chr15.8251_chr15_99683946_99686683_-_1.R fitVsDatCorrelation=0.881195163196028 cont.fitVsDatCorrelation=0.271688753741712 fstatistic=9939.06150497059,47,577 cont.fstatistic=2389.05209928178,47,577 residuals=-0.530460524714205,-0.0855520264522668,-0.00399432765490359,0.0803963942861864,0.84497546798954 cont.residuals=-0.606144007064495,-0.221046819098314,-0.0269129260736447,0.166380291556635,1.84167273335441 predictedValues: Include Exclude Both chr15.8251_chr15_99683946_99686683_-_1.R.tl.Lung 61.4667581370465 69.3769374460121 62.9242231720316 chr15.8251_chr15_99683946_99686683_-_1.R.tl.cerebhem 54.5976371721224 104.403460460370 61.8984161208579 chr15.8251_chr15_99683946_99686683_-_1.R.tl.cortex 54.8756002854991 70.892523731897 55.4079610745382 chr15.8251_chr15_99683946_99686683_-_1.R.tl.heart 57.9602058913476 75.1475358085995 56.639111283471 chr15.8251_chr15_99683946_99686683_-_1.R.tl.kidney 60.0448728356199 74.5319036022555 62.6977220833035 chr15.8251_chr15_99683946_99686683_-_1.R.tl.liver 57.9533756405943 77.3846753820498 60.694083951998 chr15.8251_chr15_99683946_99686683_-_1.R.tl.stomach 58.1880180292022 81.0453756466437 58.8224342974496 chr15.8251_chr15_99683946_99686683_-_1.R.tl.testicle 54.5774955479023 77.5986487949233 56.8075612281183 diffExp=-7.91017930896557,-49.8058232882472,-16.0169234463979,-17.1873299172518,-14.4870307666355,-19.4312997414555,-22.8573576174416,-23.0211532470210 diffExpScore=0.994176468065621 diffExp1.5=0,-1,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,-1,0,0,0,0,0,-1 diffExp1.4Score=0.666666666666667 diffExp1.3=0,-1,0,0,0,-1,-1,-1 diffExp1.3Score=0.8 diffExp1.2=0,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 61.7903556554849 64.5775480534924 65.6411161509729 cerebhem 64.1040488251096 56.5180079089323 71.4735538878749 cortex 61.3972100636065 59.9560223241316 70.8099524480565 heart 64.2820539763207 68.682120603797 60.9476178698901 kidney 57.2290451014577 64.1479401643692 69.3387444012657 liver 67.132693609362 66.3113061692288 71.478250239248 stomach 63.1858205230554 69.198429670001 64.3714766574987 testicle 63.7407825794706 63.8319489660287 74.2805555205291 cont.diffExp=-2.78719239800753,7.58604091617733,1.44118773947488,-4.40006662747632,-6.91889506291156,0.821387440133236,-6.01260914694561,-0.091166386558065 cont.diffExpScore=2.64569282843888 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=-0.496862500683664 cont.tran.correlation=0.154766268557396 tran.covariance=-0.00286037999455833 cont.tran.covariance=0.000430338989173052 tran.mean=68.1278140257553 cont.tran.mean=63.5053333871155 weightedLogRatios: wLogRatio Lung -0.505904420416855 cerebhem -2.8032112005185 cortex -1.05847540466515 heart -1.08802640619022 kidney -0.90845037382496 liver -1.21564820618271 stomach -1.40130752675820 testicle -1.46950717987113 cont.weightedLogRatios: wLogRatio Lung -0.182910942652866 cerebhem 0.516077018799426 cortex 0.0975179127249823 heart -0.277835993325197 kidney -0.468405752033525 liver 0.0517114968206215 stomach -0.381002665637424 testicle -0.00593928739842242 varWeightedLogRatios=0.456085957456814 cont.varWeightedLogRatios=0.100118593762771 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.99688566667241 0.0739622822402354 54.039512378623 6.21680431399061e-228 *** df.mm.trans1 0.0779156044112203 0.0616426162235386 1.26398925264090 0.206744273449699 df.mm.trans2 0.135288595400158 0.0578032362295015 2.34050209339507 0.0195975695715671 * df.mm.exp2 0.306639022231287 0.0759291261944273 4.03849007093924 6.10674064374921e-05 *** df.mm.exp3 0.0353906914885308 0.0759291261944273 0.466101656403998 0.64131864197105 df.mm.exp4 0.126390422694066 0.0759291261944273 1.66458418565789 0.096538631355376 . df.mm.exp5 0.0518745029500231 0.0759291261944273 0.683196364161904 0.494756998380993 df.mm.exp6 0.0864615441620151 0.0759291261944273 1.13871380451051 0.255295093536712 df.mm.exp7 0.168045517173404 0.0759291261944273 2.21318913565658 0.0272751271137169 * df.mm.exp8 0.0953823920898663 0.0759291261944273 1.25620294701701 0.209550774732526 df.mm.trans1:exp2 -0.425144926235783 0.0647525763876298 -6.56568355968925 1.15599251540879e-10 *** df.mm.trans2:exp2 0.102069300013684 0.0560773761121798 1.8201511391956 0.069253931352482 . df.mm.trans1:exp3 -0.148818391592636 0.0647525763876298 -2.29826209078943 0.0219035805418883 * df.mm.trans2:exp3 -0.0137802104060079 0.0560773761121798 -0.245735648872753 0.805974256994995 df.mm.trans1:exp4 -0.185130263530309 0.0647525763876298 -2.85904088853638 0.00440281874252719 ** df.mm.trans2:exp4 -0.0464915961637934 0.0560773761121798 -0.829061546510119 0.4074124098073 df.mm.trans1:exp5 -0.0752788502406905 0.0647525763876298 -1.16256146767114 0.245488075701474 df.mm.trans2:exp5 0.0197982682140270 0.0560773761121798 0.353052685175954 0.724177883767412 df.mm.trans1:exp6 -0.145319235739665 0.0647525763876298 -2.24422322394922 0.0251963151788945 * df.mm.trans2:exp6 0.0227727255242244 0.0560773761121798 0.406094705263470 0.68482357497851 df.mm.trans1:exp7 -0.222862570001346 0.0647525763876298 -3.44175602631189 0.000619786726455613 *** df.mm.trans2:exp7 -0.0125908250085090 0.0560773761121798 -0.224525929731836 0.822427577997663 df.mm.trans1:exp8 -0.214257274339624 0.0647525763876298 -3.30886099507471 0.00099513154876487 *** df.mm.trans2:exp8 0.0166131236847867 0.0560773761121798 0.296253584539209 0.767143026711888 df.mm.trans1:probe2 0.361422117948009 0.0443330584301015 8.15242915211571 2.22945097063243e-15 *** df.mm.trans1:probe3 0.287487009447050 0.0443330584301015 6.48470959657164 1.91181452050989e-10 *** df.mm.trans1:probe4 0.179683863750905 0.0443330584301015 4.05304461532261 5.74872988786472e-05 *** df.mm.trans1:probe5 0.0327086195012079 0.0443330584301015 0.737792984726703 0.460940194652123 df.mm.trans1:probe6 -0.0902533313152396 0.0443330584301015 -2.03580205181511 0.0422265244817249 * df.mm.trans1:probe7 0.217213041831668 0.0443330584301015 4.89957267834658 1.24941906169826e-06 *** df.mm.trans1:probe8 0.085136128730004 0.0443330584301015 1.92037571385325 0.0553030461266751 . df.mm.trans1:probe9 -0.107953450714928 0.0443330584301015 -2.43505534104159 0.0151914387562680 * df.mm.trans1:probe10 -0.0915392091893145 0.0443330584301015 -2.06480699574656 0.0393874764192480 * df.mm.trans2:probe2 0.199719244789587 0.0443330584301015 4.50497330574379 8.03803777770405e-06 *** df.mm.trans2:probe3 0.305071994563700 0.0443330584301015 6.88136585579128 1.54906546076167e-11 *** df.mm.trans2:probe4 0.318862863812486 0.0443330584301015 7.19244002340209 1.98421720896577e-12 *** df.mm.trans2:probe5 0.109673324465750 0.0443330584301015 2.47384972635417 0.0136532348741697 * df.mm.trans2:probe6 0.78475636042922 0.0443330584301015 17.7013810510394 2.5065092033023e-56 *** df.mm.trans3:probe2 -0.216046841986826 0.0443330584301015 -4.87326725557316 1.42034933174112e-06 *** df.mm.trans3:probe3 -0.119354313673915 0.0443330584301015 -2.69221925805316 0.00730391420291475 ** df.mm.trans3:probe4 0.145643278646792 0.0443330584301015 3.28520710738745 0.00108086958870299 ** df.mm.trans3:probe5 -0.320247016717628 0.0443330584301015 -7.22366171110326 1.60792733016314e-12 *** df.mm.trans3:probe6 -0.158657564858660 0.0443330584301015 -3.57876425577113 0.000374256776472674 *** df.mm.trans3:probe7 -0.245966626771961 0.0443330584301015 -5.54815380400087 4.40336816801098e-08 *** df.mm.trans3:probe8 0.242590216160038 0.0443330584301015 5.47199369388246 6.64180491158074e-08 *** df.mm.trans3:probe9 -0.0691929727639262 0.0443330584301015 -1.56075342451323 0.119130221620814 df.mm.trans3:probe10 0.0596451335853619 0.0443330584301015 1.34538729556415 0.179028671709677 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05574358927581 0.150565419765466 26.9367534430774 4.21641643364918e-104 *** df.mm.trans1 0.0310708089549477 0.125486208727205 0.247603376260197 0.80452938088222 df.mm.trans2 0.108608159650094 0.117670362015448 0.922986534500805 0.356400013308977 df.mm.exp2 -0.181672489784026 0.154569334688131 -1.17534626224910 0.240340978542378 df.mm.exp3 -0.156435656244954 0.154569334688131 -1.01207433260025 0.311926720843301 df.mm.exp4 0.175342812512205 0.154569334688131 1.1343958545593 0.257099590175072 df.mm.exp5 -0.138162122114839 0.154569334688131 -0.893852085173322 0.371773708828301 df.mm.exp6 0.0242265428068681 0.154569334688131 0.156735764281765 0.875507925014375 df.mm.exp7 0.110975639200508 0.154569334688131 0.717966726223024 0.473068391444875 df.mm.exp8 -0.104182617847212 0.154569334688131 -0.674018672962637 0.500569366094567 df.mm.trans1:exp2 0.218432720983338 0.131817171528479 1.65708851472470 0.0980451749609254 . df.mm.trans2:exp2 0.0483650041802168 0.114157019198673 0.423670874727771 0.671963744676983 df.mm.trans1:exp3 0.150052756731389 0.131817171528479 1.13833998250350 0.255450966029221 df.mm.trans2:exp3 0.082180191428764 0.114157019198673 0.719887327171197 0.471885864782488 df.mm.trans1:exp4 -0.135809613501128 0.131817171528479 -1.03028772296018 0.303306631136517 df.mm.trans2:exp4 -0.113720697401869 0.114157019198673 -0.996177880257673 0.319581298398443 df.mm.trans1:exp5 0.0614763781863933 0.131817171528479 0.466376098603448 0.641122339079672 df.mm.trans2:exp5 0.131487305959895 0.114157019198673 1.15181096075276 0.249875782181745 df.mm.trans1:exp6 0.0586973246777837 0.131817171528479 0.445293462127598 0.656274702014402 df.mm.trans2:exp6 0.0022670732758404 0.114157019198673 0.0198592543126489 0.984162514361308 df.mm.trans1:exp7 -0.0886430169631307 0.131817171528479 -0.67246942060185 0.501554099194408 df.mm.trans2:exp7 -0.0418642664907914 0.114157019198673 -0.366725294551822 0.713958379228763 df.mm.trans1:exp8 0.135259909512529 0.131817171528479 1.02611752280928 0.305266166765656 df.mm.trans2:exp8 0.0925696533219884 0.114157019198673 0.810897603772263 0.417758819113712 df.mm.trans1:probe2 0.146909774695809 0.0902490478908443 1.62782631096004 0.104107603402052 df.mm.trans1:probe3 0.0984302838242496 0.0902490478908443 1.09065177001424 0.275881380424851 df.mm.trans1:probe4 0.0185440495913224 0.0902490478908443 0.205476401410365 0.837272404069128 df.mm.trans1:probe5 0.096475166576135 0.0902490478908443 1.06898819246072 0.285521990404891 df.mm.trans1:probe6 0.200563933481941 0.0902490478908443 2.22233849740467 0.0266473605197873 * df.mm.trans1:probe7 -0.0631013956487768 0.0902490478908443 -0.69919181557569 0.484713910777041 df.mm.trans1:probe8 0.107770886105279 0.0902490478908443 1.19414984006953 0.232910041499643 df.mm.trans1:probe9 0.09554146496787 0.0902490478908443 1.05864235912413 0.290205664398686 df.mm.trans1:probe10 0.0375237704073637 0.0902490478908443 0.415780235739977 0.677725409066039 df.mm.trans2:probe2 0.00398786363827712 0.0902490478908443 0.0441873208801096 0.964770375950436 df.mm.trans2:probe3 -0.0333633549671438 0.0902490478908443 -0.369680963365914 0.711755855751434 df.mm.trans2:probe4 0.0846992012475378 0.0902490478908443 0.938505205617027 0.348377524326636 df.mm.trans2:probe5 -0.0154028377296162 0.0902490478908443 -0.170670362619734 0.864542796903114 df.mm.trans2:probe6 0.0163198974186515 0.0902490478908443 0.180831796013962 0.85656310232474 df.mm.trans3:probe2 0.00987217651373725 0.0902490478908443 0.109388151392772 0.912932662978638 df.mm.trans3:probe3 0.0113672643054922 0.0902490478908443 0.125954395876185 0.899811898404123 df.mm.trans3:probe4 -0.0736082685718596 0.0902490478908443 -0.815612688356429 0.415058239187769 df.mm.trans3:probe5 0.0257236159352799 0.0902490478908443 0.285029222318140 0.775724030165494 df.mm.trans3:probe6 0.0571204794839698 0.0902490478908443 0.6329205772127 0.52703626151127 df.mm.trans3:probe7 -0.095762226881783 0.0902490478908443 -1.06108850031977 0.289093616409524 df.mm.trans3:probe8 -0.0575668187630228 0.0902490478908443 -0.63786621696718 0.523813819134407 df.mm.trans3:probe9 0.0609034567848141 0.0902490478908443 0.674837665417551 0.500049215098797 df.mm.trans3:probe10 -0.0502525555602387 0.0902490478908443 -0.556820894343604 0.577865709289714