chr16.9484_chr16_12601501_12611962_+_0.R fitVsDatCorrelation=0.710127117566904 cont.fitVsDatCorrelation=0.281944829783433 fstatistic=9800.96715872909,42,462 cont.fstatistic=5273.02568407634,42,462 residuals=-0.392454354238283,-0.081937819741413,-0.00750283605352456,0.0797974856003763,0.725407956710976 cont.residuals=-0.50197332001896,-0.132489817850934,-0.0294096574171239,0.103073827705267,0.932667116686698 predictedValues: Include Exclude Both chr16.9484_chr16_12601501_12611962_+_0.R.tl.Lung 51.0562992903418 51.607316376165 60.4932292701181 chr16.9484_chr16_12601501_12611962_+_0.R.tl.cerebhem 60.9665022918593 63.467374179499 57.458098560929 chr16.9484_chr16_12601501_12611962_+_0.R.tl.cortex 53.9518479631606 48.8651470849108 65.8767180172351 chr16.9484_chr16_12601501_12611962_+_0.R.tl.heart 49.550471113344 50.8214392821997 59.1296404442421 chr16.9484_chr16_12601501_12611962_+_0.R.tl.kidney 50.8806330809965 52.2908048453124 61.3284034865059 chr16.9484_chr16_12601501_12611962_+_0.R.tl.liver 47.9364076369175 56.1229181688077 57.5987955964461 chr16.9484_chr16_12601501_12611962_+_0.R.tl.stomach 48.8179314436037 54.5485949496342 62.2484055846447 chr16.9484_chr16_12601501_12611962_+_0.R.tl.testicle 50.1304073780462 55.7460418338692 58.24902761674 diffExp=-0.551017085823233,-2.50087188763965,5.08670087824976,-1.27096816885575,-1.41017176431588,-8.18651053189027,-5.7306635060305,-5.61563445582301 diffExpScore=1.433133888481 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 52.9963708268355 53.7883360333161 54.9593323013804 cerebhem 56.0245219573632 52.4093577599961 56.8974894573774 cortex 55.61828853 51.6946752677356 58.0697411828455 heart 60.6932604099896 54.6255850464642 54.0298587650096 kidney 62.7709921959472 55.6421641608241 53.61514602319 liver 58.6495953398673 56.3800361239472 57.2188929549255 stomach 55.3227635442278 53.304458146367 53.1738651044803 testicle 59.3265294391035 60.2457324899251 56.4892944990905 cont.diffExp=-0.791965206480633,3.61516419736711,3.92361326226437,6.06767536352548,7.12882803512303,2.26955921592007,2.01830539786077,-0.91920305082165 cont.diffExpScore=1.09963552092892 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=0.560164766687967 cont.tran.correlation=0.535809987180702 tran.covariance=0.00308127518021496 cont.tran.covariance=0.00148608632802793 tran.mean=52.9225085574167 cont.tran.mean=56.2182917044943 weightedLogRatios: wLogRatio Lung -0.0422757237765601 cerebhem -0.166048923424836 cortex 0.390027709889343 heart -0.0991699550680412 kidney -0.107798578026299 liver -0.622588966986425 stomach -0.437716396536947 testicle -0.421286850052659 cont.weightedLogRatios: wLogRatio Lung -0.0590011503729116 cerebhem 0.266312850276448 cortex 0.291307475628486 heart 0.426921338549528 kidney 0.491756958985681 liver 0.159908402245142 stomach 0.148457357940389 testicle -0.0628958343213126 varWeightedLogRatios=0.0965434393358985 cont.varWeightedLogRatios=0.0413310276694358 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.71000484953943 0.0709184652137549 52.3136652542766 3.06126374721233e-196 *** df.mm.trans1 0.219628170054199 0.0569760267199758 3.85474703481205 0.000132256890900904 *** df.mm.trans2 0.256479328474606 0.0569760267199758 4.50153061278111 8.55076225781e-06 *** df.mm.exp2 0.435733652209619 0.0765011511891484 5.69577902340673 2.18836831323489e-08 *** df.mm.exp3 -0.0846896766024908 0.0765011511891485 -1.10703793715596 0.268853582754032 df.mm.exp4 -0.0224832001198620 0.0765011511891485 -0.293893618205463 0.768971180178566 df.mm.exp5 -0.00400112446116888 0.0765011511891485 -0.0523014934935572 0.958311069456712 df.mm.exp6 0.0698569482386427 0.0765011511891484 0.913148980803204 0.361640464499498 df.mm.exp7 -0.0180042124086404 0.0765011511891484 -0.235345640278342 0.81404475516599 df.mm.exp8 0.0966458296584726 0.0765011511891484 1.26332516774181 0.207109493423646 df.mm.trans1:exp2 -0.258338013146892 0.0604794703456519 -4.27149926529515 2.35857801153622e-05 *** df.mm.trans2:exp2 -0.228871123468365 0.0604794703456519 -3.78427790720259 0.000174446436418131 *** df.mm.trans1:exp3 0.139852689455922 0.0604794703456519 2.31239937546801 0.0211943735182161 * df.mm.trans2:exp3 0.0300906277425754 0.060479470345652 0.497534577776584 0.619048836196741 df.mm.trans1:exp4 -0.00745396283199695 0.0604794703456519 -0.123247819291341 0.901964457689866 df.mm.trans2:exp4 0.007138046115552 0.0604794703456519 0.118024282864196 0.906099712233691 df.mm.trans1:exp5 0.00055455452520276 0.060479470345652 0.00916930194714625 0.992688016049332 df.mm.trans2:exp5 0.0171582118254876 0.0604794703456519 0.283703076885844 0.776765028794772 df.mm.trans1:exp6 -0.132910588125972 0.0604794703456519 -2.19761494878117 0.0284721107731525 * df.mm.trans2:exp6 0.0140238517031695 0.0604794703456519 0.231877885553898 0.81673553136735 df.mm.trans1:exp7 -0.0268270261298435 0.0604794703456519 -0.443572438325301 0.657559392241834 df.mm.trans2:exp7 0.07343271457373 0.0604794703456519 1.21417588735562 0.225300838068308 df.mm.trans1:exp8 -0.114947003503952 0.0604794703456519 -1.9005954061272 0.0579771741580508 . df.mm.trans2:exp8 -0.0195028730063655 0.0604794703456519 -0.322470962376205 0.747241808700542 df.mm.trans1:probe2 0.0687682650351981 0.0405708620808181 1.69501611521614 0.0907461862681345 . df.mm.trans1:probe3 -0.028667085951934 0.0405708620808181 -0.706592970463099 0.480175657683478 df.mm.trans1:probe4 0.000434707267963478 0.0405708620808181 0.0107147653677541 0.991455642891615 df.mm.trans1:probe5 -0.0840940156101574 0.0405708620808181 -2.07276876302604 0.03874771634134 * df.mm.trans1:probe6 0.0929968081556022 0.0405708620808181 2.29220685452408 0.0223425056371810 * df.mm.trans2:probe2 -0.00496587114984815 0.0405708620808181 -0.122399941612185 0.902635508472238 df.mm.trans2:probe3 -0.115612691280591 0.0405708620808181 -2.84964837696295 0.00457237149061713 ** df.mm.trans2:probe4 -0.148850095149967 0.0405708620808181 -3.66889160140237 0.000271987219517703 *** df.mm.trans2:probe5 0.0771892408154125 0.0405708620808181 1.90257827555278 0.0577170672745759 . df.mm.trans2:probe6 -0.150071463365713 0.0405708620808181 -3.69899616790904 0.000242495480686112 *** df.mm.trans3:probe2 0.076628114281655 0.0405708620808181 1.88874749885792 0.0595517896928383 . df.mm.trans3:probe3 0.0159613851888193 0.0405708620808181 0.393419916910414 0.694190850482278 df.mm.trans3:probe4 -0.131989515314623 0.0405708620808181 -3.25330812669686 0.00122430463851613 ** df.mm.trans3:probe5 -0.0972503092718825 0.0405708620808181 -2.39704813464790 0.0169240515149385 * df.mm.trans3:probe6 -0.0149704380161637 0.0405708620808181 -0.368994821612176 0.71230062839114 df.mm.trans3:probe7 -0.312273885114984 0.0405708620808181 -7.69699900615684 8.5195337621614e-14 *** df.mm.trans3:probe8 -0.240095987480383 0.0405708620808181 -5.91794147736141 6.35841482615426e-09 *** df.mm.trans3:probe9 -0.0482374839352886 0.0405708620808181 -1.18896867015540 0.235062740486396 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91391924522623 0.0966395527197714 40.5001796373741 4.11644314153824e-154 *** df.mm.trans1 0.0779165936656752 0.0776403962123573 1.00355739366093 0.316117320572574 df.mm.trans2 0.0702080822719777 0.0776403962123573 0.904272591293182 0.366322157825676 df.mm.exp2 -0.00506315109165331 0.104246997043487 -0.0485687956032078 0.961283926945916 df.mm.exp3 -0.0464644400191403 0.104246997043487 -0.445714901502224 0.65601199119698 df.mm.exp4 0.168111611992952 0.104246997043487 1.61262786229540 0.107508225019308 df.mm.exp5 0.227916139386367 0.104246997043487 2.18630891872398 0.0292940475097687 * df.mm.exp6 0.108125083123150 0.104246997043487 1.03720093805719 0.300184770022665 df.mm.exp7 0.0669508346247299 0.104246997043487 0.642232740735941 0.521040625469648 df.mm.exp8 0.19875060166608 0.104246997043487 1.90653550992142 0.0572008777960015 . df.mm.trans1:exp2 0.0606292018335737 0.0824144874725642 0.735661941157581 0.462309766944250 df.mm.trans2:exp2 -0.0209083316273924 0.0824144874725642 -0.253697283919320 0.79984229349618 df.mm.trans1:exp3 0.0947530813433822 0.0824144874725642 1.14971389435535 0.250856534601255 df.mm.trans2:exp3 0.00676258203427551 0.0824144874725642 0.0820557433731147 0.934637923080217 df.mm.trans1:exp4 -0.0325023874710572 0.0824144874725642 -0.394377110964590 0.693484620633828 df.mm.trans2:exp4 -0.152665889709791 0.0824144874725642 -1.85241569039197 0.0646037649151732 . df.mm.trans1:exp5 -0.0586465165745244 0.0824144874725642 -0.7116044566078 0.477068893128557 df.mm.trans2:exp5 -0.194031518622279 0.0824144874725642 -2.35433750269784 0.0189734183765321 * df.mm.trans1:exp6 -0.00676784386570773 0.0824144874725642 -0.0821195893253689 0.93458718055876 df.mm.trans2:exp6 -0.0610665980226566 0.0824144874725642 -0.74096921421717 0.459088543404663 df.mm.trans1:exp7 -0.0239898097102687 0.0824144874725642 -0.291087288727663 0.771115196089082 df.mm.trans2:exp7 -0.0759875057378055 0.0824144874725642 -0.922016359843308 0.357001273211623 df.mm.trans1:exp8 -0.0859174552343796 0.0824144874725642 -1.04250427163042 0.297723150848853 df.mm.trans2:exp8 -0.0853755031546091 0.0824144874725642 -1.03592833945647 0.300777484557738 df.mm.trans1:probe2 -0.077022683294337 0.0552853188958375 -1.3931851137452 0.164233755794612 df.mm.trans1:probe3 -0.0578006097996539 0.0552853188958375 -1.04549654327861 0.296340233054269 df.mm.trans1:probe4 -0.0669098405067095 0.0552853188958375 -1.21026416855393 0.226796373349402 df.mm.trans1:probe5 -0.0320619936208996 0.0552853188958375 -0.579936848719409 0.56223991560393 df.mm.trans1:probe6 -0.090390912523459 0.0552853188958375 -1.63498943894605 0.102732380474424 df.mm.trans2:probe2 0.0802577376615729 0.0552853188958375 1.45170072750753 0.147263547598555 df.mm.trans2:probe3 -0.00900345067315865 0.0552853188958375 -0.162854277645064 0.870704378857558 df.mm.trans2:probe4 -0.0146972417376889 0.0552853188958375 -0.265843483066088 0.790478343715808 df.mm.trans2:probe5 -0.0252802133533958 0.0552853188958375 -0.457268111286941 0.647693290956982 df.mm.trans2:probe6 -0.0173371247505818 0.0552853188958375 -0.313593646502184 0.753971219704516 df.mm.trans3:probe2 -0.0642433427604413 0.0552853188958375 -1.16203259822886 0.245822170854874 df.mm.trans3:probe3 -0.00772361225738935 0.0552853188958375 -0.139704580015923 0.888954289132232 df.mm.trans3:probe4 -0.0651391591081226 0.0552853188958375 -1.1782361105821 0.239309007441857 df.mm.trans3:probe5 -0.108148993294199 0.0552853188958375 -1.95619733148255 0.0510440455825185 . df.mm.trans3:probe6 -0.0860888510358828 0.0552853188958375 -1.55717381676105 0.120113950272377 df.mm.trans3:probe7 -0.0558064155531224 0.0552853188958375 -1.0094255883423 0.313298943032423 df.mm.trans3:probe8 -0.0623779025875809 0.0552853188958375 -1.12829054500177 0.259782805036493 df.mm.trans3:probe9 -0.0495255226091516 0.0552853188958375 -0.895816893133277 0.3708170694053