chr4.17216_chr4_113393648_113393948_-_1.R fitVsDatCorrelation=0.856641696781327 cont.fitVsDatCorrelation=0.243762078547262 fstatistic=1747.88900922232,61,899 cont.fstatistic=484.049767468082,61,899 residuals=-1.34019506546828,-0.159354390365932,0.00176176133777631,0.164387052198936,1.60003356807071 cont.residuals=-0.97446205569537,-0.395883424498464,-0.209823544346507,0.0525770650073722,3.95373704322566 predictedValues: Include Exclude Both chr4.17216_chr4_113393648_113393948_-_1.R.tl.Lung 50.0527125785177 56.184999101953 151.823558152252 chr4.17216_chr4_113393648_113393948_-_1.R.tl.cerebhem 54.7353909658328 44.9326156226619 65.482298188011 chr4.17216_chr4_113393648_113393948_-_1.R.tl.cortex 48.0239488681818 43.7188890902338 75.6423368631864 chr4.17216_chr4_113393648_113393948_-_1.R.tl.heart 49.5101625752808 50.1467401561298 96.5553938010177 chr4.17216_chr4_113393648_113393948_-_1.R.tl.kidney 56.9304917559259 92.160983545756 285.053726497362 chr4.17216_chr4_113393648_113393948_-_1.R.tl.liver 52.8270364165031 44.8448295700404 66.3719979985428 chr4.17216_chr4_113393648_113393948_-_1.R.tl.stomach 48.5017425661619 41.4971567465711 62.3802380139798 chr4.17216_chr4_113393648_113393948_-_1.R.tl.testicle 51.1792374792535 46.9886887511056 71.3591815408084 diffExp=-6.13228652343538,9.80277534317089,4.30505977794808,-0.636577580848986,-35.2304917898302,7.98220684646269,7.00458581959082,4.19054872814793 diffExpScore=7.74996316969187 diffExp1.5=0,0,0,0,-1,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,-1,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,-1,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,1,0,0,-1,0,0,0 diffExp1.2Score=2 cont.predictedValues: Include Exclude Both Lung 85.6997207285415 63.5473451564366 60.8961440221056 cerebhem 83.0394407385061 64.9558213214446 74.5832222439974 cortex 66.5980692289056 80.414996869969 68.1501695039146 heart 60.138666671872 65.1738345635629 70.955767471234 kidney 82.3489972406512 72.8102534432491 71.5476699967502 liver 77.6528091151108 78.5143109590088 67.7641718507612 stomach 53.9289997886377 61.4916999209593 76.9513898654123 testicle 62.7662427010091 72.7245265332499 59.592294891808 cont.diffExp=22.1523755721049,18.0836194170615,-13.8169276410634,-5.03516789169085,9.53874379740205,-0.861501843898026,-7.56270013232158,-9.95828383224079 cont.diffExpScore=6.42601982133078 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=1,0,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=1,1,-1,0,0,0,0,0 cont.diffExp1.2Score=1.5 tran.correlation=0.671572172613887 cont.tran.correlation=0.102906354889965 tran.covariance=0.00997471796480961 cont.tran.covariance=0.00284064707532782 tran.mean=52.0147266118818 cont.tran.mean=70.7378584363196 weightedLogRatios: wLogRatio Lung -0.458924974504902 cerebhem 0.770408863658428 cortex 0.359217931618802 heart -0.0499341057393267 kidney -2.06299365982114 liver 0.636439506216541 stomach 0.593267723087218 testicle 0.332534977803159 cont.weightedLogRatios: wLogRatio Lung 1.28637067247508 cerebhem 1.05525896786505 cortex -0.809326593486695 heart -0.332624164026747 kidney 0.535453837838784 liver -0.0480800321921985 stomach -0.531928202013484 testicle -0.620418938739477 varWeightedLogRatios=0.865345226727523 cont.varWeightedLogRatios=0.636445769490473 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 1.76871248994535 0.186184457275291 9.49978594255128 1.84464295179011e-20 *** df.mm.trans1 2.15697981443338 0.155883950356667 13.8370872017174 1.2852205845327e-39 *** df.mm.trans2 2.24921679741902 0.140332489784013 16.0277694843212 4.83022858093944e-51 *** df.mm.exp2 0.706887130788436 0.178469428045127 3.96083037039653 8.05972962989446e-05 *** df.mm.exp3 0.404456478640832 0.178469428045126 2.26625076950750 0.0236730031951968 * df.mm.exp4 0.328007132720341 0.178469428045127 1.83788975127663 0.0664084776660752 . df.mm.exp5 -0.0063171847296479 0.178469428045127 -0.0353964530443309 0.971771470627675 df.mm.exp6 0.65594870341768 0.178469428045127 3.67541214538897 0.000251527559780334 *** df.mm.exp7 0.55496865445669 0.178469428045127 3.10960067802966 0.00193242614401917 ** df.mm.exp8 0.598507362848987 0.178469428045127 3.35355679347867 0.000831139839012877 *** df.mm.trans1:exp2 -0.617453331037603 0.15477180333337 -3.98944328191125 7.16164135509891e-05 *** df.mm.trans2:exp2 -0.930372995271444 0.115866058824295 -8.02972850472375 3.04836530561757e-15 *** df.mm.trans1:exp3 -0.445833359071298 0.15477180333337 -2.88058515484889 0.00406371853912687 ** df.mm.trans2:exp3 -0.655326026678428 0.115866058824295 -5.6558929623402 2.08288246289451e-08 *** df.mm.trans1:exp4 -0.338905881332249 0.15477180333337 -2.18971333300462 0.0288013971019403 * df.mm.trans2:exp4 -0.441703423673014 0.115866058824295 -3.81218993858101 0.000147130985076801 *** df.mm.trans1:exp5 0.135071563894645 0.15477180333337 0.872714286359436 0.383051858979119 df.mm.trans2:exp5 0.501204252398637 0.115866058824295 4.32572107383652 1.69058764920224e-05 *** df.mm.trans1:exp6 -0.602002292082765 0.15477180333337 -3.88961218463085 0.000107804362246975 *** df.mm.trans2:exp6 -0.881390205653832 0.115866058824295 -7.6069749381086 7.06886774556528e-14 *** df.mm.trans1:exp7 -0.586445629625061 0.15477180333337 -3.78909864067352 0.000161262727945292 *** df.mm.trans2:exp7 -0.857993543074034 0.115866058824295 -7.40504641117667 3.01570089585639e-13 *** df.mm.trans1:exp8 -0.576250132697091 0.15477180333337 -3.72322425846444 0.00020893556227597 *** df.mm.trans2:exp8 -0.777250256349574 0.115866058824295 -6.70817894590021 3.48186932443528e-11 *** df.mm.trans1:probe2 0.0566249634127091 0.117557623612676 0.481678360556816 0.63015168053225 df.mm.trans1:probe3 -0.0922768747620451 0.117557623612676 -0.78495015402893 0.432689593487599 df.mm.trans1:probe4 -0.0311807222573069 0.117557623612676 -0.265237772754235 0.790887103499646 df.mm.trans1:probe5 -0.110798233464338 0.117557623612676 -0.942501473399898 0.346189158358644 df.mm.trans1:probe6 0.0214120606464367 0.117557623612676 0.182140978937991 0.855513151669685 df.mm.trans1:probe7 0.0211020957203422 0.117557623612676 0.179504272643929 0.85758221188937 df.mm.trans1:probe8 -0.0565607836459433 0.117557623612676 -0.481132417513791 0.630539467325349 df.mm.trans1:probe9 -0.0942372424791686 0.117557623612676 -0.80162595655776 0.422981186311986 df.mm.trans1:probe10 -0.0791103981926159 0.117557623612676 -0.672949960721097 0.501152068114709 df.mm.trans1:probe11 0.158582038289479 0.117557623612676 1.34897281363877 0.177685369967573 df.mm.trans1:probe12 -0.110979516765537 0.117557623612676 -0.944043553748477 0.345401029120549 df.mm.trans1:probe13 -0.033929637110482 0.117557623612676 -0.288621325166217 0.772937708801411 df.mm.trans1:probe14 0.0233465800667267 0.117557623612676 0.198596903792884 0.842622977810366 df.mm.trans2:probe2 0.238223569884154 0.117557623612676 2.02644084290988 0.0430144289260736 * df.mm.trans2:probe3 -0.0936550647687583 0.117557623612676 -0.796673681303129 0.425850908978466 df.mm.trans2:probe4 0.0750662669191374 0.117557623612676 0.638548693077214 0.523279201779596 df.mm.trans2:probe5 0.111485577456934 0.117557623612676 0.948348342122432 0.343206998899284 df.mm.trans2:probe6 -0.0523869851858578 0.117557623612676 -0.445628140276637 0.655973172238547 df.mm.trans3:probe2 0.161301551377153 0.117557623612676 1.37210626091424 0.170372628389778 df.mm.trans3:probe3 -1.74386277263131 0.117557623612676 -14.8341104476296 1.07930725669014e-44 *** df.mm.trans3:probe4 -1.68723780921860 0.117557623612676 -14.3524320870728 3.25545245422001e-42 *** df.mm.trans3:probe5 -1.83613964739335 0.117557623612676 -15.6190606016585 7.75207614571753e-49 *** df.mm.trans3:probe6 -1.77504349488861 0.117557623612676 -15.0993482203838 4.43526407369312e-46 *** df.mm.trans3:probe7 -1.85466100609564 0.117557623612676 -15.7766119210295 1.10410428929565e-49 *** df.mm.trans3:probe8 -1.72245071198487 0.117557623612676 -14.6519694686916 9.4746640163858e-44 *** df.mm.trans3:probe9 -1.72276067691096 0.117557623612676 -14.6546061749856 9.18241414203049e-44 *** df.mm.trans3:probe10 -1.80042355627725 0.117557623612676 -15.3152428651434 3.22074603665639e-47 *** df.mm.trans3:probe11 -1.42301629173545 0.117557623612676 -12.1048405709862 2.35772862222950e-31 *** df.mm.trans3:probe12 -0.772060526106728 0.117557623612676 -6.56750708614594 8.64544717752507e-11 *** df.mm.trans3:probe13 -0.661568830535347 0.117557623612676 -5.62761316709714 2.44100995314760e-08 *** df.mm.trans3:probe14 -1.18847651079356 0.117557623612676 -10.1097357557116 7.86803512263959e-23 *** df.mm.trans3:probe15 -0.816190989587429 0.117557623612676 -6.94290140022377 7.35691510958199e-12 *** df.mm.trans3:probe16 -0.338408059948667 0.117557623612676 -2.87865686247316 0.00408841418994661 ** df.mm.trans3:probe17 -0.761739905043651 0.117557623612676 -6.47971506767948 1.51219609245305e-10 *** df.mm.trans3:probe18 -0.774888042651498 0.117557623612676 -6.59155926122296 7.40914702655924e-11 *** df.mm.trans3:probe19 -1.53474323136384 0.117557623612676 -13.0552420523610 8.5214736498931e-36 *** df.mm.trans3:probe20 -0.791430624878992 0.117557623612676 -6.7322781845826 2.97454868050134e-11 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.70969770184398 0.349998214279265 13.4563478032093 9.73044934126275e-38 *** df.mm.trans1 -0.282251149341779 0.293037910135326 -0.96318987946452 0.335711247264222 df.mm.trans2 -0.546263532078407 0.263803550245577 -2.07072092687868 0.0386698130548269 * df.mm.exp2 -0.212357459457334 0.335495143006902 -0.632967313786015 0.526915958480065 df.mm.exp3 -0.129302303588306 0.335495143006902 -0.385407378567165 0.700026789059931 df.mm.exp4 -0.481810536440086 0.335495143006902 -1.43611776946105 0.151316700984816 df.mm.exp5 -0.0650058035738669 0.335495143006902 -0.193760788878334 0.846406961175329 df.mm.exp6 0.00603009063268827 0.335495143006902 0.0179737047119166 0.985663818737816 df.mm.exp7 -0.730068286102028 0.335495143006902 -2.17609196830313 0.0298081354456206 * df.mm.exp8 -0.154895130868872 0.335495143006902 -0.461691127569277 0.644414528702267 df.mm.trans1:exp2 0.180823577119362 0.290947244362971 0.621499535131447 0.53442856042782 df.mm.trans2:exp2 0.234279605196477 0.217810413809776 1.07561250676051 0.282389168710903 df.mm.trans1:exp3 -0.122871676730849 0.290947244362971 -0.422316000963944 0.6728953601058 df.mm.trans2:exp3 0.364717769515436 0.217810413809776 1.67447351637632 0.094385384170491 . df.mm.trans1:exp4 0.127613976461890 0.290947244362971 0.438615518566951 0.661045534316799 df.mm.trans2:exp4 0.507083393354325 0.217810413809776 2.32809526635942 0.0201280850259252 * df.mm.trans1:exp5 0.0251225164366809 0.290947244362971 0.0863473255836696 0.931209545729727 df.mm.trans2:exp5 0.201077371762590 0.217810413809776 0.92317611562044 0.356163134047986 df.mm.trans1:exp6 -0.104631931953329 0.290947244362971 -0.359625100359416 0.719211968338106 df.mm.trans2:exp6 0.205465601653395 0.217810413809776 0.94332313161499 0.345769081077023 df.mm.trans1:exp7 0.266887081916524 0.290947244362971 0.917304037372389 0.359229357302787 df.mm.trans2:exp7 0.697185270074679 0.217810413809776 3.2008812520946 0.00141815338086574 ** df.mm.trans1:exp8 -0.156537043677997 0.290947244362971 -0.538025524251778 0.590692595183386 df.mm.trans2:exp8 0.289788603760674 0.217810413809776 1.33046257381321 0.183703289890832 df.mm.trans1:probe2 0.125387760997165 0.220990296083169 0.567390347990553 0.570590658801506 df.mm.trans1:probe3 0.036884790197947 0.220990296083169 0.166906831891232 0.8674808907765 df.mm.trans1:probe4 0.0762825605714957 0.220990296083169 0.345185114113731 0.730035955136859 df.mm.trans1:probe5 0.406817460207374 0.220990296083169 1.84088381896312 0.0659679625095394 . df.mm.trans1:probe6 0.0835955187393432 0.220990296083169 0.378276875595851 0.705314226147026 df.mm.trans1:probe7 -0.110251382439352 0.220990296083169 -0.498896939790785 0.617974039515806 df.mm.trans1:probe8 -0.0761138065955618 0.220990296083169 -0.344421487932288 0.73060988205661 df.mm.trans1:probe9 -0.099984548279151 0.220990296083169 -0.45243863667897 0.651062171630193 df.mm.trans1:probe10 -0.189688613952183 0.220990296083169 -0.858357209860446 0.390924037292973 df.mm.trans1:probe11 0.151158668070253 0.220990296083169 0.684005907722597 0.494147706199087 df.mm.trans1:probe12 -0.0735693170732912 0.220990296083169 -0.332907455111077 0.739281759087274 df.mm.trans1:probe13 0.145476358764284 0.220990296083169 0.658292971875719 0.510518433364263 df.mm.trans1:probe14 0.132482924830143 0.220990296083169 0.599496571470646 0.548992848516837 df.mm.trans2:probe2 -0.0568361720741028 0.220990296083169 -0.25718854212817 0.797092080710478 df.mm.trans2:probe3 -0.0824256198058787 0.220990296083169 -0.372982982813228 0.709249032044226 df.mm.trans2:probe4 -0.217441542769262 0.220990296083169 -0.983941587586402 0.325408960576327 df.mm.trans2:probe5 -0.260411777300720 0.220990296083169 -1.17838557582055 0.238954736138294 df.mm.trans2:probe6 0.31424244656736 0.220990296083169 1.42197396056294 0.155380644015615 df.mm.trans3:probe2 0.282380732566360 0.220990296083169 1.27779697828943 0.201650869513632 df.mm.trans3:probe3 0.253021848075554 0.220990296083169 1.14494551371763 0.252536447136435 df.mm.trans3:probe4 0.10757988486574 0.220990296083169 0.486808184669126 0.626512930562974 df.mm.trans3:probe5 0.112018268425785 0.220990296083169 0.506892250072497 0.612354726685474 df.mm.trans3:probe6 0.374703555692499 0.220990296083169 1.69556565303429 0.0903144200680736 . df.mm.trans3:probe7 0.118916273322984 0.220990296083169 0.538106312497226 0.590636845770384 df.mm.trans3:probe8 0.271622678941237 0.220990296083169 1.22911586506501 0.219349980020331 df.mm.trans3:probe9 0.134642168368415 0.220990296083169 0.609267333248621 0.542501192397829 df.mm.trans3:probe10 0.328541299370969 0.220990296083169 1.48667749305754 0.137450624512422 df.mm.trans3:probe11 0.342447713968942 0.220990296083169 1.54960520909055 0.121588243719996 df.mm.trans3:probe12 0.268921804924395 0.220990296083169 1.21689417902398 0.223963841759011 df.mm.trans3:probe13 0.136249188880210 0.220990296083169 0.616539238577848 0.537694771777284 df.mm.trans3:probe14 0.270831244013132 0.220990296083169 1.22553455429195 0.220694839217002 df.mm.trans3:probe15 0.125064573353663 0.220990296083169 0.565927896248417 0.571584024537603 df.mm.trans3:probe16 0.069214363056414 0.220990296083169 0.313200915529637 0.754200669785592 df.mm.trans3:probe17 0.34428378125205 0.220990296083169 1.55791357065959 0.119605681842233 df.mm.trans3:probe18 0.482374922448212 0.220990296083169 2.18278780108368 0.0293095293404071 * df.mm.trans3:probe19 0.274906946969633 0.220990296083169 1.24397745892957 0.213832111481646 df.mm.trans3:probe20 0.262015466253717 0.220990296083169 1.18564240556114 0.236076833503544