fitVsDatCorrelation=0.887374481352902 cont.fitVsDatCorrelation=0.271716061047992 fstatistic=7405.32046434185,51,669 cont.fstatistic=1689.49741876549,51,669 residuals=-0.81132101970237,-0.108727727886800,-0.00121168974625012,0.107322657600172,0.521255836059281 cont.residuals=-0.700755909584097,-0.28469680541601,-0.0651882442006717,0.241731602100063,1.28963631413857 predictedValues: Include Exclude Both Lung 64.2989279332798 68.8713036855552 79.8730108610472 cerebhem 61.3394124798936 66.8106687333786 76.4131716111787 cortex 68.3324840771951 95.968459219193 121.398924458820 heart 58.6478924546699 82.5691327670472 96.959442001635 kidney 65.2802063204468 63.7317876341037 72.1875337418246 liver 63.52507979164 59.2840126193304 66.0458322795853 stomach 60.5909192809467 67.6202311191904 90.2200808831131 testicle 61.4699472604897 83.1597085693284 107.097054350026 diffExp=-4.57237575227543,-5.47125625348505,-27.6359751419979,-23.9212403123773,1.54841868634307,4.24106717230961,-7.02931183824362,-21.6897613088388 diffExpScore=1.12368663562163 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,-1,-1,0,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,-1,-1,0,0,0,-1 diffExp1.3Score=0.75 diffExp1.2=0,0,-1,-1,0,0,0,-1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 68.7888203869778 76.1283294484023 63.0394167592537 cerebhem 72.0725510469762 76.0576134205901 60.7889052009687 cortex 63.3742825916364 60.6545244982932 58.9627810458225 heart 66.4507228943057 67.5726032220894 81.0608137489687 kidney 70.0396579021851 60.881803910942 72.297642677956 liver 65.0192110376222 72.6708113391184 73.0576836046026 stomach 68.5228460721441 69.9886150179923 67.3077979265636 testicle 70.2529885146 64.5711996670404 72.3744286541646 cont.diffExp=-7.3395090614245,-3.9850623736139,2.71975809334318,-1.12188032778366,9.15785399124312,-7.65160030149615,-1.46576894584813,5.68178884755956 cont.diffExpScore=7.81773339015691 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=0.207392227212899 cont.tran.correlation=0.303233552809185 tran.covariance=0.00110457224399110 cont.tran.covariance=0.00118537672278425 tran.mean=68.2187608716055 cont.tran.mean=68.3154113106822 weightedLogRatios: wLogRatio Lung -0.288381029366362 cerebhem -0.355357977489845 cortex -1.49242197640277 heart -1.45132461741302 kidney 0.10002301867185 liver 0.284456805896416 stomach -0.456503774947151 testicle -1.29035213521607 cont.weightedLogRatios: wLogRatio Lung -0.434078552226883 cerebhem -0.231663398965816 cortex 0.18103188771565 heart -0.0703971200338623 kidney 0.58559161790838 liver -0.470651864807977 stomach -0.0896935753040097 testicle 0.355042241287703 varWeightedLogRatios=0.492113239994156 cont.varWeightedLogRatios=0.139218695730638 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11751086009581 0.0987277789176 41.7056972742429 3.00881442443595e-188 *** df.mm.trans1 -0.165917342490039 0.0885736227996556 -1.87321391228771 0.0614746612110265 . df.mm.trans2 0.115429164612522 0.081588031349665 1.41478060817307 0.157598096462957 df.mm.exp2 -0.0332142726959419 0.111794986477643 -0.297099840900149 0.766482508599096 df.mm.exp3 -0.0260217171779845 0.111794986477643 -0.232762827724734 0.816016771525978 df.mm.exp4 -0.104449759834747 0.111794986477643 -0.93429735201619 0.350487624128324 df.mm.exp5 0.0387604141015368 0.111794986477643 0.346709770471579 0.72891839432233 df.mm.exp6 0.0280809757157826 0.111794986477643 0.251182781988157 0.801749926972708 df.mm.exp7 -0.199544326318255 0.111794986477643 -1.78491301448621 0.0747284224187493 . df.mm.exp8 -0.149768669879802 0.111794986477643 -1.33967250767326 0.180806728852145 df.mm.trans1:exp2 -0.0139061049711349 0.106823808417791 -0.130177955430569 0.896464766746497 df.mm.trans2:exp2 0.00283745302796002 0.093243464399617 0.0304305834862531 0.975732729825672 df.mm.trans1:exp3 0.0868640212162042 0.106823808417791 0.813152259807816 0.416420056100024 df.mm.trans2:exp3 0.357801705825867 0.093243464399617 3.83728455532736 0.000136185933010338 *** df.mm.trans1:exp4 0.0124584417956128 0.106823808417791 0.116626077839197 0.907191372682782 df.mm.trans2:exp4 0.285846076170416 0.093243464399617 3.06558832847903 0.00225990232203191 ** df.mm.trans1:exp5 -0.0236145011208603 0.106823808417791 -0.221060281136048 0.825112947433858 df.mm.trans2:exp5 -0.116316554084990 0.093243464399617 -1.24744994015332 0.212669057200584 df.mm.trans1:exp6 -0.0401891486598374 0.106823808417791 -0.37621902135015 0.706873353322714 df.mm.trans2:exp6 -0.177980906794377 0.093243464399617 -1.90877621225652 0.0567185952010939 . df.mm.trans1:exp7 0.140146403100059 0.106823808417791 1.31193977424904 0.189990448997345 df.mm.trans2:exp7 0.181211942468301 0.093243464399617 1.94342781700682 0.0523835218596885 . df.mm.trans1:exp8 0.104774104610577 0.106823808417791 0.980812294210686 0.327039868865869 df.mm.trans2:exp8 0.338292029370573 0.093243464399617 3.62805084033281 0.00030732499754924 *** df.mm.trans1:probe2 0.0113922753737896 0.0534119042088956 0.213290942207079 0.831165018846432 df.mm.trans1:probe3 -0.0801277664150137 0.0534119042088956 -1.50018554106649 0.134038112038637 df.mm.trans1:probe4 -0.158124299774711 0.0534119042088956 -2.96046924588724 0.00318041365433684 ** df.mm.trans1:probe5 -0.041483552231478 0.0534119042088956 -0.776672407507408 0.43762658367015 df.mm.trans1:probe6 -0.111992491291552 0.0534119042088956 -2.09677024158408 0.0363888131175321 * df.mm.trans1:probe7 0.0155566392953263 0.0534119042088956 0.29125790450166 0.770944358655084 df.mm.trans1:probe8 0.324444796121039 0.0534119042088957 6.07439111049337 2.08788397127443e-09 *** df.mm.trans1:probe9 0.147435129229213 0.0534119042088956 2.76034212621572 0.00593224455096744 ** df.mm.trans1:probe10 0.663963871320899 0.0534119042088957 12.4310091758593 4.56993972948879e-32 *** df.mm.trans1:probe11 0.761698380259753 0.0534119042088956 14.2608355111386 1.75215905213376e-40 *** df.mm.trans1:probe12 0.688165109023485 0.0534119042088956 12.8841148657058 4.34631257670191e-34 *** df.mm.trans1:probe13 0.672205236864079 0.0534119042088957 12.5853074669471 9.46939066408694e-33 *** df.mm.trans1:probe14 0.825614404505734 0.0534119042088956 15.4574980378294 2.5882098896425e-46 *** df.mm.trans1:probe15 0.949561042007604 0.0534119042088956 17.7780788023179 3.35975587445397e-58 *** df.mm.trans1:probe16 -0.0519289220570562 0.0534119042088956 -0.972234988177177 0.331285042791004 df.mm.trans1:probe17 -0.0264069043229044 0.0534119042088956 -0.494401102413914 0.621185238698464 df.mm.trans1:probe18 0.102386510135719 0.0534119042088956 1.91692304650444 0.0556733698384254 . df.mm.trans1:probe19 -0.035652988471349 0.0534119042088956 -0.667510155262562 0.504676510493344 df.mm.trans1:probe20 0.227828473972449 0.0534119042088956 4.26549993577096 2.28207846722945e-05 *** df.mm.trans1:probe21 0.202251631106218 0.0534119042088956 3.7866395909647 0.000166430942359089 *** df.mm.trans2:probe2 -0.291450615701243 0.0534119042088956 -5.45666027111429 6.83872593498634e-08 *** df.mm.trans2:probe3 0.139082726270495 0.0534119042088956 2.60396494621383 0.0094197223290896 ** df.mm.trans2:probe4 -0.195601096639700 0.0534119042088956 -3.66212550435757 0.000269887945698009 *** df.mm.trans2:probe5 0.130119547415562 0.0534119042088956 2.43615256454179 0.0151043249747984 * df.mm.trans2:probe6 0.211545607600023 0.0534119042088956 3.96064530432507 8.27647760186878e-05 *** df.mm.trans3:probe2 0.308991791647168 0.0534119042088956 5.78507350044461 1.11332606658553e-08 *** df.mm.trans3:probe3 0.631254189416547 0.0534119042088956 11.8186048366239 2.09494968413498e-29 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.4603147298144 0.206080723198812 21.6435320129938 3.77937084745539e-79 *** df.mm.trans1 -0.231045084568499 0.184885312350907 -1.24966705916575 0.211858199334585 df.mm.trans2 -0.153592374057627 0.170303846488224 -0.90187260725346 0.367449118525102 df.mm.exp2 0.0820555920109894 0.233356730151324 0.351631564076935 0.725225311914395 df.mm.exp3 -0.24235554601752 0.233356730151324 -1.03856248697160 0.299383471336794 df.mm.exp4 -0.405237878443506 0.233356730151324 -1.73655963631614 0.0829252533103055 . df.mm.exp5 -0.342497062541434 0.233356730151324 -1.46769738468368 0.142656407628624 df.mm.exp6 -0.250328230994602 0.233356730151324 -1.0727277110554 0.283780099353958 df.mm.exp7 -0.153477808342608 0.233356730151324 -0.657696087201268 0.510959664649179 df.mm.exp8 -0.281683212824713 0.233356730151324 -1.20709273155332 0.227823051129562 df.mm.trans1:exp2 -0.0354235642669721 0.222980076478407 -0.158864257410022 0.87382376628569 df.mm.trans2:exp2 -0.0829849292620996 0.194632967415113 -0.426366254207641 0.669978140627093 df.mm.trans1:exp3 0.160372450451387 0.222980076478407 0.719223228300026 0.472254538838569 df.mm.trans2:exp3 0.015129317091804 0.194632967415113 0.0777325511332117 0.93806404269279 df.mm.trans1:exp4 0.370657304953843 0.222980076478407 1.66228889507864 0.0969232942071809 . df.mm.trans2:exp4 0.286020039895385 0.194632967415113 1.46953542194813 0.14215771152567 df.mm.trans1:exp5 0.360517448266221 0.222980076478407 1.61681462290256 0.106389705923875 df.mm.trans2:exp5 0.119010944586053 0.194632967415113 0.611463444074339 0.541100530493909 df.mm.trans1:exp6 0.193969774287257 0.222980076478407 0.869897335002668 0.384668514896732 df.mm.trans2:exp6 0.203847578595842 0.194632967415113 1.04734352716863 0.295319405131434 df.mm.trans1:exp7 0.149603779946628 0.222980076478407 0.670928911270311 0.502497383463409 df.mm.trans2:exp7 0.0693899328940525 0.194632967415113 0.356516852286683 0.721565944560707 df.mm.trans1:exp8 0.302744823906481 0.222980076478407 1.35772141030635 0.175009872230582 df.mm.trans2:exp8 0.117031236945823 0.194632967415113 0.601291952232422 0.547849230791162 df.mm.trans1:probe2 -0.126296046844118 0.111490038239204 -1.13280117971749 0.257703576605498 df.mm.trans1:probe3 0.0982091470919686 0.111490038239204 0.880878225920592 0.378700076246759 df.mm.trans1:probe4 0.0497046422728743 0.111490038239204 0.445821376132567 0.655870521092871 df.mm.trans1:probe5 0.180318967436295 0.111490038239204 1.61735496986213 0.106273057683511 df.mm.trans1:probe6 0.0844580405945968 0.111490038239204 0.757538897003432 0.448993808029597 df.mm.trans1:probe7 -0.0912765235763383 0.111490038239204 -0.818696674769302 0.413251020008504 df.mm.trans1:probe8 0.046311797042121 0.111490038239204 0.415389552049111 0.677989945878134 df.mm.trans1:probe9 0.0262624241314795 0.111490038239204 0.235558481692625 0.813847383215396 df.mm.trans1:probe10 0.0548887315608021 0.111490038239204 0.492319604761795 0.622654941949026 df.mm.trans1:probe11 -0.0375170994558714 0.111490038239204 -0.336506292834683 0.736594632321721 df.mm.trans1:probe12 -0.0869295619768753 0.111490038239204 -0.779706988622307 0.435839110183213 df.mm.trans1:probe13 -0.103657764369634 0.111490038239204 -0.92974911486921 0.352836351973607 df.mm.trans1:probe14 -0.118447902061689 0.111490038239204 -1.06240794184281 0.288433881385180 df.mm.trans1:probe15 0.00499570798335238 0.111490038239204 0.044808559242163 0.964273273804462 df.mm.trans1:probe16 0.00800793798182069 0.111490038239204 0.0718264887903217 0.942761459261042 df.mm.trans1:probe17 0.0373575093580564 0.111490038239204 0.335074863620599 0.737673650037681 df.mm.trans1:probe18 0.115925996817565 0.111490038239204 1.03978793664815 0.298814070631401 df.mm.trans1:probe19 0.0495583265266807 0.111490038239204 0.444509010036866 0.656818376933963 df.mm.trans1:probe20 -0.0490401704945949 0.111490038239204 -0.439861455508504 0.660179515197348 df.mm.trans1:probe21 -0.100315949272041 0.111490038239204 -0.89977500103473 0.368563756553604 df.mm.trans2:probe2 0.137276266652797 0.111490038239204 1.23128728647728 0.218648099739945 df.mm.trans2:probe3 -0.0165879031705968 0.111490038239204 -0.148783724829363 0.881769129599266 df.mm.trans2:probe4 0.102882484482419 0.111490038239204 0.922795310749497 0.356446567433837 df.mm.trans2:probe5 -0.0524312841973592 0.111490038239204 -0.470277748805387 0.638309863314135 df.mm.trans2:probe6 0.0601433894523723 0.111490038239204 0.539450792216375 0.589755331396277 df.mm.trans3:probe2 -0.04240397873811 0.111490038239204 -0.380338722703921 0.70381471970637 df.mm.trans3:probe3 0.246652745947596 0.111490038239204 2.21232990716533 0.0272808955742312 *