chr13.6516_chr13_89820470_89821395_-_0.R fitVsDatCorrelation=0.770781307643052 cont.fitVsDatCorrelation=0.273579462748283 fstatistic=7550.91356036347,39,393 cont.fstatistic=3307.18290788306,39,393 residuals=-0.354253459330166,-0.0831152794783526,-0.00286316933679316,0.0625409400197706,1.52650611891826 cont.residuals=-0.434368207296137,-0.128808312876009,-0.0329177485121862,0.0909256178489623,1.40142853600566 predictedValues: Include Exclude Both chr13.6516_chr13_89820470_89821395_-_0.R.tl.Lung 42.3918757114738 44.3532793962293 57.8008362954347 chr13.6516_chr13_89820470_89821395_-_0.R.tl.cerebhem 45.3937902227999 45.549687282111 61.9162212149512 chr13.6516_chr13_89820470_89821395_-_0.R.tl.cortex 45.2798035450653 42.7834553489157 58.9427721772493 chr13.6516_chr13_89820470_89821395_-_0.R.tl.heart 47.8875808350091 43.5172635217615 57.2481120755373 chr13.6516_chr13_89820470_89821395_-_0.R.tl.kidney 45.4128160082688 44.2075102036207 57.4681672618212 chr13.6516_chr13_89820470_89821395_-_0.R.tl.liver 56.8557782650267 49.0102390903552 55.27241660026 chr13.6516_chr13_89820470_89821395_-_0.R.tl.stomach 47.2944360275487 44.1846579794609 54.3445786698769 chr13.6516_chr13_89820470_89821395_-_0.R.tl.testicle 47.462262397423 48.9857256937617 56.5058797450521 diffExp=-1.96140368475554,-0.15589705931108,2.49634819614960,4.3703173132476,1.20530580464803,7.8455391746715,3.10977804808785,-1.52346329633870 diffExpScore=1.38333498248460 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 49.8345527140831 54.4092274189711 51.3000753414289 cerebhem 53.3883407131482 46.5026829224002 53.0678858451402 cortex 49.7214940842853 50.3479480424821 52.32701318095 heart 56.3471755369613 51.1657290247917 48.7403710025525 kidney 48.4403237682424 52.5650653199777 48.0078387616196 liver 49.8922629684111 46.8652721207242 50.9250715027205 stomach 52.6938389681588 45.7827023401949 50.7852799215059 testicle 52.3426338482303 50.6070303479213 49.8711868320255 cont.diffExp=-4.57467470488805,6.88565779074801,-0.626453958196805,5.18144651216961,-4.12474155173530,3.02699084768694,6.91113662796387,1.73560350030896 cont.diffExpScore=2.14510414757919 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=0.648009848823094 cont.tran.correlation=-0.28617758600278 tran.covariance=0.00283050320327828 cont.tran.covariance=-0.000909872554550032 tran.mean=46.285635095552 cont.tran.mean=50.6816425086865 weightedLogRatios: wLogRatio Lung -0.170497411967049 cerebhem -0.0130866695054055 cortex 0.214617930711555 heart 0.365664543275093 kidney 0.102281802928698 liver 0.588946478585408 stomach 0.259979650463974 testicle -0.122449967134098 cont.weightedLogRatios: wLogRatio Lung -0.347139697519248 cerebhem 0.539702032851492 cortex -0.048989119179705 heart 0.384237832529703 kidney -0.320436448603958 liver 0.242755928403545 stomach 0.547494486403312 testicle 0.132891666704075 varWeightedLogRatios=0.065885295325573 cont.varWeightedLogRatios=0.125869252824911 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.70729348253828 0.0781858891359433 47.4164011371967 1.12129846859373e-164 *** df.mm.trans1 0.0196967704228696 0.063838511156292 0.308540566910266 0.757834519375105 df.mm.trans2 0.0634551699814324 0.063838511156292 0.993995142306482 0.320836837872363 df.mm.exp2 0.0262567357217907 0.0867394560915036 0.302708097386405 0.762272481712486 df.mm.exp3 0.0103054171996371 0.0867394560915035 0.118808875037972 0.905487481429787 df.mm.exp4 0.112479116420884 0.0867394560915036 1.29674684957936 0.195479144109621 df.mm.exp5 0.0713177247213548 0.0867394560915036 0.82220627076702 0.411458066248936 df.mm.exp6 0.438132965939113 0.0867394560915035 5.05113803661526 6.73774654558099e-07 *** df.mm.exp7 0.167285282695937 0.0867394560915036 1.92859501585373 0.0545010955831144 . df.mm.exp8 0.234979025196327 0.0867394560915035 2.70902119732503 0.00704333270910842 ** df.mm.trans1:exp2 0.042161847437086 0.0708224693302433 0.595317387769783 0.551973975587064 df.mm.trans2:exp2 0.000360373017249662 0.0708224693302433 0.00508839949605897 0.995942644001908 df.mm.trans1:exp3 0.0555989447170143 0.0708224693302433 0.785046684234602 0.432899183021721 df.mm.trans2:exp3 -0.046340596179824 0.0708224693302433 -0.654320537225821 0.51328803546324 df.mm.trans1:exp4 0.00942034832363015 0.0708224693302433 0.133013553646419 0.894250741222371 df.mm.trans2:exp4 -0.131508044053230 0.0708224693302433 -1.85686894705706 0.0640778218162594 . df.mm.trans1:exp5 -0.00248010187942226 0.0708224693302433 -0.0350185739480166 0.972082709328692 df.mm.trans2:exp5 -0.074609685529496 0.0708224693302433 -1.05347478328654 0.292770703558176 df.mm.trans1:exp6 -0.144571843661664 0.0708224693302433 -2.04132735032902 0.0418857470607776 * df.mm.trans2:exp6 -0.338290378197236 0.0708224693302433 -4.77659676930774 2.52145186861689e-06 *** df.mm.trans1:exp7 -0.057849358969876 0.0708224693302433 -0.816822111922221 0.41452495722581 df.mm.trans2:exp7 -0.171094307971185 0.0708224693302433 -2.41581957801241 0.0161550677756985 * df.mm.trans1:exp8 -0.122000839062457 0.0708224693302433 -1.72262899354117 0.085742326917315 . df.mm.trans2:exp8 -0.135636731464693 0.0708224693302433 -1.91516523989333 0.0561969770230067 . df.mm.trans1:probe2 -0.0415371557922774 0.0433697280457518 -0.957745359815466 0.338779980775051 df.mm.trans1:probe3 -0.0340388201456319 0.0433697280457518 -0.784852054172984 0.433013167052276 df.mm.trans1:probe4 0.0204432904547862 0.0433697280457518 0.471372346011027 0.637636423086117 df.mm.trans1:probe5 0.0731991845867702 0.0433697280457518 1.687794410644 0.0922440811204618 . df.mm.trans1:probe6 0.221531264960276 0.0433697280457518 5.10796988919499 5.08891118236595e-07 *** df.mm.trans2:probe2 0.0293992287236382 0.0433697280457518 0.677874407988545 0.498250222110793 df.mm.trans2:probe3 0.104234123919653 0.0433697280457518 2.40338431012743 0.0167066038472665 * df.mm.trans2:probe4 -0.0118339290308965 0.0433697280457518 -0.272861499578983 0.785103001611323 df.mm.trans2:probe5 0.067446581822691 0.0433697280457518 1.55515344139442 0.120714429245526 df.mm.trans2:probe6 0.0680099591334579 0.0433697280457518 1.56814354615534 0.117652417391960 df.mm.trans3:probe2 0.106218876326774 0.0433697280457518 2.44914785296143 0.0147553209493999 * df.mm.trans3:probe3 0.560527197132954 0.0433697280457518 12.9243881018032 4.34250297979126e-32 *** df.mm.trans3:probe4 0.203221872577274 0.0433697280457518 4.68580002076311 3.84972427678412e-06 *** df.mm.trans3:probe5 0.532060761189026 0.0433697280457518 12.2680216170076 1.62633644371569e-29 *** df.mm.trans3:probe6 0.197318221235312 0.0433697280457518 4.54967624023735 7.17013301672707e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.99723348431248 0.118039668228089 33.8634761035468 1.35857279912431e-118 *** df.mm.trans1 -0.0574419159455385 0.0963789855220777 -0.596000420987833 0.551518045236271 df.mm.trans2 -0.0158785778091737 0.0963789855220777 -0.164751452022043 0.869224323076914 df.mm.exp2 -0.122019680713226 0.130953254256964 -0.931780438795295 0.352022056681147 df.mm.exp3 -0.0996676748995972 0.130953254256964 -0.761093532689335 0.447057629690369 df.mm.exp4 0.112544295193734 0.130953254256964 0.859423431913296 0.39063086437247 df.mm.exp5 0.00346992576256916 0.130953254256964 0.0264974382061578 0.978874026366577 df.mm.exp6 -0.140762604898527 0.130953254256964 -1.07490726898863 0.283075733290259 df.mm.exp7 -0.10675177793429 0.130953254256964 -0.81518995873761 0.415457327696274 df.mm.exp8 0.00490819289578817 0.130953254256964 0.0374804957970502 0.970120922192477 df.mm.trans1:exp2 0.190903491092729 0.10692288436217 1.78543154939684 0.0749623807797171 . df.mm.trans2:exp2 -0.0350040722519994 0.10692288436217 -0.327376804888964 0.743557220205644 df.mm.trans1:exp3 0.0973964180139383 0.10692288436217 0.910903391682143 0.362904639728294 df.mm.trans2:exp3 0.0220917782104303 0.10692288436217 0.206614125144631 0.83641822948152 df.mm.trans1:exp4 0.0102792474259537 0.10692288436217 0.0961370195657624 0.923460753611632 df.mm.trans2:exp4 -0.174008103400904 0.10692288436217 -1.62741684756186 0.104450025140812 df.mm.trans1:exp5 -0.0318458962395954 0.10692288436217 -0.297839853737267 0.765982782994989 df.mm.trans2:exp5 -0.0379519452340473 0.10692288436217 -0.35494688962464 0.722819782574638 df.mm.trans1:exp6 0.141919971858958 0.10692288436217 1.32731147972258 0.185176215401869 df.mm.trans2:exp6 -0.00849422158908045 0.10692288436217 -0.0794425032559799 0.936721098263697 df.mm.trans1:exp7 0.162541745980117 0.10692288436217 1.52017734042372 0.129270512341084 df.mm.trans2:exp7 -0.065875641595644 0.10692288436217 -0.61610423239715 0.538182403561943 df.mm.trans1:exp8 0.0441944517976168 0.10692288436217 0.413330149679848 0.679590180523559 df.mm.trans2:exp8 -0.0773514477213834 0.10692288436217 -0.723432108877441 0.469844804765975 df.mm.trans1:probe2 -0.117384923792921 0.0654766271284818 -1.79277597122684 0.0737776696708546 . df.mm.trans1:probe3 -0.10313877963206 0.0654766271284818 -1.57519994775656 0.116014923149682 df.mm.trans1:probe4 -0.086155423599906 0.0654766271284819 -1.31581951267659 0.189001660501072 df.mm.trans1:probe5 -0.000572478707685106 0.0654766271284818 -0.00874325286428937 0.993028418888516 df.mm.trans1:probe6 -0.0657443381644918 0.0654766271284818 -1.00408865037420 0.315953704812683 df.mm.trans2:probe2 -0.00163905350870994 0.0654766271284818 -0.0250326502844091 0.980041626099846 df.mm.trans2:probe3 0.0430068938414556 0.0654766271284818 0.656828180185046 0.51167583874501 df.mm.trans2:probe4 0.0938287433127004 0.0654766271284818 1.43301125038992 0.152649623364342 df.mm.trans2:probe5 0.0521404806594803 0.0654766271284818 0.796322030411973 0.426325647193117 df.mm.trans2:probe6 -0.00519080854428128 0.0654766271284818 -0.079277274531805 0.93685243259933 df.mm.trans3:probe2 0.074741725665664 0.0654766271284818 1.14150237945216 0.254355977576013 df.mm.trans3:probe3 -0.00545244459887706 0.0654766271284818 -0.0832731439904192 0.933676752419533 df.mm.trans3:probe4 0.0360429112552856 0.0654766271284818 0.550469882704254 0.582309874758925 df.mm.trans3:probe5 0.0142049306716988 0.0654766271284818 0.216946585287986 0.828362549509275 df.mm.trans3:probe6 -0.0368617414235096 0.0654766271284818 -0.562975569147406 0.573772544945984