chr8.23282_chr8_21112628_21119496_-_1.R fitVsDatCorrelation=0.92752349965054 cont.fitVsDatCorrelation=0.266181089234511 fstatistic=8436.331331051,51,669 cont.fstatistic=1257.28257633759,51,669 residuals=-0.805833752604246,-0.093548798353141,-0.00674694174972613,0.0914044284176253,0.928302732100906 cont.residuals=-0.684287506648279,-0.310813656898037,-0.135840322040092,0.20954563813333,2.01105411962217 predictedValues: Include Exclude Both chr8.23282_chr8_21112628_21119496_-_1.R.tl.Lung 60.6841864655522 53.9633974679338 81.7532115924213 chr8.23282_chr8_21112628_21119496_-_1.R.tl.cerebhem 67.8839612967899 58.4851214340601 72.3962314200748 chr8.23282_chr8_21112628_21119496_-_1.R.tl.cortex 58.6196210123716 55.3870910024032 78.1674063290518 chr8.23282_chr8_21112628_21119496_-_1.R.tl.heart 64.9714904240784 58.2196247912972 86.6523896778248 chr8.23282_chr8_21112628_21119496_-_1.R.tl.kidney 93.6443801078474 57.1551298824847 126.973012066560 chr8.23282_chr8_21112628_21119496_-_1.R.tl.liver 108.110300136282 63.7164613141482 145.598149340473 chr8.23282_chr8_21112628_21119496_-_1.R.tl.stomach 62.8798681571567 55.3594693076504 90.1422936753662 chr8.23282_chr8_21112628_21119496_-_1.R.tl.testicle 65.9205976684557 59.8280338249713 84.3619561375078 diffExp=6.72078899761848,9.39883986272975,3.23253000996839,6.75186563278123,36.4892502253627,44.3938388221339,7.52039884950634,6.09256384348441 diffExpScore=0.991776320945747 diffExp1.5=0,0,0,0,1,1,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,0,0,1,1,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,0,0,1,1,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,0,0,1,1,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 83.361860019045 79.1597230231326 78.122444736304 cerebhem 73.5274809342269 76.2203683394276 81.7108964720238 cortex 78.2220000168658 66.6819596825776 80.6942199446151 heart 76.1347679129762 77.4402362654861 77.8957115402529 kidney 68.2790400537267 88.5446040411657 82.453357463404 liver 76.5119552240309 69.6402362715494 69.2478243662067 stomach 72.6335976043122 88.6126288342125 74.3482823632207 testicle 77.2635098391212 83.011234367796 68.723147360785 cont.diffExp=4.20213699591235,-2.69288740520072,11.5400403342882,-1.30546835250996,-20.2655639874390,6.87171895248144,-15.9790312299003,-5.74772452867485 cont.diffExpScore=2.81434110570214 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,-1,0,-1,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.720749944251105 cont.tran.correlation=-0.507495395390229 tran.covariance=0.00833848314705707 cont.tran.covariance=-0.00304978480002313 tran.mean=65.3017958933427 cont.tran.mean=77.2028251518533 weightedLogRatios: wLogRatio Lung 0.475024698784023 cerebhem 0.61746318926541 cortex 0.229314194604676 heart 0.451971072024806 kidney 2.11942617934263 liver 2.33625823755017 stomach 0.519389824344535 testicle 0.401479534720996 cont.weightedLogRatios: wLogRatio Lung 0.227444244296196 cerebhem -0.155231562150872 cortex 0.68311751229326 heart -0.0738034444607446 kidney -1.13150446359577 liver 0.403745446575706 stomach -0.871913558509909 testicle -0.314505609655128 varWeightedLogRatios=0.693413813015562 cont.varWeightedLogRatios=0.380065039980454 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.26354173196477 0.0852664163749818 38.2746440006635 1.15736271766784e-170 *** df.mm.trans1 0.687063009132161 0.0732320179129051 9.3820029641855 9.88058369937905e-20 *** df.mm.trans2 0.68516029099345 0.0662977360861075 10.3345955901656 2.50435507056463e-23 *** df.mm.exp2 0.314133895471549 0.0867130752024313 3.62268198582745 0.000313650381811666 *** df.mm.exp3 0.0362792183609033 0.0867130752024313 0.418382329034111 0.675801873795876 df.mm.exp4 0.0859824220135088 0.0867130752024313 0.991573898316755 0.321763923791059 df.mm.exp5 0.0510149628944383 0.0867130752024313 0.58831915227714 0.55651664820321 df.mm.exp6 0.166460484832013 0.0867130752024313 1.91966995108190 0.0553245923653103 . df.mm.exp7 -0.0365997283399361 0.0867130752024313 -0.422078541840365 0.673103292760323 df.mm.exp8 0.154524710843068 0.0867130752024313 1.78202318949398 0.0751989266111741 . df.mm.trans1:exp2 -0.202017244071342 0.0786908335176248 -2.56722714756981 0.0104677626117644 * df.mm.trans2:exp2 -0.233667500112122 0.0630930296791327 -3.70353906446506 0.000230152947510995 *** df.mm.trans1:exp3 -0.0708928928693962 0.0786908335176248 -0.900904078662706 0.36796351899848 df.mm.trans2:exp3 -0.0102386582261711 0.0630930296791327 -0.162278753742546 0.87113534577211 df.mm.trans1:exp4 -0.0177170018064132 0.0786908335176248 -0.225146958221571 0.821933706087139 df.mm.trans2:exp4 -0.0100659204564423 0.0630930296791327 -0.159540927224985 0.873290869936901 df.mm.trans1:exp5 0.382806310057352 0.0786908335176247 4.86468744763789 1.43073097341965e-06 *** df.mm.trans2:exp5 0.00644819297192685 0.0630930296791326 0.102201352585538 0.918627478974534 df.mm.trans1:exp6 0.411008374069631 0.0786908335176248 5.22307816167147 2.35286182931393e-07 *** df.mm.trans2:exp6 -0.000323528333426108 0.0630930296791327 -0.00512779834906409 0.99591015544806 df.mm.trans1:exp7 0.0721426351101526 0.0786908335176248 0.916785753629036 0.359585283482152 df.mm.trans2:exp7 0.0621414613282856 0.0630930296791327 0.98491801145568 0.325020408446965 df.mm.trans1:exp8 -0.0717569033217137 0.0786908335176248 -0.911883889317832 0.362158310016576 df.mm.trans2:exp8 -0.0513563587452775 0.0630930296791327 -0.813978327026877 0.415946989578257 df.mm.trans1:probe2 0.221150708236681 0.0515152430133834 4.2929178879973 2.02426859375680e-05 *** df.mm.trans1:probe3 -0.216118715959367 0.0515152430133834 -4.19523821140124 3.09340864121608e-05 *** df.mm.trans1:probe4 0.173544259858252 0.0515152430133834 3.36879435496725 0.000798293881171473 *** df.mm.trans1:probe5 -0.221115941106806 0.0515152430133834 -4.29224299785136 2.03026669197735e-05 *** df.mm.trans1:probe6 -0.0722560855140907 0.0515152430133834 -1.40261563932289 0.161195362038738 df.mm.trans1:probe7 0.209860028733777 0.0515152430133834 4.07374626339736 5.18102945411852e-05 *** df.mm.trans1:probe8 0.00310661051585614 0.0515152430133834 0.0603046852569261 0.95193098030393 df.mm.trans1:probe9 0.176308406559864 0.0515152430133834 3.42245122504925 0.000658401658464666 *** df.mm.trans1:probe10 0.369782552319287 0.0515152430133834 7.17811914860267 1.88449282089113e-12 *** df.mm.trans1:probe11 1.56981731956853 0.0515152430133834 30.4728703145339 1.41831408179894e-128 *** df.mm.trans1:probe12 0.232354155285495 0.0515152430133834 4.51039617973132 7.64141651370608e-06 *** df.mm.trans1:probe13 0.0546313916621197 0.0515152430133834 1.06048983691927 0.289304517004804 df.mm.trans1:probe14 -0.0350659608671128 0.0515152430133834 -0.680690972534146 0.496302563064038 df.mm.trans1:probe15 0.655527502046796 0.0515152430133834 12.7249230266951 2.25628904833610e-33 *** df.mm.trans1:probe16 0.445277049488952 0.0515152430133834 8.64359796135042 4.00529339370974e-17 *** df.mm.trans2:probe2 0.0601334210062072 0.0515152430133834 1.16729374625264 0.243507641374038 df.mm.trans2:probe3 -0.0846683422176452 0.0515152430133834 -1.64355901797161 0.100737168264854 df.mm.trans2:probe4 0.317331624679543 0.0515152430133834 6.15995589105737 1.25593295312498e-09 *** df.mm.trans2:probe5 0.161180453542702 0.0515152430133834 3.12879148217991 0.00183153095466255 ** df.mm.trans2:probe6 0.100478410545365 0.0515152430133834 1.95045979923381 0.0515385581472659 . df.mm.trans3:probe2 -0.126636102998391 0.0515152430133834 -2.45822586851608 0.0142149146981923 * df.mm.trans3:probe3 -0.520544415923782 0.0515152430133834 -10.1046677735471 1.94827865263029e-22 *** df.mm.trans3:probe4 -0.468926745112207 0.0515152430133834 -9.10267947276076 1.00078174279434e-18 *** df.mm.trans3:probe5 -0.560279050138392 0.0515152430133834 -10.875985773625 1.76029186486961e-25 *** df.mm.trans3:probe6 0.491524627108368 0.0515152430133834 9.54134346179194 2.57639352197066e-20 *** df.mm.trans3:probe7 -0.247579528353529 0.0515152430133834 -4.80594701434698 1.90312038701062e-06 *** df.mm.trans3:probe8 -0.424038284985869 0.0515152430133834 -8.23131679444289 9.6968517806446e-16 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.45827965688267 0.219898326261595 20.2742773566135 1.30723259663841e-71 *** df.mm.trans1 -0.054561903111326 0.188862143531248 -0.288898040079157 0.772748899279623 df.mm.trans2 -0.0910132228665632 0.170978936609157 -0.532306637715332 0.594690373191427 df.mm.exp2 -0.208280522582643 0.223629195557529 -0.931365522571324 0.352000491936998 df.mm.exp3 -0.267562676269025 0.223629195557529 -1.19645682041634 0.231942181157527 df.mm.exp4 -0.109740487167853 0.223629195557529 -0.490725224379847 0.623781722158898 df.mm.exp5 -0.141504617484921 0.223629195557529 -0.632764506137655 0.527103653968492 df.mm.exp6 -0.0932833262446781 0.223629195557529 -0.417133934646208 0.676714264635411 df.mm.exp7 0.0245602937127105 0.223629195557529 0.109825971745234 0.912580328705743 df.mm.exp8 0.099730548719738 0.223629195557529 0.445963902303097 0.655767615019465 df.mm.trans1:exp2 0.0827488583390027 0.202940188157511 0.407749983333895 0.683587688347498 df.mm.trans2:exp2 0.170441628820614 0.162714140162753 1.04749119314481 0.295251380068610 df.mm.trans1:exp3 0.203922723548861 0.202940188157511 1.00484150231785 0.315336476769082 df.mm.trans2:exp3 0.0960295013021235 0.162714140162753 0.590173055679557 0.555273920974647 df.mm.trans1:exp4 0.0190546282496521 0.202940188157511 0.0938928283384805 0.925222403500757 df.mm.trans2:exp4 0.087779359302036 0.162714140162753 0.539469767128018 0.589742249123275 df.mm.trans1:exp5 -0.0580834344992384 0.202940188157511 -0.286209621793379 0.774806179560272 df.mm.trans2:exp5 0.253543421219950 0.162714140162753 1.55821381575287 0.119655481609776 df.mm.trans1:exp6 0.00753944146095897 0.202940188157511 0.0371510518907535 0.970375649210216 df.mm.trans2:exp6 -0.0348417878832068 0.162714140162753 -0.214128826470501 0.830511843513524 df.mm.trans1:exp7 -0.162323592855747 0.202940188157511 -0.79985928035979 0.424076248270125 df.mm.trans2:exp7 0.0882464695945694 0.162714140162753 0.542340508982821 0.587764559374947 df.mm.trans1:exp8 -0.175699654428078 0.202940188157511 -0.865770629382236 0.386926325375111 df.mm.trans2:exp8 -0.0522222180322037 0.162714140162753 -0.320944559458502 0.748352637076886 df.mm.trans1:probe2 0.0365617980170558 0.132855539111482 0.275199651151736 0.783247826873744 df.mm.trans1:probe3 0.0464198534189518 0.132855539111482 0.349400963854430 0.726898261820326 df.mm.trans1:probe4 0.206735471972379 0.132855539111482 1.55609222885996 0.120159077883601 df.mm.trans1:probe5 0.0853459396188029 0.132855539111482 0.642396547329404 0.52083581666372 df.mm.trans1:probe6 0.192398194976698 0.132855539111482 1.44817593804088 0.148036338774940 df.mm.trans1:probe7 0.0892143971626326 0.132855539111482 0.671514321188901 0.502124741809779 df.mm.trans1:probe8 0.0665747684602273 0.132855539111482 0.501106456723364 0.616461038443862 df.mm.trans1:probe9 -0.233361825472734 0.132855539111482 -1.75650806156388 0.079459185392883 . df.mm.trans1:probe10 -0.115797054839260 0.132855539111482 -0.871601256625752 0.383738622586996 df.mm.trans1:probe11 -0.0593238929429813 0.132855539111482 -0.446529315523692 0.655359442567555 df.mm.trans1:probe12 0.0509770450372562 0.132855539111482 0.383702820207444 0.701320631434542 df.mm.trans1:probe13 -0.102740076650640 0.132855539111482 -0.773321739821687 0.439605145449315 df.mm.trans1:probe14 0.0335394970491557 0.132855539111482 0.252450874637691 0.8007701231734 df.mm.trans1:probe15 0.0880349603758399 0.132855539111482 0.66263673283481 0.507791457419549 df.mm.trans1:probe16 0.0633030764985698 0.132855539111482 0.476480521037596 0.633887662718462 df.mm.trans2:probe2 0.00266974906201704 0.132855539111482 0.0200951279854187 0.9839734783801 df.mm.trans2:probe3 0.11818139252803 0.132855539111482 0.889548101030707 0.374028336823283 df.mm.trans2:probe4 -0.0136436061014826 0.132855539111482 -0.102695049018874 0.918235777444892 df.mm.trans2:probe5 -0.0292647464365839 0.132855539111482 -0.220274943990308 0.825724233741478 df.mm.trans2:probe6 -0.0191261603795163 0.132855539111482 -0.143962084738274 0.885573763569767 df.mm.trans3:probe2 -0.0079310707895148 0.132855539111482 -0.05969695236312 0.95241482678628 df.mm.trans3:probe3 0.130063734918559 0.132855539111482 0.978986166390994 0.327940696417849 df.mm.trans3:probe4 -0.0490772707703317 0.132855539111482 -0.369403271392018 0.71194405227164 df.mm.trans3:probe5 -0.0834384564482314 0.132855539111482 -0.628038973807604 0.530192734559627 df.mm.trans3:probe6 -0.0505099289590706 0.132855539111482 -0.380186850295240 0.70392739110046 df.mm.trans3:probe7 0.199407816541082 0.132855539111482 1.50093716735253 0.13384361004794 df.mm.trans3:probe8 0.226068129440123 0.132855539111482 1.70160861151919 0.0892933910767192 .