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Together with other high-throughput technologies, such as microarrays, there are an enormous amount of resources in databases. The collection of these valuable data has been routine for more than a decade. Despite different technologies, many experiments share the same goal. For instance, the aims of RNA-seq studies often coincide with those of differential gene expression experiments based on microarrays. As such, it would be logical to utilize all available data. However, there is a lack of biostatistical tools for the integration of results obtained from different technologies. Although diverse technological platforms produce different raw data, one commonality for experiments with the same goal is that all the outcomes can be transformed into a platform-independent data format – rankings – for the same set of items. Here we present the\u003C\u002Fjats:p>",{"EN":117},"TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists",{"VOID":119},"25968440",{"VOID":121},"10.1515\u002Fsagmb-2014-0093","PUBLICATION","VERIFIED","Auto Verify",[126],"EN","https:\u002F\u002Fwww.degruyter.com\u002Fdocument\u002Fdoi\u002F10.1515\u002Fsagmb-2014-0093\u002Fhtml",[129,148,168,188,206,226],{"id":130,"sortIndex":25,"researcher":24,"roles":131,"affiliations":132,"properties":141,"displayName":145,"givenName":24,"familyName":24},"953c89f0-1e04-4241-9806-4be7dab6af0f",[],[133],{"id":134,"sortIndex":25,"affiliation":135,"properties":24},"58c61506-9966-4031-be50-7d632273f27d",{"id":134,"createTime":24,"updateTime":24,"relativeEntities":136,"slug":24,"properties":137,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":140,"statistic":24},[],{"title":138},{"EN":139},"Statistical Bioinformatics, IMI, Medical University of Graz, Auenbruggerplatz 2\u002FV, 8036 Graz, Austria",[],{"orcid":142,"title":144,"openalex":146},{"VOID":143},"https:\u002F\u002Forcid.org\u002F0000-0002-1712-6668",{"EN":145},"Michael G. 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2014, Moderated estimation of fold change and dispersion for RNA - seq data with, Genome Biol, 15, 550, 10.1186\u002Fs13059-014-0550-8",{"doi":309},"10.1186\u002Fs13059-014-0550-8",{"id":24,"text":311,"url":24,"identifiers":312},"Baker, 2010, profiling separating signal from noise, Nat Methods, 687, 10.1038\u002Fnmeth0910-687",{"doi":313},"10.1038\u002Fnmeth0910-687",{"id":24,"text":315,"url":24,"identifiers":316},"Takahashi, 2009, MiR and MiR can induce cell cycle arrest in human non small cell lung cancer cell lines One, 107",{},{"id":24,"text":318,"url":24,"identifiers":319},"Yanaihara, 2006, Unique microRNA molecular profiles in lung cancer diagnosis and prognosis, Cancer Cell, 189, 10.1016\u002Fj.ccr.2006.01.025",{"doi":320},"10.1016\u002Fj.ccr.2006.01.025",{"id":24,"text":322,"url":24,"identifiers":323},"Wang, 2011, functions as a tumor suppressor in human non - small cell lung cancer by targeting ras - related protein, Oncogene, 14, 451",{},{"id":24,"text":325,"url":24,"identifiers":326},"Plaisier, 2010, Rank - rank hypergeometric overlap : identification of statistically significant overlap between gene - expression signatures, Nucleic Acids Res, 169, 10.1093\u002Fnar\u002Fgkq636",{"doi":327},"10.1093\u002Fnar\u002Fgkq636",{"id":24,"text":329,"url":24,"identifiers":330},"Schimek, 2012, An inference and integration approach for the consolidation of ranked lists, Commun Stat Simul, 1152, 10.1080\u002F03610918.2012.625843",{"doi":331},"10.1080\u002F03610918.2012.625843",{"id":24,"text":333,"url":24,"identifiers":334},"Lin, 2010, Space oriented rank - based data integration Article, Stat Appl Genet Mol Biol, 9, 10.2202\u002F1544-6115.1534.Epub2010Apr9",{"doi":335},"10.2202\u002F1544-6115.1534.Epub2010Apr9",{"id":24,"text":337,"url":24,"identifiers":338},"Hall, 2012, Moderate - deviation - based inference for random degeneration in paired rank lists, Am Stat Assoc, 107",{},{"id":24,"text":322,"url":24,"identifiers":340},{},{"id":24,"text":307,"url":24,"identifiers":342},{"doi":309},{"id":24,"text":344,"url":24,"identifiers":345},"Lin, 2009, Integration of ranked lists via Cross Entropy Monte Carlo with applications to mRNA and microRNA studies, Biometrics, 9, 10.1111\u002Fj.1541-0420.2008.01044.x",{"doi":346},"10.1111\u002Fj.1541-0420.2008.01044.x",{"id":24,"text":311,"url":24,"identifiers":348},{"doi":313},{"id":24,"text":318,"url":24,"identifiers":350},{"doi":320},{"id":24,"text":344,"url":24,"identifiers":352},{"doi":346},{"id":24,"text":337,"url":24,"identifiers":354},{},{"id":24,"text":356,"url":24,"identifiers":357},"Yang, 2006, Similarities of ordered gene lists, Comput Biol, 693",{},{"id":24,"text":359,"url":24,"identifiers":360},"Tam, 2014, de Robust global microRNA expression profiling using next - generation sequencing technologies, Lab Invest, 350, 10.1038\u002Flabinvest.2013.157",{"doi":361},"10.1038\u002Flabinvest.2013.157",{"id":24,"text":363,"url":24,"identifiers":364},"Kugler, 2010, MADAM an open source meta - analysis toolbox for Source Code, Biol Med, 5",{},{"id":24,"text":366,"url":24,"identifiers":367},"Gao, 2010, Deregulated expression of miR miR and miR a in non small cell lung cancer is related to clinicopathologic characteristics or patient prognosis, Biomed Pharmacother, 21, 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problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.\u003C\u002Fjats:p>","\u003Cjats:p>Vấn đề xác định các gen được biểu hiện khác biệt trong các thí nghiệm vi mạch được thiết kế đã được xem xét. Lonnstedt và Speed (2002) đã đưa ra một biểu thức cho tỷ lệ hậu nghiệm của sự biểu hiện khác biệt trong một thí nghiệm hai màu được lặp lại bằng cách sử dụng một mô hình tham số phân cấp đơn giản. Mục đích của bài báo này là phát triển mô hình phân cấp của Lonnstedt và Speed (2002) thành một phương pháp thực tiễn cho các thí nghiệm vi mạch tổng quát với số lượng điều trị và mẫu RNA tùy ý. Mô hình được thiết lập lại trong bối cảnh của các mô hình tuyến tính tổng quát với các hệ số và độ tương phản của mối quan tâm tùy ý. Phương pháp này áp dụng tốt cho cả các thí nghiệm vi mạch kênh đơn và hai màu. Các ước lượng nhất quán, có hình thức kín được đưa ra cho các siêu tham số trong mô hình. Các ước lượng được đề xuất có hành vi vững bền ngay cả với số lượng vi mạch nhỏ và cho phép dữ liệu không đầy đủ phát sinh từ việc lọc điểm hoặc trọng số chất lượng điểm. Thống kê tỷ lệ hậu nghiệm được cấu trúc lại theo dạng một thống kê t đã điều chỉnh, trong đó các độ lệch chuẩn dư hậu nghiệm được sử dụng thay cho các độ lệch chuẩn thông thường. Phương pháp Bayes thực nghiệm tương đương với giảm độ biến của các phương sai mẫu ước lượng hướng tới một ước lượng tập hợp, dẫn đến suy diễn ổn định hơn khi số lượng vi mạch nhỏ. Việc sử dụng các thống kê t đã điều chỉnh có lợi thế so với tỷ lệ hậu nghiệm trong việc làm giảm số lượng siêu tham số cần ước lượng; đặc biệt, không yêu cầu biết trước thông tin về các thay đổi gấp đôi khác không. Thống kê t đã điều chỉnh được chỉ ra là tuân theo phân phối t với bậc tự do mở rộng. Phương pháp suy diễn t đã điều chỉnh có thể mở rộng để tiếp nhận các kiểm định các giả thuyết null tổng hợp thông qua việc sử dụng các thống kê F đã điều chỉnh. Hiệu suất của các phương pháp được chứng minh qua một nghiên cứu mô phỏng. Kết quả được trình bày cho hai tập dữ liệu có sẵn công khai.\u003C\u002Fjats:p>",{"EN":402,"VI":403},"Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments","Mô hình tuyến tính và phương pháp Bayes thực nghiệm để đánh giá sự biểu hiện khác biệt trong các thí nghiệm vi mạch",{"VOID":405},"16646809",{"VOID":407},"10.2202\u002F1544-6115.1027",[126],[410],"VI","https:\u002F\u002Fwww.degruyter.com\u002Fdocument\u002Fdoi\u002F10.2202\u002F1544-6115.1027\u002Fhtml",[413],{"id":414,"sortIndex":25,"researcher":24,"roles":415,"affiliations":416,"properties":425,"displayName":429,"givenName":24,"familyName":24},"98db5fad-2f8b-4095-8bc4-79ba10e897ae",[],[417],{"id":418,"sortIndex":25,"affiliation":419,"properties":24},"450d5bba-dad2-4742-b96b-246b33259526",{"id":418,"createTime":24,"updateTime":24,"relativeEntities":420,"slug":24,"properties":421,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":424,"statistic":24},[],{"title":422},{"EN":423},"Walter and Eliza Hall Institute. smyth@wehi.edu.au",[],{"orcid":426,"title":428,"openalex":430},{"VOID":427},"https:\u002F\u002Forcid.org\u002F0000-0001-9221-2892",{"EN":429},"Gordon K. Smyth",{"VOID":431},"A5054704071",{"url":24,"publisher":433,"properties":478},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":434,"slug":10,"properties":435,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":440,"manageAffiliations":457,"indexDatabases":463,"url":99,"thumbnailPath":24,"statistic":24,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":436,"eissn":437,"issn":438,"title":439},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[441,445,449,453],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":442,"label":443,"description":444,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},{"id":34,"createTime":24,"updateTime":24,"relativeEntities":446,"label":447,"description":448,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":37},{},{"id":40,"createTime":24,"updateTime":24,"relativeEntities":450,"label":451,"description":452,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":43},{},{"id":46,"createTime":24,"updateTime":24,"relativeEntities":454,"label":455,"description":456,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":49},{},[458],{"id":53,"createTime":24,"updateTime":24,"relativeEntities":459,"slug":24,"properties":460,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":462,"statistic":24},[],{"title":461},{"EN":57},[],[464,471],{"id":61,"indexDatabase":465,"url":72,"indexYears":73,"academicFieldIds":470,"indexDatabaseRanking":79},{"id":63,"createTime":24,"updateTime":24,"relativeEntities":466,"label":467,"description":468,"key":69,"publicationTags":469,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75,76,77,78],{"id":81,"indexDatabase":472,"url":94,"indexYears":24,"academicFieldIds":477,"indexDatabaseRanking":24},{"id":83,"createTime":24,"updateTime":24,"relativeEntities":473,"label":474,"description":475,"key":90,"publicationTags":476,"standard":24},[],{"EN":86,"VI":86},{"EN":88,"VI":89},[92,93],[96,97,98],{"issue":479,"pages":481,"volume":483},{"VOID":480},"1",{"VOID":482},"1-25",{"VOID":295},11417,{"total":484,"publishYear":486,"statisticByYear":487},2004,{"2012":488,"2013":489,"2014":490,"2015":491,"2016":492,"2017":493,"2018":494,"2019":495,"2020":496,"2021":497,"2022":498,"2023":499,"2024":500},876,870,851,906,706,643,576,527,532,455,400,409,155,"2004-01-12",[79,92],[],{"id":505,"createTime":506,"updateTime":506,"relativeEntities":507,"slug":508,"properties":509,"entityType":122,"verifyStatus":123,"verifyTime":522,"verifyNote":124,"languages":523,"translateLanguages":24,"viewCount":25,"primaryUrl":524,"fullTextUrl":24,"authors":525,"publicationType":246,"publisherRelationship":564,"citationCount":614,"citationInfo":615,"publishDate":631,"publishYear":616,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":632,"openAccess":24,"references":633,"isForceReanalyzing":384},"a49306c2-f166-450b-8c0c-6388e551a069","2024-10-15T23:35:37.941+00:00",[],"A-Shrinkage-Approach-to-Large-Scale-Covariance-Matrix-Estimation-and-Implications-for-Functional-Genomics",{"openalex":510,"mag":512,"abstract":514,"title":516,"pm":518,"doi":520},{"VOID":511},"W1990512452",{"VOID":513},"1990512452",{"EN":515},"\u003Cjats:p>Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity.Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data.\u003C\u002Fjats:p>",{"EN":517},"A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics",{"VOID":519},"16646851",{"VOID":521},"10.2202\u002F1544-6115.1175","2024-10-15T23:35:37.940+00:00",[126],"https:\u002F\u002Fwww.degruyter.com\u002Fdocument\u002Fdoi\u002F10.2202\u002F1544-6115.1175\u002Fhtml",[526,545],{"id":527,"sortIndex":25,"researcher":24,"roles":528,"affiliations":529,"properties":538,"displayName":542,"givenName":24,"familyName":24},"7425e303-33ba-45e3-a737-9df9b21c905b",[],[530],{"id":531,"sortIndex":25,"affiliation":532,"properties":24},"5297ffae-2d7c-4803-97d4-5de5c3564b59",{"id":531,"createTime":24,"updateTime":24,"relativeEntities":533,"slug":24,"properties":534,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":537,"statistic":24},[],{"title":535},{"EN":536},"Department of Statistics, University of Munich, Germany. schaefer@stat.math.ethz.ch",[],{"orcid":539,"title":541,"openalex":543},{"VOID":540},"https:\u002F\u002Forcid.org\u002F0000-0002-7466-5959",{"EN":542},"Juliane Schäfer",{"VOID":544},"A5084415401",{"id":546,"sortIndex":150,"researcher":24,"roles":547,"affiliations":548,"properties":557,"displayName":561,"givenName":24,"familyName":24},"94209950-4e27-42a5-b634-5e6c29aafc02",[],[549],{"id":550,"sortIndex":25,"affiliation":551,"properties":24},"07684009-5910-4c23-ba26-e97a1de33e1b",{"id":550,"createTime":24,"updateTime":24,"relativeEntities":552,"slug":24,"properties":553,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":556,"statistic":24},[],{"title":554},{"EN":555},"Statistics",[],{"orcid":558,"title":560,"openalex":562},{"VOID":559},"https:\u002F\u002Forcid.org\u002F0000-0001-7917-2056",{"EN":561},"Korbinian Strimmer",{"VOID":563},"A5000442405",{"url":24,"publisher":565,"properties":610},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":566,"slug":10,"properties":567,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":572,"manageAffiliations":589,"indexDatabases":595,"url":99,"thumbnailPath":24,"statistic":24,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":568,"eissn":569,"issn":570,"title":571},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[573,577,581,585],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":574,"label":575,"description":576,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},{"id":34,"createTime":24,"updateTime":24,"relativeEntities":578,"label":579,"description":580,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":37},{},{"id":40,"createTime":24,"updateTime":24,"relativeEntities":582,"label":583,"description":584,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":43},{},{"id":46,"createTime":24,"updateTime":24,"relativeEntities":586,"label":587,"description":588,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":49},{},[590],{"id":53,"createTime":24,"updateTime":24,"relativeEntities":591,"slug":24,"properties":592,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":594,"statistic":24},[],{"title":593},{"EN":57},[],[596,603],{"id":61,"indexDatabase":597,"url":72,"indexYears":73,"academicFieldIds":602,"indexDatabaseRanking":79},{"id":63,"createTime":24,"updateTime":24,"relativeEntities":598,"label":599,"description":600,"key":69,"publicationTags":601,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75,76,77,78],{"id":81,"indexDatabase":604,"url":94,"indexYears":24,"academicFieldIds":609,"indexDatabaseRanking":24},{"id":83,"createTime":24,"updateTime":24,"relativeEntities":605,"label":606,"description":607,"key":90,"publicationTags":608,"standard":24},[],{"EN":86,"VI":86},{"EN":88,"VI":89},[92,93],[96,97,98],{"issue":611,"volume":612},{"VOID":480},{"VOID":613},"4",1542,{"total":614,"publishYear":616,"statisticByYear":617},2005,{"2012":618,"2013":619,"2014":620,"2015":621,"2016":622,"2017":623,"2018":624,"2019":625,"2020":626,"2021":627,"2022":628,"2023":629,"2024":630},79,89,97,113,111,102,95,133,99,90,80,57,52,"2005-01-14",[79,92],[634,638,642,646,649,653,657,661,665,669,672,676,679,683,687,691,694,698,702],{"id":24,"text":635,"url":24,"identifiers":636},"Magwene, 2004, and Estimating genomic coexpression networks using first - order conditional independence, Genome Biology, 5, 10.1186\u002Fgb-2004-5-12-r100",{"doi":637},"10.1186\u002Fgb-2004-5-12-r100",{"id":24,"text":639,"url":24,"identifiers":640},"Tibshirani, 2002, Diagnosis of multiple cancer type by shrunken centroids of gene expression, Proc Natl Acad Sci USA, 99, 6567, 10.1073\u002Fpnas.082099299",{"doi":641},"10.1073\u002Fpnas.082099299",{"id":24,"text":643,"url":24,"identifiers":644},"Efron, 1977, and Stein s paradox in statistics, Sci Am, 236, 119, 10.1038\u002Fscientificamerican0577-119",{"doi":645},"10.1038\u002Fscientificamerican0577-119",{"id":24,"text":647,"url":24,"identifiers":648},"Greenland, 2000, Principles of multilevel modelling Intl, Epidemiol, 29, 158",{},{"id":24,"text":650,"url":24,"identifiers":651},"Leung, 1998, and Estimation of the scale matrix and its eigen - values in the Wishart and the multivariate F distributions Statist Math, Ann Inst, 50, 523, 10.1023\u002FA:1003529529228",{"doi":652},"10.1023\u002FA:1003529529228",{"id":24,"text":654,"url":24,"identifiers":655},"Efron, 2004, Large - scale simultaneous hypothesis testing : the choice of a null hypothesis Amer Statist, Assoc, 99, 96, 10.1198\u002F016214504000000089",{"doi":656},"10.1198\u002F016214504000000089",{"id":24,"text":658,"url":24,"identifiers":659},"Hoerl, 1970, and a Ridge regression : applications to nonorthogonal problems, Technometrics, 12, 69, 10.1080\u002F00401706.1970.10488635",{"doi":660},"10.1080\u002F00401706.1970.10488635",{"id":24,"text":662,"url":24,"identifiers":663},"Hoerl, 1970, and Ridge regression : biased estimation for nonorthogonal problems, Technometrics, 12, 55, 10.1080\u002F00401706.1970.10488634",{"doi":664},"10.1080\u002F00401706.1970.10488634",{"id":24,"text":666,"url":24,"identifiers":667},"Morris, 1983, Parametric empirical Bayes inference : theory and applica - tions Amer Statist, Assoc, 78, 47, 10.1080\u002F01621459.1983.10477920",{"doi":668},"10.1080\u002F01621459.1983.10477920",{"id":24,"text":670,"url":24,"identifiers":671},"Ledoit, 2003, and Improved estimation of the covariance matrix of stock returns with an application to portfolio selection Empir, Finance, 10, 603",{},{"id":24,"text":673,"url":24,"identifiers":674},"Efron, 1975, and Data analysis using Stein s estimator and its generalizations Amer Statist, Assoc, 70, 311, 10.1080\u002F01621459.1975.10479864",{"doi":675},"10.1080\u002F01621459.1975.10479864",{"id":24,"text":677,"url":24,"identifiers":678},"Efron, 1975, Biased versus unbiased estimation Adv, Math, 16, 259",{},{"id":24,"text":680,"url":24,"identifiers":681},"Butte, 2000, Discov - ering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks, Proc Natl Acad Sci USA, 97, 12182, 10.1073\u002Fpnas.220392197",{"doi":682},"10.1073\u002Fpnas.220392197",{"id":24,"text":684,"url":24,"identifiers":685},"Wille, 2004, von Rohr Bühlmann Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana, Genome Biology, 5, 10.1186\u002Fgb-2004-5-11-r92",{"doi":686},"10.1186\u002Fgb-2004-5-11-r92",{"id":24,"text":688,"url":24,"identifiers":689},"Eisen, 1998, Cluster analysis and display of genome - wide expression patterns, Proc Natl Acad Sci USA, 95, 14863, 10.1073\u002Fpnas.95.25.14863",{"doi":690},"10.1073\u002Fpnas.95.25.14863",{"id":24,"text":692,"url":24,"identifiers":693},"Smyth, 2004, Linear models and empirical Bayes methods for assessing differential expression in microarray experiments Statist Biol, Appl Genet Mol, 3, 3",{},{"id":24,"text":695,"url":24,"identifiers":696},"Cui, 2005, Improved statistical tests for differential gene expression by shrinking variance components estimates, Biostatistics, 6, 59, 10.1093\u002Fbiostatistics\u002Fkxh018",{"doi":697},"10.1093\u002Fbiostatistics\u002Fkxh018",{"id":24,"text":699,"url":24,"identifiers":700},"Cox, 2004, and A note on pseudolikelihood from marginal densities, Biometrika, 91, 729, 10.1093\u002Fbiomet\u002F91.3.729",{"doi":701},"10.1093\u002Fbiomet\u002F91.3.729",{"id":24,"text":703,"url":24,"identifiers":704},"Toh, 2002, and Inference of a genetic network by a combined ap - proach of cluster analysis and graphical Gaussian modeling, Bioinformatics, 18, 287, 10.1093\u002Fbioinformatics\u002F18.2.287",{"doi":705},"10.1093\u002Fbioinformatics\u002F18.2.287",{"id":707,"createTime":708,"updateTime":708,"relativeEntities":709,"slug":710,"properties":711,"entityType":122,"verifyStatus":123,"verifyTime":708,"verifyNote":124,"languages":724,"translateLanguages":24,"viewCount":25,"primaryUrl":725,"fullTextUrl":24,"authors":726,"publicationType":246,"publisherRelationship":763,"citationCount":812,"citationInfo":813,"publishDate":828,"publishYear":616,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":829,"openAccess":24,"references":830,"isForceReanalyzing":384},"3cbd5969-f1cb-45f6-9692-215265f7f81a","2024-10-15T09:41:06.766+00:00",[],"A-General-Framework-for-Weighted-Gene-Co-Expression-Network-Analysis",{"openalex":712,"mag":714,"abstract":716,"title":718,"pm":720,"doi":722},{"VOID":713},"W2060705109",{"VOID":715},"2060705109",{"EN":717},"\u003Cjats:p>Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for `soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion).We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the `weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its `unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding.We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.\u003C\u002Fjats:p>",{"EN":719},"A General Framework for Weighted Gene Co-Expression Network 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