fitVsDatCorrelation=0.896072732339065 cont.fitVsDatCorrelation=0.28254847455103 fstatistic=9546.13884117494,60,876 cont.fstatistic=2032.83256015942,60,876 residuals=-0.793636281768031,-0.0998272460147943,0.000858320891572551,0.101172661446398,0.84247767262911 cont.residuals=-0.887349979920556,-0.274448393068484,-0.0241535479429104,0.241573394686774,1.2661649663077 predictedValues: Include Exclude Both Lung 132.792573924282 55.6292485114726 80.0573003411395 cerebhem 99.013433540935 64.5684818276863 72.357752918464 cortex 100.021224765712 50.7725145268948 73.5474331504813 heart 105.572613378188 53.8777174844818 74.6326720373183 kidney 120.212780313793 54.0771334165645 75.290117285913 liver 113.650220769332 50.4892627082272 80.8905806100531 stomach 132.706567054312 51.0148305634799 91.7510665944049 testicle 120.476447887162 52.6636866722878 83.549002624881 diffExp=77.1633254128093,34.4449517132486,49.2487102388176,51.6948958937064,66.1356468972287,63.160958061105,81.6917364908323,67.8127612148746 diffExpScore=0.997968936863202 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 87.229846148646 91.2100520108978 84.0509781650725 cerebhem 99.3818099575955 94.7511480632872 88.527437854671 cortex 88.0878668355508 100.595014205496 98.0877708643618 heart 93.3434533339562 107.820929181375 83.9387492640566 kidney 88.9015918251547 94.3645990969106 91.7541560323598 liver 86.8057228966902 89.1694707457136 82.520199749986 stomach 86.8194556781611 85.2941589237134 92.0975869577278 testicle 87.665119094631 87.441852728785 82.9584680443099 cont.diffExp=-3.98020586225186,4.63066189430829,-12.5071473699455,-14.4774758474188,-5.4630072717559,-2.36374784902347,1.52529675444778,0.223266365846001 cont.diffExpScore=1.35191917948146 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=-0.339358094251142 cont.tran.correlation=0.461881651770073 tran.covariance=-0.00316276029587168 cont.tran.covariance=0.00177311084067963 tran.mean=84.8461710840508 cont.tran.mean=91.8051306704103 weightedLogRatios: wLogRatio Lung 3.87511412375851 cerebhem 1.87321505901601 cortex 2.89271431746154 heart 2.90804332556313 kidney 3.50683001250525 liver 3.51113499481519 stomach 4.21618878656546 testicle 3.62266175829145 cont.weightedLogRatios: wLogRatio Lung -0.200375545900605 cerebhem 0.218302077311464 cortex -0.603392731583597 heart -0.664463933806108 kidney -0.269396318545947 liver -0.120282695985936 stomach 0.0789632672956408 testicle 0.0114044955009132 varWeightedLogRatios=0.530978281891996 cont.varWeightedLogRatios=0.0981128884012369 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.87563991217284 0.0838207253086963 58.1674746217807 4.36676381709880e-303 *** df.mm.trans1 0.170597470576714 0.0722320474166061 2.36179752171186 0.0184044950838308 * df.mm.trans2 -0.849228829517353 0.0636664112232172 -13.33872623258 4.37732918373673e-37 *** df.mm.exp2 -0.0433954772521377 0.081558279108554 -0.532079363694988 0.594805944466523 df.mm.exp3 -0.28994777937807 0.081558279108554 -3.55509928050529 0.000398061163035588 *** df.mm.exp4 -0.191217190959824 0.081558279108554 -2.34454666098721 0.0192727196359870 * df.mm.exp5 -0.066428914802915 0.081558279108554 -0.814496278354502 0.41558206412441 df.mm.exp6 -0.262965996895453 0.081558279108554 -3.22427103378978 0.00130968248605231 ** df.mm.exp7 -0.223577096959034 0.081558279108554 -2.74131699936255 0.00624399931021794 ** df.mm.exp8 -0.194807675298769 0.081558279108554 -2.3885702031486 0.0171249202569979 * df.mm.trans1:exp2 -0.250137305719898 0.0751930180101616 -3.32660281950771 0.000915701085993298 *** df.mm.trans2:exp2 0.192412756271677 0.0547109568643883 3.51689619957862 0.00045905337198269 *** df.mm.trans1:exp3 0.0065418742680087 0.0751930180101616 0.0870010865520072 0.930690530872311 df.mm.trans2:exp3 0.198593819616425 0.0547109568643883 3.62987289929289 0.000299996452485775 *** df.mm.trans1:exp4 -0.0381721306467396 0.0751930180101616 -0.507655253863877 0.611822921172728 df.mm.trans2:exp4 0.159225063354767 0.0547109568643883 2.91029571552618 0.00370232844159329 ** df.mm.trans1:exp5 -0.0330960595761189 0.0751930180101616 -0.440148041027509 0.659938466721004 df.mm.trans2:exp5 0.0381312234781165 0.0547109568643883 0.696957714935093 0.486014164709308 df.mm.trans1:exp6 0.107303173495207 0.0751930180101616 1.42703639692593 0.153925735137895 df.mm.trans2:exp6 0.166017575626829 0.0547109568643883 3.03444840195969 0.00248093855531226 ** df.mm.trans1:exp7 0.222929208814001 0.0751930180101616 2.96475942465662 0.00311132768293933 ** df.mm.trans2:exp7 0.136984367468822 0.0547109568643883 2.50378306868886 0.0124682096716338 * df.mm.trans1:exp8 0.0974736396735093 0.0751930180101616 1.29631237384749 0.195209283720012 df.mm.trans2:exp8 0.140024720580930 0.0547109568643883 2.55935426112193 0.0106535882944449 * df.mm.trans1:probe2 -0.404232168667505 0.0523817531932531 -7.71704160370812 3.24002764856791e-14 *** df.mm.trans1:probe3 -0.437025008610535 0.0523817531932531 -8.34307715891467 2.79647169340487e-16 *** df.mm.trans1:probe4 0.470795646078557 0.0523817531932531 8.98777947239835 1.52467641031003e-18 *** df.mm.trans1:probe5 -0.385718432240356 0.0523817531932531 -7.36360294809753 4.12900514875566e-13 *** df.mm.trans1:probe6 -0.684844365527 0.0523817531932531 -13.0741016437611 8.20530603897577e-36 *** df.mm.trans1:probe7 0.096145934370662 0.0523817531932531 1.83548523120158 0.0667726785115821 . df.mm.trans1:probe8 -0.43084995650833 0.0523817531932531 -8.22519160286191 7.00621139988514e-16 *** df.mm.trans1:probe9 -0.149142653563198 0.0523817531932531 -2.84722531170278 0.00451332005860018 ** df.mm.trans1:probe10 -0.408419335453665 0.0523817531932531 -7.79697720209699 1.79687491913592e-14 *** df.mm.trans1:probe11 -0.158296607436346 0.0523817531932531 -3.0219799412276 0.00258432953336437 ** df.mm.trans1:probe12 0.0105975251199680 0.0523817531932531 0.202313295640762 0.839718781851339 df.mm.trans1:probe13 0.0530903967620625 0.0523817531932531 1.01352844312398 0.311087670136418 df.mm.trans1:probe14 -0.0608769789865017 0.0523817531932531 -1.16217910389343 0.245479249940962 df.mm.trans1:probe15 0.0112780875349025 0.0523817531932531 0.215305652204768 0.829579139641461 df.mm.trans1:probe16 -0.398070866332331 0.0523817531932531 -7.59941853919474 7.64271131365229e-14 *** df.mm.trans1:probe17 -0.278367856717571 0.0523817531932531 -5.3142142014717 1.35967120193843e-07 *** df.mm.trans1:probe18 -0.403528339674512 0.0523817531932531 -7.70360507380818 3.57575278254812e-14 *** df.mm.trans1:probe19 -0.690384571275486 0.0523817531932531 -13.1798675910758 2.55479079271320e-36 *** df.mm.trans1:probe20 -0.224834232883247 0.0523817531932531 -4.29222427996561 1.96620774781876e-05 *** df.mm.trans1:probe21 -0.374934561280512 0.0523817531932531 -7.15773219535548 1.73517820481685e-12 *** df.mm.trans1:probe22 -0.348200849724677 0.0523817531932531 -6.64736914093067 5.2421350663142e-11 *** df.mm.trans2:probe2 -0.0631363473678987 0.0523817531932531 -1.20531184084214 0.228408427120769 df.mm.trans2:probe3 -0.0837679139555265 0.0523817531932531 -1.59918118140261 0.110140937040911 df.mm.trans2:probe4 0.0967914744109509 0.0523817531932531 1.84780898901677 0.0649669257087952 . df.mm.trans2:probe5 -0.076214028593744 0.0523817531932531 -1.45497284736855 0.146035008500593 df.mm.trans2:probe6 -0.00460662946709561 0.0523817531932531 -0.0879433998724761 0.929941761511427 df.mm.trans3:probe2 0.0949697946340276 0.0523817531932531 1.81303199768159 0.0701690365719242 . df.mm.trans3:probe3 0.187193890554208 0.0523817531932531 3.57364691219078 0.000371265739833023 *** df.mm.trans3:probe4 0.381024224165955 0.0523817531932531 7.27398761855554 7.74647572131124e-13 *** df.mm.trans3:probe5 0.246637153476222 0.0523817531932531 4.7084554914819 2.90061625905676e-06 *** df.mm.trans3:probe6 0.0812123283070894 0.0523817531932531 1.55039347399220 0.121408286436605 df.mm.trans3:probe7 0.113517747766833 0.0523817531932531 2.16712387132270 0.0304942635036507 * df.mm.trans3:probe8 0.000395827400820128 0.0523817531932531 0.00755658939783466 0.993972491855834 df.mm.trans3:probe9 0.217064283764973 0.0523817531932531 4.14389115545930 3.74584982150696e-05 *** df.mm.trans3:probe10 -0.0416920326317349 0.0523817531932531 -0.795926636474339 0.426290304264247 df.mm.trans3:probe11 0.76275165506246 0.0523817531932531 14.5613998876369 3.55628070179237e-43 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.72847583726198 0.181130814218873 26.1053087938325 1.39410010548888e-111 *** df.mm.trans1 -0.198608652234334 0.156088479467126 -1.27241070521263 0.203564864795058 df.mm.trans2 -0.197594204311246 0.137578729613528 -1.43622640553745 0.151294934858974 df.mm.exp2 0.116622365491404 0.176241823806908 0.661717877018664 0.508326064208039 df.mm.exp3 -0.046713357403244 0.176241823806908 -0.26505262141649 0.79103128029381 df.mm.exp4 0.236382010201967 0.176241823806908 1.3412367456033 0.180191227523784 df.mm.exp5 -0.0347048993011365 0.176241823806908 -0.196916364977927 0.843938663824845 df.mm.exp6 -0.00911998999629233 0.176241823806908 -0.051747024623873 0.958742064379761 df.mm.exp7 -0.16320018451967 0.176241823806908 -0.926001450702607 0.354700258555846 df.mm.exp8 -0.0241301323390767 0.176241823806908 -0.136914903726336 0.891129518329688 df.mm.trans1:exp2 0.0138001883103641 0.162486932982217 0.0849310652683344 0.932335600459534 df.mm.trans2:exp2 -0.0785335152636623 0.118226609553236 -0.664262601798622 0.506697055169287 df.mm.trans1:exp3 0.0565016157927611 0.162486932982217 0.347730212859301 0.728126339062569 df.mm.trans2:exp3 0.144650942834858 0.118226609553236 1.22350580280934 0.221467716373529 df.mm.trans1:exp4 -0.168642817700652 0.162486932982217 -1.03788541395578 0.299609800170928 df.mm.trans2:exp4 -0.0690753327454821 0.118226609553236 -0.58426214713007 0.559194380315316 df.mm.trans1:exp5 0.0536884027554805 0.162486932982217 0.330416740411713 0.741164100776669 df.mm.trans2:exp5 0.0687057821279837 0.118226609553236 0.581136365050258 0.561298037668063 df.mm.trans1:exp6 0.00424599677872311 0.162486932982217 0.0261313122279674 0.979158553495676 df.mm.trans2:exp6 -0.0135063956390533 0.118226609553236 -0.114241588167776 0.90907246301224 df.mm.trans1:exp7 0.158484380127158 0.162486932982217 0.975366924702203 0.329647545589156 df.mm.trans2:exp7 0.096141049386836 0.118226609553236 0.813192983797315 0.416328380447156 df.mm.trans1:exp8 0.0291076780799882 0.162486932982217 0.179138577765967 0.857870315146774 df.mm.trans2:exp8 -0.0180609458688623 0.118226609553236 -0.152765489403041 0.878618393172709 df.mm.trans1:probe2 -0.282773236832784 0.113193360844393 -2.49814330737572 0.0126668853273529 * df.mm.trans1:probe3 -0.204938276234878 0.113193360844393 -1.81051498697532 0.070558467616993 . df.mm.trans1:probe4 -0.067658119557477 0.113193360844392 -0.597721624773444 0.550180175446646 df.mm.trans1:probe5 -0.172858163564268 0.113193360844393 -1.52710514357726 0.127095834742914 df.mm.trans1:probe6 0.0795747135261827 0.113193360844392 0.702998063955133 0.482243736189334 df.mm.trans1:probe7 -0.0897618626400409 0.113193360844393 -0.792995825642433 0.427994974076413 df.mm.trans1:probe8 -0.0955148144466002 0.113193360844393 -0.843819935498734 0.399000396808075 df.mm.trans1:probe9 -0.101312386495166 0.113193360844393 -0.895038240223652 0.371012504477431 df.mm.trans1:probe10 0.125989023064873 0.113193360844393 1.11304251525910 0.265995499474803 df.mm.trans1:probe11 -0.0652760966568419 0.113193360844392 -0.576677785427516 0.56430529070159 df.mm.trans1:probe12 -0.0358854825826656 0.113193360844392 -0.317028157084209 0.75129777675272 df.mm.trans1:probe13 -0.0577355400245776 0.113193360844393 -0.510061187280647 0.61013708103506 df.mm.trans1:probe14 -0.114529982779849 0.113193360844392 -1.01180830682547 0.311909101480063 df.mm.trans1:probe15 -0.114964684712176 0.113193360844393 -1.01564865513816 0.310077155885891 df.mm.trans1:probe16 -0.162542910103831 0.113193360844393 -1.43597565167519 0.151366259239403 df.mm.trans1:probe17 -0.0919932031597435 0.113193360844392 -0.812708470474757 0.416606033202699 df.mm.trans1:probe18 -0.0847060453453557 0.113193360844393 -0.748330509081726 0.454461686026531 df.mm.trans1:probe19 -0.152978081120148 0.113193360844393 -1.35147574008733 0.176891976298153 df.mm.trans1:probe20 -0.150176581254918 0.113193360844392 -1.32672605649872 0.184945056114518 df.mm.trans1:probe21 -0.172961871857752 0.113193360844393 -1.52802134831498 0.126868218857106 df.mm.trans1:probe22 -0.0105775280351185 0.113193360844393 -0.093446541000399 0.92557018550931 df.mm.trans2:probe2 -0.217717688938841 0.113193360844393 -1.92341394684922 0.0547519109822084 . df.mm.trans2:probe3 -0.125938241037149 0.113193360844393 -1.11259388446181 0.26618811040993 df.mm.trans2:probe4 0.226430570544296 0.113193360844393 2.0003873801006 0.0457667007635848 * df.mm.trans2:probe5 -0.0336350699689537 0.113193360844393 -0.297147020974066 0.76642471285157 df.mm.trans2:probe6 -0.150320453548510 0.113193360844393 -1.32799708770161 0.184524977086240 df.mm.trans3:probe2 -0.0395332129695028 0.113193360844393 -0.349253813780203 0.726982704632824 df.mm.trans3:probe3 0.153545849713747 0.113193360844393 1.35649165788820 0.175292274640467 df.mm.trans3:probe4 0.0539293802889003 0.113193360844393 0.476435895944792 0.633882677435376 df.mm.trans3:probe5 0.211726124932116 0.113193360844393 1.87048183173196 0.0617501989899678 . df.mm.trans3:probe6 0.174180992438478 0.113193360844393 1.53879159642522 0.124216297669873 df.mm.trans3:probe7 0.128783270618553 0.113193360844393 1.13772812873356 0.255545106222078 df.mm.trans3:probe8 0.101274925135041 0.113193360844393 0.894707290070342 0.371189339713468 df.mm.trans3:probe9 0.165539809569099 0.113193360844393 1.46245158138442 0.143976162210595 df.mm.trans3:probe10 -0.00160986809014467 0.113193360844393 -0.0142222836934559 0.988655880342848 df.mm.trans3:probe11 0.142818414983985 0.113193360844393 1.26172077512848 0.207385129488228