fitVsDatCorrelation=0.863041750559789 cont.fitVsDatCorrelation=0.271861492311247 fstatistic=12082.4284410346,46,554 cont.fstatistic=3320.25485921553,46,554 residuals=-0.44022849828479,-0.0740382672600129,-9.78538830002824e-05,0.0721298841336577,0.537913528641442 cont.residuals=-0.747517872880973,-0.170412109439299,-0.031063420861424,0.158033540101083,0.755443999475474 predictedValues: Include Exclude Both Lung 49.1587809668051 87.8949619946976 73.1450677467778 cerebhem 51.9994814664328 45.2583581603113 62.4583098361943 cortex 59.9976701616829 62.5250257693462 71.0431950105053 heart 50.6428596710745 71.7463174727845 58.9470044662178 kidney 50.0232556117625 96.5367524697344 74.3580069718804 liver 52.1088114012775 59.0020691238598 52.3808655503506 stomach 49.3049066223549 94.3544626622036 72.3463890780367 testicle 48.0155868729236 68.0776757837414 63.721081427514 diffExp=-38.7361810278925,6.74112330612153,-2.52735560766330,-21.1034578017100,-46.5134968579719,-6.89325772258233,-45.0495560398488,-20.0620889108178 diffExpScore=1.0712683695849 diffExp1.5=-1,0,0,0,-1,0,-1,0 diffExp1.5Score=0.75 diffExp1.4=-1,0,0,-1,-1,0,-1,-1 diffExp1.4Score=0.833333333333333 diffExp1.3=-1,0,0,-1,-1,0,-1,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,0,0,-1,-1,0,-1,-1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 55.9248388170857 65.0399911566614 55.029091870163 cerebhem 58.2657616550503 55.4228560915086 59.3478930169225 cortex 57.6845319062328 52.7826843570705 57.1099448977973 heart 54.0413630671027 59.0281376742166 52.7553911998377 kidney 58.9964739100573 53.8837401372878 51.308386150418 liver 56.6856610887616 55.6017174338098 67.5441740348837 stomach 54.9773984139459 57.7717820539597 52.9008670270979 testicle 57.6921496731203 56.1117456076495 60.8122136302816 cont.diffExp=-9.11515233957568,2.84290556354168,4.90184754916232,-4.9867746071139,5.11273377276952,1.08394365495178,-2.79438364001386,1.58040406547080 cont.diffExpScore=13.6527576840668 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=-0.437993920640708 cont.tran.correlation=-0.606142516789102 tran.covariance=-0.00776850392360503 cont.tran.covariance=-0.00121049353147401 tran.mean=62.290436013187 cont.tran.mean=56.86942706522 weightedLogRatios: wLogRatio Lung -2.43219716188043 cerebhem 0.538975354773451 cortex -0.169787037565389 heart -1.42782725811940 kidney -2.78832051287946 liver -0.498873708158848 stomach -2.74057681769475 testicle -1.41258487962581 cont.weightedLogRatios: wLogRatio Lung -0.618999516686722 cerebhem 0.202091345501163 cortex 0.356163592196091 heart -0.356048592380621 kidney 0.365510305335664 liver 0.0777668390833635 stomach -0.199885368096924 testicle 0.112249090833049 varWeightedLogRatios=1.55242707201826 cont.varWeightedLogRatios=0.124193778703991 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.96591825653045 0.0715127533315504 55.4574963453511 2.74999032616671e-228 *** df.mm.trans1 -0.127356425732918 0.0636741778200231 -2.00012674043306 0.0459742351092973 * df.mm.trans2 0.592767260845782 0.0593033358878831 9.99551293314171 9.71377367484476e-22 *** df.mm.exp2 -0.449631457365077 0.0814522819475006 -5.52018245056514 5.20946289880657e-08 *** df.mm.exp3 -0.112168729106771 0.0814522819475006 -1.37710971902627 0.169034492699454 df.mm.exp4 0.0425426885090025 0.0814522819475006 0.522301984570831 0.601668868321521 df.mm.exp5 0.0947671635453387 0.0814522819475006 1.16346849074677 0.245140333246432 df.mm.exp6 -0.00638806731787632 0.0814522819475006 -0.0784271129689613 0.937516625099488 df.mm.exp7 0.084863352503531 0.0814522819475006 1.04187814600736 0.297922623996492 df.mm.exp8 -0.141093703904796 0.0814522819475006 -1.73222530457449 0.0837900340104653 . df.mm.trans1:exp2 0.50580971696487 0.076793947877595 6.58658306994582 1.04747330083327e-10 *** df.mm.trans2:exp2 -0.214123666678202 0.0683279158976247 -3.13376551684999 0.00181709971591063 ** df.mm.trans1:exp3 0.311418972803776 0.076793947877595 4.05525411065152 5.72610948386176e-05 *** df.mm.trans2:exp3 -0.228406869842349 0.0683279158976247 -3.34280457470076 0.000885353696239225 *** df.mm.trans1:exp4 -0.0127999288021955 0.076793947877595 -0.166678874520135 0.867683544826222 df.mm.trans2:exp4 -0.245548647464937 0.0683279158976247 -3.59367974625249 0.000355094712736877 *** df.mm.trans1:exp5 -0.0773346411456574 0.076793947877595 -1.00704083177133 0.314354787822498 df.mm.trans2:exp5 -0.000985860955858247 0.0683279158976247 -0.0144283773755862 0.988493414265556 df.mm.trans1:exp6 0.0646666394292621 0.076793947877595 0.842079893227222 0.400106597389965 df.mm.trans2:exp6 -0.39218190735281 0.0683279158976247 -5.73970246568641 1.56322101756729e-08 *** df.mm.trans1:exp7 -0.0818952377313831 0.076793947877595 -1.06642827976391 0.286694601558994 df.mm.trans2:exp7 -0.0139472706484671 0.0683279158976247 -0.204122582479527 0.838332669544192 df.mm.trans1:exp8 0.117563901487137 0.076793947877595 1.53090060787768 0.126364751081469 df.mm.trans2:exp8 -0.114399439757009 0.0683279158976247 -1.67427087822220 0.0946417765836033 . df.mm.trans1:probe2 0.00595385075649818 0.0383969739387975 0.155060416114777 0.8768301646819 df.mm.trans1:probe3 -0.00929074243552462 0.0383969739387975 -0.241965485361776 0.808896447542319 df.mm.trans1:probe4 0.0916915131955963 0.0383969739387975 2.38798800503777 0.0172749348796995 * df.mm.trans1:probe5 -0.148806297961635 0.0383969739387975 -3.8754694106578 0.000119159571746174 *** df.mm.trans1:probe6 -0.0267367692096773 0.0383969739387975 -0.696324904464974 0.486517414581921 df.mm.trans1:probe7 -0.0683014829696637 0.0383969739387975 -1.77882463025686 0.0758167730985307 . df.mm.trans1:probe8 0.0261415684383614 0.0383969739387975 0.68082366282378 0.496267525311681 df.mm.trans1:probe9 0.0480250633751538 0.0383969739387975 1.25075125585946 0.2115532184039 df.mm.trans1:probe10 -0.0445685460291136 0.0383969739387975 -1.16073068935467 0.246251354085377 df.mm.trans1:probe11 0.123221275224446 0.0383969739387975 3.20914026769018 0.00140834377062015 ** df.mm.trans1:probe12 0.294414721747698 0.0383969739387975 7.66765428486571 7.91059956711401e-14 *** df.mm.trans1:probe13 0.090891959072858 0.0383969739387975 2.36716464213389 0.0182676892411085 * df.mm.trans1:probe14 0.264411983349777 0.0383969739387975 6.886271396575 1.55844808186695e-11 *** df.mm.trans1:probe15 0.214749782219633 0.0383969739387975 5.59288298504803 3.51198001722304e-08 *** df.mm.trans1:probe16 0.211581591590898 0.0383969739387975 5.51037151855108 5.49234671396079e-08 *** df.mm.trans2:probe2 -0.203096640943633 0.0383969739387975 -5.28939184810128 1.76971097019591e-07 *** df.mm.trans2:probe3 -0.177265812430313 0.0383969739387975 -4.61666100856967 4.85163482746394e-06 *** df.mm.trans2:probe4 -0.239391172259098 0.0383969739387975 -6.23463642318984 8.9835884249935e-10 *** df.mm.trans2:probe5 -0.0391386297652877 0.0383969739387975 -1.01931547594538 0.308498138817142 df.mm.trans2:probe6 -0.0839950102711616 0.0383969739387975 -2.18754244553346 0.0291201307929566 * df.mm.trans3:probe2 -0.122622821829792 0.0383969739387975 -3.19355431564075 0.00148515244855149 ** df.mm.trans3:probe3 -0.294030357216261 0.0383969739387975 -7.65764400301252 8.48838217039818e-14 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.13177369792200 0.136240119625990 30.3271437904243 8.54824171795055e-120 *** df.mm.trans1 -0.151711941653173 0.121306720817575 -1.25064745490336 0.211591074818912 df.mm.trans2 0.0632226494080745 0.112979758143655 0.559592713304326 0.575983442458094 df.mm.exp2 -0.194558869782101 0.155176078662342 -1.25379421531495 0.210445625639266 df.mm.exp3 -0.214954985029296 0.155176078662342 -1.38523274258677 0.166538803233117 df.mm.exp4 -0.0890509599804146 0.155176078662342 -0.573870410620355 0.56628839945156 df.mm.exp5 -0.0646967344905736 0.155176078662342 -0.416924664215491 0.676895012406146 df.mm.exp6 -0.348195402233770 0.155176078662342 -2.24387292961199 0.0252347344727227 * df.mm.exp7 -0.0961460751308672 0.155176078662342 -0.619593406146562 0.535780240114654 df.mm.exp8 -0.216473347107215 0.155176078662342 -1.39501751154734 0.163569568111957 df.mm.trans1:exp2 0.235564886733097 0.146301410000105 1.61013408369015 0.107938192632875 df.mm.trans2:exp2 0.0345486140106903 0.130172633579470 0.265406123089601 0.790795348603455 df.mm.trans1:exp3 0.245935419476747 0.146301410000105 1.68101879179818 0.0933229044642074 . df.mm.trans2:exp3 0.00613584490026704 0.130172633579470 0.0471362123631087 0.962421652769879 df.mm.trans1:exp4 0.0547920708080447 0.146301410000105 0.374514987982721 0.708164517865173 df.mm.trans2:exp4 -0.0079371293385393 0.13017263357947 -0.0609738707767151 0.951402008156885 df.mm.trans1:exp5 0.118165787128299 0.146301410000105 0.807687274703737 0.419617282922147 df.mm.trans2:exp5 -0.123476829588765 0.13017263357947 -0.948562122417098 0.343256949048227 df.mm.trans1:exp6 0.361708064888704 0.146301410000105 2.47234845438908 0.0137220662489809 * df.mm.trans2:exp6 0.191407162838692 0.130172633579470 1.47041015899734 0.142018405869696 df.mm.trans1:exp7 0.0790596127312283 0.146301410000105 0.540388590452897 0.589146248774534 df.mm.trans2:exp7 -0.0223557974294411 0.130172633579470 -0.171739610812997 0.86370495684913 df.mm.trans1:exp8 0.247585831927312 0.146301410000105 1.69229969777553 0.091151123271091 . df.mm.trans2:exp8 0.0688161775696667 0.13017263357947 0.528653186751842 0.597257776743073 df.mm.trans1:probe2 0.00346659901739529 0.0731507050000527 0.0473898237534798 0.96221961763681 df.mm.trans1:probe3 0.162984338752126 0.0731507050000527 2.22806244658898 0.0262770202624032 * df.mm.trans1:probe4 0.153923265641798 0.0731507050000527 2.10419387812718 0.0358115301911578 * df.mm.trans1:probe5 0.0118898289952432 0.0731507050000527 0.162538816204637 0.870940831052278 df.mm.trans1:probe6 0.0465623625652085 0.0731507050000527 0.636526504634165 0.524696213745454 df.mm.trans1:probe7 0.0950786616410025 0.0731507050000527 1.29976411903254 0.194222377323915 df.mm.trans1:probe8 0.00800455998789504 0.0731507050000527 0.109425602772923 0.912904551704317 df.mm.trans1:probe9 0.0276435221897836 0.0731507050000527 0.377898233376749 0.705650937919595 df.mm.trans1:probe10 0.0582366073750478 0.0731507050000527 0.796118197015405 0.426304338518292 df.mm.trans1:probe11 0.0467416289523751 0.0731507050000527 0.638977149329475 0.523101885735109 df.mm.trans1:probe12 0.0591849248761377 0.0731507050000527 0.809082084391327 0.418815265093234 df.mm.trans1:probe13 -0.016837790400377 0.0731507050000527 -0.230179468541894 0.818037282409306 df.mm.trans1:probe14 0.0590435662853662 0.0731507050000527 0.807149654748012 0.419926656830718 df.mm.trans1:probe15 0.0715808081544487 0.0731507050000527 0.978538869234372 0.328234985835891 df.mm.trans1:probe16 0.0474876265276658 0.0731507050000527 0.649175240725726 0.516494145496125 df.mm.trans2:probe2 -0.122243328149747 0.0731507050000527 -1.67111619976402 0.0952634718687666 . df.mm.trans2:probe3 -0.0284411060807483 0.0731507050000527 -0.388801530767581 0.697572350968495 df.mm.trans2:probe4 -0.0452330210912967 0.0731507050000527 -0.618353864002598 0.536596237426692 df.mm.trans2:probe5 0.0135425151737159 0.0731507050000527 0.185131710948051 0.853193485096168 df.mm.trans2:probe6 0.00242877762413805 0.0731507050000527 0.0332023816330451 0.973525157503434 df.mm.trans3:probe2 0.00499419653641169 0.0731507050000527 0.0682727054566061 0.945593184821685 df.mm.trans3:probe3 -0.111261448111335 0.0731507050000527 -1.52098941645546 0.128833017506434