chr14.7239_chr14_43749553_43750042_+_0.R fitVsDatCorrelation=0.73289495465118 cont.fitVsDatCorrelation=0.252727975411044 fstatistic=11965.6038970542,46,554 cont.fstatistic=5910.25582238904,46,554 residuals=-0.381314998369866,-0.0800109614940447,-0.00376959092522964,0.0797118610619781,0.727629577134903 cont.residuals=-0.527533847975696,-0.122951661603876,-0.0193759020824688,0.112762886727470,0.78502189720407 predictedValues: Include Exclude Both chr14.7239_chr14_43749553_43750042_+_0.R.tl.Lung 52.9793966330142 54.1645295579122 73.6252889546397 chr14.7239_chr14_43749553_43750042_+_0.R.tl.cerebhem 58.4907345088164 59.6134171387782 76.4222738973008 chr14.7239_chr14_43749553_43750042_+_0.R.tl.cortex 56.9531923931642 56.7009592656561 80.1998683063518 chr14.7239_chr14_43749553_43750042_+_0.R.tl.heart 54.4858889174767 63.9595348747954 69.444603071345 chr14.7239_chr14_43749553_43750042_+_0.R.tl.kidney 52.5559087787059 56.7016623472823 68.1584499459875 chr14.7239_chr14_43749553_43750042_+_0.R.tl.liver 55.259395118337 59.8466122310497 64.0366677472907 chr14.7239_chr14_43749553_43750042_+_0.R.tl.stomach 57.0232749706274 57.4348072754779 68.5348514169891 chr14.7239_chr14_43749553_43750042_+_0.R.tl.testicle 54.3192909697964 56.0284700875547 67.2652331566877 diffExp=-1.18513292489802,-1.12268262996184,0.252233127508092,-9.47364595731868,-4.14575356857647,-4.58721711271274,-0.411532304850482,-1.70917911775829 diffExpScore=0.978807867171793 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 57.0907606089022 55.8521882578245 58.6402158698818 cerebhem 57.5359886271069 57.528045538981 56.7723151038923 cortex 54.9506390823954 54.9518630012159 59.716323858691 heart 57.9528437002593 57.5372732786524 62.9248001063578 kidney 57.8311867355053 56.8589876610048 55.4323517699472 liver 55.7285864838266 56.8110793770817 62.3784883622454 stomach 57.8310752221825 59.7719034297925 61.7423027355323 testicle 58.01224159387 57.038218924061 52.8769828607864 cont.diffExp=1.23857235107765,0.0079430881258844,-0.00122391882046458,0.415570421606844,0.972199074500551,-1.0824928932551,-1.94082820760996,0.974022669809003 cont.diffExpScore=4.18803467502432 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=0.261438298806529 cont.tran.correlation=0.644791228323922 tran.covariance=0.000542824165512971 cont.tran.covariance=0.000325721881961215 tran.mean=56.6573171917778 cont.tran.mean=57.0801800951664 weightedLogRatios: wLogRatio Lung -0.0880715296778863 cerebhem -0.0775393257747768 cortex 0.0179320530879155 heart -0.653754976961964 kidney -0.303691878175658 liver -0.323125576904925 stomach -0.0291023757091676 testicle -0.124243361107049 cont.weightedLogRatios: wLogRatio Lung 0.0884729730489322 cerebhem 0.000559481627828909 cortex -8.92348239109273e-05 heart 0.029189924787495 kidney 0.0686471737583451 liver -0.0775318185371454 stomach -0.134481495373478 testicle 0.0686136685313241 varWeightedLogRatios=0.0488181719657238 cont.varWeightedLogRatios=0.00599447568010128 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.74306192932949 0.0719780590444099 52.0028183452411 2.65601528703168e-215 *** df.mm.trans1 0.233136742980658 0.0640884809662595 3.63773238912305 0.000300744869074211 *** df.mm.trans2 0.248427870044263 0.0596891996631502 4.16202380742647 3.65660954659279e-05 *** df.mm.exp2 0.157534042340828 0.0819822603128965 1.92156256413982 0.0551735197774233 . df.mm.exp3 0.0325582138806119 0.0819822603128965 0.397137304538189 0.691419163686945 df.mm.exp4 0.252722221824935 0.0819822603128965 3.08264520715074 0.00215377905903329 ** df.mm.exp5 0.114905133257915 0.0819822603128965 1.40158532857431 0.161599088918399 df.mm.exp6 0.281426559998521 0.0819822603128965 3.43277385771529 0.000642073971844764 *** df.mm.exp7 0.203826853119904 0.0819822603128965 2.4862311961389 0.0132039658080624 * df.mm.exp8 0.149155091559701 0.0819822603128965 1.81935812687318 0.0693964390612535 . df.mm.trans1:exp2 -0.0585687803105831 0.0772936162723332 -0.757744082049729 0.448926390113318 df.mm.trans2:exp2 -0.0616796318348948 0.0687724991101819 -0.896864773462377 0.370180639880354 df.mm.trans1:exp3 0.0397684353123593 0.0772936162723332 0.514511252420133 0.607099784018598 df.mm.trans2:exp3 0.0132066569676474 0.0687724991101819 0.192033983620236 0.847785952394044 df.mm.trans1:exp4 -0.22468356782516 0.0772936162723332 -2.90688388848982 0.00379659809115083 ** df.mm.trans2:exp4 -0.0864978640198982 0.0687724991101818 -1.25773914193984 0.209016000906293 df.mm.trans1:exp5 -0.122930696375640 0.0772936162723332 -1.59043789518802 0.112306582759351 df.mm.trans2:exp5 -0.0691278626676337 0.0687724991101818 -1.00516723344432 0.315255152518798 df.mm.trans1:exp6 -0.239291281865108 0.0772936162723332 -3.09587380440319 0.00206154894990849 ** df.mm.trans2:exp6 -0.181667991931152 0.0687724991101819 -2.64157903641247 0.0084851274359687 ** df.mm.trans1:exp7 -0.130270431011232 0.0772936162723332 -1.68539702622067 0.0924751214769043 . df.mm.trans2:exp7 -0.14520259306006 0.0687724991101819 -2.11134675835217 0.035189770903998 * df.mm.trans1:exp8 -0.124178756431995 0.0772936162723332 -1.60658489563315 0.108715241558326 df.mm.trans2:exp8 -0.115321393600466 0.0687724991101818 -1.67685332207729 0.094135282496139 . df.mm.trans1:probe2 0.0120114263791192 0.0386468081361666 0.310799958868495 0.756069601528574 df.mm.trans1:probe3 -0.138651336958080 0.0386468081361666 -3.58765299503030 0.000363209279421713 *** df.mm.trans1:probe4 0.0674006672802964 0.0386468081361666 1.74401640215201 0.081710869136083 . df.mm.trans1:probe5 0.0781885743940924 0.0386468081361666 2.02315736188629 0.0435366397662337 * df.mm.trans1:probe6 0.0986319270664776 0.0386468081361666 2.55213643307778 0.0109736907331971 * df.mm.trans1:probe7 -0.109266425961614 0.0386468081361666 -2.82730790021854 0.00486379965587757 ** df.mm.trans1:probe8 0.182933719787902 0.0386468081361666 4.73347550833592 2.80604366740786e-06 *** df.mm.trans1:probe9 0.167641535025956 0.0386468081361666 4.33778475146755 1.71045523781931e-05 *** df.mm.trans1:probe10 -0.091590195647521 0.0386468081361666 -2.36992911095829 0.0181330734311883 * df.mm.trans1:probe11 0.161462954794401 0.0386468081361666 4.17791177541776 3.41764952609236e-05 *** df.mm.trans1:probe12 -0.092948328243803 0.0386468081361666 -2.40507127823629 0.0164962404891302 * df.mm.trans1:probe13 -0.240586205184684 0.0386468081361666 -6.22525421341426 9.50066358839143e-10 *** df.mm.trans1:probe14 -0.155638467312637 0.0386468081361666 -4.02720107606989 6.43168645739471e-05 *** df.mm.trans1:probe15 -0.109994961623735 0.0386468081361666 -2.84615902136556 0.00458890445915895 ** df.mm.trans1:probe16 0.0570847281986328 0.0386468081361666 1.47708778426158 0.140220323450568 df.mm.trans2:probe2 -0.0338642795402377 0.0386468081361666 -0.876250359950082 0.381273721651435 df.mm.trans2:probe3 0.0852053845713494 0.0386468081361666 2.20471983795247 0.0278839774887257 * df.mm.trans2:probe4 -0.00402531067977578 0.0386468081361666 -0.104156355308650 0.917082964264565 df.mm.trans2:probe5 0.00292690493199851 0.0386468081361666 0.0757347132442599 0.93965749852857 df.mm.trans2:probe6 -0.0454145715762133 0.0386468081361666 -1.17511830255688 0.240452194171363 df.mm.trans3:probe2 0.0723675382545246 0.0386468081361666 1.87253596725369 0.0616589798636115 . df.mm.trans3:probe3 0.150718534617446 0.0386468081361666 3.89989605574696 0.000108044831965470 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.9102915032179 0.102362460597848 38.2004445807560 2.34792325650794e-157 *** df.mm.trans1 0.0794149828163766 0.0911424216598704 0.871328426105915 0.383952280523593 df.mm.trans2 0.111014588365241 0.0848860531911051 1.30780716256548 0.191481144393926 df.mm.exp2 0.0697041620676312 0.116589777529617 0.597858264631515 0.550178817869549 df.mm.exp3 -0.0726427793623189 0.116589777529617 -0.623063024062 0.533499508893867 df.mm.exp4 -0.0258080151454747 0.116589777529617 -0.221357443957029 0.82489568916949 df.mm.exp5 0.0870088450379513 0.116589777529617 0.74628193724659 0.455813676552511 df.mm.exp6 -0.0689261341345278 0.116589777529617 -0.591185055799757 0.554637558977437 df.mm.exp7 0.0291623976085938 0.116589777529617 0.250128255036646 0.802580891568811 df.mm.exp8 0.140477203496199 0.116589777529617 1.20488439443599 0.228762377105145 df.mm.trans1:exp2 -0.0619358137511468 0.109921896410964 -0.563452922242061 0.573354492036937 df.mm.trans2:exp2 -0.0401402904002397 0.0978037241326286 -0.410416788892483 0.681658909304342 df.mm.trans1:exp3 0.0344357975302303 0.109921896410964 0.313275140391369 0.754189541734374 df.mm.trans2:exp3 0.0563916574787502 0.0978037241326286 0.576579859088793 0.564457493055314 df.mm.trans1:exp4 0.0407953624782339 0.109921896410964 0.371130446346310 0.710682254544833 df.mm.trans2:exp4 0.055532278141652 0.0978037241326285 0.567793083894704 0.570405506385489 df.mm.trans1:exp5 -0.0741229448402869 0.109921896410964 -0.674323744954 0.500386786376021 df.mm.trans2:exp5 -0.0691432487654112 0.0978037241326286 -0.706959263347152 0.47988904047208 df.mm.trans1:exp6 0.0447770788780861 0.109921896410964 0.407353587775437 0.683905661575404 df.mm.trans2:exp6 0.085948794566381 0.0978037241326286 0.87878856688349 0.379896913545264 df.mm.trans1:exp7 -0.0162784256686145 0.109921896410964 -0.148090837222772 0.882324964331579 df.mm.trans2:exp7 0.0386646048970531 0.0978037241326286 0.395328554612309 0.692752604133135 df.mm.trans1:exp8 -0.124465446123909 0.109921896410964 -1.13230803131862 0.257994796212376 df.mm.trans2:exp8 -0.119464358640957 0.0978037241326285 -1.22147044706554 0.222427474341956 df.mm.trans1:probe2 0.000177321338335667 0.054960948205482 0.00322631512237957 0.997426938837815 df.mm.trans1:probe3 0.0986920755033012 0.054960948205482 1.79567636159263 0.073090824162776 . df.mm.trans1:probe4 0.148520243843195 0.054960948205482 2.70228678166038 0.0070975735989778 ** df.mm.trans1:probe5 0.0912663552982781 0.054960948205482 1.66056733513879 0.0973661980135457 . df.mm.trans1:probe6 0.0481866874120172 0.054960948205482 0.876744106230883 0.381005656869920 df.mm.trans1:probe7 0.00974210197885668 0.054960948205482 0.177254983710142 0.859372894580312 df.mm.trans1:probe8 0.0135359161209694 0.054960948205482 0.246282434399836 0.805554829516424 df.mm.trans1:probe9 0.052023450795581 0.054960948205482 0.946553007074793 0.344279267359404 df.mm.trans1:probe10 0.0839023613430852 0.054960948205482 1.52658140156899 0.127435821501439 df.mm.trans1:probe11 0.0739687749921937 0.054960948205482 1.34584241006264 0.178903840040389 df.mm.trans1:probe12 0.100993408619079 0.054960948205482 1.83754851247283 0.0666643205000044 . df.mm.trans1:probe13 0.0255279985759419 0.054960948205482 0.464475221215263 0.642489785384306 df.mm.trans1:probe14 0.0426856431909863 0.054960948205482 0.7766540531906 0.437694369011442 df.mm.trans1:probe15 0.0617827292607478 0.054960948205482 1.12412051243660 0.261448744052088 df.mm.trans1:probe16 0.137839454715735 0.054960948205482 2.50795263211973 0.0124280854572110 * df.mm.trans2:probe2 0.0151299049962661 0.054960948205482 0.275284642828577 0.783200202095394 df.mm.trans2:probe3 0.00104757881563071 0.054960948205482 0.0190604210777831 0.984799767611058 df.mm.trans2:probe4 -0.0390778859656737 0.054960948205482 -0.711011859176329 0.477376121800602 df.mm.trans2:probe5 0.00501834590183899 0.054960948205482 0.091307484053531 0.927281275925438 df.mm.trans2:probe6 0.030505583567299 0.054960948205482 0.555041071221115 0.579090596058561 df.mm.trans3:probe2 -0.053235468061389 0.054960948205482 -0.968605342512614 0.333164801850646 df.mm.trans3:probe3 -0.0346655386016180 0.054960948205482 -0.630730359163641 0.528476945814743