chr3.15339_chr3_97952915_97959596_+_2.R fitVsDatCorrelation=0.904793336301737 cont.fitVsDatCorrelation=0.282134784439214 fstatistic=8595.03123751412,57,807 cont.fstatistic=1682.13508875491,57,807 residuals=-0.880934183983195,-0.0952805769529655,-0.00627820709519206,0.0948247048365881,0.82655067000907 cont.residuals=-0.83142375096816,-0.267584130040908,-0.046904915236321,0.171338860551851,2.29656020874519 predictedValues: Include Exclude Both chr3.15339_chr3_97952915_97959596_+_2.R.tl.Lung 58.7917687805867 61.9783440308901 82.5380964714982 chr3.15339_chr3_97952915_97959596_+_2.R.tl.cerebhem 55.8189514832712 50.1628099173074 67.5918457061769 chr3.15339_chr3_97952915_97959596_+_2.R.tl.cortex 54.8099517298398 55.6199344627533 67.7289482562717 chr3.15339_chr3_97952915_97959596_+_2.R.tl.heart 58.1764802044467 64.3855779141633 75.4662370624489 chr3.15339_chr3_97952915_97959596_+_2.R.tl.kidney 58.0258647961643 73.3540886846295 101.73666842485 chr3.15339_chr3_97952915_97959596_+_2.R.tl.liver 61.2021653777742 150.649120493434 208.535252435773 chr3.15339_chr3_97952915_97959596_+_2.R.tl.stomach 61.2241015746818 64.5410923105103 81.8702740593248 chr3.15339_chr3_97952915_97959596_+_2.R.tl.testicle 61.6244652205812 80.9154761973714 109.184043642001 diffExp=-3.1865752503034,5.65614156596379,-0.809982732913546,-6.20909770971657,-15.3282238884652,-89.4469551156596,-3.31699073582846,-19.2910109767902 diffExpScore=1.07757522063366 diffExp1.5=0,0,0,0,0,-1,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,-1,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,-1,0,-1 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,0,0,-1,-1,0,-1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 85.903551220894 67.8125030868557 64.9876225194285 cerebhem 88.548470879801 83.5526771989265 75.1091523067332 cortex 70.6746766469005 85.157868037865 72.475166823239 heart 75.4463910184096 77.5709275629333 81.1960350678673 kidney 77.5593717257082 80.676166886649 78.5055011347154 liver 72.4382410963179 76.687154102819 77.2314696019823 stomach 70.9092376078716 67.8549793222971 71.8151959731596 testicle 69.392257589304 80.9960605770401 67.8779353431385 cont.diffExp=18.0910481340382,4.99579368087448,-14.4831913909644,-2.12453654452368,-3.11679516094077,-4.24891300650103,3.05425828557449,-11.6038029877361 cont.diffExpScore=5.91390544426775 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,-1,0,0,0,0,0 cont.diffExp1.2Score=2 tran.correlation=0.584284965097964 cont.tran.correlation=-0.080804734908693 tran.covariance=0.00974994791307386 cont.tran.covariance=-0.000738999937112113 tran.mean=66.9550120736503 cont.tran.mean=76.948783410037 weightedLogRatios: wLogRatio Lung -0.216431788223374 cerebhem 0.424013368170656 cortex -0.0588439699406404 heart -0.417213410984610 kidney -0.97938435742235 liver -4.11162982662408 stomach -0.218479915853723 testicle -1.15944076051658 cont.weightedLogRatios: wLogRatio Lung 1.02513130601705 cerebhem 0.258686074263349 cortex -0.811166521570345 heart -0.120448595373261 kidney -0.172204984925833 liver -0.245739750923299 stomach 0.186651485994697 testicle -0.667530665355371 varWeightedLogRatios=1.99811454249107 cont.varWeightedLogRatios=0.331920116297406 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.97344928888511 0.0841586523476246 47.2137941619173 2.09777160017496e-234 *** df.mm.trans1 -0.0378319532107366 0.0721701226581507 -0.524205194855159 0.600279771425581 df.mm.trans2 0.101459265558548 0.0641503794029957 1.58158480904962 0.114136205740036 df.mm.exp2 -0.0636269226604724 0.0824477055663026 -0.771724600744712 0.440503473432331 df.mm.exp3 0.0193728338296290 0.0824477055663026 0.234971169865361 0.814290717503243 df.mm.exp4 0.117158520031156 0.0824477055663025 1.42100400764870 0.155701872643044 df.mm.exp5 -0.0537276222439797 0.0824477055663025 -0.651656973046669 0.514808020271086 df.mm.exp6 0.00150096456664309 0.0824477055663025 0.0182050495684934 0.985479774233492 df.mm.exp7 0.0891801383090589 0.0824477055663025 1.08165700544986 0.279728076183365 df.mm.exp8 0.0339021979121914 0.0824477055663025 0.411196378108158 0.681037714976519 df.mm.trans1:exp2 0.0117385083216157 0.0751285548024256 0.156245629274858 0.875878488269298 df.mm.trans2:exp2 -0.147884197703635 0.0560841350062562 -2.63682764630566 0.00852944731154925 ** df.mm.trans1:exp3 -0.0895029138344336 0.0751285548024256 -1.19133016826705 0.233874252824253 df.mm.trans2:exp3 -0.127616197532952 0.0560841350062562 -2.27544202150423 0.0231408282500787 * df.mm.trans1:exp4 -0.127679225551658 0.0751285548024256 -1.69947666220163 0.0896147823655257 . df.mm.trans2:exp4 -0.0790538916374578 0.0560841350062562 -1.40955889983218 0.159055214024294 df.mm.trans1:exp5 0.0406146197672285 0.0751285548024256 0.540601637739972 0.588931359180221 df.mm.trans2:exp5 0.222240833196334 0.0560841350062562 3.96263280465221 8.06912262854714e-05 *** df.mm.trans1:exp6 0.038679747934673 0.0751285548024256 0.514847490896021 0.606800575572303 df.mm.trans2:exp6 0.886667428709019 0.0560841350062562 15.809594435398 3.00867831374295e-49 *** df.mm.trans1:exp7 -0.0486410681172182 0.0751285548024256 -0.647437825007221 0.517532750777486 df.mm.trans2:exp7 -0.0486630614824763 0.0560841350062562 -0.867679629489657 0.385827675905472 df.mm.trans1:exp8 0.0131548980700265 0.0751285548024256 0.175098510874081 0.861046149901824 df.mm.trans2:exp8 0.232717873990175 0.0560841350062562 4.14944215443841 3.68669807823700e-05 *** df.mm.trans1:probe2 0.194854845762133 0.0523368461536498 3.72309109322485 0.000210471821772957 *** df.mm.trans1:probe3 0.255616972613307 0.0523368461536498 4.88407291228191 1.25311339216088e-06 *** df.mm.trans1:probe4 -0.0779279887719383 0.0523368461536498 -1.48896990359638 0.136885968828392 df.mm.trans1:probe5 0.409391384654733 0.0523368461536498 7.82224025217047 1.62351085260134e-14 *** df.mm.trans1:probe6 0.560595435168676 0.0523368461536498 10.7112957002202 4.00541875502701e-25 *** df.mm.trans1:probe7 0.307867810411059 0.0523368461536498 5.88242955082209 5.92010754090119e-09 *** df.mm.trans1:probe8 -0.117674681034848 0.0523368461536498 -2.24840986194278 0.0248191781125001 * df.mm.trans1:probe9 -0.09265593275138 0.0523368461536498 -1.77037669559533 0.0770418974229785 . df.mm.trans1:probe10 -0.0525160226889968 0.0523368461536498 -1.00342352565191 0.315957329033389 df.mm.trans1:probe11 0.0852777095101057 0.0523368461536498 1.62940100096495 0.103618406886672 df.mm.trans1:probe12 -0.047928821957658 0.0523368461536498 -0.91577589174841 0.360058014001334 df.mm.trans1:probe13 0.00644545048429836 0.0523368461536498 0.123153207691879 0.902016442223025 df.mm.trans1:probe14 0.552665834163047 0.0523368461536498 10.5597848319048 1.66630932789552e-24 *** df.mm.trans1:probe15 0.318948235843084 0.0523368461536498 6.09414321426094 1.70300123623505e-09 *** df.mm.trans1:probe16 0.439844267820737 0.0523368461536498 8.40410342131523 1.93690004064302e-16 *** df.mm.trans1:probe17 0.48855286463834 0.0523368461536498 9.33477846953278 9.56856437302518e-20 *** df.mm.trans1:probe18 0.458423281540398 0.0523368461536498 8.75909259405058 1.14485803588317e-17 *** df.mm.trans1:probe19 0.461755035788152 0.0523368461536498 8.82275241485775 6.82582225431423e-18 *** df.mm.trans2:probe2 0.194394423866908 0.0523368461536498 3.71429381312368 0.000217801108714502 *** df.mm.trans2:probe3 0.157756294376703 0.0523368461536498 3.01424915658014 0.00265680156326076 ** df.mm.trans2:probe4 0.180369763725647 0.0523368461536498 3.44632466381562 0.000597642558363803 *** df.mm.trans2:probe5 0.112037540617149 0.0523368461536498 2.14070103284846 0.032597218478942 * df.mm.trans2:probe6 0.237342133353730 0.0523368461536498 4.53489560026113 6.63718735626314e-06 *** df.mm.trans3:probe2 0.283609533748271 0.0523368461536498 5.41892671399522 7.92090026148437e-08 *** df.mm.trans3:probe3 0.410975018472318 0.0523368461536498 7.85249873990846 1.29789831536048e-14 *** df.mm.trans3:probe4 1.16396733728912 0.0523368461536498 22.2399212568515 7.71775591227893e-86 *** df.mm.trans3:probe5 0.161343869609636 0.0523368461536498 3.08279694836721 0.00212041303150964 ** df.mm.trans3:probe6 0.197969403020415 0.0523368461536498 3.78260093164994 0.000166663132500181 *** df.mm.trans3:probe7 0.302333466715207 0.0523368461536498 5.77668485845748 1.08738233264184e-08 *** df.mm.trans3:probe8 0.219260988992781 0.0523368461536498 4.18941921622632 3.10512030966713e-05 *** df.mm.trans3:probe9 0.484685207620945 0.0523368461536498 9.26087915572927 1.7923401161123e-19 *** df.mm.trans3:probe10 0.520454078621346 0.0523368461536498 9.9443148922922 4.66329506970063e-22 *** df.mm.trans3:probe11 0.393418066187946 0.0523368461536498 7.51703809268434 1.49116557447147e-13 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.52014326986283 0.189596128079174 23.8409049575909 1.61545553859217e-95 *** df.mm.trans1 -0.00543287196857055 0.162587867525076 -0.0334149900067584 0.97335191875361 df.mm.trans2 -0.291096860104228 0.144520654862427 -2.01422322906945 0.0443174627131662 * df.mm.exp2 0.0943100438910148 0.185741635688442 0.507748537593412 0.611768444937042 df.mm.exp3 -0.0764247730664961 0.185741635688442 -0.411457413860018 0.680846410672016 df.mm.exp4 -0.218026283008980 0.185741635688442 -1.17381481109971 0.240815508127277 df.mm.exp5 -0.117456698501681 0.185741635688442 -0.632366017809273 0.527326851679079 df.mm.exp6 -0.220113377174359 0.185741635688442 -1.18505135565604 0.236345998301703 df.mm.exp7 -0.291097539972999 0.185741635688442 -1.56721748946627 0.117455872199798 df.mm.exp8 -0.079310135556464 0.185741635688442 -0.426991693394456 0.669499269778105 df.mm.trans1:exp2 -0.0639851177226434 0.169252747060249 -0.37804478115718 0.705496743349594 df.mm.trans2:exp2 0.114420664456913 0.126348682485242 0.905594440767342 0.365420841092113 df.mm.trans1:exp3 -0.118713068049405 0.169252747060249 -0.701395221710324 0.483258610147568 df.mm.trans2:exp3 0.304184988739996 0.126348682485242 2.40750423951217 0.0162854725971491 * df.mm.trans1:exp4 0.0882234648476134 0.169252747060249 0.52125277952628 0.602333697755556 df.mm.trans2:exp4 0.352472405938463 0.126348682485242 2.78968010592144 0.00540037172123869 ** df.mm.trans1:exp5 0.0152752588764066 0.169252747060249 0.0902511725317463 0.928110015148682 df.mm.trans2:exp5 0.291153311122625 0.126348682485242 2.30436364982779 0.0214553479770686 * df.mm.trans1:exp6 0.0496225595866491 0.169252747060249 0.293186139950715 0.769455252250596 df.mm.trans2:exp6 0.343100999903398 0.126348682485242 2.71550912249104 0.00675885567754392 ** df.mm.trans1:exp7 0.099273086211873 0.169252747060249 0.586537518215492 0.557678383675558 df.mm.trans2:exp7 0.29172372151739 0.126348682485242 2.30887822317787 0.0212021519968219 * df.mm.trans1:exp8 -0.134139734741278 0.169252747060249 -0.792540960611578 0.428278238646153 df.mm.trans2:exp8 0.256964064939604 0.126348682485242 2.03376924780849 0.0423015164693775 * df.mm.trans1:probe2 -0.113419381702968 0.117906633599837 -0.961942328774325 0.336366709881384 df.mm.trans1:probe3 -0.0397099829091179 0.117906633599837 -0.336791762233578 0.736361447539757 df.mm.trans1:probe4 -0.0223933662516814 0.117906633599837 -0.189924566311360 0.849416002913693 df.mm.trans1:probe5 -0.214672869275767 0.117906633599837 -1.82070221769154 0.0690223499305113 . df.mm.trans1:probe6 -0.138856038112854 0.117906633599837 -1.17767791237359 0.239272220124117 df.mm.trans1:probe7 -0.19192139282654 0.117906633599837 -1.62774041601342 0.103970268096973 df.mm.trans1:probe8 -0.0984768433405212 0.117906633599837 -0.835210372257268 0.403846464542310 df.mm.trans1:probe9 0.154330980767112 0.117906633599837 1.30892534249511 0.190932239556854 df.mm.trans1:probe10 -0.068309483591881 0.117906633599837 -0.579352335880578 0.56251314228714 df.mm.trans1:probe11 -0.193141166598919 0.117906633599837 -1.63808566746482 0.101793633882556 df.mm.trans1:probe12 -0.0469158924220465 0.117906633599837 -0.397907148984291 0.690803865800473 df.mm.trans1:probe13 -0.120551325691837 0.117906633599837 -1.02243039268660 0.306883633201707 df.mm.trans1:probe14 -0.229907006853561 0.117906633599837 -1.94990731084598 0.0515331268579708 . df.mm.trans1:probe15 -0.0910853574984972 0.117906633599837 -0.772521059397146 0.440032063507876 df.mm.trans1:probe16 -0.0239824000899902 0.117906633599837 -0.203401618363425 0.838872410921146 df.mm.trans1:probe17 -0.130755963415010 0.117906633599837 -1.10897885405483 0.267769677588225 df.mm.trans1:probe18 -0.162820262299629 0.117906633599837 -1.38092537568517 0.167684196243243 df.mm.trans1:probe19 -0.111969100517879 0.117906633599837 -0.949642077797681 0.342578451300292 df.mm.trans2:probe2 -0.150924094519543 0.117906633599837 -1.28003056241741 0.200902217039159 df.mm.trans2:probe3 -0.0395230455213456 0.117906633599837 -0.335206292594891 0.737556590443223 df.mm.trans2:probe4 -0.109534287804890 0.117906633599837 -0.928991732362052 0.353171154165069 df.mm.trans2:probe5 0.0730686953151006 0.117906633599837 0.619716576448856 0.535619329431434 df.mm.trans2:probe6 0.0178157839733017 0.117906633599837 0.151100777194323 0.87993398108647 df.mm.trans3:probe2 -0.0549124392601104 0.117906633599837 -0.465728157810676 0.641535759583854 df.mm.trans3:probe3 -0.062199308678997 0.117906633599837 -0.52753018876016 0.597970457844413 df.mm.trans3:probe4 -0.160728963460536 0.117906633599837 -1.36318847000614 0.173203319602164 df.mm.trans3:probe5 -0.0837741473396294 0.117906633599837 -0.710512587645834 0.47759160472646 df.mm.trans3:probe6 0.121978280511391 0.117906633599837 1.03453280606224 0.301197057355408 df.mm.trans3:probe7 -0.0669867531389719 0.117906633599837 -0.568133879271951 0.570102103713568 df.mm.trans3:probe8 -0.0438684701681244 0.117906633599837 -0.372061086206646 0.709945045434777 df.mm.trans3:probe9 -0.0547415039565921 0.117906633599837 -0.464278406441313 0.642573512133365 df.mm.trans3:probe10 -0.158012304861406 0.117906633599837 -1.34014770871742 0.180574519970178 df.mm.trans3:probe11 0.0196618134964328 0.117906633599837 0.166757483409822 0.867602646594745