{"data":{"id":"10.13016/m2t93b","type":"dois","attributes":{"doi":"10.13016/m2t93b","prefix":"10.13016","suffix":"m2t93b","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Okrah, Kwame","nameType":"Personal","givenName":"Kwame","familyName":"Okrah","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Shape Analysis of High-throughput Genomics Data"}],"publisher":"Digital Repository at the University of Maryland","container":{},"publicationYear":2015,"subjects":[{"subject":"Statistics","subjectScheme":"pqcontrolled"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Molecular biology","subjectScheme":"pqcontrolled"},{"subjectScheme":"pquncontrolled"}],"contributors":[],"dates":[{"date":"2015","dateType":"Issued"}],"language":"en","types":{"ris":"THES","bibtex":"phdthesis","citeproc":"thesis","schemaOrg":"Thesis","resourceType":"Dissertation","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[],"descriptions":[{"description":"RNA sequencing refers to the use of \n\nnext-generation sequencing technologies to characterize \n\nthe identity and abundance of target RNA species in a biological sample\n\nof interest. \n\nThe recent improvement and reduction in the cost of next-generation \n\nsequencing technologies have been \n\nparalleled by the development of statistical methodologies to analyze the \n\ndata they produce. \n\nCoupled with the reduction in cost is the increase in the complexity \n\nof experiments.\n\nSome of the old challenges still remain.\n\nFor example the issue of normalization is important now more than ever. \n\nSome of the crude assumptions made in the early stages of RNA sequencing\n\ndata analysis were necessary since the technology was new and untested, \n\nthe number of replicates were small, and the experiments were relatively \n\nsimple.\n\n \n\nOne of the many uses of RNA sequencing experiments is the \n\nidentification of genes whose abundance levels are significantly different\n\nacross various biological conditions of interest.\n\nSeveral methods have been developed to answer this question.\n\nSome of these newly developed methods are based on the assumption \n\nthat the data observed or a transformation of the data are relatively symmetric \n\nwith light tails, usually summarized by assuming a Gaussian random component. \n\nIt is indeed very difficult to assess this assumption for small sample sizes \n\n(e.g. sample sizes in the range of 4 to 30). \n\nIn this dissertation, we utilize L-moments statistics as the basis for \n\nnormalization, exploratory data analysis, the assessment of distributional assumptions, \n\nand the hypothesis testing of  high-throughput transcriptomic data. \n\nIn particular, we introduce a new normalization method for high-throughput \n\ntranscriptomic data that is a modification of quantile normalization.\n\nWe use L-moments ratios for assessing the shape \n\n(skewness and kurtosis statistics) of high-throughput transcriptome data. \n\nBased on these statistics, we propose a test for assessing whether \n\nthe shapes of the observed samples differ across biological conditions.\n\nWe also illustrate the utility of this framework to characterize \n\nthe robustness of distributional assumptions made by statistical methods \n\nfor differential expression.\n\nWe apply it to RNA-seq data and find that methods based on the simple t-test \n\nfor differential expression analysis using L-moments statistics as weights are robust.\n\nFinally we provide an algorithm based on L-moments ratios for identifying genes with \n\ndistributions that are markedly different from the majority in the data.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"http://hdl.handle.net/1903/16941","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datac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