chr11.3304_chr11_70482636_70483077_-_1.R fitVsDatCorrelation=0.958511452526707 cont.fitVsDatCorrelation=0.296666777250288 fstatistic=7055.54467301974,36,324 cont.fstatistic=620.43227122836,36,324 residuals=-0.682408704622589,-0.09436591706118,0.00141969713328668,0.086717005392363,0.690507933554655 cont.residuals=-0.91156748445132,-0.42104132327964,-0.142250230524261,0.452581822972466,1.16965002156982 predictedValues: Include Exclude Both chr11.3304_chr11_70482636_70483077_-_1.R.tl.Lung 126.463958772340 43.2225861125689 59.8247318093433 chr11.3304_chr11_70482636_70483077_-_1.R.tl.cerebhem 118.077355821941 50.0227952676653 57.9944326127082 chr11.3304_chr11_70482636_70483077_-_1.R.tl.cortex 113.161526422786 45.8285085260106 51.3091256636116 chr11.3304_chr11_70482636_70483077_-_1.R.tl.heart 125.350823015725 48.0006995069388 53.6985741484853 chr11.3304_chr11_70482636_70483077_-_1.R.tl.kidney 160.859928778131 42.6244986133286 64.2521076072236 chr11.3304_chr11_70482636_70483077_-_1.R.tl.liver 172.371318391661 48.8162892231873 65.6085357742315 chr11.3304_chr11_70482636_70483077_-_1.R.tl.stomach 118.500712176229 46.911173163682 56.1642355066205 chr11.3304_chr11_70482636_70483077_-_1.R.tl.testicle 167.780878887034 48.2120635315459 60.3031908350122 diffExp=83.2413726597707,68.0545605542755,67.3330178967749,77.3501235087858,118.235430164802,123.555029168474,71.5895390125468,119.568815355488 diffExpScore=0.998630001653588 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 68.1956348730854 70.8227287488878 59.688922330089 cerebhem 82.2033134422366 77.6068730335021 73.3881262169653 cortex 58.1150672960733 73.0755624895103 78.1324721106044 heart 63.8175369309208 82.5765510032717 95.9526617987336 kidney 87.0465564868475 79.1540311433525 61.5989905833037 liver 70.9104634709183 74.1948381829109 78.7178015105206 stomach 65.0680704005048 89.8139559764596 100.356123851652 testicle 68.7565603547226 89.1692619399169 79.9709022015946 cont.diffExp=-2.6270938758024,4.59644040873451,-14.960495193437,-18.7590140723509,7.89252534349501,-3.28437471199256,-24.7458855759548,-20.4127015851943 cont.diffExpScore=1.32711781002758 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,-1,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,-1,-1,0,0,-1,-1 cont.diffExp1.2Score=0.8 tran.correlation=-0.00086444148481275 cont.tran.correlation=-0.0497754810433014 tran.covariance=-0.000231933583543056 cont.tran.covariance=-0.000215826363026711 tran.mean=92.2628197631733 cont.tran.mean=75.03293786082 weightedLogRatios: wLogRatio Lung 4.61984778544555 cerebhem 3.72909733553092 cortex 3.86589718426893 heart 4.17668774005926 kidney 5.86555040602869 liver 5.70093155730579 stomach 3.99538892510573 testicle 5.61064316495333 cont.weightedLogRatios: wLogRatio Lung -0.160317928651916 cerebhem 0.252048025046141 cortex -0.956812117671162 heart -1.10420303916043 kidney 0.420006765952347 liver -0.193967409506796 stomach -1.39770986805598 testicle -1.13358812327866 varWeightedLogRatios=0.80065963696789 cont.varWeightedLogRatios=0.48466604255422 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.32226920801179 0.092949713440763 46.5011568945436 2.01113492310119e-145 *** df.mm.trans1 0.466826153967993 0.0787274363185993 5.9296501422809 7.77907662137566e-09 *** df.mm.trans2 -0.610643468185348 0.0787274363185994 -7.75642516433732 1.14540942120115e-13 *** df.mm.exp2 0.108570366758789 0.109637500589946 0.990266707783243 0.322782726707575 df.mm.exp3 0.100952574126118 0.109637500589946 0.920785074293965 0.357847720252634 df.mm.exp4 0.204044134073945 0.109637500589946 1.86107976719652 0.0636384835466226 . df.mm.exp5 0.155247233932835 0.109637500589946 1.41600486236432 0.157734681315175 df.mm.exp6 0.339107833354434 0.109637500589946 3.09299128062694 0.00215384544471951 ** df.mm.exp7 0.0799932847696087 0.109637500589946 0.729616092479075 0.46615202258443 df.mm.exp8 0.383981696756069 0.109637500589946 3.50228429770754 0.000526043832969977 *** df.mm.trans1:exp2 -0.177187755946387 0.0949488607183246 -1.86613883100748 0.062925280192866 . df.mm.trans2:exp2 0.0375452548257422 0.0949488607183246 0.395426069799026 0.692788697875027 df.mm.trans1:exp3 -0.212093695399208 0.0949488607183246 -2.2337676702452 0.026180465590265 * df.mm.trans2:exp3 -0.0424094050557777 0.0949488607183246 -0.446655228245334 0.655422405657247 df.mm.trans1:exp4 -0.212885100431637 0.0949488607183246 -2.2421027363686 0.0256309782074116 * df.mm.trans2:exp4 -0.0991917355146657 0.0949488607183246 -1.04468589474631 0.296946953729835 df.mm.trans1:exp5 0.0853293881039667 0.0949488607183246 0.898687856372548 0.369486320680378 df.mm.trans2:exp5 -0.169181246439145 0.0949488607183246 -1.78181439102295 0.0757160133241156 . df.mm.trans1:exp6 -0.0294142122851608 0.0949488607183246 -0.309790049745000 0.756919768844714 df.mm.trans2:exp6 -0.217406965939211 0.0949488607183246 -2.28972695716877 0.0226796854621487 * df.mm.trans1:exp7 -0.145031670968662 0.0949488607183246 -1.52747141852405 0.127619442038555 df.mm.trans2:exp7 0.00189941077302257 0.0949488607183246 0.0200045662333681 0.984052043503753 df.mm.trans1:exp8 -0.101280217703644 0.0949488607183246 -1.06668175834254 0.286909745695101 df.mm.trans2:exp8 -0.274735611580697 0.0949488607183246 -2.89351140711132 0.00406770337194037 ** df.mm.trans1:probe2 -0.00876127570456233 0.0474744303591623 -0.18454725287444 0.853699618058056 df.mm.trans1:probe3 0.236402568113193 0.0474744303591623 4.97957671792408 1.03789975148702e-06 *** df.mm.trans1:probe4 0.205590413669624 0.0474744303591623 4.33055040606602 1.98564388662643e-05 *** df.mm.trans1:probe5 0.0850021258158715 0.0474744303591623 1.79048226956696 0.0743099299545201 . df.mm.trans1:probe6 -0.0604758795727744 0.0474744303591623 -1.27386214253128 0.203625303881999 df.mm.trans2:probe2 0.202589766604705 0.0474744303591623 4.26734486484693 2.60032099955259e-05 *** df.mm.trans2:probe3 0.0224394570373729 0.0474744303591623 0.472664060792511 0.636771103076391 df.mm.trans2:probe4 0.0756841411184786 0.0474744303591623 1.59420851489737 0.111864334606168 df.mm.trans2:probe5 0.115447586081354 0.0474744303591623 2.43178454607984 0.0155661300884613 * df.mm.trans2:probe6 0.076476058452935 0.0474744303591623 1.61088943825896 0.108177552257057 df.mm.trans3:probe2 -0.384500867972124 0.0474744303591623 -8.09911493541317 1.13673168148828e-14 *** df.mm.trans3:probe3 0.123402639183592 0.0474744303591623 2.59934954985249 0.00976737350450793 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27668824809642 0.311397871916098 13.7338390329421 3.82666198891405e-34 *** df.mm.trans1 -0.101730793024091 0.263750744607144 -0.385708078950142 0.699966002263044 df.mm.trans2 -0.0628254085376048 0.263750744607144 -0.238199928615113 0.811876605926006 df.mm.exp2 0.0716753625345788 0.367304891022253 0.195138600891205 0.845406755697865 df.mm.exp3 -0.397900861172038 0.367304891022253 -1.08329856448313 0.279481428146499 df.mm.exp4 -0.387515270562229 0.367304891022253 -1.05502344246963 0.292200679151151 df.mm.exp5 0.323779252098013 0.367304891022253 0.8814999745767 0.378700725005812 df.mm.exp6 -0.191170826708022 0.367304891022253 -0.520469047324612 0.603092165874457 df.mm.exp7 -0.32896485160016 0.367304891022253 -0.895617944766844 0.371121745433218 df.mm.exp8 -0.0539683947270488 0.367304891022253 -0.146930781609927 0.883278038344147 df.mm.trans1:exp2 0.115139690223857 0.318095366559546 0.3619659458394 0.717613437129356 df.mm.trans2:exp2 0.0198006540438326 0.318095366559546 0.0622475399688854 0.950404086367555 df.mm.trans1:exp3 0.237945267240615 0.318095366559546 0.74803122665439 0.454983924862942 df.mm.trans2:exp3 0.429214892859138 0.318095366559546 1.34932771106174 0.178173855421706 df.mm.trans1:exp4 0.321162738660019 0.318095366559546 1.00964293234966 0.313419796652260 df.mm.trans2:exp4 0.541061047918894 0.318095366559546 1.70093973317153 0.0899137706592318 . df.mm.trans1:exp5 -0.0797167025503378 0.318095366559546 -0.250606299024526 0.802277353504112 df.mm.trans2:exp5 -0.212563513348229 0.318095366559546 -0.668238319995895 0.504457236271616 df.mm.trans1:exp6 0.230208272039556 0.318095366559546 0.723708347372176 0.469766988003862 df.mm.trans2:exp6 0.237685431485439 0.318095366559546 0.747214378053335 0.455476082879024 df.mm.trans1:exp7 0.282018252449029 0.318095366559546 0.886583968510074 0.375960514377574 df.mm.trans2:exp7 0.566525249889713 0.318095366559546 1.78099183278629 0.0758505725596681 . df.mm.trans1:exp8 0.0621599920550792 0.318095366559546 0.195413069757629 0.845192075208137 df.mm.trans2:exp8 0.284324801131633 0.318095366559546 0.89383509167968 0.372073587637857 df.mm.trans1:probe2 0.097003762042449 0.159047683279773 0.609903646768714 0.542353138874915 df.mm.trans1:probe3 0.154114151882259 0.159047683279773 0.968980803141684 0.333277533919558 df.mm.trans1:probe4 0.0076334048747293 0.159047683279773 0.0479944424044313 0.96175024218013 df.mm.trans1:probe5 0.288749472996635 0.159047683279773 1.81548996528739 0.0703729752517426 . df.mm.trans1:probe6 -0.120692865379064 0.159047683279773 -0.758847050709687 0.448495738570212 df.mm.trans2:probe2 0.170299185564350 0.159047683279773 1.07074294986608 0.285081996552734 df.mm.trans2:probe3 0.0592882653555658 0.159047683279773 0.372770380133578 0.70956308685658 df.mm.trans2:probe4 0.046466038012371 0.159047683279773 0.292151618019075 0.770357684172204 df.mm.trans2:probe5 0.00505175438515647 0.159047683279773 0.0317625147438972 0.974680997363244 df.mm.trans2:probe6 0.135748991025460 0.159047683279773 0.85351127552528 0.394006393601643 df.mm.trans3:probe2 -0.0386925385159518 0.159047683279773 -0.243276341522622 0.807945280374998 df.mm.trans3:probe3 -0.0302642583749659 0.159047683279773 -0.190284182396606 0.84920561307662