{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:16Z","timestamp":1740185116682,"version":"3.37.3"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2017,10,12]],"date-time":"2017-10-12T00:00:00Z","timestamp":1507766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1518001","DMS-1160319"],"award-info":[{"award-number":["1518001","DMS-1160319"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01-HG007031","P50 GM115318"],"award-info":[{"award-number":["U01-HG007031","P50 GM115318"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007878","name":"Diabetes Research Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007878","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007878","name":"DRC","doi-asserted-by":"publisher","award":["DK 0634591"],"award-info":[{"award-number":["DK 0634591"]}],"id":[{"id":"10.13039\/100007878","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Capturing association patterns in gene expression levels under different conditions or time points is important for inferring gene regulatory interactions. In practice, temporal changes in gene expression may result in complex association patterns that require more sophisticated detection methods than simple correlation measures. For instance, the effect of regulation may lead to time-lagged associations and interactions local to a subset of samples. Furthermore, expression profiles of interest may not be aligned or directly comparable (e.g. gene expression profiles from two species).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a count statistic for measuring association between pairs of gene expression profiles consisting of ordered samples (e.g. time-course), where correlation may only exist locally in subsequences separated by a position shift. The statistic is simple and fast to compute, and we illustrate its use in two applications. In a cross-species comparison of developmental gene expression levels, we show our method not only measures association of gene expressions between the two species, but also provides alignment between different developmental stages. In the second application, we applied our statistic to expression profiles from two distinct phenotypic conditions, where the samples in each profile are ordered by the associated phenotypic values. The detected associations can be useful in building correspondence between gene association networks under different phenotypes. On the theoretical side, we provide asymptotic distributions of the statistic for different regions of the parameter space and test its power on simulated data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code used to perform the analysis is available as part of the Supplementary Material.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx641","type":"journal-article","created":{"date-parts":[[2017,10,12]],"date-time":"2017-10-12T03:12:54Z","timestamp":1507777974000},"page":"617-624","source":"Crossref","is-referenced-by-count":9,"title":["Generalized correlation measure using count statistics for gene expression data with ordered samples"],"prefix":"10.1093","volume":"34","author":[{"given":"Y X Rachel","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, University of Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California, Berkeley, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elizabeth","family":"Theusch","sequence":"additional","affiliation":[{"name":"Children\u2019s Hospital Oakland Research Institute, Oakland, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jerome I","family":"Rotter","sequence":"additional","affiliation":[{"name":"The Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marisa W","family":"Medina","sequence":"additional","affiliation":[{"name":"Children\u2019s Hospital Oakland Research Institute, Oakland, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael S","family":"Waterman","sequence":"additional","affiliation":[{"name":"Molecular and Computational Biology, University of Southern California, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California, Berkeley, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2017,10,12]]},"reference":[{"key":"2023012712332130200_btx641-B1","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1093\/bioinformatics\/17.6.495","article-title":"Aligning gene expression time series with time warping algorithms","volume":"17","author":"Aach","year":"2001","journal-title":"Bioinformatics"},{"key":"2023012712332130200_btx641-B2","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1093\/bioinformatics\/bti095","article-title":"Clustering of gene expression data using a local shape-based similarity measure","volume":"21","author":"Balasubramaniyan","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012712332130200_btx641-B3","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1038\/ng1532","article-title":"Reverse engineering of regulatory networks in human B cells","volume":"37","author":"Basso","year":"2005","journal-title":"Nat. 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