chr5.17911_chr5_106349870_106353382_-_1.R fitVsDatCorrelation=0.84976487808883 cont.fitVsDatCorrelation=0.292086123692817 fstatistic=8508.34676354897,64,968 cont.fstatistic=2574.47473475061,64,968 residuals=-0.803418293516056,-0.105661516885449,-0.00760207084159327,0.0890372080980527,1.28458334167371 cont.residuals=-0.726402099735508,-0.231081604270871,-0.0613564939302088,0.172078670707476,1.61982326133163 predictedValues: Include Exclude Both chr5.17911_chr5_106349870_106353382_-_1.R.tl.Lung 65.7552711848402 55.8736023747339 65.0927846991939 chr5.17911_chr5_106349870_106353382_-_1.R.tl.cerebhem 72.2801367328801 65.691511021183 72.3020267994225 chr5.17911_chr5_106349870_106353382_-_1.R.tl.cortex 65.1097649412333 52.6492351209293 66.6885592982777 chr5.17911_chr5_106349870_106353382_-_1.R.tl.heart 66.1422792187533 53.3928185572799 66.1416129325756 chr5.17911_chr5_106349870_106353382_-_1.R.tl.kidney 84.8233613854777 54.3453428202422 88.341906863378 chr5.17911_chr5_106349870_106353382_-_1.R.tl.liver 87.1578863118772 57.5199695054073 98.106274024841 chr5.17911_chr5_106349870_106353382_-_1.R.tl.stomach 68.4768621511695 53.8821293631034 71.4181139740093 chr5.17911_chr5_106349870_106353382_-_1.R.tl.testicle 65.4051259349367 57.8535982515912 64.426919104478 diffExp=9.88166881010624,6.58862571169708,12.4605298203040,12.7494606614734,30.4780185652355,29.6379168064699,14.5947327880662,7.55152768334553 diffExpScore=0.99199631707948 diffExp1.5=0,0,0,0,1,1,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,0,0,1,1,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,0,0,1,1,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,1,1,1,1,1,0 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 71.2541026057104 61.3838768665109 68.3358672575986 cerebhem 67.0607732510573 62.970212569714 66.3921501027525 cortex 81.547959750141 65.5862953470225 64.2925298434027 heart 79.1622803619616 54.898674066186 67.36881410939 kidney 68.5307233754797 72.6505374750135 68.878766223969 liver 76.8776498519227 57.6727039671007 61.3435124847531 stomach 60.4074614678138 61.293277139059 69.1509397153265 testicle 74.6462942859945 60.1092794897813 65.6822302326994 cont.diffExp=9.8702257391995,4.09056068134331,15.9616644031184,24.2636062957756,-4.11981409953373,19.2049458848220,-0.885815671245268,14.5370147962132 cont.diffExpScore=1.1073761096785 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,1,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,0,0,1,0,1,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,0,1,1,0,1,0,1 cont.diffExp1.2Score=0.8 tran.correlation=0.147780610222066 cont.tran.correlation=-0.28979169905768 tran.covariance=0.00148452352842147 cont.tran.covariance=-0.00245432007033616 tran.mean=64.1474309297274 cont.tran.mean=67.2532563669043 weightedLogRatios: wLogRatio Lung 0.668411385583505 cerebhem 0.404565668333498 cortex 0.864531827064325 heart 0.874673669727279 kidney 1.87788891679800 liver 1.77037900928559 stomach 0.984351216219291 testicle 0.505371111949419 cont.weightedLogRatios: wLogRatio Lung 0.625004191432389 cerebhem 0.262709229851632 cortex 0.93496374362225 heart 1.53303395593380 kidney -0.248486960493482 liver 1.20677978384972 stomach -0.0598080471165763 testicle 0.910672509405358 varWeightedLogRatios=0.301045201902547 cont.varWeightedLogRatios=0.385782588826173 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.39578673689408 0.083315622648137 52.7606539707277 5.14705818043835e-287 *** df.mm.trans1 -0.139044158906204 0.0701466462767189 -1.98219253929394 0.0477401314819878 * df.mm.trans2 -0.362308849426737 0.062413435951174 -5.80498163424572 8.72025523558651e-09 *** df.mm.exp2 0.151448736224664 0.0789050350169151 1.91937987470884 0.0552300024862483 . df.mm.exp3 -0.0935253337173876 0.0789050350169151 -1.18528980688416 0.236193776428891 df.mm.exp4 -0.0555318416349781 0.0789050350169152 -0.703780710864313 0.481738599412147 df.mm.exp5 -0.0785028455200407 0.0789050350169152 -0.994902866505436 0.320032136973292 df.mm.exp6 -0.0994160457466264 0.0789050350169151 -1.25994552470972 0.207992681489467 df.mm.exp7 -0.0884749218483998 0.0789050350169151 -1.12128360160328 0.262445244628830 df.mm.exp8 0.0397665725077628 0.0789050350169151 0.503980164247287 0.614389996624285 df.mm.trans1:exp2 -0.0568392167473593 0.0694271744753368 -0.818688318758396 0.41316575856609 df.mm.trans2:exp2 0.0104289335233199 0.0497695947673805 0.209544272402939 0.834067469078214 df.mm.trans1:exp3 0.0836600328243976 0.0694271744753368 1.20500414220540 0.228496208574623 df.mm.trans2:exp3 0.0340850053808562 0.0497695947673805 0.684855995717205 0.493598742801834 df.mm.trans1:exp4 0.0614001711582169 0.0694271744753368 0.884382399575092 0.376709426893483 df.mm.trans2:exp4 0.0101160554179695 0.0497695947673805 0.203257741302721 0.838976300951326 df.mm.trans1:exp5 0.333134000458271 0.0694271744753368 4.79832288978739 1.85213193225335e-06 *** df.mm.trans2:exp5 0.0507697275424672 0.0497695947673806 1.02009525654692 0.307938160634381 df.mm.trans1:exp6 0.381197466906083 0.0694271744753368 5.49060896956852 5.11795790939632e-08 *** df.mm.trans2:exp6 0.128456189809626 0.0497695947673806 2.58101739445581 0.0099971363354526 ** df.mm.trans1:exp7 0.129030993285363 0.0694271744753368 1.85850849124214 0.0634002063297603 . df.mm.trans2:exp7 0.0521817538896746 0.0497695947673806 1.04846652124793 0.294685422012948 df.mm.trans1:exp8 -0.0451057768620388 0.0694271744753368 -0.649684755326779 0.516050006999955 df.mm.trans2:exp8 -0.00494296031392405 0.0497695947673806 -0.099316868803676 0.920907248532865 df.mm.trans1:probe2 0.229392928078073 0.0530258803866895 4.32605600143237 1.67550270450148e-05 *** df.mm.trans1:probe3 -0.396627955923099 0.0530258803866895 -7.4798938373244 1.66612252550917e-13 *** df.mm.trans1:probe4 -0.245772751649438 0.0530258803866895 -4.63495843646816 4.05859551094458e-06 *** df.mm.trans1:probe5 -0.327172011270488 0.0530258803866895 -6.17004392731619 1.00215671284531e-09 *** df.mm.trans1:probe6 -0.083107205623077 0.0530258803866895 -1.56729515883603 0.117372535221264 df.mm.trans1:probe7 -0.0889126380073165 0.0530258803866895 -1.67677815736248 0.0939087420475299 . df.mm.trans1:probe8 -0.087578475838306 0.0530258803866895 -1.65161757239376 0.0989368668948992 . df.mm.trans1:probe9 0.0487117532797226 0.0530258803866895 0.918641103636446 0.358512165090244 df.mm.trans1:probe10 0.258341691569606 0.0530258803866895 4.8719925003726 1.29013048877043e-06 *** df.mm.trans1:probe11 -0.326320216111619 0.0530258803866895 -6.1539801646279 1.10493876837679e-09 *** df.mm.trans1:probe12 -0.457315775974386 0.0530258803866895 -8.62438817874265 2.60358640570213e-17 *** df.mm.trans1:probe13 -0.209724624166414 0.0530258803866895 -3.95513705075717 8.20676075296203e-05 *** df.mm.trans1:probe14 -0.209886341439941 0.0530258803866895 -3.95818683083339 8.10435277067351e-05 *** df.mm.trans1:probe15 -0.35944731961183 0.0530258803866895 -6.77871478965691 2.10498398307986e-11 *** df.mm.trans1:probe16 -0.293479698456934 0.0530258803866895 -5.53465018056736 4.01513668064052e-08 *** df.mm.trans2:probe2 -0.0399880641572498 0.0530258803866895 -0.754123531106662 0.450958451587538 df.mm.trans2:probe3 -0.0289974599509653 0.0530258803866895 -0.546854851621553 0.584604490820485 df.mm.trans2:probe4 -0.0697194665499365 0.0530258803866895 -1.31481959453591 0.188881944596587 df.mm.trans2:probe5 -0.0200851294322243 0.0530258803866895 -0.378779744640808 0.704934491482667 df.mm.trans2:probe6 -0.121627783308527 0.0530258803866895 -2.29374377985920 0.022018711361924 * df.mm.trans3:probe2 0.294803728074414 0.0530258803866895 5.55961967862801 3.49635260863419e-08 *** df.mm.trans3:probe3 0.364818828879558 0.0530258803866895 6.88001455551759 1.07328249373150e-11 *** df.mm.trans3:probe4 0.377374385743428 0.0530258803866895 7.11679623216886 2.14918389642308e-12 *** df.mm.trans3:probe5 0.539119120049806 0.0530258803866895 10.1670941834119 3.85096211086012e-23 *** df.mm.trans3:probe6 0.293170470585053 0.0530258803866895 5.52881854006226 4.14666426948514e-08 *** df.mm.trans3:probe7 -0.050316862628277 0.0530258803866895 -0.948911404418805 0.34290251006358 df.mm.trans3:probe8 0.58241730045665 0.0530258803866895 10.9836422556192 1.56554619995263e-26 *** df.mm.trans3:probe9 0.523959598019488 0.0530258803866895 9.881205068139 5.29774206899602e-22 *** df.mm.trans3:probe10 0.333971778887985 0.0530258803866895 6.29827881126173 4.56002960337327e-10 *** df.mm.trans3:probe11 -0.0788217866983155 0.0530258803866895 -1.48647766191736 0.137478441616962 df.mm.trans3:probe12 0.379368015326648 0.0530258803866895 7.15439352557882 1.65764617405033e-12 *** df.mm.trans3:probe13 0.0940331470641374 0.0530258803866895 1.77334438161901 0.0764860327078538 . df.mm.trans3:probe14 0.885880654099707 0.0530258803866895 16.7065713504321 3.00790522889680e-55 *** df.mm.trans3:probe15 0.947796786239195 0.0530258803866895 17.8742300802442 5.72981890838586e-62 *** df.mm.trans3:probe16 -0.0531750453255314 0.0530258803866895 -1.00281305916572 0.316201673791011 df.mm.trans3:probe17 -0.030468263178533 0.0530258803866895 -0.574592311458937 0.565700558882336 df.mm.trans3:probe18 0.0640764912905886 0.0530258803866895 1.20840032873218 0.227188424526878 df.mm.trans3:probe19 0.222027997798684 0.0530258803866895 4.18716287555346 3.08211894114549e-05 *** df.mm.trans3:probe20 0.102316462548302 0.0530258803866895 1.92955707292670 0.0539537265396308 . df.mm.trans3:probe21 0.116626781105347 0.0530258803866895 2.19943130137303 0.0280830814596418 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.21321990573263 0.151153562265339 27.8737718290531 5.30981417990912e-126 *** df.mm.trans1 0.0913274243735625 0.127262032361824 0.717632923807988 0.473156793428721 df.mm.trans2 -0.118445489846976 0.113232223169978 -1.04604048680712 0.295803468217869 df.mm.exp2 -0.00628259345858878 0.143151749268535 -0.0438876471345341 0.965003013216083 df.mm.exp3 0.262149788978405 0.143151749268535 1.83127199156082 0.0673671753679303 . df.mm.exp4 0.00784207633492547 0.143151749268535 0.0547815613500795 0.956323794262685 df.mm.exp5 0.121630166040226 0.143151749268535 0.849658957447057 0.395724810593918 df.mm.exp6 0.121544933736339 0.143151749268535 0.849063559176881 0.396055849364464 df.mm.exp7 -0.178473685771744 0.143151749268535 -1.24674470751279 0.212792711456267 df.mm.exp8 0.0651318561774524 0.143151749268535 0.454984703367284 0.64922226570444 df.mm.trans1:exp2 -0.0543705327573751 0.125956746242945 -0.431660346739232 0.666084414093314 df.mm.trans2:exp2 0.0317971827642678 0.0902934083966802 0.352153975897945 0.724799441699367 df.mm.trans1:exp3 -0.127210876538336 0.125956746242945 -1.00995683306214 0.312768352341981 df.mm.trans2:exp3 -0.195930236165754 0.0902934083966802 -2.16992845485449 0.030254899759131 * df.mm.trans1:exp4 0.0974054530604316 0.125956746242945 0.773324621077113 0.439519086825203 df.mm.trans2:exp4 -0.119500088830198 0.0902934083966802 -1.32346414818240 0.185993559301439 df.mm.trans1:exp5 -0.160600402817208 0.125956746242945 -1.27504407352221 0.202599583615843 df.mm.trans2:exp5 0.0468834131714514 0.0902934083966802 0.519234061532836 0.603716221078164 df.mm.trans1:exp6 -0.0455821365073068 0.125956746242945 -0.361887218167641 0.717515293483511 df.mm.trans2:exp6 -0.183908149234707 0.0902934083966802 -2.03678377525361 0.0419433899264088 * df.mm.trans1:exp7 0.0133339193960227 0.125956746242945 0.105861097509650 0.915714492904464 df.mm.trans2:exp7 0.176996642313120 0.0902934083966802 1.96023879767094 0.0502544103773536 . df.mm.trans1:exp8 -0.0186233725254194 0.125956746242945 -0.147855300179783 0.882487757064002 df.mm.trans2:exp8 -0.0861148346457728 0.0902934083966802 -0.953722272476968 0.340462334180896 df.mm.trans1:probe2 -0.132759101836919 0.09620105399144 -1.38001712381167 0.167900009769916 df.mm.trans1:probe3 -0.0708446544292007 0.09620105399144 -0.736422850788147 0.461651827071374 df.mm.trans1:probe4 -0.143978217943977 0.09620105399144 -1.49663867463227 0.134813267532628 df.mm.trans1:probe5 -0.0162375493000162 0.09620105399144 -0.168787644483199 0.865998938714635 df.mm.trans1:probe6 -0.0908483965807638 0.09620105399144 -0.944359680184455 0.345221522897505 df.mm.trans1:probe7 -0.119496538878156 0.09620105399144 -1.24215415445229 0.214480541389578 df.mm.trans1:probe8 -0.00433771599416331 0.09620105399144 -0.0450901088313365 0.964044785144868 df.mm.trans1:probe9 -0.0576308277845681 0.09620105399144 -0.59906649037022 0.549268716272952 df.mm.trans1:probe10 -0.0774617950728063 0.09620105399144 -0.805207342943444 0.420897773189772 df.mm.trans1:probe11 -0.152712716108861 0.09620105399144 -1.58743287908726 0.112741205074758 df.mm.trans1:probe12 -0.156222536322257 0.09620105399144 -1.62391709695985 0.104719064901091 df.mm.trans1:probe13 -0.0581940627538326 0.09620105399144 -0.604921259584232 0.545373089841095 df.mm.trans1:probe14 -0.110052267117618 0.09620105399144 -1.14398192692785 0.252913939839726 df.mm.trans1:probe15 -0.080336506244762 0.09620105399144 -0.835089668060293 0.403873279301324 df.mm.trans1:probe16 -0.107504672259570 0.09620105399144 -1.11749994203947 0.264057899140582 df.mm.trans2:probe2 0.274524293232242 0.09620105399144 2.85365161650588 0.0044141354074169 ** df.mm.trans2:probe3 0.123936105954339 0.09620105399144 1.28830299474023 0.197948358977392 df.mm.trans2:probe4 0.0253372772772793 0.09620105399144 0.263378375038737 0.792315008425066 df.mm.trans2:probe5 0.0588520119449164 0.09620105399144 0.611760573331692 0.540839829235983 df.mm.trans2:probe6 0.121415723497262 0.09620105399144 1.26210387994362 0.207215408018739 df.mm.trans3:probe2 0.00514897088719719 0.09620105399144 0.0535230194843328 0.95732623360365 df.mm.trans3:probe3 0.0135032391179623 0.09620105399144 0.140364773125707 0.888400990457465 df.mm.trans3:probe4 0.187879210620812 0.09620105399144 1.95298495001447 0.0511092214898872 . df.mm.trans3:probe5 0.154134124981872 0.09620105399144 1.60220827721479 0.109435805490751 df.mm.trans3:probe6 -0.0157811965698531 0.09620105399144 -0.164043905082966 0.869730825650011 df.mm.trans3:probe7 0.0359174922696003 0.09620105399144 0.373358614894138 0.708963303993477 df.mm.trans3:probe8 -0.00157547236435546 0.09620105399144 -0.0163768721753885 0.986937105325865 df.mm.trans3:probe9 0.0776543513262965 0.09620105399144 0.80720894527004 0.419744405597808 df.mm.trans3:probe10 -0.0858136331100195 0.09620105399144 -0.892023834974357 0.372601837752675 df.mm.trans3:probe11 0.0150665251094323 0.09620105399144 0.156614969216168 0.875580961579095 df.mm.trans3:probe12 0.00681347982525204 0.09620105399144 0.0708254176285668 0.943551331501754 df.mm.trans3:probe13 0.236650858879128 0.09620105399144 2.45996118608206 0.0140690457011739 * df.mm.trans3:probe14 -0.00583268923180828 0.09620105399144 -0.0606302009157537 0.951666235963032 df.mm.trans3:probe15 -0.0229461484089629 0.09620105399144 -0.238522837920307 0.811526088243742 df.mm.trans3:probe16 0.02338627169457 0.09620105399144 0.243097873924031 0.80798108090062 df.mm.trans3:probe17 0.00109311957607226 0.09620105399144 0.0113628648618499 0.990936281992787 df.mm.trans3:probe18 0.0678670092218023 0.09620105399144 0.705470537025936 0.480687186802729 df.mm.trans3:probe19 0.0779811572800619 0.09620105399144 0.810606059336945 0.417791177438264 df.mm.trans3:probe20 -0.0232579234707124 0.09620105399144 -0.241763707420315 0.809014467386371 df.mm.trans3:probe21 0.0571028105494839 0.09620105399144 0.593577805858186 0.552933198828595