chr12.5696_chr12_78386146_78386443_+_1.R fitVsDatCorrelation=0.93425043488224 cont.fitVsDatCorrelation=0.242608338281274 fstatistic=10435.2345863303,50,646 cont.fstatistic=1398.93596919248,50,646 residuals=-0.593848106184159,-0.0879529830789141,0.0103217736447224,0.0965740780440584,0.456702384782928 cont.residuals=-0.668757719532972,-0.275412751712292,-0.088091529576804,0.166939647006824,1.78296595426519 predictedValues: Include Exclude Both chr12.5696_chr12_78386146_78386443_+_1.R.tl.Lung 68.1882971023537 48.6712184917381 73.7199725322786 chr12.5696_chr12_78386146_78386443_+_1.R.tl.cerebhem 66.6175860205954 50.7326214679494 78.2706678822188 chr12.5696_chr12_78386146_78386443_+_1.R.tl.cortex 74.7878662634669 47.4117437253154 83.1601085949077 chr12.5696_chr12_78386146_78386443_+_1.R.tl.heart 71.9522734354658 48.9691957060157 80.3181116899205 chr12.5696_chr12_78386146_78386443_+_1.R.tl.kidney 73.7856702872549 49.8205441622571 83.625798977436 chr12.5696_chr12_78386146_78386443_+_1.R.tl.liver 66.2098388243193 49.6132684292755 81.0671581179548 chr12.5696_chr12_78386146_78386443_+_1.R.tl.stomach 71.5551278969483 46.8681027526039 84.9719218840765 chr12.5696_chr12_78386146_78386443_+_1.R.tl.testicle 78.551975131071 49.9486713154556 86.5578493092515 diffExp=19.5170786106156,15.8849645526460,27.3761225381515,22.9830777294501,23.9651261249978,16.5965703950438,24.6870251443444,28.6033038156154 diffExpScore=0.994463308227406 diffExp1.5=0,0,1,0,0,0,1,1 diffExp1.5Score=0.75 diffExp1.4=1,0,1,1,1,0,1,1 diffExp1.4Score=0.857142857142857 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 69.9182858198788 66.1861342658228 70.4932642338756 cerebhem 63.584261972772 67.3942415281503 68.7968857412994 cortex 66.4185902733618 70.3899547873973 81.6297622956207 heart 67.7655471940943 60.120377307951 65.3128009474515 kidney 66.6847613034877 67.4631714853336 63.1028670251941 liver 69.0531817628975 63.1310751174859 77.399309987801 stomach 67.4087812070371 71.4006529834961 71.3637585308558 testicle 74.1115491340447 77.842073365339 74.1491214830922 cont.diffExp=3.73215155405602,-3.80997955537828,-3.97136451403551,7.6451698861433,-0.778410181845956,5.92210664541157,-3.99187177645904,-3.73052423129434 cont.diffExpScore=16.6469773780545 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=-0.187301932180241 cont.tran.correlation=0.439023651566191 tran.covariance=-0.000327411038333962 cont.tran.covariance=0.00138588679999432 tran.mean=60.2302500632554 cont.tran.mean=68.0545399692844 weightedLogRatios: wLogRatio Lung 1.36684060913201 cerebhem 1.10669609223422 cortex 1.86268804692969 heart 1.57141554077708 kidney 1.61210571691938 liver 1.16829136213160 stomach 1.71744693087593 testicle 1.87325840884333 cont.weightedLogRatios: wLogRatio Lung 0.231487738460813 cerebhem -0.243334332954708 cortex -0.24536165035039 heart 0.49751838308949 kidney -0.0488096684024816 liver 0.375695509048691 stomach -0.243908096023787 testicle -0.212655532327931 varWeightedLogRatios=0.0869361883807838 cont.varWeightedLogRatios=0.0953197351411655 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92142271747927 0.0751860532678895 52.1562516854975 9.51847509752047e-234 *** df.mm.trans1 0.206083201596777 0.0648300185529638 3.17882373315094 0.00154937323500858 ** df.mm.trans2 -0.0534805551485484 0.0588847885884084 -0.90822360800793 0.364098779604545 df.mm.exp2 -0.0417222511307865 0.0774866929181819 -0.538444080648028 0.590455976973377 df.mm.exp3 -0.0543292093165724 0.0774866929181819 -0.701142444857448 0.483466605157898 df.mm.exp4 -0.0258877266979031 0.0774866929181819 -0.334092548319721 0.738418150663304 df.mm.exp5 -0.0238471496451989 0.0774866929181819 -0.30775800007853 0.758365679859983 df.mm.exp6 -0.105277594978380 0.0774866929181819 -1.35865386705227 0.174730508341305 df.mm.exp7 -0.131602419299914 0.0774866929181819 -1.69838735328236 0.0899162138822082 . df.mm.exp8 0.00685639267989662 0.0774866929181819 0.088484776181328 0.929518806938623 df.mm.trans1:exp2 0.0184178947988766 0.0706787384300115 0.260586071681438 0.794494773790688 df.mm.trans2:exp2 0.0832035169609923 0.0575700577021331 1.44525679288853 0.148870674246393 df.mm.trans1:exp3 0.146711912024228 0.0706787384300115 2.07575736753574 0.0383114264436706 * df.mm.trans2:exp3 0.0281113059300654 0.0575700577021331 0.488297338097401 0.625504948199503 df.mm.trans1:exp4 0.079617803582004 0.0706787384300115 1.12647459972484 0.260382965074683 df.mm.trans2:exp4 0.0319913087400003 0.0575700577021331 0.555693532661076 0.578612772179353 df.mm.trans1:exp5 0.102738739463557 0.0706787384300115 1.45360177255133 0.146542362858646 df.mm.trans2:exp5 0.0471867226673027 0.0575700577021331 0.819640009941388 0.41272365339729 df.mm.trans1:exp6 0.0758337162166062 0.0706787384300115 1.07293533955334 0.28370076180541 df.mm.trans2:exp6 0.124448042271288 0.0575700577021331 2.16167999891855 0.0310094276860242 * df.mm.trans1:exp7 0.179797638069415 0.0706787384300115 2.54387163754284 0.0111949419449054 * df.mm.trans2:exp7 0.0938518921677057 0.0575700577021331 1.63022056801288 0.103542332936896 df.mm.trans1:exp8 0.134631163209787 0.0706787384300115 1.90483257342098 0.0572456716989141 . df.mm.trans2:exp8 0.0190516524510324 0.0575700577021331 0.330929882849962 0.740804760217972 df.mm.trans1:probe2 1.46563812619362 0.0449155543957465 32.6309704045958 9.60697263053943e-139 *** df.mm.trans1:probe3 0.394015414911704 0.0449155543957465 8.77236004792623 1.54860163894098e-17 *** df.mm.trans1:probe4 0.733703378890452 0.0449155543957465 16.3351736110360 1.82000120682972e-50 *** df.mm.trans1:probe5 0.0859538269644123 0.0449155543957465 1.91367618903424 0.0561031823701066 . df.mm.trans1:probe6 -0.0539242182245364 0.0449155543957465 -1.20056891092594 0.230358512578085 df.mm.trans1:probe7 -0.254517447816192 0.0449155543957465 -5.66657700745858 2.19557713004927e-08 *** df.mm.trans1:probe8 0.213751038097229 0.0449155543957465 4.75895357349683 2.40293772565886e-06 *** df.mm.trans1:probe9 0.117412874171858 0.0449155543957465 2.61408048395317 0.00915532666989026 ** df.mm.trans1:probe10 0.070167126232514 0.0449155543957465 1.56220104986968 0.118730358356637 df.mm.trans1:probe11 -0.0461964856565274 0.0449155543957465 -1.02851865635443 0.304090859573263 df.mm.trans1:probe12 -0.164203616670081 0.0449155543957465 -3.65582967591358 0.000277199999151019 *** df.mm.trans1:probe13 -0.183459499617036 0.0449155543957465 -4.08454269540107 4.97141797709758e-05 *** df.mm.trans1:probe14 -0.109569507606477 0.0449155543957465 -2.43945575381461 0.0149774211166823 * df.mm.trans1:probe15 -0.0800071436447709 0.0449155543957465 -1.78127921877209 0.0753365984846088 . df.mm.trans1:probe16 -0.198656145731122 0.0449155543957465 -4.42288085728125 1.14270656228505e-05 *** df.mm.trans2:probe2 0.0850805274730954 0.0449155543957465 1.89423304727488 0.0586404917495131 . df.mm.trans2:probe3 -0.0194555499672708 0.0449155543957465 -0.433158406458704 0.665044281981866 df.mm.trans2:probe4 0.00562124774281743 0.0449155543957465 0.125151471877407 0.900442570516617 df.mm.trans2:probe5 0.118368041877682 0.0449155543957465 2.63534633981699 0.00860684049630713 ** df.mm.trans2:probe6 0.0332797937169344 0.0449155543957465 0.740941399135573 0.458998209950165 df.mm.trans3:probe2 -0.0539242182245364 0.0449155543957465 -1.20056891092594 0.230358512578085 df.mm.trans3:probe3 0.473476931104284 0.0449155543957465 10.5414914159252 4.40412785541013e-24 *** df.mm.trans3:probe4 0.275139818113653 0.0449155543957465 6.12571350426678 1.56883935283790e-09 *** df.mm.trans3:probe5 -0.104923872951617 0.0449155543957465 -2.33602533383298 0.0197948897101322 * df.mm.trans3:probe6 0.174602460585431 0.0449155543957465 3.88734955928687 0.000111816464974654 *** df.mm.trans3:probe7 0.819367655324482 0.0449155543957465 18.2424032464370 2.77134207994775e-60 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05991005804623 0.204532292279173 19.8497264798887 8.09268869348812e-69 *** df.mm.trans1 0.120872847096028 0.17636026532599 0.685374604492702 0.493353424956332 df.mm.trans2 0.134069770190343 0.160187165928888 0.83695700222239 0.402926243301018 df.mm.exp2 -0.0525139561557897 0.210790834667428 -0.249128270869286 0.803340749162592 df.mm.exp3 -0.136447413983963 0.210790834667428 -0.647311891900997 0.517660059733341 df.mm.exp4 -0.0510663379598474 0.210790834667428 -0.242260713282039 0.808655042912145 df.mm.exp5 0.0825110561342569 0.210790834667428 0.391435691520637 0.695604293110365 df.mm.exp6 -0.153168826406881 0.210790834667428 -0.72663892928998 0.467710385397445 df.mm.exp7 0.0270111111579644 0.210790834667428 0.128141772390535 0.898076659457043 df.mm.exp8 0.169894190909464 0.210790834667428 0.805984715500141 0.420548249290291 df.mm.trans1:exp2 -0.0424472721187155 0.192270823619149 -0.220768140062661 0.825342727881566 df.mm.trans2:exp2 0.0706025443293901 0.156610639296456 0.450815759686309 0.652273616241455 df.mm.trans1:exp3 0.0850971905366882 0.192270823619149 0.442590242944239 0.658210294213285 df.mm.trans2:exp3 0.1980269904612 0.156610639296456 1.26445426281892 0.206523029346029 df.mm.trans1:exp4 0.0197930355323574 0.192270823619149 0.102943520809811 0.91803974418634 df.mm.trans2:exp4 -0.0450558100879395 0.156610639296456 -0.287693162420794 0.773673892186746 df.mm.trans1:exp5 -0.129861810270308 0.192270823619149 -0.675410901279223 0.499656425606902 df.mm.trans2:exp5 -0.0634002036053507 0.156610639296456 -0.404826925489637 0.68573874741456 df.mm.trans1:exp6 0.140718569574999 0.192270823619149 0.73187687516092 0.464509139984141 df.mm.trans2:exp6 0.105910960029719 0.156610639296456 0.676269252877736 0.499111754123758 df.mm.trans1:exp7 -0.0635630314958465 0.192270823619149 -0.330591143780360 0.741060528133758 df.mm.trans2:exp7 0.0488249147459831 0.156610639296456 0.311759884036742 0.755323603521525 df.mm.trans1:exp8 -0.111650026733770 0.192270823619149 -0.580691467546459 0.561650927339699 df.mm.trans2:exp8 -0.00768310596797936 0.156610639296456 -0.0490586463505563 0.960887722655907 df.mm.trans1:probe2 -0.00436688592544851 0.122185975992377 -0.0357396656202260 0.971500982702887 df.mm.trans1:probe3 0.218534424937710 0.122185975992377 1.78853934064695 0.0741575263390038 . df.mm.trans1:probe4 0.0941738586505964 0.122185975992377 0.770741960243226 0.441141684639855 df.mm.trans1:probe5 0.166463977459492 0.122185975992377 1.3623820254943 0.173552146983035 df.mm.trans1:probe6 0.0258865403312652 0.122185975992377 0.211861796094179 0.832281680707533 df.mm.trans1:probe7 0.041319037890331 0.122185975992377 0.338165141741871 0.735348612124632 df.mm.trans1:probe8 0.130921997557356 0.122185975992377 1.07149774345236 0.284345824159845 df.mm.trans1:probe9 0.134056051106054 0.122185975992377 1.09714760648487 0.27298566874731 df.mm.trans1:probe10 0.105010475615889 0.122185975992377 0.859431491732246 0.390421305999389 df.mm.trans1:probe11 0.05381231232407 0.122185975992377 0.440413164334239 0.659785218752319 df.mm.trans1:probe12 0.206523161809294 0.122185975992377 1.69023621681574 0.0914652002570785 . df.mm.trans1:probe13 0.0914073214310833 0.122185975992377 0.748099941001299 0.454672131376276 df.mm.trans1:probe14 -0.110034929017153 0.122185975992377 -0.900552850877236 0.36816170605862 df.mm.trans1:probe15 0.220554019702664 0.122185975992377 1.8050681996142 0.071529506478454 . df.mm.trans1:probe16 0.0231691376934655 0.122185975992377 0.189621906321811 0.84966498381542 df.mm.trans2:probe2 0.0515028076666557 0.122185975992377 0.421511611691581 0.673521751670444 df.mm.trans2:probe3 -0.00494237917078893 0.122185975992377 -0.0404496435098025 0.967747150585944 df.mm.trans2:probe4 -0.0119832852583274 0.122185975992377 -0.098074146079375 0.921903838853226 df.mm.trans2:probe5 -0.0379534775535470 0.122185975992377 -0.31062057036656 0.756189280574251 df.mm.trans2:probe6 -0.0162385780098703 0.122185975992377 -0.132900505790316 0.894313453263199 df.mm.trans3:probe2 -0.0716218940457475 0.122185975992377 -0.586171149872518 0.557965253599038 df.mm.trans3:probe3 -0.044297891638205 0.122185975992377 -0.362544811533598 0.717063521333724 df.mm.trans3:probe4 -0.185946120679756 0.122185975992377 -1.52182866461989 0.128541336340561 df.mm.trans3:probe5 0.0355346019189492 0.122185975992377 0.290823898817702 0.771279349596178 df.mm.trans3:probe6 -0.193409237080438 0.122185975992377 -1.58290863996130 0.113931710851817 df.mm.trans3:probe7 0.0443919514996977 0.122185975992377 0.363314620513137 0.716488728423275