chr11.4295_chr11_107979998_107980518_-_0.R fitVsDatCorrelation=0.861589036323673 cont.fitVsDatCorrelation=0.301022752134268 fstatistic=6260.43979998603,36,324 cont.fstatistic=1767.37687384881,36,324 residuals=-0.527514533428278,-0.0857260505073778,0.00120416337993309,0.082428183272053,1.05431373723684 cont.residuals=-0.592564659702458,-0.206018653503229,-0.0393128695214485,0.158661467137354,1.53465373263329 predictedValues: Include Exclude Both chr11.4295_chr11_107979998_107980518_-_0.R.tl.Lung 65.7844911758677 45.53036261914 68.5648017721569 chr11.4295_chr11_107979998_107980518_-_0.R.tl.cerebhem 104.912799008338 57.7585339251996 66.2938090145813 chr11.4295_chr11_107979998_107980518_-_0.R.tl.cortex 66.4258622358291 45.2214579696362 72.5805873871256 chr11.4295_chr11_107979998_107980518_-_0.R.tl.heart 64.1094661434791 46.6861566353591 74.5725540124696 chr11.4295_chr11_107979998_107980518_-_0.R.tl.kidney 59.9090194007485 46.6212466410274 64.6252870196519 chr11.4295_chr11_107979998_107980518_-_0.R.tl.liver 59.6449964563925 48.9875770813046 63.3422050800342 chr11.4295_chr11_107979998_107980518_-_0.R.tl.stomach 63.3362903793137 47.069907414097 65.0349223323782 chr11.4295_chr11_107979998_107980518_-_0.R.tl.testicle 72.3815235923453 51.8254029312922 69.3628161613135 diffExp=20.2541285567277,47.1542650831383,21.2044042661929,17.4233095081201,13.2877727597211,10.6574193750878,16.2663829652167,20.5561206610531 diffExpScore=0.994040659501885 diffExp1.5=0,1,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=1,1,1,0,0,0,0,0 diffExp1.4Score=0.75 diffExp1.3=1,1,1,1,0,0,1,1 diffExp1.3Score=0.857142857142857 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 56.4817343539918 54.8792533962397 61.5768088997876 cerebhem 58.0539754758968 54.1885808426642 51.8771993572408 cortex 61.9846689978979 57.4010962122409 55.2173829352807 heart 61.0186252113155 59.643218465987 57.388930155849 kidney 56.7967175793462 55.1885800586976 81.2387237563236 liver 62.4718022936818 55.5808520463417 63.7436030271777 stomach 58.831029220223 56.7455438056193 63.271866862688 testicle 59.5512328586231 52.8923420533254 67.0407228231018 cont.diffExp=1.60248095775209,3.86539463323260,4.58357278565708,1.37540674532843,1.60813752064866,6.89095024734015,2.08548541460366,6.6588908052977 cont.diffExpScore=0.966296284300236 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=0.888678979626076 cont.tran.correlation=0.455845112905182 tran.covariance=0.0130898695052773 cont.tran.covariance=0.00064303849686838 tran.mean=59.1378183505856 cont.tran.mean=57.6068283045057 weightedLogRatios: wLogRatio Lung 1.47289530199962 cerebhem 2.59913949516928 cortex 1.53953139817124 heart 1.26921795444193 kidney 0.99491811519423 liver 0.785403406303439 stomach 1.18731204420926 testicle 1.37467251644377 cont.weightedLogRatios: wLogRatio Lung 0.115689689904880 cerebhem 0.277466940295431 cortex 0.31409171080199 heart 0.093469631600428 kidney 0.115611375371664 liver 0.476421143725986 stomach 0.14641283797861 testicle 0.477580304086504 varWeightedLogRatios=0.295350502562289 cont.varWeightedLogRatios=0.0256025834103348 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.41252311194511 0.0922746482132076 36.982239195973 2.58627530242975e-118 *** df.mm.trans1 0.757865042895927 0.0781556631226858 9.69686664555913 1.09306038378498e-19 *** df.mm.trans2 0.475808160495272 0.0781556631226858 6.08795500523572 3.23225632465513e-09 *** df.mm.exp2 0.73831989859349 0.108841237088482 6.78345743160998 5.58176863325664e-11 *** df.mm.exp3 -0.0540235330268372 0.108841237088483 -0.496351699704761 0.619982858272749 df.mm.exp4 -0.0847170296231038 0.108841237088483 -0.778354159593326 0.436928298495618 df.mm.exp5 -0.0107065508089297 0.108841237088482 -0.0983685145017768 0.921700503086575 df.mm.exp6 0.054440927946514 0.108841237088482 0.50018659657696 0.617283247506702 df.mm.exp7 0.0481837502620069 0.108841237088482 0.442697561612939 0.658279907294997 df.mm.exp8 0.213496348718274 0.108841237088483 1.96153915950728 0.0506721553533733 . df.mm.trans1:exp2 -0.271574493250210 0.0942592762979509 -2.88114341544244 0.00422656613820573 ** df.mm.trans2:exp2 -0.500428200227652 0.0942592762979509 -5.30906049655859 2.04988656224838e-07 *** df.mm.trans1:exp3 0.0637258910454283 0.0942592762979509 0.676070234657781 0.499478343162697 df.mm.trans2:exp3 0.0472158273409223 0.0942592762979509 0.500914384189354 0.616771499256735 df.mm.trans1:exp4 0.0589249462567062 0.0942592762979509 0.625136841391039 0.532321275714151 df.mm.trans2:exp4 0.109785304847190 0.0942592762979509 1.16471618666116 0.244990735951609 df.mm.trans1:exp5 -0.0828504952369806 0.0942592762979509 -0.878963837735106 0.380072277203529 df.mm.trans2:exp5 0.0343835108206491 0.0942592762979509 0.364775883828810 0.71551668469219 df.mm.trans1:exp6 -0.152414778759372 0.0942592762979508 -1.61697378492057 0.106857099159380 df.mm.trans2:exp6 0.0187463953520977 0.0942592762979509 0.198881172107039 0.842480440063699 df.mm.trans1:exp7 -0.0861093917950158 0.0942592762979509 -0.913537586718006 0.361639167168801 df.mm.trans2:exp7 -0.0149292755493215 0.0942592762979509 -0.158385212953794 0.874251936489305 df.mm.trans1:exp8 -0.117929395031286 0.0942592762979509 -1.25111712780941 0.211794382108974 df.mm.trans2:exp8 -0.083995329297769 0.0942592762979509 -0.891109422824998 0.373531723074002 df.mm.trans1:probe2 0.0702511975705977 0.0471296381489754 1.49059488529353 0.137040759560914 df.mm.trans1:probe3 0.068305848951463 0.0471296381489754 1.44931834052174 0.148215891096653 df.mm.trans1:probe4 0.0416861663967888 0.0471296381489754 0.884500030851499 0.377082240780283 df.mm.trans1:probe5 -0.0740718356748081 0.0471296381489754 -1.57166145516902 0.117005088203573 df.mm.trans1:probe6 0.0377922564027594 0.0471296381489754 0.801878772828665 0.423210809837745 df.mm.trans2:probe2 -0.102522588782892 0.0471296381489754 -2.17533154951924 0.0303279052736472 * df.mm.trans2:probe3 -0.125857635256771 0.0471296381489754 -2.67045621820687 0.00795698428347019 ** df.mm.trans2:probe4 -0.134402271891064 0.0471296381489754 -2.85175692345063 0.00462694641523611 ** df.mm.trans2:probe5 -0.123410586973462 0.0471296381489754 -2.61853457442989 0.00924573266783594 ** df.mm.trans2:probe6 -0.143373645622853 0.0471296381489754 -3.04211216665092 0.00254117894300399 ** df.mm.trans3:probe2 -0.651527359718805 0.0471296381489754 -13.8241536601521 1.73894723681571e-34 *** df.mm.trans3:probe3 -0.60372365822845 0.0471296381489754 -12.8098513364371 1.12570169847543e-30 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.89357485427971 0.173352141541405 22.4604946882051 5.01566754599412e-68 *** df.mm.trans1 0.127379543490221 0.146827453024816 0.867545822433439 0.386285090071871 df.mm.trans2 0.0816415885863414 0.146827453024816 0.556037627190485 0.578568911786457 df.mm.exp2 0.186196656848239 0.204475030820043 0.910608283571354 0.363178753808397 df.mm.exp3 0.246905310810577 0.204475030820043 1.20750836823630 0.22811715352541 df.mm.exp4 0.230940813543387 0.204475030820043 1.12943283401012 0.259550777431274 df.mm.exp5 -0.265924801583838 0.204475030820043 -1.30052457024876 0.194345510235556 df.mm.exp6 0.0789178532008476 0.204475030820043 0.385953497032616 0.699784411613646 df.mm.exp7 0.0470384061735859 0.204475030820043 0.230044744265054 0.818202170949068 df.mm.exp8 -0.0689720322820053 0.204475030820043 -0.337312736941006 0.736099570968989 df.mm.trans1:exp2 -0.158740767513220 0.177080571129763 -0.89643243468475 0.370687405444011 df.mm.trans2:exp2 -0.198861835188828 0.177080571129763 -1.12300199801763 0.262268091138673 df.mm.trans1:exp3 -0.153935530684480 0.177080571129763 -0.86929655637759 0.385328451731225 df.mm.trans2:exp3 -0.201977288922352 0.177080571129763 -1.14059542294081 0.254880721641031 df.mm.trans1:exp4 -0.15367896475539 0.177080571129763 -0.867847690884029 0.386120038873191 df.mm.trans2:exp4 -0.147695739242950 0.177080571129763 -0.834059537422203 0.404861929170277 df.mm.trans1:exp5 0.271486036430843 0.177080571129763 1.53312153162133 0.126221751743072 df.mm.trans2:exp5 0.271545471439872 0.177080571129763 1.53345716984889 0.126139101970703 df.mm.trans1:exp6 0.0218801383214046 0.177080571129763 0.123560355502643 0.901739997809472 df.mm.trans2:exp6 -0.0662144779442509 0.177080571129763 -0.373922884491543 0.708706260202848 df.mm.trans1:exp7 -0.00628628274512273 0.177080571129763 -0.0354995621767912 0.971703256135218 df.mm.trans2:exp7 -0.0135966551733513 0.177080571129763 -0.076782309242654 0.938844115817469 df.mm.trans1:exp8 0.121891730670368 0.177080571129763 0.688340510157079 0.491730917595224 df.mm.trans2:exp8 0.0320952189408990 0.177080571129763 0.181246416454010 0.85628749058097 df.mm.trans1:probe2 0.0596720485370932 0.0885402855648816 0.67395364896769 0.50082130571648 df.mm.trans1:probe3 -0.0252793774220569 0.0885402855648816 -0.28551271616955 0.77543380095913 df.mm.trans1:probe4 -0.00530236818984385 0.0885402855648816 -0.059886504273338 0.952282949455913 df.mm.trans1:probe5 0.126883167181398 0.0885402855648816 1.43305577084929 0.152806095283046 df.mm.trans1:probe6 -0.0393073490078997 0.0885402855648816 -0.443948748946552 0.657375984352469 df.mm.trans2:probe2 0.140133008041206 0.0885402855648816 1.58270336657677 0.114464700692275 df.mm.trans2:probe3 0.114587299586882 0.0885402855648816 1.29418262947564 0.196524034073884 df.mm.trans2:probe4 -0.0207102345893145 0.0885402855648816 -0.233907474514956 0.815204536547299 df.mm.trans2:probe5 -0.00495364561406704 0.0885402855648816 -0.0559479290411487 0.955417773572598 df.mm.trans2:probe6 0.0402139980433407 0.0885402855648816 0.45418870954366 0.649997116495001 df.mm.trans3:probe2 0.0391145914128561 0.0885402855648816 0.44177168803226 0.658949129915252 df.mm.trans3:probe3 0.0139534117801734 0.0885402855648816 0.157593932424675 0.874874951856111