chr1.919_chr1_36930040_36941681_-_2.R fitVsDatCorrelation=0.939502678530801 cont.fitVsDatCorrelation=0.217429511008137 fstatistic=10948.6794417491,66,1014 cont.fstatistic=1334.93544452258,66,1014 residuals=-0.682796932360418,-0.099490972587252,-0.00237334662418227,0.0959202903024825,1.17509622874158 cont.residuals=-0.915324516328522,-0.372216987373633,-0.0803072391987378,0.319720721812372,1.58071291707746 predictedValues: Include Exclude Both chr1.919_chr1_36930040_36941681_-_2.R.tl.Lung 65.3751623116614 225.054039314860 71.4828840650784 chr1.919_chr1_36930040_36941681_-_2.R.tl.cerebhem 62.2088145159931 167.978474085290 73.1333526290009 chr1.919_chr1_36930040_36941681_-_2.R.tl.cortex 62.3772250284254 188.963782235341 79.652459915347 chr1.919_chr1_36930040_36941681_-_2.R.tl.heart 64.910529733331 202.503272551665 75.9475631439835 chr1.919_chr1_36930040_36941681_-_2.R.tl.kidney 68.2063732062463 277.161053052136 74.930100567199 chr1.919_chr1_36930040_36941681_-_2.R.tl.liver 66.9189794330701 305.334559414661 61.1630384958301 chr1.919_chr1_36930040_36941681_-_2.R.tl.stomach 68.2348723216316 213.063567566081 66.7046620502408 chr1.919_chr1_36930040_36941681_-_2.R.tl.testicle 66.6352766497135 289.694319006928 70.1388678523496 diffExp=-159.678877003199,-105.769659569297,-126.586557206916,-137.592742818334,-208.95467984589,-238.415579981591,-144.828695244449,-223.059042357215 diffExpScore=0.999256994928754 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 84.3909724631118 123.229355694053 87.3742628543027 cerebhem 103.985177125247 106.441027865206 86.3460886026493 cortex 94.1072875096572 104.776604562277 89.6644904190946 heart 90.2153204704046 128.466091498486 96.5226027095953 kidney 92.2553943867253 98.6371199551951 85.3565295050699 liver 98.2674940736856 99.8938517339735 83.5173973268933 stomach 94.2380608195776 89.504434192197 83.0419538607432 testicle 94.3453417044162 97.4151801650318 91.1965825054956 cont.diffExp=-38.8383832309410,-2.45585073995883,-10.6693170526198,-38.2507710280815,-6.38172556846985,-1.62635766028791,4.73362662738052,-3.06983846061557 cont.diffExpScore=1.08679144400850 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=-1,0,0,-1,0,0,0,0 cont.diffExp1.4Score=0.666666666666667 cont.diffExp1.3=-1,0,0,-1,0,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=-1,0,0,-1,0,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.719703444575509 cont.tran.correlation=-0.501144600646442 tran.covariance=0.00592746130177781 cont.tran.covariance=-0.00369289584099116 tran.mean=149.663768776690 cont.tran.mean=100.010544638703 weightedLogRatios: wLogRatio Lung -5.93157720278341 cerebhem -4.59634535811018 cortex -5.19527928860427 heart -5.39505926688135 kidney -6.90314196426199 liver -7.53262218378434 stomach -5.45664899976286 testicle -7.25101219594608 cont.weightedLogRatios: wLogRatio Lung -1.75087106748676 cerebhem -0.108681930649618 cortex -0.493816585980815 heart -1.65384214785290 kidney -0.304870789714246 liver -0.0754410113355993 stomach 0.23294549898479 testicle -0.146107066537511 varWeightedLogRatios=1.14353639444133 cont.varWeightedLogRatios=0.559810258690318 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.08472510845053 0.0787411650386827 64.5751825738493 0 *** df.mm.trans1 -1.22873412689077 0.0673132072139617 -18.2539828028862 1.37649549091257e-64 *** df.mm.trans2 0.382787949548290 0.0587937291151495 6.51069349247411 1.17470275217757e-10 *** df.mm.exp2 -0.364976896008156 0.0740890953291204 -4.92618912927532 9.78635031509471e-07 *** df.mm.exp3 -0.329942242315403 0.0740890953291204 -4.45331719667686 9.3936659694424e-06 *** df.mm.exp4 -0.173302156225678 0.0740890953291204 -2.33910476914088 0.0195234224731897 * df.mm.exp5 0.203556162998266 0.0740890953291204 2.74745105327611 0.00611253463716692 ** df.mm.exp6 0.484322740153213 0.0740890953291204 6.53703136746025 9.92436551096511e-11 *** df.mm.exp7 0.0572465567870734 0.0740890953291204 0.772671828867277 0.439896683523113 df.mm.exp8 0.290558392157405 0.0740890953291204 3.92174301584706 9.38244611459214e-05 *** df.mm.trans1:exp2 0.315331193028034 0.0675892707396537 4.66540309693021 3.49233176135755e-06 *** df.mm.trans2:exp2 0.0724721886575043 0.0459632183990647 1.57674312595523 0.115166551100560 df.mm.trans1:exp3 0.283000062388503 0.0675892707396537 4.18705601187188 3.07150063337869e-05 *** df.mm.trans2:exp3 0.155157062527741 0.0459632183990647 3.37567881301581 0.000764263464753749 *** df.mm.trans1:exp4 0.166169607120450 0.0675892707396537 2.45852049152176 0.0141173424175385 * df.mm.trans2:exp4 0.0677176552908536 0.0459632183990647 1.47330099260916 0.140980236915202 df.mm.trans1:exp5 -0.161160558903738 0.0675892707396537 -2.38441038259623 0.0172895602295563 * df.mm.trans2:exp5 0.00470204509153746 0.0459632183990647 0.102300170773793 0.918538645919782 df.mm.trans1:exp6 -0.460982519863495 0.0675892707396537 -6.82035055000295 1.55835304132085e-11 *** df.mm.trans2:exp6 -0.179255196704854 0.0459632183990647 -3.89997051878556 0.000102537806143078 *** df.mm.trans1:exp7 -0.0144332034160792 0.0675892707396537 -0.213542819121015 0.830946521886235 df.mm.trans2:exp7 -0.111996544424216 0.0459632183990647 -2.43665583754021 0.0149949605898166 * df.mm.trans1:exp8 -0.271466680577327 0.0675892707396537 -4.01641677157587 6.34512742187486e-05 *** df.mm.trans2:exp8 -0.0380726455915803 0.0459632183990647 -0.828328540030066 0.407679381978818 df.mm.trans1:probe2 0.405445194634822 0.0503232794123707 8.0568118645931 2.18771127654337e-15 *** df.mm.trans1:probe3 0.0445028878684114 0.0503232794123707 0.884339979192047 0.376722347775448 df.mm.trans1:probe4 -0.00850190025611427 0.0503232794123707 -0.168945671971138 0.865873068951927 df.mm.trans1:probe5 0.183017655124652 0.0503232794123707 3.63683880028815 0.000289891479697766 *** df.mm.trans1:probe6 0.250299538774555 0.0503232794123707 4.97383202560176 7.70586837981552e-07 *** df.mm.trans1:probe7 0.596399608390142 0.0503232794123707 11.8513661143382 1.94681061065813e-30 *** df.mm.trans1:probe8 0.0657423297567048 0.0503232794123707 1.30639995096472 0.191712821433635 df.mm.trans1:probe9 0.543067921578557 0.0503232794123707 10.7915844897234 8.91044433735911e-26 *** df.mm.trans1:probe10 0.0500261608154775 0.0503232794123707 0.99409580217421 0.320413387454251 df.mm.trans1:probe11 1.10172464243643 0.0503232794123707 21.8929420995882 2.58958590414254e-87 *** df.mm.trans1:probe12 0.838964080294773 0.0503232794123707 16.6714906121268 2.46903876605705e-55 *** df.mm.trans1:probe13 1.07088464152823 0.0503232794123706 21.2801044374103 2.27952735387417e-83 *** df.mm.trans1:probe14 1.24529322257012 0.0503232794123707 24.7458678590012 3.84479015102845e-106 *** df.mm.trans1:probe15 1.10381042669839 0.0503232794123706 21.934389801056 1.39633557447835e-87 *** df.mm.trans1:probe16 1.06599555419388 0.0503232794123707 21.1829508458432 9.53569820474476e-83 *** df.mm.trans1:probe17 0.95289697836464 0.0503232794123707 18.9355103540886 1.05436284009544e-68 *** df.mm.trans1:probe18 0.491894332712636 0.0503232794123707 9.77468754931176 1.25812508159025e-21 *** df.mm.trans1:probe19 0.677822137918217 0.0503232794123707 13.4693554520533 3.66066812334898e-38 *** df.mm.trans1:probe20 0.629611749591137 0.0503232794123707 12.5113418072743 1.67330697768701e-33 *** df.mm.trans1:probe21 0.739471708182397 0.0503232794123706 14.6944260552427 1.85006582736510e-44 *** df.mm.trans1:probe22 0.593536649260973 0.0503232794123707 11.7944747677765 3.53067104048516e-30 *** df.mm.trans2:probe2 0.0674948808201194 0.0503232794123707 1.34122580261587 0.180147479762817 df.mm.trans2:probe3 -0.237120358748222 0.0503232794123707 -4.71194170008587 2.7955429276833e-06 *** df.mm.trans2:probe4 -0.448809334803227 0.0503232794123707 -8.91852319729582 2.16123704041741e-18 *** df.mm.trans2:probe5 -0.176664486108243 0.0503232794123707 -3.51059168184525 0.000466774775813919 *** df.mm.trans2:probe6 -0.381868428837998 0.0503232794123707 -7.58830571650156 7.3238065055152e-14 *** df.mm.trans3:probe2 0.320433531659248 0.0503232794123707 6.36750099359537 2.90730206643848e-10 *** df.mm.trans3:probe3 -0.0319959410283463 0.0503232794123707 -0.635807948169628 0.525045074311162 df.mm.trans3:probe4 -0.264402562974839 0.0503232794123707 -5.25408053811857 1.81300228833223e-07 *** df.mm.trans3:probe5 0.0472071004866137 0.0503232794123707 0.938076791454276 0.348428328908204 df.mm.trans3:probe6 0.481014471246005 0.0503232794123707 9.55848817610563 8.69892408912899e-21 *** df.mm.trans3:probe7 0.247756973157065 0.0503232794123707 4.92330738477589 9.92820765343033e-07 *** df.mm.trans3:probe8 -0.299986564620981 0.0503232794123707 -5.96118870081502 3.45285123451841e-09 *** df.mm.trans3:probe9 -0.178970770022510 0.0503232794123707 -3.55642104633019 0.000393310764868228 *** df.mm.trans3:probe10 0.310995717255114 0.0503232794123707 6.17995728590501 9.27456067430155e-10 *** df.mm.trans3:probe11 0.0132507122778045 0.0503232794123707 0.263311780005879 0.792363775873424 df.mm.trans3:probe12 -0.281624614976126 0.0503232794123707 -5.59630887065948 2.81710692249787e-08 *** df.mm.trans3:probe13 -0.146414577421761 0.0503232794123707 -2.90948004842803 0.00369937376832834 ** df.mm.trans3:probe14 0.0569265821504871 0.0503232794123707 1.13121765543152 0.258230959363522 df.mm.trans3:probe15 0.205867409978631 0.0503232794123707 4.09089813665887 4.63785517734205e-05 *** df.mm.trans3:probe16 0.0428657728467561 0.0503232794123707 0.851808017031153 0.394521781092406 df.mm.trans3:probe17 -0.00137843410557769 0.0503232794123707 -0.0273915794374649 0.9781528041118 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.96027745264286 0.224373838025649 22.1072006268211 1.05847962892442e-88 *** df.mm.trans1 -0.44859441233636 0.191809743289836 -2.33874674269548 0.0195420370304557 * df.mm.trans2 -0.0997289116787198 0.16753339434241 -0.595277807568864 0.55179061110309 df.mm.exp2 0.0741689875575519 0.211117712910093 0.351315796932385 0.725424428298487 df.mm.exp3 -0.0791157943139152 0.211117712910093 -0.374747306719865 0.707926766436961 df.mm.exp4 0.00878007683231262 0.211117712910093 0.0415885370833461 0.966834898649248 df.mm.exp5 -0.110135501219889 0.211117712910093 -0.521678165710288 0.602008354353433 df.mm.exp6 -0.0125605002432808 0.211117712910093 -0.0594952458992859 0.95256937493471 df.mm.exp7 -0.158540607595319 0.211117712910093 -0.750958341723015 0.452851961616476 df.mm.exp8 -0.166380443455445 0.211117712910093 -0.788093245052819 0.430826357684163 df.mm.trans1:exp2 0.134618939240335 0.192596389420459 0.698969174060928 0.484731581609785 df.mm.trans2:exp2 -0.220625184191244 0.130972709321012 -1.68451263881619 0.092390374304446 . df.mm.trans1:exp3 0.188090847673907 0.192596389420459 0.97660630212171 0.328997025599982 df.mm.trans2:exp3 -0.0831009970836967 0.130972709321012 -0.634490937192245 0.525903599410493 df.mm.trans1:exp4 0.0579587512742707 0.192596389420459 0.300933737380405 0.763526716998436 df.mm.trans2:exp4 0.0328376138379884 0.130972709321012 0.250721039583169 0.80208058581334 df.mm.trans1:exp5 0.199235823624144 0.192596389420459 1.03447330567132 0.301161595777065 df.mm.trans2:exp5 -0.112464137338266 0.130972709321012 -0.858683751151681 0.390717950799921 df.mm.trans1:exp6 0.164793357328457 0.192596389420459 0.855640948536659 0.392398546345491 df.mm.trans2:exp6 -0.19737865965407 0.130972709321012 -1.50702127700739 0.132116667210192 df.mm.trans1:exp7 0.268904315701451 0.192596389420459 1.39620642168116 0.162957916823946 df.mm.trans2:exp7 -0.161218523757710 0.130972709321012 -1.23093218880099 0.218633665888281 df.mm.trans1:exp8 0.277881907005328 0.192596389420459 1.44281991911428 0.149380029916922 df.mm.trans2:exp8 -0.0686848036430532 0.130972709321012 -0.524420728555807 0.600100559774664 df.mm.trans1:probe2 -0.18855290294392 0.143396752362551 -1.31490357931678 0.188839600833954 df.mm.trans1:probe3 -0.245120328130061 0.143396752362551 -1.70938549229011 0.0876855329489295 . df.mm.trans1:probe4 -0.128591047425290 0.143396752362551 -0.896750067952531 0.370065144985163 df.mm.trans1:probe5 0.0102423512936888 0.143396752362551 0.0714266615173611 0.943072275779535 df.mm.trans1:probe6 -0.297700217028035 0.143396752362551 -2.07605968840465 0.0381399409396604 * df.mm.trans1:probe7 -0.147394477646643 0.143396752362551 -1.02787877143818 0.304251981417917 df.mm.trans1:probe8 -0.069055320430926 0.143396752362551 -0.481568231450132 0.630216607892754 df.mm.trans1:probe9 -0.036200674722555 0.143396752362551 -0.252451147784914 0.800743532824904 df.mm.trans1:probe10 -0.226483504851099 0.143396752362551 -1.57941864874652 0.114551937950549 df.mm.trans1:probe11 -0.0753001777696817 0.143396752362551 -0.525117734739905 0.59961614189777 df.mm.trans1:probe12 -0.149102206754562 0.143396752362551 -1.03978789127376 0.298686318961691 df.mm.trans1:probe13 -0.156982633864579 0.143396752362551 -1.09474329981811 0.273889079934871 df.mm.trans1:probe14 -0.200706580287462 0.143396752362551 -1.39965917624142 0.161921256297523 df.mm.trans1:probe15 -0.108338132011969 0.143396752362551 -0.755513149545097 0.450116602670717 df.mm.trans1:probe16 -0.124528972815212 0.143396752362551 -0.868422546281694 0.385368542549286 df.mm.trans1:probe17 -0.212810131590628 0.143396752362551 -1.48406521127187 0.138102386801082 df.mm.trans1:probe18 -0.134074888409133 0.143396752362551 -0.93499250296932 0.350014767660276 df.mm.trans1:probe19 -0.0416065896740828 0.143396752362551 -0.290150153253741 0.771760692567174 df.mm.trans1:probe20 -0.106651987554870 0.143396752362551 -0.743754553696034 0.457197279285862 df.mm.trans1:probe21 -0.149066800001270 0.143396752362551 -1.03954097666302 0.298801017668301 df.mm.trans1:probe22 -0.184656402134896 0.143396752362551 -1.28773071281299 0.198133527673060 df.mm.trans2:probe2 -0.110719105293187 0.143396752362551 -0.772117244421653 0.440224901564145 df.mm.trans2:probe3 -0.343627347423959 0.143396752362551 -2.39633981776076 0.0167400556068746 * df.mm.trans2:probe4 -0.302195050819161 0.143396752362551 -2.10740512487423 0.0353275091828927 * df.mm.trans2:probe5 -0.226430296992561 0.143396752362551 -1.57904759530450 0.114637020531144 df.mm.trans2:probe6 -0.0865567542166654 0.143396752362551 -0.603617256252942 0.546233138970346 df.mm.trans3:probe2 -0.0165407676456086 0.143396752362551 -0.115349667081640 0.908190810283903 df.mm.trans3:probe3 0.107284251816598 0.143396752362551 0.748163748822922 0.454534878204216 df.mm.trans3:probe4 -0.0594583312832588 0.143396752362551 -0.414642105233528 0.678491624448705 df.mm.trans3:probe5 0.102001955237463 0.143396752362551 0.711326815683882 0.477045335606241 df.mm.trans3:probe6 0.139079772684257 0.143396752362551 0.96989485739971 0.332330189968612 df.mm.trans3:probe7 0.102138132677966 0.143396752362551 0.712276470667407 0.476457443444342 df.mm.trans3:probe8 -0.0363893908373908 0.143396752362551 -0.25376718954825 0.799726867587321 df.mm.trans3:probe9 0.0574982135373623 0.143396752362551 0.400972913193943 0.68852454246499 df.mm.trans3:probe10 0.204560764326521 0.143396752362551 1.42653694003703 0.154021226599873 df.mm.trans3:probe11 0.0659084706459929 0.143396752362551 0.459623175281936 0.645885245384039 df.mm.trans3:probe12 0.0836900359535696 0.143396752362551 0.58362574169027 0.559601928767413 df.mm.trans3:probe13 0.038572897111164 0.143396752362551 0.268994216923686 0.787988854171239 df.mm.trans3:probe14 0.0985766465480562 0.143396752362551 0.687439882172676 0.491962773716291 df.mm.trans3:probe15 -0.0788899953678866 0.143396752362551 -0.550151897223088 0.582336355179417 df.mm.trans3:probe16 0.157389287360681 0.143396752362551 1.09757916248168 0.272648861941622 df.mm.trans3:probe17 0.024770857608181 0.143396752362551 0.172743504996213 0.862887518775167