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Browsing by Author "Ye, Jieping"
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Item ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide(Elsevier, 2017-01-15) Thompson, Paul M.; Andreassen, Ole A.; Arias-Vasquez, Alejandro; Bearden, Carrie E.; Boedhoe, Premika S.; Brouwer, Rachel M.; Buckner, Randy L.; Buitelaar, Jan K.; Bulayeva, Kazima B.; Cannon, Dara M.; Cohen, Ronald A.; Conrod, Patricia J.; Dale, Anders M.; Deary, Ian J.; Dennis, Emily L.; de Reus, Marcel A.; Desrivieres, Sylvane; Dima, Danai; Donohoe, Gary; Fisher, Simon E.; Fouche, Jean-Paul; Francks, Clyde; Frangou, Sophia; Franke, Barbara; Ganjgahi, Habib; Garavan, Hugh; Glahn, David C.; Grabe, Hans J.; Guadalupe, Tulio; Gutman, Boris A.; Hashimoto, Ryota; Hibar, Derrek P.; Holland, Dominic; Hoogman, Martine; Pol, Hilleke E. Hulshoff; Hosten, Norbert; Jahanshad, Neda; Kelly, Sinead; Kochunov, Peter; Kremen, William S.; Lee, Phil H.; Mackey, Scott; Martin, Nicholas G.; Mazoyer, Bernard; McDonald, Colm; Medland, Sarah E.; Morey, Rajendra A.; Nichols, Thomas E.; Paus, Tomas; Pausova, Zdenka; Schmaal, Lianne; Schumann, Gunter; Shen, Li; Sisodiya, Sanjay M.; Smit, Dirk J.A.; Smoller, Jordan W.; Stein, Dan J.; Stein, Jason L.; Toro, Roberto; Turner, Jessica A.; Heuvel, Martijn P. van den; Heuvel, Odile L. van den; Erp, Theo G.M. van; Rooij, Daan van; Veltman, Dick J.; Walter, Henrik; Wang, Yalin; Wardlaw, Joanna M.; Whelan, Christopher D.; Wright, Margaret J.; Ye, Jieping; ENIGMA Consortium; Radiology and Imaging Sciences, School of MedicineIn this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) – a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date – of schizophrenia and major depression – ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens – now numbering over 30,000 MRI scans – have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants – and genetic variants in general – may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures – from tens of thousands of people – that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.Item A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq data(IEEE, 2018-01) Liu, Kefei; Ye, Jieping; Yang, Yang; Shen, Li; Jiang, Hui; Radiology and Imaging Sciences, School of MedicineThe RNA-sequencing (RNA-seq) is becoming increasingly popular for quantifying gene expression levels. Since the RNA-seq measurements are relative in nature, between-sample normalization of counts is an essential step in differential expression (DE) analysis. The normalization of existing DE detection algorithms is ad hoc and performed once for all prior to DE detection, which may be suboptimal since ideally normalization should be based on non-DE genes only and thus coupled with DE detection. We propose a unified statistical model for joint normalization and DE detection of log-transformed RNA-seq data. Sample-specific normalization factors are modeled as unknown parameters in the gene-wise linear models and jointly estimated with the regression coefficients. By imposing sparsity-inducing L1 penalty (or mixed L1/L2 penalty for multiple treatment conditions) on the regression coefficients, we formulate the problem as a penalized least-squares regression problem and apply the augmented lagrangian method to solve it. Simulation studies show that the proposed model and algorithms perform better than or comparably to existing methods in terms of detection power and false-positive rate. The performance gain increases with increasingly larger sample size or higher signal to noise ratio, and is more significant when a large proportion of genes are differentially expressed in an asymmetric manner.