chr16.9620_chr16_29355726_29357223_+_2.R fitVsDatCorrelation=0.86169217937052 cont.fitVsDatCorrelation=0.257879677881987 fstatistic=12453.1541613089,59,853 cont.fstatistic=3424.48126012321,59,853 residuals=-0.505644915746195,-0.0818581075647773,-0.000553755534439648,0.0812345151933244,0.492478395623969 cont.residuals=-0.488869751922288,-0.167617011949174,-0.0619035873226354,0.105554713165277,1.37234910450651 predictedValues: Include Exclude Both chr16.9620_chr16_29355726_29357223_+_2.R.tl.Lung 48.1379539297822 49.7832323239116 59.5529753456616 chr16.9620_chr16_29355726_29357223_+_2.R.tl.cerebhem 53.8456152243105 59.8800122081446 67.3201717750916 chr16.9620_chr16_29355726_29357223_+_2.R.tl.cortex 48.1560785494861 58.231823861109 94.1660247685259 chr16.9620_chr16_29355726_29357223_+_2.R.tl.heart 55.3397854474047 53.7056256697579 96.0444636228073 chr16.9620_chr16_29355726_29357223_+_2.R.tl.kidney 52.3917401402636 51.6053842921532 68.0505164715926 chr16.9620_chr16_29355726_29357223_+_2.R.tl.liver 54.3153143459614 52.9294907494681 64.3082883578778 chr16.9620_chr16_29355726_29357223_+_2.R.tl.stomach 50.8609501024141 50.8494541658955 63.523229453654 chr16.9620_chr16_29355726_29357223_+_2.R.tl.testicle 53.2219169865233 54.0390544414111 68.4437449076097 diffExp=-1.64527839412941,-6.03439698383411,-10.0757453116228,1.63415977764684,0.786355848110368,1.38582359649328,0.0114959365186209,-0.817137454887835 diffExpScore=1.42118609915002 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,-1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 58.4012912969562 59.6365678119238 60.4244480601843 cerebhem 55.325639505608 57.1410768812707 55.6705795876876 cortex 61.0664096423819 58.0007673704327 55.1380334004329 heart 57.7162094365476 60.7388618737365 57.6274022316079 kidney 58.5942938394419 54.5743484541825 54.8246988145025 liver 60.9658527632702 55.3391253881709 59.8634203002403 stomach 56.6520931096559 55.5934442800489 64.2266146356737 testicle 59.9277420695869 59.5512461428097 55.9466669717628 cont.diffExp=-1.23527651496766,-1.81543737566268,3.06564227194922,-3.02265243718893,4.0199453852594,5.62672737509935,1.05864882960699,0.376495926777203 cont.diffExpScore=2.22841281101002 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=0.149939194452962 cont.tran.correlation=0.0146892254044144 tran.covariance=0.0005516262290886 cont.tran.covariance=2.5562164343036e-05 tran.mean=52.9558395273748 cont.tran.mean=58.0765606166265 weightedLogRatios: wLogRatio Lung -0.130761781644094 cerebhem -0.429054434799088 cortex -0.754132549854503 heart 0.119852547214173 kidney 0.0597535482854136 liver 0.102913986366389 stomach 0.00088815560091339 testicle -0.0606740120487604 cont.weightedLogRatios: wLogRatio Lung -0.0853522555952888 cerebhem -0.130095773281580 cortex 0.21046311577362 heart -0.208320453198233 kidney 0.286788133272491 liver 0.393328709677817 stomach 0.0759732927035834 testicle 0.0257763994383889 varWeightedLogRatios=0.093323877183699 cont.varWeightedLogRatios=0.0449836568522381 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.78008520787162 0.0664635571511168 56.8745545664509 1.73904507984649e-292 *** df.mm.trans1 0.0287692174389159 0.0573963022023801 0.501238169272217 0.616332839033058 df.mm.trans2 0.148823811233826 0.0507093735691441 2.93483829041782 0.00342675362021033 ** df.mm.exp2 0.174120919453649 0.0652284682325523 2.66940071063563 0.0077431101460157 ** df.mm.exp3 -0.301062963616306 0.0652284682325523 -4.61551484764225 4.52382077059608e-06 *** df.mm.exp4 -0.262684290102997 0.0652284682325523 -4.0271417867192 6.14891480521131e-05 *** df.mm.exp5 -0.0127582544661964 0.0652284682325523 -0.195593347688477 0.844975049370274 df.mm.exp6 0.105195462053354 0.0652284682325523 1.61272316986369 0.107174546640035 df.mm.exp7 0.0116762740345671 0.0652284682325523 0.179005798402913 0.85797565696547 df.mm.exp8 0.0432822372675768 0.0652284682325523 0.663548270262413 0.507158764168692 df.mm.trans1:exp2 -0.0620708738533181 0.0602920296122654 -1.02950380427550 0.30353481716111 df.mm.trans2:exp2 0.0105436169633202 0.044528614914863 0.236782953691222 0.812882026676312 df.mm.trans1:exp3 0.301439406879741 0.0602920296122654 4.99965598800175 6.9718092944036e-07 *** df.mm.trans2:exp3 0.457816743638102 0.0445286149148630 10.2814054403765 1.86978116306084e-23 *** df.mm.trans1:exp4 0.402105458510409 0.0602920296122654 6.66929710438223 4.61651015273102e-11 *** df.mm.trans2:exp4 0.338523820195055 0.0445286149148630 7.60238828093576 7.6646835555302e-14 *** df.mm.trans1:exp5 0.0974362735027193 0.0602920296122654 1.61607220936708 0.106448461975000 df.mm.trans2:exp5 0.0487060412458587 0.044528614914863 1.09381442335412 0.274345162289307 df.mm.trans1:exp6 0.0155398283225121 0.0602920296122654 0.257742663871955 0.796667683904888 df.mm.trans2:exp6 -0.0439130244488679 0.0445286149148630 -0.986175396042027 0.324326691411417 df.mm.trans1:exp7 0.0433482375700697 0.0602920296122654 0.718971277776511 0.472355522941203 df.mm.trans2:exp7 0.00951488719044466 0.044528614914863 0.213680286454828 0.83084744826739 df.mm.trans1:exp8 0.0571171187187407 0.0602920296122654 0.9473411176578 0.343733276280254 df.mm.trans2:exp8 0.0387465512768599 0.0445286149148630 0.870149483673405 0.384463515478280 df.mm.trans1:probe2 -0.063978327327323 0.041279130820509 -1.54989521473975 0.121537586484191 df.mm.trans1:probe3 0.0263424243111183 0.041279130820509 0.63815356058879 0.523545002339424 df.mm.trans1:probe4 0.0693651184461697 0.041279130820509 1.68039193334242 0.0932472140804736 . df.mm.trans1:probe5 0.0142323898260211 0.041279130820509 0.344784144993429 0.730341645232214 df.mm.trans1:probe6 0.0836545985345404 0.041279130820509 2.02655910799793 0.0430182025771589 * df.mm.trans1:probe7 -0.00233381701844648 0.041279130820509 -0.0565374554177132 0.954926898344634 df.mm.trans1:probe8 0.190320118084397 0.041279130820509 4.61056505554712 4.63019424204593e-06 *** df.mm.trans1:probe9 0.0362853011231920 0.041279130820509 0.87902289612077 0.379636410270885 df.mm.trans1:probe10 0.0655541308994193 0.041279130820509 1.58806955467312 0.112641186207162 df.mm.trans1:probe11 0.337615901753074 0.041279130820509 8.17885200202264 1.03498705534424e-15 *** df.mm.trans1:probe12 0.210947012925054 0.041279130820509 5.11025810699113 3.97192650194947e-07 *** df.mm.trans1:probe13 0.270382045876463 0.041279130820509 6.55009057850916 9.93019489373437e-11 *** df.mm.trans1:probe14 0.196244102340091 0.041279130820509 4.75407544779479 2.33942121695813e-06 *** df.mm.trans1:probe15 0.235144997435944 0.041279130820509 5.69646193517028 1.68353922289727e-08 *** df.mm.trans1:probe16 0.281102389006507 0.041279130820509 6.80979428149307 1.84349624526103e-11 *** df.mm.trans1:probe17 0.00767149869917524 0.041279130820509 0.185844482349511 0.852610903953133 df.mm.trans1:probe18 0.0404635952899928 0.041279130820509 0.9802433938335 0.327243920764073 df.mm.trans1:probe19 0.051424154518518 0.041279130820509 1.24576640777932 0.213192225667688 df.mm.trans1:probe20 0.0431471685163455 0.041279130820509 1.04525380400957 0.296201792575394 df.mm.trans1:probe21 -0.0193787069252796 0.041279130820509 -0.469455304413811 0.638864200234264 df.mm.trans1:probe22 0.0127220202892009 0.041279130820509 0.30819496526027 0.758009227001971 df.mm.trans2:probe2 -0.0935272716787765 0.041279130820509 -2.26572773747234 0.0237179675024032 * df.mm.trans2:probe3 -0.114743381637472 0.041279130820509 -2.77969471150936 0.00556072709952924 ** df.mm.trans2:probe4 -0.0662014054282775 0.041279130820509 -1.60374998485642 0.109139349657162 df.mm.trans2:probe5 -0.0432268563639345 0.041279130820509 -1.04718426732129 0.295311229969626 df.mm.trans2:probe6 -0.0219937583984044 0.041279130820509 -0.532805753445688 0.594306821986135 df.mm.trans3:probe2 0.267736384581517 0.041279130820509 6.48599859686231 1.49164626073245e-10 *** df.mm.trans3:probe3 0.484953586423945 0.041279130820509 11.7481540135289 1.20808927159914e-29 *** df.mm.trans3:probe4 0.751514065350895 0.041279130820509 18.2056659239907 8.07328563281155e-63 *** df.mm.trans3:probe5 0.119211131417691 0.041279130820509 2.88792736300694 0.00397597149923631 ** df.mm.trans3:probe6 -0.145131873535495 0.041279130820509 -3.51586553909193 0.000461430331267811 *** df.mm.trans3:probe7 -0.096407649909947 0.041279130820509 -2.33550581113612 0.0197479769930558 * df.mm.trans3:probe8 0.124863521044064 0.041279130820509 3.02485828945862 0.00256209096570181 ** df.mm.trans3:probe9 -0.168842564287225 0.041279130820509 -4.09026452183285 4.71691872855027e-05 *** df.mm.trans3:probe10 -0.046016382767144 0.041279130820509 -1.11476142671786 0.265266649075371 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.08710357041769 0.126550244202398 32.2962914546493 4.09714327813863e-150 *** df.mm.trans1 0.0367771082486644 0.109285695370035 0.336522617385006 0.736559569463821 df.mm.trans2 -0.00906400353556611 0.096553417896893 -0.0938755326636426 0.925230075404924 df.mm.exp2 -0.0149052737439071 0.124198567419576 -0.120011639856948 0.904502187382541 df.mm.exp3 0.108365307842530 0.124198567419576 0.872516568379109 0.383172165702501 df.mm.exp4 0.0539104457741175 0.124198567419576 0.434066566903251 0.664349868562321 df.mm.exp5 0.0118473318014214 0.124198567419576 0.0953902452143263 0.924027268109826 df.mm.exp6 -0.0224847009551658 0.124198567419576 -0.181038327754670 0.856380497449524 df.mm.exp7 -0.161636610820391 0.124198567419576 -1.30143699865989 0.19346030287623 df.mm.exp8 0.101364749369981 0.124198567419576 0.816150713136197 0.414641793465389 df.mm.trans1:exp2 -0.039196282153677 0.114799318573068 -0.341433055882898 0.732861682919024 df.mm.trans2:exp2 -0.0278404230819115 0.0847849157194223 -0.328365285802057 0.742716076091812 df.mm.trans1:exp3 -0.0637413540314078 0.114799318573068 -0.555241571323763 0.578875013881974 df.mm.trans2:exp3 -0.136178006674341 0.0847849157194223 -1.60615842474850 0.108609207845275 df.mm.trans1:exp4 -0.0657103864413859 0.114799318573068 -0.572393523395019 0.567206228111664 df.mm.trans2:exp4 -0.035595663820567 0.0847849157194223 -0.419834867069554 0.674711813576815 df.mm.trans1:exp5 -0.00854801555221445 0.114799318573068 -0.0744605077666358 0.940661421968008 df.mm.trans2:exp5 -0.100552307792033 0.0847849157194223 -1.18596930761586 0.235964664577558 df.mm.trans1:exp6 0.0654606167820853 0.114799318573068 0.570217816584168 0.568680130930547 df.mm.trans2:exp6 -0.0523040689600928 0.0847849157194223 -0.616903001156267 0.537463325466432 df.mm.trans1:exp7 0.13122754486917 0.114799318573068 1.14310386594887 0.253316189244599 df.mm.trans2:exp7 0.0914329566775345 0.0847849157194223 1.07841065715171 0.281155390661957 df.mm.trans1:exp8 -0.0755632125776665 0.114799318573068 -0.658220044481984 0.510574350635885 df.mm.trans2:exp8 -0.102796467611260 0.0847849157194223 -1.21243816472547 0.225680393426146 df.mm.trans1:probe2 0.0274977957871199 0.0785977204608641 0.349854876526753 0.726533973707881 df.mm.trans1:probe3 -0.0998789988448313 0.0785977204608641 -1.27076203049125 0.204159775538957 df.mm.trans1:probe4 -0.0204330323375911 0.0785977204608641 -0.259969783064704 0.794949817518198 df.mm.trans1:probe5 -0.144527604046848 0.0785977204608641 -1.83882691761795 0.0662881149637823 . df.mm.trans1:probe6 -0.090587222243849 0.0785977204608641 -1.15254261462907 0.249421057096466 df.mm.trans1:probe7 -0.159851723048523 0.0785977204608641 -2.03379591814138 0.0422811932988409 * df.mm.trans1:probe8 -0.147376633912944 0.0785977204608641 -1.87507516819558 0.0611238384961296 . df.mm.trans1:probe9 -0.0555895420697162 0.0785977204608641 -0.707266594295132 0.479593996006345 df.mm.trans1:probe10 -0.121225362478705 0.0785977204608641 -1.54235214161289 0.123358925123953 df.mm.trans1:probe11 -0.109326781793265 0.0785977204608641 -1.39096631749901 0.164598356703968 df.mm.trans1:probe12 -0.104223822407152 0.0785977204608641 -1.32604128715219 0.185180984745215 df.mm.trans1:probe13 -0.172019919470269 0.0785977204608641 -2.18861206739351 0.0288955426141696 * df.mm.trans1:probe14 -0.0756612722284205 0.0785977204608641 -0.962639524209792 0.336001261713904 df.mm.trans1:probe15 0.0808814531519834 0.0785977204608641 1.02905596597113 0.303745077680003 df.mm.trans1:probe16 -0.0778425207708462 0.0785977204608641 -0.990391582789045 0.322263607895313 df.mm.trans1:probe17 -0.110407783783126 0.0785977204608641 -1.40471992235577 0.160468616081780 df.mm.trans1:probe18 -0.0314347581180213 0.0785977204608641 -0.399944908499904 0.689297224765125 df.mm.trans1:probe19 -0.112870838557513 0.0785977204608641 -1.43605740593603 0.151352625574742 df.mm.trans1:probe20 -0.097146436678109 0.0785977204608641 -1.23599560023475 0.216800451874483 df.mm.trans1:probe21 -0.0237502436878605 0.0785977204608641 -0.302174713828837 0.762592540812883 df.mm.trans1:probe22 -0.163590442937354 0.0785977204608641 -2.08136370849088 0.0376983597116546 * df.mm.trans2:probe2 0.00118315380561549 0.0785977204608641 0.0150532839715703 0.987993190765997 df.mm.trans2:probe3 -0.0170673693116655 0.0785977204608641 -0.217148400889867 0.828144622236823 df.mm.trans2:probe4 0.0618883952578302 0.0785977204608641 0.787406999782469 0.431262380630278 df.mm.trans2:probe5 0.111681549805422 0.0785977204608641 1.42092606694658 0.155703700671657 df.mm.trans2:probe6 0.00598423737540192 0.0785977204608641 0.0761375436884538 0.939327524525705 df.mm.trans3:probe2 -0.0914718549441156 0.0785977204608641 -1.16379781000979 0.244831382047695 df.mm.trans3:probe3 -0.0196671020831452 0.0785977204608641 -0.250224840718351 0.802473728326483 df.mm.trans3:probe4 0.000385595984010351 0.0785977204608641 0.00490594360433582 0.99608678612657 df.mm.trans3:probe5 0.0176863364031881 0.0785977204608641 0.225023528665754 0.822014865518546 df.mm.trans3:probe6 0.047801177051063 0.0785977204608641 0.608175106997721 0.543233238582981 df.mm.trans3:probe7 0.0112112052649498 0.0785977204608641 0.142640336121862 0.88660792469597 df.mm.trans3:probe8 -0.108427804676678 0.0785977204608641 -1.37952861788997 0.168093359699604 df.mm.trans3:probe9 -0.0261647408908957 0.0785977204608641 -0.332894398685822 0.739295796588674 df.mm.trans3:probe10 0.0344183591053672 0.0785977204608641 0.437905309512189 0.661565789210705