chr2.13766_chr2_36514513_36523668_-_2.R fitVsDatCorrelation=0.942900309702338 cont.fitVsDatCorrelation=0.267285267127876 fstatistic=6303.8051327959,53,715 cont.fstatistic=741.264724354093,53,715 residuals=-0.945411726383536,-0.11058525248667,-0.0108403923004485,0.0916313059574644,1.14302613304689 cont.residuals=-0.937760263174293,-0.429717098141913,-0.172927969206544,0.310634964959481,1.97933752068723 predictedValues: Include Exclude Both chr2.13766_chr2_36514513_36523668_-_2.R.tl.Lung 101.340989213881 45.202309596694 62.7759089279979 chr2.13766_chr2_36514513_36523668_-_2.R.tl.cerebhem 90.8158576989163 55.6741317615718 62.2859374842064 chr2.13766_chr2_36514513_36523668_-_2.R.tl.cortex 81.0433359979865 47.6331393966327 69.9695597742956 chr2.13766_chr2_36514513_36523668_-_2.R.tl.heart 86.7104800874056 46.2728783868061 65.488986336739 chr2.13766_chr2_36514513_36523668_-_2.R.tl.kidney 101.553793577316 45.0265160736158 62.1323368043774 chr2.13766_chr2_36514513_36523668_-_2.R.tl.liver 93.4009593360532 50.8526904453263 72.138897338873 chr2.13766_chr2_36514513_36523668_-_2.R.tl.stomach 87.1754778765932 46.3775012392742 71.9748239215962 chr2.13766_chr2_36514513_36523668_-_2.R.tl.testicle 89.8165452455264 49.0916527888702 63.5006717529322 diffExp=56.1386796171874,35.1417259373445,33.4101966013539,40.4376017005995,56.5272775037007,42.5482688907269,40.797976637319,40.7248924566562 diffExpScore=0.997115883395716 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 73.821802482225 95.222834158655 80.0246779114342 cerebhem 77.2513767089961 66.0245157207917 86.2614112755875 cortex 81.8912441095055 93.4563392118194 81.6984866452087 heart 70.6668135953963 85.2821334992464 73.604456762162 kidney 81.4366882558726 65.3835928400158 107.094049228274 liver 76.4023312355294 75.6665944744814 82.627598296072 stomach 85.2238632552423 91.8705320171912 60.0881395659569 testicle 75.0521690093604 72.6012064674337 73.4856045086824 cont.diffExp=-21.4010316764301,11.2268609882045,-11.5650951023139,-14.6153199038501,16.0530954158568,0.73573676104796,-6.64666876194887,2.45096254192663 cont.diffExpScore=3.42042723043862 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=-1,0,0,-1,1,0,0,0 cont.diffExp1.2Score=1.5 tran.correlation=-0.223037698061033 cont.tran.correlation=0.0635237677099123 tran.covariance=-0.00121508287973398 cont.tran.covariance=0.00026957764430573 tran.mean=69.8742661701544 cont.tran.mean=79.2033773151101 weightedLogRatios: wLogRatio Lung 3.40280414432789 cerebhem 2.08653873209987 cortex 2.19451518156230 heart 2.60537682296124 kidney 3.4273374794961 liver 2.57348075941768 stomach 2.62059571984557 testicle 2.53455682862686 cont.weightedLogRatios: wLogRatio Lung -1.12745522486036 cerebhem 0.670326456981122 cortex -0.59068789503478 heart -0.81812220556939 kidney 0.941899626291482 liver 0.0419103312475928 stomach -0.33665542521973 testicle 0.142820792924453 varWeightedLogRatios=0.244349298448679 cont.varWeightedLogRatios=0.516668974053902 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.1590621068882 0.101359222576954 41.0328927269599 4.21073712689320e-190 *** df.mm.trans1 0.29769055153719 0.0879418092133278 3.38508559466928 0.000750421596839007 *** df.mm.trans2 -0.373048611702976 0.079070448723866 -4.717927085576 2.86619904486302e-06 *** df.mm.exp2 0.106546095066241 0.103707098529336 1.02737514188677 0.304591281799269 df.mm.exp3 -0.279615298430823 0.103707098529336 -2.69620211534245 0.00717852202509368 ** df.mm.exp4 -0.174818977486394 0.103707098529336 -1.68569924301703 0.0922900474311643 . df.mm.exp5 0.00850587142384835 0.103707098529336 0.0820182180821718 0.93465518035744 df.mm.exp6 -0.102826503417910 0.103707098529336 -0.99150882510538 0.321772603504783 df.mm.exp7 -0.261646621269311 0.103707098529336 -2.52293840035741 0.0118536697121369 * df.mm.exp8 -0.0496600248112381 0.103707098529336 -0.47884884945644 0.632192563509819 df.mm.trans1:exp2 -0.216203141783356 0.0958755877167124 -2.25503850283745 0.0244325820351193 * df.mm.trans2:exp2 0.101821340387867 0.076194190910048 1.33633993840912 0.181863299744504 df.mm.trans1:exp3 0.056108361018996 0.0958755877167124 0.585220517080758 0.558584093141665 df.mm.trans2:exp3 0.331995840436211 0.076194190910048 4.35723296580645 1.51011892206138e-05 *** df.mm.trans1:exp4 0.0189027702248266 0.0958755877167124 0.197159367415608 0.843758858332598 df.mm.trans2:exp4 0.198226804155112 0.076194190910048 2.60159996172321 0.00947099076108481 ** df.mm.trans1:exp5 -0.00640818868858421 0.0958755877167123 -0.0668385857254796 0.94672889000749 df.mm.trans2:exp5 -0.0124024919152389 0.076194190910048 -0.162774770190562 0.870741766304785 df.mm.trans1:exp6 0.0212371585268882 0.0958755877167124 0.221507466422407 0.824760569431482 df.mm.trans2:exp6 0.220611351154011 0.076194190910048 2.89538281749663 0.00390245213753253 ** df.mm.trans1:exp7 0.111078734286758 0.0958755877167124 1.15857161277558 0.247017657742490 df.mm.trans2:exp7 0.287312893016398 0.076194190910048 3.77079787297155 0.000176172523746437 *** df.mm.trans1:exp8 -0.0710617326929074 0.0958755877167123 -0.74118693178577 0.458823461691848 df.mm.trans2:exp8 0.132200858054592 0.076194190910048 1.73505166831764 0.0831625527440164 . df.mm.trans1:probe2 0.762194298889916 0.060927872666805 12.5097802619519 1.30188379895983e-32 *** df.mm.trans1:probe3 0.117879845172219 0.060927872666805 1.93474414931350 0.0534158285862078 . df.mm.trans1:probe4 0.459970919025651 0.060927872666805 7.54943343485968 1.33560401697384e-13 *** df.mm.trans1:probe5 -0.236263967854097 0.060927872666805 -3.87776492946257 0.000115135247283075 *** df.mm.trans1:probe6 -0.0685447527831636 0.060927872666805 -1.12501470645484 0.260960314509416 df.mm.trans1:probe7 0.245492028914397 0.060927872666805 4.02922370615029 6.19568934635168e-05 *** df.mm.trans1:probe8 0.843019486748454 0.060927872666805 13.8363519001994 9.4395697509536e-39 *** df.mm.trans1:probe9 0.926760182341229 0.060927872666805 15.2107753278927 1.78507445098308e-45 *** df.mm.trans1:probe10 0.00718709601956335 0.060927872666805 0.117960724787935 0.906131907104932 df.mm.trans1:probe11 1.27756256920152 0.060927872666805 20.9684420821337 1.85476310940963e-76 *** df.mm.trans1:probe12 1.31710496547158 0.060927872666805 21.617445478105 3.86994229021781e-80 *** df.mm.trans1:probe13 1.09592969581766 0.060927872666805 17.9873290802545 5.87569503297938e-60 *** df.mm.trans1:probe14 -0.344132338890615 0.060927872666805 -5.64819226124248 2.34074901118287e-08 *** df.mm.trans1:probe15 -0.486550647078655 0.060927872666805 -7.98568250920961 5.58309579897751e-15 *** df.mm.trans1:probe16 -0.432080203279135 0.060927872666805 -7.09166731689522 3.19025264948308e-12 *** df.mm.trans1:probe17 -0.419816355915964 0.060927872666805 -6.89038263672531 1.22205155781423e-11 *** df.mm.trans1:probe18 -0.394692341006807 0.060927872666805 -6.47802596301455 1.72905374815540e-10 *** df.mm.trans1:probe19 -0.465824605091312 0.060927872666805 -7.64550910284949 6.72119419241653e-14 *** df.mm.trans2:probe2 0.106440205825987 0.060927872666805 1.7469870712223 0.0810689338497899 . df.mm.trans2:probe3 -0.0388400489188265 0.060927872666805 -0.637475874649854 0.524018993736079 df.mm.trans2:probe4 0.0994058302731134 0.060927872666805 1.63153292445860 0.103218257680412 df.mm.trans2:probe5 0.147813307539049 0.060927872666805 2.42603755997510 0.0155110641798155 * df.mm.trans2:probe6 0.0119316443346083 0.060927872666805 0.195832281882853 0.844797083154128 df.mm.trans3:probe2 0.119172584807877 0.060927872666805 1.95596169030213 0.0508586853264333 . df.mm.trans3:probe3 -0.103698424271600 0.060927872666805 -1.70198662340786 0.0891925748748426 . df.mm.trans3:probe4 -0.274678255589256 0.060927872666805 -4.50825284991293 7.63619617642277e-06 *** df.mm.trans3:probe5 -0.303622087884034 0.060927872666805 -4.98330361121331 7.84812030496492e-07 *** df.mm.trans3:probe6 0.092042321782849 0.060927872666805 1.51067676835196 0.131312622902601 df.mm.trans3:probe7 0.861855222647321 0.060927872666805 14.1454999973580 3.11051810926765e-40 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.4652791436834 0.293241742027508 15.2272971535701 1.47510179346547e-45 *** df.mm.trans1 -0.157872230295978 0.254423906134324 -0.620508633385374 0.53512069930435 df.mm.trans2 0.0776389753003584 0.228758227788097 0.339393149051132 0.734413240802078 df.mm.exp2 -0.395830312549883 0.300034367472304 -1.31928324039885 0.187496755819067 df.mm.exp3 0.0643121429563332 0.300034367472305 0.214349254380899 0.830335826287005 df.mm.exp4 -0.0703033560794867 0.300034367472305 -0.234317677244012 0.814805443068102 df.mm.exp5 -0.569148994235888 0.300034367472304 -1.89694600332219 0.0582377880462981 . df.mm.exp6 -0.227532581747418 0.300034367472305 -0.75835506333594 0.448488342863074 df.mm.exp7 0.394310506734559 0.300034367472305 1.31421780130224 0.189194368662484 df.mm.exp8 -0.169463353001755 0.300034367472304 -0.564813139339439 0.572378024701262 df.mm.trans1:exp2 0.441240935079616 0.27737707181618 1.59076210658114 0.112105138648586 df.mm.trans2:exp2 0.029636668276221 0.220436944037091 0.134445105858636 0.89308845133413 df.mm.trans1:exp3 0.0394258180926132 0.27737707181618 0.142137985070161 0.887011056171201 df.mm.trans2:exp3 -0.0830375436675264 0.220436944037091 -0.376695222437644 0.706511875573723 df.mm.trans1:exp4 0.0266253066963649 0.27737707181618 0.0959895730459285 0.923555750836407 df.mm.trans2:exp4 -0.0399514338801553 0.220436944037091 -0.181237469312009 0.856232547968565 df.mm.trans1:exp5 0.667320766811694 0.27737707181618 2.40582526321402 0.0163888535932897 * df.mm.trans2:exp5 0.193200579574495 0.220436944037091 0.876443739584717 0.381083208257788 df.mm.trans1:exp6 0.261891676443415 0.27737707181618 0.944172042514647 0.345400671606099 df.mm.trans2:exp6 -0.00235041116601015 0.220436944037091 -0.0106625102079743 0.991495683205346 df.mm.trans1:exp7 -0.250683141525387 0.27737707181618 -0.903763025126737 0.366425460409023 df.mm.trans2:exp7 -0.430149949134014 0.220436944037091 -1.95135144434608 0.0514053986457411 . df.mm.trans1:exp8 0.185992696956172 0.27737707181618 0.670540992225308 0.502729509323668 df.mm.trans2:exp8 -0.101774874941366 0.220436944037091 -0.461696089037786 0.644439709591125 df.mm.trans1:probe2 0.199790195989786 0.176270052833914 1.13343243947418 0.257412597243738 df.mm.trans1:probe3 -0.0943608940028962 0.176270052833914 -0.53532005287254 0.592595016771415 df.mm.trans1:probe4 -0.0259200153951768 0.176270052833914 -0.147047186850277 0.883136227701903 df.mm.trans1:probe5 -0.175671047505525 0.176270052833914 -0.996601774840603 0.31929499506109 df.mm.trans1:probe6 0.275143986584199 0.176270052833914 1.56092303917017 0.118984392810469 df.mm.trans1:probe7 0.0721415335586078 0.176270052833914 0.40926710123915 0.682466224258917 df.mm.trans1:probe8 0.0257585556025857 0.176270052833914 0.146131207136223 0.883858999918728 df.mm.trans1:probe9 0.0297350169919029 0.176270052833914 0.168690123556722 0.866088095874503 df.mm.trans1:probe10 -0.0106958424631222 0.176270052833914 -0.0606787272776284 0.951632029919924 df.mm.trans1:probe11 -0.165206395795532 0.176270052833914 -0.937234618924145 0.348954349150508 df.mm.trans1:probe12 0.118315968923451 0.176270052833914 0.671219909572109 0.502297229012456 df.mm.trans1:probe13 -0.000160781536530077 0.176270052833914 -0.000912131890500818 0.99927247858438 df.mm.trans1:probe14 -0.234956671338985 0.176270052833914 -1.33293584225771 0.182977428993134 df.mm.trans1:probe15 0.0651767596579946 0.176270052833914 0.369755149046252 0.711674424736973 df.mm.trans1:probe16 -0.183266716290355 0.176270052833914 -1.03969286525961 0.298834063268647 df.mm.trans1:probe17 -0.203649870993870 0.176270052833914 -1.15532881348685 0.248341740153313 df.mm.trans1:probe18 -0.00977982557857685 0.176270052833914 -0.0554820596087961 0.955769921757972 df.mm.trans1:probe19 0.168033274209156 0.176270052833914 0.953271820752677 0.340774516683571 df.mm.trans2:probe2 0.07803727523271 0.176270052833914 0.44271431237522 0.65810628794499 df.mm.trans2:probe3 -0.065422639912778 0.176270052833914 -0.371150055616202 0.710635714875976 df.mm.trans2:probe4 0.115955263848999 0.176270052833914 0.657827361964061 0.510860796361142 df.mm.trans2:probe5 0.0514852958740033 0.176270052833914 0.292081922290646 0.77030871622211 df.mm.trans2:probe6 -0.00713376245295373 0.176270052833914 -0.0404706434148252 0.96772920880003 df.mm.trans3:probe2 -0.174646245867242 0.176270052833914 -0.990787958926852 0.322124302458661 df.mm.trans3:probe3 0.0219714840693994 0.176270052833914 0.124646720847707 0.900838273735094 df.mm.trans3:probe4 -0.0620434205798036 0.176270052833914 -0.351979361112817 0.72495745266197 df.mm.trans3:probe5 0.236152015863126 0.176270052833914 1.33971716730371 0.180762955445245 df.mm.trans3:probe6 0.0525971734819161 0.176270052833914 0.298389730055136 0.765492419654854 df.mm.trans3:probe7 -0.0930068007921797 0.176270052833914 -0.527638128524378 0.597914215633952