chr7.20841_chr7_131513959_131515003_+_1.R fitVsDatCorrelation=0.892838168414178 cont.fitVsDatCorrelation=0.239988541653258 fstatistic=9890.0598288696,67,1037 cont.fstatistic=2116.55500513778,67,1037 residuals=-0.654172946186228,-0.0835426417320041,-0.00463914734902841,0.0882120828478426,1.35748126841722 cont.residuals=-0.711593340305547,-0.203326643681260,-0.066241791950949,0.0993943359293617,2.12234144375962 predictedValues: Include Exclude Both chr7.20841_chr7_131513959_131515003_+_1.R.tl.Lung 60.87064079681 70.4141191521455 76.4739706521683 chr7.20841_chr7_131513959_131515003_+_1.R.tl.cerebhem 73.3812482432297 59.1896635249125 74.213772832828 chr7.20841_chr7_131513959_131515003_+_1.R.tl.cortex 56.4464475573917 70.6533621719763 75.7647918890294 chr7.20841_chr7_131513959_131515003_+_1.R.tl.heart 57.4729015467423 69.2203744238371 73.3596661967981 chr7.20841_chr7_131513959_131515003_+_1.R.tl.kidney 58.9453727782317 61.2154772715611 71.6184429167814 chr7.20841_chr7_131513959_131515003_+_1.R.tl.liver 57.5566579534914 59.001850713689 71.1000663102339 chr7.20841_chr7_131513959_131515003_+_1.R.tl.stomach 58.345266976158 63.1465193756231 76.3738464372687 chr7.20841_chr7_131513959_131515003_+_1.R.tl.testicle 59.9598064158813 66.8481495096395 75.5052995695311 diffExp=-9.54347835533557,14.1915847183172,-14.2069146145846,-11.7474728770948,-2.27010449332938,-1.44519276019769,-4.80125239946508,-6.88834309375827 diffExpScore=1.72612880010246 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=0,1,-1,-1,0,0,0,0 diffExp1.2Score=1.5 cont.predictedValues: Include Exclude Both Lung 73.3409112342096 69.3969181681664 63.7068715456117 cerebhem 74.1431401737188 59.3665019510205 63.6643970557742 cortex 69.3444320471455 58.4001464047761 68.0139792819036 heart 67.2818712942682 65.4076437215471 64.8138987682587 kidney 70.5911066682508 58.1885490750059 68.7650196031424 liver 62.9806119323867 62.7556188807198 70.7115488485152 stomach 69.6180467631657 67.185597005577 67.212166019412 testicle 66.2415979176305 65.0629595265787 67.4278578270881 cont.diffExp=3.94399306604318,14.7766382226984,10.9442856423694,1.87422757272103,12.4025575932449,0.224993051666949,2.43244975758867,1.17863839105178 cont.diffExpScore=0.979498863367708 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=0,1,0,0,1,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=-0.436760868478974 cont.tran.correlation=-0.0574189873944174 tran.covariance=-0.00279209801382782 cont.tran.covariance=-0.000272808182097118 tran.mean=62.6667411507075 cont.tran.mean=66.2066032977605 weightedLogRatios: wLogRatio Lung -0.609016033592552 cerebhem 0.900135898941292 cortex -0.930645899449886 heart -0.77076472896807 kidney -0.154764750561060 liver -0.100812105670615 stomach -0.324693897979343 testicle -0.451096919597484 cont.weightedLogRatios: wLogRatio Lung 0.235889798075811 cerebhem 0.932381581261232 cortex 0.713384976208842 heart 0.118509244668056 kidney 0.803834096482188 liver 0.0148200030843361 stomach 0.150270421751779 testicle 0.0751222489765337 varWeightedLogRatios=0.319877571420582 cont.varWeightedLogRatios=0.137733113538914 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.23131459812344 0.0777718511737715 54.4067620130205 4.32268870575999e-306 *** df.mm.trans1 -0.0887739846567974 0.0653645911890819 -1.35813569765928 0.174716032580321 df.mm.trans2 0.0177328029001662 0.0580662999412714 0.305388890253061 0.760131269573258 df.mm.exp2 0.0432712639152713 0.0731678296264904 0.591397396043643 0.554383073764952 df.mm.exp3 -0.0627499817518873 0.0731678296264904 -0.857617098555684 0.391302089676439 df.mm.exp4 -0.0329598100077188 0.0731678296264904 -0.450468603154872 0.652466750406152 df.mm.exp5 -0.106535778325039 0.0731678296264904 -1.4560467198342 0.145682383058451 df.mm.exp6 -0.159943978552465 0.0731678296264904 -2.18598774036284 0.0290395220006192 * df.mm.exp7 -0.149998680802775 0.0731678296264904 -2.05006328011222 0.0406092918076963 * df.mm.exp8 -0.0542991190824744 0.0731678296264904 -0.742117394484193 0.458184261556194 df.mm.trans1:exp2 0.143646195789408 0.0641724317292038 2.23844089928786 0.0254038740488491 * df.mm.trans2:exp2 -0.216918139083578 0.0453767616409508 -4.78037945501641 2.0021284032137e-06 *** df.mm.trans1:exp3 -0.0127086301654267 0.0641724317292038 -0.198038781186521 0.843053535582893 df.mm.trans2:exp3 0.0661418796855386 0.0453767616409508 1.45761568903692 0.145249258479104 df.mm.trans1:exp4 -0.0244776004744453 0.0641724317292038 -0.381434828241142 0.702958759575196 df.mm.trans2:exp4 0.0158612581922961 0.0453767616409508 0.349545838413951 0.726750535007495 df.mm.trans1:exp5 0.0743959384682689 0.0641724317292038 1.15931306424862 0.24659546938645 df.mm.trans2:exp5 -0.0334579667292322 0.0453767616409508 -0.737337031539898 0.461084212523112 df.mm.trans1:exp6 0.103962827172999 0.0641724317292038 1.62005435000038 0.105524634782333 df.mm.trans2:exp6 -0.0168810091988875 0.0453767616409508 -0.372018817306985 0.70995484528547 df.mm.trans1:exp7 0.107625952117960 0.0641724317292038 1.67713688289267 0.0938170858948134 . df.mm.trans2:exp7 0.0410626121109917 0.0453767616409508 0.904926015565075 0.365714769585346 df.mm.trans1:exp8 0.0392225940059204 0.0641724317292038 0.61120629137809 0.541196970557868 df.mm.trans2:exp8 0.00232894182154093 0.0453767616409508 0.0513245488950703 0.959076790957624 df.mm.trans1:probe2 -0.0259531249654975 0.0497077518745678 -0.522114237453093 0.601702300192824 df.mm.trans1:probe3 -0.0155393276648153 0.0497077518745678 -0.312613769056125 0.754636968922752 df.mm.trans1:probe4 -0.0467842268235586 0.0497077518745678 -0.941185731787138 0.346828878632788 df.mm.trans1:probe5 -0.129704135187546 0.0497077518745678 -2.60933416411268 0.00920248822644497 ** df.mm.trans1:probe6 0.000237797381156250 0.0497077518745678 0.00478390939417874 0.996183927213356 df.mm.trans1:probe7 0.0518218418822886 0.0497077518745678 1.04253038868174 0.29740882209213 df.mm.trans1:probe8 0.0169038832311094 0.0497077518745678 0.340065333748438 0.733876195736967 df.mm.trans1:probe9 -0.125371329570020 0.0497077518745678 -2.52216857214507 0.0118121649776143 * df.mm.trans1:probe10 0.100274605040976 0.0497077518745678 2.01728304458444 0.0439232470570583 * df.mm.trans1:probe11 -0.230491653835619 0.0497077518745678 -4.63693579257497 3.98704877232461e-06 *** df.mm.trans1:probe12 -0.196575512341248 0.0497077518745678 -3.9546248809901 8.18604852077581e-05 *** df.mm.trans1:probe13 -0.228299122346138 0.0497077518745678 -4.59282735059566 4.90933385457553e-06 *** df.mm.trans1:probe14 -0.0864970951449839 0.0497077518745678 -1.74011279695871 0.0821358682340906 . df.mm.trans1:probe15 -0.122514563148501 0.0497077518745678 -2.46469732643821 0.0138741162442955 * df.mm.trans1:probe16 -0.144145566541347 0.0497077518745678 -2.89986090912104 0.00381204117898176 ** df.mm.trans2:probe2 0.0026028578277412 0.0497077518745678 0.0523632175985193 0.958249366830457 df.mm.trans2:probe3 0.0227757894480663 0.0497077518745678 0.458193915217461 0.646909129510558 df.mm.trans2:probe4 0.0735228990959286 0.0497077518745678 1.47910328516678 0.139416403848450 df.mm.trans2:probe5 0.0024480076564176 0.0497077518745678 0.0492480058763247 0.960731137870015 df.mm.trans2:probe6 0.0590423910757054 0.0497077518745678 1.18779041194003 0.235187972576603 df.mm.trans3:probe2 -0.0155393276648152 0.0497077518745678 -0.312613769056124 0.754636968922752 df.mm.trans3:probe3 -0.0467842268235585 0.0497077518745678 -0.941185731787136 0.346828878632788 df.mm.trans3:probe4 -0.129704135187546 0.0497077518745678 -2.60933416411269 0.00920248822644497 ** df.mm.trans3:probe5 0.0518218418822887 0.0497077518745678 1.04253038868174 0.29740882209213 df.mm.trans3:probe6 -0.148313440570891 0.0497077518745678 -2.9837084755944 0.00291447649412746 ** df.mm.trans3:probe7 0.246349105367408 0.0497077518745678 4.95594944605508 8.40205500393967e-07 *** df.mm.trans3:probe8 1.77688226515810 0.0497077518745678 35.7465827390842 5.03398744137394e-183 *** df.mm.trans3:probe9 -0.137905022386147 0.0497077518745678 -2.77431622202782 0.00563112143077896 ** df.mm.trans3:probe10 0.525301823622096 0.0497077518745678 10.5678048958569 7.32198617750224e-25 *** df.mm.trans3:probe11 0.155198277321563 0.0497077518745678 3.12221477473352 0.00184466856692220 ** df.mm.trans3:probe12 0.0205770541879213 0.0497077518745678 0.413960668344955 0.678988513166638 df.mm.trans3:probe13 0.542810496081598 0.0497077518745678 10.9200371292454 2.36147524428464e-26 *** df.mm.trans3:probe14 0.17091755683149 0.0497077518745678 3.43844874060654 0.000608308514380476 *** df.mm.trans3:probe15 0.115205557599578 0.0497077518745678 2.3176577747931 0.0206618449737569 * df.mm.trans3:probe16 0.258535745343077 0.0497077518745678 5.20111523038628 2.38615581984565e-07 *** df.mm.trans3:probe17 -0.172426765529698 0.0497077518745678 -3.46881037719829 0.0005442463261066 *** df.mm.trans3:probe18 0.753168965655967 0.0497077518745678 15.1519418451373 5.3574276856648e-47 *** df.mm.trans3:probe19 -0.239486861217423 0.0497077518745678 -4.81789765551543 1.66693212690830e-06 *** df.mm.trans3:probe20 -0.120380839926461 0.0497077518745678 -2.42177196486835 0.0156157067204344 * df.mm.trans3:probe21 0.433370587915309 0.0497077518745678 8.71837030588053 1.10345965281277e-17 *** df.mm.trans3:probe22 -0.057474972424011 0.0497077518745678 -1.15625773157159 0.247842056588736 df.mm.trans3:probe23 0.0959058831665778 0.0497077518745678 1.92939490421104 0.0539544325139776 . df.mm.trans3:probe24 0.162218759286327 0.0497077518745678 3.26344992820574 0.00113658867301313 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.48215003889322 0.167634937208203 26.7375650537952 3.2360628436766e-120 *** df.mm.trans1 -0.167749653746846 0.140891453324657 -1.19063044484536 0.234071166454129 df.mm.trans2 -0.257446567688054 0.125160201250944 -2.05693635129171 0.0399420479043235 * df.mm.exp2 -0.144566428621095 0.157711104210334 -0.916653455347647 0.359537384835670 df.mm.exp3 -0.293977625903444 0.157711104210334 -1.86402617225593 0.0626004453659797 . df.mm.exp4 -0.162658727374126 0.157711104210334 -1.03137143188849 0.302607150999974 df.mm.exp5 -0.290771043625344 0.157711104210334 -1.8436941715756 0.0655128922841277 . df.mm.exp6 -0.35720247653828 0.157711104210334 -2.26491646435937 0.0237234976821295 * df.mm.exp7 -0.138040169279332 0.157711104210334 -0.875272353018546 0.381628412278519 df.mm.exp8 -0.223062828031829 0.157711104210334 -1.41437617312182 0.157551647688526 df.mm.trans1:exp2 0.155445392738809 0.138321788681440 1.12379542095718 0.261359904488547 df.mm.trans2:exp2 -0.0115459040309023 0.0978082747624986 -0.118046290653204 0.90605384008793 df.mm.trans1:exp3 0.237944894371154 0.138321788681440 1.72022713586469 0.0856895000202637 . df.mm.trans2:exp3 0.121453562937588 0.0978082747624986 1.24175140838039 0.214609072426507 df.mm.trans1:exp4 0.0764309688240738 0.138321788681440 0.55255914164107 0.580684403918779 df.mm.trans2:exp4 0.103455395756018 0.0978082747624986 1.05773663841052 0.290421855976348 df.mm.trans1:exp5 0.252556624960615 0.138321788681440 1.82586291984889 0.0681583682096798 . df.mm.trans2:exp5 0.114617167988111 0.0978082747624987 1.17185553335266 0.241524221464129 df.mm.trans1:exp6 0.204910821103476 0.138321788681440 1.48140667538209 0.138802014887940 df.mm.trans2:exp6 0.256608134729217 0.0978082747624986 2.62358307977849 0.00882877687053072 ** df.mm.trans1:exp7 0.0859454082259104 0.138321788681440 0.621343962113199 0.534509890355306 df.mm.trans2:exp7 0.105656603834277 0.0978082747624987 1.08024197432002 0.280285608177216 df.mm.trans1:exp8 0.121252873613003 0.138321788681440 0.876599954127638 0.380906981495931 df.mm.trans2:exp8 0.158575777502554 0.0978082747624986 1.62129204188105 0.105259012325762 df.mm.trans1:probe2 0.171498884441415 0.107143596796168 1.60064520484298 0.109760101122007 df.mm.trans1:probe3 -0.139161194953140 0.107143596796168 -1.29882885318740 0.194291396294878 df.mm.trans1:probe4 -0.119401895107083 0.107143596796168 -1.11440999441372 0.265361549469116 df.mm.trans1:probe5 -0.0834483569080821 0.107143596796168 -0.778845954432874 0.436248149549444 df.mm.trans1:probe6 -0.103586098442337 0.107143596796168 -0.966796911246146 0.333870982569773 df.mm.trans1:probe7 -0.142040187823200 0.107143596796168 -1.32569926780991 0.185231286763789 df.mm.trans1:probe8 -0.0196286123557426 0.107143596796168 -0.183199117284485 0.854677598214135 df.mm.trans1:probe9 0.0226708405025483 0.107143596796168 0.211593050639114 0.832466107866387 df.mm.trans1:probe10 0.00371178187284264 0.107143596796168 0.0346430583239054 0.972371033435875 df.mm.trans1:probe11 0.0436285740375637 0.107143596796168 0.407197213292771 0.683947184792033 df.mm.trans1:probe12 -0.0366703383294942 0.107143596796168 -0.342254128347555 0.73222900617328 df.mm.trans1:probe13 0.00227701737855185 0.107143596796168 0.0212520154879969 0.983048709615967 df.mm.trans1:probe14 -0.116600872451102 0.107143596796168 -1.08826729676554 0.276729978190714 df.mm.trans1:probe15 -0.0762250539866162 0.107143596796168 -0.71142892590799 0.476978475887942 df.mm.trans1:probe16 -0.0818874104863269 0.107143596796168 -0.764277221737398 0.444875922785985 df.mm.trans2:probe2 -0.0129429425712620 0.107143596796168 -0.120799963397579 0.90387288029764 df.mm.trans2:probe3 0.132781568979030 0.107143596796168 1.23928608847840 0.215519969667259 df.mm.trans2:probe4 0.117189062941117 0.107143596796167 1.09375703677431 0.274315549598918 df.mm.trans2:probe5 0.0559502897893559 0.107143596796168 0.522199099735255 0.601643239832256 df.mm.trans2:probe6 0.161191676546662 0.107143596796168 1.50444526193495 0.132771251177144 df.mm.trans3:probe2 0.0634087812727315 0.107143596796168 0.59181120635106 0.554106012622712 df.mm.trans3:probe3 0.0475070013015039 0.107143596796168 0.443395617863029 0.657572093711976 df.mm.trans3:probe4 0.0386741510113966 0.107143596796168 0.3609562509365 0.718205670301882 df.mm.trans3:probe5 0.0666309397202442 0.107143596796168 0.621884477585762 0.534154527847888 df.mm.trans3:probe6 0.0874796816341946 0.107143596796168 0.816471392131982 0.414418093537667 df.mm.trans3:probe7 0.184021150567291 0.107143596796168 1.71751888185514 0.0861829607620483 . df.mm.trans3:probe8 0.165109775444348 0.107143596796168 1.54101393253072 0.123618471088574 df.mm.trans3:probe9 0.151309636481357 0.107143596796168 1.41221352470752 0.158187110713547 df.mm.trans3:probe10 -0.0235192049996106 0.107143596796168 -0.219511064616900 0.826295161166056 df.mm.trans3:probe11 0.101631670183717 0.107143596796167 0.948555706759257 0.343067622169424 df.mm.trans3:probe12 0.137351425536933 0.107143596796168 1.28193778857577 0.200150995386568 df.mm.trans3:probe13 0.0620181062936176 0.107143596796168 0.578831662816045 0.562828461545766 df.mm.trans3:probe14 0.0288397834245444 0.107143596796168 0.269169453769691 0.78785283017366 df.mm.trans3:probe15 0.120382403108568 0.107143596796167 1.12356133925190 0.261459198657633 df.mm.trans3:probe16 0.178762458502516 0.107143596796168 1.66843809474305 0.0955306660876164 . df.mm.trans3:probe17 -0.022648989282178 0.107143596796168 -0.211389107323567 0.832625190588958 df.mm.trans3:probe18 0.224983979950511 0.107143596796168 2.09983598346550 0.0359844107995848 * df.mm.trans3:probe19 0.0523834774427843 0.107143596796168 0.488909080982598 0.625009447781391 df.mm.trans3:probe20 0.195840590956844 0.107143596796168 1.82783289727912 0.067861843212871 . df.mm.trans3:probe21 0.0600332106088199 0.107143596796167 0.560306097647893 0.575391690500752 df.mm.trans3:probe22 0.250256209307314 0.107143596796168 2.33570849579940 0.0196963142947832 * df.mm.trans3:probe23 0.0120686099499246 0.107143596796168 0.112639581933059 0.910338103054417 df.mm.trans3:probe24 0.153601860207042 0.107143596796168 1.43360746512232 0.151985745760921