chr18.11275_chr18_36843658_36845019_+_1.R fitVsDatCorrelation=0.831132714020993 cont.fitVsDatCorrelation=0.249224848040912 fstatistic=11217.4093274525,47,577 cont.fstatistic=3690.11584279172,47,577 residuals=-0.503180055934275,-0.0835847706427111,-0.00147668671307524,0.0881643515483809,0.733996527476848 cont.residuals=-0.493918092153652,-0.165293776912848,-0.0274309958619169,0.152805207611639,1.16624261428328 predictedValues: Include Exclude Both chr18.11275_chr18_36843658_36845019_+_1.R.tl.Lung 56.5186540503876 49.9208017523016 73.6159657898142 chr18.11275_chr18_36843658_36845019_+_1.R.tl.cerebhem 66.0201319895612 51.0290807556726 74.3032346522401 chr18.11275_chr18_36843658_36845019_+_1.R.tl.cortex 55.9556578391701 46.6773402435619 70.4149439665469 chr18.11275_chr18_36843658_36845019_+_1.R.tl.heart 54.4846574961241 49.8523915715664 71.4417390122016 chr18.11275_chr18_36843658_36845019_+_1.R.tl.kidney 50.7374498119726 50.003483701769 64.548238973151 chr18.11275_chr18_36843658_36845019_+_1.R.tl.liver 50.939010396602 55.2499491952079 62.0675991509484 chr18.11275_chr18_36843658_36845019_+_1.R.tl.stomach 54.9522711752237 47.3134774490556 68.2979898928892 chr18.11275_chr18_36843658_36845019_+_1.R.tl.testicle 59.269045839825 50.9040331314955 75.5108077355809 diffExp=6.59785229808607,14.9910512338887,9.27831759560823,4.63226592455768,0.733966110203546,-4.3109387986059,7.63879372616812,8.36501270832942 diffExpScore=1.15578276626692 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=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 62.2451308401739 61.5328642870099 61.5181925052149 cerebhem 58.0805243078937 62.6109135111374 61.8800089688887 cortex 56.2402176767436 59.1978524589041 63.2669520859402 heart 56.7667664599447 66.1899068650096 61.5789478755249 kidney 60.0864652994563 66.524740070835 64.3431760121431 liver 54.3870932599221 60.9215519129815 61.3817750997352 stomach 63.829845933217 61.5300044552357 63.6214912413893 testicle 56.1524171879568 64.9739944316086 62.6528804044044 cont.diffExp=0.712266553163978,-4.53038920324366,-2.95763478216052,-9.42314040506486,-6.43827477137864,-6.53445865305937,2.29984147798129,-8.82157724365176 cont.diffExpScore=1.13692436724427 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=-0.114965606661445 cont.tran.correlation=-0.00995644454855438 tran.covariance=-0.000559739010058425 cont.tran.covariance=1.47026131938397e-05 tran.mean=53.1142147749686 cont.tran.mean=60.7043930598769 weightedLogRatios: wLogRatio Lung 0.493118709637875 cerebhem 1.04601335491064 cortex 0.713220590135199 heart 0.351278421947598 kidney 0.0571116780770498 liver -0.322617766305523 stomach 0.588446374463944 testicle 0.609495182997447 cont.weightedLogRatios: wLogRatio Lung 0.0474779715438695 cerebhem -0.307901537334110 cortex -0.207844770687538 heart -0.632082640837781 kidney -0.422087564582497 liver -0.459837686357274 stomach 0.151843187320087 testicle -0.598411301977936 varWeightedLogRatios=0.175930970556171 cont.varWeightedLogRatios=0.0818765532289462 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.41785111781939 0.0700831768561644 48.7684958236701 7.87064968474009e-207 *** df.mm.trans1 0.60055584957147 0.0555636923903044 10.8084222580619 6.36140377419286e-25 *** df.mm.trans2 0.492837745416759 0.0555636923903044 8.86978032264027 9.14518741867625e-18 *** df.mm.exp2 0.168054321151885 0.0738439959695689 2.27580210070349 0.0232235535237755 * df.mm.exp3 -0.032733732638638 0.0738439959695689 -0.443282249407623 0.657727749591455 df.mm.exp4 -0.00804325954847512 0.0738439959695689 -0.108922322564853 0.913301969105227 df.mm.exp5 0.0251975348844014 0.0738439959695689 0.341226589292180 0.733057228486059 df.mm.exp6 0.168125763577615 0.0738439959695689 2.27676957848949 0.0231652989451656 * df.mm.exp7 -0.00676673333052657 0.0738439959695689 -0.0916355248883761 0.927019429382366 df.mm.exp8 0.0416069392827966 0.0738439959695689 0.563443767316477 0.573351635184959 df.mm.trans1:exp2 -0.0126653393381950 0.0566357595452457 -0.223627959435714 0.823125958911464 df.mm.trans2:exp2 -0.146096424715899 0.0566357595452457 -2.57957915438891 0.01013834051192 * df.mm.trans1:exp3 0.0227225415694398 0.0566357595452457 0.401204852762449 0.68841784326984 df.mm.trans2:exp3 -0.0344452247430075 0.0566357595452457 -0.608188625341726 0.543301697470299 df.mm.trans1:exp4 -0.0286083360927355 0.0566357595452457 -0.505128496950423 0.613661464154687 df.mm.trans2:exp4 0.00667194548752193 0.0566357595452456 0.117804467373511 0.906263585883185 df.mm.trans1:exp5 -0.133103985735527 0.0566357595452456 -2.35017569825635 0.0191004032928768 * df.mm.trans2:exp5 -0.0235426425234174 0.0566357595452457 -0.415685120363036 0.677794977601182 df.mm.trans1:exp6 -0.272067464903679 0.0566357595452457 -4.80381065051891 1.98702823472352e-06 *** df.mm.trans2:exp6 -0.0666961273993434 0.0566357595452456 -1.17763278774536 0.239428530525279 df.mm.trans1:exp7 -0.0213389988501168 0.0566357595452456 -0.376776069067623 0.706478544571326 df.mm.trans2:exp7 -0.046875860935412 0.0566357595452457 -0.8276725042941 0.408198175542483 df.mm.trans1:exp8 0.00590949400690679 0.0566357595452457 0.104342098602664 0.916934128668081 df.mm.trans2:exp8 -0.0221025671656174 0.0566357595452456 -0.390258157444855 0.69648968759768 df.mm.trans1:probe2 -0.0259763148568723 0.0410365064438375 -0.633005026693085 0.526981151556555 df.mm.trans1:probe3 0.0764710585251586 0.0410365064438375 1.86348851673855 0.0629013419805598 . df.mm.trans1:probe4 0.125073998414919 0.0410365064438375 3.0478714991515 0.00241017337708854 ** df.mm.trans1:probe5 0.0708618181328266 0.0410365064438375 1.72679948352348 0.0847388940079651 . df.mm.trans1:probe6 0.0768449688741603 0.0410365064438375 1.87260016832403 0.0616290967795709 . df.mm.trans2:probe2 -0.104890490828891 0.0410365064438375 -2.55602876361915 0.0108425000664805 * df.mm.trans2:probe3 -0.0785825828130793 0.0410365064438375 -1.91494329373840 0.0559938581067253 . df.mm.trans2:probe4 -0.0996177041040603 0.0410365064438375 -2.42753861711881 0.0155065767729921 * df.mm.trans2:probe5 0.0761840456908735 0.0410365064438375 1.85649443124840 0.0638926174712159 . df.mm.trans2:probe6 0.201885160840650 0.0410365064438375 4.91964785347774 1.13250123837828e-06 *** df.mm.trans3:probe2 -0.220390851805459 0.0410365064438375 -5.37060463729012 1.13941734508598e-07 *** df.mm.trans3:probe3 -0.353647301207301 0.0410365064438375 -8.61787057071494 6.55534417188306e-17 *** df.mm.trans3:probe4 -0.449054372475084 0.0410365064438375 -10.9428021873563 1.87265483331174e-25 *** df.mm.trans3:probe5 -0.0561596310615492 0.0410365064438375 -1.36852855976932 0.171679284167961 df.mm.trans3:probe6 -0.141622904911417 0.0410365064438375 -3.45114429039524 0.000599045250263297 *** df.mm.trans3:probe7 -0.473314502265572 0.0410365064438375 -11.5339862791037 7.70943357709467e-28 *** df.mm.trans3:probe8 -0.137561011797387 0.0410365064438375 -3.35216186069965 0.000854319127818827 *** df.mm.trans3:probe9 0.0519997239371218 0.0410365064438375 1.26715767114066 0.205610112024726 df.mm.trans3:probe10 -0.246969716146674 0.0410365064438375 -6.01829291888375 3.13489055257854e-09 *** df.mm.trans3:probe11 -0.00838900248650706 0.0410365064438375 -0.204427794017706 0.838091298588077 df.mm.trans3:probe12 -0.414636441125832 0.0410365064438375 -10.1040872398167 3.28246886839575e-22 *** df.mm.trans3:probe13 -0.276351449449100 0.0410365064438375 -6.73428304203512 3.98950166171057e-11 *** df.mm.trans3:probe14 -0.24726151668774 0.0410365064438375 -6.0254036738274 3.00793199601405e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.16040326562204 0.122055220570128 34.0862377388572 2.45691943305965e-140 *** df.mm.trans1 -0.0554756113660301 0.0967684262416943 -0.573282149153393 0.566677023581731 df.mm.trans2 -0.0256116660077632 0.0967684262416942 -0.264669655201316 0.791358470974938 df.mm.exp2 -0.0577459521024618 0.128604975118970 -0.449018026316962 0.653587246847583 df.mm.exp3 -0.168164505097577 0.128604975118970 -1.30760497361794 0.191528093021650 df.mm.exp4 -0.0201597922843392 0.128604975118970 -0.156757483648590 0.875490814828857 df.mm.exp5 -0.00219114017362134 0.128604975118970 -0.0170377559001459 0.986412384878242 df.mm.exp6 -0.142717876266573 0.128604975118970 -1.10973837625292 0.267574060959541 df.mm.exp7 -0.00852428656967761 0.128604975118970 -0.0662827123273573 0.947175705879173 df.mm.exp8 -0.066871602644007 0.128604975118970 -0.519976793915986 0.603279179397226 df.mm.trans1:exp2 -0.0115039629325360 0.098635513307837 -0.1166310444052 0.907193018081455 df.mm.trans2:exp2 0.075114141518546 0.098635513307837 0.76153242376423 0.446650254478167 df.mm.trans1:exp3 0.0667163102388002 0.098635513307837 0.67639238648844 0.499062586417703 df.mm.trans2:exp3 0.129478359563906 0.098635513307837 1.31269514621788 0.189807566893859 df.mm.trans1:exp4 -0.0719694636116595 0.098635513307837 -0.729650621749654 0.465899868065509 df.mm.trans2:exp4 0.0931163685950486 0.098635513307837 0.944045055095284 0.345541748041228 df.mm.trans1:exp5 -0.0331045595018357 0.098635513307837 -0.335625155602099 0.737275549578967 df.mm.trans2:exp5 0.080193639055765 0.098635513307837 0.813030077772133 0.416536154384323 df.mm.trans1:exp6 0.00776443287676037 0.098635513307837 0.0787184312867914 0.937283876164656 df.mm.trans2:exp6 0.132733467841184 0.098635513307837 1.34569652845957 0.178928940428115 df.mm.trans1:exp7 0.0336648592792829 0.098635513307837 0.341305663146056 0.732997737112058 df.mm.trans2:exp7 0.00847780899599624 0.098635513307837 0.0859508782555567 0.931535282315167 df.mm.trans1:exp8 -0.0361389811871977 0.098635513307837 -0.366389142969323 0.714209026552974 df.mm.trans2:exp8 0.121287296082845 0.098635513307837 1.2296513904106 0.219328835506205 df.mm.trans1:probe2 0.0824623779538877 0.0714682191948824 1.15383283483007 0.249046409044331 df.mm.trans1:probe3 0.111946614182947 0.0714682191948824 1.56638314825903 0.117807180404896 df.mm.trans1:probe4 0.17113252247127 0.0714682191948824 2.3945261879916 0.0169599217998847 * df.mm.trans1:probe5 0.140289621664491 0.0714682191948824 1.96296512274839 0.0501302040672727 . df.mm.trans1:probe6 0.0172220356184408 0.0714682191948824 0.240974741115055 0.809660294985079 df.mm.trans2:probe2 -0.092184787933185 0.0714682191948824 -1.28987106397337 0.19761216120204 df.mm.trans2:probe3 -0.0743014651401984 0.0714682191948824 -1.03964343840148 0.298941032320165 df.mm.trans2:probe4 -0.0395767257696277 0.0714682191948824 -0.553766782151214 0.579952990730026 df.mm.trans2:probe5 -0.112186084388025 0.0714682191948824 -1.56973387124859 0.117025231324683 df.mm.trans2:probe6 0.0138452856178097 0.0714682191948824 0.193726467145569 0.846458245147422 df.mm.trans3:probe2 0.0160769794075478 0.0714682191948824 0.224952847414716 0.822095599559155 df.mm.trans3:probe3 0.096873268947124 0.0714682191948824 1.35547338437196 0.17579711142433 df.mm.trans3:probe4 0.00367417149992509 0.0714682191948824 0.0514098649905661 0.959016716203205 df.mm.trans3:probe5 0.0717917347750647 0.0714682191948824 1.00452670548989 0.315545876555843 df.mm.trans3:probe6 -0.0520568274114147 0.0714682191948824 -0.728391276540193 0.466669604937969 df.mm.trans3:probe7 0.109369505813554 0.0714682191948824 1.53032364658927 0.126484672177558 df.mm.trans3:probe8 0.0844921207768124 0.0714682191948824 1.18223347004654 0.237600036418031 df.mm.trans3:probe9 0.0254298766381858 0.0714682191948824 0.355820767953411 0.722104845431237 df.mm.trans3:probe10 0.0660966011808737 0.0714682191948824 0.924839067287221 0.355436242785875 df.mm.trans3:probe11 -0.0060271118167466 0.0714682191948824 -0.0843327549593986 0.93282112136018 df.mm.trans3:probe12 0.0395620241839338 0.0714682191948824 0.553561074133588 0.580093705707214 df.mm.trans3:probe13 0.0853783246850777 0.0714682191948824 1.19463344192563 0.232721107588659 df.mm.trans3:probe14 0.0195767141032405 0.0714682191948824 0.273921951935837 0.784242631571549