fitVsDatCorrelation=0.70925866641292 cont.fitVsDatCorrelation=0.270184824116936 fstatistic=16788.1886954771,58,830 cont.fstatistic=8993.28024422316,58,830 residuals=-0.369599164311992,-0.0680847670172011,-0.00530208249859608,0.0611857233966682,0.517698748820424 cont.residuals=-0.42011225821269,-0.107801007654150,-0.0222196253528279,0.08535147546988,0.648090742228736 predictedValues: Include Exclude Both Lung 48.8379926331528 44.9029709605094 46.7713830489285 cerebhem 52.7228988145708 45.8722860141942 54.0735073651785 cortex 52.5607408243485 47.8594634483072 48.4017357322231 heart 51.2908883986285 50.2853971873049 46.5662747556972 kidney 49.8866538103457 47.2027159453019 46.2868914239393 liver 53.728771844899 54.4425610185485 48.2605311856104 stomach 52.0413719213201 48.412080608669 48.5268542193563 testicle 51.4880423917423 45.2248857999347 48.8341152414377 diffExp=3.93502167264333,6.85061280037662,4.7012773760413,1.00549121132358,2.68393786504383,-0.713789173649502,3.62929131265108,6.2631565918076 diffExpScore=1.01456577592595 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 50.1128753712119 54.0998334313376 47.9612243149369 cerebhem 48.1056392424333 49.0548315249423 52.2282377084714 cortex 50.3424650783125 50.2709157116712 50.1564612816319 heart 49.7927870993093 53.2005692717224 51.2887759151445 kidney 48.2227701619736 52.1618062575523 49.7527957049371 liver 50.7308732487409 53.1305716063928 48.7575889436319 stomach 48.0825421107032 48.8941680722955 48.153452065122 testicle 48.8178085248419 50.0115520451747 49.5826008218599 cont.diffExp=-3.98695806012562,-0.949192282509017,0.0715493666412428,-3.40778217241308,-3.93903609557871,-2.39969835765189,-0.811625961592327,-1.19374352033280 cont.diffExpScore=0.951357994210033 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=0.598642768565366 cont.tran.correlation=0.647102728155522 tran.covariance=0.00117265545384566 cont.tran.covariance=0.000570350917221005 tran.mean=49.7974826013611 cont.tran.mean=50.3145005474135 weightedLogRatios: wLogRatio Lung 0.323124167648245 cerebhem 0.542203503559941 cortex 0.366849004946690 heart 0.0777604783400919 kidney 0.214688326326516 liver -0.0526655412827482 stomach 0.283078559172364 testicle 0.502789956148713 cont.weightedLogRatios: wLogRatio Lung -0.302580454692562 cerebhem -0.075874300073135 cortex 0.00557262512928611 heart -0.260888092801098 kidney -0.307409912445334 liver -0.182543971262217 stomach -0.0649687574684756 testicle -0.0942237118906651 varWeightedLogRatios=0.0405130046189833 cont.varWeightedLogRatios=0.0143704753814464 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81567065210048 0.055238968739799 69.0757039667782 0 *** df.mm.trans1 0.146360989198392 0.047823044383994 3.06046992791154 0.00228103530904992 ** df.mm.trans2 -0.0397988367697638 0.0423683879291908 -0.939352161245279 0.347823244917599 df.mm.exp2 -0.0471743163356539 0.0547597782699638 -0.861477489976059 0.389223918263355 df.mm.exp3 0.102961699270658 0.0547597782699638 1.88024317343033 0.0604249345035665 . df.mm.exp4 0.166610315614474 0.0547597782699638 3.042567389391 0.00241957769818795 ** df.mm.exp5 0.0816051761301167 0.0547597782699638 1.49023934552485 0.136541217821687 df.mm.exp6 0.25673983344572 0.0547597782699638 4.68847467168332 3.21677114768089e-06 *** df.mm.exp7 0.101930092214124 0.0547597782699638 1.86140439998884 0.0630404102255734 . df.mm.exp8 0.0168269829547576 0.0547597782699638 0.307287273366980 0.758701804680494 df.mm.trans1:exp2 0.123715642599613 0.0507636730949249 2.43709004997082 0.0150150954948210 * df.mm.trans2:exp2 0.0685314995621182 0.0381111927459973 1.79819876063354 0.0725087927024881 . df.mm.trans1:exp3 -0.0295007778825839 0.0507636730949249 -0.581139544954111 0.561304178798334 df.mm.trans2:exp3 -0.0391967886565661 0.0381111927459973 -1.02848496287702 0.304021381201393 df.mm.trans1:exp4 -0.117605740795361 0.0507636730949249 -2.31673032358091 0.0207610540645767 * df.mm.trans2:exp4 -0.0533995559705389 0.0381111927459973 -1.40115152854006 0.161542546192867 df.mm.trans1:exp5 -0.0603602153771517 0.0507636730949249 -1.18904349699601 0.234762611694255 df.mm.trans2:exp5 -0.0316577049247047 0.0381111927459973 -0.830666863031455 0.406400726020510 df.mm.trans1:exp6 -0.161299734389271 0.0507636730949249 -3.17746381526906 0.00154067420807010 ** df.mm.trans2:exp6 -0.0640975747571902 0.0381111927459973 -1.68185695956530 0.0929727413552048 . df.mm.trans1:exp7 -0.0383996236146387 0.0507636730949249 -0.756439029595707 0.449600662776017 df.mm.trans2:exp7 -0.0266846712282972 0.0381111927459973 -0.700179377909915 0.484011495704892 df.mm.trans1:exp8 0.0360140636668048 0.0507636730949249 0.70944558325125 0.478247249377575 df.mm.trans2:exp8 -0.00968343777473813 0.0381111927459973 -0.254083828844616 0.799493726710919 df.mm.trans1:probe2 0.00927004079408364 0.0340533071483488 0.272221454254059 0.785519449752063 df.mm.trans1:probe3 -0.120488720710359 0.0340533071483488 -3.538238450247 0.000425174050952764 *** df.mm.trans1:probe4 -0.223548386033405 0.0340533071483488 -6.56466008013687 9.18317691162465e-11 *** df.mm.trans1:probe5 0.116126196410597 0.0340533071483488 3.4101297681517 0.000680660907833054 *** df.mm.trans1:probe6 -0.0322586048970138 0.0340533071483488 -0.947297270026766 0.343763031830546 df.mm.trans1:probe7 0.120344260659232 0.0340533071483488 3.53399627633721 0.000431953081893585 *** df.mm.trans1:probe8 0.0163880815501369 0.0340533071483488 0.48124787054439 0.630467185666068 df.mm.trans1:probe9 -0.174803643554718 0.0340533071483488 -5.13323545326182 3.54960491463908e-07 *** df.mm.trans1:probe10 -0.231445208950247 0.0340533071483488 -6.7965559979824 2.04561885504870e-11 *** df.mm.trans1:probe11 -0.175953816777279 0.0340533071483488 -5.16701112202581 2.98123887645784e-07 *** df.mm.trans1:probe12 -0.145886782551011 0.0340533071483488 -4.28407091021952 2.04988404555382e-05 *** df.mm.trans1:probe13 -0.137789903155601 0.0340533071483488 -4.04630018915159 5.68916843649467e-05 *** df.mm.trans1:probe14 -0.256238961789965 0.0340533071483488 -7.52464248696005 1.37593442447074e-13 *** df.mm.trans1:probe15 -0.108490826661713 0.0340533071483488 -3.18591161172943 0.00149692019536004 ** df.mm.trans1:probe16 -0.151659373075532 0.0340533071483488 -4.45358720710585 9.60015430249777e-06 *** df.mm.trans1:probe17 -0.221437695192367 0.0340533071483488 -6.50267811662765 1.36147844015633e-10 *** df.mm.trans1:probe18 -0.0668613827737983 0.0340533071483488 -1.96343287547742 0.0499293997756459 * df.mm.trans1:probe19 0.0621540589880948 0.0340533071483488 1.82519890703504 0.0683302792960093 . df.mm.trans1:probe20 -0.187022145421640 0.0340533071483488 -5.49204059996 5.28428976849626e-08 *** df.mm.trans1:probe21 -0.209654346471554 0.0340533071483488 -6.15665155687295 1.15606378028453e-09 *** df.mm.trans1:probe22 -0.159958747514029 0.0340533071483488 -4.6973043416074 3.08428769939134e-06 *** df.mm.trans2:probe2 -0.0166448347438105 0.0340533071483488 -0.48878761382263 0.625121190931419 df.mm.trans2:probe3 0.250613311923341 0.0340533071483488 7.35944120879588 4.4447723604556e-13 *** df.mm.trans2:probe4 0.114602909303182 0.0340533071483488 3.36539734023275 0.00079943100471751 *** df.mm.trans2:probe5 -0.02991646817436 0.0340533071483488 -0.878518730766231 0.379916523658825 df.mm.trans2:probe6 0.110827265901298 0.0340533071483488 3.25452284027786 0.00118180697362297 ** df.mm.trans3:probe2 -0.138579588993534 0.0340533071483488 -4.06948988507431 5.16153438162302e-05 *** df.mm.trans3:probe3 0.00740431189512785 0.0340533071483488 0.217432975389789 0.827924373806884 df.mm.trans3:probe4 -0.192665180359565 0.0340533071483488 -5.65775240332001 2.11122525495780e-08 *** df.mm.trans3:probe5 -0.0321806735597235 0.0340533071483488 -0.945008759928444 0.344929415253775 df.mm.trans3:probe6 -0.118571875469968 0.0340533071483488 -3.4819489030368 0.000523782501287337 *** df.mm.trans3:probe7 -0.205482436314355 0.0340533071483488 -6.03414039697224 2.40622072344288e-09 *** df.mm.trans3:probe8 0.108552452077057 0.0340533071483488 3.1877212866334 0.00148769769649617 ** df.mm.trans3:probe9 -0.121128366780472 0.0340533071483488 -3.55702211984265 0.000396336306872908 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.06576882797961 0.075444603856152 53.8907837031219 2.82697267651990e-273 *** df.mm.trans1 -0.152996810371676 0.0653160390401366 -2.34240796931455 0.0193952048729671 * df.mm.trans2 -0.0672804069002689 0.0578661462417664 -1.16269029942256 0.245289362143989 df.mm.exp2 -0.224001425278895 0.0747901322757962 -2.99506657446289 0.00282539557831424 ** df.mm.exp3 -0.113587912517393 0.0747901322757961 -1.51875533658004 0.129204894105253 df.mm.exp4 -0.0902489198845614 0.0747901322757961 -1.20669555111575 0.227893221254501 df.mm.exp5 -0.111601009192282 0.0747901322757961 -1.49218895322637 0.136029598798848 df.mm.exp6 -0.0222899096266703 0.0747901322757962 -0.298032761119796 0.76575274683133 df.mm.exp7 -0.146531759050302 0.0747901322757961 -1.95923920163627 0.0504191786280999 . df.mm.exp8 -0.138007005373203 0.0747901322757961 -1.84525686977379 0.0653562274697583 . df.mm.trans1:exp2 0.183122866974945 0.0693323082291805 2.64123424781453 0.00841580354237505 ** df.mm.trans2:exp2 0.126109001532312 0.0520517291470652 2.42276296289808 0.0156160336181786 * df.mm.trans1:exp3 0.11815890109452 0.0693323082291805 1.70424011708858 0.0887105059660163 . df.mm.trans2:exp3 0.0401834989791997 0.0520517291470651 0.771991625209351 0.440339132729109 df.mm.trans1:exp4 0.083841087550932 0.0693323082291805 1.20926433422341 0.226905648964258 df.mm.trans2:exp4 0.0734869098266865 0.0520517291470651 1.41180535269173 0.158382057314681 df.mm.trans1:exp5 0.0731543601867883 0.0693323082291805 1.05512656444343 0.291674475438993 df.mm.trans2:exp5 0.0751204484374588 0.0520517291470651 1.44318833722538 0.149344716898388 df.mm.trans1:exp6 0.0345466062015385 0.0693323082291805 0.498275725760398 0.61842171939545 df.mm.trans2:exp6 0.00421130172430955 0.0520517291470651 0.080906086950754 0.935536143412677 df.mm.trans1:exp7 0.105172951887311 0.0693323082291805 1.51694000349242 0.129662592722411 df.mm.trans2:exp7 0.0453587791490583 0.0520517291470651 0.871417336029379 0.383778307006194 df.mm.trans1:exp8 0.111824211938707 0.0693323082291805 1.61287305723426 0.107152214760491 df.mm.trans2:exp8 0.0594299180744947 0.0520517291470652 1.14174723968503 0.253888379532613 df.mm.trans1:probe2 -0.0216124919760200 0.0465095262712247 -0.464689574561236 0.642275648240123 df.mm.trans1:probe3 0.0128780732758607 0.0465095262712247 0.276891086801464 0.781932677452718 df.mm.trans1:probe4 -0.00403427488982471 0.0465095262712247 -0.086740829530244 0.930898442378736 df.mm.trans1:probe5 0.00212047394515722 0.0465095262712247 0.0455922499143827 0.963646213170067 df.mm.trans1:probe6 0.0268654706211313 0.0465095262712247 0.577633718831337 0.563668097267772 df.mm.trans1:probe7 0.0161140120020756 0.0465095262712247 0.346466913210537 0.729079658624167 df.mm.trans1:probe8 0.00727841392317911 0.0465095262712247 0.156492970509619 0.875682515424066 df.mm.trans1:probe9 0.0246112818338983 0.0465095262712247 0.52916646990501 0.596831463091247 df.mm.trans1:probe10 -0.0643333439561449 0.0465095262712247 -1.38322939651070 0.166966487728457 df.mm.trans1:probe11 0.101904775031652 0.0465095262712247 2.19105166621961 0.0287253939532369 * df.mm.trans1:probe12 0.0380945764646966 0.0465095262712247 0.819070403825325 0.412981359360143 df.mm.trans1:probe13 -0.018873893440953 0.0465095262712247 -0.405807045440285 0.684988972299943 df.mm.trans1:probe14 -0.0261625772828635 0.0465095262712247 -0.562520829180111 0.573913110157262 df.mm.trans1:probe15 0.0303330358093779 0.0465095262712247 0.652189739204995 0.514459345939054 df.mm.trans1:probe16 -0.0580681949963652 0.0465095262712247 -1.24852260712644 0.212191785284746 df.mm.trans1:probe17 0.00315286514428311 0.0465095262712247 0.0677896636894747 0.945969398514438 df.mm.trans1:probe18 0.00623843861941002 0.0465095262712247 0.134132491116550 0.893330324328755 df.mm.trans1:probe19 -0.0506512694371932 0.0465095262712247 -1.08905150187546 0.276447275974160 df.mm.trans1:probe20 -0.0250887196874396 0.0465095262712247 -0.539431847598971 0.589733584473871 df.mm.trans1:probe21 0.053209573840995 0.0465095262712247 1.14405753201394 0.252929609693899 df.mm.trans1:probe22 -0.00729174684897443 0.0465095262712247 -0.156779641367489 0.87545664531491 df.mm.trans2:probe2 -0.0707957735905214 0.0465095262712247 -1.52217791206191 0.128345383298118 df.mm.trans2:probe3 -0.0527750055260795 0.0465095262712247 -1.13471389104927 0.256822805247611 df.mm.trans2:probe4 -0.0310172395579831 0.0465095262712247 -0.666900784521071 0.505020856314049 df.mm.trans2:probe5 0.0323107844430935 0.0465095262712247 0.69471325626217 0.487429438203567 df.mm.trans2:probe6 0.00741752220905259 0.0465095262712247 0.159483933803080 0.87332641577193 df.mm.trans3:probe2 0.0575941627176795 0.0465095262712247 1.23833045260048 0.215943683519293 df.mm.trans3:probe3 0.0177259784555815 0.0465095262712247 0.381125758026664 0.703207469430493 df.mm.trans3:probe4 -0.00378939676593898 0.0465095262712247 -0.0814757119614755 0.935083282175707 df.mm.trans3:probe5 0.0493600251698893 0.0465095262712247 1.06128849565230 0.28886752253906 df.mm.trans3:probe6 0.00586924591628032 0.0465095262712247 0.126194489319312 0.899608535236634 df.mm.trans3:probe7 0.0167438138647081 0.0465095262712247 0.360008265125405 0.718932537916275 df.mm.trans3:probe8 0.0207051358257638 0.0465095262712247 0.44518053581152 0.656305369608525 df.mm.trans3:probe9 0.0599022574578593 0.0465095262712247 1.28795673188614 0.198119984401048