chr5.18536_chr5_27698479_27699392_+_1.R fitVsDatCorrelation=0.610610194126171 cont.fitVsDatCorrelation=0.221213733490010 fstatistic=18385.2462974769,56,784 cont.fstatistic=12118.9131888888,56,784 residuals=-0.320103560574053,-0.0704315952901595,-0.00303113542521768,0.0664747915074083,0.403396949965968 cont.residuals=-0.342075353032727,-0.0858123149326909,-0.0129937332770126,0.0753611968214076,0.539855703767975 predictedValues: Include Exclude Both chr5.18536_chr5_27698479_27699392_+_1.R.tl.Lung 44.441457621023 43.0131571791774 46.5024852433747 chr5.18536_chr5_27698479_27699392_+_1.R.tl.cerebhem 48.1484452936313 46.7709030654478 45.8724028394462 chr5.18536_chr5_27698479_27699392_+_1.R.tl.cortex 48.5251726065987 46.1535368634562 51.5273609032516 chr5.18536_chr5_27698479_27699392_+_1.R.tl.heart 46.1891764971425 45.2639162508474 47.7221117766395 chr5.18536_chr5_27698479_27699392_+_1.R.tl.kidney 45.2862192048548 41.9948914447951 48.4583403160554 chr5.18536_chr5_27698479_27699392_+_1.R.tl.liver 47.2098092899042 46.356762544564 48.686396823482 chr5.18536_chr5_27698479_27699392_+_1.R.tl.stomach 45.4587772877001 44.877643639504 46.9738171365043 chr5.18536_chr5_27698479_27699392_+_1.R.tl.testicle 47.5753511110822 47.5640943833361 46.9711100562024 diffExp=1.42830044184566,1.37754222818351,2.37163574314255,0.925260246295132,3.29132776005968,0.853046745340173,0.581133648196086,0.0112567277461224 diffExpScore=0.9155369989499 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 44.9805996472237 46.975325974405 46.1584189760324 cerebhem 46.4322587168937 48.6118086855794 47.3640183950724 cortex 46.3519739281737 47.2311624440796 46.0778412380891 heart 46.2659594508853 49.887777044065 47.1948826995338 kidney 48.0463217018153 47.0705376182257 47.655382361667 liver 45.9028419557535 46.5364113822808 47.145028936851 stomach 46.16523148121 48.7470270713615 45.0747525564617 testicle 47.546506904406 47.0527627729793 48.1734685158416 cont.diffExp=-1.99472632718130,-2.17954996868568,-0.879188515905966,-3.62181759317971,0.975784083589609,-0.633569426527295,-2.58179559015149,0.493744131426716 cont.diffExpScore=1.16977814476444 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=0.839224178961092 cont.tran.correlation=-0.0885050455648716 tran.covariance=0.00113822763325044 cont.tran.covariance=-4.01922595064671e-05 tran.mean=45.9268321426915 cont.tran.mean=47.1127816737086 weightedLogRatios: wLogRatio Lung 0.123409600139846 cerebhem 0.112039885893003 cortex 0.193272169005550 heart 0.0773521408345912 kidney 0.284862632528846 liver 0.0701205315795318 stomach 0.0490248870278737 testicle 0.000913936258495969 cont.weightedLogRatios: wLogRatio Lung -0.166098366654363 cerebhem -0.177108717369216 cortex -0.0722599976451097 heart -0.291837775327969 kidney 0.0792397701013912 liver -0.0525480457769142 stomach -0.210019921063244 testicle 0.0402568875260567 varWeightedLogRatios=0.00797843842682703 cont.varWeightedLogRatios=0.0162462470073912 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.72964376705113 0.0508767258997781 73.307464289234 0 *** df.mm.trans1 0.0508051527235323 0.0431845314071281 1.17646645843068 0.239765609027099 df.mm.trans2 0.0391568246045602 0.0387026095358252 1.01173603212245 0.311976418767292 df.mm.exp2 0.177513381660891 0.0496490532631849 3.5753628718741 0.000371162460845197 *** df.mm.exp3 0.0557702797597504 0.0496490532631849 1.12328989364847 0.261658242955638 df.mm.exp4 0.0636877537353907 0.0496490532631849 1.28275867412391 0.19995573832041 df.mm.exp5 -0.0463267697849183 0.0496490532631849 -0.933084656002291 0.351063528296692 df.mm.exp6 0.0893961564309743 0.0496490532631849 1.80056114981878 0.0721563146244974 . df.mm.exp7 0.0549822570701677 0.0496490532631849 1.10741803632613 0.268452875059005 df.mm.exp8 0.158687160640502 0.0496490532631849 3.19617697037074 0.00144842265336483 ** df.mm.trans1:exp2 -0.0973972967380463 0.0442910607830188 -2.19902831443108 0.0281672553879686 * df.mm.trans2:exp2 -0.093758151263968 0.0335084172143241 -2.79804774616118 0.00526737389837634 ** df.mm.trans1:exp3 0.0321396423678789 0.0442910607830188 0.725646254564335 0.468272205483109 df.mm.trans2:exp3 0.0146972668785709 0.0335084172143241 0.438614178179926 0.661061960473619 df.mm.trans1:exp4 -0.0251150220345088 0.0442910607830188 -0.567044942941125 0.570845958803825 df.mm.trans2:exp4 -0.0126836392174961 0.0335084172143241 -0.378520988812151 0.705146079249343 df.mm.trans1:exp5 0.0651567801922244 0.0442910607830188 1.47110453080874 0.141664092716538 df.mm.trans2:exp5 0.0223686986016481 0.0335084172143241 0.667554616458757 0.504614379598157 df.mm.trans1:exp6 -0.0289672254797426 0.0442910607830188 -0.654019681796573 0.513290913476452 df.mm.trans2:exp6 -0.0145350231471913 0.0335084172143241 -0.4337722982922 0.66457313278767 df.mm.trans1:exp7 -0.0323490994370339 0.0442910607830188 -0.730375359387115 0.465378965338286 df.mm.trans2:exp7 -0.0125485506122537 0.0335084172143241 -0.374489506084146 0.708141429973793 df.mm.trans1:exp8 -0.0905451313126703 0.0442910607830188 -2.04432067581875 0.0412554508563484 * df.mm.trans2:exp8 -0.0581150535252303 0.0335084172143241 -1.73434194618979 0.0832504855424481 . df.mm.trans1:probe2 0.0198350180688202 0.0317279190822692 0.625159753382781 0.532048126723696 df.mm.trans1:probe3 0.116009340778898 0.0317279190822692 3.65638037836929 0.000272875339566181 *** df.mm.trans1:probe4 -0.0585496236313858 0.0317279190822692 -1.84536601595488 0.0653612350651338 . df.mm.trans1:probe5 -0.0250273701545271 0.0317279190822692 -0.788812215816366 0.430460230545184 df.mm.trans1:probe6 0.128428979462293 0.0317279190822692 4.0478223336766 5.68235001634094e-05 *** df.mm.trans1:probe7 0.0414075109161352 0.0317279190822692 1.30508120651617 0.192248099088052 df.mm.trans1:probe8 0.113388217755352 0.0317279190822692 3.57376786865036 0.000373395096254702 *** df.mm.trans1:probe9 0.044806550834961 0.0317279190822692 1.41221208736632 0.158284319269577 df.mm.trans1:probe10 -0.0946833038633666 0.0317279190822692 -2.98422671899335 0.00293126120628086 ** df.mm.trans1:probe11 0.0289162717805032 0.0317279190822692 0.91138254940466 0.362373979480388 df.mm.trans1:probe12 0.00286903465554157 0.0317279190822692 0.0904261842102623 0.927971646617435 df.mm.trans1:probe13 -0.000135247049217623 0.0317279190822692 -0.00426271413725346 0.996599940887797 df.mm.trans1:probe14 -0.0375470077233787 0.0317279190822692 -1.18340593425055 0.237007011711838 df.mm.trans1:probe15 0.0600231661907326 0.0317279190822692 1.89180910462785 0.0588847049327018 . df.mm.trans1:probe16 0.0170784114869228 0.0317279190822692 0.538277075235826 0.59053855592783 df.mm.trans2:probe2 0.0226308576673540 0.0317279190822692 0.713278977063485 0.475885400600098 df.mm.trans2:probe3 -0.0508271806161798 0.0317279190822692 -1.60197019175405 0.109565008795633 df.mm.trans2:probe4 -0.00248943811528131 0.0317279190822692 -0.0784620670780932 0.937480524111777 df.mm.trans2:probe5 -0.0581291028145958 0.0317279190822692 -1.83211204818915 0.0673139223176369 . df.mm.trans2:probe6 -0.0497814313404814 0.0317279190822692 -1.56901028433035 0.117049032525130 df.mm.trans3:probe2 0.108593961393473 0.0317279190822692 3.42266258029382 0.000652268839486363 *** df.mm.trans3:probe3 0.0249715327661408 0.0317279190822692 0.78705233398354 0.431489083772196 df.mm.trans3:probe4 0.164098605695956 0.0317279190822692 5.17205699089358 2.94230346023769e-07 *** df.mm.trans3:probe5 0.0663922942788616 0.0317279190822692 2.09255117257168 0.0367102217382132 * df.mm.trans3:probe6 -0.100534368302166 0.0317279190822692 -3.16864046587755 0.00159095772644557 ** df.mm.trans3:probe7 -0.0720560826544424 0.0317279190822692 -2.2710623557632 0.0234135753497869 * df.mm.trans3:probe8 -0.0108908581994580 0.0317279190822692 -0.343257878690957 0.731496488906476 df.mm.trans3:probe9 0.189127950392666 0.0317279190822692 5.96093144029601 3.78954342531614e-09 *** df.mm.trans3:probe10 -0.00880330567640473 0.0317279190822692 -0.277462434696021 0.781498192086861 df.mm.trans3:probe11 -0.0895020224574277 0.0317279190822692 -2.82092318205152 0.00490957245996466 ** df.mm.trans3:probe12 -0.0861226351026736 0.0317279190822692 -2.71441171037285 0.00678526033314417 ** df.mm.trans3:probe13 0.0734250953051465 0.0317279190822692 2.31421087259957 0.0209137939294012 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81110508169078 0.0626522434090827 60.8295070426525 4.04151835121757e-299 *** df.mm.trans1 -0.0169204100182411 0.0531796754876942 -0.318174375136163 0.750437401959018 df.mm.trans2 0.0458704321540459 0.0476604040515859 0.96244320766558 0.336123764145361 df.mm.exp2 0.0402237004624912 0.0611404235446122 0.657890445151092 0.510801648920556 df.mm.exp3 0.0372112156151809 0.0611404235446122 0.608618872063081 0.54295338410957 df.mm.exp4 0.0661226362560481 0.0611404235446122 1.08148803071017 0.279812622733467 df.mm.exp5 0.0360428863501608 0.0611404235446122 0.589509922577842 0.555689095493369 df.mm.exp6 -0.010240886440901 0.0611404235446122 -0.167497800099938 0.867021572676567 df.mm.exp7 0.086774490588176 0.0611404235446122 1.41926544759474 0.156219092435103 df.mm.exp8 0.0143950934405638 0.0611404235446122 0.235443142294560 0.8139262896355 df.mm.trans1:exp2 -0.008460529685081 0.0545423132473285 -0.155118644248104 0.876767710370575 df.mm.trans2:exp2 -0.00597970659287113 0.0412640057793909 -0.144913381043041 0.884816506394453 df.mm.trans1:exp3 -0.007178614924266 0.0545423132473285 -0.131615520077297 0.89532217651005 df.mm.trans2:exp3 -0.0317798041931975 0.0412640057793909 -0.770158000730741 0.441438173023652 df.mm.trans1:exp4 -0.0379474404732125 0.0545423132473285 -0.695743143513833 0.486795881918565 df.mm.trans2:exp4 -0.00596909711243557 0.0412640057793909 -0.144656268815685 0.8850194458909 df.mm.trans1:exp5 0.0298914168036191 0.0545423132473285 0.548040869995245 0.583819841036744 df.mm.trans2:exp5 -0.0340180939199052 0.0412640057793909 -0.824401152466282 0.409962396571289 df.mm.trans1:exp6 0.0305366399767799 0.0545423132473285 0.559870642785316 0.575727597096591 df.mm.trans2:exp6 0.000853448504903333 0.0412640057793909 0.0206826382650805 0.983504081232029 df.mm.trans1:exp7 -0.0607788190229497 0.0545423132473285 -1.11434252425894 0.265473762223957 df.mm.trans2:exp7 -0.0497527627476434 0.0412640057793909 -1.20571819938267 0.228289923636774 df.mm.trans1:exp8 0.0410819534644759 0.0545423132473285 0.753212524708753 0.451548332984091 df.mm.trans2:exp8 -0.0127479937895412 0.0412640057793909 -0.308937378927669 0.757451239916665 df.mm.trans1:probe2 0.01973805261485 0.0390714078795443 0.505178945066471 0.613575135031703 df.mm.trans1:probe3 0.0421792136754703 0.0390714078795443 1.07954168955230 0.280678315755863 df.mm.trans1:probe4 0.0276122303811825 0.0390714078795443 0.706711937954986 0.479955489244628 df.mm.trans1:probe5 0.0124887848602877 0.0390714078795443 0.31964000116889 0.749326402847805 df.mm.trans1:probe6 0.0395681037912436 0.0390714078795443 1.01271251635545 0.311509931449212 df.mm.trans1:probe7 0.00592565825082666 0.0390714078795443 0.151662265897744 0.879492337158063 df.mm.trans1:probe8 -0.003375166601985 0.0390714078795443 -0.0863845657261829 0.931182772443599 df.mm.trans1:probe9 0.0289112163499527 0.0390714078795443 0.739958397175882 0.459546755228098 df.mm.trans1:probe10 -0.00468490857703928 0.0390714078795443 -0.119906315930121 0.904588080649977 df.mm.trans1:probe11 0.0212148799659155 0.0390714078795443 0.542977105696324 0.587299894354177 df.mm.trans1:probe12 0.068588676341971 0.0390714078795443 1.75546979400966 0.0795694951978791 . df.mm.trans1:probe13 -0.0346035432812284 0.0390714078795443 -0.885648743139991 0.376078393324318 df.mm.trans1:probe14 0.00180695800314717 0.0390714078795443 0.0462475785033894 0.963124698191582 df.mm.trans1:probe15 0.0477835226657244 0.0390714078795443 1.22297928994622 0.221705004195393 df.mm.trans1:probe16 0.0400580824660317 0.0390714078795443 1.02525311065138 0.305559999974075 df.mm.trans2:probe2 -0.0502401582202757 0.0390714078795443 -1.28585482189852 0.198873379976733 df.mm.trans2:probe3 -0.00438909524244003 0.0390714078795443 -0.112335221089843 0.91058637131083 df.mm.trans2:probe4 -0.0563497055184068 0.0390714078795443 -1.44222357413203 0.149638663640877 df.mm.trans2:probe5 -0.0106389818714095 0.0390714078795443 -0.272295841097129 0.785466249659066 df.mm.trans2:probe6 -0.0180896149469953 0.0390714078795443 -0.462988561936771 0.643501050296253 df.mm.trans3:probe2 -0.0242802372246055 0.0390714078795443 -0.621432360447838 0.534495829348852 df.mm.trans3:probe3 -0.00343115493286459 0.0390714078795443 -0.0878175402187378 0.930044121798399 df.mm.trans3:probe4 0.0172243216531170 0.0390714078795443 0.440842103929783 0.659448842721674 df.mm.trans3:probe5 -0.00522502465339376 0.0390714078795443 -0.133730135077352 0.893650294100306 df.mm.trans3:probe6 -0.00729986646898523 0.0390714078795443 -0.186833975665542 0.85183914314205 df.mm.trans3:probe7 -0.0278962084150613 0.0390714078795443 -0.713980118173991 0.475451973618135 df.mm.trans3:probe8 -0.0263950765226163 0.0390714078795443 -0.675559903139179 0.499519310733082 df.mm.trans3:probe9 -0.0108001428395895 0.0390714078795443 -0.276420621260590 0.782297872807802 df.mm.trans3:probe10 0.0220809870439636 0.0390714078795443 0.565144391828379 0.57213723417317 df.mm.trans3:probe11 0.00217005585804812 0.0390714078795443 0.0555407643548017 0.955721805370455 df.mm.trans3:probe12 -0.0406953019713539 0.0390714078795443 -1.04156221083243 0.297935773830839 df.mm.trans3:probe13 0.0008631173176878 0.0390714078795443 0.0220907657166785 0.982381173541382