fitVsDatCorrelation=0.754574937428406 cont.fitVsDatCorrelation=0.263094040248239 fstatistic=11238.4797303369,62,922 cont.fstatistic=5191.37797354516,62,922 residuals=-0.450687238332169,-0.0850576199811535,-0.00617648050265728,0.0733893875314673,1.57303321114238 cont.residuals=-0.513837816448807,-0.152707504685683,-0.0363691606898395,0.119595016084502,1.58504892939587 predictedValues: Include Exclude Both Lung 53.1184371833511 69.5033595855528 83.0678384991573 cerebhem 59.5260643304935 55.2924047648359 64.456168647039 cortex 51.8821904961413 64.1259566921383 69.2460275087306 heart 55.6017807745954 87.3849560246311 99.433413177646 kidney 54.3932591196342 66.3632298317491 80.0401209801297 liver 52.944048992419 62.7670186055787 78.2698817097628 stomach 53.448403467567 58.2506933468222 67.469223835209 testicle 54.2696655856577 57.3373078952006 61.7648248122008 diffExp=-16.3849224022017,4.2336595656576,-12.2437661959970,-31.7831752500357,-11.9699707121150,-9.82296961315975,-4.80228987925522,-3.06764230954282 diffExpScore=1.08598832956437 diffExp1.5=0,0,0,-1,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,-1,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=-1,0,0,-1,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,0,-1,-1,-1,0,0,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 55.3143524056264 52.9027676697721 58.2555053437677 cerebhem 55.8120912073346 56.3820079891777 56.7868708649726 cortex 56.249471335505 56.3374320204468 67.4119970257096 heart 57.9125355113636 52.2080279502206 55.8482148557618 kidney 56.3205994117792 52.8974812212739 68.5485255023443 liver 56.9227227289217 56.0405001709833 51.4680492234746 stomach 57.9340874266498 55.9022292952905 52.0918082197453 testicle 58.4645351134687 54.1607942465737 58.0170054987059 cont.diffExp=2.41158473585436,-0.569916781843055,-0.0879606849418124,5.704507561143,3.4231181905053,0.882222557938398,2.03185813135935,4.30374086689497 cont.diffExpScore=1.01653240368825 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=-0.093912327119954 cont.tran.correlation=-0.0802598053280164 tran.covariance=-0.000846339257170536 cont.tran.covariance=-4.85948302983813e-05 tran.mean=59.763048543523 cont.tran.mean=55.7351022315242 weightedLogRatios: wLogRatio Lung -1.10415753033367 cerebhem 0.298768762802318 cortex -0.859129057072527 heart -1.91886767438576 kidney -0.814644963984067 liver -0.690023416507898 stomach -0.346027093242042 testicle -0.221124251050075 cont.weightedLogRatios: wLogRatio Lung 0.177894421601523 cerebhem -0.0409132871141088 cortex -0.0062979344029064 heart 0.415525059506341 kidney 0.250800216677199 liver 0.0630091153374663 stomach 0.144286741663509 testicle 0.308160487260076 varWeightedLogRatios=0.434189094983864 cont.varWeightedLogRatios=0.0247647171776119 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.66693722402887 0.0803940718380053 45.612035069175 1.32742422798292e-238 *** df.mm.trans1 0.365270803071379 0.0727379621450513 5.02173545009372 6.14676084384864e-07 *** df.mm.trans2 0.569466756127064 0.0661736190179357 8.60564624662157 3.23639173671106e-17 *** df.mm.exp2 0.138822819013671 0.0904251520365945 1.53522350681247 0.125072003604600 df.mm.exp3 0.077917454821275 0.0904251520365945 0.861679002648978 0.389088225592395 df.mm.exp4 0.0948085934734125 0.0904251520365945 1.04847590894892 0.294694154397769 df.mm.exp5 0.0146137775083262 0.0904251520365945 0.161611865495256 0.871646919769866 df.mm.exp6 -0.0457390156112341 0.0904251520365945 -0.50582182701472 0.613102642232977 df.mm.exp7 0.0375596422579283 0.0904251520365945 0.415367200518813 0.677969757330618 df.mm.exp8 0.125341341154317 0.0904251520365945 1.38613359592243 0.166041237396449 df.mm.trans1:exp2 -0.0249326306471554 0.0877252830728035 -0.284212598396106 0.77631127665609 df.mm.trans2:exp2 -0.367562356705788 0.0749097213814073 -4.90673773613871 1.09454154020975e-06 *** df.mm.trans1:exp3 -0.101465958143600 0.0877252830728036 -1.15663300920207 0.247721919314127 df.mm.trans2:exp3 -0.158443323028895 0.0749097213814073 -2.11512364626443 0.034687127452946 * df.mm.trans1:exp4 -0.0491174486509498 0.0877252830728035 -0.559900714258021 0.575683160670802 df.mm.trans2:exp4 0.134139455744835 0.0749097213814073 1.79068154668279 0.0736723852669038 . df.mm.trans1:exp5 0.00910237119243101 0.0877252830728035 0.103759952360335 0.917382413097996 df.mm.trans2:exp5 -0.0608457327688898 0.0749097213814073 -0.81225415936458 0.416855470813814 df.mm.trans1:exp6 0.0424506079118244 0.0877252830728035 0.4839039148679 0.628568972745413 df.mm.trans2:exp6 -0.0562063210734267 0.0749097213814073 -0.750320786633939 0.453252954445764 df.mm.trans1:exp7 -0.0313669591174983 0.0877252830728035 -0.357558938755053 0.720755242919143 df.mm.trans2:exp7 -0.214178737696129 0.0749097213814072 -2.85915811387985 0.00434344531663353 ** df.mm.trans1:exp8 -0.10389999938718 0.0877252830728036 -1.18437918633962 0.236568219289968 df.mm.trans2:exp8 -0.317764922421164 0.0749097213814073 -4.24197175695322 2.43925663559838e-05 *** df.mm.trans1:probe2 0.0233718019072978 0.0438626415364018 0.532840729345984 0.594272234603173 df.mm.trans1:probe3 0.00332051866392640 0.0438626415364018 0.0757026605698312 0.939672081710108 df.mm.trans1:probe4 -0.113414093838589 0.0438626415364018 -2.58566492728136 0.0098715490910246 ** df.mm.trans1:probe5 -0.244980691681439 0.0438626415364018 -5.58517871018161 3.07193583438607e-08 *** df.mm.trans1:probe6 -0.0977612168246644 0.0438626415364018 -2.22880367894697 0.0260673359332281 * df.mm.trans1:probe7 0.0310600605362997 0.0438626415364018 0.708121067230364 0.479049051043876 df.mm.trans1:probe8 -0.202922328393270 0.0438626415364018 -4.62631344774035 4.25435433127269e-06 *** df.mm.trans1:probe9 0.175811094673131 0.0438626415364018 4.00821948963619 6.61175527958103e-05 *** df.mm.trans1:probe10 0.0397859509546486 0.0438626415364018 0.907057795906572 0.364613232147681 df.mm.trans1:probe11 -0.0343823366071931 0.0438626415364018 -0.783863793945448 0.433321358007133 df.mm.trans1:probe12 -0.156080249720567 0.0438626415364018 -3.55838691545823 0.000392167199269238 *** df.mm.trans1:probe13 -0.121284925925362 0.0438626415364018 -2.76510765601537 0.00580401012318031 ** df.mm.trans1:probe14 -0.188479471683784 0.0438626415364018 -4.29703878019669 1.91482492724581e-05 *** df.mm.trans1:probe15 -0.178022939438962 0.0438626415364018 -4.0586461098386 5.35430110502458e-05 *** df.mm.trans1:probe16 -0.0540827247347655 0.0438626415364018 -1.23300199988826 0.217889293152974 df.mm.trans1:probe17 -0.129580446894292 0.0438626415364018 -2.95423263067164 0.00321414481834309 ** df.mm.trans1:probe18 0.0971832076022724 0.0438626415364018 2.21562596775253 0.0269607467436550 * df.mm.trans1:probe19 -0.0884701356545935 0.0438626415364018 -2.01698148026885 0.0439868781248064 * df.mm.trans1:probe20 -0.0308010565118622 0.0438626415364018 -0.70221617834622 0.482721590741416 df.mm.trans1:probe21 -0.112626694891073 0.0438626415364018 -2.56771345605356 0.0103937664785643 * df.mm.trans1:probe22 -0.108707296294501 0.0438626415364018 -2.47835726455928 0.0133769084927354 * df.mm.trans1:probe23 0.0450955195140919 0.0438626415364018 1.02810770018643 0.304168804664847 df.mm.trans1:probe24 -0.218956892519172 0.0438626415364018 -4.99187656852491 7.14827239242886e-07 *** df.mm.trans1:probe25 0.245158154124448 0.0438626415364018 5.58922457784468 3.0035141484004e-08 *** df.mm.trans1:probe26 0.00405157031535082 0.0438626415364018 0.0923695010932802 0.926424536163154 df.mm.trans1:probe27 -0.0318166697587062 0.0438626415364018 -0.725370580618166 0.468408777984651 df.mm.trans1:probe28 -0.113651255313403 0.0438626415364018 -2.59107184000953 0.00971891486313718 ** df.mm.trans1:probe29 -0.0611452953381207 0.0438626415364018 -1.39401762402695 0.163648141247327 df.mm.trans1:probe30 -0.238124066126524 0.0438626415364018 -5.42885831280598 7.25442869137218e-08 *** df.mm.trans1:probe31 -0.0126927110775356 0.0438626415364018 -0.289374069434506 0.772360192047033 df.mm.trans1:probe32 -0.215792379128267 0.0438626415364018 -4.91973058551843 1.02606797578473e-06 *** df.mm.trans2:probe2 -0.0829147277417786 0.0438626415364018 -1.89032682112789 0.0590276593683017 . df.mm.trans2:probe3 -0.0239088452311300 0.0438626415364018 -0.545084481774496 0.585827240131208 df.mm.trans2:probe4 0.168532653649546 0.0438626415364018 3.84228235569624 0.000130243652133532 *** df.mm.trans2:probe5 -0.0608977958828826 0.0438626415364018 -1.38837502142554 0.165358214732129 df.mm.trans2:probe6 0.0439287106690219 0.0438626415364018 1.00150627345517 0.316844873492316 df.mm.trans3:probe2 -0.397130654905833 0.0438626415364018 -9.05396120697045 8.03895938703313e-19 *** df.mm.trans3:probe3 -0.148920878940917 0.0438626415364018 -3.39516439786981 0.00071514240077781 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.86405368183611 0.118195920446343 32.6919378202251 2.79949003317796e-156 *** df.mm.trans1 0.121847218462138 0.106939855023754 1.13939951045447 0.254832487876496 df.mm.trans2 0.0845283669195536 0.0972889123572546 0.868838646372744 0.385161496337928 df.mm.exp2 0.0981860141759734 0.132943683932342 0.73855343309083 0.460366163416324 df.mm.exp3 -0.0663166436669815 0.132943683932342 -0.498832601184209 0.618016306243337 df.mm.exp4 0.0748831092307483 0.132943683932342 0.563269401116174 0.573388402969595 df.mm.exp5 -0.144775285542679 0.132943683932342 -1.08899709456192 0.276439722639531 df.mm.exp6 0.210158573251491 0.132943683932342 1.58080900901200 0.11426468381138 df.mm.exp7 0.213253027440872 0.132943683932342 1.60408543778131 0.109037689328850 df.mm.exp8 0.0829919924119244 0.132943683932342 0.624264274594353 0.532608472823863 df.mm.trans1:exp2 -0.0892278919853515 0.128974317908653 -0.691826818177459 0.489220267015562 df.mm.trans2:exp2 -0.0344915702446426 0.110132790473599 -0.313181660941487 0.754213480764376 df.mm.trans1:exp3 0.0830808742008243 0.128974317908653 0.6441660289273 0.519627990417976 df.mm.trans2:exp3 0.129220168639688 0.110132790473599 1.17331239936815 0.24097356756579 df.mm.trans1:exp4 -0.0289816573248194 0.128974317908653 -0.224708746630828 0.822255649189547 df.mm.trans2:exp4 -0.0881024904120868 0.110132790473599 -0.799966023136466 0.423936540621073 df.mm.trans1:exp5 0.162803228274050 0.128974317908653 1.26229183386227 0.207162985541225 df.mm.trans2:exp5 0.144675352913825 0.110132790473599 1.31364466742088 0.189292588131864 df.mm.trans1:exp6 -0.181496378890536 0.128974317908653 -1.40722883310057 0.159696556376626 df.mm.trans2:exp6 -0.152539582967943 0.110132790473599 -1.38505146661574 0.166371751110349 df.mm.trans1:exp7 -0.166979498818535 0.128974317908653 -1.29467247066039 0.195757373054353 df.mm.trans2:exp7 -0.158104424411094 0.110132790473599 -1.43557993701244 0.151461063499280 df.mm.trans1:exp8 -0.027604071413509 0.128974317908653 -0.214027659623366 0.830572809640524 df.mm.trans2:exp8 -0.0594903554918138 0.110132790473599 -0.540169328643995 0.589210709316212 df.mm.trans1:probe2 0.0570960764961186 0.0644871589543266 0.885386756401491 0.376178919434468 df.mm.trans1:probe3 -0.0363232445561596 0.0644871589543266 -0.563263216199147 0.573392612166518 df.mm.trans1:probe4 0.0464439303160492 0.0644871589543266 0.72020431771453 0.471581742885787 df.mm.trans1:probe5 -0.000695323109179859 0.0644871589543266 -0.0107823498577806 0.991399428751539 df.mm.trans1:probe6 -0.0769860386356588 0.0644871589543266 -1.19381966710899 0.232855581657152 df.mm.trans1:probe7 0.0818595066466095 0.0644871589543266 1.26939235615244 0.20462148114673 df.mm.trans1:probe8 0.0600226619994394 0.0644871589543266 0.930769210067865 0.352216598768446 df.mm.trans1:probe9 0.0246604407230102 0.0644871589543266 0.382408546490257 0.702246483268067 df.mm.trans1:probe10 0.0219339219393991 0.0644871589543266 0.340128520081555 0.733837217612784 df.mm.trans1:probe11 0.092974310864154 0.0644871589543266 1.44174921599513 0.149712696941325 df.mm.trans1:probe12 0.109171088505940 0.0644871589543266 1.69291205064967 0.0908100288373024 . df.mm.trans1:probe13 0.0599543353493157 0.0644871589543266 0.92970967121964 0.352764769990179 df.mm.trans1:probe14 -0.0346371014318263 0.0644871589543266 -0.537116256840501 0.591316904164512 df.mm.trans1:probe15 0.0636665044954609 0.0644871589543266 0.987274141516346 0.32376727454364 df.mm.trans1:probe16 0.0636835903844841 0.0644871589543266 0.987539091768462 0.323637509847216 df.mm.trans1:probe17 -0.00173496852318211 0.0644871589543266 -0.026904093021231 0.978542050853148 df.mm.trans1:probe18 0.0261587960863279 0.0644871589543266 0.405643487951686 0.685098684251343 df.mm.trans1:probe19 -0.0406670446788668 0.0644871589543266 -0.630622364797765 0.528443746640835 df.mm.trans1:probe20 0.0782414448686137 0.0644871589543266 1.21328720534934 0.225330768542593 df.mm.trans1:probe21 -0.0267854461157264 0.0644871589543266 -0.415360926889295 0.677974347621578 df.mm.trans1:probe22 0.0867075488774234 0.0644871589543266 1.34457076855928 0.179094574315840 df.mm.trans1:probe23 0.0805419534588888 0.0644871589543266 1.24896110737229 0.211996304852127 df.mm.trans1:probe24 0.100616309869324 0.0644871589543266 1.56025341325063 0.119043167406616 df.mm.trans1:probe25 0.0276634116403671 0.0644871589543266 0.428975505960184 0.66804126041669 df.mm.trans1:probe26 0.0584304103147019 0.0644871589543266 0.906078221806695 0.365131172529579 df.mm.trans1:probe27 -0.0279317529194535 0.0644871589543266 -0.433136664296785 0.665016795091558 df.mm.trans1:probe28 0.0585585618059884 0.0644871589543266 0.908065462264555 0.364080918325937 df.mm.trans1:probe29 -0.0198951346541156 0.0644871589543266 -0.30851312070061 0.75776160897964 df.mm.trans1:probe30 -0.0109787540353916 0.0644871589543266 -0.170247134676337 0.864853134026493 df.mm.trans1:probe31 0.0188524740187075 0.0644871589543266 0.292344620609816 0.770088917467473 df.mm.trans1:probe32 0.0090004404920284 0.0644871589543266 0.139569499385188 0.889030621613744 df.mm.trans2:probe2 -0.00685997628380848 0.0644871589543266 -0.106377399703204 0.915306064032754 df.mm.trans2:probe3 0.141142103335424 0.0644871589543266 2.18868540069176 0.0288699252366747 * df.mm.trans2:probe4 -0.0144244965286710 0.0644871589543266 -0.223680136674765 0.823055754152383 df.mm.trans2:probe5 0.0426937058655812 0.0644871589543266 0.662049725214585 0.508104789799632 df.mm.trans2:probe6 0.0163111323650732 0.0644871589543266 0.252936129138914 0.800373951663498 df.mm.trans3:probe2 -0.00769655546029665 0.0644871589543266 -0.119350202196809 0.905023911431561 df.mm.trans3:probe3 -0.00907544523562446 0.0644871589543266 -0.140732595183053 0.888111933896498