fitVsDatCorrelation=0.878243033463662 cont.fitVsDatCorrelation=0.225437002943516 fstatistic=5724.81074338567,57,807 cont.fstatistic=1368.55407329801,57,807 residuals=-0.79403295352513,-0.121108621651779,-0.00422001392988781,0.108200367287343,0.919358250667451 cont.residuals=-0.79995870814292,-0.309964029860401,-0.108606430830828,0.239512138186725,1.73654458859756 predictedValues: Include Exclude Both Lung 94.8501088254335 46.2667183986118 113.263116537762 cerebhem 68.5944175187165 55.3887092212603 101.739298123333 cortex 101.140524145834 45.4276907294108 112.445390048821 heart 57.9558203004796 44.3986586622274 72.5382183104012 kidney 48.3322527276234 45.1343964865243 68.6033975570776 liver 50.5327068288682 50.7204786475392 69.1889211622546 stomach 54.166687388687 47.1152145058126 72.5086674596147 testicle 77.3782367103603 50.1519340054771 100.740965455809 diffExp=48.5833904268217,13.2057082974562,55.7128334164231,13.5571616382521,3.19785624109907,-0.187771818671003,7.05147288287444,27.2263027048832 diffExpScore=0.996312562176728 diffExp1.5=1,0,1,0,0,0,0,1 diffExp1.5Score=0.75 diffExp1.4=1,0,1,0,0,0,0,1 diffExp1.4Score=0.75 diffExp1.3=1,0,1,1,0,0,0,1 diffExp1.3Score=0.8 diffExp1.2=1,1,1,1,0,0,0,1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 62.5418142669474 78.6327645938169 63.8125204628051 cerebhem 61.6189196551786 64.0856908186241 70.7635396069887 cortex 60.5884532261108 66.3866767630518 69.4936106108136 heart 62.2904447260131 59.2086159181912 72.538692155156 kidney 68.6807709216089 67.8789768546522 61.7532559452603 liver 65.4094225896847 68.0496653231125 67.0888569481099 stomach 62.0420282725196 75.0716687016132 67.3346274098307 testicle 62.9675264795568 71.7947342922444 68.35730740353 cont.diffExp=-16.0909503268695,-2.4667711634455,-5.79822353694101,3.08182880782191,0.801794066956646,-2.64024273342777,-13.0296404290936,-8.82720781268765 cont.diffExpScore=1.14721192395387 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=-1,0,0,0,0,0,-1,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=-0.0952050095282863 cont.tran.correlation=0.0116017194878828 tran.covariance=-0.000857586166196115 cont.tran.covariance=0.00011736758359619 tran.mean=58.5971596939291 cont.tran.mean=66.0780108376829 weightedLogRatios: wLogRatio Lung 3.01030842712067 cerebhem 0.881278377402179 cortex 3.37469336755669 heart 1.04628667625948 kidney 0.2631302063508 liver -0.0145557238419220 stomach 0.547047984846745 testicle 1.79178406845077 cont.weightedLogRatios: wLogRatio Lung -0.97312210378041 cerebhem -0.162527241644360 cortex -0.379258766496734 heart 0.208364618144015 kidney 0.049597258904981 liver -0.166218132721391 stomach -0.805059927696508 testicle -0.552084135202193 varWeightedLogRatios=1.57855758388634 cont.varWeightedLogRatios=0.168196731911267 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91528105563054 0.102111598114378 38.3431571724588 5.69325996664169e-184 *** df.mm.trans1 0.718239089860742 0.088670480732345 8.10009243131062 2.02369661570031e-15 *** df.mm.trans2 -0.0624020437547476 0.0788153195354402 -0.791750184133781 0.428739009186372 df.mm.exp2 -0.0368338624656685 0.102435819915627 -0.359579905701025 0.719255384452606 df.mm.exp3 0.0531578988163018 0.102435819915627 0.518938578908101 0.603945847212067 df.mm.exp4 -0.0882304563476341 0.102435819915627 -0.861324255717446 0.389315323407721 df.mm.exp5 -0.197605481347292 0.102435819915627 -1.92906623396047 0.0540730013727886 . df.mm.exp6 -0.0448973615136316 0.102435819915627 -0.438297477880413 0.661287953408874 df.mm.exp7 -0.096051180035953 0.102435819915627 -0.937671803818887 0.348693632891284 df.mm.exp8 -0.00579702850330942 0.102435819915627 -0.0565918104436926 0.954884366723059 df.mm.trans1:exp2 -0.287252827114825 0.0952801006458013 -3.01482497570686 0.00265181987325173 ** df.mm.trans2:exp2 0.216786753247936 0.0728388535410403 2.97625158427006 0.00300511687493241 ** df.mm.trans1:exp3 0.0110551354784842 0.0952801006458013 0.116027747699187 0.907659416699795 df.mm.trans2:exp3 -0.071458929268455 0.0728388535410403 -0.9810551071921 0.326859583967394 df.mm.trans1:exp4 -0.404386386041323 0.0952801006458013 -4.24418512680427 2.44867956421008e-05 *** df.mm.trans2:exp4 0.0470168374896943 0.0728388535410404 0.645491179555746 0.518792412766268 df.mm.trans1:exp5 -0.47659326623253 0.0952801006458013 -5.00202311922654 6.96429643462814e-07 *** df.mm.trans2:exp5 0.172827231181747 0.0728388535410403 2.37273409423405 0.0178904242170162 * df.mm.trans1:exp6 -0.584779695591517 0.0952801006458013 -6.13747982661568 1.31344991168931e-09 *** df.mm.trans2:exp6 0.136804231125648 0.0728388535410403 1.87817661145047 0.0607173603183909 . df.mm.trans1:exp7 -0.4641805680249 0.0952801006458013 -4.87174724710322 1.33150469396299e-06 *** df.mm.trans2:exp7 0.114224276968319 0.0728388535410404 1.56817785310089 0.117231631528790 df.mm.trans1:exp8 -0.197795253614910 0.0952801006458013 -2.07593455794304 0.038216113047129 * df.mm.trans2:exp8 0.086431229054374 0.0728388535410404 1.18660886124018 0.235731144460168 df.mm.trans1:probe2 0.256737502264035 0.0623754676331993 4.11600124224779 4.25138516142317e-05 *** df.mm.trans1:probe3 0.178783570906323 0.0623754676331993 2.86624818522668 0.00426174468384023 ** df.mm.trans1:probe4 -0.0229991653642202 0.0623754676331993 -0.368721329665494 0.712432157895495 df.mm.trans1:probe5 0.497803628212402 0.0623754676331993 7.98075985802223 4.98562601736257e-15 *** df.mm.trans1:probe6 -0.137075422286636 0.0623754676331993 -2.19758548493315 0.0282620931375818 * df.mm.trans1:probe7 -0.225149622836467 0.0623754676331993 -3.60958613024701 0.000325601325766755 *** df.mm.trans1:probe8 0.0509649993390249 0.0623754676331993 0.817068012038418 0.414130704576432 df.mm.trans1:probe9 -0.337260845860764 0.0623754676331993 -5.40694697223011 8.44930495705827e-08 *** df.mm.trans1:probe10 0.611412506240651 0.0623754676331993 9.8021310210623 1.65237969326216e-21 *** df.mm.trans1:probe11 -0.235332679380288 0.0623754676331993 -3.77284032184205 0.000173203989734397 *** df.mm.trans1:probe12 -0.146392014763368 0.0623754676331993 -2.34694857318314 0.0191687312359936 * df.mm.trans1:probe13 -0.299744058932416 0.0623754676331993 -4.80547994758242 1.84088269534104e-06 *** df.mm.trans1:probe14 -0.143253923464712 0.0623754676331993 -2.29663886942092 0.0218947065088884 * df.mm.trans1:probe15 -0.353704510861897 0.0623754676331993 -5.67057088761028 1.98231167611647e-08 *** df.mm.trans1:probe16 -0.205082760268107 0.0623754676331993 -3.28787531460448 0.00105314668854413 ** df.mm.trans1:probe17 -0.539971902996139 0.0623754676331993 -8.65679927518073 2.61164660192748e-17 *** df.mm.trans1:probe18 -0.470909311951116 0.0623754676331993 -7.54959168755755 1.18120735288688e-13 *** df.mm.trans1:probe19 -0.0388134720188728 0.0623754676331993 -0.622255407320014 0.533949706575809 df.mm.trans1:probe20 -0.466932359690041 0.0623754676331993 -7.48583341187676 1.86289628726548e-13 *** df.mm.trans1:probe21 -0.367408475855647 0.0623754676331993 -5.89027208607401 5.65692954068492e-09 *** df.mm.trans1:probe22 -0.0423407334848674 0.0623754676331993 -0.678804265386085 0.497456506849719 df.mm.trans2:probe2 -0.0219337024286017 0.0623754676331993 -0.35163988761709 0.725200140678612 df.mm.trans2:probe3 -0.0941544848616495 0.0623754676331993 -1.50947942250834 0.131567720214756 df.mm.trans2:probe4 -0.00668659771702706 0.0623754676331993 -0.107199159713684 0.914657625888018 df.mm.trans2:probe5 -0.101632860425260 0.0623754676331993 -1.62937232026725 0.103624475980506 df.mm.trans2:probe6 -0.0339782354143049 0.0623754676331993 -0.544737165164274 0.586084795271602 df.mm.trans3:probe2 0.169605862633667 0.0623754676331993 2.71911168074986 0.00668635127662983 ** df.mm.trans3:probe3 0.219634934982743 0.0623754676331993 3.52117496375839 0.000453740410298087 *** df.mm.trans3:probe4 0.166761777537851 0.0623754676331993 2.67351546794804 0.00765776529496735 ** df.mm.trans3:probe5 0.285452391836080 0.0623754676331993 4.57635674195969 5.47606556332015e-06 *** df.mm.trans3:probe6 0.272184019963573 0.0623754676331993 4.36363894799409 1.44518538281194e-05 *** df.mm.trans3:probe7 0.29389182253175 0.0623754676331993 4.71165722171398 2.89288663519917e-06 *** df.mm.trans3:probe8 -0.138767007581173 0.0623754676331993 -2.22470488553604 0.0263768811376551 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.35007010579228 0.208030080929599 20.9107744723919 6.32741869252864e-78 *** df.mm.trans1 -0.281548014918217 0.180646739679407 -1.55855575039926 0.119493619797926 df.mm.trans2 0.0527240933878258 0.160569001016753 0.328357858951397 0.742726281662445 df.mm.exp2 -0.322828232894402 0.208690612042603 -1.54692264177412 0.122273945704519 df.mm.exp3 -0.286308505033273 0.208690612042603 -1.37192805287678 0.170467090392830 df.mm.exp4 -0.415919404728787 0.208690612042603 -1.99299527974876 0.0465983025292585 * df.mm.exp5 -0.0206254834111502 0.208690612042603 -0.0988328282200812 0.921295546659145 df.mm.exp6 -0.149788238110507 0.208690612042603 -0.71775264179075 0.473117535029047 df.mm.exp7 -0.108093766438949 0.208690612042603 -0.51796180662349 0.604626881697452 df.mm.exp8 -0.152992603013899 0.208690612042603 -0.73310726110989 0.463705905763399 df.mm.trans1:exp2 0.307961832222407 0.194112396773227 1.58651295508028 0.113014722631051 df.mm.trans2:exp2 0.118260874827269 0.148393256758052 0.796942377375595 0.425718896572563 df.mm.trans1:exp3 0.254577477816210 0.194112396773227 1.31149520611825 0.190063510892142 df.mm.trans2:exp3 0.117016425308666 0.148393256758052 0.788556217884322 0.430603010007084 df.mm.trans1:exp4 0.411892082260768 0.194112396773227 2.12192569412228 0.0341480764337784 * df.mm.trans2:exp4 0.132198010272586 0.148393256758052 0.890862652122587 0.373268375616300 df.mm.trans1:exp5 0.114259382897700 0.194112396773227 0.588624862693257 0.556277660529612 df.mm.trans2:exp5 -0.126436614105690 0.148393256758052 -0.852037463614933 0.394446075729072 df.mm.trans1:exp6 0.194619201176672 0.194112396773227 1.00261088117951 0.316349171999785 df.mm.trans2:exp6 0.00523758417820589 0.148393256758051 0.0352952977286935 0.971853001094237 df.mm.trans1:exp7 0.100070435879666 0.194112396773227 0.515528310108778 0.606325086569794 df.mm.trans2:exp7 0.0617485414699502 0.148393256758052 0.416114201001925 0.677437074757481 df.mm.trans1:exp8 0.159776382494702 0.194112396773227 0.823112717944345 0.410687059194022 df.mm.trans2:exp8 0.0620152729155474 0.148393256758052 0.417911664386883 0.676122876317675 df.mm.trans1:probe2 0.0943084472063824 0.127076393077516 0.742139786331947 0.458218587100118 df.mm.trans1:probe3 -0.0815726761578592 0.127076393077516 -0.641918409724615 0.521108451498654 df.mm.trans1:probe4 0.101653493584477 0.127076393077516 0.799940029163944 0.423980949111008 df.mm.trans1:probe5 0.0325923971080049 0.127076393077516 0.256478770908485 0.797646539591928 df.mm.trans1:probe6 0.0884825334777345 0.127076393077516 0.696294027040573 0.486445203786621 df.mm.trans1:probe7 0.113187468230772 0.127076393077516 0.890704130717088 0.373353383542597 df.mm.trans1:probe8 0.0601280502581407 0.127076393077516 0.473164596523154 0.636223710264733 df.mm.trans1:probe9 0.188443262384815 0.127076393077516 1.48291321323438 0.138487875299214 df.mm.trans1:probe10 0.154428833782646 0.127076393077516 1.21524407517960 0.224628426907511 df.mm.trans1:probe11 0.0111753193066648 0.127076393077516 0.087941741467653 0.92994479747423 df.mm.trans1:probe12 0.221377895730103 0.127076393077516 1.74208513767827 0.0818745251443757 . df.mm.trans1:probe13 -0.067321938759193 0.127076393077516 -0.529775335361673 0.596413422301372 df.mm.trans1:probe14 0.0919677278512604 0.127076393077516 0.723720005140219 0.469447377013491 df.mm.trans1:probe15 0.0790710491063549 0.127076393077516 0.622232400459476 0.533964824849133 df.mm.trans1:probe16 0.200759684086447 0.127076393077516 1.57983461148433 0.114536598431883 df.mm.trans1:probe17 0.129743240335460 0.127076393077516 1.02098617369724 0.307566957698090 df.mm.trans1:probe18 0.226834012755487 0.127076393077516 1.78502086234946 0.0746334427422395 . df.mm.trans1:probe19 0.200923046784380 0.127076393077516 1.58112015865777 0.114242396019854 df.mm.trans1:probe20 -0.0390647274620583 0.127076393077516 -0.307411365053688 0.758609592640243 df.mm.trans1:probe21 0.0421044067141474 0.127076393077516 0.331331458931667 0.740480150223515 df.mm.trans1:probe22 0.170176586869952 0.127076393077516 1.33916758847684 0.180893177976613 df.mm.trans2:probe2 -0.0154336289199265 0.127076393077516 -0.121451581573551 0.903363597190313 df.mm.trans2:probe3 -0.0977066958471218 0.127076393077516 -0.7688815639229 0.442188581341532 df.mm.trans2:probe4 -0.0885494383391554 0.127076393077516 -0.696820520276653 0.486115790321927 df.mm.trans2:probe5 -0.204100994103765 0.127076393077516 -1.60612832297863 0.108636914158202 df.mm.trans2:probe6 -0.126289522501567 0.127076393077516 -0.993807893371124 0.320614288065593 df.mm.trans3:probe2 0.0641469947524971 0.127076393077516 0.504790804955942 0.613843584975341 df.mm.trans3:probe3 0.0324675656559641 0.127076393077516 0.255496436983060 0.798404799354517 df.mm.trans3:probe4 0.0452765830012371 0.127076393077516 0.356294209370725 0.721713372897879 df.mm.trans3:probe5 -0.064061707175337 0.127076393077516 -0.50411965294183 0.614314896543817 df.mm.trans3:probe6 0.200329549039911 0.127076393077516 1.57644975741254 0.11531409261604 df.mm.trans3:probe7 0.0246324705618099 0.127076393077516 0.193839862505258 0.8463500530094 df.mm.trans3:probe8 -0.0251655971662065 0.127076393077516 -0.198035186211617 0.843067407786449