chr11.4404_chr11_54419498_54421898_-_2.R fitVsDatCorrelation=0.724362350387133 cont.fitVsDatCorrelation=0.277850691167711 fstatistic=10468.0413496984,36,324 cont.fstatistic=5387.3314370983,36,324 residuals=-0.353736949498264,-0.078915239520647,-0.0025536549047178,0.0701782252861858,0.527677421449661 cont.residuals=-0.357640709802138,-0.109013993562882,-0.0135524152437458,0.0866346935735684,0.641198797972824 predictedValues: Include Exclude Both chr11.4404_chr11_54419498_54421898_-_2.R.tl.Lung 49.034283540854 40.5526757894634 50.1782389046543 chr11.4404_chr11_54419498_54421898_-_2.R.tl.cerebhem 57.749052993228 41.3496461456601 52.1868639084719 chr11.4404_chr11_54419498_54421898_-_2.R.tl.cortex 48.202413774796 43.4584036199141 48.683868291603 chr11.4404_chr11_54419498_54421898_-_2.R.tl.heart 46.2145053979705 45.1760094852940 52.486094978405 chr11.4404_chr11_54419498_54421898_-_2.R.tl.kidney 47.1568497155176 42.3337723350858 54.1756822318054 chr11.4404_chr11_54419498_54421898_-_2.R.tl.liver 50.8619574042834 46.9284703607282 50.6344897588258 chr11.4404_chr11_54419498_54421898_-_2.R.tl.stomach 51.3558359624706 43.3706638983589 54.5700392068726 chr11.4404_chr11_54419498_54421898_-_2.R.tl.testicle 52.3260491811358 42.3943092268365 52.4670346092077 diffExp=8.4816077513906,16.3994068475679,4.74401015488184,1.03849591267659,4.82307738043183,3.9334870435552,7.98517206411174,9.93173995429931 diffExpScore=0.982858219490918 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=0,1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=1,1,0,0,0,0,0,1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 46.3471335967357 46.237058920457 47.9479835278557 cerebhem 46.9490356170508 46.8245658080463 46.4144563233189 cortex 46.1833742656805 49.8475057365249 45.8415455329074 heart 48.0164073886752 47.3518084783049 48.1849031504195 kidney 46.9716957142480 46.0492827840514 51.3578495844206 liver 49.5487582067178 46.832886459893 49.6551284149147 stomach 48.303522177706 46.820142497319 50.087273559137 testicle 47.408550831782 51.3805422269059 47.1825060964012 cont.diffExp=0.110074676278764,0.124469809004445,-3.66413147084445,0.664598910370344,0.922412930196508,2.71587174682476,1.48337968038694,-3.97199139512389 cont.diffExpScore=5.22190635905905 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=-0.297273250681516 cont.tran.correlation=-0.187335662878158 tran.covariance=-0.000988583887048176 cont.tran.covariance=-0.000168898902701558 tran.mean=46.7790561769748 cont.tran.mean=47.5670169193811 weightedLogRatios: wLogRatio Lung 0.721224957761687 cerebhem 1.29912209505189 cortex 0.396144218962847 heart 0.0868630510299643 kidney 0.409947130860775 liver 0.313017899202875 stomach 0.651355419033326 testicle 0.810823172930847 cont.weightedLogRatios: wLogRatio Lung 0.00911890739305613 cerebhem 0.0102145412865415 cortex -0.295529679875436 heart 0.053863504796119 kidney 0.0761514081187473 liver 0.218426835545902 stomach 0.120456708257501 testicle -0.313704090603607 varWeightedLogRatios=0.138906060293006 cont.varWeightedLogRatios=0.0364322533162185 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.4963107814697 0.0674778050006174 51.814234049814 7.61163788767703e-159 *** df.mm.trans1 0.416093623619551 0.0571529959529196 7.28034666743127 2.54043113284172e-12 *** df.mm.trans2 0.194516549304831 0.0571529959529196 3.40343574403459 0.000748965719684881 *** df.mm.exp2 0.143799962223387 0.0795924765306378 1.80670295097595 0.0717362554574688 . df.mm.exp3 0.0823255728734227 0.0795924765306378 1.03433862673858 0.301749283975296 df.mm.exp4 0.00377158524038874 0.0795924765306378 0.0473862028773151 0.962234619942217 df.mm.exp5 -0.0727077376784435 0.0795924765306378 -0.913500130259873 0.361658827600906 df.mm.exp6 0.173566782112745 0.0795924765306378 2.18069332276567 0.0299250607441663 * df.mm.exp7 0.0295368078459475 0.0795924765306378 0.371100500115458 0.71080520991818 df.mm.exp8 0.0647833574870151 0.0795924765306378 0.813938205102561 0.416278132733522 df.mm.trans1:exp2 0.0197872710995271 0.0689291066256491 0.287066989087656 0.77424452749672 df.mm.trans2:exp2 -0.124337864125826 0.068929106625649 -1.80385138024637 0.0721833127969607 . df.mm.trans1:exp3 -0.0994361923143464 0.0689291066256491 -1.44258640771858 0.150103016350676 df.mm.trans2:exp3 -0.0131230966833177 0.0689291066256491 -0.190385416636671 0.849126355300852 df.mm.trans1:exp4 -0.0629975843366806 0.0689291066256491 -0.913947495051947 0.361424055336749 df.mm.trans2:exp4 0.104192831299197 0.068929106625649 1.51159410588423 0.131611854895004 df.mm.trans1:exp5 0.0336672936321179 0.0689291066256491 0.488433628118285 0.625573135108157 df.mm.trans2:exp5 0.115691139741243 0.0689291066256491 1.67840764815881 0.0942317341763448 . df.mm.trans1:exp6 -0.136971254293883 0.068929106625649 -1.98713230156555 0.0477495071062487 * df.mm.trans2:exp6 -0.0275440126924321 0.0689291066256491 -0.399599153983271 0.689715068297763 df.mm.trans1:exp7 0.016722055193025 0.0689291066256491 0.242597880802978 0.808470420960115 df.mm.trans2:exp7 0.0376446918687387 0.068929106625649 0.546136366936908 0.58534805338052 df.mm.trans1:exp8 0.000191244325208199 0.0689291066256491 0.00277450752766663 0.997787973597279 df.mm.trans2:exp8 -0.0203709863991684 0.0689291066256491 -0.295535331827269 0.767774272183361 df.mm.trans1:probe2 -0.0925329273867099 0.0344645533128245 -2.68487238313568 0.00762905514582902 ** df.mm.trans1:probe3 -0.0759586884982137 0.0344645533128245 -2.20396555872230 0.0282296657373085 * df.mm.trans1:probe4 0.0150826155248125 0.0344645533128245 0.4376268970589 0.661948348108864 df.mm.trans1:probe5 -0.0561322219225793 0.0344645533128245 -1.62869431131411 0.104349644886199 df.mm.trans1:probe6 0.0305790345092945 0.0344645533128245 0.887260433400591 0.375596837303331 df.mm.trans2:probe2 0.0089636963495579 0.0344645533128245 0.260084506774165 0.794963788727825 df.mm.trans2:probe3 -0.0423475825374343 0.0344645533128245 -1.22872860567952 0.220065284013982 df.mm.trans2:probe4 -0.0159599131718401 0.0344645533128245 -0.463081967927358 0.643616502176564 df.mm.trans2:probe5 0.0333855020037431 0.0344645533128245 0.968690982317768 0.333421937282798 df.mm.trans2:probe6 0.121928212531665 0.0344645533128245 3.53778595140806 0.000462433855694336 *** df.mm.trans3:probe2 -0.310480532134820 0.0344645533128245 -9.0086916060301 1.83530596468087e-17 *** df.mm.trans3:probe3 -0.263537996768842 0.0344645533128245 -7.64663897938225 2.36801376813969e-13 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.8422998505701 0.0940223210386905 40.8658264135915 6.86813345031574e-130 *** df.mm.trans1 0.000688545741703534 0.0796359237494347 0.00864617008612802 0.993106761495546 df.mm.trans2 0.0042639131959745 0.0796359237494347 0.0535425847434183 0.957332599492863 df.mm.exp2 0.0580353526366521 0.110902679489346 0.52329982380838 0.601123328430663 df.mm.exp3 0.116573222666864 0.110902679489346 1.05113080408542 0.293981860633721 df.mm.exp4 0.0542777080886068 0.110902679489346 0.489417463478156 0.624877345874257 df.mm.exp5 -0.0593847181305752 0.110902679489346 -0.535466937354566 0.592694361642046 df.mm.exp6 0.044616862424093 0.110902679489346 0.402306442274725 0.687723794321378 df.mm.exp7 0.0102267130559573 0.110902679489346 0.0922133991987065 0.926585481346667 df.mm.exp8 0.144214645877933 0.110902679489346 1.30037115912773 0.194397997588213 df.mm.trans1:exp2 -0.0451321353576479 0.0960445377855372 -0.4699084028956 0.638736576331932 df.mm.trans2:exp2 -0.0454089946818606 0.0960445377855372 -0.472791016843214 0.636680613368368 df.mm.trans1:exp3 -0.120112801251516 0.0960445377855372 -1.25059481799707 0.211984729692745 df.mm.trans2:exp3 -0.0413863805663185 0.0960445377855373 -0.430908217380694 0.666821639236783 df.mm.trans1:exp4 -0.0188943825170760 0.0960445377855373 -0.196725216787094 0.844165916945026 df.mm.trans2:exp4 -0.0304543130261514 0.0960445377855372 -0.317085320293325 0.751383085893339 df.mm.trans1:exp5 0.0727704720618317 0.0960445377855372 0.757674238844531 0.449196722626644 df.mm.trans2:exp5 0.0553152883087317 0.0960445377855373 0.575933723917211 0.565059798039044 df.mm.trans1:exp6 0.0221808890930413 0.0960445377855372 0.230943785086146 0.817504238830532 df.mm.trans2:exp6 -0.0318128218254306 0.0960445377855373 -0.331229891453766 0.740684968651784 df.mm.trans1:exp7 0.0311183203512827 0.0960445377855372 0.323998855830494 0.746147952058982 df.mm.trans2:exp7 0.00230517483617618 0.0960445377855372 0.024001102918768 0.98086650380624 df.mm.trans1:exp8 -0.121571483677969 0.0960445377855372 -1.26578238056007 0.206500423297411 df.mm.trans2:exp8 -0.0387367186638198 0.0960445377855373 -0.403320371537599 0.686978584304852 df.mm.trans1:probe2 -0.0459611045142534 0.0480222688927686 -0.95707898801871 0.339240974062681 df.mm.trans1:probe3 -0.0255167276252976 0.0480222688927686 -0.53135197927185 0.59553897142826 df.mm.trans1:probe4 0.00880200961323813 0.0480222688927686 0.183290165504104 0.85468499606608 df.mm.trans1:probe5 0.013686948669954 0.0480222688927686 0.285012536590395 0.775816633012187 df.mm.trans1:probe6 -0.0124716652121696 0.0480222688927686 -0.259705871874113 0.795255608383408 df.mm.trans2:probe2 -0.0564536307682712 0.0480222688927686 -1.17557191840164 0.240629098582943 df.mm.trans2:probe3 -0.0927783126774247 0.0480222688927686 -1.93198519804623 0.0542329939902137 . df.mm.trans2:probe4 0.0284644329484244 0.0480222688927686 0.592734029539173 0.553772628929492 df.mm.trans2:probe5 -0.00470037518743505 0.0480222688927686 -0.0978790735175541 0.922088839496252 df.mm.trans2:probe6 0.0104285709088150 0.0480222688927686 0.21716114521997 0.828219446986798 df.mm.trans3:probe2 0.0606247091844476 0.0480222688927686 1.26242908930063 0.207702321206151 df.mm.trans3:probe3 0.00796860270107772 0.0480222688927686 0.165935572908294 0.868311147678261