fitVsDatCorrelation=0.926406788583338 cont.fitVsDatCorrelation=0.243678534502146 fstatistic=10528.6193100103,59,853 cont.fstatistic=1574.59604228410,59,853 residuals=-0.725190945046509,-0.090827492324829,-0.0116057209041612,0.0783671553859462,0.845621586399829 cont.residuals=-0.626842831672218,-0.257651373702592,-0.0974785469286524,0.115052717191286,2.12843654943715 predictedValues: Include Exclude Both Lung 54.9471783402176 143.231589162471 67.8462399643854 cerebhem 60.930161148048 67.7563880955328 71.4681624949799 cortex 54.706786410991 89.0382614298924 63.1340279878179 heart 53.8069060959252 118.341574098108 61.6668069314391 kidney 56.7761084755229 161.608050436275 69.033953518426 liver 54.8988651713533 133.053022970880 62.2100163653554 stomach 54.9333901744876 101.320306651804 64.9378397020151 testicle 54.8400508646506 106.304395903763 63.5924818806914 diffExp=-88.2844108222536,-6.82622694748472,-34.3314750189013,-64.5346680021826,-104.831941960752,-78.1541577995263,-46.3869164773163,-51.464345039112 diffExpScore=0.99789833905387 diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.875 diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.875 diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 68.3762553933152 73.1686281634598 64.0920337429165 cerebhem 67.1976442389566 75.8730678716788 75.8065137808289 cortex 68.7543227594186 65.4545244891353 73.1959015739582 heart 66.4586904199502 66.8824336891658 65.514835143954 kidney 68.1441018690517 62.6353453940271 64.358829559457 liver 71.4388137693732 78.7364490386102 56.6622378322581 stomach 68.6834603175657 70.3216324764983 73.6169854710128 testicle 64.6134998718895 76.5652460451327 69.04670276307 cont.diffExp=-4.79237277014465,-8.67542363272227,3.29979827028335,-0.423743269215649,5.5087564750246,-7.29763526923705,-1.63817215893258,-11.9517461732432 cont.diffExpScore=1.61612078947734 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=-0.406601336850432 cont.tran.correlation=0.0647271044882931 tran.covariance=-0.00564482968460675 cont.tran.covariance=0.000105152193331304 tran.mean=85.4058147143701 cont.tran.mean=69.5815072379518 weightedLogRatios: wLogRatio Lung -4.29743594687931 cerebhem -0.442052043157046 cortex -2.06790454235568 heart -3.45179594870484 kidney -4.7722696486272 liver -3.93772082459824 stomach -2.63978253258353 testicle -2.86951592058819 cont.weightedLogRatios: wLogRatio Lung -0.288502444966206 cerebhem -0.518278393757053 cortex 0.206865294537252 heart -0.0266928208591678 kidney 0.352308470137176 liver -0.419938460656913 stomach -0.099971752119374 testicle -0.721866825480883 varWeightedLogRatios=1.91968350000799 cont.varWeightedLogRatios=0.134228055195399 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.90621337493255 0.0749712840446828 65.4412344332858 0 *** df.mm.trans1 -0.99101016776716 0.0647433670416599 -15.3067443515794 6.533660845519e-47 *** df.mm.trans2 0.0713035868051651 0.057200472146507 1.24655591342910 0.21290257945041 df.mm.exp2 -0.697196204124897 0.0735780964678637 -9.4755944716429 2.53880258355464e-20 *** df.mm.exp3 -0.407797133187692 0.0735780964678637 -5.54237134098465 3.9781444206534e-08 *** df.mm.exp4 -0.116360034291425 0.0735780964678637 -1.58144936981683 0.114146024359293 df.mm.exp5 0.136099923175072 0.0735780964678637 1.84973422402298 0.0646975856311432 . df.mm.exp6 0.012133186612631 0.0735780964678637 0.164902154242742 0.86906006642356 df.mm.exp7 -0.30261347182084 0.0735780964678637 -4.11282006939403 4.28685876193888e-05 *** df.mm.exp8 -0.235359020480759 0.0735780964678637 -3.19876473813854 0.00143111830214946 ** df.mm.trans1:exp2 0.800552183243487 0.0680097646205447 11.7711359201154 9.55833889671715e-30 *** df.mm.trans2:exp2 -0.0513478762363904 0.0502285399697779 -1.02228486568166 0.306935927197968 df.mm.trans1:exp3 0.403412570989919 0.0680097646205447 5.93168603421477 4.35446088988775e-09 *** df.mm.trans2:exp3 -0.0675995106647385 0.0502285399697779 -1.34583865478496 0.178712144898351 df.mm.trans1:exp4 0.095389529371587 0.0680097646205447 1.40258578902317 0.161104231306584 df.mm.trans2:exp4 -0.0745276519694433 0.0502285399697779 -1.48377101970884 0.138239071921502 df.mm.trans1:exp5 -0.103356641311210 0.0680097646205447 -1.51973237795897 0.128948808182834 df.mm.trans2:exp5 -0.0153887861818506 0.0502285399697779 -0.306375343402574 0.759393644307227 df.mm.trans1:exp6 -0.0130128390868601 0.0680097646205447 -0.191337805085258 0.848306477631093 df.mm.trans2:exp6 -0.085848293793351 0.0502285399697779 -1.70915367727203 0.0877862224463113 . df.mm.trans1:exp7 0.302362505412698 0.0680097646205447 4.44586901748164 9.90946843841588e-06 *** df.mm.trans2:exp7 -0.0435625013920195 0.0502285399697779 -0.867285838255117 0.386029320969373 df.mm.trans1:exp8 0.233407472739863 0.0680097646205447 3.43197001257309 0.000628033892964729 *** df.mm.trans2:exp8 -0.0627971661841915 0.0502285399697779 -1.25022877873767 0.211558855874672 df.mm.trans1:probe2 -0.0086184749673869 0.0465631027666114 -0.185092368319726 0.853200586540425 df.mm.trans1:probe3 -0.0864645333926677 0.0465631027666114 -1.85693238326609 0.063665312590097 . df.mm.trans1:probe4 -0.0603848695160983 0.0465631027666114 -1.29683947005778 0.195037107519351 df.mm.trans1:probe5 0.266471162205970 0.0465631027666114 5.72279651426163 1.45045432181231e-08 *** df.mm.trans1:probe6 0.0790245859580211 0.0465631027666114 1.69715034571722 0.0900330311824108 . df.mm.trans1:probe7 -0.0218704273818069 0.0465631027666114 -0.469694373491995 0.638693432756467 df.mm.trans1:probe8 0.223869773286183 0.0465631027666114 4.80787920015311 1.80261851975614e-06 *** df.mm.trans1:probe9 0.62662927418698 0.0465631027666114 13.4576357019814 1.41158096649537e-37 *** df.mm.trans1:probe10 0.0133954711938207 0.0465631027666114 0.287684247782261 0.77365832700782 df.mm.trans1:probe11 0.130994854449263 0.0465631027666114 2.81327589155408 0.00501667784429976 ** df.mm.trans1:probe12 0.232249325999061 0.0465631027666114 4.98784041869301 7.39897803854509e-07 *** df.mm.trans1:probe13 0.103805621001694 0.0465631027666114 2.22935360476298 0.0260500752865343 * df.mm.trans1:probe14 0.113026616093226 0.0465631027666114 2.42738583508385 0.0154141304176761 * df.mm.trans1:probe15 0.228135804256117 0.0465631027666114 4.89949747119739 1.14966959078951e-06 *** df.mm.trans1:probe16 0.118760010229129 0.0465631027666114 2.55051753798262 0.0109299857108497 * df.mm.trans1:probe17 0.132975728678018 0.0465631027666114 2.85581760615339 0.00439683532154731 ** df.mm.trans1:probe18 0.173344140397246 0.0465631027666114 3.72277898373955 0.000209974005740634 *** df.mm.trans1:probe19 0.199832480182361 0.0465631027666114 4.29164871559318 1.97675378181053e-05 *** df.mm.trans1:probe20 0.0424147308303072 0.0465631027666114 0.910908601664776 0.362600869269956 df.mm.trans1:probe21 0.260882104105916 0.0465631027666114 5.60276460556199 2.84692679783083e-08 *** df.mm.trans1:probe22 0.148938551140081 0.0465631027666114 3.19863888552716 0.00143173563780393 ** df.mm.trans2:probe2 -0.0131507114355168 0.0465631027666114 -0.28242773041634 0.777684068292818 df.mm.trans2:probe3 0.359195125750710 0.0465631027666114 7.7141578719766 3.39577959470412e-14 *** df.mm.trans2:probe4 -0.177175673682644 0.0465631027666114 -3.80506588168539 0.000151890883969336 *** df.mm.trans2:probe5 -0.475633347265822 0.0465631027666114 -10.2148121367651 3.45260685890658e-23 *** df.mm.trans2:probe6 0.0978984173755316 0.0465631027666114 2.10248912891885 0.0358029936184848 * df.mm.trans3:probe2 1.62309645201054 0.0465631027666114 34.8579960434768 3.24913099611264e-166 *** df.mm.trans3:probe3 0.180156794918048 0.0465631027666114 3.86908913310717 0.000117566333755433 *** df.mm.trans3:probe4 -0.045506661678897 0.0465631027666114 -0.977311626052722 0.328691982559921 df.mm.trans3:probe5 -0.132890497150134 0.0465631027666114 -2.85398715408254 0.00442200380514435 ** df.mm.trans3:probe6 -0.187510487565521 0.0465631027666114 -4.02701874283124 6.15207186410616e-05 *** df.mm.trans3:probe7 0.253806346708522 0.0465631027666114 5.45080399776358 6.56689941287225e-08 *** df.mm.trans3:probe8 0.269344912050296 0.0465631027666114 5.78451383277303 1.02049089412461e-08 *** df.mm.trans3:probe9 0.187317775746679 0.0465631027666114 4.02288001909094 6.25916317489991e-05 *** df.mm.trans3:probe10 0.159557103862834 0.0465631027666114 3.42668538783988 0.000640212245405064 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.4274894389066 0.19311204114735 22.9270500824353 5.21875618274936e-91 *** df.mm.trans1 -0.098828024255274 0.166766835055345 -0.592611979608992 0.553597922197028 df.mm.trans2 -0.117729173943838 0.147337744998747 -0.79904286538958 0.424487985612288 df.mm.exp2 -0.148956542827705 0.189523449860848 -0.785953099403119 0.432113232025609 df.mm.exp3 -0.238716495461462 0.189523449860848 -1.25956178845801 0.208172014243152 df.mm.exp4 -0.140231988255233 0.189523449860848 -0.739918930128144 0.459552776926922 df.mm.exp5 -0.162992081823972 0.189523449860848 -0.860010103993173 0.390025112837552 df.mm.exp6 0.240367234164747 0.189523449860848 1.26827173281845 0.205046960248977 df.mm.exp7 -0.173760189405186 0.189523449860848 -0.916826859857 0.359492508882636 df.mm.exp8 -0.0856887906322599 0.189523449860848 -0.452127642754364 0.651291979740004 df.mm.trans1:exp2 0.131569112786715 0.175180465843385 0.751048994836794 0.452830311844651 df.mm.trans2:exp2 0.185251575159487 0.129379348386698 1.43184810767321 0.152553602565101 df.mm.trans1:exp3 0.244230485544422 0.175180465843384 1.39416506497231 0.163630798048209 df.mm.trans2:exp3 0.127305362268283 0.129379348386698 0.983969728211838 0.325409403627742 df.mm.trans1:exp4 0.111786925214246 0.175180465843384 0.638124374633109 0.523563990249386 df.mm.trans2:exp4 0.0504015931626698 0.129379348386698 0.389564438151491 0.696955893067195 df.mm.trans1:exp5 0.159591068924603 0.175180465843384 0.911009501865812 0.362547734060076 df.mm.trans2:exp5 0.00755507139828808 0.129379348386698 0.0583947244478847 0.953447893501705 df.mm.trans1:exp6 -0.196551523294116 0.175180465843384 -1.12199452346381 0.262180499663694 df.mm.trans2:exp6 -0.167027799065893 0.129379348386698 -1.29099273685217 0.197055961869889 df.mm.trans1:exp7 0.178242986368674 0.175180465843384 1.01748208917327 0.30921264795827 df.mm.trans2:exp7 0.134072905379934 0.129379348386698 1.03627748208476 0.300366385610218 df.mm.trans1:exp8 0.0290865349456294 0.175180465843384 0.166037547654620 0.868166747159905 df.mm.trans2:exp8 0.131065305340069 0.129379348386698 1.01303111334533 0.311332557196456 df.mm.trans1:probe2 -0.167897918812928 0.119937865970856 -1.39987415528742 0.161914591056977 df.mm.trans1:probe3 -0.101598514993253 0.119937865970856 -0.84709290240282 0.397180940565966 df.mm.trans1:probe4 -0.197069540738738 0.119937865970856 -1.64309694143319 0.100731407460319 df.mm.trans1:probe5 -0.149655746593477 0.119937865970856 -1.24777730020511 0.212455049787457 df.mm.trans1:probe6 -0.149169110473052 0.119937865970856 -1.24371989834552 0.213944355649369 df.mm.trans1:probe7 -0.142339764117286 0.119937865970856 -1.18677919575352 0.235645142809669 df.mm.trans1:probe8 -0.127770854305691 0.119937865970856 -1.06530871857215 0.287037696140584 df.mm.trans1:probe9 -0.222918997200957 0.119937865970856 -1.85862067326698 0.063425181913846 . df.mm.trans1:probe10 -0.257327706409764 0.119937865970856 -2.14550846245916 0.0321939048224921 * df.mm.trans1:probe11 -0.199951340663184 0.119937865970856 -1.66712438181759 0.0958567505536362 . df.mm.trans1:probe12 -0.236986120751581 0.119937865970856 -1.97590743201289 0.0484871397540842 * df.mm.trans1:probe13 -0.175672304764871 0.119937865970856 -1.46469426767655 0.143372802188665 df.mm.trans1:probe14 -0.214146367369982 0.119937865970856 -1.78547755236881 0.0745390869986115 . df.mm.trans1:probe15 -0.249910071703718 0.119937865970856 -2.08366281724942 0.0374880207143773 * df.mm.trans1:probe16 -0.141752462284834 0.119937865970856 -1.18188247837659 0.237581707713655 df.mm.trans1:probe17 -0.0418148776887584 0.119937865970856 -0.348637833016965 0.72744725297686 df.mm.trans1:probe18 -0.0970438929155337 0.119937865970856 -0.809118055669879 0.418672868124291 df.mm.trans1:probe19 -0.042807128890936 0.119937865970856 -0.356910876681246 0.721246800514826 df.mm.trans1:probe20 -0.200761903746529 0.119937865970856 -1.67388257345943 0.0945202860770616 . df.mm.trans1:probe21 -0.191266628923564 0.119937865970856 -1.59471429123176 0.111146564624617 df.mm.trans1:probe22 -0.00848414388691903 0.119937865970856 -0.0707378259421474 0.943622996067548 df.mm.trans2:probe2 -0.0414449712240157 0.119937865970856 -0.34555368222148 0.729763359315177 df.mm.trans2:probe3 -0.0714732902413793 0.119937865970856 -0.59591930924256 0.551387220163231 df.mm.trans2:probe4 0.112762498247563 0.119937865970856 0.940174292203623 0.347394323985896 df.mm.trans2:probe5 -0.118040398712169 0.119937865970856 -0.984179581291294 0.325306290255023 df.mm.trans2:probe6 -0.153700043874457 0.119937865970856 -1.28149723717616 0.200367309532792 df.mm.trans3:probe2 -0.0483479900144799 0.119937865970856 -0.403108639820456 0.686969320071413 df.mm.trans3:probe3 -0.0467528703248797 0.119937865970856 -0.389809089451702 0.696775030153671 df.mm.trans3:probe4 -0.0217001910620273 0.119937865970856 -0.180928607378259 0.856466592701758 df.mm.trans3:probe5 -0.223137108969375 0.119937865970856 -1.86043921294711 0.0631673645919242 . df.mm.trans3:probe6 -0.129241236011309 0.119937865970856 -1.07756824723489 0.281531119005285 df.mm.trans3:probe7 -0.129396158113166 0.119937865970856 -1.07885993356434 0.280955145648604 df.mm.trans3:probe8 0.0272585167749802 0.119937865970856 0.227271984158896 0.820266715174858 df.mm.trans3:probe9 0.0275108178243038 0.119937865970856 0.22937558211173 0.818631999412838 df.mm.trans3:probe10 0.0376845890386819 0.119937865970856 0.314200930070233 0.753445264482044