fitVsDatCorrelation=0.821923552896274 cont.fitVsDatCorrelation=0.254590576330476 fstatistic=15026.1451083293,69,1083 cont.fstatistic=5202.74464515877,69,1083 residuals=-0.511704516201302,-0.0788233735675749,-0.00846229351329725,0.0736533849620908,1.02845579637548 cont.residuals=-0.430326189257438,-0.154498295298616,-0.0365029196324461,0.112171950849766,0.946229852137 predictedValues: Include Exclude Both Lung 64.6344458588615 45.9202107760111 53.03581463939 cerebhem 73.077745837242 44.3924114671477 58.4221055051354 cortex 65.6508868422684 46.1667049463162 55.864832425797 heart 62.9100570709195 47.0430431066611 54.6434859591939 kidney 64.971135200841 45.6532989736142 53.5307734753433 liver 68.5039426036234 51.7926624201397 58.1876290200184 stomach 66.3279341382383 47.5051917152294 54.2709459907199 testicle 65.8138745376358 45.888107985204 56.5077182523575 diffExp=18.7142350828504,28.6853343700943,19.4841818959522,15.8670139642584,19.3178362272267,16.7112801834836,18.8227424230089,19.9257665524318 diffExpScore=0.993691981634402 diffExp1.5=0,1,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=1,1,1,0,1,0,0,1 diffExp1.4Score=0.833333333333333 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 53.6303049758574 48.9235567032602 58.2508688112589 cerebhem 52.460490610561 53.100060132645 53.4517946726454 cortex 54.3449746360989 55.7471347233657 52.4347433738692 heart 50.8919865722517 51.6517087282522 54.2628966976953 kidney 51.5769248211663 58.4771098124881 55.6370784353343 liver 52.8004863189941 50.1335732617691 51.899829648905 stomach 50.9030902865626 50.3352101540241 56.0680333733738 testicle 51.4582837525157 51.1704696747564 53.2514570300668 cont.diffExp=4.70674827259712,-0.639569522083953,-1.40216008726679,-0.75972215600045,-6.90018499132174,2.66691305722508,0.567880132538534,0.287814077759265 cont.diffExpScore=7.2528125751786 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=-0.0470319374455982 cont.tran.correlation=0.0624940180797064 tran.covariance=-0.000115945961191389 cont.tran.covariance=8.60164852981172e-05 tran.mean=56.6407283424971 cont.tran.mean=52.3503353227855 weightedLogRatios: wLogRatio Lung 1.36662555371493 cerebhem 2.014904149719 cortex 1.41129309446162 heart 1.16152137452135 kidney 1.41059008394036 liver 1.14291972095485 stomach 1.34434239015652 testicle 1.44484938161326 cont.weightedLogRatios: wLogRatio Lung 0.361559554930309 cerebhem -0.048060378214463 cortex -0.102101530667461 heart -0.0583393789985534 kidney -0.502979198709574 liver 0.204239591688171 stomach 0.0440260939201587 testicle 0.0220874809834308 varWeightedLogRatios=0.0723364253483866 cont.varWeightedLogRatios=0.0633142825252704 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.42312239320408 0.0596943733524407 74.0961357796586 0 *** df.mm.trans1 -0.0250001307757319 0.0509105238328867 -0.491060175648449 0.623483378019833 df.mm.trans2 -0.605128029151483 0.0443455119896874 -13.6457558386564 3.07286697566575e-39 *** df.mm.exp2 -0.00778730444197176 0.0555972151339756 -0.140066447990358 0.888633534131426 df.mm.exp3 -0.031010486076055 0.0555972151339756 -0.557770492664558 0.577116386326039 df.mm.exp4 -0.0327463728670224 0.0555972151339756 -0.588993041973627 0.555988770639807 df.mm.exp5 -0.00992312027473778 0.0555972151339756 -0.178482325973082 0.858377581003197 df.mm.exp6 0.0857816195019069 0.0555972151339756 1.54291216376925 0.123144220382178 df.mm.exp7 0.0367757279360912 0.0555972151339756 0.661467086929997 0.508453515791855 df.mm.exp8 -0.046025956640785 0.0555972151339756 -0.827846440327519 0.407939852467137 df.mm.trans1:exp2 0.130563704029072 0.0505473049934507 2.58300030132148 0.0099247428766379 ** df.mm.trans2:exp2 -0.0260494956999076 0.0335505976990060 -0.77642419171207 0.437668097911895 df.mm.trans1:exp3 0.0466141090664313 0.0505473049934507 0.922187821338268 0.356636005089176 df.mm.trans2:exp3 0.0363640101263733 0.033550597699006 1.08385580646304 0.278669968305435 df.mm.trans1:exp4 0.00570492732415082 0.0505473049934507 0.112863135332141 0.910159979546239 df.mm.trans2:exp4 0.0569040241875647 0.033550597699006 1.6960658852659 0.0901607978911113 . df.mm.trans1:exp5 0.0151187313991872 0.0505473049934507 0.299100642480269 0.764920641390826 df.mm.trans2:exp5 0.00409364930672042 0.033550597699006 0.122014199074662 0.902910408886335 df.mm.trans1:exp6 -0.0276378059407354 0.0505473049934507 -0.546771107664718 0.584648628968181 df.mm.trans2:exp6 0.0345615255383629 0.0335505976990060 1.03013144053129 0.303178306834579 df.mm.trans1:exp7 -0.0109120764013693 0.0505473049934507 -0.215878500402409 0.82912310717749 df.mm.trans2:exp7 -0.00284206567094316 0.033550597699006 -0.0847098372565614 0.932507731127783 df.mm.trans1:exp8 0.0641091456709736 0.0505473049934507 1.26829997522677 0.20496341212488 df.mm.trans2:exp8 0.0453266127785142 0.033550597699006 1.35099270615549 0.176979942570378 df.mm.trans1:probe2 -0.519650292506435 0.0383934342501393 -13.5348739349763 1.12106315210268e-38 *** df.mm.trans1:probe3 -0.56590483762037 0.0383934342501393 -14.7396253727502 5.96565579887537e-45 *** df.mm.trans1:probe4 -0.425762222247524 0.0383934342501393 -11.0894539799075 3.83047447502547e-27 *** df.mm.trans1:probe5 -0.46347738393882 0.0383934342501393 -12.0717876113710 1.43074943859535e-31 *** df.mm.trans1:probe6 -0.557903064361857 0.0383934342501393 -14.5312102253482 7.70413755131787e-44 *** df.mm.trans1:probe7 -0.460234148975563 0.0383934342501393 -11.9873139239659 3.52789541429794e-31 *** df.mm.trans1:probe8 -0.284669405714679 0.0383934342501392 -7.41453353352068 2.45656864919564e-13 *** df.mm.trans1:probe9 -0.638361690532735 0.0383934342501393 -16.6268452666596 1.83491380358813e-55 *** df.mm.trans1:probe10 -0.465693112027407 0.0383934342501393 -12.1294987313024 7.70197571831098e-32 *** df.mm.trans1:probe11 -0.208182170530242 0.0383934342501393 -5.42233781885471 7.2514389869094e-08 *** df.mm.trans1:probe12 -0.414310821139268 0.0383934342501393 -10.7911894111886 7.41006732947396e-26 *** df.mm.trans1:probe13 -0.262466852605425 0.0383934342501393 -6.83624316843896 1.35525448854397e-11 *** df.mm.trans1:probe14 -0.372654864200133 0.0383934342501393 -9.70621335336213 2.05211237425917e-21 *** df.mm.trans1:probe15 -0.214094078365854 0.0383934342501393 -5.57632008043348 3.10113917190392e-08 *** df.mm.trans1:probe16 -0.32652975400771 0.0383934342501393 -8.50483319310061 5.96968192548614e-17 *** df.mm.trans1:probe17 -0.639005623949566 0.0383934342501392 -16.6436172337787 1.46811458129796e-55 *** df.mm.trans1:probe18 -0.486298573628031 0.0383934342501393 -12.6661910591201 2.18740454424230e-34 *** df.mm.trans1:probe19 -0.584133305904374 0.0383934342501393 -15.2144062471373 1.60750220295053e-47 *** df.mm.trans1:probe20 -0.603548451246356 0.0383934342501393 -15.7200954547109 2.58499513007489e-50 *** df.mm.trans1:probe21 -0.60263579939929 0.0383934342501393 -15.6963244150816 3.5080362557879e-50 *** df.mm.trans1:probe22 -0.538224144153762 0.0383934342501392 -14.0186507059292 3.74980804486147e-41 *** df.mm.trans2:probe2 -0.00416326537611121 0.0383934342501393 -0.108436910045267 0.913669210140546 df.mm.trans2:probe3 0.0160030996833022 0.0383934342501393 0.416818656519224 0.676893656157476 df.mm.trans2:probe4 0.101709345623148 0.0383934342501393 2.64913383263649 0.00818728747637256 ** df.mm.trans2:probe5 -0.0160998518696991 0.0383934342501393 -0.419338675587239 0.675051796890452 df.mm.trans2:probe6 0.134236100410056 0.0383934342501393 3.49632959467721 0.000490815721316069 *** df.mm.trans3:probe2 0.349126494262833 0.0383934342501393 9.09339060393034 4.47326188026738e-19 *** df.mm.trans3:probe3 0.462233990436381 0.0383934342501393 12.0394020348598 2.02336709270904e-31 *** df.mm.trans3:probe4 0.218729352345465 0.0383934342501393 5.69705098325951 1.56989548799625e-08 *** df.mm.trans3:probe5 -0.0390826196697639 0.0383934342501393 -1.01795060621914 0.308928777153715 df.mm.trans3:probe6 0.100836656423704 0.0383934342501393 2.62640366492712 0.00875101259435136 ** df.mm.trans3:probe7 -0.0960025595849891 0.0383934342501393 -2.50049419803181 0.0125489204338026 * df.mm.trans3:probe8 0.209207433599837 0.0383934342501393 5.449041943912 6.26766570209536e-08 *** df.mm.trans3:probe9 0.0268506935036314 0.0383934342501393 0.699356388092165 0.484479508469353 df.mm.trans3:probe10 0.00419654484672089 0.0383934342501393 0.109303711134038 0.912981851984333 df.mm.trans3:probe11 0.566279945015684 0.0383934342501393 14.7493954650236 5.28832191469079e-45 *** df.mm.trans3:probe12 0.114893036578400 0.0383934342501393 2.99251783077946 0.00282957445128841 ** df.mm.trans3:probe13 0.0675502137843508 0.0383934342501393 1.75942098183378 0.0787883599612412 . df.mm.trans3:probe14 0.519673698984898 0.0383934342501393 13.5354835829257 1.11313642453265e-38 *** df.mm.trans3:probe15 0.118797381225840 0.0383934342501393 3.09421086042619 0.00202396635521073 ** df.mm.trans3:probe16 0.109443278662798 0.0383934342501393 2.85057278152713 0.00444684651398836 ** df.mm.trans3:probe17 -0.110636944595445 0.0383934342501393 -2.88166314778272 0.00403389811592329 ** df.mm.trans3:probe18 0.180065885837536 0.0383934342501393 4.69001769063893 3.08037141528005e-06 *** df.mm.trans3:probe19 0.135553155497725 0.0383934342501393 3.53063376968508 0.000431993840420875 *** df.mm.trans3:probe20 0.621748362313485 0.0383934342501393 16.1941325243972 5.52622712706778e-53 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.73345717379291 0.10134761460902 36.8381356403492 3.06674343746806e-193 *** df.mm.trans1 0.222246791504545 0.08643461450706 2.5712706971857 0.0102651531628511 * df.mm.trans2 0.152511814772480 0.075288701537006 2.02569325355568 0.0430410522281620 * df.mm.exp2 0.145843868848984 0.0943915618221756 1.54509434989263 0.122615585445697 df.mm.exp3 0.248994442366973 0.0943915618221755 2.63788878539857 0.00846197243089244 ** df.mm.exp4 0.0727737582153816 0.0943915618221755 0.770977371393433 0.440888509809935 df.mm.exp5 0.185245609409551 0.0943915618221756 1.96252298228242 0.0499573422960951 * df.mm.exp6 0.124281497275058 0.0943915618221756 1.31665897751742 0.188231514164644 df.mm.exp7 0.0144483301088332 0.0943915618221756 0.153068026737946 0.878373143494927 df.mm.exp8 0.0932945856529976 0.0943915618221756 0.988378450912333 0.32318810143957 df.mm.trans1:exp2 -0.167897842009434 0.0858179506426024 -1.95644198856089 0.0506698176636261 . df.mm.trans2:exp2 -0.0639248208183521 0.0569613659469316 -1.12224873395607 0.262005440051028 df.mm.trans1:exp3 -0.235756595715895 0.0858179506426024 -2.74717112154924 0.00611090161414733 ** df.mm.trans2:exp3 -0.118427441102243 0.0569613659469316 -2.07908358820918 0.0378445667386221 * df.mm.trans1:exp4 -0.125182581426796 0.0858179506426023 -1.45869926384203 0.144937848300395 df.mm.trans2:exp4 -0.0185094930235034 0.0569613659469316 -0.324948194549055 0.745283039585899 df.mm.trans1:exp5 -0.224285529974982 0.0858179506426024 -2.61350368187003 0.00908616734445798 ** df.mm.trans2:exp5 -0.00686922965855084 0.0569613659469316 -0.120594538848500 0.904034563356794 df.mm.trans1:exp6 -0.139875395662163 0.0858179506426024 -1.62990836549673 0.103411652690423 df.mm.trans2:exp6 -0.0998496012904613 0.0569613659469316 -1.75293551393214 0.0798960027364353 . df.mm.trans1:exp7 -0.0666389951401552 0.0858179506426024 -0.776515806322153 0.437614047274274 df.mm.trans2:exp7 0.0139974925129555 0.0569613659469316 0.245736601997859 0.805932697287151 df.mm.trans1:exp8 -0.134637430097110 0.0858179506426024 -1.56887258538510 0.116969740405752 df.mm.trans2:exp8 -0.0483909968230279 0.0569613659469316 -0.849540667056891 0.395768265749078 df.mm.trans1:probe2 0.0399474445748353 0.0651834127636548 0.61284677928231 0.540106322404027 df.mm.trans1:probe3 0.0643812413122771 0.0651834127636548 0.987693626071923 0.323523326677993 df.mm.trans1:probe4 0.133353753416720 0.0651834127636548 2.0458234351773 0.041014897274766 * df.mm.trans1:probe5 0.0165276487893107 0.0651834127636548 0.253556051893715 0.79988667790219 df.mm.trans1:probe6 -0.0103339255888709 0.0651834127636548 -0.158536123696686 0.874063914385839 df.mm.trans1:probe7 0.0505935364026182 0.0651834127636549 0.776171947088175 0.437816936655299 df.mm.trans1:probe8 0.0308103797989771 0.0651834127636548 0.472672087770102 0.636542381513625 df.mm.trans1:probe9 0.134816962454043 0.0651834127636548 2.06827100236173 0.0388515024237428 * df.mm.trans1:probe10 0.0192813039599378 0.0651834127636549 0.295800774191571 0.767438937302058 df.mm.trans1:probe11 0.109385419472366 0.0651834127636548 1.67811740494442 0.0936126929776183 . df.mm.trans1:probe12 0.146527287961415 0.0651834127636548 2.24792292623126 0.0247816590051931 * df.mm.trans1:probe13 -0.0225745560802907 0.0651834127636548 -0.346323629327949 0.729166783597637 df.mm.trans1:probe14 0.109386054046893 0.0651834127636549 1.67812714015927 0.0936107923020753 . df.mm.trans1:probe15 -0.0342212284408294 0.0651834127636549 -0.524999029506331 0.599691318522316 df.mm.trans1:probe16 0.0380466984851533 0.0651834127636548 0.583686813439868 0.559552578825784 df.mm.trans1:probe17 0.0642930469312991 0.0651834127636548 0.986340607301676 0.324186303013410 df.mm.trans1:probe18 0.0657595170251004 0.0651834127636548 1.0088382034174 0.313277575767967 df.mm.trans1:probe19 0.0305644440920187 0.0651834127636548 0.468899107858018 0.639236125237563 df.mm.trans1:probe20 0.0848749241042241 0.0651834127636548 1.30209389943984 0.193161146774769 df.mm.trans1:probe21 0.0182149895184363 0.0651834127636548 0.279442096480604 0.779958925457774 df.mm.trans1:probe22 0.0195991003156664 0.0651834127636548 0.300676191759548 0.763719135727066 df.mm.trans2:probe2 0.0476029351874924 0.0651834127636548 0.730292158222729 0.46536956120163 df.mm.trans2:probe3 0.0696647562781912 0.0651834127636549 1.06874975280575 0.28542059997967 df.mm.trans2:probe4 -0.0263015546451576 0.0651834127636549 -0.403500730170772 0.68665954921053 df.mm.trans2:probe5 -0.00218589356537366 0.0651834127636548 -0.0335345062907243 0.973254528528188 df.mm.trans2:probe6 0.0227603831564578 0.0651834127636549 0.349174463125818 0.727026214194703 df.mm.trans3:probe2 -0.0522109489387475 0.0651834127636549 -0.800985200453626 0.423315899265312 df.mm.trans3:probe3 -0.106942519357333 0.0651834127636549 -1.64064007733211 0.10116252383647 df.mm.trans3:probe4 -0.0907936838638096 0.0651834127636549 -1.39289552378937 0.163937239669659 df.mm.trans3:probe5 -0.0673386887722695 0.0651834127636549 -1.03306479236412 0.301804210348006 df.mm.trans3:probe6 -0.0115654634992946 0.0651834127636548 -0.177429548545260 0.859204177228621 df.mm.trans3:probe7 -0.062524455873125 0.0651834127636548 -0.959208074910548 0.337668021963634 df.mm.trans3:probe8 -0.0641509386216545 0.0651834127636549 -0.984160477363406 0.325256424728396 df.mm.trans3:probe9 -0.0547527565561169 0.0651834127636549 -0.839979900325902 0.401105047633158 df.mm.trans3:probe10 -0.058863886037515 0.0651834127636548 -0.903050078874306 0.366700125970937 df.mm.trans3:probe11 -0.0479830158056705 0.0651834127636548 -0.736123098979945 0.461815192916504 df.mm.trans3:probe12 -0.133264501296493 0.0651834127636549 -2.04445418928416 0.0411501030348702 * df.mm.trans3:probe13 -0.0321218492703124 0.0651834127636548 -0.492791768770705 0.622259620815607 df.mm.trans3:probe14 -0.0260500915936629 0.0651834127636549 -0.39964295346297 0.689498316506509 df.mm.trans3:probe15 0.0279010821650620 0.0651834127636548 0.428039603667687 0.668707392422749 df.mm.trans3:probe16 -0.0255039428247499 0.0651834127636549 -0.391264307028896 0.695678842504581 df.mm.trans3:probe17 0.0245294333682972 0.0651834127636549 0.376314039543115 0.706757150922608 df.mm.trans3:probe18 -0.0717398243041622 0.0651834127636549 -1.10058404834187 0.271322291873489 df.mm.trans3:probe19 -0.0546044268852089 0.0651834127636548 -0.837704326454896 0.402381610821311 df.mm.trans3:probe20 0.0388450275545278 0.0651834127636549 0.595934240132132 0.551343680707256