chr16.9725_chr16_25638541_25640716_+_0.R fitVsDatCorrelation=0.899858795372204 cont.fitVsDatCorrelation=0.317947497791522 fstatistic=8305.5844550611,36,324 cont.fstatistic=1750.78158054852,36,324 residuals=-0.380979194632756,-0.0822920388502169,-0.010860038851029,0.0709041530843385,0.761713173486042 cont.residuals=-0.535306792289069,-0.177265775338794,-0.0532907935680765,0.096519081784139,1.4805814360784 predictedValues: Include Exclude Both chr16.9725_chr16_25638541_25640716_+_0.R.tl.Lung 60.0275712336861 58.1434113339629 53.4686768352885 chr16.9725_chr16_25638541_25640716_+_0.R.tl.cerebhem 60.2793660466057 67.8389656989536 61.3266612776619 chr16.9725_chr16_25638541_25640716_+_0.R.tl.cortex 56.6610374506164 54.9123208610824 51.7993524353082 chr16.9725_chr16_25638541_25640716_+_0.R.tl.heart 64.5082422163518 57.4443385590347 61.977242666125 chr16.9725_chr16_25638541_25640716_+_0.R.tl.kidney 66.6327916044518 61.9699344803792 58.3755374460414 chr16.9725_chr16_25638541_25640716_+_0.R.tl.liver 67.4435689257987 59.3108699146996 57.1706623342129 chr16.9725_chr16_25638541_25640716_+_0.R.tl.stomach 63.5286334753073 58.2697379478075 94.5351153250933 chr16.9725_chr16_25638541_25640716_+_0.R.tl.testicle 105.827318969094 72.561140145225 239.576634466397 diffExp=1.88415989972327,-7.55959965234794,1.74871658953407,7.06390365731712,4.66285712407258,8.13269901109907,5.25889552749979,33.2661788238691 diffExpScore=1.2545935199208 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,1 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,0,0,1 diffExp1.3Score=0.5 diffExp1.2=0,0,0,0,0,0,0,1 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 63.8294050605403 64.1497974373043 63.4418013548508 cerebhem 61.6758579441395 60.2688254773779 70.5549405603554 cortex 61.3063871614298 60.3331507728121 60.2675279423818 heart 59.093007692636 71.3475483320141 80.9678156548326 kidney 74.6049082852712 63.7562085865687 71.9618244120312 liver 60.2156208454382 71.86555356799 69.3058131075122 stomach 61.5983838124932 73.9071011134068 69.1824978105067 testicle 62.7472801941395 65.1374847792976 62.5457984988958 cont.diffExp=-0.320392376764033,1.40703246676161,0.973236388617686,-12.2545406393781,10.8486996987025,-11.6499327225518,-12.3087173009136,-2.39020458515810 cont.diffExpScore=1.95366584207800 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,-1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.76546913940169 cont.tran.correlation=-0.325088806099371 tran.covariance=0.0136360242367605 cont.tran.covariance=-0.00188117472576081 tran.mean=64.7099530539411 cont.tran.mean=64.7397825664287 weightedLogRatios: wLogRatio Lung 0.130080538859337 cerebhem -0.491262240342988 cortex 0.126067483879755 heart 0.476524988888373 kidney 0.302009591938483 liver 0.532889242951357 stomach 0.354988193796009 testicle 1.68806202408347 cont.weightedLogRatios: wLogRatio Lung -0.0208225106518725 cerebhem 0.0948570732786624 cortex 0.0657356395043705 heart -0.786467084338267 kidney 0.665272489866875 liver -0.740422436660111 stomach -0.767263264421372 testicle -0.155439445873939 varWeightedLogRatios=0.376525521525275 cont.varWeightedLogRatios=0.271126057242462 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11413924152482 0.0822837687903898 49.9994020935673 2.19950013677489e-154 *** df.mm.trans1 -0.0759526219886659 0.069693492617685 -1.08980938012845 0.276606991764411 df.mm.trans2 -0.00650669971414472 0.069693492617685 -0.0933616535741475 0.925673958069228 df.mm.exp2 0.0212913801759895 0.0970566386420187 0.219370673391236 0.826499447350597 df.mm.exp3 -0.0831736389550641 0.0970566386420188 -0.856959813556285 0.392100486468892 df.mm.exp4 -0.0877783504431758 0.0970566386420188 -0.904403363554916 0.366453528365763 df.mm.exp5 0.0803286973766944 0.0970566386420188 0.827647634418669 0.408479261374381 df.mm.exp6 0.0694224123555713 0.0970566386420188 0.71527731978878 0.474952806237469 df.mm.exp7 -0.51101828953739 0.0970566386420187 -5.26515544621545 2.55644094604171e-07 *** df.mm.exp8 -0.711255666725588 0.0970566386420188 -7.32825365350809 1.87100415792855e-12 *** df.mm.trans1:exp2 -0.0171055005207444 0.0840535146699146 -0.203507260676952 0.838866324603937 df.mm.trans2:exp2 0.132932797124341 0.0840535146699146 1.58152574162282 0.114733539126901 df.mm.trans1:exp3 0.0254564660596961 0.0840535146699147 0.302860221368087 0.762190740586717 df.mm.trans2:exp3 0.0259988181062347 0.0840535146699147 0.309312682620522 0.757282503318899 df.mm.trans1:exp4 0.159767375144662 0.0840535146699147 1.90078161242967 0.0582178771998086 . df.mm.trans2:exp4 0.0756822364631512 0.0840535146699146 0.900405375793764 0.368573318286725 df.mm.trans1:exp5 0.0240641481939830 0.0840535146699146 0.286295561684541 0.774834729531184 df.mm.trans2:exp5 -0.0165919255671174 0.0840535146699147 -0.197397165749407 0.843640525235871 df.mm.trans1:exp6 0.0470648427163271 0.0840535146699146 0.559939021005305 0.575907945021392 df.mm.trans2:exp6 -0.0495423872449928 0.0840535146699147 -0.589414820302875 0.555993798930991 df.mm.trans1:exp7 0.567705037436456 0.0840535146699147 6.75409041092312 6.66778828671839e-11 *** df.mm.trans2:exp7 0.513188605592064 0.0840535146699146 6.1054984744826 2.92936491714135e-09 *** df.mm.trans1:exp8 1.27826038890064 0.0840535146699146 15.2076970715678 8.56588622032423e-40 *** df.mm.trans2:exp8 0.932772617661863 0.0840535146699147 11.0973660212180 1.80937690906578e-24 *** df.mm.trans1:probe2 0.198862404051369 0.0420267573349573 4.73180460882138 3.32881599903477e-06 *** df.mm.trans1:probe3 -0.0465172035432338 0.0420267573349573 -1.10684731568717 0.269181036589656 df.mm.trans1:probe4 0.300840977166051 0.0420267573349573 7.15831999048414 5.50235154344607e-12 *** df.mm.trans1:probe5 0.00502571197420263 0.0420267573349573 0.119583624645300 0.90488710591095 df.mm.trans1:probe6 0.0513443296528832 0.0420267573349573 1.22170571580538 0.222707073504996 df.mm.trans2:probe2 0.0386501843736053 0.0420267573349573 0.919656590813314 0.358436416916563 df.mm.trans2:probe3 -0.126081810779910 0.0420267573349573 -3.00003661417478 0.00290877670871994 ** df.mm.trans2:probe4 -0.0453141183679103 0.0420267573349573 -1.07822066800806 0.281737363222534 df.mm.trans2:probe5 -0.0557775661014111 0.0420267573349573 -1.32719176159270 0.185379727885726 df.mm.trans2:probe6 -0.213956454462518 0.0420267573349573 -5.09095795226989 6.05333248093517e-07 *** df.mm.trans3:probe2 0.0603968938245845 0.0420267573349573 1.43710573107545 0.151652933914679 df.mm.trans3:probe3 -0.218148645107956 0.0420267573349573 -5.19070846625853 3.70538585378663e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.9864305377079 0.178856639181646 22.2884124176083 2.28364137104109e-67 *** df.mm.trans1 0.143008900786109 0.151489705025347 0.944017289902182 0.345864222848611 df.mm.trans2 0.1648783470178 0.151489705025347 1.08837988027116 0.277236355421874 df.mm.exp2 -0.202996092788666 0.210967782017860 -0.962213712667657 0.336659819967292 df.mm.exp3 -0.0503396413021523 0.210967782017861 -0.238612933314579 0.811556583209223 df.mm.exp4 -0.214688092082420 0.210967782017861 -1.01763449389748 0.309610969303779 df.mm.exp5 0.0238251663350711 0.210967782017860 0.112932724168537 0.910153831175826 df.mm.exp6 -0.0331118633117048 0.210967782017860 -0.156952227468086 0.875380256017356 df.mm.exp7 0.0193846983405972 0.210967782017860 0.0918846382854615 0.9268464807933 df.mm.exp8 0.0124044153356545 0.210967782017861 0.0587976761996974 0.953149506606408 df.mm.trans1:exp2 0.168674687297778 0.182703458607525 0.9232156226452 0.356581852245127 df.mm.trans2:exp2 0.140590138996006 0.182703458607525 0.769499056380836 0.442157724292596 df.mm.trans1:exp3 0.0100096954083483 0.182703458607525 0.0547865677236609 0.956342268573443 df.mm.trans2:exp3 -0.0109995753849642 0.182703458607525 -0.0602045274282024 0.952029857483953 df.mm.trans1:exp4 0.137586717804158 0.182703458607525 0.753060280592254 0.451960515841656 df.mm.trans2:exp4 0.321030140850579 0.182703458607525 1.75711036505445 0.0798434877477358 . df.mm.trans1:exp5 0.132167154881601 0.182703458607525 0.723397115133526 0.469957862502918 df.mm.trans2:exp5 -0.0299795308606222 0.182703458607525 -0.164088469310386 0.869763812626233 df.mm.trans1:exp6 -0.0251703140883085 0.182703458607525 -0.137765942036041 0.890510977686449 df.mm.trans2:exp6 0.146687991757191 0.182703458607525 0.802874739620007 0.422635692797789 df.mm.trans1:exp7 -0.054963043492884 0.182703458607525 -0.300831981571586 0.763735573094065 df.mm.trans2:exp7 0.122203282426345 0.182703458607525 0.668861352476397 0.504060202647091 df.mm.trans1:exp8 -0.029503160320203 0.182703458607525 -0.161481126548241 0.871815122549248 df.mm.trans2:exp8 0.00287483781403916 0.182703458607525 0.0157349939401790 0.98745549404329 df.mm.trans1:probe2 0.110878031197448 0.0913517293037625 1.21374857424710 0.225727768172959 df.mm.trans1:probe3 -0.0325543834160488 0.0913517293037625 -0.356363077789136 0.721800622469058 df.mm.trans1:probe4 -0.00603048430857882 0.0913517293037625 -0.0660139042198782 0.947407469650887 df.mm.trans1:probe5 0.103230431631249 0.0913517293037625 1.13003259399707 0.259298354374699 df.mm.trans1:probe6 0.0654472652864048 0.0913517293037625 0.716431596700044 0.474240965066075 df.mm.trans2:probe2 0.0129914125162472 0.0913517293037625 0.142213098922825 0.887000076525589 df.mm.trans2:probe3 -0.0260360358075809 0.0913517293037625 -0.285008680251755 0.775819584824956 df.mm.trans2:probe4 0.0451271538548099 0.0913517293037625 0.493993427368553 0.621645547562721 df.mm.trans2:probe5 0.0359464357056725 0.0913517293037625 0.39349485750996 0.694212831903108 df.mm.trans2:probe6 0.0211794719369055 0.0913517293037625 0.231845331208559 0.81680450775466 df.mm.trans3:probe2 -0.168529005849367 0.0913517293037625 -1.84483651413948 0.0659738666930375 . df.mm.trans3:probe3 -0.264055436048171 0.0913517293037625 -2.89053571356197 0.00410541971606895 **