fitVsDatCorrelation=0.924534244863023 cont.fitVsDatCorrelation=0.231195130274976 fstatistic=11280.9508994266,66,1014 cont.fstatistic=1717.9187637039,66,1014 residuals=-0.827256560593898,-0.0911591333484832,-0.00079547736780141,0.0922707658803144,1.1268394207478 cont.residuals=-0.865655705847884,-0.308578887198926,-0.0823776830738046,0.281052586537703,1.23319755997430 predictedValues: Include Exclude Both Lung 99.1145608543098 65.0699439316528 88.7459879499538 cerebhem 80.2246524107651 53.9059400144505 84.3050904790613 cortex 86.607890479987 56.2719050877581 84.6731225180305 heart 90.5023751726303 63.6998913385582 86.6431210416891 kidney 93.8362005256824 66.3426909976269 83.4339118973358 liver 88.4844581197081 61.5344413918636 84.7904465248934 stomach 90.0095431240545 63.2234321132605 84.7952385099932 testicle 90.5573366545018 56.1143090228599 85.2406944303653 diffExp=34.0446169226570,26.3187123963146,30.3359853922288,26.8024838340721,27.4935095280555,26.9500167278445,26.786111010794,34.4430276316419 diffExpScore=0.995729679550474 diffExp1.5=1,0,1,0,0,0,0,1 diffExp1.5Score=0.75 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 84.4381328987941 92.6136248633445 82.514085685402 cerebhem 81.1811357767514 79.3709965115746 81.9506179500089 cortex 82.7772715229858 76.5955037951684 86.013065372499 heart 82.914416358384 92.071837684468 85.6911606212278 kidney 91.8088080760964 106.832306189622 91.4772932597375 liver 82.0172420232565 81.546016235448 83.5758764663836 stomach 85.9203347547024 82.8578668566834 81.4707743859066 testicle 84.0014557080564 96.441987801616 72.2137421177646 cont.diffExp=-8.17549196455039,1.81013926517684,6.18176772781739,-9.15742132608388,-15.0234981135260,0.471225787808464,3.06246789801899,-12.4405320935596 cont.diffExpScore=1.64342974461694 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=0.787236081698603 cont.tran.correlation=0.768648371152002 tran.covariance=0.00378581702910013 cont.tran.covariance=0.00327431399179772 tran.mean=75.3437232024794 cont.tran.mean=86.4618085660595 weightedLogRatios: wLogRatio Lung 1.84563359945611 cerebhem 1.66432670549775 cortex 1.83076705808598 heart 1.52058927669453 kidney 1.51452699611514 liver 1.56232871803370 stomach 1.52716389916014 testicle 2.04200381832354 cont.weightedLogRatios: wLogRatio Lung -0.414234737473812 cerebhem 0.098890440207817 cortex 0.339748351081865 heart -0.468297759094505 kidney -0.69645544266337 liver 0.025376199777821 stomach 0.16097308635581 testicle -0.621468431248287 varWeightedLogRatios=0.0386984677287977 cont.varWeightedLogRatios=0.157666915432376 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.55334561719741 0.076236874160361 59.726289506829 0 *** df.mm.trans1 0.0736487154863795 0.0651723721026995 1.13006037850399 0.258718019442762 df.mm.trans2 -0.377100409808009 0.056923848228154 -6.62464716539487 5.63958100250545e-11 *** df.mm.exp2 -0.348331610137357 0.0717327592814549 -4.85596279338179 1.38678965968513e-06 *** df.mm.exp3 -0.233172803262548 0.0717327592814549 -3.25057624435801 0.00118962058048539 ** df.mm.exp4 -0.0881995652115229 0.0717327592814549 -1.22955768180418 0.219148003403886 df.mm.exp5 0.0263685747268272 0.0717327592814549 0.367594596819647 0.713252254515409 df.mm.exp6 -0.123719806903838 0.0717327592814549 -1.72473230004166 0.0848804578307815 . df.mm.exp7 -0.0796095906033652 0.0717327592814549 -1.10980800684112 0.267344887621538 df.mm.exp8 -0.198065799045880 0.0717327592814549 -2.76116241770008 0.00586357709128924 ** df.mm.trans1:exp2 0.136886102980076 0.0654396556799504 2.09179130846215 0.0367054700449733 * df.mm.trans2:exp2 0.160109534485477 0.0445014001935741 3.59785386053082 0.000336346639286134 *** df.mm.trans1:exp3 0.0982873673038687 0.0654396556799504 1.50195422458469 0.133420264297017 df.mm.trans2:exp3 0.0879054404791752 0.0445014001935741 1.97534100268307 0.0485003142800119 * df.mm.trans1:exp4 -0.00270070088959220 0.0654396556799504 -0.04127009626702 0.967088696908108 df.mm.trans2:exp4 0.0669196701125662 0.0445014001935741 1.50376549550073 0.132953139804249 df.mm.trans1:exp5 -0.0810942215220965 0.0654396556799504 -1.23922139686536 0.215550267977206 df.mm.trans2:exp5 -0.00699773014845013 0.0445014001935741 -0.157247415092809 0.875081165959715 df.mm.trans1:exp6 0.0102703675650786 0.0654396556799504 0.156944095416830 0.875320150251224 df.mm.trans2:exp6 0.0678540957495765 0.0445014001935741 1.52476316373017 0.127629996464375 df.mm.trans1:exp7 -0.0167510714399787 0.0654396556799504 -0.25597737741629 0.79802022422025 df.mm.trans2:exp7 0.050821832482454 0.0445014001935741 1.14202771736141 0.253712098944451 df.mm.trans1:exp8 0.107772641870378 0.0654396556799504 1.64690111447817 0.0998882191111272 . df.mm.trans2:exp8 0.0499938900031128 0.0445014001935741 1.12342285378993 0.261523850571968 df.mm.trans1:probe2 0.301352927767181 0.0487227934462597 6.18505029067283 8.99056755168891e-10 *** df.mm.trans1:probe3 0.0165536729360335 0.0487227934462597 0.339752131705910 0.734113558559631 df.mm.trans1:probe4 -0.0310386702233485 0.0487227934462597 -0.637046195998255 0.524238548643151 df.mm.trans1:probe5 0.596923095869686 0.0487227934462597 12.251413633089 2.78926223340655e-32 *** df.mm.trans1:probe6 -0.366551417761203 0.0487227934462597 -7.52320201356069 1.17599279960981e-13 *** df.mm.trans1:probe7 -0.0862119596865889 0.0487227934462597 -1.76943794862006 0.0771213385798406 . df.mm.trans1:probe8 -0.240412319607416 0.0487227934462597 -4.93428850446733 9.3980348207069e-07 *** df.mm.trans1:probe9 -0.70974766460048 0.0487227934462597 -14.5670560819407 8.6799883316735e-44 *** df.mm.trans1:probe10 -0.292303305511239 0.0487227934462597 -5.99931335697417 2.75420607461462e-09 *** df.mm.trans1:probe11 -0.68101213233645 0.0487227934462597 -13.9772801222408 9.93662920356378e-41 *** df.mm.trans1:probe12 -0.53876702625792 0.0487227934462597 -11.0578024811358 6.46465887444445e-27 *** df.mm.trans1:probe13 -0.511454712436968 0.0487227934462597 -10.4972370478120 1.52907306765218e-24 *** df.mm.trans1:probe14 -0.619927386302907 0.0487227934462597 -12.7235600107099 1.63030930257699e-34 *** df.mm.trans1:probe15 -0.459351245588057 0.0487227934462597 -9.42785117800588 2.75080735916377e-20 *** df.mm.trans1:probe16 -0.547174670324835 0.0487227934462597 -11.2303632780899 1.14981216530984e-27 *** df.mm.trans1:probe17 0.510498792826614 0.0487227934462597 10.477617491076 1.84400776947555e-24 *** df.mm.trans1:probe18 0.461120391528865 0.0487227934462597 9.46416161539407 2.00009497747960e-20 *** df.mm.trans1:probe19 0.225200609645742 0.0487227934462597 4.62207918957139 4.28871908660558e-06 *** df.mm.trans1:probe20 0.633092303312151 0.0487227934462597 12.9937603846635 8.07567974354719e-36 *** df.mm.trans1:probe21 0.59188251405549 0.0487227934462597 12.1479593469599 8.44547277570978e-32 *** df.mm.trans1:probe22 0.549327325125339 0.0487227934462597 11.2745449566892 7.36536409276373e-28 *** df.mm.trans2:probe2 -0.0724573895190666 0.0487227934462597 -1.48713537123001 0.13728993589706 df.mm.trans2:probe3 -0.07012644796627 0.0487227934462597 -1.43929448633971 0.150375720994874 df.mm.trans2:probe4 0.00151883594956961 0.0487227934462597 0.0311730063516341 0.975137701743214 df.mm.trans2:probe5 0.0368828525140968 0.0487227934462597 0.756993799109196 0.449229432114564 df.mm.trans2:probe6 0.0861856714890601 0.0487227934462597 1.76889840243091 0.0772114081067072 . df.mm.trans3:probe2 0.149589733483349 0.0487227934462597 3.07022079200659 0.00219594265255437 ** df.mm.trans3:probe3 0.18449649290554 0.0487227934462597 3.78665671353668 0.000161657175457782 *** df.mm.trans3:probe4 -0.159748798831119 0.0487227934462597 -3.27872824055787 0.00107820635397111 ** df.mm.trans3:probe5 0.27451454462303 0.0487227934462597 5.6342119407791 2.27759306247869e-08 *** df.mm.trans3:probe6 0.809949102500987 0.0487227934462597 16.6236179252395 4.62725233060605e-55 *** df.mm.trans3:probe7 -0.0314647778420389 0.0487227934462597 -0.645791745843636 0.518560344832257 df.mm.trans3:probe8 1.01272324772647 0.0487227934462597 20.7854101970464 3.24338509034324e-80 *** df.mm.trans3:probe9 0.336950183791386 0.0487227934462597 6.91565815418681 8.23185905944245e-12 *** df.mm.trans3:probe10 0.131254433905269 0.0487227934462597 2.69390206557102 0.00717871353960017 ** df.mm.trans3:probe11 0.20798036327071 0.0487227934462597 4.26864612145254 2.15103081691465e-05 *** df.mm.trans3:probe12 0.504622059980585 0.0487227934462597 10.3570018114247 5.79625691701174e-24 *** df.mm.trans3:probe13 0.0594719510477387 0.0487227934462597 1.22061866410298 0.222514202555680 df.mm.trans3:probe14 -0.118774401872837 0.0487227934462597 -2.43775845906377 0.0149495764082747 * df.mm.trans3:probe15 0.4351859321616 0.0487227934462597 8.93187564546358 1.93254160055547e-18 *** df.mm.trans3:probe16 0.286581185806744 0.0487227934462597 5.88187099992199 5.50390505033426e-09 *** df.mm.trans3:probe17 -0.0732981822339388 0.0487227934462597 -1.50439203193030 0.132791852347575 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.61362569374392 0.194625080773905 23.7051960384466 3.39712992629094e-99 *** df.mm.trans1 -0.150262517441886 0.166378518589761 -0.903136526971895 0.366667932577316 df.mm.trans2 -0.0583396052238054 0.145320865806515 -0.401453741002896 0.68817067860463 df.mm.exp2 -0.186787289683371 0.183126527983321 -1.01999034077894 0.307976332257959 df.mm.exp3 -0.251293669019731 0.183126527983321 -1.37224066762528 0.170292001700636 df.mm.exp4 -0.0618579770539196 0.183126527983321 -0.337788182493983 0.735592731869087 df.mm.exp5 0.123391469976034 0.183126527983321 0.67380445277307 0.500589203385013 df.mm.exp6 -0.169144312797936 0.183126527983321 -0.92364724357872 0.355889742391766 df.mm.exp7 -0.0811835045945392 0.183126527983321 -0.443319192956759 0.657629443051173 df.mm.exp8 0.168659067221971 0.183126527983321 0.92099746049536 0.357270813829082 df.mm.trans1:exp2 0.147451080677818 0.167060866710469 0.88261891358054 0.377651393933396 df.mm.trans2:exp2 0.0324840403929866 0.113607603966133 0.285931920566427 0.774988666363639 df.mm.trans1:exp3 0.231428083051797 0.167060866710469 1.38529200529578 0.166267849749674 df.mm.trans2:exp3 0.061395779270753 0.113607603966133 0.540419629737594 0.589026350359207 df.mm.trans1:exp4 0.0436478134969868 0.167060866710469 0.261268927645588 0.793938181961203 df.mm.trans2:exp4 0.0559908262172059 0.113607603966133 0.492844002184017 0.62222951973974 df.mm.trans1:exp5 -0.0397023395738731 0.167060866710469 -0.237651943005184 0.812199068894038 df.mm.trans2:exp5 0.0194326356388853 0.113607603966133 0.171050483950689 0.864218195826375 df.mm.trans1:exp6 0.140054695372277 0.167060866710469 0.838345317667983 0.402034362812844 df.mm.trans2:exp6 0.0418755225129959 0.113607603966133 0.368597884746157 0.712504413677387 df.mm.trans1:exp7 0.09858492030547 0.167060866710469 0.5901137845545 0.555245845797933 df.mm.trans2:exp7 -0.0301260706147599 0.113607603966133 -0.265176533638897 0.790927368389017 df.mm.trans1:exp8 -0.173844049833876 0.167060866710469 -1.04060306436194 0.298307857426565 df.mm.trans2:exp8 -0.128153669907084 0.113607603966133 -1.12803778473566 0.259570793272009 df.mm.trans1:probe2 -0.124963935067457 0.124384396847937 -1.00465925175670 0.315300635486809 df.mm.trans1:probe3 -0.124444281660610 0.124384396847937 -1.0004814495563 0.317316256430028 df.mm.trans1:probe4 -0.125005287056686 0.124384396847937 -1.00499170494437 0.315140602878698 df.mm.trans1:probe5 -0.0257618808612245 0.124384396847937 -0.207115052322189 0.835961590781954 df.mm.trans1:probe6 -0.0799910074823621 0.124384396847937 -0.643095191273491 0.520307730682756 df.mm.trans1:probe7 -0.0619990192276551 0.124384396847937 -0.498446917770966 0.618277172028171 df.mm.trans1:probe8 0.0639951355806642 0.124384396847937 0.514494882014017 0.607018153103389 df.mm.trans1:probe9 -0.147082657311115 0.124384396847937 -1.18248478939788 0.237290595388185 df.mm.trans1:probe10 -0.0511323353381445 0.124384396847937 -0.411083195592892 0.681098392639574 df.mm.trans1:probe11 -0.0562634132718053 0.124384396847937 -0.452334976874861 0.651124432807511 df.mm.trans1:probe12 0.0371989879833635 0.124384396847937 0.299064745466749 0.764951926777749 df.mm.trans1:probe13 0.0727261491550762 0.124384396847937 0.584688682809512 0.558887132403505 df.mm.trans1:probe14 -0.0609015032787487 0.124384396847937 -0.489623335579639 0.624506363685666 df.mm.trans1:probe15 -0.029206639397893 0.124384396847937 -0.234809510983912 0.81440399188895 df.mm.trans1:probe16 -0.068809122532358 0.124384396847937 -0.553197380668885 0.580250201938252 df.mm.trans1:probe17 -0.0284206485713578 0.124384396847937 -0.228490464170539 0.81931103867361 df.mm.trans1:probe18 -0.0284135386522138 0.124384396847937 -0.228433303310141 0.819355459526797 df.mm.trans1:probe19 0.0684717627821513 0.124384396847937 0.5504851453825 0.582107909697512 df.mm.trans1:probe20 -0.079275974122684 0.124384396847937 -0.637346613656057 0.52404296920445 df.mm.trans1:probe21 -0.173382135496124 0.124384396847937 -1.39392190572011 0.163646574052342 df.mm.trans1:probe22 -0.0437571966002359 0.124384396847937 -0.351790077446209 0.725068791801687 df.mm.trans2:probe2 0.00095346861599846 0.124384396847937 0.00766550017655427 0.993885383448785 df.mm.trans2:probe3 -0.223064584493248 0.124384396847937 -1.79334860437478 0.0732151103966368 . df.mm.trans2:probe4 -0.107508005100668 0.124384396847937 -0.864320669031335 0.387616170991052 df.mm.trans2:probe5 -0.121712175809743 0.124384396847937 -0.97851642886156 0.328052362955848 df.mm.trans2:probe6 -0.166214584794434 0.124384396847937 -1.33629771101945 0.181751769706179 df.mm.trans3:probe2 0.023884750112733 0.124384396847937 0.192023683982908 0.847762100338273 df.mm.trans3:probe3 -0.0814549956061288 0.124384396847937 -0.654865060813932 0.512703155352837 df.mm.trans3:probe4 0.144638471699953 0.124384396847937 1.16283453041764 0.245170229980529 df.mm.trans3:probe5 0.0308469921950776 0.124384396847937 0.247997280822841 0.804186721690029 df.mm.trans3:probe6 0.0133929302797822 0.124384396847937 0.107673716472295 0.914275819822115 df.mm.trans3:probe7 0.0970150799706153 0.124384396847937 0.77996181538122 0.435595365719269 df.mm.trans3:probe8 0.150902101181534 0.124384396847937 1.21319156586831 0.225339122091346 df.mm.trans3:probe9 0.0531968156275554 0.124384396847937 0.427680778101048 0.668974356114984 df.mm.trans3:probe10 -0.0473654290126805 0.124384396847937 -0.380798799632287 0.703432312359617 df.mm.trans3:probe11 -0.0510550906421858 0.124384396847937 -0.410462179630149 0.68155365578579 df.mm.trans3:probe12 0.0502279331269202 0.124384396847937 0.403812169369804 0.686435991151029 df.mm.trans3:probe13 0.132941397718036 0.124384396847937 1.06879480937276 0.285416503954988 df.mm.trans3:probe14 -0.199394390509424 0.124384396847937 -1.60304986447125 0.109235071679248 df.mm.trans3:probe15 -0.0826363437639016 0.124384396847937 -0.66436261989457 0.506609295029865 df.mm.trans3:probe16 -0.057635224288041 0.124384396847937 -0.463363779932151 0.643203073235128 df.mm.trans3:probe17 -0.0424324625104739 0.124384396847937 -0.341139753745388 0.733069048439639