chr3.15555_chr3_90883689_90884336_-_1.R fitVsDatCorrelation=0.846415638350159 cont.fitVsDatCorrelation=0.322191255533874 fstatistic=6935.2904326442,37,347 cont.fstatistic=2188.10985344292,37,347 residuals=-0.521001365282805,-0.124358699463712,-0.0003124998347559,0.117841854461291,0.588704513480804 cont.residuals=-0.874242788567102,-0.194962165700973,-0.00940075592862217,0.228073405839861,0.80283064040177 predictedValues: Include Exclude Both chr3.15555_chr3_90883689_90884336_-_1.R.tl.Lung 185.41823691509 160.47108505954 91.758471418996 chr3.15555_chr3_90883689_90884336_-_1.R.tl.cerebhem 133.917650377177 90.6627447648778 67.2715201262101 chr3.15555_chr3_90883689_90884336_-_1.R.tl.cortex 124.251567189581 110.989613879889 117.924653881362 chr3.15555_chr3_90883689_90884336_-_1.R.tl.heart 117.39500593747 113.235070289277 102.169472806913 chr3.15555_chr3_90883689_90884336_-_1.R.tl.kidney 175.509309996992 151.121658855644 82.6592512319371 chr3.15555_chr3_90883689_90884336_-_1.R.tl.liver 150.977021843149 132.302489106947 81.4315951754853 chr3.15555_chr3_90883689_90884336_-_1.R.tl.stomach 129.526399346448 110.920668247955 86.3210674192945 chr3.15555_chr3_90883689_90884336_-_1.R.tl.testicle 134.033424283114 116.120490831585 84.239730817927 diffExp=24.9471518555500,43.2549056122992,13.2619533096925,4.15993564819308,24.3876511413478,18.6745327362024,18.6057310984933,17.9129334515294 diffExpScore=0.993983326408347 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,1,0,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 124.335130927980 98.0296624201452 128.446164103204 cerebhem 126.891333406437 119.833060122337 105.040331850618 cortex 98.2988724765323 105.489181346657 121.234456074517 heart 107.457459249110 115.653241608533 129.679105393469 kidney 114.341048903806 116.782019146977 118.717280699527 liver 96.6637063834313 119.148068939807 120.794509912912 stomach 122.225244913969 105.047619703920 116.285061510518 testicle 112.153765460024 108.118888996368 112.710233103237 cont.diffExp=26.3054685078350,7.05827328410014,-7.1903088701243,-8.19578235942359,-2.44097024317099,-22.4843625563756,17.1776252100494,4.03487646365669 cont.diffExpScore=6.21610153262329 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=1,0,0,0,0,-1,0,0 cont.diffExp1.2Score=2 tran.correlation=0.89142526138704 cont.tran.correlation=-0.230105433592208 tran.covariance=0.0256150665483642 cont.tran.covariance=-0.0018100383276971 tran.mean=133.553277307796 cont.tran.mean=111.904269000377 weightedLogRatios: wLogRatio Lung 0.744228831825359 cerebhem 1.83422177377125 cortex 0.537931886566667 heart 0.17128258732019 kidney 0.761931229808415 liver 0.653728460662208 stomach 0.74221675953543 testicle 0.692393474396735 cont.weightedLogRatios: wLogRatio Lung 1.11821988595081 cerebhem 0.275553232641998 cortex -0.326386506828724 heart -0.346474149381153 kidney -0.100331163017327 liver -0.977845837296538 stomach 0.716388378099816 testicle 0.172261965435647 varWeightedLogRatios=0.223695469367007 cont.varWeightedLogRatios=0.432393820412517 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.9360209900118 0.100047313388998 59.3321378549338 9.62948906832422e-184 *** df.mm.trans1 -0.614626655059033 0.0833531417287864 -7.3737671107695 1.22913929410320e-12 *** df.mm.trans2 -0.854110748850585 0.0833531417287864 -10.2468932920331 1.05988972847891e-21 *** df.mm.exp2 -0.585933357397771 0.114843119112267 -5.10203277242045 5.54389704559447e-07 *** df.mm.exp3 -1.01986895797913 0.114843119112267 -8.8805403916463 3.63917553515909e-17 *** df.mm.exp4 -0.91319060927192 0.114843119112267 -7.95163538164803 2.61890278734233e-14 *** df.mm.exp5 -0.01051743077589 0.114843119112267 -0.0915808527074966 0.927083909772693 df.mm.exp6 -0.279112778016564 0.114843119112267 -2.43038311893734 0.0155891801122104 * df.mm.exp7 -0.666941695334326 0.114843119112267 -5.80741537229014 1.43443770424855e-08 *** df.mm.exp8 -0.562517072789838 0.114843119112267 -4.89813475233061 1.48643386774932e-06 *** df.mm.trans1:exp2 0.260544405479708 0.0970601507440262 2.68436019810884 0.00761547448054932 ** df.mm.trans2:exp2 0.0149661068724844 0.0970601507440262 0.154194144123618 0.877546299079245 df.mm.trans1:exp3 0.619563222540129 0.0970601507440262 6.38329136922612 5.55640801627822e-10 *** df.mm.trans2:exp3 0.651191815316134 0.0970601507440262 6.70915726304096 7.97812919632877e-11 *** df.mm.trans1:exp4 0.456120963365973 0.0970601507440262 4.69936384674373 3.76862842676713e-06 *** df.mm.trans2:exp4 0.564542764321644 0.0970601507440262 5.81642167247912 1.36580010095732e-08 *** df.mm.trans1:exp5 -0.0444044928413209 0.0970601507440262 -0.457494579401876 0.647601906439747 df.mm.trans2:exp5 -0.0495111399635036 0.0970601507440262 -0.510107799997939 0.610300247470882 df.mm.trans1:exp6 0.0736264164957682 0.0970601507440262 0.75856482739184 0.44862783523125 df.mm.trans2:exp6 0.0860898921240914 0.0970601507440262 0.886974638553093 0.375706879405839 df.mm.trans1:exp7 0.308212398123221 0.0970601507440262 3.17547825508803 0.00162991399064241 ** df.mm.trans2:exp7 0.297643169709786 0.0970601507440262 3.06658466351192 0.00233518282478768 ** df.mm.trans1:exp8 0.237992263053614 0.0970601507440262 2.45200796855616 0.0146982671780482 * df.mm.trans2:exp8 0.239031667923542 0.0970601507440262 2.46271684199145 0.0142739975879592 * df.mm.trans1:probe2 0.0632396990990572 0.053162033997355 1.18956507763047 0.235030683280690 df.mm.trans1:probe3 -0.532803423577071 0.053162033997355 -10.0222542953037 6.24315638334964e-21 *** df.mm.trans1:probe4 -0.121514932404550 0.053162033997355 -2.28574648612195 0.0228715153063488 * df.mm.trans1:probe5 -0.149027447620929 0.053162033997355 -2.80326835554004 0.00534311894917658 ** df.mm.trans1:probe6 -0.247697109703908 0.053162033997355 -4.65928579249303 4.5291320322651e-06 *** df.mm.trans2:probe2 0.0638952022615553 0.053162033997355 1.20189536511591 0.230223664919642 df.mm.trans2:probe3 -0.106069798316987 0.053162033997355 -1.99521708146578 0.0468008043773735 * df.mm.trans2:probe4 0.0491723527279661 0.053162033997355 0.924952433731422 0.355633435946159 df.mm.trans2:probe5 0.0282012588055425 0.053162033997355 0.530477423172817 0.596120269377457 df.mm.trans2:probe6 -0.073163717611551 0.053162033997355 -1.37623999892839 0.169634812276336 df.mm.trans3:probe2 -0.0676361113425411 0.053162033997355 -1.27226342291392 0.204131435659093 df.mm.trans3:probe3 0.376189943721473 0.053162033997355 7.0762895140579 8.23852049821222e-12 *** df.mm.trans3:probe4 -0.101048919929343 0.053162033997355 -1.90077226793787 0.0581602533855549 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.38014314581281 0.177855754567537 24.6275030935225 3.65527669401435e-78 *** df.mm.trans1 0.381333577404149 0.148178251025164 2.57347873096025 0.0104827968501575 * df.mm.trans2 0.177376242712068 0.148178251025164 1.19704640515662 0.232105610366876 df.mm.exp2 0.422345457802822 0.204158501759908 2.06871354443766 0.0393131920349853 * df.mm.exp3 -0.103846205449831 0.204158501759908 -0.50865481748076 0.611317440153234 df.mm.exp4 0.00988764104189057 0.204158501759908 0.0484311990764829 0.96140045812688 df.mm.exp5 0.170009039017418 0.204158501759908 0.832730636010203 0.405569424098701 df.mm.exp6 0.00477333072921909 0.204158501759908 0.0233805141009145 0.981360187228922 df.mm.exp7 0.151493937997592 0.204158501759908 0.742040800121812 0.458564577044169 df.mm.exp8 0.12554122568345 0.204158501759908 0.614920390780922 0.539010321186653 df.mm.trans1:exp2 -0.401994968821177 0.172545426401385 -2.32979208551163 0.0203902481495951 * df.mm.trans2:exp2 -0.221515959866777 0.172545426401385 -1.28381241095011 0.200064185341069 df.mm.trans1:exp3 -0.131121826428379 0.172545426401385 -0.75992640989136 0.447814556972508 df.mm.trans2:exp3 0.177184496001030 0.172545426401385 1.02688607688072 0.305189395535876 df.mm.trans1:exp4 -0.155773188447371 0.172545426401385 -0.902795233094166 0.367260837464379 df.mm.trans2:exp4 0.155438666086088 0.172545426401385 0.900856483581881 0.368289430296649 df.mm.trans1:exp5 -0.253803988409792 0.172545426401385 -1.47094010953022 0.142213536112852 df.mm.trans2:exp5 0.00502996323692764 0.172545426401385 0.0291515303641062 0.976760496609466 df.mm.trans1:exp6 -0.256515909242383 0.172545426401385 -1.48665725074428 0.138013389546298 df.mm.trans2:exp6 0.190323555087083 0.172545426401385 1.10303448231854 0.270776577905255 df.mm.trans1:exp7 -0.168608914475850 0.172545426401385 -0.977185648975833 0.329158005169491 df.mm.trans2:exp7 -0.0823502802894018 0.172545426401385 -0.477267244962111 0.633472288527725 df.mm.trans1:exp8 -0.228650978805865 0.172545426401385 -1.32516395000795 0.185988668052851 df.mm.trans2:exp8 -0.0275798906107604 0.172545426401385 -0.159841330981457 0.87309906638323 df.mm.trans1:probe2 0.0952188360246593 0.0945070222343862 1.00753186137330 0.314381299784503 df.mm.trans1:probe3 0.106573820764677 0.0945070222343862 1.12768150180802 0.260233581826307 df.mm.trans1:probe4 0.139461697961076 0.0945070222343862 1.47567550710886 0.140937830401787 df.mm.trans1:probe5 0.201468616927886 0.0945070222343862 2.13178462472582 0.0337266031910963 * df.mm.trans1:probe6 0.0723156835219406 0.0945070222343862 0.765188467610279 0.444679431828519 df.mm.trans2:probe2 0.0830006313123488 0.0945070222343862 0.878248296793221 0.380416689419603 df.mm.trans2:probe3 0.180571174836132 0.0945070222343862 1.91066410269809 0.0568716505778739 . df.mm.trans2:probe4 0.0460608585236970 0.0945070222343862 0.487380275398602 0.626296623652091 df.mm.trans2:probe5 0.0286006750246053 0.0945070222343862 0.302630157510126 0.762353057010493 df.mm.trans2:probe6 -0.0607261186507275 0.0945070222343862 -0.642556682191521 0.520936113018723 df.mm.trans3:probe2 -0.102130022029975 0.0945070222343862 -1.08066067066089 0.280598576727043 df.mm.trans3:probe3 -0.172614705975446 0.0945070222343862 -1.82647492106296 0.0686377921919749 . df.mm.trans3:probe4 -0.0586276950978164 0.0945070222343862 -0.62035279190592 0.53543274125556