chr7.22027_chr7_19806024_19809201_+_0.R fitVsDatCorrelation=0.906800096007871 cont.fitVsDatCorrelation=0.229699388985823 fstatistic=7250.43761024453,45,531 cont.fstatistic=1350.68548665134,45,531 residuals=-0.582274090361825,-0.099726231827266,-0.0106453033737683,0.101412515000541,1.25361324762214 cont.residuals=-0.832967700158679,-0.326702454444089,-0.0859328669500542,0.282890923962186,1.30923365802098 predictedValues: Include Exclude Both chr7.22027_chr7_19806024_19809201_+_0.R.tl.Lung 114.336782195438 153.740973004172 76.03953571488 chr7.22027_chr7_19806024_19809201_+_0.R.tl.cerebhem 117.627085016853 84.255266669519 73.0969230811485 chr7.22027_chr7_19806024_19809201_+_0.R.tl.cortex 103.69568484458 109.782018238515 75.2962575148611 chr7.22027_chr7_19806024_19809201_+_0.R.tl.heart 108.260715210541 121.764639656220 91.219216324858 chr7.22027_chr7_19806024_19809201_+_0.R.tl.kidney 124.090240407692 143.108252134788 76.5155312713748 chr7.22027_chr7_19806024_19809201_+_0.R.tl.liver 114.989988174613 134.445501940990 67.8412822106592 chr7.22027_chr7_19806024_19809201_+_0.R.tl.stomach 117.455128914537 130.757083096255 71.46878749064 chr7.22027_chr7_19806024_19809201_+_0.R.tl.testicle 110.130398247320 127.962466171733 73.9247926169303 diffExp=-39.4041908087340,33.3718183473339,-6.08633339393452,-13.5039244456793,-19.0180117270961,-19.455513766377,-13.3019541817181,-17.8320679244129 diffExpScore=1.68319146996241 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=-1,1,0,0,0,0,0,0 diffExp1.3Score=2 diffExp1.2=-1,1,0,0,0,0,0,0 diffExp1.2Score=2 cont.predictedValues: Include Exclude Both Lung 74.8786399942913 97.7171657540682 96.7784043636451 cerebhem 102.595788584104 98.197549016737 92.457886158695 cortex 81.0303240599256 88.4074001649761 93.1109576374435 heart 92.839032398289 97.2624914371462 88.4311735779834 kidney 85.8002769080421 100.391140131712 89.6853089996825 liver 87.5938402022072 82.4395748903147 97.3673602417544 stomach 93.7773743805166 78.2462695658138 84.9232680991396 testicle 85.1315495389659 81.8650041847725 93.452502598728 cont.diffExp=-22.8385257597769,4.39823956736655,-7.37707610505049,-4.4234590388572,-14.5908632236701,5.15426531189246,15.5311048147028,3.26654535419341 cont.diffExpScore=3.54574487942212 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=-1,0,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=-1,0,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.252118999420479 cont.tran.correlation=0.000903994542210665 tran.covariance=0.00205037725245993 cont.tran.covariance=-0.000332174506063621 tran.mean=119.775138995235 cont.tran.mean=89.2608388257426 weightedLogRatios: wLogRatio Lung -1.44720450817523 cerebhem 1.53510255262519 cortex -0.266358044472087 heart -0.557565671977687 kidney -0.697605078311488 liver -0.753902066886222 stomach -0.517080883649302 testicle -0.71684853258693 cont.weightedLogRatios: wLogRatio Lung -1.18435474051769 cerebhem 0.201941373627397 cortex -0.386726748143453 heart -0.211978206224944 kidney -0.711530388236351 liver 0.269408978423337 stomach 0.805798995997583 testicle 0.173118704687946 varWeightedLogRatios=0.743675996539882 cont.varWeightedLogRatios=0.394254506203127 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.31209908351941 0.0939142976322915 56.5632626495084 6.07924602434348e-227 *** df.mm.trans1 -0.461777292001852 0.0760272336845617 -6.0738405124376 2.38211106915286e-09 *** df.mm.trans2 -0.303931971999561 0.0743426347521415 -4.08825935498347 5.01941193643842e-05 *** df.mm.exp2 -0.533579974577012 0.0986800051271887 -5.4071741675457 9.69318595569354e-08 *** df.mm.exp3 -0.424637286784315 0.0986800051271887 -4.30317455128827 2.00485946613311e-05 *** df.mm.exp4 -0.469797338134171 0.0986800051271887 -4.76081590722101 2.49002289653633e-06 *** df.mm.exp5 0.00395255056627588 0.0986800051271886 0.0400542193039151 0.96806495513083 df.mm.exp6 -0.0143310037054689 0.0986800051271887 -0.145227026356532 0.884586736049076 df.mm.exp7 -0.0730272664404279 0.0986800051271886 -0.7400411699037 0.459602077350964 df.mm.exp8 -0.192810274217674 0.0986800051271887 -1.95389404336938 0.0512383434118537 . df.mm.trans1:exp2 0.561950975274768 0.0791437783666054 7.10038093799022 4.01211723759419e-12 *** df.mm.trans2:exp2 -0.0678381385414969 0.0753971928822006 -0.899743557395912 0.36866458136019 df.mm.trans1:exp3 0.326949466223039 0.0791437783666054 4.13108235379618 4.19386166256947e-05 *** df.mm.trans2:exp3 0.0878648413336966 0.0753971928822006 1.16535958402291 0.244396686611088 df.mm.trans1:exp4 0.415191362879112 0.0791437783666054 5.24603918902994 2.24900761478501e-07 *** df.mm.trans2:exp4 0.236618143656091 0.0753971928822006 3.13828850400013 0.00179375292839210 ** df.mm.trans1:exp5 0.0779081725663813 0.0791437783666054 0.984387834069526 0.325373168631953 df.mm.trans2:exp5 -0.0756203915517844 0.0753971928822005 -1.00296030476801 0.316336834221441 df.mm.trans1:exp6 0.0200277459100923 0.0791437783666054 0.253055215753295 0.800323481869275 df.mm.trans2:exp6 -0.119779262189123 0.0753971928822006 -1.58864352385459 0.112736108238236 df.mm.trans1:exp7 0.0999353224933404 0.0791437783666054 1.26270598341193 0.207249087266651 df.mm.trans2:exp7 -0.0889006520565412 0.0753971928822005 -1.17909763822956 0.238887341607858 df.mm.trans1:exp8 0.155327053526978 0.0791437783666054 1.96259335519060 0.0502151549395135 . df.mm.trans2:exp8 0.00927806930772844 0.0753971928822005 0.123055898410228 0.90210939486001 df.mm.trans1:probe2 -0.287951094227229 0.0551339735375537 -5.22275242924573 2.53540665366584e-07 *** df.mm.trans1:probe3 -0.190589729463678 0.0551339735375537 -3.45684733449985 0.000590333292886765 *** df.mm.trans1:probe4 -0.236588677976157 0.0551339735375537 -4.29115956634267 2.11262000251385e-05 *** df.mm.trans1:probe5 -0.331256811151999 0.0551339735375537 -6.0082158041152 3.48536346593551e-09 *** df.mm.trans1:probe6 -0.701956974210688 0.0551339735375537 -12.7318408083277 1.35952731017316e-32 *** df.mm.trans1:probe7 -0.252779145970337 0.0551339735375537 -4.5848163981535 5.67204380596273e-06 *** df.mm.trans2:probe2 0.0158170195343156 0.0551339735375537 0.286883359196704 0.77431353996203 df.mm.trans2:probe3 -0.220368248441321 0.0551339735375537 -3.99695930298258 7.32380940910543e-05 *** df.mm.trans2:probe4 0.395767821057958 0.0551339735375537 7.17829308617464 2.39223645312779e-12 *** df.mm.trans2:probe5 0.200547257764803 0.0551339735375537 3.63745336853335 0.000302211227039936 *** df.mm.trans2:probe6 0.0689715317620396 0.0551339735375537 1.25098060844573 0.211492450639442 df.mm.trans3:probe2 -0.335635842910735 0.0551339735375537 -6.08764109269434 2.19793685438784e-09 *** df.mm.trans3:probe3 -0.339249267458006 0.0551339735375537 -6.15318007556505 1.49680126635805e-09 *** df.mm.trans3:probe4 -0.420444480894634 0.0551339735375537 -7.62586938538456 1.12649768393610e-13 *** df.mm.trans3:probe5 -0.321681010791341 0.0551339735375537 -5.83453341290978 9.38715439169221e-09 *** df.mm.trans3:probe6 -0.418166347845221 0.0551339735375537 -7.58454943503017 1.50263397000007e-13 *** df.mm.trans3:probe7 -0.274830301137930 0.0551339735375537 -4.98477224665719 8.41106967799664e-07 *** df.mm.trans3:probe8 1.00881205605993 0.0551339735375537 18.2974669034655 2.35944733649342e-58 *** df.mm.trans3:probe9 -0.418954493437689 0.0551339735375537 -7.59884453371249 1.36026931532416e-13 *** df.mm.trans3:probe10 -0.488692390331841 0.0551339735375537 -8.86372519475629 1.17057792140459e-17 *** df.mm.trans3:probe11 -0.357658406145792 0.0551339735375537 -6.48707835110375 2.00686286036692e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.35356352348041 0.216820828505149 20.0790835156182 3.84729311334525e-67 *** df.mm.trans1 -0.0743213296572708 0.175524794541756 -0.423423538829954 0.67215772079117 df.mm.trans2 0.207970625542249 0.171635545029862 1.21169904232871 0.226166525929043 df.mm.exp2 0.365502847051231 0.227823462539666 1.60432487056768 0.109237031381068 df.mm.exp3 0.0174652214943029 0.227823462539666 0.0766612064429583 0.938921941789136 df.mm.exp4 0.300533992458282 0.227823462539666 1.31915294898986 0.187686539849933 df.mm.exp5 0.239267177352066 0.227823462539666 1.05023062455829 0.294089825837580 df.mm.exp6 -0.0192368042576241 0.227823462539666 -0.0844373272321536 0.932740554362415 df.mm.exp7 0.133514612301005 0.227823462539666 0.586044171274762 0.558094843878325 df.mm.exp8 -0.0137060494372317 0.227823462539666 -0.0601608336755278 0.95205017434497 df.mm.trans1:exp2 -0.050574631630568 0.182719990769275 -0.276787621418117 0.782050894328526 df.mm.trans2:exp2 -0.360598833322885 0.174070213373563 -2.07157115703089 0.0387877606478399 * df.mm.trans1:exp3 0.0614895646939541 0.182719990769275 0.336523466507823 0.736609108987039 df.mm.trans2:exp3 -0.117586785305121 0.174070213373563 -0.675513535752236 0.49964361492883 df.mm.trans1:exp4 -0.0855355028915812 0.182719990769275 -0.468123397617663 0.639888431727434 df.mm.trans2:exp4 -0.305197813778058 0.174070213373563 -1.75330292221271 0.08012701113305 . df.mm.trans1:exp5 -0.103113613031895 0.182719990769275 -0.564325844138745 0.572770680333631 df.mm.trans2:exp5 -0.212270461912620 0.174070213373563 -1.21945310342717 0.22321364939487 df.mm.trans1:exp6 0.176078812866193 0.182719990769275 0.963653796855384 0.335658164189951 df.mm.trans2:exp6 -0.150774838552559 0.174070213373563 -0.866172538256095 0.386786744364019 df.mm.trans1:exp7 0.0915403337849212 0.182719990769275 0.500986965900799 0.616587900639338 df.mm.trans2:exp7 -0.35573069952171 0.174070213373563 -2.04360466174816 0.04148556688183 * df.mm.trans1:exp8 0.142035081664332 0.182719990769275 0.777337395138568 0.437305906610838 df.mm.trans2:exp8 -0.16329959257757 0.174070213373563 -0.938124848661624 0.348606754310106 df.mm.trans1:probe2 0.134819374579487 0.12728832693823 1.05916526536571 0.290006075712399 df.mm.trans1:probe3 0.127216210619854 0.12728832693823 0.999433441226616 0.318039982898259 df.mm.trans1:probe4 0.249579068366283 0.12728832693823 1.96073806899354 0.0504319156883819 . df.mm.trans1:probe5 0.0235165554672343 0.12728832693823 0.184750291192423 0.85349543756481 df.mm.trans1:probe6 0.135578396231202 0.12728832693823 1.06512827603583 0.287301965668526 df.mm.trans1:probe7 -0.0114330424506967 0.12728832693823 -0.0898200386925104 0.928464092290742 df.mm.trans2:probe2 0.0366330447850502 0.12728832693823 0.287795791383347 0.77361534733956 df.mm.trans2:probe3 0.115401471352543 0.12728832693823 0.906614723662327 0.365021789066801 df.mm.trans2:probe4 0.0428178731284478 0.12728832693823 0.336384915713648 0.736713513570776 df.mm.trans2:probe5 0.0209293537396916 0.12728832693823 0.164424768893758 0.869459335728114 df.mm.trans2:probe6 0.133450841420841 0.12728832693823 1.04841382262493 0.294924938489862 df.mm.trans3:probe2 0.0401816563904521 0.12728832693823 0.315674322673368 0.752373749283854 df.mm.trans3:probe3 0.0673319461918735 0.12728832693823 0.528971884629672 0.597045995140551 df.mm.trans3:probe4 0.134978681612566 0.12728832693823 1.06041681008242 0.289437104535525 df.mm.trans3:probe5 0.082905871687732 0.12728832693823 0.651323445613078 0.51511948622624 df.mm.trans3:probe6 0.00923505851264417 0.12728832693823 0.0725522813818247 0.942189719906739 df.mm.trans3:probe7 0.0344909171382907 0.12728832693823 0.270966851147539 0.78652182587198 df.mm.trans3:probe8 0.135106557979938 0.12728832693823 1.06142142983388 0.288980935041697 df.mm.trans3:probe9 0.215623473707680 0.12728832693823 1.69397680756945 0.090856099601128 . df.mm.trans3:probe10 0.122073905889695 0.12728832693823 0.959034569987983 0.337977790240302 df.mm.trans3:probe11 0.095393531607566 0.12728832693823 0.749428748905295 0.453930703940917