fitVsDatCorrelation=0.72873336719832 cont.fitVsDatCorrelation=0.251850521346092 fstatistic=17218.0485541343,58,830 cont.fstatistic=8614.04991987895,58,830 residuals=-0.326687213779431,-0.0680949141642312,-0.00625422956088833,0.0588291884767025,0.56402635896036 cont.residuals=-0.358186093280145,-0.0973897234656426,-0.0283517080212633,0.0749460104549186,0.901214639927915 predictedValues: Include Exclude Both Lung 46.2373109837294 44.8823375106357 49.5721576669596 cerebhem 51.8594204219667 43.2190470548467 53.3781680582464 cortex 46.7027654170436 43.830542686833 51.4066151984125 heart 47.8530809902521 45.8208947868771 50.7100672478423 kidney 45.4249726755966 43.8735194224712 48.1395001826676 liver 50.4714330872467 45.3053023153966 52.3707406882636 stomach 48.1188996383324 46.1489056761274 51.4867160197122 testicle 47.6009608950433 44.7763051973408 53.3857932692886 diffExp=1.35497347309373,8.64037336712003,2.87222273021056,2.032186203375,1.55145325312542,5.16613077185009,1.96999396220498,2.82465569770248 diffExpScore=0.963519612412398 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 47.1232503070197 47.130345398646 44.6525395477892 cerebhem 48.6217138057497 43.425775631551 49.292325408161 cortex 46.5787271957091 46.6308101153934 49.7403377269275 heart 47.4384208895156 48.555964701199 47.628624049612 kidney 47.3648718843105 45.0652702192768 49.1089887030239 liver 50.0446117985274 46.698133571259 49.1151427614514 stomach 48.3284171641843 45.0270356037139 47.8703505819606 testicle 48.7161670842315 44.7833321523022 46.8273438515887 cont.diffExp=-0.0070950916263186,5.19593817419871,-0.0520829196842385,-1.11754381168340,2.29960166503368,3.34647822726847,3.30138156047039,3.93283493192935 cont.diffExpScore=1.07561343517876 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=-0.0848299288648598 cont.tran.correlation=-0.320817364140599 tran.covariance=-7.51420673184694e-05 cont.tran.covariance=-0.000267004814688079 tran.mean=46.3828561724837 cont.tran.mean=46.9708029701618 weightedLogRatios: wLogRatio Lung 0.113584962624719 cerebhem 0.70303323408506 cortex 0.241961368795999 heart 0.166917321555624 kidney 0.132008445766078 liver 0.417616783911322 stomach 0.161052799373187 testicle 0.234434467781708 cont.weightedLogRatios: wLogRatio Lung -0.000580057776884224 cerebhem 0.43257970807356 cortex -0.00429327556744256 heart -0.0901363359270265 kidney 0.190764267086906 liver 0.268420307580350 stomach 0.27189237304889 testicle 0.323561982344104 varWeightedLogRatios=0.0396211946906596 cont.varWeightedLogRatios=0.0342922987157404 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91682251140528 0.0536821390474543 72.9632347165388 0 *** df.mm.trans1 -0.0498225600920509 0.0462909941755292 -1.07629056103506 0.282110068722083 df.mm.trans2 -0.102401590249716 0.0410004109420173 -2.49757472905654 0.0126972115005945 * df.mm.exp2 0.00301400797397503 0.0527898529875301 0.0570944566692956 0.954483693712034 df.mm.exp3 -0.0500346628667937 0.0527898529875301 -0.947808338822476 0.343502900778911 df.mm.exp4 0.032349179670491 0.0527898529875301 0.61279162262741 0.540181990896305 df.mm.exp5 -0.0111321749815467 0.0527898529875301 -0.210877173387400 0.833034877656728 df.mm.exp6 0.0420814438927705 0.0527898529875301 0.797150238374615 0.425591755146530 df.mm.exp7 0.0298223652468954 0.0527898529875301 0.564926090132131 0.572276687388731 df.mm.exp8 -0.0474146952348876 0.0527898529875301 -0.898178201899668 0.369351022491327 df.mm.trans1:exp2 0.111735527046853 0.0486264425497515 2.29783470038000 0.0218191049171657 * df.mm.trans2:exp2 -0.0407770492746149 0.0361792310109318 -1.12708446628658 0.260032501854004 df.mm.trans1:exp3 0.060050973220373 0.0486264425497515 1.23494481750197 0.217200517747045 df.mm.trans2:exp3 0.0263212154221808 0.0361792310109318 0.727522799316207 0.467110918405469 df.mm.trans1:exp4 0.00199925545955271 0.0486264425497514 0.0411145737734608 0.967214444013125 df.mm.trans2:exp4 -0.0116533179605349 0.0361792310109318 -0.322099658696829 0.747458304629595 df.mm.trans1:exp5 -0.006592881471351 0.0486264425497515 -0.135582229043500 0.892184429265 df.mm.trans2:exp5 -0.0116012325870450 0.0361792310109318 -0.320660010256702 0.748548770280822 df.mm.trans1:exp6 0.0455389816826885 0.0486264425497515 0.936506544481348 0.349284849365999 df.mm.trans2:exp6 -0.0327017127769220 0.0361792310109318 -0.903880814023963 0.366320866780714 df.mm.trans1:exp7 0.0100655893572192 0.0486264425497515 0.206998267391671 0.836061982869414 df.mm.trans2:exp7 -0.0019934603342295 0.0361792310109318 -0.0550995772582111 0.95607239288292 df.mm.trans1:exp8 0.0764805739320833 0.0486264425497515 1.57281861312049 0.116141916597455 df.mm.trans2:exp8 0.0450494494643853 0.0361792310109318 1.24517432254912 0.213419103802353 df.mm.trans1:probe2 0.199580502102934 0.0332922493446599 5.99480377660175 3.03652463273491e-09 *** df.mm.trans1:probe3 -0.0272595132768705 0.0332922493446599 -0.818794578722059 0.41313864771365 df.mm.trans1:probe4 0.00706247940488794 0.0332922493446599 0.212135843744687 0.832053139428015 df.mm.trans1:probe5 0.0236166516020035 0.0332922493446599 0.709373865295515 0.478291718305069 df.mm.trans1:probe6 0.0165912183683641 0.0332922493446599 0.49835077818271 0.618368851025892 df.mm.trans1:probe7 -0.0598560325217093 0.0332922493446599 -1.79789691895091 0.0725566641084423 . df.mm.trans1:probe8 -0.0530243980004341 0.0332922493446599 -1.59269496787364 0.111609373790128 df.mm.trans1:probe9 -0.0421138271549674 0.0332922493446599 -1.26497391987491 0.206235714206402 df.mm.trans1:probe10 -0.122791372874634 0.0332922493446599 -3.68828707256845 0.000240468982634233 *** df.mm.trans1:probe11 -0.144887420961111 0.0332922493446599 -4.35198653780213 1.51730573149676e-05 *** df.mm.trans1:probe12 -0.150579939099049 0.0332922493446599 -4.52297282590195 6.98589047136799e-06 *** df.mm.trans1:probe13 -0.160066472695329 0.0332922493446599 -4.80792003682997 1.81047011461327e-06 *** df.mm.trans1:probe14 -0.160159946489141 0.0332922493446599 -4.81072771115812 1.78590208320462e-06 *** df.mm.trans1:probe15 -0.122955517533191 0.0332922493446599 -3.69321748916053 0.000235926755045492 *** df.mm.trans1:probe16 0.0235924750925562 0.0332922493446599 0.708647674968242 0.478742122226021 df.mm.trans1:probe17 -0.0684542830731616 0.0332922493446599 -2.05616275321276 0.0400790392724677 * df.mm.trans1:probe18 -0.0279088643247557 0.0332922493446599 -0.838299149926087 0.402104057542812 df.mm.trans1:probe19 -0.0634855182455762 0.0332922493446599 -1.90691585865343 0.0568764942287121 . df.mm.trans1:probe20 -0.0694213163805397 0.0332922493446599 -2.08520955318614 0.037355309125653 * df.mm.trans1:probe21 -0.0270782492307123 0.0332922493446599 -0.813349946721329 0.416250700982976 df.mm.trans2:probe2 -0.0463078308487517 0.0332922493446599 -1.39094929781846 0.164613564506407 df.mm.trans2:probe3 -0.0269732581826678 0.0332922493446599 -0.810196328383391 0.418059573755742 df.mm.trans2:probe4 -0.0143243734273906 0.0332922493446599 -0.430261508590085 0.667117115633766 df.mm.trans2:probe5 -0.0460982382751224 0.0332922493446599 -1.38465376123697 0.166530465891719 df.mm.trans2:probe6 -0.0323215428998408 0.0332922493446599 -0.97084286991937 0.331909359084218 df.mm.trans3:probe2 0.0773671174503876 0.0332922493446599 2.32387774852460 0.0203726761407055 * df.mm.trans3:probe3 0.0298065237903467 0.0332922493446599 0.895299187560834 0.370886736896027 df.mm.trans3:probe4 0.166425909721205 0.0332922493446599 4.99893858171826 7.03399170344407e-07 *** df.mm.trans3:probe5 0.0881564741182893 0.0332922493446599 2.64795788369973 0.00825164802563631 ** df.mm.trans3:probe6 0.160555543163679 0.0332922493446599 4.82261025686547 1.68543363450941e-06 *** df.mm.trans3:probe7 -0.00137208221689651 0.0332922493446599 -0.0412132626633891 0.967135792130805 df.mm.trans3:probe8 0.0792976611356056 0.0332922493446599 2.38186553016206 0.0174496095934274 * df.mm.trans3:probe9 0.248032469845896 0.0332922493446599 7.45015655980844 2.34092178820628e-13 *** df.mm.trans3:probe10 0.540040159980332 0.0332922493446599 16.2211977445422 1.31507214482569e-51 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88056185538203 0.0758644027457663 51.1512872300124 8.12365153521187e-259 *** df.mm.trans1 -0.0250703994070612 0.0654191261367184 -0.383227366178309 0.701649319607402 df.mm.trans2 -0.0362645654214087 0.0579423946891816 -0.625872741641788 0.531570477640567 df.mm.exp2 -0.149417410454777 0.0746034107246645 -2.00282277986224 0.0455211042822436 * df.mm.exp3 -0.130183208689654 0.0746034107246645 -1.74500344454914 0.0813543594492141 . df.mm.exp4 -0.0280568177447161 0.0746034107246644 -0.37607955818889 0.70695385619334 df.mm.exp5 -0.134821748072953 0.0746034107246645 -1.80717941396183 0.0710962909774683 . df.mm.exp6 -0.044320756070602 0.0746034107246644 -0.594084850010071 0.552617245772443 df.mm.exp7 -0.08998589509283 0.0746034107246645 -1.20619009531531 0.228087905154027 df.mm.exp8 -0.0653927380216331 0.0746034107246644 -0.876538182188148 0.380991144615722 df.mm.trans1:exp2 0.180721111393182 0.0687196167505019 2.62983293474003 0.00870086056392564 ** df.mm.trans2:exp2 0.0675535142828777 0.0511290310175449 1.32123595809388 0.186786806086553 df.mm.trans1:exp3 0.118560631332814 0.0687196167505019 1.72528074135320 0.0848491190452212 . df.mm.trans2:exp3 0.119527623079516 0.0511290310175449 2.33776429360651 0.0196362239298323 * df.mm.trans1:exp4 0.0347227690874754 0.0687196167505019 0.505281762754037 0.613495092266629 df.mm.trans2:exp4 0.0578567922636192 0.0511290310175449 1.13158397709054 0.258136210246661 df.mm.trans1:exp5 0.139936086221508 0.0687196167505019 2.0363339150969 0.0420338191819892 * df.mm.trans2:exp5 0.0900165667295222 0.0511290310175449 1.76057642670038 0.078678372590518 . df.mm.trans1:exp6 0.104469083348014 0.0687196167505019 1.52022214744454 0.128835987272473 df.mm.trans2:exp6 0.0351078840471309 0.0511290310175449 0.686652638401922 0.492493434737528 df.mm.trans1:exp7 0.115239113512250 0.0687196167505019 1.67694639407908 0.0939295018146542 . df.mm.trans2:exp7 0.0443319261085656 0.0511290310175449 0.867059774579986 0.386159850754039 df.mm.trans1:exp8 0.0986371696861294 0.0687196167505019 1.43535680712901 0.151562170129283 df.mm.trans2:exp8 0.0143116884974604 0.0511290310175449 0.279913157214917 0.779613875843421 df.mm.trans1:probe2 -0.0107951375858940 0.0470491052966997 -0.229444056753428 0.818580324639056 df.mm.trans1:probe3 -0.0318028673553203 0.0470491052966997 -0.675950523495951 0.499260352292667 df.mm.trans1:probe4 -0.0087325716400627 0.0470491052966997 -0.185605477192257 0.85279950562477 df.mm.trans1:probe5 -0.00685787554560578 0.0470491052966997 -0.145759956589160 0.884146279038905 df.mm.trans1:probe6 0.00379986581302228 0.0470491052966997 0.0807638272621696 0.935649245467948 df.mm.trans1:probe7 0.00631375685709209 0.0470491052966997 0.134195046160314 0.893280875241126 df.mm.trans1:probe8 0.0135525597292573 0.0470491052966997 0.288051380441616 0.773379320890793 df.mm.trans1:probe9 -0.00766414290353811 0.0470491052966997 -0.162896676891233 0.870639438808228 df.mm.trans1:probe10 -0.048739090832928 0.0470491052966997 -1.03591961049144 0.300541360625487 df.mm.trans1:probe11 0.0161667708329294 0.0470491052966997 0.343614840940735 0.731222984859364 df.mm.trans1:probe12 -0.0170242523000831 0.0470491052966997 -0.361840085857643 0.717563632790883 df.mm.trans1:probe13 0.030184205152348 0.0470491052966997 0.641546846895414 0.521344574998341 df.mm.trans1:probe14 -1.81790175955091e-05 0.0470491052966997 -0.000386383916992027 0.999691803053053 df.mm.trans1:probe15 -0.0144263181736908 0.0470491052966997 -0.306622582570189 0.75920756589226 df.mm.trans1:probe16 0.0770583740033328 0.0470491052966997 1.63782867957615 0.101836459267065 df.mm.trans1:probe17 -0.0399287413164843 0.0470491052966997 -0.848661012035975 0.396314633764772 df.mm.trans1:probe18 0.0150398507618833 0.0470491052966997 0.319662843045354 0.749304372917846 df.mm.trans1:probe19 -0.0402076978664284 0.0470491052966997 -0.854590063145129 0.393024725017498 df.mm.trans1:probe20 0.0170558152513957 0.0470491052966997 0.362510937112169 0.71706253790295 df.mm.trans1:probe21 -0.0374474533110682 0.0470491052966997 -0.795922750813606 0.426304508958594 df.mm.trans2:probe2 0.0316234062522378 0.0470491052966997 0.672136187347564 0.5016840517155 df.mm.trans2:probe3 0.00231935714276548 0.0470491052966997 0.0492965196285714 0.96069485292758 df.mm.trans2:probe4 0.0133752775296027 0.0470491052966997 0.284283355554924 0.77626414712999 df.mm.trans2:probe5 0.0837866173831413 0.0470491052966997 1.78083338364818 0.0753053387370204 . df.mm.trans2:probe6 0.00681181504945812 0.0470491052966997 0.144780968872875 0.884918956856339 df.mm.trans3:probe2 -0.0321407242221624 0.0470491052966997 -0.683131464870108 0.494714410617452 df.mm.trans3:probe3 0.0105595800953760 0.0470491052966997 0.224437426148393 0.822472189067704 df.mm.trans3:probe4 -0.0374770984346133 0.0470491052966997 -0.796552839810159 0.425938553940033 df.mm.trans3:probe5 -0.0369491742677557 0.0470491052966997 -0.785332134049052 0.432482971334158 df.mm.trans3:probe6 0.00731325044020237 0.0470491052966997 0.155438671874497 0.876513292315965 df.mm.trans3:probe7 -0.0484274774643752 0.0470491052966997 -1.02929645864641 0.303640236719703 df.mm.trans3:probe8 -0.0401837831595463 0.0470491052966997 -0.854081770655159 0.393306114686146 df.mm.trans3:probe9 -0.060936090527263 0.0470491052966997 -1.29515939023685 0.195625377683663 df.mm.trans3:probe10 0.0350844367573138 0.0470491052966997 0.745698277067447 0.456060825747785