fitVsDatCorrelation=0.72162135362298 cont.fitVsDatCorrelation=0.254099032735014 fstatistic=14352.1744882719,59,853 cont.fstatistic=7346.18152087402,59,853 residuals=-0.471055627969307,-0.0830714991862146,-0.00484571546423794,0.0742154002839459,0.779494387685762 cont.residuals=-0.477428951288944,-0.124184115121853,-0.0188383922457717,0.0943501024271946,1.14197274662148 predictedValues: Include Exclude Both Lung 50.6794343641441 47.4926121972427 51.1068286720079 cerebhem 54.6759687581876 52.7486323260885 59.6428436960279 cortex 51.1418660143995 47.416464971054 55.5786313166404 heart 49.1761464335491 47.7626597170745 48.7163707712572 kidney 50.3454724104281 47.5515930409014 52.385223999534 liver 50.6880479662121 47.6477114604118 51.7925295161543 stomach 49.6055833085472 51.7449232546719 54.9751116357288 testicle 49.5669809368547 46.0373330334975 51.4512079279025 diffExp=3.18682216690139,1.92733643209904,3.72540104334544,1.41348671647459,2.79387936952668,3.0403365058003,-2.13933994612465,3.52964790335722 diffExpScore=1.17744107359846 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 51.37466056499 49.723755961888 50.1511002580436 cerebhem 50.3497416412533 51.5811499181361 48.6693267035845 cortex 50.6511786944678 46.9812055248631 49.5528913142154 heart 52.0604336446588 44.6503935965145 50.1951932107409 kidney 51.3161199476839 48.4773263627129 46.5974422452209 liver 47.9692018003431 47.630672769734 51.8284900463656 stomach 48.9715561622007 50.1319360105368 51.7933077544896 testicle 51.997397235969 49.3134443104853 50.5529886284165 cont.diffExp=1.65090460310198,-1.23140827688285,3.66997316960467,7.41004004814424,2.83879358497101,0.338529030609095,-1.16037984833617,2.68395292548369 cont.diffExpScore=1.21997018084005 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=0.599764837474324 cont.tran.correlation=-0.218178813519987 tran.covariance=0.000932437772548298 cont.tran.covariance=-0.000282912779347500 tran.mean=49.6425893870791 cont.tran.mean=49.5737608841523 weightedLogRatios: wLogRatio Lung 0.252837951989299 cerebhem 0.142952955569456 cortex 0.294729264869190 heart 0.113182441954931 kidney 0.222114000808042 liver 0.240912636139695 stomach -0.165734079625856 testicle 0.285618839010229 cont.weightedLogRatios: wLogRatio Lung 0.12812796762116 cerebhem -0.0949858006922706 cortex 0.292386877117251 heart 0.595073036566457 kidney 0.222487599779015 liver 0.0273871352264794 stomach -0.0914016285056111 testicle 0.207996743087179 varWeightedLogRatios=0.0228743306177465 cont.varWeightedLogRatios=0.0514056434356987 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.76956268496731 0.0598102008323675 63.0254142689207 0 *** df.mm.trans1 0.199960638294042 0.0516506264320812 3.87140780484016 0.000116472650939776 *** df.mm.trans2 0.0941100993088986 0.0456330949960763 2.06232120168467 0.0394794853498359 * df.mm.exp2 0.0264117207319335 0.0586987508975225 0.449953709884622 0.652858140681654 df.mm.exp3 -0.0764020492838208 0.0586987508975225 -1.30159582811575 0.193405997565461 df.mm.exp4 0.0234614660963881 0.0586987508975225 0.399692765819628 0.689482881029835 df.mm.exp5 -0.0300768220982939 0.0586987508975225 -0.512392881252323 0.608508717048113 df.mm.exp6 -0.00989741663390111 0.0586987508975225 -0.168613752125326 0.866140435229914 df.mm.exp7 -0.00862708595986253 0.0586987508975225 -0.146972223905137 0.88318867332083 df.mm.exp8 -0.0600326184951486 0.0586987508975225 -1.02272395199610 0.306728337904115 df.mm.trans1:exp2 0.0494923696971314 0.0542564760941334 0.912192852541022 0.361924932564259 df.mm.trans2:exp2 0.078551957566098 0.0400710632262685 1.96031627916984 0.0502839890464206 . df.mm.trans1:exp3 0.0854853123725425 0.0542564760941334 1.57557804204291 0.115493864512380 df.mm.trans2:exp3 0.0747974136725679 0.0400710632262684 1.86661914235245 0.0622977025824068 . df.mm.trans1:exp4 -0.0535729832140713 0.0542564760941334 -0.98740255672196 0.323725324684313 df.mm.trans2:exp4 -0.0177914755986565 0.0400710632262684 -0.443998091545357 0.65715657260774 df.mm.trans1:exp5 0.0234653204223531 0.0542564760941334 0.432488840256441 0.665495477105155 df.mm.trans2:exp5 0.0313179467114579 0.0400710632262684 0.781560163118595 0.434689975769972 df.mm.trans1:exp6 0.0100673646663606 0.0542564760941334 0.185551392038326 0.85284068677847 df.mm.trans2:exp6 0.0131578512131407 0.0400710632262684 0.328362917121579 0.74271786620231 df.mm.trans1:exp7 -0.012789714401995 0.0542564760941334 -0.235726964276213 0.813701128951701 df.mm.trans2:exp7 0.0943792456795845 0.0400710632262684 2.35529676731199 0.0187334966592373 * df.mm.trans1:exp8 0.0378373291643756 0.0542564760941334 0.697379039116527 0.485755642206278 df.mm.trans2:exp8 0.0289111072552637 0.0400710632262684 0.721495885747078 0.470802182272745 df.mm.trans1:probe2 -0.175350521967663 0.0371468698093708 -4.72046562382031 2.74952994593202e-06 *** df.mm.trans1:probe3 -0.0913963349689448 0.0371468698093708 -2.46040475113972 0.0140752612732485 * df.mm.trans1:probe4 0.0626019055201556 0.0371468698093708 1.68525385426589 0.0923053623866234 . df.mm.trans1:probe5 0.13042341119134 0.0371468698093708 3.51102022487071 0.000469799909746365 *** df.mm.trans1:probe6 0.28736714644658 0.0371468698093708 7.73597204613152 2.89355058314453e-14 *** df.mm.trans1:probe7 -0.105752700648887 0.0371468698093708 -2.84688053641089 0.00452096337500013 ** df.mm.trans1:probe8 0.245564282877106 0.0371468698093708 6.61063190888722 6.7400620681233e-11 *** df.mm.trans1:probe9 -0.102260484538887 0.0371468698093708 -2.75286948977569 0.00603304231951644 ** df.mm.trans1:probe10 -0.0726006170121251 0.0371468698093708 -1.95442085388876 0.0509778379855148 . df.mm.trans1:probe11 -0.139651410077135 0.0371468698093708 -3.7594395111565 0.000181901739021903 *** df.mm.trans1:probe12 -0.114361428425561 0.0371468698093708 -3.07862894000052 0.00214611815581934 ** df.mm.trans1:probe13 -0.181044655775286 0.0371468698093708 -4.87375266622371 1.30553088557136e-06 *** df.mm.trans1:probe14 -0.126982354263077 0.0371468698093708 -3.41838639203575 0.000659782094375821 *** df.mm.trans1:probe15 -0.0156697017630234 0.0371468698093708 -0.421831014118732 0.673254653360461 df.mm.trans1:probe16 -0.115477951515225 0.0371468698093708 -3.10868592987327 0.00194157219898115 ** df.mm.trans1:probe17 -0.154337001443092 0.0371468698093708 -4.15477810741832 3.58408069917299e-05 *** df.mm.trans1:probe18 -0.0992094044048333 0.0371468698093708 -2.67073389800953 0.00771268508996355 ** df.mm.trans1:probe19 -0.201021502808454 0.0371468698093708 -5.4115327574046 8.12355532663134e-08 *** df.mm.trans1:probe20 -0.150214966870941 0.0371468698093708 -4.04381224156462 5.73510423263148e-05 *** df.mm.trans1:probe21 -0.100053386084595 0.0371468698093708 -2.69345402716422 0.00721042281261123 ** df.mm.trans1:probe22 -0.188672445041321 0.0371468698093708 -5.079094039674 4.65932591567334e-07 *** df.mm.trans2:probe2 -0.00339311678108359 0.0371468698093708 -0.0913432759878903 0.927241278130875 df.mm.trans2:probe3 0.0178619091403081 0.0371468698093708 0.480845606425826 0.630749540171483 df.mm.trans2:probe4 0.0280935773656673 0.0371468698093708 0.756283840599141 0.449687846612335 df.mm.trans2:probe5 -0.00943241029350506 0.0371468698093708 -0.253922075854843 0.799616952272676 df.mm.trans2:probe6 -0.0827078477785678 0.0371468698093708 -2.22650921068196 0.0262405415478801 * df.mm.trans3:probe2 -0.341789309069567 0.0371468698093708 -9.20102584211134 2.66792649261696e-19 *** df.mm.trans3:probe3 -0.154683641965403 0.0371468698093708 -4.16410972873901 3.44342170234914e-05 *** df.mm.trans3:probe4 -0.130187591381999 0.0371468698093708 -3.50467191583278 0.000480980495682616 *** df.mm.trans3:probe5 -0.0558505791708716 0.0371468698093708 -1.50350701034795 0.133078482106521 df.mm.trans3:probe6 -0.340862496258638 0.0371468698093708 -9.17607588493634 3.2947090301687e-19 *** df.mm.trans3:probe7 -0.089195087443925 0.0371468698093708 -2.40114679653100 0.0165569141283821 * df.mm.trans3:probe8 0.0400916037872080 0.0371468698093708 1.07927273530580 0.280771243123609 df.mm.trans3:probe9 -0.0941544734642218 0.0371468698093708 -2.53465430458612 0.0114335805834580 * df.mm.trans3:probe10 -0.130521432889186 0.0371468698093708 -3.51365898550785 0.000465224400238311 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88158421887718 0.0835593364589732 46.4530282715086 7.8567226289743e-236 *** df.mm.trans1 0.0520153928297923 0.0721597990358089 0.720836165355449 0.471207822211075 df.mm.trans2 0.00899887773485948 0.063752856291663 0.141152542149492 0.887782763509413 df.mm.exp2 0.0465133737960559 0.0820065575388136 0.567190907556889 0.570733713907574 df.mm.exp3 -0.0589178877901329 0.0820065575388136 -0.718453372003174 0.47267452917839 df.mm.exp4 -0.0952383360037519 0.0820065575388135 -1.16135024883438 0.245824368519980 df.mm.exp5 0.0469680522487219 0.0820065575388135 0.572735323348892 0.566974847146611 df.mm.exp6 -0.144491301861269 0.0820065575388135 -1.76194814412105 0.078436216900614 . df.mm.exp7 -0.0719504719248353 0.0820065575388135 -0.877374615935821 0.380530234439512 df.mm.exp8 -0.00421907217091199 0.0820065575388135 -0.0514479853506241 0.958980593649505 df.mm.trans1:exp2 -0.0666649516878688 0.0758003664581321 -0.879480598879305 0.379388438273399 df.mm.trans2:exp2 -0.0098398857789618 0.0559822807446677 -0.175767861688969 0.860518035956469 df.mm.trans1:exp3 0.044735323788629 0.0758003664581321 0.59017292236098 0.555231030857675 df.mm.trans2:exp3 0.00218272101233055 0.0559822807446677 0.0389894978070978 0.968907884926758 df.mm.trans1:exp4 0.108498499457560 0.075800366458132 1.43137170078840 0.152689985089160 df.mm.trans2:exp4 -0.0123813476249250 0.0559822807446677 -0.221165473436062 0.825016521645818 df.mm.trans1:exp5 -0.048108186218981 0.0758003664581321 -0.63466957307592 0.525814125779429 df.mm.trans2:exp5 -0.0723546673562482 0.0559822807446677 -1.29245658436558 0.196549068501323 df.mm.trans1:exp6 0.0759054118904813 0.075800366458132 1.00138581694598 0.316924298050412 df.mm.trans2:exp6 0.101485435426132 0.0559822807446677 1.81281352021012 0.0702119967994541 . df.mm.trans1:exp7 0.0240450492151741 0.075800366458132 0.317215474524852 0.751157734545762 df.mm.trans2:exp7 0.0801259161899475 0.0559822807446677 1.43127280854093 0.152718306934284 df.mm.trans1:exp8 0.0162676705581179 0.075800366458132 0.214612030498603 0.830121108341125 df.mm.trans2:exp8 -0.00406698595463343 0.0559822807446677 -0.0726477360431732 0.9421034697492 df.mm.trans1:probe2 0.00423976552947676 0.0518969632203461 0.0816958308615362 0.93490775866951 df.mm.trans1:probe3 0.0160923369095815 0.0518969632203461 0.310082438566898 0.756574007525513 df.mm.trans1:probe4 -0.0130369449373278 0.0518969632203461 -0.251208242801704 0.801713614251015 df.mm.trans1:probe5 0.0436962473867349 0.0518969632203461 0.841980814969994 0.400034687744113 df.mm.trans1:probe6 0.0367165894943004 0.0518969632203461 0.707490134603978 0.479455187494495 df.mm.trans1:probe7 -0.0043662011015061 0.0518969632203461 -0.0841321116029066 0.932971134130638 df.mm.trans1:probe8 0.0138446514148367 0.0518969632203461 0.266771898695778 0.789709269338111 df.mm.trans1:probe9 0.0188353397502515 0.0518969632203461 0.362937223711523 0.716741693696151 df.mm.trans1:probe10 -0.00932055309160458 0.0518969632203461 -0.179597273390179 0.857511398291458 df.mm.trans1:probe11 0.017596196648091 0.0518969632203461 0.339060236981119 0.734647803862808 df.mm.trans1:probe12 -0.0314506339392574 0.0518969632203461 -0.606020699240592 0.544662233267927 df.mm.trans1:probe13 -0.0219976737515247 0.0518969632203461 -0.423872080108543 0.671765972360869 df.mm.trans1:probe14 0.00112897394285902 0.0518969632203461 0.021754142685875 0.982649161945005 df.mm.trans1:probe15 -0.014779967868227 0.0518969632203461 -0.284794464860567 0.775870740702104 df.mm.trans1:probe16 0.008682507802174 0.0518969632203461 0.167302810480634 0.867171446247966 df.mm.trans1:probe17 0.0181751119143523 0.0518969632203461 0.350215326418691 0.726263563847569 df.mm.trans1:probe18 -0.0521407087449671 0.0518969632203461 -1.00469672037622 0.315327823473765 df.mm.trans1:probe19 0.00244066584534486 0.0518969632203462 0.0470290686370643 0.962501067842757 df.mm.trans1:probe20 -0.00534583768827441 0.0518969632203462 -0.103008680210764 0.917980293224651 df.mm.trans1:probe21 0.0708543595537617 0.0518969632203461 1.36528912593451 0.172522141666669 df.mm.trans1:probe22 0.0775903054942852 0.0518969632203461 1.49508373283518 0.135262436995684 df.mm.trans2:probe2 0.108005762342064 0.0518969632203461 2.08115765624839 0.0377172598695617 * df.mm.trans2:probe3 -0.00228904316402720 0.0518969632203461 -0.0441074587410498 0.964829070237134 df.mm.trans2:probe4 0.0202905105613611 0.0518969632203461 0.390976837608221 0.69591198916732 df.mm.trans2:probe5 0.0563516454017403 0.0518969632203461 1.08583704912521 0.277857854164936 df.mm.trans2:probe6 0.0720364760875562 0.0518969632203461 1.3880672705588 0.165478984626299 df.mm.trans3:probe2 -0.0293471979351673 0.0518969632203461 -0.565489695621762 0.571889438944614 df.mm.trans3:probe3 0.0355553335300734 0.0518969632203461 0.685113951255898 0.493458143857984 df.mm.trans3:probe4 -0.0783260019564379 0.0518969632203461 -1.50925983132921 0.131602696780273 df.mm.trans3:probe5 -0.0647681491183609 0.0518969632203461 -1.24801424012742 0.212368311261099 df.mm.trans3:probe6 -0.0449791267460044 0.0518969632203461 -0.866700553460716 0.386349826843128 df.mm.trans3:probe7 0.00887133050762682 0.0518969632203461 0.170941225789274 0.86431051951259 df.mm.trans3:probe8 -0.0226923697485394 0.0518969632203461 -0.437258142681515 0.662034825105001 df.mm.trans3:probe9 -0.0429414877585087 0.0518969632203461 -0.827437389278175 0.408220613215621 df.mm.trans3:probe10 -0.0369526900917546 0.0518969632203461 -0.712039545259314 0.476634984355741