chr7.21812_chr7_4444157_4450857_+_1.R fitVsDatCorrelation=0.92046019468746 cont.fitVsDatCorrelation=0.233432910907699 fstatistic=7901.29261300055,74,1198 cont.fstatistic=1262.93056310769,74,1198 residuals=-0.925752341584263,-0.119914476013031,0.00351655770692587,0.121379367112611,0.916568528034919 cont.residuals=-1.21044837619403,-0.383727436812903,-0.0639073466444417,0.332169054098582,1.95527414058328 predictedValues: Include Exclude Both chr7.21812_chr7_4444157_4450857_+_1.R.tl.Lung 78.9962547441751 47.7822475064735 114.670904631706 chr7.21812_chr7_4444157_4450857_+_1.R.tl.cerebhem 83.277864124095 51.468995432368 142.382623324753 chr7.21812_chr7_4444157_4450857_+_1.R.tl.cortex 139.501462664242 47.6401342549746 211.361790949221 chr7.21812_chr7_4444157_4450857_+_1.R.tl.heart 98.5252141679246 47.0829630776038 144.573139667273 chr7.21812_chr7_4444157_4450857_+_1.R.tl.kidney 87.5521496078153 47.6444360193441 116.651783781275 chr7.21812_chr7_4444157_4450857_+_1.R.tl.liver 75.467484778568 48.2058385178262 100.532917759745 chr7.21812_chr7_4444157_4450857_+_1.R.tl.stomach 94.0223568407333 45.9660728076920 137.850828128334 chr7.21812_chr7_4444157_4450857_+_1.R.tl.testicle 86.4350882395279 48.9817343864324 120.591266687516 diffExp=31.2140072377016,31.8088686917269,91.8613284092672,51.4422510903208,39.9077135884712,27.2616462607418,48.0562840330414,37.4533538530955 diffExpScore=0.99722226429847 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 106.883307643501 123.610231370163 107.473656160359 cerebhem 117.727569404068 140.660603599403 96.6435965037292 cortex 110.804409055651 93.132455971312 111.113571015694 heart 104.747775820974 111.669035970449 103.565086085745 kidney 118.952981147258 95.2631227364374 104.221475312241 liver 103.597486951585 115.733932442239 114.205574879724 stomach 98.5645401590474 121.260977377476 104.778488862376 testicle 118.157233056163 138.327898346095 97.0438882467743 cont.diffExp=-16.7269237266625,-22.9330341953346,17.6719530843386,-6.92126014947569,23.6898584108208,-12.1364454906536,-22.6964372184285,-20.1706652899323 cont.diffExpScore=2.33485264729861 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=0,0,0,0,1,0,-1,0 cont.diffExp1.2Score=2 tran.correlation=-0.274033688434842 cont.tran.correlation=0.116425360487197 tran.covariance=-0.00190161298242302 cont.tran.covariance=0.000575599333182044 tran.mean=70.5343935731122 cont.tran.mean=113.693347565739 weightedLogRatios: wLogRatio Lung 2.07032284414854 cerebhem 2.01219016903535 cortex 4.72829835223805 heart 3.11687439552503 kidney 2.53609791606140 liver 1.83752587636017 stomach 2.99542131427635 testicle 2.37141444730791 cont.weightedLogRatios: wLogRatio Lung -0.68982034383395 cerebhem -0.864497186633212 cortex 0.8028519294464 heart -0.299672729670796 kidney 1.03662538186392 liver -0.520215935924685 stomach -0.972822072873485 testicle -0.764541485438633 varWeightedLogRatios=0.874677343421348 cont.varWeightedLogRatios=0.598343602857194 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.68933711996398 0.0981717921627455 37.5804193718694 7.85183996575846e-205 *** df.mm.trans1 0.680850426535637 0.0822758864184779 8.27521229090919 3.38401722701873e-16 *** df.mm.trans2 0.170124024460329 0.0728995643575408 2.33367683277151 0.0197773676046174 * df.mm.exp2 -0.0893438867866358 0.09135823252377 -0.977951130604349 0.328295932056871 df.mm.exp3 -0.0458089607982062 0.09135823252377 -0.501421268042674 0.61616680003737 df.mm.exp4 -0.0255501175141664 0.0913582325237699 -0.27966956899608 0.779779304577077 df.mm.exp5 0.0828188813868126 0.0913582325237699 0.906528936680825 0.364838248798683 df.mm.exp6 0.094708533877394 0.09135823252377 1.03667213409314 0.300097881779773 df.mm.exp7 -0.0487242709399365 0.09135823252377 -0.533332022675233 0.593902690643366 df.mm.exp8 0.0644460066400626 0.09135823252377 0.70542090033643 0.480685475754247 df.mm.trans1:exp2 0.142126220797936 0.0796927371899372 1.78342752187313 0.0747698303296992 . df.mm.trans2:exp2 0.163669303287076 0.0550025018741851 2.97567015517693 0.00298212719707046 ** df.mm.trans1:exp3 0.614483604011356 0.0796927371899372 7.7106600385279 2.61860816248751e-14 *** df.mm.trans2:exp3 0.0428303439591278 0.0550025018741851 0.778698104626215 0.436311350017914 df.mm.trans1:exp4 0.246462171293179 0.0796927371899372 3.09265536589324 0.00202960553974973 ** df.mm.trans2:exp4 0.0108071555664798 0.0550025018741851 0.196484799749665 0.844264054068074 df.mm.trans1:exp5 0.0200152869182214 0.0796927371899372 0.251155721637689 0.801736787715921 df.mm.trans2:exp5 -0.0857072052359545 0.0550025018741851 -1.55824194019400 0.119440022792222 df.mm.trans1:exp6 -0.140407078611461 0.0796927371899372 -1.76185539062134 0.0783487534498175 . df.mm.trans2:exp6 -0.0858825685180872 0.0550025018741851 -1.56143021847512 0.118686385009714 df.mm.trans1:exp7 0.222856420620367 0.0796927371899372 2.7964458052083 0.00524941808820289 ** df.mm.trans2:exp7 0.0099736682113734 0.0550025018741851 0.181331173519844 0.856138311901498 df.mm.trans1:exp8 0.0255472575698166 0.0796927371899372 0.320571967667870 0.748590691040799 df.mm.trans2:exp8 -0.0396527250850891 0.0550025018741851 -0.72092584398783 0.471095795200083 df.mm.trans1:probe2 1.16157999979484 0.0631726885456492 18.3873763573552 1.04988296815773e-66 *** df.mm.trans1:probe3 -0.209537652489884 0.0631726885456493 -3.31690256206953 0.000937519686762925 *** df.mm.trans1:probe4 0.0674131606338265 0.0631726885456493 1.06712508499797 0.28613035044987 df.mm.trans1:probe5 -0.295057398682574 0.0631726885456493 -4.67064811511643 3.34156608366464e-06 *** df.mm.trans1:probe6 -0.134894602143484 0.0631726885456493 -2.13533103068754 0.0329363416693504 * df.mm.trans1:probe7 0.0535601020766766 0.0631726885456493 0.847836356338919 0.396698466877185 df.mm.trans1:probe8 -0.0201747146363861 0.0631726885456493 -0.319358176782481 0.74951059958082 df.mm.trans1:probe9 0.287905784421647 0.0631726885456493 4.55744073981597 5.70803951113095e-06 *** df.mm.trans1:probe10 -0.0611469296550607 0.0631726885456493 -0.967932995456973 0.333273197125555 df.mm.trans1:probe11 -0.178951707682589 0.0631726885456493 -2.83273851093541 0.00469243954550817 ** df.mm.trans1:probe12 -0.26668003727412 0.0631726885456493 -4.22144511201885 2.61087892648541e-05 *** df.mm.trans1:probe13 -0.102872426995488 0.0631726885456493 -1.62843199116263 0.103696242788774 df.mm.trans1:probe14 -0.181158282241300 0.0631726885456493 -2.86766775978503 0.00420754099041226 ** df.mm.trans1:probe15 -0.064080853299778 0.0631726885456493 -1.01437590792851 0.310608233833983 df.mm.trans1:probe16 -0.0913240974355155 0.0631726885456493 -1.44562625935294 0.148543593153698 df.mm.trans2:probe2 -0.00591099667288717 0.0631726885456493 -0.093568863522657 0.925467290027315 df.mm.trans2:probe3 0.09087513226581 0.0631726885456493 1.43851930886466 0.15054794907191 df.mm.trans2:probe4 0.0806181598675932 0.0631726885456493 1.27615527728154 0.202147983589582 df.mm.trans2:probe5 0.0965308909627303 0.0631726885456493 1.52804785082047 0.126764629218733 df.mm.trans2:probe6 0.00402910903542876 0.0631726885456493 0.0637792870334668 0.949156611117417 df.mm.trans3:probe2 0.0312520530115875 0.0631726885456493 0.494708294534662 0.620896754647389 df.mm.trans3:probe3 0.0160190516466761 0.0631726885456493 0.253575588050216 0.799866967791778 df.mm.trans3:probe4 0.177569540766953 0.0631726885456493 2.81085932631535 0.00502142516342704 ** df.mm.trans3:probe5 0.0101700481462520 0.0631726885456493 0.160988053229727 0.872129952881206 df.mm.trans3:probe6 -0.0844558749151825 0.0631726885456493 -1.33690486916912 0.181507499792925 df.mm.trans3:probe7 0.780462732734034 0.0631726885456493 12.3544327572834 4.33729892011337e-33 *** df.mm.trans3:probe8 -0.0734547822453185 0.0631726885456493 -1.16276169237659 0.245157767251143 df.mm.trans3:probe9 0.829935077432871 0.0631726885456493 13.1375614452937 6.26675254369621e-37 *** df.mm.trans3:probe10 -0.170638019217630 0.0631726885456493 -2.7011359362096 0.00700778020801779 ** df.mm.trans3:probe11 -0.307023373126929 0.0631726885456493 -4.86006500902792 1.32908719524446e-06 *** df.mm.trans3:probe12 -0.192303485619831 0.0631726885456493 -3.04409215512286 0.00238472917053865 ** df.mm.trans3:probe13 -0.46434544806323 0.0631726885456493 -7.35041453440262 3.64864368976566e-13 *** df.mm.trans3:probe14 0.537234111223238 0.0631726885456493 8.50421477368383 5.3760868553935e-17 *** df.mm.trans3:probe15 0.0840227352521422 0.0631726885456493 1.33004843052432 0.183755469023229 df.mm.trans3:probe16 0.617361571475697 0.0631726885456492 9.7726024598365 9.3508739283308e-22 *** df.mm.trans3:probe17 0.605855619930894 0.0631726885456493 9.59046755613537 4.88092064246267e-21 *** df.mm.trans3:probe18 -0.0560402323090887 0.0631726885456493 -0.887095889050113 0.375205325941726 df.mm.trans3:probe19 0.723200622464077 0.0631726885456493 11.4479949977352 7.16983853132413e-29 *** df.mm.trans3:probe20 0.453013293008562 0.0631726885456493 7.1710307640494 1.29969248925531e-12 *** df.mm.trans3:probe21 -0.246128409047578 0.0631726885456493 -3.89612053426732 0.000103153021869168 *** df.mm.trans3:probe22 0.167788379949184 0.0631726885456493 2.65602721384794 0.00801169663102563 ** df.mm.trans3:probe23 0.539023105371163 0.0631726885456493 8.53253388102454 4.26932677937702e-17 *** df.mm.trans3:probe24 0.150247379340453 0.0631726885456493 2.37835974373455 0.0175459105288849 * df.mm.trans3:probe25 0.204238598016437 0.0631726885456493 3.23302051437707 0.00125827952440105 ** df.mm.trans3:probe26 -0.179225611068288 0.0631726885456493 -2.83707429894768 0.00462961208095474 ** df.mm.trans3:probe27 0.169335011197767 0.0631726885456493 2.68050980726273 0.00745187724709394 ** df.mm.trans3:probe28 0.332925102042274 0.0631726885456493 5.27007967694296 1.61605272889714e-07 *** df.mm.trans3:probe29 0.807110419453722 0.0631726885456493 12.7762556578623 3.89679514331107e-35 *** df.mm.trans3:probe30 -0.0721329111660331 0.0631726885456493 -1.14183696826373 0.253749962939696 df.mm.trans3:probe31 0.863381132169388 0.0631726885456493 13.6669999654281 1.26648362392513e-39 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.89220649430392 0.244244421613402 20.0299620437082 3.3096892273338e-77 *** df.mm.trans1 -0.203716776978811 0.204696541117408 -0.995213577458375 0.319833367641585 df.mm.trans2 -0.070077454928103 0.181368920136036 -0.386380725405109 0.699283334678084 df.mm.exp2 0.332067976776603 0.227292770874550 1.46097025215061 0.144285820806990 df.mm.exp3 -0.280388754167104 0.227292770874550 -1.23360172471943 0.217593318093603 df.mm.exp4 -0.0847306978733506 0.227292770874550 -0.372782194292118 0.709376498128076 df.mm.exp5 -0.122772348163167 0.227292770874550 -0.540150695029932 0.589193505812975 df.mm.exp6 -0.15781838748032 0.227292770874550 -0.694339669814772 0.487603841287759 df.mm.exp7 -0.0748170851142318 0.227292770874550 -0.32916614473201 0.742087646704717 df.mm.exp8 0.314854612738486 0.227292770874550 1.38523812933876 0.166237531906323 df.mm.trans1:exp2 -0.235432411982727 0.198269849953208 -1.18743425709097 0.235291756966588 df.mm.trans2:exp2 -0.202851374384978 0.136842304307534 -1.48237327200438 0.138503992519249 df.mm.trans1:exp3 0.316417664036833 0.198269849953208 1.59589400058308 0.110776201604892 df.mm.trans2:exp3 -0.00272182789009111 0.136842304307534 -0.0198902518038149 0.984134233645389 df.mm.trans1:exp4 0.0645483666647364 0.198269849953208 0.325558155614532 0.74481552670394 df.mm.trans2:exp4 -0.0168631612223981 0.136842304307534 -0.123230614302581 0.901945153778992 df.mm.trans1:exp5 0.229762990213275 0.198269849953208 1.15883978460416 0.246752435578541 df.mm.trans2:exp5 -0.13771819548192 0.136842304307534 -1.00640073388722 0.31442606569629 df.mm.trans1:exp6 0.126593803214637 0.198269849953208 0.638492454826152 0.523275201008133 df.mm.trans2:exp6 0.0919789385438233 0.136842304307534 0.672152803983142 0.501616028809166 df.mm.trans1:exp7 -0.0062090078088861 0.198269849953208 -0.0313159454670059 0.975022790224346 df.mm.trans2:exp7 0.0556288262323094 0.136842304307534 0.406517754241344 0.684434823028638 df.mm.trans1:exp8 -0.214576048287136 0.198269849953208 -1.08224245056814 0.279362595861448 df.mm.trans2:exp8 -0.202360990685534 0.136842304307534 -1.47878970402863 0.139459403465925 df.mm.trans1:probe2 0.0804000134476056 0.157169146408340 0.511550869142718 0.609059649922877 df.mm.trans1:probe3 -0.165133845672493 0.157169146408340 -1.05067597200954 0.293619327296090 df.mm.trans1:probe4 -0.062527948915574 0.157169146408340 -0.397838572929069 0.690820061635879 df.mm.trans1:probe5 0.139399691165227 0.157169146408340 0.886940562768304 0.37528891629343 df.mm.trans1:probe6 -0.176138688897298 0.157169146408340 -1.12069507866177 0.262642281816349 df.mm.trans1:probe7 -0.0812055496231288 0.157169146408340 -0.516676151005803 0.605477609436374 df.mm.trans1:probe8 -0.0185269404933892 0.157169146408340 -0.117878991626350 0.906183299553735 df.mm.trans1:probe9 -0.202440510015694 0.157169146408340 -1.28804230755147 0.197979964924325 df.mm.trans1:probe10 0.0732154360708092 0.157169146408340 0.46583847875962 0.641415798860853 df.mm.trans1:probe11 -0.102660852032467 0.157169146408340 -0.653187055974362 0.513761027521247 df.mm.trans1:probe12 -0.0136978449352610 0.157169146408340 -0.0871535237563277 0.930564046025066 df.mm.trans1:probe13 0.103873885324397 0.157169146408340 0.660905067553929 0.508800265840622 df.mm.trans1:probe14 -0.0655573311906134 0.157169146408340 -0.417113235572898 0.676670316328128 df.mm.trans1:probe15 -0.193500184581758 0.157169146408340 -1.23115884385493 0.218505099513172 df.mm.trans1:probe16 -0.0693420635054815 0.157169146408340 -0.441193867181312 0.659152263612814 df.mm.trans2:probe2 -0.0505577191019563 0.157169146408340 -0.321677124660349 0.747753425482656 df.mm.trans2:probe3 -0.101300027270992 0.157169146408340 -0.644528710538419 0.519356035819987 df.mm.trans2:probe4 0.189554875323105 0.157169146408340 1.20605652989057 0.228033823730637 df.mm.trans2:probe5 -0.089722682689083 0.157169146408340 -0.570867022818684 0.568196849421537 df.mm.trans2:probe6 -0.132816075249398 0.157169146408340 -0.845051832910824 0.3982506322205 df.mm.trans3:probe2 -0.0333802908161569 0.157169146408340 -0.212384501532074 0.83184320573505 df.mm.trans3:probe3 -0.130537115612877 0.157169146408340 -0.830551788286292 0.406392365274285 df.mm.trans3:probe4 0.050868616496927 0.157169146408340 0.323655231700283 0.746255558257349 df.mm.trans3:probe5 -0.067393377255249 0.157169146408340 -0.428795210735285 0.668149354291952 df.mm.trans3:probe6 0.0483067264271315 0.157169146408340 0.307355021841412 0.75862657969145 df.mm.trans3:probe7 0.0621736565193818 0.157169146408340 0.39558436207224 0.692482107415177 df.mm.trans3:probe8 0.131170811932149 0.157169146408340 0.834583726702663 0.40411851740966 df.mm.trans3:probe9 0.106503854743589 0.157169146408340 0.677638437170628 0.498131813071635 df.mm.trans3:probe10 -0.080289257885072 0.157169146408340 -0.510846178908887 0.60955289269987 df.mm.trans3:probe11 0.102219222689090 0.157169146408340 0.650377157508475 0.515573330769678 df.mm.trans3:probe12 -0.0903237747795345 0.157169146408340 -0.574691514483796 0.565607847038204 df.mm.trans3:probe13 -0.0737766139776289 0.157169146408340 -0.469409013560144 0.638862726002654 df.mm.trans3:probe14 0.12653217386164 0.157169146408340 0.805070058298194 0.420938999770243 df.mm.trans3:probe15 0.188722447238787 0.157169146408340 1.20076014632330 0.230081615793725 df.mm.trans3:probe16 0.111934936011926 0.157169146408340 0.712194082425745 0.47648327234774 df.mm.trans3:probe17 0.0956127827473725 0.157169146408340 0.608343208144437 0.543075186518736 df.mm.trans3:probe18 0.242162467771034 0.157169146408340 1.54077612117249 0.123635364217522 df.mm.trans3:probe19 -0.107301989916206 0.157169146408340 -0.682716629620326 0.494917911523489 df.mm.trans3:probe20 0.157240722019153 0.157169146408340 1.00045540497259 0.317292197379899 df.mm.trans3:probe21 0.0731348066688525 0.157169146408340 0.465325468389587 0.641782971473727 df.mm.trans3:probe22 0.18002836701193 0.157169146408340 1.14544343547047 0.25225427912911 df.mm.trans3:probe23 0.10650088492624 0.157169146408340 0.677619541494108 0.49814379254896 df.mm.trans3:probe24 0.064502193609287 0.157169146408340 0.410399846810291 0.681586043525467 df.mm.trans3:probe25 0.0109651176519880 0.157169146408340 0.0697663498375155 0.94439127411684 df.mm.trans3:probe26 -0.0199399941626105 0.157169146408340 -0.126869647244915 0.899064860417034 df.mm.trans3:probe27 0.0383086013823844 0.157169146408340 0.243741232028168 0.807472969582183 df.mm.trans3:probe28 0.217757588166538 0.157169146408340 1.38549831912164 0.166158031972661 df.mm.trans3:probe29 0.045326301344056 0.157169146408340 0.288391852853193 0.773096691227367 df.mm.trans3:probe30 0.166981794963902 0.157169146408340 1.06243368230853 0.288252966360515 df.mm.trans3:probe31 0.0998267213509906 0.157169146408340 0.635154695640017 0.525448817399209