fitVsDatCorrelation=0.958417353492238 cont.fitVsDatCorrelation=0.244865740579303 fstatistic=6095.86105907867,51,669 cont.fstatistic=516.106068186399,51,669 residuals=-1.25178481800629,-0.118320829786928,0.0120010426758241,0.130388500487506,0.824281235287242 cont.residuals=-1.55289687106319,-0.613824325898923,-0.170679344815303,0.546734592279799,2.34576298851424 predictedValues: Include Exclude Both Lung 73.845521202997 62.7874680674716 131.037089329073 cerebhem 98.5231393905952 237.100124762512 572.108450892174 cortex 126.039311867598 214.771327949872 474.641797127903 heart 256.435116678846 204.400149376943 555.149738060681 kidney 76.7006140040744 50.1255329821027 120.200332051195 liver 237.267766808707 55.8025929198598 425.893372985407 stomach 84.7445868526877 58.1286059876991 135.488524322392 testicle 79.4626739324166 51.2691909218328 134.240814054331 diffExp=11.0580531355253,-138.576985371917,-88.732016082274,52.0349673019032,26.5750810219717,181.465173888847,26.6159808649886,28.1934830105838 diffExpScore=5.55285541888661 diffExp1.5=0,-1,-1,0,1,1,0,1 diffExp1.5Score=2.5 diffExp1.4=0,-1,-1,0,1,1,1,1 diffExp1.4Score=2 diffExp1.3=0,-1,-1,0,1,1,1,1 diffExp1.3Score=2 diffExp1.2=0,-1,-1,1,1,1,1,1 diffExp1.2Score=1.75 cont.predictedValues: Include Exclude Both Lung 125.514385674530 196.573024133797 154.378359312128 cerebhem 138.829460009846 176.808533902689 200.852625429157 cortex 167.021130892550 225.161051055520 158.532067336262 heart 148.503675994457 295.343561780173 152.541159621435 kidney 125.408133649340 183.739003379972 189.719609275443 liver 132.539875053568 170.944947805539 171.973857309089 stomach 164.761215790748 207.427742918937 149.980760179337 testicle 150.668594586609 247.944698485905 132.897954054836 cont.diffExp=-71.0586384592667,-37.9790738928428,-58.1399201629699,-146.839885785717,-58.3308697306319,-38.4050727519714,-42.6665271281886,-97.276103899296 cont.diffExpScore=0.99818740785943 cont.diffExp1.5=-1,0,0,-1,0,0,0,-1 cont.diffExp1.5Score=0.75 cont.diffExp1.4=-1,0,0,-1,-1,0,0,-1 cont.diffExp1.4Score=0.8 cont.diffExp1.3=-1,0,-1,-1,-1,0,0,-1 cont.diffExp1.3Score=0.833333333333333 cont.diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 cont.diffExp1.2Score=0.888888888888889 tran.correlation=0.29042462051681 cont.tran.correlation=0.474664436254401 tran.covariance=0.140187462688021 cont.tran.covariance=0.0110774875587362 tran.mean=122.962732731638 cont.tran.mean=178.574314694636 weightedLogRatios: wLogRatio Lung 0.684708196743752 cerebhem -4.41676278771626 cortex -2.71984170910201 heart 1.23229146620540 kidney 1.75563357049938 liver 6.86849650601677 stomach 1.60261536886164 testicle 1.82123106771461 cont.weightedLogRatios: wLogRatio Lung -2.26851662340753 cerebhem -1.22220199166076 cortex -1.57336951180747 heart -3.67441557270445 kidney -1.91832487328119 liver -1.27588114255159 stomach -1.20200902563438 testicle -2.62219153916635 varWeightedLogRatios=11.3154456648205 cont.varWeightedLogRatios=0.744328039789449 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.39485724036879 0.116348106628706 29.1784485260476 2.27162722483884e-121 *** df.mm.trans1 0.825873354718343 0.0975608533031925 8.46521249821128 1.61414445185961e-16 *** df.mm.trans2 0.708017024819917 0.0896522372151152 7.89737151925246 1.1706565676746e-14 *** df.mm.exp2 0.143194932025986 0.116348106628706 1.23074570076980 0.218850523040452 df.mm.exp3 0.477356867544145 0.116348106628706 4.10283313906863 4.58465377552768e-05 *** df.mm.exp4 0.98146693702789 0.116348106628706 8.43560729492558 2.02977821829312e-16 *** df.mm.exp5 -0.100970004985019 0.116348106628706 -0.867826799341377 0.385800338935364 df.mm.exp6 -0.129429811885703 0.116348106628706 -1.11243591009817 0.266350348444955 df.mm.exp7 0.0271623747340470 0.116348106628706 0.233457814837749 0.815477338562413 df.mm.exp8 -0.153508353449118 0.116348106628706 -1.31938849627351 0.187490628839970 df.mm.trans1:exp2 0.145121146313341 0.100760416022680 1.44025949913383 0.150261796820760 df.mm.trans2:exp2 1.18553208675226 0.0822705351753734 14.4101662183806 3.38010199510578e-41 *** df.mm.trans1:exp3 0.0572616297740759 0.100760416022680 0.56829489232346 0.570025440687174 df.mm.trans2:exp3 0.752461503143431 0.0822705351753734 9.14618461566384 7.00208407675752e-19 *** df.mm.trans1:exp4 0.263433379245411 0.100760416022680 2.61445307238624 0.00913825527859381 ** df.mm.trans2:exp4 0.198857151613302 0.0822705351753734 2.41711265387303 0.0159106025443722 * df.mm.trans1:exp5 0.138904358673534 0.100760416022680 1.37856078960878 0.168491008968792 df.mm.trans2:exp5 -0.124254976880425 0.0822705351753734 -1.51032172837522 0.131433505481632 df.mm.trans1:exp6 1.29664377309444 0.100760416022680 12.8685829641928 5.10670073359363e-34 *** df.mm.trans2:exp6 0.0114946477961549 0.0822705351753734 0.139717673789919 0.88892511308434 df.mm.trans1:exp7 0.110504137621027 0.10076041602268 1.09670187939829 0.273166339826733 df.mm.trans2:exp7 -0.104259974747837 0.0822705351753734 -1.26728207766716 0.205495397273145 df.mm.trans1:exp8 0.226820394649973 0.100760416022680 2.25108632539706 0.0247038778561580 * df.mm.trans2:exp8 -0.0491571421075742 0.0822705351753734 -0.597506045181153 0.550371755496731 df.mm.trans1:probe2 -0.226309673952744 0.0712483734448146 -3.17634863802235 0.00155997380775276 ** df.mm.trans1:probe3 -0.00327075486715819 0.0712483734448146 -0.0459063794584946 0.963398568888667 df.mm.trans1:probe4 0.202618094720477 0.0712483734448146 2.84382765421886 0.00459361636179596 ** df.mm.trans1:probe5 0.125801716636926 0.0712483734448146 1.76567843663640 0.0779059088212745 . df.mm.trans1:probe6 0.0909358859941042 0.0712483734448146 1.27632227372234 0.202284513498928 df.mm.trans1:probe7 0.284353409496367 0.0712483734448146 3.99101615585109 7.30635128011195e-05 *** df.mm.trans1:probe8 0.203565736939907 0.0712483734448146 2.85712819953116 0.00440770473744972 ** df.mm.trans1:probe9 0.157117945731531 0.0712483734448146 2.20521449311719 0.0277784688571043 * df.mm.trans1:probe10 -0.0301109937539833 0.0712483734448146 -0.422620086580724 0.672708267486026 df.mm.trans1:probe11 0.593979128242964 0.0712483734448146 8.3367394864534 4.34285785833469e-16 *** df.mm.trans1:probe12 0.551193860821902 0.0712483734448146 7.73623079618553 3.78108264225756e-14 *** df.mm.trans2:probe2 0.0100825131159637 0.0712483734448146 0.141512186573257 0.88750793522903 df.mm.trans2:probe3 -0.0663423344998411 0.0712483734448146 -0.931141741098504 0.352116136499603 df.mm.trans2:probe4 0.405908280628026 0.0712483734448146 5.69708838254985 1.82678346715243e-08 *** df.mm.trans2:probe5 0.00848975672934457 0.0712483734448146 0.119157200633083 0.905186585015257 df.mm.trans2:probe6 0.305724019540652 0.0712483734448146 4.29096138984072 2.04170372376066e-05 *** df.mm.trans3:probe2 0.351490197874456 0.0712483734448146 4.93330838137242 1.02133340345037e-06 *** df.mm.trans3:probe3 -0.0193479527180585 0.0712483734448146 -0.271556412905965 0.786046892949346 df.mm.trans3:probe4 -0.256256173505945 0.0712483734448146 -3.59665998135982 0.00034607717744536 *** df.mm.trans3:probe5 0.284561293144896 0.0712483734448146 3.99393388770206 7.21905256320307e-05 *** df.mm.trans3:probe6 -0.309362571113296 0.0712483734448146 -4.34202994617011 1.63040561744121e-05 *** df.mm.trans3:probe7 -0.551960709749076 0.0712483734448146 -7.74699383385358 3.49839671237678e-14 *** df.mm.trans3:probe8 0.301048302105576 0.0712483734448146 4.22533578733208 2.71692910514182e-05 *** df.mm.trans3:probe9 0.570217274729757 0.0712483734448146 8.0032321744358 5.36236598021599e-15 *** df.mm.trans3:probe10 -0.380068210161792 0.0712483734448146 -5.3344124474108 1.31321089760112e-07 *** df.mm.trans3:probe11 -1.10230375831838 0.0712483734448146 -15.4712831328307 2.21062734970025e-46 *** df.mm.trans3:probe12 0.472775762167406 0.0712483734448146 6.6356007766773 6.67787889740865e-11 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.03067454741124 0.39529741768301 12.7263025822378 2.22443107087584e-33 *** df.mm.trans1 -0.222842192117372 0.331466961476002 -0.67229081029696 0.50163069520169 df.mm.trans2 0.339008309592625 0.304597117112828 1.11297281079338 0.266119850889209 df.mm.exp2 -0.268305638515819 0.39529741768301 -0.678743716790413 0.497534986684525 df.mm.exp3 0.394931477509098 0.39529741768301 0.999074266216923 0.318119936377809 df.mm.exp4 0.587266650839718 0.39529741768301 1.4856323987187 0.137847442126625 df.mm.exp5 -0.27450536042767 0.39529741768301 -0.694427406170907 0.487655290370276 df.mm.exp6 -0.193165264294116 0.39529741768301 -0.488658047467985 0.625243951227833 df.mm.exp7 0.354725383048999 0.39529741768301 0.897363269226955 0.369847919471213 df.mm.exp8 0.564658949970139 0.39529741768301 1.42844077575771 0.153631717418351 df.mm.trans1:exp2 0.369131533085309 0.342337605763875 1.07826755480645 0.281302996851314 df.mm.trans2:exp2 0.162339069870076 0.279517484629187 0.580783238248719 0.56158214278333 df.mm.trans1:exp3 -0.109231519697527 0.342337605763875 -0.319075432726104 0.749768912305842 df.mm.trans2:exp3 -0.259149535250552 0.279517484629188 -0.927131752041717 0.354192481837540 df.mm.trans1:exp4 -0.419077317623284 0.342337605763875 -1.22416383875846 0.221321350702770 df.mm.trans2:exp4 -0.180161342300054 0.279517484629187 -0.644544088320839 0.519443615648951 df.mm.trans1:exp5 0.273658469269183 0.342337605763875 0.799381851896042 0.42435275296334 df.mm.trans2:exp5 0.206987664813873 0.279517484629187 0.740517771503513 0.459245693386879 df.mm.trans1:exp6 0.247628429355035 0.342337605763875 0.723345683283874 0.469720358480348 df.mm.trans2:exp6 0.0534728399396907 0.279517484629187 0.191304096810361 0.848345395644777 df.mm.trans1:exp7 -0.082648513237605 0.342337605763875 -0.241423997381729 0.809300492355386 df.mm.trans2:exp7 -0.300976317284756 0.279517484629187 -1.07677098512831 0.281970723004583 df.mm.trans1:exp8 -0.381996641850433 0.342337605763875 -1.11584773457203 0.264887954947349 df.mm.trans2:exp8 -0.332487205012066 0.279517484629187 -1.18950413943925 0.234663332920296 df.mm.trans1:probe2 0.152831623897618 0.242069242490803 0.631354988866149 0.528024091223814 df.mm.trans1:probe3 -0.0757235427045844 0.242069242490803 -0.31281769598408 0.754516658736602 df.mm.trans1:probe4 0.157393022852777 0.242069242490803 0.650198353302803 0.515787314423243 df.mm.trans1:probe5 -0.037882776947506 0.242069242490803 -0.156495623143636 0.875689582162094 df.mm.trans1:probe6 0.00963051428417624 0.242069242490803 0.0397841302971076 0.968277097716027 df.mm.trans1:probe7 0.312625688242881 0.242069242490803 1.29147216319628 0.196986024700207 df.mm.trans1:probe8 -0.117478834396025 0.242069242490803 -0.485310868853935 0.627614731792053 df.mm.trans1:probe9 0.0683889067983121 0.242069242490803 0.282517952692443 0.777633766705965 df.mm.trans1:probe10 -0.214457188736629 0.242069242490803 -0.885933241786292 0.37597183447754 df.mm.trans1:probe11 0.00359730836043551 0.242069242490803 0.0148606585595945 0.988147776936073 df.mm.trans1:probe12 0.331187844688576 0.242069242490803 1.36815334852448 0.171723407368496 df.mm.trans2:probe2 -0.223414222955349 0.242069242490803 -0.922935192660165 0.356373715571669 df.mm.trans2:probe3 -0.356255343396198 0.242069242490803 -1.47170842412883 0.141569864876256 df.mm.trans2:probe4 -0.198880242668720 0.242069242490803 -0.821584108011065 0.411606326312784 df.mm.trans2:probe5 -0.413476546967295 0.242069242490803 -1.70809204305667 0.0880831602714865 . df.mm.trans2:probe6 -0.403653315516336 0.242069242490803 -1.66751178862252 0.0958806730506939 . df.mm.trans3:probe2 -0.135810322576443 0.242069242490803 -0.561039152182264 0.574958708802381 df.mm.trans3:probe3 -0.211037076456450 0.242069242490803 -0.871804588988494 0.38362774886957 df.mm.trans3:probe4 0.164858887238282 0.242069242490803 0.681040207925407 0.496081703715892 df.mm.trans3:probe5 -0.0483852772411763 0.242069242490803 -0.199881970725854 0.841633632176292 df.mm.trans3:probe6 -0.206439180750223 0.242069242490803 -0.85281045467008 0.394069635235698 df.mm.trans3:probe7 -0.331633467586967 0.242069242490803 -1.36999423873344 0.171148292606468 df.mm.trans3:probe8 -0.185769511210460 0.242069242490803 -0.767423028629992 0.443100796479408 df.mm.trans3:probe9 -0.0879742478882104 0.242069242490803 -0.363425964335609 0.716401517958524 df.mm.trans3:probe10 -0.0492252132409556 0.242069242490803 -0.203351787837424 0.838921915443171 df.mm.trans3:probe11 -0.170210055618191 0.242069242490803 -0.70314614887374 0.482209291732799 df.mm.trans3:probe12 -0.0275848988453106 0.242069242490803 -0.113954579943624 0.909307987838953