chr4.16718_chr4_100693723_100696008_+_1.R fitVsDatCorrelation=0.881525870580328 cont.fitVsDatCorrelation=0.299124740070660 fstatistic=6049.02843758918,55,761 cont.fstatistic=1470.45793902084,55,761 residuals=-0.816454352462548,-0.114756868912466,-0.000596051697308074,0.0999662400518505,1.27016646718728 cont.residuals=-1.05540146567369,-0.312051831213192,-0.0325831603653368,0.208969406256747,2.55309622518643 predictedValues: Include Exclude Both chr4.16718_chr4_100693723_100696008_+_1.R.tl.Lung 58.9197215198372 111.467655711676 76.8201614576089 chr4.16718_chr4_100693723_100696008_+_1.R.tl.cerebhem 82.5704298557693 103.873573822142 72.629492165255 chr4.16718_chr4_100693723_100696008_+_1.R.tl.cortex 56.1855464597936 90.3161344630036 78.1819781578345 chr4.16718_chr4_100693723_100696008_+_1.R.tl.heart 60.4844547958688 89.8879797992686 83.1208446110794 chr4.16718_chr4_100693723_100696008_+_1.R.tl.kidney 58.1400390847566 120.658876354996 75.0820466453927 chr4.16718_chr4_100693723_100696008_+_1.R.tl.liver 59.7400422374101 107.160016648311 76.5667864375318 chr4.16718_chr4_100693723_100696008_+_1.R.tl.stomach 65.6466511578139 94.1463662278863 110.652350053667 chr4.16718_chr4_100693723_100696008_+_1.R.tl.testicle 60.3258244947231 90.7675458837644 79.3203671953316 diffExp=-52.5479341918389,-21.3031439663724,-34.1305880032100,-29.4035250033998,-62.5188372702394,-47.4199744109007,-28.4997150700724,-30.4417213890413 diffExpScore=0.996745484938815 diffExp1.5=-1,0,-1,0,-1,-1,0,-1 diffExp1.5Score=0.833333333333333 diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.875 diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 85.7166225579762 79.5052435826422 84.8587121119763 cerebhem 109.852054603521 81.3673886333784 84.4047960945158 cortex 99.9938755310486 108.249158438468 83.3782545544966 heart 102.170791517906 74.234173391914 82.7167943220523 kidney 93.9499991400976 78.1419233269959 83.1627086342198 liver 88.9375653314204 79.9842471767289 88.867304114616 stomach 89.7896583316237 82.7106963598133 83.6683902217877 testicle 85.2228072600798 109.837071972467 81.6613401284273 cont.diffExp=6.21137897533409,28.4846659701425,-8.25528290741956,27.9366181259926,15.8080758131017,8.9533181546915,7.07896197181039,-24.6142647123874 cont.diffExpScore=2.03411350522402 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=0,1,0,1,0,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,1,0,1,1,0,0,-1 cont.diffExp1.2Score=1.33333333333333 tran.correlation=0.0116511477187616 cont.tran.correlation=-0.173838455514281 tran.covariance=0.000342462017616843 cont.tran.covariance=-0.00250567580000741 tran.mean=81.8931786573138 cont.tran.mean=90.603954822255 weightedLogRatios: wLogRatio Lung -2.80204146378516 cerebhem -1.03937449639434 cortex -2.02487918313192 heart -1.70375301651500 kidney -3.23287444145909 liver -2.56058838317719 stomach -1.57370662634155 testicle -1.75837537631385 cont.weightedLogRatios: wLogRatio Lung 0.331995308928662 cerebhem 1.36544393848446 cortex -0.368454238687585 heart 1.42683397673280 kidney 0.819969038967592 liver 0.470562751950075 stomach 0.365964225027492 testicle -1.16008295403897 varWeightedLogRatios=0.524045591324634 cont.varWeightedLogRatios=0.742520842694643 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.71088021658216 0.105398198122077 44.6960223278751 5.10178894159679e-215 *** df.mm.trans1 -0.722295775291581 0.0840786759105932 -8.5907130133644 4.86247740381563e-17 *** df.mm.trans2 0.0046626867045824 0.082052385954564 0.0568257296888884 0.954698934251072 df.mm.exp2 0.323011874182710 0.107431791311006 3.00666935030076 0.00272817593778974 ** df.mm.exp3 -0.275506704579094 0.107431791311006 -2.5644802271008 0.0105238093074702 * df.mm.exp4 -0.267788101403461 0.107431791311006 -2.49263368073457 0.0128915327531387 * df.mm.exp5 0.0887972509818441 0.107431791311006 0.826545381941772 0.40875385646487 df.mm.exp6 -0.0222808714561649 0.107431791311006 -0.207395512857676 0.835756471007289 df.mm.exp7 -0.425699263199322 0.107431791311006 -3.96250735470806 8.11423909558847e-05 *** df.mm.exp8 -0.213876062444738 0.107431791311006 -1.99080793343178 0.0468592228350344 * df.mm.trans1:exp2 0.0144638850170556 0.0832163077201513 0.173810703855022 0.86206045568031 df.mm.trans2:exp2 -0.393571816366613 0.0784570873256339 -5.01639596602796 6.55775065038148e-07 *** df.mm.trans1:exp3 0.227990382799656 0.0832163077201513 2.73973201943021 0.00629291563750463 ** df.mm.trans2:exp3 0.0650883597505568 0.078457087325634 0.8296045898366 0.407022533233964 df.mm.trans1:exp4 0.293998622729463 0.0832163077201513 3.53294481315072 0.000435744998563465 *** df.mm.trans2:exp4 0.0526178620785101 0.078457087325634 0.670657857334432 0.502641997163902 df.mm.trans1:exp5 -0.102118548770713 0.0832163077201513 -1.22714587523070 0.220147258253929 df.mm.trans2:exp5 -0.009564356016783 0.0784570873256339 -0.121905570838826 0.903005986629715 df.mm.trans1:exp6 0.0361075260926615 0.0832163077201513 0.43389964157131 0.664484299859841 df.mm.trans2:exp6 -0.0171303940225816 0.0784570873256339 -0.218340937785294 0.827222017039188 df.mm.trans1:exp7 0.533809986931655 0.0832163077201513 6.41472809304167 2.47724904479169e-10 *** df.mm.trans2:exp7 0.256815456481652 0.078457087325634 3.27332386704272 0.00111090964982532 ** df.mm.trans1:exp8 0.237460476236803 0.0832163077201513 2.85353295216317 0.00444100608832854 ** df.mm.trans2:exp8 0.00844339445859965 0.0784570873256339 0.107617995345093 0.914327087427696 df.mm.trans1:probe2 0.230973414727435 0.0635575048637219 3.63408562407667 0.000297716795020315 *** df.mm.trans1:probe3 0.405937458602893 0.0635575048637219 6.38693195199043 2.94441110354042e-10 *** df.mm.trans1:probe4 0.350533453719691 0.0635575048637219 5.51521735271538 4.77531645031608e-08 *** df.mm.trans1:probe5 0.368307277287841 0.0635575048637219 5.79486683874004 1.00106940350498e-08 *** df.mm.trans1:probe6 0.531916815242485 0.0635575048637219 8.36906383255613 2.77162536112376e-16 *** df.mm.trans1:probe7 0.477300025212962 0.0635575048637219 7.50973510109269 1.66186792788129e-13 *** df.mm.trans2:probe2 0.0785920619480461 0.0635575048637219 1.23655046113847 0.216635510104321 df.mm.trans2:probe3 -0.0560283182065897 0.0635575048637219 -0.881537409731926 0.37830535366035 df.mm.trans2:probe4 -0.140310464156751 0.0635575048637219 -2.20761441874725 0.0275687810195348 * df.mm.trans2:probe5 0.270810688224638 0.0635575048637219 4.260876647145 2.29164237206708e-05 *** df.mm.trans2:probe6 -0.201891787659222 0.0635575048637219 -3.17652160971568 0.00155067918892103 ** df.mm.trans3:probe2 0.0918321881148704 0.0635575048637219 1.44486773531740 0.148906660461918 df.mm.trans3:probe3 1.19264949600322 0.0635575048637219 18.7648885613187 7.36699074634149e-65 *** df.mm.trans3:probe4 0.434176453835651 0.063557504863722 6.8312381797649 1.72420168190502e-11 *** df.mm.trans3:probe5 1.38469194665600 0.0635575048637219 21.7864428382614 3.70436850223122e-82 *** df.mm.trans3:probe6 0.728582604431653 0.0635575048637219 11.4633607155262 3.55785215258163e-28 *** df.mm.trans3:probe7 0.29066265124876 0.0635575048637219 4.57322312875545 5.60484493213314e-06 *** df.mm.trans3:probe8 1.14692357053585 0.0635575048637219 18.0454467650209 7.21105747707062e-61 *** df.mm.trans3:probe9 0.766366354259528 0.063557504863722 12.0578420424582 9.19907796348578e-31 *** df.mm.trans3:probe10 -0.203554323563411 0.063557504863722 -3.20267958913531 0.00141821251971084 ** df.mm.trans3:probe11 -0.176977936534311 0.0635575048637219 -2.78453247832465 0.00549362903333619 ** df.mm.trans3:probe12 0.305388099100864 0.0635575048637219 4.80491013226004 1.86533638124107e-06 *** df.mm.trans3:probe13 0.442530151103715 0.0635575048637219 6.96267344120218 7.22082979136426e-12 *** df.mm.trans3:probe14 0.274931442191403 0.0635575048637219 4.32571169653218 1.72299411381068e-05 *** df.mm.trans3:probe15 0.0440291352941175 0.0635575048637219 0.692744867636378 0.488681017846819 df.mm.trans3:probe16 0.488201068624113 0.0635575048637219 7.68124975439012 4.85170607143953e-14 *** df.mm.trans3:probe17 -0.173704234704193 0.0635575048637219 -2.73302476358449 0.00642121381981598 ** df.mm.trans3:probe18 0.253563544340024 0.063557504863722 3.98951382505823 7.26044838187457e-05 *** df.mm.trans3:probe19 0.0168056817897442 0.0635575048637219 0.264416953210773 0.791530243565718 df.mm.trans3:probe20 0.144369400231287 0.0635575048637219 2.27147683882241 0.0233965595430646 * df.mm.trans3:probe21 -0.138847362559729 0.0635575048637219 -2.18459429547213 0.0292230278902221 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.46431257431526 0.213015947362253 20.9576448599094 2.43288457623552e-77 *** df.mm.trans1 0.0313443788118455 0.169927941095488 0.184456885723297 0.853704189726626 df.mm.trans2 -0.0813686043635918 0.165832690111081 -0.490666854099081 0.623803542896276 df.mm.exp2 0.276602755890916 0.217125958609195 1.27392762091968 0.203077990367727 df.mm.exp3 0.480274910997861 0.217125958609195 2.21196449321064 0.0272654426975931 * df.mm.exp4 0.132565702556808 0.217125958609195 0.610547460128492 0.541681506617635 df.mm.exp5 0.0946083036452055 0.217125958609195 0.435730044676469 0.66315617004364 df.mm.exp6 -0.00326205914005427 0.217125958609195 -0.0150238099624267 0.98801712271724 df.mm.exp7 0.100075405355192 0.217125958609195 0.460909446278223 0.644995264832342 df.mm.exp8 0.355804413497598 0.217125958609195 1.63870048416464 0.101688987339745 df.mm.trans1:exp2 -0.0285150224819008 0.168185044344546 -0.169545530002564 0.865412621235932 df.mm.trans2:exp2 -0.253451170695639 0.158566380466919 -1.59839160072470 0.110370944352223 df.mm.trans1:exp3 -0.326212740540405 0.168185044344546 -1.93960611546483 0.0527967877984268 . df.mm.trans2:exp3 -0.171662294885546 0.158566380466919 -1.08258947691222 0.279333584568995 df.mm.trans1:exp4 0.0430333681186227 0.168185044344546 0.255869172472101 0.798121004524447 df.mm.trans2:exp4 -0.201164077064763 0.158566380466919 -1.26864267490000 0.204956458114263 df.mm.trans1:exp5 -0.00289235590408659 0.168185044344546 -0.0171974619702883 0.986283594618323 df.mm.trans2:exp5 -0.111904576961096 0.158566380466919 -0.705727006138243 0.480573895999523 df.mm.trans1:exp6 0.0401499006010247 0.168185044344546 0.238724559353643 0.81138344243259 df.mm.trans2:exp6 0.0092687876340852 0.158566380466919 0.0584536747751453 0.953402606822615 df.mm.trans1:exp7 -0.0536523689893827 0.168185044344546 -0.319007966484045 0.7498080126321 df.mm.trans2:exp7 -0.0605494488923251 0.158566380466919 -0.381855527723025 0.70267513072312 df.mm.trans1:exp8 -0.361582093604711 0.168185044344546 -2.14990634282540 0.0318771417077341 * df.mm.trans2:exp8 -0.0326292860990113 0.158566380466919 -0.205776823579691 0.837020280286767 df.mm.trans1:probe2 -0.154055828914577 0.128453449411398 -1.19931251064486 0.230779733776434 df.mm.trans1:probe3 -0.234583326714953 0.128453449411398 -1.82621274702911 0.0682098902392319 . df.mm.trans1:probe4 -0.297077121865876 0.128453449411398 -2.31272202675093 0.0210038189436476 * df.mm.trans1:probe5 -0.291146835153114 0.128453449411398 -2.26655521114624 0.0236973336135008 * df.mm.trans1:probe6 -0.126334316401839 0.128453449411398 -0.983502716203656 0.32567265384637 df.mm.trans1:probe7 -0.101277543310481 0.128453449411398 -0.78843770855946 0.430686254760636 df.mm.trans2:probe2 0.112912507889124 0.128453449411398 0.879014992641414 0.379670610867181 df.mm.trans2:probe3 -0.160776849205821 0.128453449411398 -1.25163512496189 0.211087437879484 df.mm.trans2:probe4 -0.186556361856858 0.128453449411398 -1.4523266032302 0.146822978401048 df.mm.trans2:probe5 0.147135393016728 0.128453449411398 1.14543746151571 0.252388071808324 df.mm.trans2:probe6 -0.104981513049308 0.128453449411398 -0.817272821635825 0.414028322262218 df.mm.trans3:probe2 -0.00244772651630856 0.128453449411398 -0.0190553584004523 0.984801938613691 df.mm.trans3:probe3 0.203762755503695 0.128453449411398 1.58627702438027 0.113091888079212 df.mm.trans3:probe4 0.0810203159322031 0.128453449411398 0.63073678677728 0.52840201091976 df.mm.trans3:probe5 -0.193610059488586 0.128453449411398 -1.50723908447573 0.132164377902649 df.mm.trans3:probe6 0.0926754364679023 0.128453449411398 0.721470983399524 0.470841385334317 df.mm.trans3:probe7 -0.132727227810477 0.128453449411398 -1.03327102867741 0.30180533042887 df.mm.trans3:probe8 0.0879986280467997 0.128453449411398 0.685062397701492 0.493513151294099 df.mm.trans3:probe9 -0.0908344443398579 0.128453449411398 -0.70713900448825 0.479696576229993 df.mm.trans3:probe10 0.119547999939701 0.128453449411398 0.930671776332174 0.352318508605139 df.mm.trans3:probe11 0.214443053638544 0.128453449411398 1.66942230528778 0.095444989238573 . df.mm.trans3:probe12 -0.00938490257082114 0.128453449411398 -0.0730607283325191 0.94177699056001 df.mm.trans3:probe13 0.124180009049225 0.128453449411398 0.96673160291331 0.333985343568352 df.mm.trans3:probe14 -0.00782686177731399 0.128453449411398 -0.0609315033047255 0.951429758157578 df.mm.trans3:probe15 -0.0790094227956464 0.128453449411398 -0.615082141878517 0.538684352692416 df.mm.trans3:probe16 -0.0993879794739383 0.128453449411398 -0.773727602717995 0.439332177315466 df.mm.trans3:probe17 0.0762595951349907 0.128453449411398 0.593674949831467 0.552905955538137 df.mm.trans3:probe18 -0.0234922497175468 0.128453449411398 -0.182885316238633 0.854936715449574 df.mm.trans3:probe19 0.221877219563320 0.128453449411398 1.72729670226849 0.0845201413948868 . df.mm.trans3:probe20 -0.0428165949413976 0.128453449411398 -0.333323823825616 0.738981672638079 df.mm.trans3:probe21 0.0204580559286098 0.128453449411398 0.15926435625009 0.873502860150037