fitVsDatCorrelation=0.933861890066154 cont.fitVsDatCorrelation=0.270120776249696 fstatistic=11692.1027153049,49,623 cont.fstatistic=1602.18648256678,49,623 residuals=-0.754728285402569,-0.0806958334779111,-0.00287878652065258,0.0754464314831738,0.509607447906108 cont.residuals=-0.81256319414265,-0.256363929675216,-0.104199918601686,0.172275790948155,1.20006625564679 predictedValues: Include Exclude Both Lung 49.7828683723535 56.5266929102646 109.305844767972 cerebhem 51.5858322478051 53.7224155760881 86.6375847646835 cortex 49.647861344492 69.8412579374748 129.403140615844 heart 52.427667737662 55.0974283502276 102.784743022888 kidney 47.6012670070743 57.4611183240363 99.6966478472466 liver 50.338849878176 56.3489179876502 81.7963216583425 stomach 51.9603606511809 57.6584083525159 111.326327393787 testicle 52.6505875389556 54.1056743573798 106.135118630951 diffExp=-6.74382453791108,-2.13658332828293,-20.1933965929828,-2.66976061256558,-9.85985131696198,-6.01006810947417,-5.69804770133496,-1.45508681842421 diffExpScore=0.98206812574242 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,-1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,-1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,-1,0,-1,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 80.9859623464979 66.8833923612371 66.1315493370765 cerebhem 72.6471316987523 67.9178405263397 75.2390197097323 cortex 68.762053436318 76.741286202933 68.8990938975615 heart 78.04117663323 99.4259867762933 63.0654360708695 kidney 77.100623474483 67.9057365940117 70.0404259489352 liver 66.6597740570261 62.3537993908348 60.4383750347456 stomach 69.2208525730843 62.0466920318619 71.0564541887369 testicle 66.3098723543542 64.1170910028821 68.0567456661447 cont.diffExp=14.1025699852608,4.72929117241262,-7.97923276661497,-21.3848101430633,9.19488688047127,4.30597466619125,7.17416054122236,2.19278135147202 cont.diffExpScore=5.32886348853971 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=1,0,0,-1,0,0,0,0 cont.diffExp1.2Score=2 tran.correlation=-0.409818718221839 cont.tran.correlation=0.427634423770946 tran.covariance=-0.00120138799437831 cont.tran.covariance=0.00531101936794477 tran.mean=54.1723255358335 cont.tran.mean=71.6949544662587 weightedLogRatios: wLogRatio Lung -0.504508370075978 cerebhem -0.16085336422903 cortex -1.39087504286447 heart -0.197892972948577 kidney -0.744898814539845 liver -0.448343121575039 stomach -0.416480853718538 testicle -0.108428060374394 cont.weightedLogRatios: wLogRatio Lung 0.822432804518431 cerebhem 0.286221124841342 cortex -0.470500666694938 heart -1.08454698153346 kidney 0.543726048659619 liver 0.278207534403618 stomach 0.457638412937475 testicle 0.140481103396796 varWeightedLogRatios=0.174688516036818 cont.varWeightedLogRatios=0.377090716386168 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.43660401042204 0.0692890534784893 49.5980798971215 1.66907693030595e-218 *** df.mm.trans1 0.429096518891381 0.0583626152860826 7.35224966852548 6.15594511085447e-13 *** df.mm.trans2 0.567944566056999 0.0537803798675192 10.5604416974379 4.2894459041293e-24 *** df.mm.exp2 0.217109661574443 0.0702361004158937 3.09114059990314 0.00208288843068181 ** df.mm.exp3 0.0400135701949719 0.0702361004158937 0.56970090819446 0.569085934873199 df.mm.exp4 0.0876665616308863 0.0702361004158938 1.24816954688231 0.212437859926282 df.mm.exp5 0.0636018252495008 0.0702361004158937 0.905543230231905 0.365527734406322 df.mm.exp6 0.297873890500539 0.0702361004158938 4.24103685621381 2.56264344242167e-05 *** df.mm.exp7 0.0443174303677612 0.0702361004158937 0.63097794589024 0.528286352651343 df.mm.exp8 0.0416694627940795 0.0702361004158938 0.593276997830734 0.553211102862817 df.mm.trans1:exp2 -0.181533511887637 0.0613070946046385 -2.96105227393866 0.00318257310083656 ** df.mm.trans2:exp2 -0.267992292359517 0.050833157447602 -5.27199776318753 1.86319736860073e-07 *** df.mm.trans1:exp3 -0.0427291715229166 0.0613070946046385 -0.696969442092656 0.486081898710629 df.mm.trans2:exp3 0.171498385402158 0.050833157447602 3.37375040255833 0.000787514976453144 *** df.mm.trans1:exp4 -0.0359030156503338 0.0613070946046386 -0.585625789019487 0.558339086867678 df.mm.trans2:exp4 -0.113276486447846 0.050833157447602 -2.22839760769552 0.0262101012764601 * df.mm.trans1:exp5 -0.108413362829496 0.0613070946046386 -1.76836569288823 0.0774891136652945 . df.mm.trans2:exp5 -0.0472062767118152 0.050833157447602 -0.928651279639174 0.353429490968517 df.mm.trans1:exp6 -0.286767664389529 0.0613070946046386 -4.67756083107275 3.56247356628057e-06 *** df.mm.trans2:exp6 -0.301023819314943 0.050833157447602 -5.92180054180646 5.26463568479739e-09 *** df.mm.trans1:exp7 -0.00150721390943489 0.0613070946046385 -0.0245846572758784 0.980394129592442 df.mm.trans2:exp7 -0.0244943100301172 0.050833157447602 -0.48185694652876 0.63007679616378 df.mm.trans1:exp8 0.0143370187992294 0.0613070946046385 0.233855786050325 0.81517376328381 df.mm.trans2:exp8 -0.0854433634607792 0.050833157447602 -1.68085886753843 0.0932915678564415 . df.mm.trans1:probe2 0.0542960678295049 0.0419740983125798 1.29356126783627 0.196296399956723 df.mm.trans1:probe3 0.0395825055405397 0.0419740983125798 0.943022176337655 0.346035102921397 df.mm.trans1:probe4 0.149434841462555 0.0419740983125798 3.56016799574153 0.000398857838831071 *** df.mm.trans1:probe5 0.134721795400506 0.0419740983125798 3.20964120294466 0.00139729340355752 ** df.mm.trans1:probe6 0.107718721396446 0.0419740983125798 2.56631412530337 0.0105111423350579 * df.mm.trans1:probe7 0.0216058373962607 0.0419740983125798 0.514742144914294 0.606915782117325 df.mm.trans1:probe8 0.0312357420594615 0.0419740983125798 0.744167077201991 0.457055986335303 df.mm.trans1:probe9 0.158351972993648 0.0419740983125798 3.77261166670946 0.000176957682899724 *** df.mm.trans1:probe10 0.0665279642582302 0.0419740983125798 1.58497661493044 0.11347908251863 df.mm.trans1:probe11 0.0475382575555592 0.0419740983125798 1.13256173370404 0.257834040625835 df.mm.trans1:probe12 0.112334806905693 0.0419740983125798 2.67628874524331 0.00764009652031307 ** df.mm.trans2:probe2 0.101811754484258 0.0419740983125798 2.42558526751592 0.0155667657041574 * df.mm.trans2:probe3 0.0480527116747988 0.0419740983125798 1.14481819995159 0.252724090503377 df.mm.trans2:probe4 0.213936873513141 0.0419740983125798 5.0968783634126 4.58539455071695e-07 *** df.mm.trans2:probe5 0.0507509869310597 0.0419740983125798 1.20910249347392 0.227082129632691 df.mm.trans2:probe6 0.0680779293637344 0.0419740983125798 1.62190331896495 0.105329980839562 df.mm.trans3:probe2 -0.0871971145789823 0.0419740983125798 -2.07740292428984 0.0381734702990074 * df.mm.trans3:probe3 0.650980206123529 0.0419740983125798 15.5090932811874 4.09817741575701e-46 *** df.mm.trans3:probe4 0.261156214905813 0.0419740983125798 6.22184216944914 9.01909708294548e-10 *** df.mm.trans3:probe5 0.665356320011595 0.0419740983125798 15.8515929289703 8.58088039902116e-48 *** df.mm.trans3:probe6 0.711430089384769 0.0419740983125798 16.9492643793506 2.84341116413312e-53 *** df.mm.trans3:probe7 0.0919039444774436 0.0419740983125798 2.18953945819248 0.0289278391326462 * df.mm.trans3:probe8 -0.0343308784194545 0.0419740983125798 -0.817906275527196 0.413723407999047 df.mm.trans3:probe9 0.332841292012284 0.0419740983125798 7.92968295670405 1.01979898364868e-14 *** df.mm.trans3:probe10 0.0129076553752239 0.0419740983125798 0.307514774447351 0.758554350249003 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.41260818857618 0.186540880422325 23.6549124169786 9.23487019509917e-89 *** df.mm.trans1 0.0266714287000081 0.157124583071338 0.169747013348629 0.86526420349446 df.mm.trans2 -0.195863402269295 0.144788229977028 -1.35275776422138 0.176623762845852 df.mm.exp2 -0.222337916045383 0.189090532360629 -1.17582786017729 0.240112801623219 df.mm.exp3 -0.0671317336732966 0.189090532360629 -0.35502429886476 0.722691541679216 df.mm.exp4 0.406896657869958 0.189090532360629 2.15186161247848 0.0317918529822686 * df.mm.exp5 -0.091421300701495 0.189090532360629 -0.483478995802595 0.628925546215443 df.mm.exp6 -0.174778557104162 0.189090532360629 -0.924311518520813 0.355682011073893 df.mm.exp7 -0.303865932437660 0.189090532360629 -1.60698649818244 0.108564002968741 df.mm.exp8 -0.270872698991215 0.189090532360629 -1.43250270444325 0.152501562389087 df.mm.trans1:exp2 0.113675988902184 0.165051748141348 0.68872938446452 0.491249974268465 df.mm.trans2:exp2 0.237685972577594 0.136853679894273 1.7367890491598 0.082918692126622 . df.mm.trans1:exp3 -0.0964920577264506 0.165051748141348 -0.584616999292949 0.559016921081257 df.mm.trans2:exp3 0.204620888285139 0.136853679894273 1.49518002324248 0.135373812771709 df.mm.trans1:exp4 -0.443935900208531 0.165051748141348 -2.6896770570909 0.00734370478572026 ** df.mm.trans2:exp4 -0.0104338325894403 0.136853679894273 -0.076240789414658 0.939252008195188 df.mm.trans1:exp5 0.0422568325179748 0.165051748141348 0.256021720423019 0.798018577271756 df.mm.trans2:exp5 0.106591127023361 0.136853679894273 0.778869279260215 0.436352456159694 df.mm.trans1:exp6 -0.0198955947948238 0.165051748141348 -0.120541557534946 0.904093025735411 df.mm.trans2:exp6 0.104652473279919 0.136853679894273 0.764703392417133 0.444737739001657 df.mm.trans1:exp7 0.146892252140234 0.165051748141348 0.88997695446665 0.373821794616269 df.mm.trans2:exp7 0.228802440740683 0.136853679894273 1.67187642244948 0.095050823095601 . df.mm.trans1:exp8 0.0709356540975116 0.165051748141348 0.429778265885213 0.66750555067211 df.mm.trans2:exp8 0.228632967093774 0.136853679894273 1.67063806592855 0.095295437251676 . df.mm.trans1:probe2 -0.0724745474656695 0.113003207015847 -0.641349474758763 0.521531521269434 df.mm.trans1:probe3 -0.165409111106867 0.113003207015847 -1.46375590104863 0.143765015249540 df.mm.trans1:probe4 -0.118549434356542 0.113003207015847 -1.04908026495139 0.294547970425570 df.mm.trans1:probe5 -0.00779178367187674 0.113003207015847 -0.0689518809035573 0.945050046494746 df.mm.trans1:probe6 -0.139229590800495 0.113003207015847 -1.2320853051628 0.218382024457755 df.mm.trans1:probe7 -0.127217811877701 0.113003207015847 -1.12578939339182 0.260688165045548 df.mm.trans1:probe8 -0.119317975805505 0.113003207015847 -1.05588132369352 0.291431716728996 df.mm.trans1:probe9 -0.133155516851634 0.113003207015847 -1.17833396385786 0.239113366055565 df.mm.trans1:probe10 -0.107012814101197 0.113003207015847 -0.94698917780439 0.344011451616309 df.mm.trans1:probe11 -0.0367607322648186 0.113003207015847 -0.325306982302399 0.745057965680833 df.mm.trans1:probe12 0.0368361145902835 0.113003207015847 0.325974063595537 0.744553440474074 df.mm.trans2:probe2 -0.0508286513653721 0.113003207015847 -0.449798308451936 0.653012333963953 df.mm.trans2:probe3 0.0104267610830694 0.113003207015847 0.0922696033007909 0.92651350006458 df.mm.trans2:probe4 -0.00118422653066050 0.113003207015847 -0.0104795833846948 0.991642010102873 df.mm.trans2:probe5 -0.0731340133634106 0.113003207015847 -0.647185290530333 0.517750415663239 df.mm.trans2:probe6 -0.105985401218660 0.113003207015847 -0.937897286435392 0.348660647435257 df.mm.trans3:probe2 0.0336452946556832 0.113003207015847 0.297737520413600 0.766002763248275 df.mm.trans3:probe3 -0.0345675403418811 0.113003207015847 -0.305898755041823 0.75978392302542 df.mm.trans3:probe4 -0.0593402084439078 0.113003207015847 -0.525119684750062 0.599686814324248 df.mm.trans3:probe5 -0.0978232359076306 0.113003207015847 -0.865667784932086 0.387005602142502 df.mm.trans3:probe6 -0.0438479146963231 0.113003207015847 -0.388023630959199 0.698131092311402 df.mm.trans3:probe7 -0.0722715697281241 0.113003207015847 -0.639553262572352 0.522698171755425 df.mm.trans3:probe8 -0.0521646603805991 0.113003207015847 -0.461621061544595 0.644514225094561 df.mm.trans3:probe9 -0.0636507230833649 0.113003207015847 -0.563264749419356 0.573457278914729 df.mm.trans3:probe10 -0.127682291453189 0.113003207015847 -1.12989971545925 0.258953315234723