standard microarray analysis and a dynamic bayesian network modeling approach (DBN). Both analytical methods confirmed increased expression of immune response genes to Salmonella infection and novel gene expression. Gene expression changes mapped to 219 molecular interaction pathways and 1620 gene ontology groups. Bayesian network modeling identified effects of infection on several interrelated signaling pathways including MAPK, Phosphatidylinositol, mTOR, Calcium, Toll-like Receptor, CCR3, Wnt, TGF-β, and Regulation of Actin Cytoskeleton and Apoptosis that were used to model of host-pathogen interactions. Comparison of WT and MUT demonstrated significantly different patterns of host response at early time points of infection (15 minutes, 30 minutes and one hour) within phosphatidylinositol, CCR3, Wnt, and TGF-β signaling pathways and the regulation of actin cytoskeleton pathway.
Collapse
Affiliation(s)
- Sara D. Lawhon
- Department of Veterinary Pathobiology, College of Veterinary Medicine, Texas A &M University, College Station, Texas, United States of America
| | - Sangeeta Khare
- Department of Veterinary Pathobiology, College of Veterinary Medicine, Texas A &M University, College Station, Texas, United States of America
| | - Carlos A. Rossetti
- Department of Veterinary Pathobiology, College of Veterinary Medicine, Texas A &M University, College Station, Texas, United States of America
| | - Robin E. Everts
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Cristi L. Galindo
- Eugene McDermott Center for Human Growth and Development, The University of Texas Southwestern Medical School, Dallas, Texas, United States of America
| | - Sarah A. Luciano
- Department of Veterinary Pathobiology, College of Veterinary Medicine, Texas A &M University, College Station, Texas, United States of America
| | - Josely F. Figueiredo
- Department of Veterinary Pathobiology, College of Veterinary Medicine, Texas A &M University, College Station, Texas, United States of America
| | - Jairo E. S. Nunes
- Department of Veterinary Pathobiology, College of Veterinary Medicine, Texas A &M University, College Station, Texas, United States of America
| | - Tamara Gull
- Department of Veterinary Pathobiology, College of Veterinary Medicine, Texas A &M University, College Station, Texas, United States of America
| | - George S. Davidson
- Sandia National Laboratories, Computation, Computers and Mathematics Center, Albuquerque, New Mexico, United States of America
| | | | - Harold R. Garner
- Eugene McDermott Center for Human Growth and Development, The University of Texas Southwestern Medical School, Dallas, Texas, United States of America
| | - Harris A. Lewin
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Andreas J. Bäumler
- Department of Medical Microbiology and Immunology, School of Medicine, University of California Davis, Davis, California, United States of America
| | - Leslie Garry Adams
- Department of Veterinary Pathobiology, College of Veterinary Medicine, Texas A &M University, College Station, Texas, United States of America
| |
Collapse
|
21
|
Ruan L, Yuan M. An empirical Bayes' approach to joint analysis of multiple microarray gene expression studies. Biometrics 2011; 67:1617-26. [PMID: 21517790 DOI: 10.1111/j.1541-0420.2011.01602.x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
With the prevalence of gene expression studies and the relatively low reproducibility caused by insufficient sample sizes, it is natural to consider joint analysis that could combine data from different experiments effectively to achieve improved accuracy. We present in this article a model-based approach for better identification of differentially expressed genes by incorporating data from different studies. The model can accommodate in a seamless fashion a wide range of studies including those performed at different platforms by fitting each data with different set of parameters, and/or under different but overlapping biological conditions. Model-based inferences can be done in an empirical Bayes' fashion. Because of the information sharing among studies, the joint analysis dramatically improves inferences based on individual analysis. Simulation studies and real data examples are presented to demonstrate the effectiveness of the proposed approach under a variety of complications that often arise in practice.
Collapse
Affiliation(s)
- Lingyan Ruan
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0205, USA.
| | | |
Collapse
|
22
|
Unifying gene expression measures from multiple platforms using factor analysis. PLoS One 2011; 6:e17691. [PMID: 21436879 PMCID: PMC3059153 DOI: 10.1371/journal.pone.0017691] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2010] [Accepted: 02/10/2011] [Indexed: 11/19/2022] Open
Abstract
In the Cancer Genome Atlas (TCGA) project, gene expression of the same set of samples is measured multiple times on different microarray platforms. There are two main advantages to combining these measurements. First, we have the opportunity to obtain a more precise and accurate estimate of expression levels than using the individual platforms alone. Second, the combined measure simplifies downstream analysis by eliminating the need to work with three sets of expression measures and to consolidate results from the three platforms. We propose to use factor analysis (FA) to obtain a unified gene expression measure (UE) from multiple platforms. The UE is a weighted average of the three platforms, and is shown to perform well in terms of accuracy and precision. In addition, the FA model produces parameter estimates that allow the assessment of the model fit. The R code is provided in File S2. Gene-level FA measurements for the TCGA data sets are available from http://tcga-data.nci.nih.gov/docs/publications/unified_expression/.
Collapse
|
23
|
Scharpf RB, Iacobuzio-Donahue CA, Cope L, Ruczinski I, Garrett-Mayer E, Lakkur S, Campagna D, Parmigiani G. Cross-platform Comparison of Two Pancreatic Cancer Phenotypes. Cancer Inform 2010; 9:257-64. [PMID: 21082040 PMCID: PMC2978933 DOI: 10.4137/cin.s5755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Model-based approaches for combining gene expression data from multiple high throughput platforms can be sensitive to technological artifacts when the number of samples in each platform is small. This paper proposes simple tools for quantifying concordance in a small study of pancreatic cancer cells lines with an emphasis on visualizations that uncover intra- and inter-platform variation. Using this approach, we identify several transcripts from the integrative analysis whose over-or under-expression in pancreatic cancer cell lines was validated by qPCR.
Collapse
|
24
|
Scharpf RB, Tjelmeland H, Parmigiani G, Nobel AB. Rejoinder. J Am Stat Assoc 2009; 104:1318-1323. [PMID: 21904418 DOI: 10.1198/jasa.2009.ap09575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- Robert B Scharpf
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | | | | | | |
Collapse
|