chr11.4234_chr11_84606011_84607037_+_0.R fitVsDatCorrelation=0.897639569905779 cont.fitVsDatCorrelation=0.362156693669999 fstatistic=6409.54054243958,36,324 cont.fstatistic=1425.96413751424,36,324 residuals=-0.571775238816622,-0.0867203004122052,-0.00629076434233865,0.0827292243810019,0.939188514261652 cont.residuals=-0.75234409243699,-0.2726240492177,-0.0546729655519375,0.254253823612070,1.45989234076533 predictedValues: Include Exclude Both chr11.4234_chr11_84606011_84607037_+_0.R.tl.Lung 111.296495625254 64.0299402221412 57.5994297209374 chr11.4234_chr11_84606011_84607037_+_0.R.tl.cerebhem 153.116587112787 67.0318514459096 56.4429147823599 chr11.4234_chr11_84606011_84607037_+_0.R.tl.cortex 96.0426822465158 58.2981341044628 63.3746132087675 chr11.4234_chr11_84606011_84607037_+_0.R.tl.heart 100.337011192581 59.4901531820064 61.5319615803774 chr11.4234_chr11_84606011_84607037_+_0.R.tl.kidney 103.602748261261 63.6275005692987 60.9414690342626 chr11.4234_chr11_84606011_84607037_+_0.R.tl.liver 105.251423955526 58.6612693523453 64.5004760543593 chr11.4234_chr11_84606011_84607037_+_0.R.tl.stomach 117.745769294060 56.8469257381328 60.4177046293333 chr11.4234_chr11_84606011_84607037_+_0.R.tl.testicle 98.7114294483372 59.3008639942581 56.7125903000941 diffExp=47.2665554031132,86.0847356668776,37.7445481420530,40.8468580105743,39.9752476919625,46.590154603181,60.8988435559277,39.4105654540791 diffExpScore=0.9974988589077 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 77.4834439058177 84.7162879431052 87.7464080028713 cerebhem 84.234399050389 71.4953738553113 78.484422884675 cortex 78.6295130341407 69.1814775112028 84.6494110005547 heart 80.284969261503 59.2554783464126 69.5836616181131 kidney 89.362722354573 81.3849424637649 82.074527028027 liver 92.3359992367916 67.7688031662085 92.2810486072406 stomach 72.7334116598754 87.603680606249 71.8784651853593 testicle 82.9989387671566 70.3471013003916 90.007688282083 cont.diffExp=-7.23284403728755,12.7390251950776,9.44803552293793,21.0294909150904,7.97777989080816,24.5671960705831,-14.8702689463736,12.651837466765 cont.diffExpScore=1.64189665962787 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=0,0,0,1,0,1,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,0,0,1,0,1,-1,0 cont.diffExp1.2Score=1.5 tran.correlation=0.650957395097839 cont.tran.correlation=-0.351274454159419 tran.covariance=0.00536826284823978 cont.tran.covariance=-0.00344952294616763 tran.mean=85.8369241090549 cont.tran.mean=78.1135339039308 weightedLogRatios: wLogRatio Lung 2.45230442405114 cerebhem 3.81476636275136 cortex 2.15423612491527 heart 2.27237075462146 kidney 2.14352184802432 liver 2.55111200936205 stomach 3.20716558322108 testicle 2.21024496090922 cont.weightedLogRatios: wLogRatio Lung -0.392197599354523 cerebhem 0.713537560911432 cortex 0.550554741791261 heart 1.28588325164055 kidney 0.415755372632617 liver 1.35201898678481 stomach -0.814742039677618 testicle 0.717137027390994 varWeightedLogRatios=0.362119276575992 cont.varWeightedLogRatios=0.566687353946663 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.77130674451313 0.0971995220572184 49.0877593174214 4.27136582136112e-152 *** df.mm.trans1 -0.0661610510840793 0.0823269798226409 -0.80363753445847 0.422195531162847 df.mm.trans2 -0.605060293794146 0.0823269798226409 -7.3494775965017 1.63319053751359e-12 *** df.mm.exp2 0.385101875161435 0.114650301355499 3.3589259741005 0.000875779389439506 *** df.mm.exp3 -0.336736466412124 0.114650301355499 -2.93707441176275 0.00355055240434078 ** df.mm.exp4 -0.243247197281952 0.114650301355499 -2.12164463944765 0.0346263172334044 * df.mm.exp5 -0.134340141541347 0.114650301355499 -1.17173823315819 0.242163084325897 df.mm.exp6 -0.256576795163948 0.114650301355499 -2.23790772575795 0.0259062677828746 * df.mm.exp7 -0.110428135196674 0.114650301355499 -0.963173527597343 0.336178745856557 df.mm.exp8 -0.181207516734441 0.114650301355499 -1.58052368456116 0.114962690220499 df.mm.trans1:exp2 -0.066100008615058 0.0992900735254035 -0.66572625306946 0.506059755036095 df.mm.trans2:exp2 -0.33928476395852 0.0992900735254035 -3.41710658388941 0.000713595872832305 *** df.mm.trans1:exp3 0.189331393867172 0.0992900735254035 1.90685118002991 0.0574241620813363 . df.mm.trans2:exp3 0.242955764248296 0.0992900735254035 2.44692903954931 0.0149387602984267 * df.mm.trans1:exp4 0.139584057156309 0.0992900735254035 1.40582086607677 0.160735556242019 df.mm.trans2:exp4 0.169707213459862 0.0992900735254035 1.70920624221758 0.0883702988364772 . df.mm.trans1:exp5 0.0627062266941912 0.0992900735254035 0.631545777616408 0.528129039969069 df.mm.trans2:exp5 0.128035127379455 0.0992900735254035 1.28950581698076 0.198142043344110 df.mm.trans1:exp6 0.200731024964539 0.0992900735254035 2.02166256743864 0.0440329142696398 * df.mm.trans2:exp6 0.169005707686977 0.0992900735254035 1.70214102665295 0.0896881279085036 . df.mm.trans1:exp7 0.166758165946561 0.0992900735254035 1.67950490946002 0.09401765898681 . df.mm.trans2:exp7 -0.00856051273372886 0.0992900735254035 -0.0862172061091147 0.931347008354547 df.mm.trans1:exp8 0.0612104844123846 0.0992900735254035 0.616481408856282 0.538009740619413 df.mm.trans2:exp8 0.104480602579436 0.0992900735254035 1.05227641464788 0.293456897695099 df.mm.trans1:probe2 -0.00604897893068388 0.0496450367627017 -0.121844585584605 0.903097637712899 df.mm.trans1:probe3 -0.165343415107204 0.0496450367627017 -3.33051249206498 0.000966904599746268 *** df.mm.trans1:probe4 -0.0146550815433896 0.0496450367627017 -0.295197314757554 0.768032226902255 df.mm.trans1:probe5 0.230675797883905 0.0496450367627017 4.64650271056323 4.91779988893387e-06 *** df.mm.trans1:probe6 0.0188403842872634 0.0496450367627017 0.379501869991930 0.704563826962939 df.mm.trans2:probe2 0.0192400436798720 0.0496450367627017 0.387552209334389 0.698601909009466 df.mm.trans2:probe3 -0.237159230491831 0.0496450367627017 -4.77709849678284 2.69960551673544e-06 *** df.mm.trans2:probe4 -0.0236311190701945 0.0496450367627017 -0.476001643087684 0.634394007276115 df.mm.trans2:probe5 -0.0907398470189345 0.0496450367627017 -1.82777278326254 0.0685031846372699 . df.mm.trans2:probe6 0.270229205879900 0.0496450367627017 5.44322702733746 1.03472409704202e-07 *** df.mm.trans3:probe2 -0.105634731574390 0.0496450367627017 -2.12780045021042 0.0341082870155737 * df.mm.trans3:probe3 -0.034083466406336 0.0496450367627017 -0.686543280635514 0.492861619414043 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.39540593361887 0.205570953437954 21.3814542381130 7.00724030439033e-64 *** df.mm.trans1 0.0907839155003924 0.174116450138971 0.521397693485786 0.602445960611811 df.mm.trans2 0.0116267631003677 0.174116450138971 0.0667757876472202 0.946801385005245 df.mm.exp2 0.0254147091943148 0.242478268028154 0.104812317412973 0.916589590096554 df.mm.exp3 -0.151959121063724 0.242478268028154 -0.626691712620121 0.531302644795196 df.mm.exp4 -0.0900103786888228 0.242478268028154 -0.371210085838999 0.710723672003686 df.mm.exp5 0.169344948319801 0.242478268028154 0.698392271179285 0.485432861414677 df.mm.exp6 -0.0982239922867804 0.242478268028154 -0.405083693006979 0.685683319641971 df.mm.exp7 0.169725921323220 0.242478268028154 0.699963434675775 0.484452408875282 df.mm.exp8 -0.142546935714351 0.242478268028154 -0.587875098554398 0.557025640758738 df.mm.trans1:exp2 0.0581243818990596 0.209992339978033 0.276792867326208 0.782115654508347 df.mm.trans2:exp2 -0.195089847653812 0.209992339978033 -0.929033162229727 0.35356351577021 df.mm.trans1:exp3 0.166641947401277 0.209992339978033 0.79356202906596 0.428031219302478 df.mm.trans2:exp3 -0.0506156029065987 0.209992339978033 -0.241035472588636 0.809680081844837 df.mm.trans1:exp4 0.125528513307057 0.209992339978033 0.597776629948447 0.550406564929466 df.mm.trans2:exp4 -0.267439268769335 0.209992339978033 -1.27356678247079 0.203729887265407 df.mm.trans1:exp5 -0.0267056155146793 0.209992339978033 -0.127174236533927 0.898881385683542 df.mm.trans2:exp5 -0.209462559182143 0.209992339978033 -0.99747714228078 0.319277325981057 df.mm.trans1:exp6 0.273593795473743 0.209992339978033 1.30287512155140 0.193542609782555 df.mm.trans2:exp6 -0.124981933693128 0.209992339978033 -0.59517377493961 0.552142772721243 df.mm.trans1:exp7 -0.232989346208818 0.209992339978033 -1.10951354812843 0.268031520844437 df.mm.trans2:exp7 -0.136210792952374 0.209992339978033 -0.648646483803279 0.517026369709854 df.mm.trans1:exp8 0.211310470975244 0.209992339978033 1.00627704323571 0.315033234032791 df.mm.trans2:exp8 -0.0433193702906136 0.209992339978033 -0.206290240373269 0.836693769925223 df.mm.trans1:probe2 -0.208522578684378 0.104996169989017 -1.98600176278993 0.0478755383348871 * df.mm.trans1:probe3 -0.241373877251571 0.104996169989017 -2.29888268569054 0.022147622283036 * df.mm.trans1:probe4 -0.164577980840984 0.104996169989017 -1.5674665167139 0.117981787088649 df.mm.trans1:probe5 -0.348257161780808 0.104996169989017 -3.31685586071605 0.00101377395956606 ** df.mm.trans1:probe6 -0.262398464870825 0.104996169989017 -2.49912415755998 0.0129446189290803 * df.mm.trans2:probe2 0.123739943811895 0.104996169989017 1.17851864334517 0.239454698876885 df.mm.trans2:probe3 0.135532367998532 0.104996169989017 1.29083154188110 0.197682399200856 df.mm.trans2:probe4 0.0370346078040480 0.104996169989017 0.352723416558168 0.724525149797716 df.mm.trans2:probe5 -0.0469741169047253 0.104996169989017 -0.447388861037876 0.65489326525443 df.mm.trans2:probe6 0.0411438894943862 0.104996169989016 0.391860860245571 0.695418638552316 df.mm.trans3:probe2 -0.100090081414714 0.104996169989017 -0.953273642507 0.341162074154199 df.mm.trans3:probe3 0.0299930259595928 0.104996169989017 0.285658286037770 0.775322393648108