fitVsDatCorrelation=0.911908539638163 cont.fitVsDatCorrelation=0.245299474634480 fstatistic=6792.13950443189,49,623 cont.fstatistic=1206.75611601935,49,623 residuals=-0.843625599060757,-0.109678336292158,0.00728161600828923,0.11031846987858,1.05649606435038 cont.residuals=-1.03834419343881,-0.370741748298571,-0.0119119071952631,0.280947147362524,1.69546196076178 predictedValues: Include Exclude Both Lung 103.396725502611 63.9753261155906 110.659069813012 cerebhem 141.367384745774 84.8522588636234 154.338930984064 cortex 194.215293643341 63.0408467314328 314.665551250697 heart 107.178401938126 62.511403530968 128.542934436626 kidney 105.111675870361 63.422358701087 116.405334013356 liver 98.5972813473561 57.946803958881 105.352271482658 stomach 92.522570999059 70.1952528282826 98.2424882759245 testicle 115.518406513334 70.7130487022248 134.511307689665 diffExp=39.4213993870203,56.5151258821506,131.174446911908,44.6669984071581,41.6893171692742,40.6504773884752,22.3273181707764,44.8053578111097 diffExpScore=0.997631737228435 diffExp1.5=1,1,1,1,1,1,0,1 diffExp1.5Score=0.875 diffExp1.4=1,1,1,1,1,1,0,1 diffExp1.4Score=0.875 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 115.471794028062 102.976463771520 107.727082217213 cerebhem 99.9834722470562 106.501593262144 118.094271541182 cortex 92.4961371747028 100.783245872507 116.234276923990 heart 98.9081205859253 101.763527472643 105.741227849761 kidney 93.8089147829783 108.840653455927 97.8726182615005 liver 102.311290817002 95.0516048122538 96.6502127303398 stomach 105.142151254047 117.316413495944 116.671783986460 testicle 108.160144610306 93.2285756963227 107.296225518992 cont.diffExp=12.4953302565426,-6.51812101508759,-8.28710869780402,-2.85540688671784,-15.0317386729483,7.25968600474799,-12.1742622418967,14.9315689139831 cont.diffExpScore=7.11563955840634 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=0.173679808941306 cont.tran.correlation=-0.117892740869274 tran.covariance=0.0063581297650697 cont.tran.covariance=-0.000706422116888007 tran.mean=93.4103149995033 cont.tran.mean=102.671506458709 weightedLogRatios: wLogRatio Lung 2.11163045491151 cerebhem 2.39714518299395 cortex 5.29553996310341 heart 2.37489494020852 kidney 2.22413293627247 liver 2.29896777185305 stomach 1.21221990606715 testicle 2.21057684675165 cont.weightedLogRatios: wLogRatio Lung 0.537328129943702 cerebhem -0.292823596636701 cortex -0.392135942232751 heart -0.131157609751690 kidney -0.685989568166074 liver 0.337914176662417 stomach -0.516044570565782 testicle 0.684756464143379 varWeightedLogRatios=1.40697262889584 cont.varWeightedLogRatios=0.262737602874644 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05181690669287 0.100058931431851 40.4943051930602 1.21787629849289e-176 *** df.mm.trans1 0.662138192613083 0.0842802813420822 7.85638327339647 1.73973348820772e-14 *** df.mm.trans2 0.143373593573856 0.077663167143907 1.84609511621114 0.0653525892382973 . df.mm.exp2 0.262505904610521 0.101426542906035 2.58813814499934 0.00987468777554103 ** df.mm.exp3 -0.429376892097857 0.101426542906035 -4.23337796789196 2.64899171875261e-05 *** df.mm.exp4 -0.137035959459695 0.101426542906035 -1.35108577630068 0.177158407832264 df.mm.exp5 -0.0428552755316322 0.101426542906035 -0.422525251317447 0.672787459726886 df.mm.exp6 -0.0973573373410846 0.101426542906035 -0.959880269519585 0.3374878499761 df.mm.exp7 0.100677776874391 0.101426542906035 0.992617652044621 0.321281683384435 df.mm.exp8 0.0157949924513293 0.101426542906035 0.155728392181939 0.876297560693066 df.mm.trans1:exp2 0.0502828694198078 0.0885323448275412 0.567960438840264 0.570266461120574 df.mm.trans2:exp2 0.0199082289810517 0.073407142400829 0.271202887483969 0.786324827237154 df.mm.trans1:exp3 1.05977090356015 0.0885323448275412 11.9704375347175 7.24358026671561e-30 *** df.mm.trans2:exp3 0.414662289630523 0.0734071424008289 5.64880032199484 2.45855776014162e-08 *** df.mm.trans1:exp4 0.172957420020314 0.0885323448275412 1.95360712920493 0.0511948580407773 . df.mm.trans2:exp4 0.113887476474185 0.073407142400829 1.55144952860743 0.121301815391854 df.mm.trans1:exp5 0.0593053468870964 0.0885323448275412 0.669872090280923 0.503187533353316 df.mm.trans2:exp5 0.0341742561248065 0.0734071424008289 0.465544019384422 0.641704618491956 df.mm.trans1:exp6 0.0498277327079188 0.0885323448275412 0.5628195300258 0.573760254511861 df.mm.trans2:exp6 -0.00161472567605383 0.073407142400829 -0.0219968469449043 0.982457513905113 df.mm.trans1:exp7 -0.211798444640767 0.0885323448275412 -2.39232842023269 0.0170371801844504 * df.mm.trans2:exp7 -0.00789457123812994 0.0734071424008289 -0.107545001479867 0.91439125026979 df.mm.trans1:exp8 0.0950615952456795 0.0885323448275412 1.07374988690130 0.283350502956898 df.mm.trans2:exp8 0.0843376482289982 0.073407142400829 1.14890248374586 0.251037096255730 df.mm.trans1:probe2 -0.444918230509137 0.0606139529135877 -7.34019494065039 6.68836743404486e-13 *** df.mm.trans1:probe3 -0.561415330929622 0.0606139529135877 -9.26214681510683 3.20230876998772e-19 *** df.mm.trans1:probe4 0.27681842744367 0.0606139529135877 4.56690933584727 5.96707772736994e-06 *** df.mm.trans1:probe5 -0.348657109495439 0.0606139529135877 -5.75209325140848 1.38223304110272e-08 *** df.mm.trans1:probe6 -0.457115535227961 0.0606139529135877 -7.54142426380991 1.65093528665986e-13 *** df.mm.trans1:probe7 0.290740910269434 0.0606139529135877 4.796600391397 2.02133992836395e-06 *** df.mm.trans1:probe8 -0.115968518865585 0.0606139529135877 -1.91323141440573 0.0561764999469534 . df.mm.trans1:probe9 -0.080059731497873 0.0606139529135877 -1.32081356931147 0.187048486955210 df.mm.trans1:probe10 0.022581752467587 0.0606139529135877 0.372550401056666 0.709609796218284 df.mm.trans1:probe11 -0.074435156112844 0.0606139529135877 -1.22802015930161 0.219903144300989 df.mm.trans1:probe12 -0.165971209242062 0.0606139529135877 -2.73816837978994 0.00635480395150166 ** df.mm.trans2:probe2 -0.127049573315245 0.0606139529135877 -2.09604500627717 0.0364810502585519 * df.mm.trans2:probe3 -0.226115267464224 0.0606139529135877 -3.73041612690362 0.000208616539118826 *** df.mm.trans2:probe4 -0.18949659706034 0.0606139529135876 -3.12628673682592 0.00185270884927806 ** df.mm.trans2:probe5 -0.00657282729519278 0.0606139529135877 -0.108437529302257 0.913683547169686 df.mm.trans2:probe6 -0.0378540658495844 0.0606139529135877 -0.624510760807002 0.532520838389595 df.mm.trans3:probe2 -0.0831813675831302 0.0606139529135877 -1.37231385819227 0.170459622722145 df.mm.trans3:probe3 -0.270756085456300 0.0606139529135877 -4.46689371739697 9.42497368178049e-06 *** df.mm.trans3:probe4 0.211062284509974 0.0606139529135877 3.48207424800141 0.000532363955309692 *** df.mm.trans3:probe5 -0.424486521140664 0.0606139529135877 -7.00311563157445 6.50466975482236e-12 *** df.mm.trans3:probe6 -0.269123341013885 0.0606139529135877 -4.43995694188683 1.06439323597206e-05 *** df.mm.trans3:probe7 0.0248704363895778 0.0606139529135877 0.410308768758797 0.681720525457915 df.mm.trans3:probe8 -0.0304079002166753 0.0606139529135877 -0.501665025213342 0.616080400815738 df.mm.trans3:probe9 0.0256894390095998 0.0606139529135877 0.423820552443149 0.671842977096441 df.mm.trans3:probe10 -0.692413557055107 0.0606139529135877 -11.4233361094635 1.42866688815074e-27 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.7993463412579 0.236363073074242 20.3049752181485 9.8707644388882e-71 *** df.mm.trans1 -0.0791067994466785 0.199090136307761 -0.397341630850019 0.691251645255927 df.mm.trans2 -0.148473086494446 0.183458933531734 -0.809298754965036 0.41865229320147 df.mm.exp2 -0.202244112140368 0.239593697728971 -0.844112821236 0.398930370065947 df.mm.exp3 -0.319394504752957 0.239593697728971 -1.33306722080084 0.182997010674866 df.mm.exp4 -0.148077490086507 0.239593697728971 -0.618035831034306 0.536777579539836 df.mm.exp5 -0.0564477452899325 0.239593697728971 -0.235597788359969 0.813822214588103 df.mm.exp6 -0.092584152801801 0.239593697728971 -0.386421486372034 0.69931647825567 df.mm.exp7 -0.0431025335006085 0.239593697728971 -0.179898444362949 0.857290793696057 df.mm.exp8 -0.160851977255676 0.239593697728971 -0.671353123142797 0.502244443086888 df.mm.trans1:exp2 0.0582227143500999 0.209134524929025 0.278398386731504 0.780798933020948 df.mm.trans2:exp2 0.235903602340575 0.173405187474701 1.36041836911593 0.174189567196909 df.mm.trans1:exp3 0.0975350955367842 0.209134524929025 0.466374911411137 0.641110194548544 df.mm.trans2:exp3 0.297866179947984 0.173405187474701 1.71774665040769 0.0863397502187992 . df.mm.trans1:exp4 -0.00675745818577499 0.209134524929025 -0.0323115381741408 0.974233956648027 df.mm.trans2:exp4 0.136228798661185 0.173405187474701 0.785609707789509 0.432394846886429 df.mm.trans1:exp5 -0.151318655472759 0.209134524929025 -0.723546987395376 0.469615472712595 df.mm.trans2:exp5 0.111832207963113 0.173405187474701 0.644918468655554 0.519217430376276 df.mm.trans1:exp6 -0.0284221032523236 0.209134524929025 -0.135903449045390 0.891941509541767 df.mm.trans2:exp6 0.0125036504227327 0.173405187474701 0.0721065534706508 0.94254025296427 df.mm.trans1:exp7 -0.0506105030538484 0.209134524929025 -0.241999751456745 0.808859992993259 df.mm.trans2:exp7 0.173476751827460 0.173405187474701 1.00041270018390 0.317499226629260 df.mm.trans1:exp8 0.0954386339639188 0.209134524929025 0.456350447140702 0.648297039135297 df.mm.trans2:exp8 0.0614058030915785 0.173405187474701 0.354117451650848 0.723370681152998 df.mm.trans1:probe2 0.120636072888034 0.143184621070942 0.842521158946698 0.399819623377282 df.mm.trans1:probe3 0.00118473446863816 0.143184621070942 0.0082741739984155 0.993400888409724 df.mm.trans1:probe4 -0.0315646830741973 0.143184621070942 -0.220447439383580 0.825594923552059 df.mm.trans1:probe5 0.0876001579644598 0.143184621070942 0.611798650645992 0.5408941769198 df.mm.trans1:probe6 0.0097605166444777 0.143184621070942 0.0681673532497725 0.945674286666943 df.mm.trans1:probe7 0.0779675490013139 0.143184621070942 0.544524603397766 0.586275366069404 df.mm.trans1:probe8 -0.111563154717995 0.143184621070942 -0.77915598675028 0.436183692310208 df.mm.trans1:probe9 0.155558072731559 0.143184621070942 1.08641606597183 0.277715124522373 df.mm.trans1:probe10 0.184787462325378 0.143184621070942 1.29055383841693 0.197337174606641 df.mm.trans1:probe11 0.0326589555590045 0.143184621070942 0.228089827767351 0.819651248369227 df.mm.trans1:probe12 0.106282833322250 0.143184621070942 0.742278273513685 0.458198510765729 df.mm.trans2:probe2 -0.123490114757626 0.143184621070942 -0.862453759586665 0.388769728458258 df.mm.trans2:probe3 0.0450243018697652 0.143184621070942 0.314449286054664 0.753285103556385 df.mm.trans2:probe4 -0.155278635589553 0.143184621070942 -1.08446447969170 0.278578393694122 df.mm.trans2:probe5 0.138097038561799 0.143184621070942 0.96446837327158 0.335185372886149 df.mm.trans2:probe6 -0.166317385575032 0.143184621070942 -1.16155900215449 0.245859469926414 df.mm.trans3:probe2 0.104582901627919 0.143184621070942 0.730405967105243 0.465416643244567 df.mm.trans3:probe3 0.205280498441732 0.143184621070942 1.43367700320291 0.152166135538263 df.mm.trans3:probe4 0.0907635849015176 0.143184621070942 0.633891993586017 0.526383971198244 df.mm.trans3:probe5 0.251513082887966 0.143184621070942 1.75656492301189 0.07948325727612 . df.mm.trans3:probe6 -0.00526071803929032 0.143184621070942 -0.0367408035859093 0.970703443209903 df.mm.trans3:probe7 0.249615048027479 0.143184621070942 1.74330906601908 0.0817729583425298 . df.mm.trans3:probe8 -0.0291362119843026 0.143184621070942 -0.203487020927107 0.838820835120867 df.mm.trans3:probe9 0.127418852315553 0.143184621070942 0.889892024454376 0.373867365065723 df.mm.trans3:probe10 0.0835685318684863 0.143184621070942 0.58364181322994 0.55967255656973