fitVsDatCorrelation=0.765869027484219 cont.fitVsDatCorrelation=0.229026958452449 fstatistic=10004.3912324516,60,876 cont.fstatistic=4357.00338276376,60,876 residuals=-0.474759069752923,-0.0888670077991879,-0.0074088924771706,0.0776038545233438,0.888715856827324 cont.residuals=-0.613626780793446,-0.156196038747481,-0.0218387006081243,0.132518542738304,0.93836442357196 predictedValues: Include Exclude Both Lung 56.453988769614 76.2139623046331 47.1516425933518 cerebhem 69.1610093857424 76.0609362752037 43.8425091924828 cortex 54.5829322275293 67.2123715067596 46.9758847348122 heart 52.8478084981043 72.0116043479093 47.4046009768647 kidney 56.5969888393546 86.6394740202097 50.0837871037198 liver 57.735014506931 75.385726667823 46.9737570720363 stomach 57.0291353954225 61.3521588071415 48.7578877285405 testicle 57.848044957111 75.6865016608234 47.9250519587203 diffExp=-19.7599735350191,-6.89992688946131,-12.6294392792303,-19.163795849805,-30.0424851808552,-17.6507121608920,-4.323023411719,-17.8384567037124 diffExpScore=0.9922665152498 diffExp1.5=0,0,0,0,-1,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,-1,0,0,0 diffExp1.4Score=0.5 diffExp1.3=-1,0,0,-1,-1,-1,0,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,0,-1,-1,-1,-1,0,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 58.8053195989386 55.8198495150517 54.1112229226549 cerebhem 58.2096995395423 62.5857240380374 58.0958717360581 cortex 60.4434037301582 60.248408681628 53.8622374004495 heart 61.0733582810893 54.5357280904697 60.3254944818614 kidney 56.5080049825265 59.7493475170668 53.9034966653331 liver 59.8458550425739 59.878964480822 57.0446901746234 stomach 59.1397858867672 66.1264031691224 64.1083875939603 testicle 59.1481456938198 64.2988234239695 56.2551305225332 cont.diffExp=2.98547008388692,-4.37602449849506,0.194995048530231,6.53763019061966,-3.24134253454034,-0.033109438248097,-6.98661728235518,-5.1506777301497 cont.diffExpScore=2.66546793043871 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=0.183825444054200 cont.tran.correlation=-0.27466688188688 tran.covariance=0.00160066094026268 cont.tran.covariance=-0.000437098666660008 tran.mean=65.8011036356445 cont.tran.mean=59.776051354474 weightedLogRatios: wLogRatio Lung -1.25554229890068 cerebhem -0.407396603784842 cortex -0.854146668326907 heart -1.27542995033435 kidney -1.80916167134153 liver -1.11749730481481 stomach -0.298124781541509 testicle -1.12678286002638 cont.weightedLogRatios: wLogRatio Lung 0.210921512478898 cerebhem -0.297210682308387 cortex 0.0132485921014255 heart 0.459158583959206 kidney -0.226577030528421 liver -0.00226328071984307 stomach -0.461813717955588 testicle -0.344153632024443 varWeightedLogRatios=0.241181641239169 cont.varWeightedLogRatios=0.0963257609554898 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.70080108594757 0.0852822991453114 55.1204779075895 5.26420346194323e-287 *** df.mm.trans1 -0.653653766622186 0.0770770682961204 -8.48052191231418 9.45469801070555e-17 *** df.mm.trans2 -0.342317802203015 0.07023336859642 -4.87400517793851 1.29800337560202e-06 *** df.mm.exp2 0.273766411037827 0.0960070660601526 2.85152356250841 0.00445331022032218 ** df.mm.exp3 -0.155657592071701 0.0960070660601525 -1.62131391427143 0.105310186086650 df.mm.exp4 -0.128077548969665 0.0960070660601526 -1.33404294314565 0.182536488934607 df.mm.exp5 0.0704121893800267 0.0960070660601526 0.733406323821108 0.463506864949991 df.mm.exp6 0.0152909161869820 0.0960070660601526 0.159268654011379 0.87349392846631 df.mm.exp7 -0.240276120957022 0.0960070660601526 -2.50269204983807 0.0125064266721898 * df.mm.exp8 0.00117933539017703 0.0960070660601526 0.0122838394982107 0.990201957521293 df.mm.trans1:exp2 -0.0707551037165046 0.0929584419923015 -0.761147693529161 0.446773698040361 df.mm.trans2:exp2 -0.275776277172951 0.0796048538360976 -3.46431484869956 0.000557371326063418 *** df.mm.trans1:exp3 0.121952881065861 0.0929584419923015 1.31190754117803 0.189895018923709 df.mm.trans2:exp3 0.0299702442076154 0.0796048538360976 0.376487648219575 0.706645565302769 df.mm.trans1:exp4 0.0620678456530943 0.0929584419923015 0.667694556006377 0.504504446249346 df.mm.trans2:exp4 0.0713601482458587 0.0796048538360977 0.896429612103623 0.370269631572766 df.mm.trans1:exp5 -0.0678823546270248 0.0929584419923015 -0.730244108788383 0.465436224220765 df.mm.trans2:exp5 0.0577986641768709 0.0796048538360977 0.726069597412683 0.467990047827522 df.mm.trans1:exp6 0.00714696216152911 0.0929584419923015 0.0768834116445389 0.938733849940593 df.mm.trans2:exp6 -0.0262176388324891 0.0796048538360977 -0.329347239132803 0.74197197077113 df.mm.trans1:exp7 0.2504124571656 0.0929584419923016 2.69381082340363 0.00719911708210807 ** df.mm.trans2:exp7 0.0233618016234392 0.0796048538360977 0.29347207485036 0.769230852504277 df.mm.trans1:exp8 0.023214374318658 0.0929584419923016 0.249728521919295 0.80285584736135 df.mm.trans2:exp8 -0.00812418269386922 0.0796048538360977 -0.102056373479392 0.918735284707214 df.mm.trans1:probe2 -0.0135620784814127 0.0464792209961508 -0.291787990219025 0.770517811891432 df.mm.trans1:probe3 0.0527313677759541 0.0464792209961508 1.13451487881695 0.256888983633882 df.mm.trans1:probe4 -0.0253168942168923 0.0464792209961508 -0.544692739557511 0.586103489500255 df.mm.trans1:probe5 -0.147566984677691 0.0464792209961508 -3.17490227923381 0.00155124004636924 ** df.mm.trans1:probe6 0.00726904342142195 0.0464792209961508 0.156393400440682 0.875758973031307 df.mm.trans1:probe7 -0.102765809455963 0.0464792209961508 -2.21100541819479 0.0272930844871218 * df.mm.trans1:probe8 0.0341397340239132 0.0464792209961508 0.734516054534144 0.462830845539964 df.mm.trans1:probe9 -0.0182344270959482 0.0464792209961508 -0.392313526456443 0.694922001495844 df.mm.trans1:probe10 0.299432923515407 0.0464792209961508 6.44229651654887 1.93922194511650e-10 *** df.mm.trans1:probe11 -0.29522943539159 0.0464792209961508 -6.35185850933344 3.41402847686172e-10 *** df.mm.trans1:probe12 -0.221014804187016 0.0464792209961508 -4.75513142109932 2.31793787129870e-06 *** df.mm.trans1:probe13 -0.246179060919960 0.0464792209961508 -5.29654016663376 1.49349079663199e-07 *** df.mm.trans1:probe14 0.008732261744775 0.0464792209961508 0.187874528824357 0.851018519364526 df.mm.trans1:probe15 -0.137185727671927 0.0464792209961508 -2.95154963297014 0.00324616814183329 ** df.mm.trans1:probe16 -0.170311965799685 0.0464792209961508 -3.66426033288703 0.000262961476062283 *** df.mm.trans1:probe17 0.166941669789818 0.0464792209961508 3.59174844612055 0.000346751276964408 *** df.mm.trans1:probe18 -0.0943375967618887 0.0464792209961508 -2.02967250181971 0.0426916630613817 * df.mm.trans1:probe19 0.183825115790872 0.0464792209961508 3.95499562710175 8.27174689694593e-05 *** df.mm.trans1:probe20 0.163047359020381 0.0464792209961508 3.50796238675953 0.000474525191447625 *** df.mm.trans1:probe21 -0.00707773248635653 0.0464792209961508 -0.15227734748271 0.879003255207529 df.mm.trans1:probe22 0.0796067641017999 0.0464792209961508 1.71273877650386 0.0871144715125792 . df.mm.trans1:probe23 0.0424654869015647 0.0464792209961508 0.91364454892825 0.361155041177984 df.mm.trans1:probe24 -0.0379537913420044 0.0464792209961508 -0.816575461648713 0.414393084248826 df.mm.trans1:probe25 -0.0251625909083124 0.0464792209961508 -0.541372905333252 0.588388228941642 df.mm.trans1:probe26 -0.140852126495928 0.0464792209961508 -3.03043216898134 0.00251382054410766 ** df.mm.trans1:probe27 -0.0912338371718192 0.0464792209961508 -1.96289514360352 0.0499744507410356 * df.mm.trans1:probe28 0.0269790700872668 0.0464792209961508 0.580454437683048 0.561757485184247 df.mm.trans1:probe29 0.000345430241731309 0.0464792209961508 0.00743192838279102 0.994071925776653 df.mm.trans1:probe30 0.255663393564505 0.0464792209961508 5.50059549375147 4.96902632547883e-08 *** df.mm.trans2:probe2 -0.184130656343432 0.0464792209961508 -3.96156932919941 8.0511533138476e-05 *** df.mm.trans2:probe3 0.129861775692869 0.0464792209961508 2.79397487543139 0.00532008824291774 ** df.mm.trans2:probe4 0.105148905906031 0.0464792209961508 2.26227771577193 0.0239244956113844 * df.mm.trans2:probe5 -0.149958291881799 0.0464792209961508 -3.22635123110643 0.00130031199714913 ** df.mm.trans2:probe6 -0.12536918280784 0.0464792209961508 -2.69731678201368 0.00712438063777434 ** df.mm.trans3:probe2 0.170803569420573 0.0464792209961508 3.67483718013946 0.000252463274133140 *** df.mm.trans3:probe3 0.274814417462832 0.0464792209961508 5.91262959173931 4.82156285489612e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.23516969203998 0.129107217594132 32.8035083627460 1.37813890274330e-154 *** df.mm.trans1 -0.0998592428722458 0.116685477851263 -0.855798379636707 0.392343328141461 df.mm.trans2 -0.184103007584612 0.106324933692239 -1.73151302513392 0.0837123677109589 . df.mm.exp2 0.0331743540043834 0.145343234089906 0.228248354401296 0.819506439508854 df.mm.exp3 0.108433743895932 0.145343234089906 0.746052917942213 0.455835600792128 df.mm.exp4 -0.094143264171911 0.145343234089906 -0.647730627169588 0.51732890965353 df.mm.exp5 0.0320249904372755 0.145343234089906 0.220340428213298 0.82565737618592 df.mm.exp6 0.0349422009030273 0.145343234089906 0.240411609950918 0.810067428160445 df.mm.exp7 0.00557655649347893 0.145343234089906 0.0383681877481095 0.969402866665617 df.mm.exp8 0.108369087036566 0.145343234089906 0.745608061600798 0.456104225150539 df.mm.trans1:exp2 -0.0433546745801747 0.140727981278533 -0.308074301828902 0.758099032542788 df.mm.trans2:exp2 0.0812333153756717 0.120512243323252 0.674066909183476 0.500446602926216 df.mm.trans1:exp3 -0.0809586124062034 0.140727981278533 -0.575284400946304 0.56524670104963 df.mm.trans2:exp3 -0.0320871160050965 0.120512243323252 -0.266256067601601 0.790104661022595 df.mm.trans1:exp4 0.131986680397129 0.140727981278533 0.937885125601973 0.348561953824119 df.mm.trans2:exp4 0.0708697801397518 0.120512243323252 0.588071204928584 0.556636070925645 df.mm.trans1:exp5 -0.0718750013962148 0.140727981278533 -0.510737102481117 0.609663838544968 df.mm.trans2:exp5 0.0360037478903236 0.120512243323252 0.298755934645993 0.765197130811518 df.mm.trans1:exp6 -0.0174023472426914 0.140727981278533 -0.123659467609701 0.901613272260352 df.mm.trans2:exp6 0.0352535330514847 0.120512243323252 0.292530717870079 0.76995014914829 df.mm.trans1:exp7 9.50172951667271e-05 0.140727981278533 0.0006751840984535 0.999461434798092 df.mm.trans2:exp7 0.163862021200825 0.120512243323252 1.35971264563796 0.174270737158438 df.mm.trans1:exp8 -0.102556166502655 0.140727981278533 -0.728754619876718 0.466346551348252 df.mm.trans2:exp8 0.0330427136041176 0.120512243323252 0.274185532464834 0.78400668702268 df.mm.trans1:probe2 -0.078982256757037 0.0703639906392664 -1.12248120152755 0.26196545400461 df.mm.trans1:probe3 -0.0425618224452951 0.0703639906392664 -0.604880735993158 0.5454148913672 df.mm.trans1:probe4 -0.0297708362322834 0.0703639906392664 -0.423097609470573 0.672327892687269 df.mm.trans1:probe5 -0.0764400712856485 0.0703639906392664 -1.08635213254933 0.277622068987566 df.mm.trans1:probe6 -0.125602430062693 0.0703639906392664 -1.78503846813659 0.0746010047906306 . df.mm.trans1:probe7 0.0158733603617602 0.0703639906392664 0.225589256913210 0.821573515511278 df.mm.trans1:probe8 -0.0954934306625201 0.0703639906392664 -1.35713494636886 0.175087898614747 df.mm.trans1:probe9 -0.0448541116223357 0.0703639906392664 -0.637458325129515 0.52399291704716 df.mm.trans1:probe10 -0.0361718370686356 0.0703639906392664 -0.514067447568132 0.607334485345166 df.mm.trans1:probe11 -0.0818973689749065 0.0703639906392664 -1.16391023634188 0.244777295793006 df.mm.trans1:probe12 -0.0216125447120762 0.0703639906392664 -0.307153481713065 0.758799552829072 df.mm.trans1:probe13 -0.0608421666207454 0.0703639906392664 -0.864677600971549 0.387452480795059 df.mm.trans1:probe14 -0.104992590760308 0.0703639906392664 -1.49213524995437 0.136023723194995 df.mm.trans1:probe15 -0.08382898205466 0.0703639906392664 -1.19136196359903 0.233834202145358 df.mm.trans1:probe16 -0.00477980632831444 0.0703639906392664 -0.0679297220764378 0.945857079998305 df.mm.trans1:probe17 -0.119142059330521 0.0703639906392664 -1.69322487607794 0.090768204284871 . df.mm.trans1:probe18 -0.0901051687979738 0.0703639906392664 -1.28055796692820 0.200687921549265 df.mm.trans1:probe19 -0.00306420561248210 0.0703639906392664 -0.0435479225189387 0.965274688215642 df.mm.trans1:probe20 -0.0582819861563504 0.0703639906392664 -0.828292790486307 0.407730310383675 df.mm.trans1:probe21 -0.0884297462309763 0.0703639906392664 -1.25674717177892 0.209180202720072 df.mm.trans1:probe22 -0.165056256768519 0.0703639906392664 -2.34574894443821 0.0192110674200010 * df.mm.trans1:probe23 -0.0756291524084273 0.0703639906392664 -1.07482750368940 0.282747917845603 df.mm.trans1:probe24 -0.113627449336736 0.0703639906392664 -1.61485226043059 0.106702789819384 df.mm.trans1:probe25 -0.101039545547624 0.0703639906392664 -1.43595530369534 0.151372048140232 df.mm.trans1:probe26 -0.0231538891973991 0.0703639906392664 -0.32905878400362 0.74218991015862 df.mm.trans1:probe27 -0.0165287239525777 0.0703639906392664 -0.234903162859468 0.814338791121418 df.mm.trans1:probe28 -0.0967156034585473 0.0703639906392664 -1.37450423973787 0.169636705115674 df.mm.trans1:probe29 -0.0706169224212044 0.0703639906392664 -1.00359461962916 0.315851183090461 df.mm.trans1:probe30 -0.122230652078347 0.0703639906392664 -1.73711938404665 0.0827175908232936 . df.mm.trans2:probe2 -0.0310207985877759 0.0703639906392664 -0.440861842910667 0.659421795678785 df.mm.trans2:probe3 -0.0782099180913368 0.0703639906392664 -1.11150486748675 0.266656058680495 df.mm.trans2:probe4 -0.0382727002790216 0.0703639906392664 -0.543924526328153 0.586631813960788 df.mm.trans2:probe5 -0.0310702298252982 0.0703639906392664 -0.441564350501171 0.658913458653205 df.mm.trans2:probe6 -0.0818607234434525 0.0703639906392664 -1.16338943683746 0.244988325387436 df.mm.trans3:probe2 0.111988365982092 0.0703639906392664 1.59155791143542 0.111844845938905 df.mm.trans3:probe3 0.00751414549853194 0.0703639906392664 0.106789643825839 0.914980314197702