02676nas a2200541   4500008004100000022001400041245013100055210006900186260000900255300000900264490000600273520115300279653001201432100002001444700002001464700001601484700001701500700002501517700001601542700002001558700001801578700002101596700001801617700002001635700002001655700002101675700001401696700001501710700001801725700001901743700002601762700002701788700002701815700001601842700001301858700002101871700002601892700001801918700001601936700001801952700001501970700001501985700001302000700001502013700001302028700001602041856007702057       2014                        eng d  a2041-172300aAssessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.0 aAssessing technical performance in differential gene expression   c2014  a51250 v53 aThere is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard ’dashboard’ of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.10aRNA-seq1 aMunro, Sarah, A1 aLund, Steven, P1 aPine, Scott1 aBinder, Hans1 aClevert, Djork-Arné1 aConesa, Ana1 aDopazo, Joaquin1 aFasold, Mario1 aHochreiter, Sepp1 aHong, Huixiao1 aJafari, Nadereh1 aKreil, David, P1 aLabaj, Paweł, P1 aLi, Sheng1 aLiao, Yang1 aLin, Simon, M1 aMeehan, Joseph1 aMason, Christopher, E1 aSantoyo-López, Javier1 aSetterquist, Robert, A1 aShi, Leming1 aShi, Wei1 aSmyth, Gordon, K1 aStralis-Pavese, Nancy1 aSu, Zhenqiang1 aTong, Weida1 aWang, Charles1 aWang, Jian1 aXu, Joshua1 aYe, Zhan1 aYang, Yong1 aYu, Ying1 aSalit, Marc  uhttp://www.nature.com/ncomms/2014/140925/ncomms6125/full/ncomms6125.html07842nas a2202545   4500008004100000245014500041210006900186260001300255300001100268490000700279520110300286100001601389700002201405700002201427700002101449700001701470700002301487700001801510700001801528700002601546700001901572700002501591700002201616700002301638700002301661700001801684700001901702700001701721700001201738700002201750700002301772700002301795700001401818700002401832700002001856700001701876700001601893700001701909700002001926700002201946700001701968700001701985700001502002700001602017700001502033700002402048700002102072700001802093700002702111700001802138700002002156700002002176700001902196700001802215700001602233700001702249700001502266700002002281700002102301700001802322700001402340700002402354700002002378700002202398700002002420700002702440700001102467700001402478700001302492700001802505700001702523700002602540700001302566700001302579700002302592700001902615700002202634700002202656700001802678700001902696700002102715700001502736700002102751700001602772700001602788700001702804700002702821700002302848700002102871700001702892700002702909700002002936700001702956700001502973700001402988700002203002700001703024700001903041700001703060700001703077700001503094700002203109700001603131700002703147700002003174700002403194700002003218700002603238700001703264700001503281700001603296700001403312700001703326700002603343700001603369700002203385700001703407700001503424700002103439700003003460700002103490700001903511700001603530700002603546700001203572700002303584700001403607700002303621700002103644700001803665700002003683700002403703700001503727700002403742700002103766700002003787700002103807700001903828700001703847700001103864700001703875700001803892700001303910700001703923700001303940700001403953700001703967700001303984700001903997700003204016700002004048700001904068700001904087700002404106700002004130700002104150700002404171700002104195700002404216700001804240700001904258700001704277700002004294700001704314700002104331700001804352700001704370700001204387700002504399700001904424700002404443700002604467700002504493700001504518700002004533700001704553700001904570700002304589700001604612700002104628700001904649700002004668700001704688700001604705700001504721700001804736700001704754700002204771700001804793700002004811700002504831700002304856700001804879700001504897700001704912700001404929700002204943700002104965700001904986700001605005700001905021700001505040700001205055700001405067700001505081700001705096700001405113700001505127700001505142700001705157700001605174700001605190700002605206856006405232       2010                        eng d00aThe MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.0 aMicroArray Quality Control MAQCII study of common practices for   c2010 Aug  a827-380 v283 a<p>Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, &gt;30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.</p>1 aShi, Leming1 aCampbell, Gregory1 aJones, Wendell, D1 aCampagne, Fabien1 aWen, Zhining1 aWalker, Stephen, J1 aSu, Zhenqiang1 aChu, Tzu-Ming1 aGoodsaid, Federico, M1 aPusztai, Lajos1 aShaughnessy, John, D1 aOberthuer, André1 aThomas, Russell, S1 aPaules, Richard, S1 aFielden, Mark1 aBarlogie, Bart1 aChen, Weijie1 aDu, Pan1 aFischer, Matthias1 aFurlanello, Cesare1 aGallas, Brandon, D1 aGe, Xijin1 aMegherbi, Dalila, B1 aSymmans, Fraser1 aWang, May, D1 aZhang, John1 aBitter, Hans1 aBrors, Benedikt1 aBushel, Pierre, R1 aBylesjo, Max1 aChen, Minjun1 aCheng, Jie1 aCheng, Jing1 aChou, Jeff1 aDavison, Timothy, S1 aDelorenzi, Mauro1 aDeng, Youping1 aDevanarayan, Viswanath1 aDix, David, J1 aDopazo, Joaquin1 aDorff, Kevin, C1 aElloumi, Fathi1 aFan, Jianqing1 aFan, Shicai1 aFan, Xiaohui1 aFang, Hong1 aGonzaludo, Nina1 aHess, Kenneth, R1 aHong, Huixiao1 aHuan, Jun1 aIrizarry, Rafael, A1 aJudson, Richard1 aJuraeva, Dilafruz1 aLababidi, Samir1 aLambert, Christophe, G1 aLi, Li1 aLi, Yanen1 aLi, Zhen1 aLin, Simon, M1 aLiu, Guozhen1 aLobenhofer, Edward, K1 aLuo, Jun1 aLuo, Wen1 aMcCall, Matthew, N1 aNikolsky, Yuri1 aPennello, Gene, A1 aPerkins, Roger, G1 aPhilip, Reena1 aPopovici, Vlad1 aPrice, Nathan, D1 aQian, Feng1 aScherer, Andreas1 aShi, Tieliu1 aShi, Weiwei1 aSung, Jaeyun1 aThierry-Mieg, Danielle1 aThierry-Mieg, Jean1 aThodima, Venkata1 aTrygg, Johan1 aVishnuvajjala, Lakshmi1 aWang, Sue, Jane1 aWu, Jianping1 aWu, Yichao1 aXie, Qian1 aYousef, Waleed, A1 aZhang, Liang1 aZhang, Xuegong1 aZhong, Sheng1 aZhou, Yiming1 aZhu, Sheng1 aArasappan, Dhivya1 aBao, Wenjun1 aLucas, Anne, Bergstrom1 aBerthold, Frank1 aBrennan, Richard, J1 aBuness, Andreas1 aCatalano, Jennifer, G1 aChang, Chang1 aChen, Rong1 aCheng, Yiyu1 aCui, Jian1 aCzika, Wendy1 aDemichelis, Francesca1 aDeng, Xutao1 aDosymbekov, Damir1 aEils, Roland1 aFeng, Yang1 aFostel, Jennifer1 aFulmer-Smentek, Stephanie1 aFuscoe, James, C1 aGatto, Laurent1 aGe, Weigong1 aGoldstein, Darlene, R1 aGuo, Li1 aHalbert, Donald, N1 aHan, Jing1 aHarris, Stephen, C1 aHatzis, Christos1 aHerman, Damir1 aHuang, Jianping1 aJensen, Roderick, V1 aJiang, Rui1 aJohnson, Charles, D1 aJurman, Giuseppe1 aKahlert, Yvonne1 aKhuder, Sadik, A1 aKohl, Matthias1 aLi, Jianying1 aLi, Li1 aLi, Menglong1 aLi, Quan-Zhen1 aLi, Shao1 aLi, Zhiguang1 aLiu, Jie1 aLiu, Ying1 aLiu, Zhichao1 aMeng, Lu1 aMadera, Manuel1 aMartinez-Murillo, Francisco1 aMedina, Ignacio1 aMeehan, Joseph1 aMiclaus, Kelci1 aMoffitt, Richard, A1 aMontaner, David1 aMukherjee, Piali1 aMulligan, George, J1 aNeville, Padraic1 aNikolskaya, Tatiana1 aNing, Baitang1 aPage, Grier, P1 aParker, Joel1 aParry, Mitchell1 aPeng, Xuejun1 aPeterson, Ron, L1 aPhan, John, H1 aQuanz, Brian1 aRen, Yi1 aRiccadonna, Samantha1 aRoter, Alan, H1 aSamuelson, Frank, W1 aSchumacher, Martin, M1 aShambaugh, Joseph, D1 aShi, Qiang1 aShippy, Richard1 aSi, Shengzhu1 aSmalter, Aaron1 aSotiriou, Christos1 aSoukup, Mat1 aStaedtler, Frank1 aSteiner, Guido1 aStokes, Todd, H1 aSun, Qinglan1 aTan, Pei-Yi1 aTang, Rong1 aTezak, Zivana1 aThorn, Brett1 aTsyganova, Marina1 aTurpaz, Yaron1 aVega, Silvia, C1 aVisintainer, Roberto1 avon Frese, Juergen1 aWang, Charles1 aWang, Eric1 aWang, Junwei1 aWang, Wei1 aWestermann, Frank1 aWilley, James, C1 aWoods, Matthew1 aWu, Shujian1 aXiao, Nianqing1 aXu, Joshua1 aXu, Lei1 aYang, Lun1 aZeng, Xiao1 aZhang, Jialu1 aZhang, Li1 aZhang, Min1 aZhao, Chen1 aPuri, Raj, K1 aScherf, Uwe1 aTong, Weida1 aWolfinger, Russell, D  uhttp://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html