fitVsDatCorrelation=0.794272887053142 cont.fitVsDatCorrelation=0.277769993883991 fstatistic=9340.11137822834,59,853 cont.fstatistic=3727.29951187453,59,853 residuals=-0.420041127312381,-0.0969862061161937,-0.00448062691388907,0.0797483482689537,0.744439449384592 cont.residuals=-0.547477845877212,-0.172624938183828,-0.052638216188027,0.114857652614587,1.27168230692987 predictedValues: Include Exclude Both Lung 56.8440287696596 47.5490166507144 59.0820914322872 cerebhem 56.7487621882258 54.403293925686 67.2246519695408 cortex 56.5322773873867 48.0643681611367 58.2927971986885 heart 57.9351949915777 51.8936100323013 56.3333119040844 kidney 79.1976263298421 50.7300962141956 83.2445708016562 liver 58.5310501810265 51.2475913343806 54.0375805090176 stomach 76.6767710693808 49.2880458081239 80.4301489588939 testicle 54.4647916251077 50.4435138673385 55.2778559843248 diffExp=9.29501211894512,2.34546826253977,8.46790922625002,6.04158495927636,28.4675301156465,7.28345884664585,27.3887252612569,4.02127775776921 diffExpScore=0.989396779223045 diffExp1.5=0,0,0,0,1,0,1,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,0,0,1,0,1,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,0,0,1,0,1,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,0,0,1,0,1,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 62.9273851725435 52.8645410746126 56.6782779320568 cerebhem 58.6407626233205 54.7746152282908 59.7370648413592 cortex 56.9352976237649 54.1732349326297 58.5919595072471 heart 59.8351550892809 67.6904470252337 61.8422212824881 kidney 58.882746398156 59.744654241609 60.7607091844365 liver 58.0939747994505 61.2712326673735 59.2294875120813 stomach 58.1354064648805 50.6709174945157 59.2986785955968 testicle 59.6351876414545 65.431755635614 60.7167990545351 cont.diffExp=10.0628440979309,3.86614739502973,2.76206269113521,-7.85529193595276,-0.861907843452933,-3.17725786792293,7.46448897036478,-5.79656799415956 cont.diffExpScore=5.60606478894647 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=-0.0894556429017296 cont.tran.correlation=0.0584522359266741 tran.covariance=-0.000501167738709255 cont.tran.covariance=0.000191882846550671 tran.mean=56.2843774085052 cont.tran.mean=58.7317071320456 weightedLogRatios: wLogRatio Lung 0.705457785600307 cerebhem 0.169576464907871 cortex 0.641565606833358 heart 0.440986776121249 kidney 1.84818025075303 liver 0.531968751789818 stomach 1.82009808507576 testicle 0.303672138171763 cont.weightedLogRatios: wLogRatio Lung 0.706553308708189 cerebhem 0.275358727793769 cortex 0.199762043093893 heart -0.512312803740325 kidney -0.0593298216908785 liver -0.217716368822575 stomach 0.548875219558807 testicle -0.383535850651677 varWeightedLogRatios=0.431050740301038 cont.varWeightedLogRatios=0.191117311262715 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92102008382197 0.0772882117563264 50.7324467046039 8.29951719670828e-260 *** df.mm.trans1 0.179078538275544 0.0667442091394761 2.6830573106548 0.0074364898486021 ** df.mm.trans2 -0.0676837495511567 0.0589682070962827 -1.14780070285406 0.251372650900463 df.mm.exp2 0.00387409029277171 0.0758519688291105 0.0510743538048403 0.95927822936505 df.mm.exp3 0.0187299023573413 0.0758519688291105 0.246927042850246 0.805024102219989 df.mm.exp4 0.154090242851806 0.0758519688291105 2.03146002971870 0.0425179058350754 * df.mm.exp5 0.0535383335605572 0.0758519688291105 0.705826551202323 0.480488724475608 df.mm.exp6 0.193401833187876 0.0758519688291105 2.54972726711416 0.0109545984437977 * df.mm.exp7 0.0267468570308491 0.0758519688291105 0.352619153381609 0.724461085754475 df.mm.exp8 0.0828918153354306 0.0758519688291105 1.09281033327138 0.274785615760782 df.mm.trans1:exp2 -0.00555142567587333 0.0701115521291832 -0.0791798998493802 0.936908102968354 df.mm.trans2:exp2 0.130789503384468 0.0517808129187382 2.52582947258324 0.0117225757404091 * df.mm.trans1:exp3 -0.0242293259767558 0.0701115521291832 -0.345582507317943 0.729741701042166 df.mm.trans2:exp3 -0.00794989489050311 0.0517808129187382 -0.15352974282152 0.878016849656018 df.mm.trans1:exp4 -0.135076364558308 0.0701115521291832 -1.92659213005903 0.0543624239573169 . df.mm.trans2:exp4 -0.0666556894809502 0.0517808129187382 -1.28726618459150 0.198350698428373 df.mm.trans1:exp5 0.278096814382378 0.0701115521291832 3.96649062725033 7.9061922011394e-05 *** df.mm.trans2:exp5 0.0112199061537929 0.0517808129187382 0.216680764965987 0.828508949562257 df.mm.trans1:exp6 -0.164155627280150 0.0701115521291832 -2.34134921129242 0.0194435596145273 * df.mm.trans2:exp6 -0.118494323083461 0.0517808129187382 -2.28838282762031 0.0223590126292418 * df.mm.trans1:exp7 0.272540771251458 0.0701115521291832 3.88724486870996 0.000109255634820206 *** df.mm.trans2:exp7 0.00917360762198484 0.0517808129187382 0.177162294388490 0.859422969315577 df.mm.trans1:exp8 -0.125648527503343 0.0701115521291832 -1.79212303375956 0.0734675707443506 . df.mm.trans2:exp8 -0.0237987508203301 0.0517808129187382 -0.459605585135917 0.645916461096398 df.mm.trans1:probe2 -0.193425118240873 0.0480020983035662 -4.02951381453471 6.08835284486189e-05 *** df.mm.trans1:probe3 -0.166021588726089 0.0480020983035662 -3.45863190555056 0.000569816898887011 *** df.mm.trans1:probe4 -0.0757936727076754 0.0480020983035662 -1.57896582412616 0.114714633232043 df.mm.trans1:probe5 -0.0571389953705358 0.0480020983035662 -1.19034370141879 0.234242499021195 df.mm.trans1:probe6 -0.0333321068695296 0.0480020983035662 -0.694388538157993 0.48762765478305 df.mm.trans1:probe7 -0.220170640591452 0.0480020983035662 -4.58668784016666 5.17794702733597e-06 *** df.mm.trans1:probe8 -0.201081372433745 0.0480020983035662 -4.18901213780494 3.09331254374548e-05 *** df.mm.trans1:probe9 0.112390895005886 0.0480020983035662 2.34137462689910 0.0194422445860808 * df.mm.trans1:probe10 0.208651380438806 0.0480020983035662 4.34671374403865 1.54856935225200e-05 *** df.mm.trans1:probe11 0.0375421349618242 0.0480020983035662 0.782093622749718 0.434376593548992 df.mm.trans1:probe12 -0.00535871190665139 0.0480020983035662 -0.111634951304895 0.911139120809582 df.mm.trans1:probe13 -0.0879032582802012 0.0480020983035662 -1.83123782890280 0.0674137028384814 . df.mm.trans1:probe14 0.0878664233009805 0.0480020983035662 1.83047046704732 0.0675283872544705 . df.mm.trans1:probe15 0.302848520242883 0.0480020983035662 6.30906837296285 4.50481355556424e-10 *** df.mm.trans1:probe16 0.165203863805152 0.0480020983035662 3.44159671438527 0.000606402381398676 *** df.mm.trans1:probe17 -0.258967001894137 0.0480020983035662 -5.39491003614934 8.88536138159494e-08 *** df.mm.trans1:probe18 -0.31705935264469 0.0480020983035662 -6.60511443978137 6.9832357420997e-11 *** df.mm.trans1:probe19 -0.268997048501552 0.0480020983035662 -5.60386020628535 2.82961673530906e-08 *** df.mm.trans1:probe20 -0.33908279962452 0.0480020983035662 -7.06391619549949 3.36122504443248e-12 *** df.mm.trans1:probe21 -0.301412851562517 0.0480020983035662 -6.2791599162265 5.4160462197848e-10 *** df.mm.trans1:probe22 -0.301956850465469 0.0480020983035662 -6.29049273129454 5.05132923081491e-10 *** df.mm.trans2:probe2 0.0229741695016220 0.0480020983035662 0.478607609116019 0.632340454088003 df.mm.trans2:probe3 0.00283577509823649 0.0480020983035662 0.0590760653899543 0.952905359135151 df.mm.trans2:probe4 0.0580281829613447 0.0480020983035662 1.20886763312665 0.227048681770700 df.mm.trans2:probe5 0.032442894764629 0.0480020983035662 0.67586409576222 0.499310125759805 df.mm.trans2:probe6 0.0185153646386326 0.0480020983035662 0.385719901691403 0.699800296618414 df.mm.trans3:probe2 0.108081503938583 0.0480020983035662 2.25159957081613 0.0246013077041712 * df.mm.trans3:probe3 0.200040092704322 0.0480020983035662 4.16731975838358 3.39626168170341e-05 *** df.mm.trans3:probe4 -0.04142479062979 0.0480020983035662 -0.862978746633507 0.388391718169354 df.mm.trans3:probe5 0.111475111758577 0.0480020983035662 2.32229664323434 0.0204515274452414 * df.mm.trans3:probe6 -0.0103424174557568 0.0480020983035662 -0.215457611672538 0.829462062401969 df.mm.trans3:probe7 -0.0541439343907525 0.0480020983035662 -1.1279493252221 0.259658478500053 df.mm.trans3:probe8 0.191946812270997 0.0480020983035662 3.99871711976259 6.92053925073421e-05 *** df.mm.trans3:probe9 -0.00257745209549654 0.0480020983035662 -0.0536945714163719 0.957191080451501 df.mm.trans3:probe10 -0.0379250811495563 0.0480020983035662 -0.790071319585185 0.429705695941063 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.0620629952099 0.122204543350893 33.2398688610640 4.59562262772079e-156 *** df.mm.trans1 0.091784480973691 0.105532854414095 0.869724234062144 0.384695790279951 df.mm.trans2 -0.119077858724266 0.0932377998748594 -1.27714144782575 0.201899837371381 df.mm.exp2 -0.0876189821261987 0.119933622507043 -0.73056229182983 0.465247155234395 df.mm.exp3 -0.108818243969678 0.119933622507043 -0.907320580292554 0.3644935374119 df.mm.exp4 0.109628878710434 0.119933622507043 0.914079608526763 0.360933316507797 df.mm.exp5 -0.0136387404827143 0.119933622507043 -0.113719073914519 0.90948725095429 df.mm.exp6 0.0236296556146113 0.119933622507043 0.197022779106199 0.843856646502864 df.mm.exp7 -0.166783238260775 0.119933622507043 -1.39062953969377 0.164700476020931 df.mm.exp8 0.0907098558150597 0.119933622507043 0.75633382798667 0.449657899124687 df.mm.trans1:exp2 0.0170675994829629 0.110857141300954 0.153960306775617 0.877677449786128 df.mm.trans2:exp2 0.123113029931808 0.0818734248506463 1.50369952345821 0.133028889569176 df.mm.trans1:exp3 0.00875229233959348 0.110857141300954 0.0789510918005074 0.937090041641898 df.mm.trans2:exp3 0.13327239694958 0.0818734248506463 1.62778578266020 0.103939596201772 df.mm.trans1:exp4 -0.160016958048759 0.110857141300954 -1.44345196142435 0.149260336028371 df.mm.trans2:exp4 0.137583370730227 0.0818734248506463 1.68043991052295 0.0932378823395493 . df.mm.trans1:exp5 -0.0527945875036935 0.110857141300954 -0.476239842414545 0.634025474205107 df.mm.trans2:exp5 0.135985645467038 0.0818734248506463 1.66092533340462 0.0970959265782857 . df.mm.trans1:exp6 -0.103549146412177 0.110857141300954 -0.93407736476861 0.350528321306387 df.mm.trans2:exp6 0.123947976467794 0.0818734248506463 1.51389753993925 0.130422251299353 df.mm.trans1:exp7 0.0875766767885508 0.110857141300954 0.789995806862798 0.429749770758887 df.mm.trans2:exp7 0.124402551772734 0.0818734248506463 1.51944971154767 0.129019888664841 df.mm.trans1:exp8 -0.144445504600256 0.110857141300954 -1.30298781752017 0.192930542869279 df.mm.trans2:exp8 0.122565031914434 0.0818734248506463 1.49700628913494 0.134761536847348 df.mm.trans1:probe2 0.0889579567495127 0.075898696188838 1.17206172459383 0.241499521562792 df.mm.trans1:probe3 -0.0395780158240618 0.075898696188838 -0.521458441467698 0.602182806754071 df.mm.trans1:probe4 0.00423784481036078 0.075898696188838 0.0558355416253385 0.955485896306884 df.mm.trans1:probe5 -0.0195441596328389 0.075898696188838 -0.257503232785613 0.796852425634649 df.mm.trans1:probe6 -0.0528774982841624 0.075898696188838 -0.696685199342579 0.486189629030151 df.mm.trans1:probe7 -0.103004527460595 0.075898696188838 -1.35713171151606 0.17509834857491 df.mm.trans1:probe8 -0.0571891328534 0.075898696188838 -0.753492954755269 0.451361664946163 df.mm.trans1:probe9 -0.0472591795317027 0.075898696188838 -0.622661282798859 0.533673549193007 df.mm.trans1:probe10 0.0389082397037940 0.075898696188838 0.512633835066011 0.60834019743612 df.mm.trans1:probe11 -0.0355842037860805 0.075898696188838 -0.46883814311574 0.639305128340663 df.mm.trans1:probe12 0.0432593336905933 0.075898696188838 0.569961486333875 0.56885389901991 df.mm.trans1:probe13 0.0521895177703238 0.075898696188838 0.687620741738105 0.491878566121266 df.mm.trans1:probe14 0.0261273177643877 0.075898696188838 0.344239348978832 0.730751137238688 df.mm.trans1:probe15 0.0402057588298623 0.075898696188838 0.529729242381573 0.596437513997322 df.mm.trans1:probe16 -0.0845596533467843 0.0758986961888379 -1.11411206770137 0.265544932707271 df.mm.trans1:probe17 -0.0702996037401804 0.075898696188838 -0.92622939879327 0.354588753038941 df.mm.trans1:probe18 -0.142158902668073 0.075898696188838 -1.87300849430111 0.0614090257778678 . df.mm.trans1:probe19 0.0093680943131633 0.075898696188838 0.123428922808572 0.901796531570178 df.mm.trans1:probe20 -0.0210051400267109 0.075898696188838 -0.276752316989077 0.782037343219775 df.mm.trans1:probe21 -0.0310841152004968 0.075898696188838 -0.409547419934048 0.68224079558331 df.mm.trans1:probe22 0.0211770807569717 0.075898696188838 0.279017714669072 0.780298861545816 df.mm.trans2:probe2 0.0886585930245183 0.075898696188838 1.16811747073933 0.243085763527382 df.mm.trans2:probe3 0.163030839382163 0.075898696188838 2.14800579678652 0.0319943436344406 * df.mm.trans2:probe4 0.048483050698592 0.075898696188838 0.638786344602876 0.523133410340365 df.mm.trans2:probe5 0.0629115037363023 0.075898696188838 0.828887805658437 0.407399771107722 df.mm.trans2:probe6 0.0328788384289751 0.075898696188838 0.433193718468782 0.664983558683867 df.mm.trans3:probe2 -0.0557218154456978 0.075898696188838 -0.734160377499246 0.463052750798862 df.mm.trans3:probe3 -0.0338034461081601 0.075898696188838 -0.445375847090393 0.656161168445617 df.mm.trans3:probe4 -0.0669305913145692 0.075898696188838 -0.88184112080192 0.378111151314157 df.mm.trans3:probe5 0.0561001768606454 0.075898696188838 0.739145462012506 0.460022016388174 df.mm.trans3:probe6 0.035042602053968 0.075898696188838 0.461702292840197 0.644412546298194 df.mm.trans3:probe7 -0.0356943123941411 0.075898696188838 -0.470288874334978 0.638268862940452 df.mm.trans3:probe8 -0.00762220262149001 0.075898696188838 -0.100426002082114 0.920029725757616 df.mm.trans3:probe9 -0.0186830484179418 0.075898696188838 -0.246157699092194 0.80561937950059 df.mm.trans3:probe10 0.153526783548203 0.075898696188838 2.02278551882136 0.0434068072155114 *