fitVsDatCorrelation=0.89485976008676 cont.fitVsDatCorrelation=0.227890316696265 fstatistic=5874.21050841961,58,830 cont.fstatistic=1223.09982353994,58,830 residuals=-0.630289960173249,-0.113100070144056,-0.0129952175139969,0.102222514009758,1.23880586809218 cont.residuals=-0.869954990044818,-0.346289762184586,-0.118663498780398,0.248579631134591,1.70259433399060 predictedValues: Include Exclude Both Lung 117.229947542490 61.1608674698452 60.0087750786513 cerebhem 181.492463285103 65.424973601729 86.847105237164 cortex 197.828861627080 65.8996485186666 118.901106313517 heart 116.603460575271 59.1452071069609 65.7484959210333 kidney 122.124898436936 70.2756494764146 67.6851933021128 liver 128.895093127858 65.6056839497811 62.0358850015534 stomach 143.805598843111 56.8479742143809 64.1101143148882 testicle 116.165203738985 59.3426689250633 57.6909476894223 diffExp=56.0690800726443,116.067489683374,131.929213108413,57.4582534683097,51.849248960521,63.2894091780768,86.9576246287297,56.8225348139214 diffExpScore=0.998390841581488 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 80.7610142989548 78.3336036113548 79.547443771626 cerebhem 77.2816414691266 62.7857042627411 71.1630096266983 cortex 75.4323298000887 73.2807397009765 75.4356771144545 heart 83.4098010371536 71.4714306974408 80.3705334428569 kidney 80.6744656694342 63.4456090281081 80.5113535858163 liver 84.1026765807205 78.2826172719718 85.1882606404522 stomach 70.7861334024805 74.88185634422 72.4815841150474 testicle 87.2863950815568 66.280592640576 65.9799540045575 cont.diffExp=2.42741068760004,14.4959372063855,2.15159009911213,11.9383703397128,17.2288566413261,5.8200593087487,-4.09572294173944,21.0058024409808 cont.diffExpScore=1.09991962887903 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=0,0,0,0,0,0,0,1 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,1,0,0,1,0,0,1 cont.diffExp1.2Score=0.75 tran.correlation=0.301325613278068 cont.tran.correlation=-0.113323439599355 tran.covariance=0.00455908612072948 cont.tran.covariance=-0.00071594479783856 tran.mean=101.740512527480 cont.tran.mean=75.5310381810566 weightedLogRatios: wLogRatio Lung 2.88803019634433 cerebhem 4.78633488747221 cortex 5.20808219098524 heart 2.99980668410557 kidney 2.50266454472178 liver 3.05341959475142 stomach 4.18047156944662 testicle 2.96828978057641 cont.weightedLogRatios: wLogRatio Lung 0.133552541728162 cerebhem 0.881517198273883 cortex 0.124687724479366 heart 0.67140016997827 kidney 1.02589375507855 liver 0.315262589438756 stomach -0.241181595955717 testicle 1.19246364450001 varWeightedLogRatios=1.01338375291758 cont.varWeightedLogRatios=0.255469018170302 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.42887958266836 0.102592387680357 52.9169824917506 3.50240199427474e-268 *** df.mm.trans1 -0.407448028901876 0.085260373406243 -4.77886751633721 2.08455314707469e-06 *** df.mm.trans2 -1.32692929204888 0.0779089303164361 -17.0317996494036 5.16244490988428e-56 *** df.mm.exp2 0.134814880297088 0.099784766672097 1.35105672732697 0.177045428841550 df.mm.exp3 -0.0859108099053208 0.0997847666720969 -0.860961174440911 0.389508066239080 df.mm.exp4 -0.130216416231815 0.099784766672097 -1.30497289891672 0.192263746784934 df.mm.exp5 0.0594480605222769 0.099784766672097 0.595762885507658 0.55149605817522 df.mm.exp6 0.131794040413057 0.099784766672097 1.32078316970110 0.186937713279009 df.mm.exp7 0.0650866927032444 0.099784766672097 0.652270831249483 0.514407067270583 df.mm.exp8 8.75320309695562e-05 0.099784766672097 0.000877208354429445 0.999300299858086 df.mm.trans1:exp2 0.302261878337168 0.0849424342998605 3.55843202315270 0.000394247323547359 *** df.mm.trans2:exp2 -0.0674183999444957 0.0668837396514832 -1.00799387557871 0.313751165441233 df.mm.trans1:exp3 0.609175762021525 0.0849424342998604 7.17163061128006 1.64130218068371e-12 *** df.mm.trans2:exp3 0.160536353269178 0.0668837396514832 2.40022992293341 0.0166040867882232 * df.mm.trans1:exp4 0.124857999132490 0.0849424342998604 1.46991312600862 0.141964231815524 df.mm.trans2:exp4 0.0967044093305418 0.0668837396514832 1.44585828834404 0.148594431810939 df.mm.trans1:exp5 -0.0185411514002263 0.0849424342998605 -0.218279020998775 0.827265374716681 df.mm.trans2:exp5 0.0794697335800493 0.0668837396514832 1.18817718617633 0.235103482693476 df.mm.trans1:exp6 -0.0369325680790528 0.0849424342998604 -0.434795263209375 0.66382406418086 df.mm.trans2:exp6 -0.0616392672150805 0.0668837396514832 -0.92158822960961 0.35701119397263 df.mm.trans1:exp7 0.139238317148488 0.0849424342998604 1.63920799181425 0.101548900184084 df.mm.trans2:exp7 -0.138213671588015 0.0668837396514832 -2.06647643071719 0.0390931981927927 * df.mm.trans1:exp8 -0.0092115535379339 0.0849424342998604 -0.108444661538845 0.913669185280736 df.mm.trans2:exp8 -0.0302665059246954 0.0668837396514832 -0.452524127424807 0.65100969510545 df.mm.trans1:probe2 -0.447084314548347 0.0641301315492809 -6.97151719086658 6.39706047156094e-12 *** df.mm.trans1:probe3 -0.287264086611576 0.064130131549281 -4.47939337830342 8.53392966735764e-06 *** df.mm.trans1:probe4 -0.207650170732001 0.0641301315492809 -3.23795017592365 0.00125172745954885 ** df.mm.trans1:probe5 -0.436593732656856 0.0641301315492809 -6.80793446246645 1.89813004966396e-11 *** df.mm.trans1:probe6 -0.245910083218453 0.064130131549281 -3.83454824241992 0.000135314976170670 *** df.mm.trans1:probe7 -1.01504864161235 0.0641301315492809 -15.8279519640832 1.6484076126142e-49 *** df.mm.trans1:probe8 -1.12751191005647 0.0641301315492809 -17.5816247810132 4.63663903589927e-59 *** df.mm.trans1:probe9 -0.985278288594003 0.0641301315492809 -15.3637340948983 4.55531956820343e-47 *** df.mm.trans1:probe10 -1.03343548494558 0.0641301315492809 -16.1146634191985 4.89761588917486e-51 *** df.mm.trans1:probe11 -1.15185582228776 0.064130131549281 -17.9612265632515 3.44619480105763e-61 *** df.mm.trans1:probe12 -1.03848717363406 0.0641301315492809 -16.1934358864684 1.85328411752275e-51 *** df.mm.trans2:probe2 0.0445768510448835 0.064130131549281 0.695099947060428 0.487187213785568 df.mm.trans2:probe3 0.239706756512179 0.064130131549281 3.73781794487628 0.000198348394895135 *** df.mm.trans2:probe4 0.23860553751137 0.064130131549281 3.72064631316113 0.000212095029547674 *** df.mm.trans2:probe5 -0.0621262179983062 0.064130131549281 -0.96875238670866 0.332950949648664 df.mm.trans2:probe6 -0.171831078194777 0.064130131549281 -2.67941253266779 0.00752119170608345 ** df.mm.trans3:probe2 0.082169455625704 0.0641301315492809 1.2812924851489 0.200448803802726 df.mm.trans3:probe3 1.13882803041212 0.0641301315492809 17.7580803734510 4.77659530491234e-60 *** df.mm.trans3:probe4 0.317859362325205 0.0641301315492809 4.9564745096608 8.70332675754377e-07 *** df.mm.trans3:probe5 0.280291375385936 0.0641301315492809 4.37066584169636 1.39580399083811e-05 *** df.mm.trans3:probe6 -0.0526789087095859 0.0641301315492809 -0.821437714798771 0.411632870185681 df.mm.trans3:probe7 0.759608686066396 0.0641301315492809 11.8448016200103 5.13677762870021e-30 *** df.mm.trans3:probe8 0.69590076258026 0.0641301315492809 10.8513852344977 9.56147540680519e-26 *** df.mm.trans3:probe9 0.153038679530006 0.0641301315492809 2.38637713400608 0.0172384578884897 * df.mm.trans3:probe10 0.860678436399915 0.0641301315492809 13.4208119585491 2.61368080155566e-37 *** df.mm.trans3:probe11 1.15300806575780 0.064130131549281 17.9791938345202 2.72949576950086e-61 *** df.mm.trans3:probe12 0.056866964889674 0.0641301315492809 0.886743306396846 0.375473974371709 df.mm.trans3:probe13 0.619508553280697 0.0641301315492809 9.6601790502275 5.39320699407861e-21 *** df.mm.trans3:probe14 0.343902208356017 0.064130131549281 5.36256826624072 1.06438461160573e-07 *** df.mm.trans3:probe15 0.267023329386729 0.0641301315492809 4.16377329869555 3.45753675446608e-05 *** df.mm.trans3:probe16 -0.0879490502222277 0.0641301315492809 -1.37141540329826 0.170616131906751 df.mm.trans3:probe17 0.453520890776177 0.0641301315492809 7.07188461679154 3.24670878091885e-12 *** df.mm.trans3:probe18 0.169977801804987 0.0641301315492809 2.65051384892868 0.00819000176601685 ** df.mm.trans3:probe19 0.388227637666050 0.064130131549281 6.05374772025401 2.14169394765400e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.44983125664338 0.223800682138107 19.8830102488132 3.01029726626835e-72 *** df.mm.trans1 -0.0406448612652589 0.185991672083097 -0.218530543921879 0.827069482773391 df.mm.trans2 -0.0951575582905834 0.169954829434261 -0.559899113236972 0.575699323893625 df.mm.exp2 -0.153906725003129 0.21767598311276 -0.707045043749282 0.479736940485435 df.mm.exp3 -0.0818639675452481 0.217675983112760 -0.376081763245517 0.706952217559036 df.mm.exp4 -0.069701397419121 0.21767598311276 -0.320207109771107 0.748891925318438 df.mm.exp5 -0.223910517988713 0.21767598311276 -1.02864135393717 0.303947902473369 df.mm.exp6 -0.0286170770304398 0.217675983112760 -0.131466396160093 0.895438227327437 df.mm.exp7 -0.083875176938514 0.21767598311276 -0.385321227170318 0.700098162358173 df.mm.exp8 0.0976233670554298 0.217675983112760 0.448480193631923 0.653923730603318 df.mm.trans1:exp2 0.109868802436154 0.185298102214068 0.592929992932288 0.553389519424529 df.mm.trans2:exp2 -0.0673425423925798 0.145903871587302 -0.461554183997683 0.644521993565615 df.mm.trans1:exp3 0.0136055750618017 0.185298102214068 0.0734253340926485 0.941485349075704 df.mm.trans2:exp3 0.0151851062050740 0.145903871587302 0.104076101887317 0.91713409858938 df.mm.trans1:exp4 0.101972865521981 0.185298102214068 0.550317916392772 0.582249405968554 df.mm.trans2:exp4 -0.021977479223528 0.145903871587302 -0.150629856387175 0.880304306614396 df.mm.trans1:exp5 0.222838279868201 0.185298102214068 1.20259342759358 0.229476643512745 df.mm.trans2:exp5 0.0131168302718909 0.145903871587302 0.0899004949573421 0.928387976805402 df.mm.trans1:exp6 0.069161116882611 0.185298102214068 0.37324244585469 0.709063311683723 df.mm.trans2:exp6 0.0279659779182704 0.145903871587302 0.191673994761249 0.848044457655915 df.mm.trans1:exp7 -0.0479560502294399 0.185298102214068 -0.258804864466653 0.795849972243317 df.mm.trans2:exp7 0.0388101238944846 0.145903871587302 0.265997903086914 0.790306883715324 df.mm.trans1:exp8 -0.0199231101327182 0.185298102214068 -0.107519234653044 0.914403050585012 df.mm.trans2:exp8 -0.2647029088957 0.145903871587302 -1.81422813538786 0.0700035464806906 . df.mm.trans1:probe2 -0.05733026041919 0.139896999288609 -0.409803360406017 0.682055944760428 df.mm.trans1:probe3 0.00254927604991653 0.139896999288609 0.0182225213041014 0.985465715639291 df.mm.trans1:probe4 -0.139412766849192 0.139896999288609 -0.996538650279285 0.319278930337187 df.mm.trans1:probe5 -0.156295437566888 0.139896999288609 -1.11721794149744 0.264224441974953 df.mm.trans1:probe6 -0.0363834641941369 0.139896999288609 -0.260073228011685 0.79487178931989 df.mm.trans1:probe7 -0.154393998313432 0.139896999288609 -1.10362623286091 0.270075168515738 df.mm.trans1:probe8 -0.123186142337554 0.139896999288609 -0.880548853542027 0.378816943787042 df.mm.trans1:probe9 0.197418421386717 0.139896999288609 1.41116980629042 0.158569268792483 df.mm.trans1:probe10 -0.0588718364430309 0.139896999288609 -0.420822724879021 0.673993474537484 df.mm.trans1:probe11 0.00493548743049095 0.139896999288609 0.0352794374117274 0.97186540227201 df.mm.trans1:probe12 -0.0274825990314865 0.139896999288609 -0.196448809990482 0.844306984963308 df.mm.trans2:probe2 0.128045068560858 0.139896999288609 0.915281022552168 0.360309949702337 df.mm.trans2:probe3 -0.00772566758485647 0.139896999288609 -0.0552239692355255 0.955973323356898 df.mm.trans2:probe4 -0.0256607832576126 0.139896999288609 -0.183426259234297 0.854508371835427 df.mm.trans2:probe5 0.0738186940645875 0.139896999288609 0.527664599240609 0.597873172084275 df.mm.trans2:probe6 -0.0109028749074057 0.139896999288609 -0.0779350162108408 0.93789852884091 df.mm.trans3:probe2 0.147255953796438 0.139896999288609 1.05260266156708 0.292829476454331 df.mm.trans3:probe3 0.105799214309984 0.139896999288609 0.756265072503233 0.449704875676871 df.mm.trans3:probe4 0.0228016129214034 0.139896999288609 0.162988577577447 0.870567102585791 df.mm.trans3:probe5 0.026300142386442 0.139896999288609 0.187996472548954 0.850925367282278 df.mm.trans3:probe6 0.0933904989191013 0.139896999288609 0.6675661336126 0.504596144608935 df.mm.trans3:probe7 0.00906166924528104 0.139896999288609 0.064773864281297 0.948369648101826 df.mm.trans3:probe8 -0.0214689935645596 0.139896999288609 -0.153462859630526 0.878070579217998 df.mm.trans3:probe9 0.0173035438900248 0.139896999288609 0.123687741538526 0.90159246364001 df.mm.trans3:probe10 0.214554301417872 0.139896999288609 1.53365906709153 0.125494615240341 df.mm.trans3:probe11 0.00436504872466315 0.139896999288609 0.0312018752858166 0.975116045539722 df.mm.trans3:probe12 0.0582758546395436 0.139896999288609 0.416562577724201 0.677106087067837 df.mm.trans3:probe13 0.111471259745128 0.139896999288609 0.796809512083687 0.425789531613437 df.mm.trans3:probe14 0.0359160871351092 0.139896999288609 0.256732362507747 0.797449011211525 df.mm.trans3:probe15 0.0368113512045757 0.139896999288609 0.263131814061527 0.792514288556071 df.mm.trans3:probe16 -0.0629304646123 0.139896999288609 -0.449834270444028 0.652947399089988 df.mm.trans3:probe17 0.0688672048163526 0.139896999288609 0.492270778977031 0.622658113576235 df.mm.trans3:probe18 0.201817281881603 0.139896999288609 1.44261337203703 0.149506666681473 df.mm.trans3:probe19 0.114579321044694 0.139896999288609 0.81902629525538 0.41300650973889