fitVsDatCorrelation=0.92907181003404 cont.fitVsDatCorrelation=0.223642224763468 fstatistic=10290.3591085229,64,968 cont.fstatistic=1469.16686734773,64,968 residuals=-0.87211313338598,-0.104634692288852,-0.00400657046933699,0.0999387822683204,0.871595026522818 cont.residuals=-0.761344002730639,-0.327605214009728,-0.106556239441619,0.25665929830271,1.48777318937895 predictedValues: Include Exclude Both Lung 74.9107377905127 47.8791835943807 80.5189843193986 cerebhem 83.8198936160468 51.5342885485825 91.4821660986494 cortex 83.9156617527592 45.388020260048 90.279603833653 heart 90.2110886350759 45.6051113744254 104.904357852948 kidney 73.8139567483125 45.7399908077756 75.3355591309362 liver 82.1357570820594 50.8790079048666 82.4377100027222 stomach 81.6026964847682 47.9368152278943 85.086220348754 testicle 92.2843271112562 48.3120826626461 100.054958739863 diffExp=27.0315541961320,32.2856050674644,38.5276414927113,44.6059772606505,28.0739659405369,31.2567491771927,33.6658812568739,43.9722444486101 diffExpScore=0.996433915700563 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 74.8163015470955 81.6751202571678 71.8530544240822 cerebhem 77.093241357589 79.78894745809 91.5456825916751 cortex 72.5644665139622 76.4198454641658 84.5492307381553 heart 76.2632468125509 69.6264365627027 75.354038015871 kidney 76.6979038957274 69.1016501244061 68.8793756529226 liver 70.6182934199525 62.8291142300681 78.5157419136641 stomach 76.3826658555416 64.9196669055048 66.2021675405321 testicle 73.103622043342 62.9104140332463 71.1204956366216 cont.diffExp=-6.85881871007237,-2.69570610050106,-3.85537895020359,6.63681024984815,7.59625377132133,7.78917918988439,11.4629989500368,10.1932080100958 cont.diffExpScore=1.82574377403609 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=0.0504912068384104 cont.tran.correlation=0.32951232179636 tran.covariance=0.000246686835484626 cont.tran.covariance=0.00115813926147264 tran.mean=65.3730387250881 cont.tran.mean=72.8006835300695 weightedLogRatios: wLogRatio Lung 1.83186526023625 cerebhem 2.03590342256151 cortex 2.53355895246913 heart 2.83841226352837 kidney 1.94409563291787 liver 1.99658794620323 stomach 2.20019559219942 testicle 2.71903618407810 cont.weightedLogRatios: wLogRatio Lung -0.382334309197737 cerebhem -0.149926101720095 cortex -0.223134606273887 heart 0.390469465340147 kidney 0.447191730628914 liver 0.490722809744691 stomach 0.691796093708547 testicle 0.633220052532695 varWeightedLogRatios=0.146619693153346 cont.varWeightedLogRatios=0.177278381363538 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.83606891144313 0.0840061546877683 45.664141225139 1.07811835570890e-243 *** df.mm.trans1 0.879000834295677 0.0746351552817885 11.7773029476119 5.11755363135895e-30 *** df.mm.trans2 0.0081035785670514 0.066438998157048 0.121970210145196 0.902947926336849 df.mm.exp2 0.0582886244990177 0.08812514422629 0.661430117485455 0.508493919429347 df.mm.exp3 -0.0543361814500629 0.08812514422629 -0.616579773311202 0.53765691516145 df.mm.exp4 -0.127361987027287 0.08812514422629 -1.44524004068854 0.148714113495435 df.mm.exp5 0.00608347630360551 0.08812514422629 0.0690322422393341 0.944978212180382 df.mm.exp6 0.129295809980846 0.08812514422629 1.46718409502782 0.142650791452508 df.mm.exp7 0.0315959167709714 0.08812514422629 0.358534638988375 0.720021427426662 df.mm.exp8 0.000351304186606748 0.08812514422629 0.00398642396209486 0.996820123660109 df.mm.trans1:exp2 0.0540845068648072 0.0845939863910224 0.639342217717581 0.522751593253047 df.mm.trans2:exp2 0.0152779292196911 0.0671807188426223 0.227415387672186 0.820148777524037 df.mm.trans1:exp3 0.167851207231063 0.0845939863910224 1.98419786549834 0.0475158418616525 * df.mm.trans2:exp3 0.00090355128695022 0.0671807188426223 0.0134495626500645 0.989271896511626 df.mm.trans1:exp4 0.313217098473180 0.0845939863910224 3.70259296004071 0.000225463456001358 *** df.mm.trans2:exp4 0.078700959343953 0.0671807188426223 1.17148135208732 0.24169361666607 df.mm.trans1:exp5 -0.0208328886834882 0.0845939863910224 -0.246269144797024 0.805526103895438 df.mm.trans2:exp5 -0.051791318142159 0.0671807188426223 -0.770925334447305 0.440939314452895 df.mm.trans1:exp6 -0.0372195994342868 0.0845939863910224 -0.439979258835788 0.660050346745944 df.mm.trans2:exp6 -0.0685262193304896 0.0671807188426223 -1.02002807518358 0.307970003890074 df.mm.trans1:exp7 0.0539691478726265 0.0845939863910224 0.637978539315579 0.523638535746246 df.mm.trans2:exp7 -0.0303929518972122 0.0671807188426223 -0.452405875090602 0.651077980998457 df.mm.trans1:exp8 0.208225777188576 0.0845939863910224 2.46147257118355 0.0140102845571215 * df.mm.trans2:exp8 0.00864955436807695 0.0671807188426223 0.128750548030595 0.897581781376863 df.mm.trans1:probe2 0.204774191645336 0.0493922468158689 4.14587723471501 3.68191934507766e-05 *** df.mm.trans1:probe3 -0.400391495523733 0.0493922468158689 -8.10636327228374 1.56660618507802e-15 *** df.mm.trans1:probe4 0.115097873025970 0.0493922468158689 2.33028218892425 0.0199959749802193 * df.mm.trans1:probe5 0.05388074733943 0.0493922468158689 1.09087459698470 0.275599522983288 df.mm.trans1:probe6 -0.782864304683418 0.0493922468158689 -15.8499431621705 1.77836941548000e-50 *** df.mm.trans1:probe7 -0.671332453692012 0.0493922468158689 -13.5918589853727 1.23056428509378e-38 *** df.mm.trans1:probe8 -0.412318308353075 0.0493922468158689 -8.34783462858393 2.38241873288511e-16 *** df.mm.trans1:probe9 0.519237997251401 0.0493922468158689 10.5125405448164 1.49915338363873e-24 *** df.mm.trans1:probe10 -0.348634353672591 0.0493922468158689 -7.05848338854227 3.20759410813024e-12 *** df.mm.trans1:probe11 -0.820242476005295 0.0493922468158689 -16.6067050778861 1.10092685398500e-54 *** df.mm.trans1:probe12 -0.74718405117323 0.0493922468158689 -15.1275574476027 1.44525054246188e-46 *** df.mm.trans1:probe13 -0.744795663083913 0.0493922468158689 -15.0792019213149 2.61665149894328e-46 *** df.mm.trans1:probe14 -0.90413450979057 0.0493922468158689 -18.3051909576239 1.66029623871224e-64 *** df.mm.trans1:probe15 -0.774404937421453 0.0493922468158689 -15.6786740297194 1.53733236600746e-49 *** df.mm.trans1:probe16 -0.873837238557935 0.0493922468158689 -17.6917896004112 6.66052604428553e-61 *** df.mm.trans1:probe17 -0.69308569480985 0.0493922468158689 -14.0322771181807 7.44450243798782e-41 *** df.mm.trans1:probe18 -0.804587546669678 0.0493922468158689 -16.2897539297843 6.57279737649517e-53 *** df.mm.trans1:probe19 -0.791619375524142 0.0493922468158689 -16.0271991366428 1.88074414139068e-51 *** df.mm.trans1:probe20 -0.919245019278743 0.0493922468158689 -18.6111197311114 2.51748460726993e-66 *** df.mm.trans1:probe21 -0.788377371067868 0.0493922468158689 -15.9615612143924 4.32870096302747e-51 *** df.mm.trans1:probe22 -0.793011844129413 0.0493922468158689 -16.05539118489 1.31393610328292e-51 *** df.mm.trans1:probe23 -0.422626746677737 0.0493922468158689 -8.5565402248912 4.5067974521998e-17 *** df.mm.trans1:probe24 -0.237896594888528 0.0493922468158689 -4.81647647606296 1.69500337670193e-06 *** df.mm.trans1:probe25 0.164520108913815 0.0493922468158689 3.33088935045081 0.000898412462848625 *** df.mm.trans1:probe26 -0.182549845641945 0.0493922468158689 -3.69592106879606 0.000231383091179358 *** df.mm.trans1:probe27 -0.641828177025173 0.0493922468158689 -12.9945126695262 1.05301152632422e-35 *** df.mm.trans1:probe28 -0.845141044111773 0.0493922468158689 -17.1108037919879 1.50932777233506e-57 *** df.mm.trans1:probe29 -0.66243880552901 0.0493922468158689 -13.4117973616090 9.62547216117871e-38 *** df.mm.trans1:probe30 -0.336691108749715 0.0493922468158689 -6.8166793465557 1.6370284676343e-11 *** df.mm.trans1:probe31 0.498021105585802 0.0493922468158689 10.0829813926545 8.37933097828393e-23 *** df.mm.trans1:probe32 -0.71087569836923 0.0493922468158689 -14.3924551766055 1.05569931897013e-42 *** df.mm.trans2:probe2 0.178532308807061 0.0493922468158689 3.61458164623727 0.000316332647769583 *** df.mm.trans2:probe3 -0.0104194704549211 0.0493922468158689 -0.210953563091879 0.83296790677712 df.mm.trans2:probe4 0.0232416398533573 0.0493922468158689 0.470552391350015 0.638066418158394 df.mm.trans2:probe5 0.000623148482780859 0.0493922468158689 0.0126163218511585 0.989936498277145 df.mm.trans2:probe6 0.077614107329106 0.0493922468158689 1.57138239972047 0.116420637049132 df.mm.trans3:probe2 -0.216822922783335 0.0493922468158689 -4.38981696037511 1.25921106671986e-05 *** df.mm.trans3:probe3 -0.62492679424806 0.0493922468158689 -12.6523257096958 4.58283597773792e-34 *** df.mm.trans3:probe4 -0.1778573317378 0.0493922468158689 -3.6009159980278 0.000333201065565586 *** df.mm.trans3:probe5 -0.653794639381967 0.0493922468158689 -13.2367867738285 6.98151193077792e-37 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.51402028123087 0.221353121487682 20.3928467368917 3.48937600693647e-77 *** df.mm.trans1 -0.137107353259290 0.196660883428667 -0.697176534900608 0.485859720587184 df.mm.trans2 -0.136044461414876 0.175064311480957 -0.777111338479028 0.437282946910424 df.mm.exp2 -0.235599606648122 0.232206507113005 -1.01461242226716 0.310544144596187 df.mm.exp3 -0.259778300599292 0.232206507113005 -1.11873824652497 0.263529362787592 df.mm.exp4 -0.188024157268435 0.232206507113006 -0.809728201014329 0.418295402387199 df.mm.exp5 -0.100065909176062 0.232206507113005 -0.430934991530468 0.666611580291047 df.mm.exp6 -0.408753449137813 0.232206507113005 -1.76030144124639 0.0786724894412565 . df.mm.exp7 -0.126968912688368 0.232206507113006 -0.546793086321986 0.584646912202515 df.mm.exp8 -0.273948034084157 0.232206507113006 -1.17976036714095 0.238385388452353 df.mm.trans1:exp2 0.265579426285699 0.222902036360745 1.19146253942644 0.233764096947526 df.mm.trans2:exp2 0.212235168607446 0.177018718150732 1.19894195836808 0.230843928009507 df.mm.trans1:exp3 0.229217864300167 0.222902036360745 1.02833454571586 0.304049367099472 df.mm.trans2:exp3 0.193271290552471 0.177018718150732 1.09181273354324 0.275187086731104 df.mm.trans1:exp4 0.207179489998447 0.222902036360745 0.929464321551317 0.352880225241496 df.mm.trans2:exp4 0.0284190582879637 0.177018718150732 0.160542673593223 0.87248710487839 df.mm.trans1:exp5 0.124904492209712 0.222902036360745 0.560355994269905 0.57536630125521 df.mm.trans2:exp5 -0.0671049099705718 0.177018718150732 -0.379083696185349 0.704708848232564 df.mm.trans1:exp6 0.351006877312320 0.222902036360745 1.57471364121703 0.115649309184278 df.mm.trans2:exp6 0.146422587698645 0.177018718150732 0.827158784270292 0.408350922408891 df.mm.trans1:exp7 0.147688900052969 0.222902036360746 0.662573130619358 0.507761704783217 df.mm.trans2:exp7 -0.102629905396542 0.177018718150732 -0.579768662143134 0.562205543073598 df.mm.trans1:exp8 0.250790152411276 0.222902036360746 1.12511377870679 0.260819715447989 df.mm.trans2:exp8 0.0129103191301128 0.177018718150732 0.0729319433842002 0.941875341068585 df.mm.trans1:probe2 -0.0422863962819361 0.130146749968720 -0.324913194467778 0.74531697626557 df.mm.trans1:probe3 -0.190621397581363 0.130146749968720 -1.46466506176433 0.14333700979414 df.mm.trans1:probe4 -0.0325307283956711 0.130146749968720 -0.249954212483136 0.802675792570994 df.mm.trans1:probe5 -0.0499323932226194 0.130146749968720 -0.383662236933464 0.70131305411506 df.mm.trans1:probe6 -0.0872377763386452 0.130146749968720 -0.67030314901918 0.502824437887062 df.mm.trans1:probe7 -0.0994198876665962 0.130146749968720 -0.763906034461029 0.445109393763089 df.mm.trans1:probe8 -0.0280038047020311 0.130146749968720 -0.215170987433505 0.829679334247403 df.mm.trans1:probe9 -0.0891251269325045 0.130146749968720 -0.684804860312877 0.49363100014541 df.mm.trans1:probe10 0.0537839754531893 0.130146749968720 0.41325638531977 0.679510308157936 df.mm.trans1:probe11 -0.0479887478550738 0.130146749968720 -0.368727977199642 0.71241112150524 df.mm.trans1:probe12 -0.106830489326099 0.130146749968720 -0.820846385728228 0.411935870026161 df.mm.trans1:probe13 -0.163756439766109 0.130146749968720 -1.25824455705169 0.208606729458388 df.mm.trans1:probe14 -0.0362978627641644 0.130146749968720 -0.278899494400654 0.780381527466975 df.mm.trans1:probe15 -0.236190214926685 0.130146749968720 -1.81479917849236 0.0698641776561668 . df.mm.trans1:probe16 -0.204378014470050 0.130146749968720 -1.57036587174994 0.116656811990487 df.mm.trans1:probe17 0.0065482138760237 0.130146749968720 0.0503140791268127 0.95988248419525 df.mm.trans1:probe18 -0.191521906156396 0.130146749968720 -1.47158424011684 0.141458195882297 df.mm.trans1:probe19 0.0219317080084597 0.130146749968720 0.168515218503196 0.866213175352631 df.mm.trans1:probe20 -0.0508559292565143 0.130146749968720 -0.390758349853047 0.696061845733333 df.mm.trans1:probe21 -0.208838365818800 0.130146749968720 -1.60463757926335 0.108899767328898 df.mm.trans1:probe22 0.0740602477635476 0.130146749968720 0.569051841719807 0.569452965154443 df.mm.trans1:probe23 0.0779877961244369 0.130146749968720 0.599229686051942 0.549159943719835 df.mm.trans1:probe24 -0.00683683297876416 0.130146749968720 -0.0525317226930933 0.958115857630601 df.mm.trans1:probe25 -0.0283000110431705 0.130146749968720 -0.217446928563120 0.827905886658637 df.mm.trans1:probe26 0.00913455663415194 0.130146749968720 0.0701865904169512 0.944059643927482 df.mm.trans1:probe27 -0.175156838426749 0.130146749968720 -1.345841048423 0.178668902115360 df.mm.trans1:probe28 -0.222439746858465 0.130146749968720 -1.70914561379310 0.0877445052282426 . df.mm.trans1:probe29 0.0519832195912548 0.130146749968720 0.399420036257137 0.68967184030379 df.mm.trans1:probe30 0.00383087625873626 0.130146749968720 0.0294350512760171 0.976523685414188 df.mm.trans1:probe31 -0.185418276561281 0.130146749968720 -1.42468618391043 0.154570244182067 df.mm.trans1:probe32 -0.104747276096406 0.130146749968720 -0.804839737616046 0.421109797852432 df.mm.trans2:probe2 0.0134403590669286 0.130146749968720 0.103270800616680 0.917769439726316 df.mm.trans2:probe3 0.0649282202926077 0.130146749968720 0.498884684467439 0.61797397202827 df.mm.trans2:probe4 0.0435277515807017 0.130146749968720 0.334451314313751 0.738111472793808 df.mm.trans2:probe5 0.0316890401442002 0.130146749968720 0.243486987971781 0.807679753438496 df.mm.trans2:probe6 0.118924339537760 0.130146749968720 0.913771105051354 0.361064670873024 df.mm.trans3:probe2 -0.0238716609601417 0.130146749968720 -0.183421107064748 0.854506027291343 df.mm.trans3:probe3 0.0720249328503054 0.130146749968720 0.55341322674301 0.580108277178053 df.mm.trans3:probe4 0.0668159473818517 0.130146749968720 0.513389288613894 0.607796188011268 df.mm.trans3:probe5 0.0538040616594571 0.130146749968720 0.413410720378253 0.679397287195325