chr4.16298_chr4_105762989_105764682_-_1.R fitVsDatCorrelation=0.83505690168601 cont.fitVsDatCorrelation=0.306577819451173 fstatistic=8306.6466188805,40,416 cont.fstatistic=2768.16018818474,40,416 residuals=-0.452007783243072,-0.0829476241649896,-0.00802569702904442,0.067315400068931,0.677249580067926 cont.residuals=-0.586599135380618,-0.188473401721765,-0.0488896326942013,0.189088230079134,0.872299326721955 predictedValues: Include Exclude Both chr4.16298_chr4_105762989_105764682_-_1.R.tl.Lung 66.3420350399947 62.4325661408208 81.7942257597238 chr4.16298_chr4_105762989_105764682_-_1.R.tl.cerebhem 59.6281475081124 59.99197204284 69.8297159278861 chr4.16298_chr4_105762989_105764682_-_1.R.tl.cortex 61.8098512091643 60.6998004430204 68.4181139026785 chr4.16298_chr4_105762989_105764682_-_1.R.tl.heart 67.6944631611232 64.2629292562301 85.7057847002931 chr4.16298_chr4_105762989_105764682_-_1.R.tl.kidney 60.4220496927441 60.8090909943149 72.6103820443735 chr4.16298_chr4_105762989_105764682_-_1.R.tl.liver 62.2256325034882 61.490127458861 70.776157164192 chr4.16298_chr4_105762989_105764682_-_1.R.tl.stomach 70.7632608203081 66.9801950915894 74.9756016120576 chr4.16298_chr4_105762989_105764682_-_1.R.tl.testicle 65.1851156575185 62.1483451142613 83.0347707548783 diffExp=3.90946889917389,-0.363824534727613,1.11005076614392,3.4315339048931,-0.387041301570747,0.735505044627274,3.78306572871874,3.03677054325723 diffExpScore=1.03086529334342 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 71.2843728601076 60.6402351426721 66.4631452726886 cerebhem 75.0130092493191 66.0355400115385 67.0834786313206 cortex 72.0516062021428 69.2830263156703 71.569625509259 heart 74.9978896561785 58.786890923491 66.4594746236484 kidney 61.4648073394878 71.8736568712615 62.9560411530096 liver 69.977946142836 72.3478100728326 67.0072782802867 stomach 63.7793687489588 61.4245573360796 79.6578425149313 testicle 61.8001198563744 68.2277039699484 64.9835453982881 cont.diffExp=10.6441377174355,8.97746923778061,2.76857988647247,16.2109987326876,-10.4088495317736,-2.36986392999668,2.35481141287926,-6.42758411357405 cont.diffExpScore=2.64453140559301 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=0,0,0,1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.954503665030031 cont.tran.correlation=-0.363862985870314 tran.covariance=0.00205467249229431 cont.tran.covariance=-0.0024155846647411 tran.mean=63.3053488833995 cont.tran.mean=67.4367837936812 weightedLogRatios: wLogRatio Lung 0.252935178407537 cerebhem -0.0248866552299363 cortex 0.0745734823766075 heart 0.217917543589093 kidney -0.0262083567906379 liver 0.049045699652091 stomach 0.232511426392225 testicle 0.198145043653578 cont.weightedLogRatios: wLogRatio Lung 0.676924229358951 cerebhem 0.542241619701216 cortex 0.166831628084900 heart 1.02182277164576 kidney -0.656550908350994 liver -0.142040451342657 stomach 0.155619851335610 testicle -0.412936535036879 varWeightedLogRatios=0.0136382238158971 cont.varWeightedLogRatios=0.320245333428216 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.02002177441061 0.0806603354385767 49.8388923446999 1.66285299708772e-177 *** df.mm.trans1 0.0695596235182631 0.0654241712186496 1.06320985382899 0.288303577843252 df.mm.trans2 0.166220623535475 0.0654241712186496 2.54066074417604 0.0114267988465533 * df.mm.exp2 0.0115747513019533 0.0884696266680459 0.130833052403215 0.895970620628713 df.mm.exp3 0.0796614641007089 0.0884696266680459 0.900438569720806 0.368407865190164 df.mm.exp4 0.00236291590512597 0.0884696266680459 0.0267087812407309 0.978704816722522 df.mm.exp5 -0.000718604881395653 0.0884696266680459 -0.00812261686253056 0.993523054408003 df.mm.exp6 0.065417374923913 0.0884696266680459 0.739433152231679 0.460061054611078 df.mm.exp7 0.221870092227217 0.0884696266680459 2.5078673956624 0.0125260917729884 * df.mm.exp8 -0.0372081891988405 0.0884696266680459 -0.420575858632843 0.674281997073508 df.mm.trans1:exp2 -0.118270724050990 0.0713264933093533 -1.65815980238973 0.0980390136897047 . df.mm.trans2:exp2 -0.0514510298366068 0.0713264933093533 -0.721345287696344 0.471102307331376 df.mm.trans1:exp3 -0.150422416621904 0.0713264933093533 -2.10892768791395 0.0355479506041198 * df.mm.trans2:exp3 -0.107808086153884 0.0713264933093533 -1.51147324299689 0.131427158141969 df.mm.trans1:exp4 0.0178177667912967 0.0713264933093533 0.249805730866629 0.80286089547847 df.mm.trans2:exp4 0.0265329886666163 0.0713264933093533 0.371993454823846 0.710087137389231 df.mm.trans1:exp5 -0.0927510046185912 0.0713264933093533 -1.30037241865118 0.194193303692393 df.mm.trans2:exp5 -0.0256291269099444 0.0713264933093533 -0.359321280506349 0.719537072580765 df.mm.trans1:exp6 -0.129474070901371 0.0713264933093533 -1.81523112793198 0.0702083308510915 . df.mm.trans2:exp6 -0.0806277746406645 0.0713264933093533 -1.13040429859590 0.258957212262096 df.mm.trans1:exp7 -0.157353850138208 0.0713264933093533 -2.20610663496019 0.0279224306433007 * df.mm.trans2:exp7 -0.151560144700007 0.0713264933093533 -2.12487867646414 0.0341856832455173 * df.mm.trans1:exp8 0.0196156356482274 0.0713264933093533 0.275011916864490 0.783443616613425 df.mm.trans2:exp8 0.0326453469976029 0.0713264933093533 0.457688938330604 0.647414888862118 df.mm.trans1:probe2 0.512630076313425 0.0453271954375146 11.3095476427635 4.84849378325391e-26 *** df.mm.trans1:probe3 0.133625820221434 0.0453271954375146 2.94802753472013 0.00337825749604622 ** df.mm.trans1:probe4 -0.103261517127673 0.0453271954375146 -2.27813603138149 0.0232248633019427 * df.mm.trans1:probe5 0.306254499245676 0.0453271954375146 6.75652875254239 4.78378851968752e-11 *** df.mm.trans1:probe6 0.51890116475571 0.0453271954375146 11.4478992081263 1.44627241033604e-26 *** df.mm.trans2:probe2 -0.0687777258070941 0.0453271954375146 -1.51736115908400 0.129935061273235 df.mm.trans2:probe3 -0.100394034662338 0.0453271954375146 -2.21487417638127 0.0273098405671333 * df.mm.trans2:probe4 -0.215511280204917 0.0453271954375146 -4.75456904237564 2.74536752710365e-06 *** df.mm.trans2:probe5 -0.121761532306517 0.0453271954375146 -2.68627986203934 0.00751462009764087 ** df.mm.trans2:probe6 -0.171575177573386 0.0453271954375146 -3.78525906836459 0.000176122828582197 *** df.mm.trans3:probe2 0.0534139856358376 0.0453271954375146 1.17840923357967 0.239307115602916 df.mm.trans3:probe3 0.0191722911606022 0.0453271954375146 0.422975456026878 0.672531733569776 df.mm.trans3:probe4 -0.156471397649360 0.0453271954375146 -3.45204233659377 0.000613346579171893 *** df.mm.trans3:probe5 0.276918170598356 0.0453271954375146 6.10931622672527 2.30090234564554e-09 *** df.mm.trans3:probe6 0.410356148775001 0.0453271954375146 9.05319962583375 5.41618307625732e-18 *** df.mm.trans3:probe7 0.435443210908053 0.0453271954375146 9.6066656386965 7.22119207032465e-20 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.08287949307406 0.139551557300170 29.2571403147581 5.21354069336638e-103 *** df.mm.trans1 0.172243823852903 0.113191259731223 1.52170604216176 0.128842482229406 df.mm.trans2 -0.0192093553627575 0.113191259731223 -0.169707055194641 0.865323020821343 df.mm.exp2 0.126928683382058 0.153062519615878 0.829260381317354 0.407432652968767 df.mm.exp3 0.0699236412101068 0.153062519615878 0.456830590438375 0.648031259780711 df.mm.exp4 0.0197983437220671 0.153062519615878 0.129348084506596 0.89714474047891 df.mm.exp5 0.0759497008778819 0.153062519615878 0.496200513806277 0.620015014564596 df.mm.exp6 0.149875916541815 0.153062519615878 0.979181036075588 0.328059627836838 df.mm.exp7 -0.279489178026684 0.153062519615878 -1.82598051259109 0.068569858940772 . df.mm.exp8 -0.00236624763817034 0.153062519615878 -0.0154593537602061 0.987673122994156 df.mm.trans1:exp2 -0.0759442571554977 0.123402948474721 -0.615416876939978 0.538615971592554 df.mm.trans2:exp2 -0.0416942202725414 0.123402948474721 -0.337870535411741 0.735631184560219 df.mm.trans1:exp3 -0.0592181550566036 0.123402948474721 -0.479876338357786 0.631567533795503 df.mm.trans2:exp3 0.0633176854663808 0.123402948474721 0.513097022794001 0.608155818251839 df.mm.trans1:exp4 0.0309845025752732 0.123402948474721 0.251083972937813 0.801873160596524 df.mm.trans2:exp4 -0.0508380761603085 0.123402948474721 -0.411968083329247 0.680575008163931 df.mm.trans1:exp5 -0.224162057164064 0.123402948474721 -1.81650487232875 0.0700125124104158 . df.mm.trans2:exp5 0.0940014920473064 0.123402948474721 0.761744295490336 0.446644261859789 df.mm.trans1:exp6 -0.168372908160557 0.123402948474721 -1.3644156014234 0.173174428046654 df.mm.trans2:exp6 0.0266506485519098 0.123402948474721 0.215964439110377 0.829121319951563 df.mm.trans1:exp7 0.168241813314591 0.123402948474721 1.36335326986985 0.173508575109007 df.mm.trans2:exp7 0.292340270822942 0.123402948474721 2.36898935103506 0.0182926515827104 * df.mm.trans1:exp8 -0.140405577402003 0.123402948474721 -1.13778138316335 0.255866652680352 df.mm.trans2:exp8 0.120258327562946 0.123402948474721 0.97451745723548 0.330365996646445 df.mm.trans1:probe2 0.0180611391704287 0.0784212051308812 0.230309380483066 0.817964583345941 df.mm.trans1:probe3 0.0737366660398342 0.0784212051308812 0.940264382787427 0.347627610089592 df.mm.trans1:probe4 -0.0114878279532571 0.0784212051308811 -0.146488796417812 0.88360649463191 df.mm.trans1:probe5 0.0289191681780483 0.0784212051308812 0.368767199251575 0.712488857443794 df.mm.trans1:probe6 0.0409704105455485 0.0784212051308811 0.522440460806116 0.601641789440572 df.mm.trans2:probe2 0.145628254086707 0.0784212051308812 1.85700097114881 0.0640176169707967 . df.mm.trans2:probe3 0.0927307780702954 0.0784212051308812 1.18247070949155 0.237694387219370 df.mm.trans2:probe4 0.0640978994361878 0.0784212051308812 0.817354175177639 0.414193917487828 df.mm.trans2:probe5 0.143441595003053 0.0784212051308812 1.82911745316405 0.0680977007381653 . df.mm.trans2:probe6 0.0908517302762516 0.0784212051308812 1.15850974394776 0.247320703329006 df.mm.trans3:probe2 -0.0677078055133351 0.0784212051308812 -0.863386445035295 0.388422529150814 df.mm.trans3:probe3 -0.0922263087636923 0.0784212051308812 -1.17603789191675 0.240252299983005 df.mm.trans3:probe4 -0.075338813364551 0.0784212051308812 -0.960694409615539 0.337263888705785 df.mm.trans3:probe5 -0.0138978565498918 0.0784212051308812 -0.177220644935218 0.859421333641829 df.mm.trans3:probe6 -0.0338077873941181 0.0784212051308812 -0.431105175413902 0.666615198610867 df.mm.trans3:probe7 0.00811395388465419 0.0784212051308812 0.103466324843037 0.917642735078095