fitVsDatCorrelation=0.841203674899487 cont.fitVsDatCorrelation=0.281258249225556 fstatistic=7114.40160529645,69,1083 cont.fstatistic=2248.05290648919,69,1083 residuals=-0.669115523337822,-0.105920502525190,-0.00561753896380229,0.0923800495744801,1.17510421339376 cont.residuals=-0.614397012401419,-0.219922311642544,-0.0845701748218994,0.127695276357564,1.66490067750741 predictedValues: Include Exclude Both Lung 47.3655440430797 44.3674844317675 55.0619189344384 cerebhem 51.1585271310136 44.1523957447697 56.3778940516236 cortex 48.9406474935167 46.0671597227609 55.6856571331874 heart 49.4831058019641 48.9523543363345 60.9993974003924 kidney 73.9575771861098 68.0538154971103 113.119767955701 liver 56.1062749861608 53.3427224808113 78.5332153649013 stomach 49.1745902539977 44.7722366366784 55.5157195222146 testicle 47.7822536344777 44.435473540029 57.1169819900803 diffExp=2.99805961131228,7.00613138624391,2.87348777075582,0.530751465629663,5.90376168899954,2.76355250534947,4.40235361731926,3.34678009444863 diffExpScore=0.967558671425843 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 55.8839021752531 67.0088417873631 57.6759361417445 cerebhem 62.1847828955071 63.1604996538987 57.690463502447 cortex 54.4605920110981 76.8379276643342 59.628086084836 heart 61.1963556711737 59.1770084882711 61.5004332247366 kidney 55.6778655137233 56.5423119941276 52.5526793772167 liver 54.7281374449418 56.3117798429513 55.2114640300331 stomach 57.5369790296481 56.517204301993 57.2293915065778 testicle 59.8570580347812 61.2767025448196 53.8658138913622 cont.diffExp=-11.1249396121100,-0.975716758391542,-22.3773356532361,2.01934718290258,-0.864446480404311,-1.58364239800954,1.01977472765509,-1.41964451003843 cont.diffExpScore=1.13987107939984 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,-1,0,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,0,-1,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,-1,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.97532752660821 cont.tran.correlation=-0.232238149645607 tran.covariance=0.0215113442924464 cont.tran.covariance=-0.00115238979024374 tran.mean=51.1320101825364 cont.tran.mean=59.8973718158678 weightedLogRatios: wLogRatio Lung 0.250122916896474 cerebhem 0.568698237262443 cortex 0.233582173928631 heart 0.0420164604040812 kidney 0.354559214171777 liver 0.202141055961178 stomach 0.360944577176723 testicle 0.278145200082956 cont.weightedLogRatios: wLogRatio Lung -0.746898516740562 cerebhem -0.0644219659952885 cortex -1.43525974666902 heart 0.137483379243510 kidney -0.062046632152814 liver -0.114577788721679 stomach 0.0723087059836827 testicle -0.0961917278664321 varWeightedLogRatios=0.0229845028540535 cont.varWeightedLogRatios=0.286507865279415 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.72544848582305 0.0882987030487699 42.1914292870800 7.25171740264646e-231 *** df.mm.trans1 0.107407950380907 0.075305811478019 1.42629032571089 0.154072748084530 df.mm.trans2 0.0590105247345181 0.0655949794732701 0.899619531988188 0.368522741436771 df.mm.exp2 0.0485556807574232 0.0822382699365881 0.590426826766459 0.555027708405614 df.mm.exp3 0.0590424391367053 0.082238269936588 0.717943594657712 0.472946904214005 df.mm.exp4 0.03967123035106 0.082238269936588 0.482393785541081 0.629623715916012 df.mm.exp5 0.153399730087119 0.0822382699365881 1.86530833157606 0.0624084455203589 . df.mm.exp6 -0.00148000669226969 0.0822382699365881 -0.0179965689138510 0.985644905410346 df.mm.exp7 0.038355482762662 0.0822382699365881 0.466394572651358 0.641026892332806 df.mm.exp8 -0.0263526317677903 0.0822382699365881 -0.320442438637269 0.748694774777087 df.mm.trans1:exp2 0.0284784606296401 0.0747685455575659 0.380888252102135 0.703360870695225 df.mm.trans2:exp2 -0.0534153599587911 0.0496273617924184 -1.07632882405108 0.282019928122629 df.mm.trans1:exp3 -0.0263291963008596 0.0747685455575658 -0.352142683858792 0.724799769100824 df.mm.trans2:exp3 -0.0214489819633228 0.0496273617924184 -0.432200729368603 0.66568156774878 df.mm.trans1:exp4 0.00406503859351926 0.0747685455575658 0.0543682983693925 0.95665176309044 df.mm.trans2:exp4 0.0586693656936617 0.0496273617924184 1.18219795642300 0.23738665654741 df.mm.trans1:exp5 0.292196872014731 0.0747685455575659 3.90801867062885 9.88296970821548e-05 *** df.mm.trans2:exp5 0.274392197929138 0.0496273617924184 5.52905066920276 4.03406552911518e-08 *** df.mm.trans1:exp6 0.170832621106533 0.0747685455575659 2.28481936932966 0.0225161209269843 * df.mm.trans2:exp6 0.185710695745024 0.0496273617924184 3.74210292543488 0.000192065469687094 *** df.mm.trans1:exp7 -0.00087349637197392 0.0747685455575659 -0.0116826717098756 0.990680940305803 df.mm.trans2:exp7 -0.0292741219113427 0.0496273617924184 -0.58987866479364 0.555395043945398 df.mm.trans1:exp8 0.0351118940080440 0.0747685455575659 0.469607824335844 0.63872976746508 df.mm.trans2:exp8 0.0278838672828858 0.0496273617924184 0.561864791433375 0.574324401099051 df.mm.trans1:probe2 -0.0368563191059009 0.0567907871293016 -0.648984121702454 0.516486235066223 df.mm.trans1:probe3 -0.00627490790362344 0.0567907871293016 -0.110491652269878 0.912039941345865 df.mm.trans1:probe4 -0.10505004805018 0.0567907871293016 -1.84977270716466 0.0646185984872618 . df.mm.trans1:probe5 -0.0277849670079688 0.0567907871293017 -0.489251310158949 0.624762857884462 df.mm.trans1:probe6 -0.0814171677406657 0.0567907871293016 -1.43363337358390 0.151965549189572 df.mm.trans1:probe7 -0.0789846892384208 0.0567907871293017 -1.39080110051280 0.164571438379370 df.mm.trans1:probe8 0.163456467494861 0.0567907871293017 2.87822155242719 0.00407782921294087 ** df.mm.trans1:probe9 0.0784002136334844 0.0567907871293017 1.38050936774273 0.167714718654751 df.mm.trans1:probe10 -0.101186559676828 0.0567907871293016 -1.78174251127116 0.0750713748167696 . df.mm.trans1:probe11 -0.0515455646049028 0.0567907871293017 -0.90763955230174 0.3642706026799 df.mm.trans1:probe12 -0.106517659681200 0.0567907871293017 -1.87561513170578 0.0609769982159385 . df.mm.trans1:probe13 -0.143899123807575 0.0567907871293017 -2.53384626418267 0.0114218919840463 * df.mm.trans1:probe14 -0.093209971395979 0.0567907871293016 -1.64128683731321 0.101028233795131 df.mm.trans1:probe15 -0.0648150966691915 0.0567907871293017 -1.14129597326447 0.253999071035398 df.mm.trans1:probe16 -0.0206206535719543 0.0567907871293017 -0.363098569579693 0.716602109152618 df.mm.trans1:probe17 0.351553460796021 0.0567907871293017 6.19032555396004 8.50629117620092e-10 *** df.mm.trans1:probe18 0.384485196031217 0.0567907871293016 6.77020368032263 2.10388593145337e-11 *** df.mm.trans1:probe19 0.199648978314597 0.0567907871293016 3.51551701264563 0.000457052243317852 *** df.mm.trans1:probe20 0.245231080298702 0.0567907871293016 4.31814899378588 1.71769866135595e-05 *** df.mm.trans1:probe21 0.193562181445403 0.0567907871293016 3.40833771162037 0.000677647162586965 *** df.mm.trans1:probe22 0.353446738563562 0.0567907871293016 6.22366331635502 6.93130799100141e-10 *** df.mm.trans2:probe2 0.0476393698119168 0.0567907871293016 0.838857360850646 0.401734471003622 df.mm.trans2:probe3 -0.0187817882933086 0.0567907871293016 -0.330718928944338 0.740920774025915 df.mm.trans2:probe4 -0.00669499450217893 0.0567907871293016 -0.117888742885985 0.906177672894025 df.mm.trans2:probe5 0.0431223691905114 0.0567907871293016 0.759319801155954 0.44782651249534 df.mm.trans2:probe6 0.143959351175131 0.0567907871293016 2.53490677717433 0.0113875803917165 * df.mm.trans3:probe2 0.242645638457932 0.0567907871293016 4.27262326731754 2.10173002784858e-05 *** df.mm.trans3:probe3 0.90644399011092 0.0567907871293017 15.9611098195755 1.15079825995039e-51 *** df.mm.trans3:probe4 -0.207986122044675 0.0567907871293017 -3.66232152357971 0.000262019924389694 *** df.mm.trans3:probe5 0.169235306186317 0.0567907871293017 2.97997817499872 0.00294702283340831 ** df.mm.trans3:probe6 -0.0274247393135855 0.0567907871293017 -0.482908244450718 0.62925848767639 df.mm.trans3:probe7 0.299967927060731 0.0567907871293017 5.28198220563068 1.54409399245570e-07 *** df.mm.trans3:probe8 0.147986789827522 0.0567907871293017 2.60582388989582 0.00929110271744256 ** df.mm.trans3:probe9 0.186469257765556 0.0567907871293017 3.28344203683955 0.00105827043780666 ** df.mm.trans3:probe10 0.068919946749541 0.0567907871293016 1.21357618433116 0.225174204032156 df.mm.trans3:probe11 0.437652872278058 0.0567907871293017 7.706406169043 2.91506826096825e-14 *** df.mm.trans3:probe12 0.251446439192428 0.0567907871293017 4.42759207791808 1.04940849348004e-05 *** df.mm.trans3:probe13 0.461835318743694 0.0567907871293017 8.13222253271793 1.14541170216325e-15 *** df.mm.trans3:probe14 -0.00959591628214707 0.0567907871293017 -0.168969594668569 0.865852110009934 df.mm.trans3:probe15 0.119783059911646 0.0567907871293017 2.10919879731413 0.0351564824797937 * df.mm.trans3:probe16 -0.208002681774238 0.0567907871293016 -3.66261311541 0.000261725419867185 *** df.mm.trans3:probe17 -0.210184611331682 0.0567907871293017 -3.70103359992409 0.000225526961558212 *** df.mm.trans3:probe18 -0.06841909938751 0.0567907871293016 -1.20475701862932 0.228560318570548 df.mm.trans3:probe19 -0.156017899715861 0.0567907871293017 -2.74723960702708 0.00610963462122167 ** df.mm.trans3:probe20 -0.072927679120539 0.0567907871293017 -1.28414629919632 0.199365411125343 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.2397764581166 0.156706841803093 27.0554649007859 1.41569147926998e-123 *** df.mm.trans1 -0.214131716798475 0.133647896046916 -1.60220791446882 0.109401247059700 df.mm.trans2 -0.0209006417094426 0.116413737874694 -0.179537588011647 0.857549189961333 df.mm.exp2 0.0474366338151389 0.145951176089132 0.325017139883610 0.745230873181334 df.mm.exp3 0.077788113999046 0.145951176089132 0.532973533228268 0.594161245762929 df.mm.exp4 -0.0976843885749956 0.145951176089132 -0.669294973788631 0.503449984604645 df.mm.exp5 -0.0805050657775045 0.145951176089132 -0.551589017195313 0.581343750637357 df.mm.exp6 -0.151149799540218 0.145951176089132 -1.03561892127482 0.300611142664938 df.mm.exp7 -0.133355562264379 0.145951176089132 -0.913699812757512 0.361077967529231 df.mm.exp8 0.0476021363588136 0.145951176089132 0.326151098157257 0.744373049487369 df.mm.trans1:exp2 0.0593973247263957 0.13269439115165 0.447624984077235 0.654513387242026 df.mm.trans2:exp2 -0.106582111195617 0.0880754401252527 -1.21012294737382 0.226495773802042 df.mm.trans1:exp3 -0.103587119535364 0.13269439115165 -0.780644295786243 0.435182322764746 df.mm.trans2:exp3 0.0590856763872618 0.0880754401252526 0.670853035797899 0.502457198274206 df.mm.trans1:exp4 0.188495665173394 0.13269439115165 1.42052473761285 0.155742798449818 df.mm.trans2:exp4 -0.02660709276047 0.0880754401252526 -0.302094349146957 0.762638143254495 df.mm.trans1:exp5 0.0768113828958261 0.13269439115165 0.578859303917692 0.562804477613599 df.mm.trans2:exp5 -0.0893302691522837 0.0880754401252526 -1.01424720699944 0.310691341762270 df.mm.trans1:exp6 0.130251409124612 0.13269439115165 0.981589410028297 0.326521392741385 df.mm.trans2:exp6 -0.0227710314150992 0.0880754401252527 -0.25854008089788 0.796039246108707 df.mm.trans1:exp7 0.162507053627677 0.13269439115165 1.22467160983432 0.220965286796531 df.mm.trans2:exp7 -0.0369239226736510 0.0880754401252526 -0.419230634796048 0.675130723057768 df.mm.trans1:exp8 0.0210808541367793 0.13269439115165 0.158867710638101 0.87380272044127 df.mm.trans2:exp8 -0.137026999681128 0.0880754401252526 -1.55579125674831 0.120049899038188 df.mm.trans1:probe2 -0.0794423819558195 0.100788625282856 -0.788207813459806 0.430747646913786 df.mm.trans1:probe3 -0.0512905690639957 0.100788625282856 -0.508892436225343 0.610931278380674 df.mm.trans1:probe4 0.0260671367228838 0.100788625282856 0.258631732000792 0.795968542003163 df.mm.trans1:probe5 0.000909871738642524 0.100788625282856 0.00902752404935613 0.992798838373658 df.mm.trans1:probe6 0.0378753720710647 0.100788625282856 0.375790144619695 0.707146509486517 df.mm.trans1:probe7 0.00219276110070636 0.100788625282856 0.0217560373956142 0.982646570550458 df.mm.trans1:probe8 0.105151067933003 0.100788625282856 1.04328308514879 0.297050023167225 df.mm.trans1:probe9 0.0601454965520973 0.100788625282856 0.596748853189568 0.55079979211967 df.mm.trans1:probe10 -0.03863932640239 0.100788625282856 -0.383369911971232 0.701520749532052 df.mm.trans1:probe11 -0.038031084585586 0.100788625282856 -0.37733508596684 0.705998530324663 df.mm.trans1:probe12 -0.0374825101217479 0.100788625282856 -0.371892264792341 0.710045815491915 df.mm.trans1:probe13 0.0230317837348463 0.100788625282856 0.2285157047257 0.819288488714438 df.mm.trans1:probe14 -0.0560008712785629 0.100788625282856 -0.55562689858504 0.578580696059158 df.mm.trans1:probe15 0.0026771519005558 0.100788625282856 0.0265620440108452 0.978813940520085 df.mm.trans1:probe16 0.00154594098280297 0.100788625282856 0.0153384469573268 0.987764994949718 df.mm.trans1:probe17 -0.135235196065847 0.100788625282856 -1.34177041988934 0.179951700004293 df.mm.trans1:probe18 0.0629121512107826 0.100788625282856 0.6241989215968 0.532628415728692 df.mm.trans1:probe19 0.129661070639466 0.100788625282856 1.28646531566019 0.198555650956112 df.mm.trans1:probe20 -0.0952487739384398 0.100788625282856 -0.945034954799028 0.344851817116544 df.mm.trans1:probe21 -0.108562161493447 0.100788625282856 -1.07712711815223 0.281663348196021 df.mm.trans1:probe22 0.0882911931460633 0.100788625282856 0.876003546018022 0.381222356571425 df.mm.trans2:probe2 0.0313778402302709 0.100788625282856 0.311323228610484 0.755614825623342 df.mm.trans2:probe3 -0.0913219698549367 0.100788625282856 -0.906074168574562 0.365098133109345 df.mm.trans2:probe4 -0.0622696800852406 0.100788625282856 -0.617824480793198 0.536820934442837 df.mm.trans2:probe5 -0.128011521439896 0.100788625282856 -1.27009889340827 0.204322212843716 df.mm.trans2:probe6 -0.115106876285194 0.100788625282856 -1.14206217181905 0.253680611930149 df.mm.trans3:probe2 0.005886367170583 0.100788625282856 0.0584030901707742 0.953438327333464 df.mm.trans3:probe3 0.360131574232316 0.100788625282856 3.57313707991981 0.000368248658073145 *** df.mm.trans3:probe4 -0.023854844009407 0.100788625282856 -0.236681906737592 0.812948330621966 df.mm.trans3:probe5 0.162998449022251 0.100788625282856 1.61723060082235 0.106119720328281 df.mm.trans3:probe6 0.148426842809505 0.100788625282856 1.47265470079541 0.141134716559570 df.mm.trans3:probe7 0.0956588295535976 0.100788625282856 0.949103425958416 0.342779750065492 df.mm.trans3:probe8 -0.0607511665474359 0.100788625282856 -0.602758162212672 0.546795774345809 df.mm.trans3:probe9 0.124581138246104 0.100788625282856 1.23606347339768 0.216702991038102 df.mm.trans3:probe10 0.132208738933599 0.100788625282856 1.31174265511177 0.189884971634162 df.mm.trans3:probe11 0.00468277499868962 0.100788625282856 0.046461344080721 0.9629511094104 df.mm.trans3:probe12 -0.00390412764597775 0.100788625282856 -0.0387357961776054 0.969108172623012 df.mm.trans3:probe13 0.0268807941439297 0.100788625282856 0.266704641208179 0.789747352359751 df.mm.trans3:probe14 0.0108976768236456 0.100788625282856 0.108124074448501 0.913917299224859 df.mm.trans3:probe15 -0.0063313667151458 0.100788625282856 -0.0628182664202164 0.949922811625552 df.mm.trans3:probe16 0.0195742718929263 0.100788625282856 0.194211120927511 0.846046997361171 df.mm.trans3:probe17 0.0548986333570552 0.100788625282856 0.544690764488415 0.586078354390511 df.mm.trans3:probe18 0.0427556048156777 0.100788625282856 0.424210615986549 0.671496468098897 df.mm.trans3:probe19 -0.0519460003714313 0.100788625282856 -0.515395464772425 0.606381886408546 df.mm.trans3:probe20 -0.0408754777963126 0.100788625282856 -0.40555645720535 0.685148633077268