chr15.8358_chr15_89931910_89933531_+_0.R fitVsDatCorrelation=0.831786878852544 cont.fitVsDatCorrelation=0.316447988336993 fstatistic=7254.07000984354,36,324 cont.fstatistic=2478.02283250025,36,324 residuals=-0.556864593231622,-0.0844357226185194,-0.0095533736522471,0.0739356687389209,1.09978610953196 cont.residuals=-0.425262990845616,-0.172476391756422,-0.0644293647196238,0.117644141444071,1.17297759744449 predictedValues: Include Exclude Both chr15.8358_chr15_89931910_89933531_+_0.R.tl.Lung 50.2082781771176 53.4397251379967 77.8529260250662 chr15.8358_chr15_89931910_89933531_+_0.R.tl.cerebhem 55.5940257825286 68.8717252244124 101.654796783589 chr15.8358_chr15_89931910_89933531_+_0.R.tl.cortex 51.0403639232303 50.3255536316051 85.878163196907 chr15.8358_chr15_89931910_89933531_+_0.R.tl.heart 52.9228838407143 51.3867721121684 78.5062220967037 chr15.8358_chr15_89931910_89933531_+_0.R.tl.kidney 56.8502096706757 52.3743295273281 87.0416091164611 chr15.8358_chr15_89931910_89933531_+_0.R.tl.liver 61.7912257712431 55.692111418896 101.03365865937 chr15.8358_chr15_89931910_89933531_+_0.R.tl.stomach 49.3529955912642 51.5641623593516 84.3353270998282 chr15.8358_chr15_89931910_89933531_+_0.R.tl.testicle 59.889190514836 56.2963563256955 85.1261135550132 diffExp=-3.23144696087908,-13.2776994418838,0.714810291625156,1.53611172854593,4.47588014334765,6.09911435234704,-2.21116676808742,3.59283418914042 diffExpScore=10.6431619087579 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 58.6672750524983 56.3686834173983 56.4768641221051 cerebhem 62.0514795044741 58.7250484375846 53.5882320724319 cortex 65.2384230062116 61.9586268149256 66.7299307676237 heart 62.7414226985475 61.5240531653585 63.9683338895196 kidney 54.3123666069102 61.9296793474804 62.0521609258454 liver 62.5097369665535 53.0383581625762 55.5622964224686 stomach 58.0294095078148 61.6642757266734 64.2627832238278 testicle 65.0596679216242 56.2410809298112 64.7879468635242 cont.diffExp=2.29859163509995,3.32643106688950,3.27979619128599,1.21736953318903,-7.61731274057024,9.47137880397738,-3.63486621885864,8.81858699181304 cont.diffExpScore=2.18416229139242 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=0.349490546604599 cont.tran.correlation=-0.276866299973874 tran.covariance=0.00325424585048443 cont.tran.covariance=-0.00105246937728516 tran.mean=54.8499943130665 cont.tran.mean=60.0037242041526 weightedLogRatios: wLogRatio Lung -0.246214929269471 cerebhem -0.883485567520728 cortex 0.0553653683938553 heart 0.116468436892207 kidney 0.327965620172775 liver 0.423153119515147 stomach -0.171847479643995 testicle 0.251273528379459 cont.weightedLogRatios: wLogRatio Lung 0.161947596060432 cerebhem 0.225924684208715 cortex 0.214180730125426 heart 0.0809066805907876 kidney -0.53291453179736 liver 0.665963795040043 stomach -0.248567861580006 testicle 0.59755580157137 varWeightedLogRatios=0.176437479248793 cont.varWeightedLogRatios=0.158057233541461 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.60958200876864 0.086169277843002 41.8894308867855 8.09229586339415e-133 *** df.mm.trans1 0.238656265636596 0.0729844781966757 3.26995919589196 0.00119129011916001 ** df.mm.trans2 0.406377526618374 0.0729844781966757 5.56799934259012 5.41466782865613e-08 *** df.mm.exp2 0.088825917172411 0.101639735085016 0.873929050465477 0.382804182969321 df.mm.exp3 -0.141712662877325 0.101639735085016 -1.39426438645073 0.164193125817557 df.mm.exp4 0.00512591564469584 0.101639735085016 0.0504322019376406 0.959809051610205 df.mm.exp5 -0.0074626048006308 0.101639735085016 -0.073422119748627 0.941515522264492 df.mm.exp6 -0.0117666480070602 0.101639735085016 -0.115768188467021 0.907907980545507 df.mm.exp7 -0.132888292906734 0.101639735085016 -1.30744430606377 0.191988876282588 df.mm.exp8 0.139079178465787 0.101639735085016 1.36835439751446 0.172149278342565 df.mm.trans1:exp2 0.0130699113272728 0.0880225926175447 0.148483598796744 0.882053497737519 df.mm.trans2:exp2 0.164865416679937 0.0880225926175447 1.87298978338746 0.0619700581543324 . df.mm.trans1:exp3 0.158149515016601 0.0880225926175447 1.79669230720976 0.0733157900935237 . df.mm.trans2:exp3 0.0816712496281054 0.0880225926175447 0.927844172722386 0.354179080908247 df.mm.trans1:exp4 0.0475299994326894 0.0880225926175447 0.539974999818578 0.589585208351964 df.mm.trans2:exp4 -0.0442995140469513 0.0880225926175447 -0.503274360929486 0.61511335447372 df.mm.trans1:exp5 0.131702596099739 0.0880225926175447 1.49623627506614 0.135565528464699 df.mm.trans2:exp5 -0.0126752042658888 0.0880225926175447 -0.143999442517697 0.885590433107336 df.mm.trans1:exp6 0.219348107550131 0.0880225926175446 2.49195236163051 0.0132038014272849 * df.mm.trans2:exp6 0.0530507728393352 0.0880225926175447 0.602694958893554 0.547133233921467 df.mm.trans1:exp7 0.115706840852297 0.0880225926175447 1.31451298367272 0.189603384356077 df.mm.trans2:exp7 0.0971608103391087 0.0880225926175447 1.10381672988512 0.270491761858673 df.mm.trans1:exp8 0.0372369344909694 0.0880225926175447 0.423038374395113 0.67254790903293 df.mm.trans2:exp8 -0.0870037501580179 0.0880225926175447 -0.988425216421951 0.323682007121222 df.mm.trans1:probe2 0.120784692983693 0.0440112963087723 2.74440207660092 0.00640002456792853 ** df.mm.trans1:probe3 0.187485219678875 0.0440112963087723 4.25993404882976 2.68329821531988e-05 *** df.mm.trans1:probe4 0.0769624212110789 0.0440112963087723 1.74869698613578 0.0812904269484592 . df.mm.trans1:probe5 0.0970768756244977 0.0440112963087723 2.20572634224246 0.0281048439223757 * df.mm.trans1:probe6 0.129165574117248 0.0440112963087723 2.93482775901566 0.00357567924780165 ** df.mm.trans2:probe2 0.0403978206371098 0.0440112963087723 0.91789663166676 0.359355756480241 df.mm.trans2:probe3 -0.0974045187765651 0.0440112963087723 -2.21317086625214 0.0275823693919248 * df.mm.trans2:probe4 -0.0804222672825131 0.0440112963087723 -1.82730966882435 0.0685729319249873 . df.mm.trans2:probe5 -0.0803781479899953 0.0440112963087723 -1.82630721499504 0.0687241071934458 . df.mm.trans2:probe6 -0.118839232517007 0.0440112963087723 -2.70019841459020 0.00729382211480778 ** df.mm.trans3:probe2 0.0562664832217516 0.0440112963087723 1.27845548622335 0.202003907237476 df.mm.trans3:probe3 0.244350562693082 0.0440112963087723 5.55199649150933 5.88733828812653e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.97307965145468 0.147255892507621 26.9807855142303 7.41169776380986e-85 *** df.mm.trans1 0.110604457341027 0.124724202698274 0.886792258023852 0.375848511730792 df.mm.trans2 0.0216822609378087 0.124724202698274 0.173841647961953 0.862098476157706 df.mm.exp2 0.149536464856348 0.173693574773271 0.860921108057935 0.389918140365215 df.mm.exp3 0.0338971161750876 0.173693574773271 0.195154692505665 0.845394169049686 df.mm.exp4 0.0300971548601264 0.173693574773271 0.173277306886069 0.86254166174454 df.mm.exp5 -0.0771886048697018 0.173693574773271 -0.444395280426803 0.657053508516699 df.mm.exp6 0.0188681249018315 0.173693574773271 0.108628801764606 0.913564157769386 df.mm.exp7 -0.0502907110317432 0.173693574773271 -0.289536968177375 0.772355684336269 df.mm.exp8 -0.0361320492916284 0.173693574773271 -0.208021795502757 0.835342648085308 df.mm.trans1:exp2 -0.0934541862727395 0.150423048227785 -0.621275711227593 0.534855077505567 df.mm.trans2:exp2 -0.108583855225326 0.150423048227785 -0.72185650074647 0.470903334046529 df.mm.trans1:exp3 0.072269412564533 0.150423048227785 0.480441085431906 0.631238008925076 df.mm.trans2:exp3 0.0606559909491509 0.150423048227785 0.403236017776343 0.687040570349814 df.mm.trans1:exp4 0.0370426472165308 0.150423048227785 0.246256459052986 0.805639649212516 df.mm.trans2:exp4 0.057417306135086 0.150423048227785 0.381705508640798 0.702930024822174 df.mm.trans1:exp5 5.84753337147959e-05 0.150423048227785 0.000388739188599921 0.999690070261364 df.mm.trans2:exp5 0.171274396411564 0.150423048227785 1.13861804044952 0.255703685590773 df.mm.trans1:exp6 0.0445721346418408 0.150423048227785 0.296311869537077 0.767181762253922 df.mm.trans2:exp6 -0.0797664799400186 0.150423048227785 -0.53028097010259 0.596280369110744 df.mm.trans1:exp7 0.0393585770166919 0.150423048227785 0.261652569073666 0.793755566593445 df.mm.trans2:exp7 0.140081728956288 0.150423048227785 0.931251763653687 0.352416716634777 df.mm.trans1:exp8 0.139554789607964 0.150423048227785 0.92774871439008 0.354228531195168 df.mm.trans2:exp8 0.0338657708220677 0.150423048227785 0.225136847185711 0.822014746670225 df.mm.trans1:probe2 -0.00990469455361694 0.0752115241138925 -0.131691182572212 0.895310297843216 df.mm.trans1:probe3 -0.0049329145390801 0.0752115241138925 -0.0655872168154738 0.947746916433571 df.mm.trans1:probe4 -0.043950964933092 0.0752115241138925 -0.584364769241177 0.559381579828448 df.mm.trans1:probe5 -0.0482587299837064 0.0752115241138925 -0.641640101730001 0.521560548764417 df.mm.trans1:probe6 0.000829013611891579 0.0752115241138925 0.0110224280342492 0.991212336687327 df.mm.trans2:probe2 0.101623902992969 0.0752115241138925 1.35117462636551 0.177582232348066 df.mm.trans2:probe3 0.066187923319031 0.0752115241138925 0.880023694491324 0.379498730081245 df.mm.trans2:probe4 -0.0458730686329344 0.0752115241138925 -0.609920742511068 0.542341827566073 df.mm.trans2:probe5 0.0362429204536782 0.0752115241138925 0.481879883178484 0.630216611895764 df.mm.trans2:probe6 0.176184821941964 0.0752115241138925 2.34252428757019 0.0197586847776932 * df.mm.trans3:probe2 -0.0644118999390686 0.0752115241138925 -0.856409981022721 0.392403985969037 df.mm.trans3:probe3 -0.150194000082932 0.0752115241138925 -1.9969546137038 0.0466662717558366 *