chr1.361_chr1_132709145_132711631_+_1.R fitVsDatCorrelation=0.863647235287587 cont.fitVsDatCorrelation=0.250874292903585 fstatistic=9271.058595795,49,623 cont.fstatistic=2504.86871214006,49,623 residuals=-0.578893184438102,-0.115690490226895,-0.0073004599032457,0.098486963977882,0.819636432583783 cont.residuals=-0.817757913609764,-0.220277279458232,-0.0134263203958599,0.207667061899535,1.5009611977155 predictedValues: Include Exclude Both chr1.361_chr1_132709145_132711631_+_1.R.tl.Lung 141.323348697204 84.3095506523849 96.5700797788849 chr1.361_chr1_132709145_132711631_+_1.R.tl.cerebhem 184.093874146799 90.0075541510078 66.9811983207445 chr1.361_chr1_132709145_132711631_+_1.R.tl.cortex 118.110303801920 72.9118078208414 78.5113051099375 chr1.361_chr1_132709145_132711631_+_1.R.tl.heart 119.232698793531 74.4358658295074 87.1894169490623 chr1.361_chr1_132709145_132711631_+_1.R.tl.kidney 144.278593736135 91.4322807621756 104.669685073479 chr1.361_chr1_132709145_132711631_+_1.R.tl.liver 135.139612040574 89.2548822423477 96.0274796421512 chr1.361_chr1_132709145_132711631_+_1.R.tl.stomach 120.923913812193 78.3449871319687 85.0879353730693 chr1.361_chr1_132709145_132711631_+_1.R.tl.testicle 124.896755337802 78.6027591904701 90.9536202834914 diffExp=57.013798044819,94.0863199957916,45.1984959810788,44.7968329640238,52.8463129739593,45.8847297982264,42.5789266802246,46.2939961473318 diffExpScore=0.997672791791864 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 105.880190162621 95.9088264458584 90.4745857031704 cerebhem 92.5288872169576 91.7117649687386 94.8167252048247 cortex 89.7748279295472 93.4414041890745 89.7100616089658 heart 103.000524208903 97.9857434979688 100.444265941442 kidney 88.0319017542614 83.820945299775 82.7369202551338 liver 92.4961931795262 103.850129652452 98.6694317875866 stomach 108.824352072092 106.899699091025 86.1293047304513 testicle 107.584957485322 87.9098392673308 94.264384975696 cont.diffExp=9.97136371676243,0.817122248218993,-3.66657625952725,5.01478071093457,4.21095645448644,-11.3539364729255,1.92465298106745,19.6751182179912 cont.diffExpScore=2.05245963119254 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=0,0,0,0,0,0,0,1 cont.diffExp1.2Score=0.5 tran.correlation=0.758900299238818 cont.tran.correlation=0.376441281863029 tran.covariance=0.0105091084112774 cont.tran.covariance=0.00273863441696142 tran.mean=109.206174259179 cont.tran.mean=96.8531366513408 weightedLogRatios: wLogRatio Lung 2.42407698867991 cerebhem 3.47591630078749 cortex 2.18533842802722 heart 2.14156648891298 kidney 2.16381434124335 liver 1.94916238620160 stomach 1.98709459007900 testicle 2.12829411433215 cont.weightedLogRatios: wLogRatio Lung 0.456258027054302 cerebhem 0.0401207682504710 cortex -0.180827956288632 heart 0.230083628745871 kidney 0.218279166844390 liver -0.530864037971823 stomach 0.083525038798305 testicle 0.924472421264005 varWeightedLogRatios=0.243581030289074 cont.varWeightedLogRatios=0.184854525135129 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.64962745793426 0.0854836192917299 54.3920285132813 8.66839106522492e-239 *** df.mm.trans1 0.392685843752706 0.0675563331164749 5.81271696727043 9.81771797357673e-09 *** df.mm.trans2 -0.258991445503847 0.0675563331164749 -3.83371082408093 0.000139046301190456 *** df.mm.exp2 0.695650746809637 0.0895529318665496 7.76803988780943 3.29454258462273e-14 *** df.mm.exp3 -0.117649774161696 0.0895529318665496 -1.31374564416290 0.189415380635308 df.mm.exp4 -0.192344739421110 0.0895529318665496 -2.14783296774403 0.0321120407507752 * df.mm.exp5 0.0212584612566587 0.0895529318665496 0.237384313540262 0.812436699336416 df.mm.exp6 0.0178934225352946 0.0895529318665496 0.199808338625463 0.841695679595244 df.mm.exp7 -0.10267844304703 0.0895529318665495 -1.14656707387358 0.252000759416898 df.mm.exp8 -0.133732177808164 0.0895529318665496 -1.49333109503830 0.135856793196293 df.mm.trans1:exp2 -0.431255451778586 0.068139564079076 -6.32900221196182 4.71884460416642e-10 *** df.mm.trans2:exp2 -0.630252297203688 0.068139564079076 -9.2494324805524 3.55553073694986e-19 *** df.mm.trans1:exp3 -0.0617817780810736 0.068139564079076 -0.90669464819845 0.364918841240764 df.mm.trans2:exp3 -0.0275947793019979 0.068139564079076 -0.404974403269944 0.685635341026622 df.mm.trans1:exp4 0.0223712571135809 0.068139564079076 0.328315236763462 0.74278364105924 df.mm.trans2:exp4 0.0677874804906378 0.068139564079076 0.99483290518223 0.320203768976612 df.mm.trans1:exp5 -0.000562870096006413 0.068139564079076 -0.00826054735767317 0.993411756162966 df.mm.trans2:exp5 0.0598449838418982 0.068139564079076 0.87827071761786 0.380135409846036 df.mm.trans1:exp6 -0.0626355332697416 0.068139564079076 -0.919224155837761 0.358334097812458 df.mm.trans2:exp6 0.0391075474106236 0.068139564079076 0.573933043734169 0.566220251654005 df.mm.trans1:exp7 -0.0532105386685061 0.068139564079076 -0.780905181705526 0.435154885705988 df.mm.trans2:exp7 0.0293052771419427 0.068139564079076 0.430077261837688 0.667288162658078 df.mm.trans1:exp8 0.0101690984642605 0.068139564079076 0.149239265053990 0.881413134485235 df.mm.trans2:exp8 0.0636438286184735 0.068139564079076 0.934021658028437 0.350654582547556 df.mm.trans1:probe2 -0.315074352217924 0.0503244032230515 -6.26086614125215 7.13145926888797e-10 *** df.mm.trans1:probe3 -0.340419996646773 0.0503244032230515 -6.76451134726703 3.08700176323494e-11 *** df.mm.trans1:probe4 -0.468498717619354 0.0503244032230515 -9.30957324109418 2.16548715870808e-19 *** df.mm.trans1:probe5 -0.445667887764729 0.0503244032230515 -8.85590010455579 8.59836484561133e-18 *** df.mm.trans1:probe6 -0.43812028558955 0.0503244032230515 -8.70592113427915 2.81700761258969e-17 *** df.mm.trans2:probe2 -0.0979510129659474 0.0503244032230515 -1.94639194292681 0.0520568481290532 . df.mm.trans2:probe3 0.217613258192801 0.0503244032230515 4.3242094144322 1.78221638814911e-05 *** df.mm.trans2:probe4 0.334382324962545 0.0503244032230515 6.64453631929764 6.64200220959077e-11 *** df.mm.trans2:probe5 0.353991735771487 0.0503244032230515 7.03419639578236 5.29318376875619e-12 *** df.mm.trans2:probe6 0.156864769259765 0.0503244032230515 3.11707162357192 0.00191068032428300 ** df.mm.trans3:probe2 -0.55855737009647 0.0503244032230515 -11.0991354953737 3.03735648996557e-26 *** df.mm.trans3:probe3 -0.283525760509636 0.0503244032230515 -5.63396170348951 2.66865765304493e-08 *** df.mm.trans3:probe4 0.198111860174033 0.0503244032230515 3.93669566822179 9.19255881375372e-05 *** df.mm.trans3:probe5 -0.661610663554414 0.0503244032230515 -13.1469152375633 5.11927037636514e-35 *** df.mm.trans3:probe6 -0.172427833339882 0.0503244032230515 -3.42632643999045 0.000652058456043838 *** df.mm.trans3:probe7 -0.603518350230026 0.0503244032230515 -11.9925585119225 5.83126003579504e-30 *** df.mm.trans3:probe8 -0.290443276475469 0.0503244032230515 -5.77142018332825 1.23982327514708e-08 *** df.mm.trans3:probe9 0.0368821839915109 0.0503244032230515 0.732888651019646 0.463902001349654 df.mm.trans3:probe10 0.170396541912244 0.0503244032230515 3.38596249531265 0.000753899510282897 *** df.mm.trans3:probe11 -0.273472317421447 0.0503244032230515 -5.43418898003308 7.90651489209836e-08 *** df.mm.trans3:probe12 0.248819955260978 0.0503244032230515 4.94432003809643 9.8374309589606e-07 *** df.mm.trans3:probe13 -0.454500430289618 0.0503244032230515 -9.03141222112754 2.10337576319726e-18 *** df.mm.trans3:probe14 -0.202763315536174 0.0503244032230515 -4.02912508743465 6.29051602616042e-05 *** df.mm.trans3:probe15 -0.614646009029538 0.0503244032230515 -12.2136770565417 6.58329993010169e-31 *** df.mm.trans3:probe16 0.0524084478687185 0.0503244032230515 1.04141220783941 0.298088241805452 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.74012970952686 0.164154675392572 28.8759957533403 5.09295762787577e-117 *** df.mm.trans1 -0.0533277665411745 0.129728806820891 -0.411071124818107 0.681161740890846 df.mm.trans2 -0.168275603984071 0.129728806820891 -1.29713367530158 0.19506534949031 df.mm.exp2 -0.226411459156575 0.171968999239937 -1.31658298970897 0.188462570827738 df.mm.exp3 -0.182580960967917 0.171968999239937 -1.06170857407371 0.288779398068335 df.mm.exp4 -0.110684133461703 0.171968999239937 -0.643628409486021 0.520053274916482 df.mm.exp5 -0.229920948234699 0.171968999239937 -1.33699067419649 0.181713661924994 df.mm.exp6 -0.142296101241799 0.171968999239937 -0.827452051652997 0.408297717332171 df.mm.exp7 0.185139227550554 0.171968999239937 1.07658489825972 0.282082466539008 df.mm.exp8 -0.112148066042631 0.171968999239937 -0.652141179737625 0.514550626075143 df.mm.trans1:exp2 0.0916241757811312 0.130848788521922 0.700229454289373 0.484045421462035 df.mm.trans2:exp2 0.181664112977530 0.130848788521922 1.38835150886471 0.165526244296231 df.mm.trans1:exp3 0.0175774110150854 0.130848788521922 0.134333769640829 0.893182035714242 df.mm.trans2:exp3 0.156517491971888 0.130848788521922 1.19617073829969 0.232084979458319 df.mm.trans1:exp4 0.0831100377077091 0.130848788521922 0.63516092618454 0.525556670065392 df.mm.trans2:exp4 0.132108111367862 0.130848788521922 1.00962426064593 0.313067299197706 df.mm.trans1:exp5 0.0453120435757116 0.130848788521922 0.346293183815913 0.729239310005287 df.mm.trans2:exp5 0.0952058527319095 0.130848788521922 0.727602095574303 0.467130543895888 df.mm.trans1:exp6 0.00715541671985623 0.130848788521922 0.0546846233785147 0.956407251220836 df.mm.trans2:exp6 0.221846884350545 0.130848788521922 1.69544469502961 0.0904907455755725 . df.mm.trans1:exp7 -0.157712267359092 0.130848788521922 -1.20530170084585 0.228544460294583 df.mm.trans2:exp7 -0.0766462400590904 0.130848788521922 -0.585761938837127 0.558247634655924 df.mm.trans1:exp8 0.128120730297545 0.130848788521922 0.979151062419503 0.327885410559588 df.mm.trans2:exp8 0.0250617858550629 0.130848788521922 0.191532425620158 0.848170884647714 df.mm.trans1:probe2 -0.205691243873395 0.0966382348320169 -2.12846648359153 0.0336901934238484 * df.mm.trans1:probe3 -0.0661957042688061 0.0966382348320169 -0.684984616946615 0.493608392385918 df.mm.trans1:probe4 -0.132238886031546 0.0966382348320169 -1.36839095065646 0.171683000328426 df.mm.trans1:probe5 -0.097224613286936 0.0966382348320169 -1.00606776868326 0.314773559919777 df.mm.trans1:probe6 -0.037512483919203 0.0966382348320169 -0.388174349256376 0.69801961760733 df.mm.trans2:probe2 -0.0157321219836154 0.0966382348320169 -0.162793970843549 0.870733444863597 df.mm.trans2:probe3 -0.0370645123026037 0.0966382348320169 -0.383538796699171 0.701451156361361 df.mm.trans2:probe4 -0.0901468550545327 0.0966382348320169 -0.932828038625002 0.351270133323133 df.mm.trans2:probe5 -0.0118821444342413 0.0966382348320169 -0.122954899320085 0.902182465682592 df.mm.trans2:probe6 -0.0312083433944778 0.0966382348320169 -0.322939915538878 0.746849103025502 df.mm.trans3:probe2 0.0176810380208789 0.0966382348320169 0.182961102834849 0.854888062853586 df.mm.trans3:probe3 0.0546200678401341 0.0966382348320169 0.565201422967612 0.572140241511799 df.mm.trans3:probe4 0.0249862303686865 0.0966382348320169 0.258554291809234 0.796064550347171 df.mm.trans3:probe5 0.0295031218649069 0.0966382348320169 0.305294502907583 0.760243833939323 df.mm.trans3:probe6 0.0183486319938163 0.0966382348320169 0.189869279232088 0.849473411893011 df.mm.trans3:probe7 -0.0946349704541106 0.0966382348320169 -0.979270478383753 0.327826466255377 df.mm.trans3:probe8 -0.0519719241984208 0.0966382348320169 -0.537798773836897 0.590908033128844 df.mm.trans3:probe9 -0.0251505837858069 0.0966382348320169 -0.260254999788907 0.794753072927375 df.mm.trans3:probe10 0.0201512927560865 0.0966382348320169 0.208522980486087 0.834888796319987 df.mm.trans3:probe11 -0.052023432530772 0.0966382348320169 -0.538331775422043 0.590540293205989 df.mm.trans3:probe12 -0.0443768107848945 0.0966382348320169 -0.459205519037401 0.646246765551371 df.mm.trans3:probe13 0.0217202892036659 0.0966382348320169 0.224758753524643 0.822240652288559 df.mm.trans3:probe14 -0.0428835240059946 0.0966382348320169 -0.44375317989342 0.657375118130272 df.mm.trans3:probe15 -0.0667521578268444 0.0966382348320169 -0.690742726653457 0.489984503004855 df.mm.trans3:probe16 -0.0245348475455644 0.0966382348320169 -0.253883440526542 0.799669373981207