chr15.8584_chr15_27737689_27739663_-_0.R fitVsDatCorrelation=0.738837476833215 cont.fitVsDatCorrelation=0.260181394661535 fstatistic=8496.20541440862,47,577 cont.fstatistic=4132.14206269921,47,577 residuals=-0.406831640976200,-0.0928245748699055,-0.00108377710698801,0.0783468812437423,1.23050866148549 cont.residuals=-0.408557439473981,-0.144714918551408,-0.0356964437924895,0.100853384353980,1.32101308084355 predictedValues: Include Exclude Both chr15.8584_chr15_27737689_27739663_-_0.R.tl.Lung 56.5504746910803 53.9755446651897 70.103589719238 chr15.8584_chr15_27737689_27739663_-_0.R.tl.cerebhem 63.7573250269411 62.972640492976 50.4913087836883 chr15.8584_chr15_27737689_27739663_-_0.R.tl.cortex 56.3885440680852 50.4061036798459 71.2357552034095 chr15.8584_chr15_27737689_27739663_-_0.R.tl.heart 52.8554036867079 50.673136364091 57.529782349618 chr15.8584_chr15_27737689_27739663_-_0.R.tl.kidney 52.6436958953945 51.7427047544042 57.4583050512598 chr15.8584_chr15_27737689_27739663_-_0.R.tl.liver 56.1381445076132 51.9340474175023 55.3369862388874 chr15.8584_chr15_27737689_27739663_-_0.R.tl.stomach 55.7173736613108 50.8352468400334 57.8899812538488 chr15.8584_chr15_27737689_27739663_-_0.R.tl.testicle 56.1340584851387 52.7827123290441 56.5090280394301 diffExp=2.57493002589065,0.784684533965091,5.98244038823931,2.1822673226168,0.900991140990378,4.20409709011095,4.88212682127742,3.35134615609452 diffExpScore=0.961334551083416 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 60.5441497713759 54.3859083370254 56.9792685853057 cerebhem 57.1332169851594 53.9319171046549 54.1306706014672 cortex 56.2355155117461 50.463232643454 59.3820891993745 heart 57.088188371435 59.8783675367197 60.4553285492667 kidney 57.3204162445821 52.1635899103592 58.8704244557707 liver 59.7835250934005 54.3813852067492 50.8782934169433 stomach 57.6962872404536 52.0609125630429 54.1176033164533 testicle 55.8982239981946 53.8059872437548 54.5809957939073 cont.diffExp=6.15824143435051,3.20129988050446,5.7722828682921,-2.79017916528477,5.15682633422289,5.4021398866513,5.63537467741074,2.09223675443978 cont.diffExpScore=1.14481870759147 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,0,0 cont.diffExp1.2Score=0 tran.correlation=0.896817194641622 cont.tran.correlation=0.143695467455192 tran.covariance=0.00377284339798134 cont.tran.covariance=0.000226600556576964 tran.mean=54.7191972853349 cont.tran.mean=55.7981764851317 weightedLogRatios: wLogRatio Lung 0.186961533786824 cerebhem 0.0513787429453312 cortex 0.445944507507887 heart 0.166399443190173 kidney 0.0682738714077281 liver 0.310499279097519 stomach 0.364464440426960 testicle 0.246049108543672 cont.weightedLogRatios: wLogRatio Lung 0.434406446791112 cerebhem 0.231607458288021 cortex 0.430549387517557 heart -0.194138841523271 kidney 0.377231867213656 liver 0.382940910145560 stomach 0.411504518878812 testicle 0.152761531318594 varWeightedLogRatios=0.0193440940455010 cont.varWeightedLogRatios=0.046738329961041 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.47082865235360 0.0822015052173474 42.2234196706794 1.27533297264699e-178 *** df.mm.trans1 0.402049280693939 0.072307917181178 5.56023871751893 4.1235358572048e-08 *** df.mm.trans2 0.505070577187574 0.0667310378164763 7.5687505202095 1.49967131731884e-13 *** df.mm.exp2 0.602292552974078 0.09029293284763 6.6704285039722 5.98457604202072e-11 *** df.mm.exp3 -0.0873072440219797 0.09029293284763 -0.96693330550367 0.333982478131212 df.mm.exp4 0.0669624377753113 0.09029293284763 0.74161327651535 0.458623417045767 df.mm.exp5 0.0850797285366778 0.09029293284763 0.942263429190528 0.346452125500770 df.mm.exp6 0.190657937191195 0.09029293284763 2.11154883531064 0.0351544918853433 * df.mm.exp7 0.116646965534272 0.09029293284763 1.29187259573365 0.196918469680656 df.mm.exp8 0.185835373983770 0.09029293284763 2.05813863968034 0.0400253833839203 * df.mm.trans1:exp2 -0.482342069863383 0.0841985940305347 -5.72862380206089 1.63128978496563e-08 *** df.mm.trans2:exp2 -0.448123266211916 0.0729181213933758 -6.14556790066488 1.48659644914171e-09 *** df.mm.trans1:exp3 0.0844396658238115 0.0841985940305347 1.00286313323937 0.316347278924039 df.mm.trans2:exp3 0.0188884491114125 0.0729181213933758 0.259036419897790 0.79569952502467 df.mm.trans1:exp4 -0.134536081668582 0.0841985940305347 -1.59784237750802 0.110625511842290 df.mm.trans2:exp4 -0.130097589766610 0.0729181213933758 -1.78415992184940 0.0749230600797694 . df.mm.trans1:exp5 -0.156666829915965 0.0841985940305347 -1.86068225627556 0.0632975327882079 . df.mm.trans2:exp5 -0.12732734467207 0.0729181213933758 -1.74616874706864 0.0813138120828469 . df.mm.trans1:exp6 -0.197976014678828 0.0841985940305347 -2.35129834361643 0.0190434280150282 * df.mm.trans2:exp6 -0.229214409915245 0.0729181213933758 -3.14344919390734 0.00175545309615543 ** df.mm.trans1:exp7 -0.131488549022048 0.0841985940305347 -1.56164779870747 0.118919258133036 df.mm.trans2:exp7 -0.176588083522777 0.0729181213933758 -2.42173111633152 0.0157539913861147 * df.mm.trans1:exp8 -0.193226239256223 0.0841985940305347 -2.29488676718461 0.0220976715108563 * df.mm.trans2:exp8 -0.208182722309208 0.0729181213933758 -2.85502037533458 0.00445814281986663 ** df.mm.trans1:probe2 0.0501957398132262 0.0461174692607437 1.08843222791399 0.276858748022378 df.mm.trans1:probe3 0.269336394254565 0.0461174692607437 5.84022494234806 8.71081396794411e-09 *** df.mm.trans1:probe4 0.105353153543438 0.0461174692607437 2.28445218769012 0.0227072063485768 * df.mm.trans1:probe5 0.181238453897766 0.0461174692607437 3.92993060553825 9.52750976704705e-05 *** df.mm.trans1:probe6 0.296923119641996 0.0461174692607437 6.43840879392627 2.54317986341227e-10 *** df.mm.trans1:probe7 0.591313573062028 0.0461174692607437 12.8218998687634 2.71961799073964e-33 *** df.mm.trans1:probe8 0.0244819096195776 0.0461174692607437 0.530859780729929 0.59572021509678 df.mm.trans1:probe9 -0.0429194603335967 0.0461174692607437 -0.930655151325288 0.352421180805328 df.mm.trans1:probe10 0.308722646730762 0.0461174692607437 6.69426687282588 5.14571552272644e-11 *** df.mm.trans1:probe11 0.00858069622953465 0.0461174692607437 0.186061732507919 0.852461743348263 df.mm.trans1:probe12 0.455643202038665 0.0461174692607437 9.88005650228773 2.25464694132271e-21 *** df.mm.trans1:probe13 0.101601754283598 0.0461174692607437 2.20310775747801 0.0279816198263692 * df.mm.trans1:probe14 0.195981319862019 0.0461174692607437 4.24961132958011 2.4967869934708e-05 *** df.mm.trans1:probe15 0.342489956276773 0.0461174692607437 7.42646900983157 4.03085856763192e-13 *** df.mm.trans1:probe16 0.0316594870121182 0.0461174692607437 0.686496625240178 0.49267576987128 df.mm.trans2:probe2 -0.0600178780952713 0.0461174692607437 -1.30141308830144 0.193636481716371 df.mm.trans2:probe3 -0.089594443663768 0.0461174692607437 -1.94274415096825 0.0525331673281412 . df.mm.trans2:probe4 0.0887245967936195 0.0461174692607437 1.92388260275037 0.0548608874653737 . df.mm.trans2:probe5 0.116486428943242 0.0461174692607437 2.52586342682074 0.0118079438873036 * df.mm.trans2:probe6 0.0707196747518716 0.0461174692607437 1.53346824718480 0.125708623157207 df.mm.trans3:probe2 -0.310830469264655 0.0461174692607437 -6.73997238459139 3.84734175438640e-11 *** df.mm.trans3:probe3 -0.0951594725260698 0.0461174692607437 -2.06341488488988 0.0395199287949182 * df.mm.trans3:probe4 -0.105908094038694 0.0461174692607437 -2.29648538257595 0.0220055602790323 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.04362347826492 0.117780665109491 34.3318105268457 1.52947429379061e-141 *** df.mm.trans1 0.0626255456092892 0.103604849519032 0.604465388444827 0.545771880562343 df.mm.trans2 -0.068825368155094 0.0956141374381126 -0.71982418080843 0.471924718427913 df.mm.exp2 -0.0150832485799354 0.129374293784053 -0.116586132675722 0.907228593819296 df.mm.exp3 -0.189989578635884 0.129374293784053 -1.46852649841713 0.1425062011688 df.mm.exp4 -0.0217826245902201 0.129374293784053 -0.168369031846302 0.86635196966408 df.mm.exp5 -0.129087665069404 0.129374293784053 -0.997784500256845 0.318802116526634 df.mm.exp6 0.100525220433666 0.129374293784053 0.777010776201484 0.437470839829839 df.mm.exp7 -0.0403427006269502 0.129374293784053 -0.311829339870939 0.755282868347365 df.mm.exp8 -0.0475588400656233 0.129374293784053 -0.367606567538114 0.713301415744527 df.mm.trans1:exp2 -0.042903917546205 0.120642151016325 -0.355629580414224 0.722247961689213 df.mm.trans2:exp2 0.00670062293262227 0.104479167547336 0.0641335788743384 0.948886076642713 df.mm.trans1:exp3 0.116165237360489 0.120642151016325 0.962890966232607 0.336005596756745 df.mm.trans2:exp3 0.115129500898385 0.104479167547336 1.10193738714681 0.270948321579399 df.mm.trans1:exp4 -0.0369929859770734 0.120642151016325 -0.306634005324290 0.759232587064386 df.mm.trans2:exp4 0.117992839140745 0.104479167547336 1.12934321655355 0.259222362604101 df.mm.trans1:exp5 0.0743716825845252 0.120642151016325 0.616465157144468 0.537830696577094 df.mm.trans2:exp5 0.087367322904563 0.104479167547336 0.836217639894384 0.403378636389552 df.mm.trans1:exp6 -0.113167944731753 0.120642151016325 -0.938046476943536 0.348613004488161 df.mm.trans2:exp6 -0.100608391212884 0.104479167547336 -0.962951692425214 0.335975145995030 df.mm.trans1:exp7 -0.00783732103581787 0.120642151016325 -0.0649633728327454 0.948225663300811 df.mm.trans2:exp7 -0.00334795328027054 0.104479167547336 -0.0320442185639899 0.974447868759652 df.mm.trans1:exp8 -0.0322813985171955 0.120642151016325 -0.267579765821876 0.789118426404993 df.mm.trans2:exp8 0.0368385057166042 0.104479167547336 0.352591876269631 0.724523184551097 df.mm.trans1:probe2 0.0164649919190941 0.0660784274975857 0.249173482823811 0.803315262077411 df.mm.trans1:probe3 -0.0200411693819415 0.0660784274975857 -0.303293679055449 0.76177538526087 df.mm.trans1:probe4 -0.0371957021313296 0.0660784274975857 -0.562902350130662 0.573720028936011 df.mm.trans1:probe5 0.00725106909567566 0.0660784274975857 0.109734286517950 0.9126582614596 df.mm.trans1:probe6 -0.0419917273307975 0.0660784274975857 -0.635483151174137 0.525365293579993 df.mm.trans1:probe7 -0.0210106596932615 0.0660784274975857 -0.317965491748864 0.750626154942353 df.mm.trans1:probe8 0.0929980925679682 0.0660784274975857 1.407389613976 0.159850345568079 df.mm.trans1:probe9 0.0337700913376143 0.0660784274975857 0.511060759411204 0.60950400093186 df.mm.trans1:probe10 -0.0101405455723532 0.0660784274975857 -0.153462271370239 0.878087395325515 df.mm.trans1:probe11 0.00117025711968326 0.0660784274975857 0.0177101236212986 0.985876226522142 df.mm.trans1:probe12 0.0141639335905882 0.0660784274975857 0.214350342872576 0.830349591450883 df.mm.trans1:probe13 -0.0518249777392716 0.0660784274975857 -0.784294961334622 0.433188910746934 df.mm.trans1:probe14 0.027638818801591 0.0660784274975857 0.41827295001233 0.675903191547749 df.mm.trans1:probe15 0.0276829184816145 0.0660784274975857 0.418940333933127 0.675415644620115 df.mm.trans1:probe16 -0.0907065714312615 0.0660784274975857 -1.37271080542247 0.170375558954799 df.mm.trans2:probe2 0.11025918839655 0.0660784274975857 1.66861096082498 0.0957369952404723 . df.mm.trans2:probe3 -0.0187265833920645 0.0660784274975857 -0.283399349852093 0.776972378061263 df.mm.trans2:probe4 0.055679147356777 0.0660784274975857 0.84262216074696 0.399788928487285 df.mm.trans2:probe5 0.0171348698470719 0.0660784274975857 0.259311101912920 0.795487702723524 df.mm.trans2:probe6 0.0487231005279262 0.0660784274975857 0.737352603157912 0.461207680607638 df.mm.trans3:probe2 0.0525852997140265 0.0660784274975857 0.795801318303887 0.426474788977031 df.mm.trans3:probe3 0.00342419692368905 0.0660784274975857 0.0518201938721098 0.958689899329982 df.mm.trans3:probe4 -0.0349541489275551 0.0660784274975857 -0.528979732891377 0.597022937054071