fitVsDatCorrelation=0.857207539138351 cont.fitVsDatCorrelation=0.274229283022586 fstatistic=12987.2393796872,62,922 cont.fstatistic=3713.61523926952,62,922 residuals=-0.666764110289366,-0.085089075911319,-0.00364855045382509,0.0750588590296866,0.931329493680963 cont.residuals=-0.585880523357339,-0.185868525482670,-0.0500545706626883,0.136444206797919,1.41750637888891 predictedValues: Include Exclude Both Lung 55.1779702058976 68.3671279666972 55.4626752545259 cerebhem 54.0551904284799 88.803067409528 56.6425603162614 cortex 56.7808204236937 62.4044572963947 61.432648272853 heart 57.3750457538647 65.5258830523007 60.8153800123103 kidney 57.1345135470808 72.7141653457867 55.3887167621953 liver 56.2825170244615 73.4132132422744 54.3382937595633 stomach 54.4572805788249 64.2314272165496 58.8435607380779 testicle 54.1885421050567 72.9815851965374 56.5408713901341 diffExp=-13.1891577607996,-34.747876981048,-5.62363687270094,-8.15083729843597,-15.5796517987059,-17.1306962178128,-9.77414663772475,-18.7930430914807 diffExpScore=0.991934771441928 diffExp1.5=0,-1,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,-1,0,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,-1,0,0,0,-1,0,-1 diffExp1.3Score=0.75 diffExp1.2=-1,-1,0,0,-1,-1,0,-1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 58.199645042106 59.6328399919654 64.7068655753167 cerebhem 59.1912724999013 63.6269074466453 62.259864606758 cortex 59.8620573625336 62.121468395911 60.5084128463881 heart 59.4127426401504 57.3740739348674 63.5941447900905 kidney 56.9464105363466 63.5572681661246 58.6353976365779 liver 61.7077776450899 67.1106845097719 62.8963286692096 stomach 59.3941771670415 60.3076569824053 64.4923402538726 testicle 60.979882475538 62.6975293644434 65.1627562165047 cont.diffExp=-1.43319494985941,-4.43563494674398,-2.25941103337738,2.038668705283,-6.61085762977799,-5.40290686468192,-0.913479815363793,-1.71764688890541 cont.diffExpScore=1.14158791733727 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=-0.463270444309225 cont.tran.correlation=0.374121914977369 tran.covariance=-0.00125336211895640 cont.tran.covariance=0.00042456299426849 tran.mean=63.3683004245893 cont.tran.mean=60.7576496350526 weightedLogRatios: wLogRatio Lung -0.882545799625922 cerebhem -2.10391533463205 cortex -0.385913534179545 heart -0.546755620062604 kidney -1.00453189085837 liver -1.1062571056091 stomach -0.673502826134611 testicle -1.23303240483075 cont.weightedLogRatios: wLogRatio Lung -0.0991585998441809 cerebhem -0.297497237543319 cortex -0.152291438761019 heart 0.142005945944429 kidney -0.449979237779976 liver -0.349529810361796 stomach -0.0624530867246237 testicle -0.114568618175272 varWeightedLogRatios=0.283809724838899 cont.varWeightedLogRatios=0.0348749698155742 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.2102247194674 0.0657961203926698 63.9889509342021 0 *** df.mm.trans1 -0.345620189971017 0.056507606923378 -6.11634802442906 1.41295126400200e-09 *** df.mm.trans2 0.0231049648067845 0.0496173204904708 0.465663292140533 0.641566507375969 df.mm.exp2 0.219920479811456 0.0631316998782636 3.48351905992595 0.000518142452943518 *** df.mm.exp3 -0.164851751105291 0.0631316998782636 -2.61123574089045 0.00916811060974792 ** df.mm.exp4 -0.095533656595192 0.0631316998782636 -1.51324385022752 0.130560421805497 df.mm.exp5 0.097823051733212 0.0631316998782636 1.54950764705914 0.121602898368581 df.mm.exp6 0.111513056273395 0.0631316998782636 1.76635599054714 0.0776670035106404 . df.mm.exp7 -0.134718876771030 0.0631316998782636 -2.13393393542084 0.0331115118327841 * df.mm.exp8 0.0279672430900297 0.0631316998782636 0.442998416706009 0.657870797335325 df.mm.trans1:exp2 -0.240478693360082 0.0579555172047821 -4.14936670326599 3.64231796891099e-05 *** df.mm.trans2:exp2 0.0416085886619841 0.0410786622756008 1.01290028343250 0.311373524802111 df.mm.trans1:exp3 0.193486568060195 0.0579555172047821 3.33853578385854 0.000875943540643042 *** df.mm.trans2:exp3 0.0735963311333835 0.0410786622756008 1.79159512643373 0.0735257031860487 . df.mm.trans1:exp4 0.134579339276181 0.0579555172047822 2.32211436920929 0.0204438003505431 * df.mm.trans2:exp4 0.0530867584564582 0.0410786622756008 1.29231955267418 0.196570294787518 df.mm.trans1:exp5 -0.0629784604199369 0.0579555172047822 -1.08666893951453 0.277467153511786 df.mm.trans2:exp5 -0.0361789634300159 0.0410786622756008 -0.880723992112687 0.378696741971581 df.mm.trans1:exp6 -0.0916928849305059 0.0579555172047822 -1.58212521176396 0.113963938664074 df.mm.trans2:exp6 -0.0403012437574897 0.0410786622756008 -0.98107488231006 0.326813175485871 df.mm.trans1:exp7 0.121571645332245 0.0579555172047821 2.09767164880401 0.0362058961270763 * df.mm.trans2:exp7 0.0723193643696916 0.0410786622756008 1.76050923675396 0.0786530147227936 . df.mm.trans1:exp8 -0.0460615404207079 0.0579555172047821 -0.794774037784054 0.426949487563794 df.mm.trans2:exp8 0.0373477848262051 0.0410786622756008 0.909177240866197 0.363494170779829 df.mm.trans1:probe2 0.272227226761885 0.0415164578977387 6.55709182687073 9.12560190779114e-11 *** df.mm.trans1:probe3 -0.0124235897893442 0.0415164578977387 -0.299244936067171 0.764820604615713 df.mm.trans1:probe4 0.0708323702338464 0.0415164578977387 1.70612749306112 0.0883211135243663 . df.mm.trans1:probe5 0.150158550970362 0.0415164578977387 3.61684398366126 0.000314400304768839 *** df.mm.trans1:probe6 0.138071044271449 0.0415164578977387 3.32569422496347 0.000916802556719084 *** df.mm.trans1:probe7 0.0472307240584239 0.0415164578977387 1.13763857636315 0.255566978420808 df.mm.trans1:probe8 -0.0370465897125539 0.0415164578977387 -0.892335030213927 0.372446209931616 df.mm.trans1:probe9 0.0991662418343037 0.0415164578977387 2.38860073464275 0.0171129669604357 * df.mm.trans1:probe10 0.00170250892448948 0.0415164578977387 0.0410080486317743 0.967298357557182 df.mm.trans1:probe11 0.743630484253206 0.0415164578977387 17.9117035004499 8.50109127580177e-62 *** df.mm.trans1:probe12 0.299555033229171 0.0415164578977387 7.215332145315 1.12271040377312e-12 *** df.mm.trans1:probe13 0.222898500686592 0.0415164578977387 5.36891902569397 1.00282601976776e-07 *** df.mm.trans1:probe14 0.158270395731150 0.0415164578977387 3.81223263605468 0.000146866587945307 *** df.mm.trans1:probe15 0.817794354905036 0.0415164578977387 19.6980762886705 2.28705659032569e-72 *** df.mm.trans1:probe16 0.184989005615437 0.0415164578977387 4.45579933796599 9.38457706125944e-06 *** df.mm.trans1:probe17 0.505961253168485 0.0415164578977387 12.1870043541466 8.76336477171218e-32 *** df.mm.trans1:probe18 0.325525094821961 0.0415164578977387 7.84086868932264 1.23280335414089e-14 *** df.mm.trans1:probe19 0.304610700299322 0.0415164578977387 7.33710715518226 4.78334404660328e-13 *** df.mm.trans1:probe20 0.307024653457161 0.0415164578977387 7.39525164245487 3.16912205077112e-13 *** df.mm.trans1:probe21 0.229396578761745 0.0415164578977387 5.52543714896832 4.27696276547043e-08 *** df.mm.trans1:probe22 0.278999335477778 0.0415164578977387 6.72021048050378 3.17452092727160e-11 *** df.mm.trans2:probe2 -0.0419588101073864 0.0415164578977387 -1.01065486392739 0.312446796923969 df.mm.trans2:probe3 -0.0796549783923747 0.0415164578977387 -1.91863618492158 0.0553389106318681 . df.mm.trans2:probe4 -0.145206602377836 0.0415164578977387 -3.49756722347319 0.00049194492962891 *** df.mm.trans2:probe5 0.248349029275853 0.0415164578977387 5.98194166485913 3.15182792912412e-09 *** df.mm.trans2:probe6 -0.141842286872382 0.0415164578977387 -3.41653151677249 0.000661957562468204 *** df.mm.trans3:probe2 0.809949972838202 0.0415164578977387 19.5091299655965 3.14483790204546e-71 *** df.mm.trans3:probe3 0.034479277412097 0.0415164578977387 0.830496606840224 0.406473060789391 df.mm.trans3:probe4 0.0924959463356664 0.0415164578977387 2.22793443900002 0.0261254680804745 * df.mm.trans3:probe5 0.0815445500801927 0.0415164578977387 1.96414998314763 0.0498127736054773 * df.mm.trans3:probe6 -0.141474379529103 0.0415164578977387 -3.40766979393029 0.000683551773420403 *** df.mm.trans3:probe7 -0.0276837242853776 0.0415164578977387 -0.666813251592098 0.505058257245547 df.mm.trans3:probe8 0.355921602004924 0.0415164578977387 8.57302429030946 4.20939988031221e-17 *** df.mm.trans3:probe9 0.254995993982358 0.0415164578977387 6.14204599560134 1.20986459048883e-09 *** df.mm.trans3:probe10 -0.0291091153758275 0.0415164578977387 -0.701146409154836 0.483388568115692 df.mm.trans3:probe11 0.0151090875630097 0.0415164578977387 0.363930073230853 0.715993664087594 df.mm.trans3:probe12 -0.116734889144182 0.0415164578977387 -2.8117738134529 0.00503144209560452 ** df.mm.trans3:probe13 0.334515847642604 0.0415164578977387 8.05742745362734 2.40163682115733e-15 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92561842376747 0.12286860852239 31.9497263863952 2.16733276173672e-151 *** df.mm.trans1 0.119459048072130 0.105523106714650 1.13206530580231 0.257901312961696 df.mm.trans2 0.184003874338792 0.0926560880928902 1.98588002284667 0.0473424455424967 * df.mm.exp2 0.120275195785856 0.117893031859665 1.02020614695045 0.307898318465551 df.mm.exp3 0.136133721962780 0.117893031859665 1.15472237684775 0.248503322352438 df.mm.exp4 -0.000638524269789963 0.117893031859665 -0.00541613240170152 0.995679744325718 df.mm.exp5 0.140495075660096 0.117893031859665 1.19171653696492 0.233679069749725 df.mm.exp6 0.205047073659484 0.117893031859665 1.73926372428493 0.082322126182273 . df.mm.exp7 0.0348904353989495 0.117893031859665 0.295949937401572 0.767334955899886 df.mm.exp8 0.0897596002177874 0.117893031859665 0.761364762631886 0.446633916463645 df.mm.trans1:exp2 -0.103380344537939 0.108226954912380 -0.955218084271475 0.33971779774789 df.mm.trans2:exp2 -0.0554451707490124 0.0767108766237618 -0.722781086454667 0.469997686443953 df.mm.trans1:exp3 -0.107970106343527 0.108226954912380 -0.99762675971932 0.318722126714609 df.mm.trans2:exp3 -0.0952485148072449 0.0767108766237617 -1.24165592937235 0.214679307536621 df.mm.trans1:exp4 0.0212679944072618 0.108226954912380 0.196512915146511 0.844252031067955 df.mm.trans2:exp4 -0.0379753769480426 0.0767108766237618 -0.495045534863298 0.620685928862681 df.mm.trans1:exp5 -0.162263671873742 0.108226954912380 -1.49929074513008 0.134140533113923 df.mm.trans2:exp5 -0.0767601442092994 0.0767108766237618 -1.00064225032624 0.317262334500583 df.mm.trans1:exp6 -0.146516350642276 0.108226954912380 -1.35378797972182 0.176135781002994 df.mm.trans2:exp6 -0.0869102386230856 0.0767108766237618 -1.13295848578746 0.257526215985978 df.mm.trans1:exp7 -0.0145734971121493 0.108226954912380 -0.134656815614446 0.892912607259413 df.mm.trans2:exp7 -0.0236377871607039 0.0767108766237618 -0.308141272803314 0.758044437408044 df.mm.trans1:exp8 -0.0430948417078562 0.108226954912380 -0.39818954291697 0.69058265825536 df.mm.trans2:exp8 -0.0396439863306571 0.0767108766237618 -0.516797461787539 0.605421489872466 df.mm.trans1:probe2 0.0638926684273661 0.077528422378561 0.824119290282817 0.410084914296522 df.mm.trans1:probe3 -0.000857898998289359 0.077528422378561 -0.0110656062895276 0.99117349758858 df.mm.trans1:probe4 0.0937881445137249 0.077528422378561 1.20972595128751 0.226694145374896 df.mm.trans1:probe5 0.00109737972718368 0.077528422378561 0.0141545473713539 0.988709744464696 df.mm.trans1:probe6 0.0973989818110214 0.077528422378561 1.25630031958390 0.209325325865609 df.mm.trans1:probe7 -0.0882368859920929 0.077528422378561 -1.13812306874044 0.255364748495566 df.mm.trans1:probe8 0.197413627405484 0.077528422378561 2.54633876646606 0.0110475768378778 * df.mm.trans1:probe9 0.0613951943264605 0.077528422378561 0.791905632061954 0.42861939110239 df.mm.trans1:probe10 -0.0242132790360223 0.077528422378561 -0.312314868446466 0.754871859858293 df.mm.trans1:probe11 -0.0306764929059559 0.077528422378561 -0.395680602865445 0.69243220428345 df.mm.trans1:probe12 0.0821644476679068 0.077528422378561 1.05979775090365 0.289514170490489 df.mm.trans1:probe13 -0.0350214939457595 0.077528422378561 -0.451724578822901 0.651573649559368 df.mm.trans1:probe14 0.0331439744318622 0.077528422378561 0.42750740199542 0.669109619043103 df.mm.trans1:probe15 0.0568113110259141 0.077528422378561 0.732780434361375 0.463878605619292 df.mm.trans1:probe16 -0.01374992032285 0.077528422378561 -0.177353284137667 0.859269853028457 df.mm.trans1:probe17 0.115145664809059 0.077528422378561 1.48520582873231 0.137831121392234 df.mm.trans1:probe18 0.0372858296139205 0.077528422378561 0.480931102039697 0.630679569998315 df.mm.trans1:probe19 -0.0712259415950566 0.077528422378561 -0.91870748055816 0.358488868891549 df.mm.trans1:probe20 -0.0592333002273351 0.077528422378561 -0.7640204509529 0.445050527283705 df.mm.trans1:probe21 0.0471402381238476 0.077528422378561 0.608038144948547 0.543311917895911 df.mm.trans1:probe22 0.0946001892604487 0.077528422378561 1.22020010672381 0.222701023815556 df.mm.trans2:probe2 -0.0295371225056199 0.077528422378561 -0.380984438989279 0.703302562670305 df.mm.trans2:probe3 -0.065608020971874 0.077528422378561 -0.846244757200382 0.397635794972503 df.mm.trans2:probe4 -0.108288582615894 0.077528422378561 -1.39675978555497 0.162821925043018 df.mm.trans2:probe5 -0.147761563521526 0.077528422378561 -1.90590184848630 0.0569736903941157 . df.mm.trans2:probe6 -0.0557062253130807 0.077528422378561 -0.718526491369509 0.472614761585985 df.mm.trans3:probe2 -0.0703017374706541 0.077528422378561 -0.906786637903968 0.364756558284723 df.mm.trans3:probe3 -0.104899369033686 0.077528422378561 -1.35304402972985 0.176373231721951 df.mm.trans3:probe4 -0.00151094912739961 0.077528422378561 -0.0194889703807186 0.984455252327028 df.mm.trans3:probe5 0.0234635154954550 0.077528422378561 0.302644046861754 0.762229406804406 df.mm.trans3:probe6 -0.109437972256134 0.077528422378561 -1.41158518255102 0.158409508715279 df.mm.trans3:probe7 -0.188654364848089 0.077528422378561 -2.43335745859647 0.0151486427176697 * df.mm.trans3:probe8 -0.0911912933759464 0.077528422378561 -1.17623047881294 0.239806391270382 df.mm.trans3:probe9 -0.136638865642176 0.077528422378561 -1.76243578097058 0.0783269965854963 . df.mm.trans3:probe10 0.0174071411720101 0.077528422378561 0.224525930464745 0.822397839367605 df.mm.trans3:probe11 -0.0327647717726501 0.077528422378561 -0.422616258237065 0.672673802393233 df.mm.trans3:probe12 -0.0523141393025697 0.077528422378561 -0.674773685541113 0.499988725024509 df.mm.trans3:probe13 -0.0229396658511660 0.077528422378561 -0.295887174630675 0.767382872896262