chr3.15214_chr3_89187045_89188998_-_2.R fitVsDatCorrelation=0.884366088669784 cont.fitVsDatCorrelation=0.252193364671595 fstatistic=13182.5813372547,66,1014 cont.fstatistic=3055.75148796363,66,1014 residuals=-0.651619391520819,-0.0828112284718437,-0.00576445290697969,0.0827137975856658,0.597684069030274 cont.residuals=-0.593005626066992,-0.211235948681204,-0.0571806177854747,0.142370748562041,1.13293395967705 predictedValues: Include Exclude Both chr3.15214_chr3_89187045_89188998_-_2.R.tl.Lung 63.4423184549711 66.1146451939498 69.9170513036677 chr3.15214_chr3_89187045_89188998_-_2.R.tl.cerebhem 61.1506909021565 62.983692572006 63.7657049616581 chr3.15214_chr3_89187045_89188998_-_2.R.tl.cortex 59.3967697333901 67.5975091655806 62.7377953986455 chr3.15214_chr3_89187045_89188998_-_2.R.tl.heart 62.3870317464586 71.9261457373205 71.0715331425592 chr3.15214_chr3_89187045_89188998_-_2.R.tl.kidney 63.2874119092276 67.8473396132738 72.892047839252 chr3.15214_chr3_89187045_89188998_-_2.R.tl.liver 62.3116313643672 68.5069002915073 75.9641536088479 chr3.15214_chr3_89187045_89188998_-_2.R.tl.stomach 66.6812007288993 106.182989978408 74.1833090885118 chr3.15214_chr3_89187045_89188998_-_2.R.tl.testicle 60.6884234661712 81.310787550247 79.4150427351916 diffExp=-2.67232673897868,-1.83300166984947,-8.20073943219047,-9.53911399086192,-4.55992770404622,-6.19526892714009,-39.5017892495086,-20.6223640840758 diffExpScore=0.989375777165506 diffExp1.5=0,0,0,0,0,0,-1,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,0,-1,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,0,-1,-1 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,0,0,0,0,-1,-1 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 65.9122798412384 71.4901551909316 63.69419043504 cerebhem 65.2725917555416 72.401154005062 63.7928749556145 cortex 65.7511845353756 61.3203732124025 68.2205862101944 heart 63.8545912456511 61.4736910479171 66.5087030278922 kidney 69.4989508277279 78.4389039213652 65.6499130819016 liver 64.9133797145715 65.4676555003054 67.8690686689653 stomach 67.752417022966 65.5193216153031 63.3026417702558 testicle 66.537134963027 62.765392272962 60.6750875718516 cont.diffExp=-5.57787534969326,-7.12856224952043,4.43081132297316,2.38090019773404,-8.9399530936373,-0.55427578573395,2.23309540766282,3.771742690065 cont.diffExpScore=3.37219010230922 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=0.671131138879218 cont.tran.correlation=0.597021733384983 tran.covariance=0.00372332041061911 cont.tran.covariance=0.00137270169867321 tran.mean=68.2384680254959 cont.tran.mean=66.7730735420218 weightedLogRatios: wLogRatio Lung -0.172082088183286 cerebhem -0.121922381259675 cortex -0.536583309044026 heart -0.598226197325233 kidney -0.290990270114316 liver -0.396163533625234 stomach -2.06220035337299 testicle -1.24382404832973 cont.weightedLogRatios: wLogRatio Lung -0.343538064664866 cerebhem -0.438480751005072 cortex 0.289596428033016 heart 0.157225791327233 kidney -0.52055572873827 liver -0.0355173273219538 stomach 0.140733130176343 testicle 0.243263311770931 varWeightedLogRatios=0.436701869209854 cont.varWeightedLogRatios=0.105418092536767 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92627017527738 0.0668038832882678 58.7730829708656 0 *** df.mm.trans1 0.236376355884578 0.0571084214498397 4.13908053984989 3.77655517861438e-05 *** df.mm.trans2 0.290916938067132 0.049880509336647 5.83227681384955 7.34621948209515e-09 *** df.mm.exp2 0.00678965157346795 0.0628570745031321 0.108017301586787 0.914003336902196 df.mm.exp3 0.0646350677878411 0.0628570745031321 1.02828628756212 0.304060398618667 df.mm.exp4 0.0510985414681852 0.0628570745031321 0.812932225562598 0.416447741571566 df.mm.exp5 -0.018244776620555 0.0628570745031321 -0.290258125513714 0.771678119179812 df.mm.exp6 -0.065390831724907 0.0628570745031321 -1.04030981781770 0.29844396645044 df.mm.exp7 0.464335963606002 0.0628570745031321 7.38717109054867 3.1275236637115e-13 *** df.mm.exp8 0.0351320054542997 0.0628570745031321 0.558918876387559 0.57634049577713 df.mm.trans1:exp2 -0.0435796133144356 0.0573426333203577 -0.759986257885439 0.447439456974056 df.mm.trans2:exp2 -0.0553040902481319 0.0389951237827868 -1.41823091923468 0.156430605231247 df.mm.trans1:exp3 -0.130526346782765 0.0573426333203577 -2.27625309869446 0.0230386485961951 * df.mm.trans2:exp3 -0.0424542155766444 0.0389951237827868 -1.08870575236857 0.276542346772367 df.mm.trans1:exp4 -0.0678722345593387 0.0573426333203578 -1.18362604975176 0.236838527023066 df.mm.trans2:exp4 0.0331510139583792 0.0389951237827868 0.850132292002433 0.395452223748164 df.mm.trans1:exp5 0.0158000997093430 0.0573426333203578 0.275538439629587 0.782958736372171 df.mm.trans2:exp5 0.044114668810376 0.0389951237827868 1.13128680027037 0.258201878727655 df.mm.trans1:exp6 0.0474078170676755 0.0573426333203578 0.826746424476547 0.408575300182307 df.mm.trans2:exp6 0.100935022606470 0.0389951237827868 2.58840113365725 0.0097802309294868 ** df.mm.trans1:exp7 -0.414544020845547 0.0573426333203578 -7.22924631887068 9.54884560346392e-13 *** df.mm.trans2:exp7 0.00943767918260314 0.0389951237827868 0.24202203422083 0.808812027508386 df.mm.trans1:exp8 -0.0795101650662363 0.0573426333203578 -1.38658028873621 0.165874546533565 df.mm.trans2:exp8 0.171756406996227 0.0389951237827868 4.40456114341259 1.17254945288474e-05 *** df.mm.trans1:probe2 -0.0310319238992412 0.0426941928392267 -0.726841798276828 0.467490634333171 df.mm.trans1:probe3 -0.189251248714712 0.0426941928392267 -4.43271639839578 1.03189766377658e-05 *** df.mm.trans1:probe4 0.0166883096935065 0.0426941928392267 0.390880084238846 0.695967985810281 df.mm.trans1:probe5 0.210463878966977 0.0426941928392267 4.92956687949388 9.6225634080831e-07 *** df.mm.trans1:probe6 -0.00978514204973566 0.0426941928392267 -0.229191405177362 0.818766370597249 df.mm.trans1:probe7 -0.1639881297192 0.0426941928392267 -3.84099379362269 0.000130142205053159 *** df.mm.trans1:probe8 -0.0879323292465152 0.0426941928392267 -2.05958523627889 0.0396930456957472 * df.mm.trans1:probe9 -0.210392736946877 0.0426941928392267 -4.92790056341272 9.70302990875822e-07 *** df.mm.trans1:probe10 -0.178975217966664 0.0426941928392267 -4.19202720708715 3.00607293049993e-05 *** df.mm.trans1:probe11 -0.0266069329519641 0.0426941928392267 -0.623197938233841 0.533294597526178 df.mm.trans1:probe12 -0.125917271870530 0.0426941928392267 -2.94928334503701 0.00325823381962957 ** df.mm.trans1:probe13 -0.219409623209758 0.0426941928392267 -5.1390975825679 3.31069814050553e-07 *** df.mm.trans1:probe14 -0.152020092471229 0.0426941928392267 -3.56067376759389 0.000387072764917500 *** df.mm.trans1:probe15 -0.179292621974253 0.0426941928392267 -4.19946156727717 2.91069660109695e-05 *** df.mm.trans1:probe16 -0.064944860086656 0.0426941928392267 -1.5211637875722 0.128530497849345 df.mm.trans1:probe17 0.140260158333689 0.0426941928392267 3.28522801360518 0.00105389527403303 ** df.mm.trans1:probe18 0.122034341175531 0.0426941928392267 2.85833583117674 0.00434587053685538 ** df.mm.trans1:probe19 0.239640320612096 0.0426941928392267 5.61294885031574 2.56648233433578e-08 *** df.mm.trans1:probe20 -0.00468062992687102 0.0426941928392267 -0.109631535710181 0.912723291654997 df.mm.trans1:probe21 0.224224452419143 0.0426941928392267 5.25187238609952 1.83429365250641e-07 *** df.mm.trans1:probe22 0.20281635390597 0.0426941928392267 4.75044357132396 2.32202822059204e-06 *** df.mm.trans2:probe2 -0.127888723071589 0.0426941928392267 -2.99545944229884 0.00280691856885652 ** df.mm.trans2:probe3 -0.251652130243024 0.0426941928392267 -5.89429413013307 5.11815794065725e-09 *** df.mm.trans2:probe4 0.0486542669005249 0.0426941928392267 1.13959917414863 0.254722454195454 df.mm.trans2:probe5 -0.182225473293447 0.0426941928392267 -4.2681559522545 2.15567784311702e-05 *** df.mm.trans2:probe6 -0.0802150268116115 0.0426941928392267 -1.87882757530226 0.0605547174212401 . df.mm.trans3:probe2 -0.0183857784154158 0.0426941928392267 -0.430638857248128 0.666822500813742 df.mm.trans3:probe3 -0.515465119040593 0.0426941928392267 -12.0734246219779 1.86813611733082e-31 *** df.mm.trans3:probe4 -0.194668521678214 0.0426941928392267 -4.55960187398967 5.75025416849754e-06 *** df.mm.trans3:probe5 -0.635611480506309 0.0426941928392267 -14.8875394576454 1.74675143854357e-45 *** df.mm.trans3:probe6 0.346557647702429 0.0426941928392267 8.1172080944933 1.37330602859267e-15 *** df.mm.trans3:probe7 -0.46958750187746 0.0426941928392267 -10.9988612185686 1.16050999598273e-26 *** df.mm.trans3:probe8 -0.509449644531915 0.0426941928392267 -11.9325278369905 8.29658548421402e-31 *** df.mm.trans3:probe9 -0.607016556656225 0.0426941928392267 -14.2177780229284 5.75419512325399e-42 *** df.mm.trans3:probe10 0.678636907345748 0.0426941928392267 15.8952977493049 5.75024021497884e-51 *** df.mm.trans3:probe11 -0.0269405362353663 0.0426941928392267 -0.631011724166238 0.528175059706688 df.mm.trans3:probe12 -0.200910655931586 0.0426941928392267 -4.70580757172655 2.87907969053687e-06 *** df.mm.trans3:probe13 -0.463639887699537 0.0426941928392267 -10.8595538846574 4.58193933985158e-26 *** df.mm.trans3:probe14 -0.437647159801424 0.0426941928392267 -10.2507420962253 1.57599691286194e-23 *** df.mm.trans3:probe15 -0.228492161519032 0.0426941928392267 -5.35183232950354 1.07657666394992e-07 *** df.mm.trans3:probe16 -0.536873579332124 0.0426941928392267 -12.5748619104670 8.35902904639832e-34 *** df.mm.trans3:probe17 0.313177537751883 0.0426941928392267 7.33536616867811 4.52079582615225e-13 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.2987992013869 0.138486301496808 31.041331560768 3.00273073415100e-149 *** df.mm.trans1 -0.108408139133509 0.118387340400288 -0.91570719273667 0.360038204435868 df.mm.trans2 -0.0368483977358306 0.103403678271236 -0.356354806249486 0.72164900278865 df.mm.exp2 0.00136179352833914 0.130304457501153 0.0104508591221992 0.99166362836826 df.mm.exp3 -0.224547701764505 0.130304457501153 -1.72325418539513 0.0851474231783208 . df.mm.exp4 -0.225906188423328 0.130304457501153 -1.73367966649436 0.083278896316174 . df.mm.exp5 0.115504248622628 0.130304457501153 0.886418245681318 0.375602365187477 df.mm.exp6 -0.166761574238242 0.130304457501153 -1.27978411050724 0.200913799329587 df.mm.exp7 -0.0535130008421701 0.130304457501153 -0.410676671146852 0.681396400178837 df.mm.exp8 -0.0721602982963134 0.130304457501153 -0.553782270231813 0.579849954984842 df.mm.trans1:exp2 -0.0111143381331690 0.118872868098951 -0.09349768631743 0.925526657862906 df.mm.trans2:exp2 0.0113006944501857 0.0808379723344134 0.139794382811045 0.88885021284563 df.mm.trans1:exp3 0.22210062372331 0.118872868098951 1.86838786070536 0.0619962271624509 . df.mm.trans2:exp3 0.0711000914749761 0.0808379723344134 0.879538284073315 0.379317871378285 df.mm.trans1:exp4 0.194189910377497 0.118872868098951 1.63359321166417 0.102654721377264 df.mm.trans2:exp4 0.0749557332871784 0.0808379723344134 0.927234208412587 0.354025589458800 df.mm.trans1:exp5 -0.0625173568536872 0.118872868098951 -0.525917796495387 0.599060319297492 df.mm.trans2:exp5 -0.0227439714401781 0.0808379723344134 -0.281352572106707 0.778497394051493 df.mm.trans1:exp6 0.151490570946732 0.118872868098951 1.27439148536931 0.202816682312613 df.mm.trans2:exp6 0.0787580351773584 0.0808379723344134 0.974270295295747 0.330154707045131 df.mm.trans1:exp7 0.0810483708974093 0.118872868098951 0.681807145680575 0.495516601178308 df.mm.trans2:exp7 -0.0337016641059605 0.0808379723344134 -0.416903877382553 0.676836958558961 df.mm.trans1:exp8 0.081595745912118 0.118872868098951 0.686411855093768 0.49261035708851 df.mm.trans2:exp8 -0.057995609167647 0.0808379723344134 -0.717430280508877 0.473273875554918 df.mm.trans1:probe2 -0.128869563533008 0.0885062450064845 -1.4560505139899 0.145688198458830 df.mm.trans1:probe3 -0.0386978978470888 0.0885062450064845 -0.437233529049318 0.662035097123904 df.mm.trans1:probe4 0.0868165663996813 0.0885062450064845 0.98090893352577 0.32687162651647 df.mm.trans1:probe5 -0.055353897822288 0.0885062450064845 -0.625423638956014 0.531833778754779 df.mm.trans1:probe6 -0.0201823207411103 0.0885062450064845 -0.228032730793535 0.819666768759965 df.mm.trans1:probe7 0.0453571667444588 0.0885062450064845 0.512474195929741 0.608430782839476 df.mm.trans1:probe8 0.000710566478458686 0.0885062450064845 0.008028433229843 0.993595885079176 df.mm.trans1:probe9 -0.0706490924414317 0.0885062450064845 -0.798238502110846 0.424919002616101 df.mm.trans1:probe10 0.0222811646174214 0.0885062450064845 0.251746807423464 0.801287787068961 df.mm.trans1:probe11 0.000279194614985426 0.0885062450064845 0.00315451881350260 0.997483682787896 df.mm.trans1:probe12 -0.00814204525251663 0.0885062450064845 -0.091994019765725 0.926720961612286 df.mm.trans1:probe13 0.154489973626846 0.0885062450064845 1.74552624637422 0.0811961917540041 . df.mm.trans1:probe14 -0.0510066485325672 0.0885062450064845 -0.576305644068734 0.564536502226594 df.mm.trans1:probe15 0.0579275785078996 0.0885062450064845 0.654502724679547 0.512936394171905 df.mm.trans1:probe16 0.0200659511150074 0.0885062450064845 0.226717912544332 0.820688793676342 df.mm.trans1:probe17 0.00706978797753285 0.0885062450064845 0.0798789732522815 0.936349274793875 df.mm.trans1:probe18 -0.0415190351195048 0.0885062450064845 -0.469108537103375 0.63909290048183 df.mm.trans1:probe19 -0.0673215728985793 0.0885062450064845 -0.760642064225489 0.447047721734033 df.mm.trans1:probe20 -0.00283382100997629 0.0885062450064845 -0.0320183170099315 0.974463744522732 df.mm.trans1:probe21 -0.058703799730665 0.0885062450064845 -0.663272967081182 0.507306500435276 df.mm.trans1:probe22 0.0676961396378708 0.0885062450064845 0.76487415812196 0.444524452634374 df.mm.trans2:probe2 0.0195003979104022 0.0885062450064845 0.220327931763159 0.825660119021359 df.mm.trans2:probe3 -0.00615203845168222 0.0885062450064845 -0.0695096538242187 0.944597653554483 df.mm.trans2:probe4 -0.00626423881505329 0.0885062450064845 -0.0707773650841738 0.943588902814957 df.mm.trans2:probe5 0.154817892765848 0.0885062450064845 1.74923128593361 0.0805535825235957 . df.mm.trans2:probe6 0.0131037660145404 0.0885062450064845 0.148054705219732 0.88232903059502 df.mm.trans3:probe2 -0.00511797813703996 0.0885062450064845 -0.0578261809284191 0.953898476663222 df.mm.trans3:probe3 -0.0198934600535012 0.0885062450064845 -0.224768998527094 0.822204269872887 df.mm.trans3:probe4 -0.0159444059528113 0.0885062450064845 -0.180150066830235 0.857070753122384 df.mm.trans3:probe5 -0.0206787790073408 0.0885062450064845 -0.233642032896388 0.815310052406835 df.mm.trans3:probe6 -0.0048780309562218 0.0885062450064845 -0.0551151046557721 0.95605761983107 df.mm.trans3:probe7 -0.0411797799156793 0.0885062450064845 -0.465275415454155 0.641834142477339 df.mm.trans3:probe8 -0.0182393348609457 0.0885062450064845 -0.206079637200848 0.836770072394935 df.mm.trans3:probe9 -0.0069538500941517 0.0885062450064845 -0.0785690331076888 0.937390920806643 df.mm.trans3:probe10 -0.0233139824214634 0.0885062450064845 -0.263416241642104 0.79228329104735 df.mm.trans3:probe11 -0.0377613579625286 0.0885062450064845 -0.426651904165202 0.669723450491343 df.mm.trans3:probe12 0.213482875409002 0.0885062450064845 2.41206567280490 0.0160391725464842 * df.mm.trans3:probe13 0.085969792765726 0.0885062450064845 0.971341544988463 0.331609867916216 df.mm.trans3:probe14 -0.0611546011082529 0.0885062450064845 -0.690963684017691 0.489746506825677 df.mm.trans3:probe15 -0.0145239725238556 0.0885062450064845 -0.164101104083576 0.869684255986883 df.mm.trans3:probe16 -0.0966822030059503 0.0885062450064845 -1.09237718760825 0.274926810638879 df.mm.trans3:probe17 0.0762308308000289 0.0885062450064845 0.861304541780569 0.389273956148631