chr2.12973_chr2_25782315_25783329_+_2.R fitVsDatCorrelation=0.93945274254595 cont.fitVsDatCorrelation=0.243559264266850 fstatistic=7767.54740101266,60,876 cont.fstatistic=956.875237022818,60,876 residuals=-1.24517870358426,-0.115015249048513,-0.00517324191664209,0.121394884281213,0.903792199663399 cont.residuals=-1.13808069784803,-0.425437511394761,-0.143334966462299,0.340698695904019,1.79807579596895 predictedValues: Include Exclude Both chr2.12973_chr2_25782315_25783329_+_2.R.tl.Lung 113.805930807758 141.170001033671 96.5662400627038 chr2.12973_chr2_25782315_25783329_+_2.R.tl.cerebhem 95.2717489263495 117.972503156413 101.176611609471 chr2.12973_chr2_25782315_25783329_+_2.R.tl.cortex 120.904419589309 144.862961874300 103.316116152498 chr2.12973_chr2_25782315_25783329_+_2.R.tl.heart 135.449598453803 181.781693419315 109.641529395409 chr2.12973_chr2_25782315_25783329_+_2.R.tl.kidney 113.314764472357 157.255072347122 96.7815003491639 chr2.12973_chr2_25782315_25783329_+_2.R.tl.liver 101.190573467883 180.857406599942 92.8192743861584 chr2.12973_chr2_25782315_25783329_+_2.R.tl.stomach 117.016326401961 156.312785789072 101.195354882101 chr2.12973_chr2_25782315_25783329_+_2.R.tl.testicle 112.976099866235 173.059295155618 94.1977235635067 diffExp=-27.364070225913,-22.7007542300634,-23.9585422849914,-46.3320949655122,-43.9403078747644,-79.6668331320589,-39.2964593871106,-60.0831952893831 diffExpScore=0.997095912631867 diffExp1.5=0,0,0,0,0,-1,0,-1 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,0,0,0,-1,0,-1 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,0,-1,-1,-1,-1,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,-1,0,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 106.166012959986 117.51697220647 135.060105019953 cerebhem 121.879206933250 125.776352794280 110.902861443325 cortex 114.013442733799 108.258017415017 97.1242713143668 heart 103.147935163200 105.249740446856 123.001857196688 kidney 133.513019618622 105.952581216122 102.233951846356 liver 114.962260012354 99.576966642316 113.866924784904 stomach 139.586918179696 105.849016921671 120.662477516567 testicle 129.669201306964 104.808752119739 97.0142738846962 cont.diffExp=-11.3509592464844,-3.89714586102954,5.75542531878128,-2.10180528365656,27.5604384024999,15.3852933700383,33.7379012580254,24.8604491872247 cont.diffExpScore=1.37053293076676 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=0,0,0,0,0,0,1,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,0,1,0,1,1 cont.diffExp1.2Score=0.75 tran.correlation=0.459808439642903 cont.tran.correlation=-0.149394546144512 tran.covariance=0.00764146241090297 cont.tran.covariance=-0.00114110105671239 tran.mean=135.200073835069 cont.tran.mean=114.745399791896 weightedLogRatios: wLogRatio Lung -1.04335625342012 cerebhem -0.996694817137497 cortex -0.88322013122781 heart -1.48742260014071 kidney -1.60376855766652 liver -2.84971858850093 stomach -1.42082508583812 testicle -2.10687531620835 cont.weightedLogRatios: wLogRatio Lung -0.479024344422489 cerebhem -0.151670261083517 cortex 0.243994565048500 heart -0.0937231582931933 kidney 1.10484636755082 liver 0.671350609505293 stomach 1.32813107942923 testicle 1.01285667877127 varWeightedLogRatios=0.432899048569118 cont.varWeightedLogRatios=0.446962744613556 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.26572988966003 0.0987993272974237 53.2972241178138 3.81952973266632e-277 *** df.mm.trans1 -0.758503735432927 0.0851397750114183 -8.90892341835767 2.93345871904424e-18 *** df.mm.trans2 -0.0813488942957316 0.0750434761466137 -1.08402353506118 0.278652623322221 df.mm.exp2 -0.403913006455352 0.0961325863237868 -4.2016242556392 2.92159837070094e-05 *** df.mm.exp3 0.0187648574338765 0.0961325863237867 0.195197675954271 0.845283462408943 df.mm.exp4 0.299959580587275 0.0961325863237868 3.12026953666859 0.00186603758298857 ** df.mm.exp5 0.101352477113625 0.0961325863237868 1.05429886981566 0.292036804692771 df.mm.exp6 0.169829917322268 0.0961325863237868 1.76662174416342 0.0776398000383446 . df.mm.exp7 0.0828893652585733 0.0961325863237868 0.862240041887476 0.388791413717924 df.mm.exp8 0.221184274542671 0.0961325863237868 2.30082517282635 0.0216355907450111 * df.mm.trans1:exp2 0.226151693187049 0.0886298653406698 2.55164207141443 0.0108904807635147 * df.mm.trans2:exp2 0.22439973445021 0.0644876993618561 3.47972926109596 0.000526682995829511 *** df.mm.trans1:exp3 0.041740818866189 0.0886298653406698 0.470956586763935 0.637788988877241 df.mm.trans2:exp3 0.00705850236672385 0.0644876993618561 0.109455019121040 0.91286666488639 df.mm.trans1:exp4 -0.125854612990435 0.0886298653406698 -1.42000230403920 0.155962790366364 df.mm.trans2:exp4 -0.047117945395779 0.0644876993618561 -0.73065012183779 0.465188253959002 df.mm.trans1:exp5 -0.105677640881581 0.0886298653406698 -1.19234797971749 0.233447713706553 df.mm.trans2:exp5 0.00655182930788504 0.0644876993618561 0.101598124490705 0.919098917900482 df.mm.trans1:exp6 -0.287318948753339 0.0886298653406698 -3.24178478269104 0.00123270201787016 ** df.mm.trans2:exp6 0.0779141494007193 0.0644876993618561 1.20820172175044 0.227295735693093 df.mm.trans1:exp7 -0.0550705346941525 0.0886298653406698 -0.621354150573014 0.534528283963771 df.mm.trans2:exp7 0.0190048264780476 0.0644876993618561 0.294704674939742 0.768289316020428 df.mm.trans1:exp8 -0.228502620204709 0.0886298653406698 -2.57816729526109 0.0100948641320076 * df.mm.trans2:exp8 -0.0175148374230876 0.0644876993618562 -0.271599663135874 0.785993851314816 df.mm.trans1:probe2 0.210888160015918 0.0617422714858822 3.41562036738702 0.000665627280830038 *** df.mm.trans1:probe3 -0.48746493808114 0.0617422714858822 -7.89515718080768 8.65012358148243e-15 *** df.mm.trans1:probe4 0.185466710883391 0.0617422714858822 3.00388544865569 0.00274140313424829 ** df.mm.trans1:probe5 0.695340787197917 0.0617422714858822 11.2619890791823 1.42886067158753e-27 *** df.mm.trans1:probe6 -0.0682735563025384 0.0617422714858822 -1.10578303420777 0.269124030792887 df.mm.trans1:probe7 0.255982603569053 0.0617422714858822 4.14598616812447 3.71241512384235e-05 *** df.mm.trans1:probe8 -0.052035445950403 0.0617422714858822 -0.842784768006168 0.399578874999581 df.mm.trans1:probe9 0.129453431487815 0.0617422714858822 2.09667426177242 0.0363086763352477 * df.mm.trans1:probe10 -0.439208758809423 0.0617422714858822 -7.11358277302531 2.35011715713314e-12 *** df.mm.trans1:probe11 1.65480728482107 0.0617422714858822 26.8018530092378 4.89232055832553e-116 *** df.mm.trans1:probe12 1.38735265598904 0.0617422714858822 22.4700618004678 1.18419401478533e-88 *** df.mm.trans1:probe13 0.81329641524722 0.0617422714858821 13.1724407877217 2.77348642807209e-36 *** df.mm.trans1:probe14 1.17586122254635 0.0617422714858822 19.0446706000316 6.22009977529184e-68 *** df.mm.trans1:probe15 1.46908117273393 0.0617422714858822 23.7937662055380 6.36727670368124e-97 *** df.mm.trans1:probe16 0.72381409991031 0.0617422714858822 11.7231530763460 1.38043959761829e-29 *** df.mm.trans1:probe17 0.0149262374407491 0.0617422714858821 0.241750701448717 0.809029919504712 df.mm.trans1:probe18 -0.0117054663392675 0.0617422714858822 -0.189585936143345 0.84967752735094 df.mm.trans1:probe19 -0.0496477495656115 0.0617422714858822 -0.80411277996087 0.421550001224087 df.mm.trans1:probe20 0.0228958424976204 0.0617422714858822 0.370829286753011 0.710854341936055 df.mm.trans1:probe21 -0.118264407011033 0.0617422714858822 -1.91545280348286 0.0557600994656413 . df.mm.trans1:probe22 -0.0127063907277583 0.0617422714858821 -0.205797266960994 0.836997091741177 df.mm.trans2:probe2 -0.139825259244248 0.0617422714858822 -2.26466010853230 0.0237771641072463 * df.mm.trans2:probe3 -1.13670365517259 0.0617422714858822 -18.4104605777666 3.07255161402676e-64 *** df.mm.trans2:probe4 -0.577973885986079 0.0617422714858822 -9.36107260190835 6.46437902857722e-20 *** df.mm.trans2:probe5 -1.21860977445094 0.0617422714858822 -19.7370414972437 5.05424998205729e-72 *** df.mm.trans2:probe6 -0.911961977628411 0.0617422714858822 -14.7704636658361 2.99912154880973e-44 *** df.mm.trans3:probe2 -0.195765129253430 0.0617422714858822 -3.17068233063296 0.00157368779157976 ** df.mm.trans3:probe3 0.83323196174293 0.0617422714858822 13.4953240574159 7.5879695001877e-38 *** df.mm.trans3:probe4 0.370435154424578 0.0617422714858822 5.99970078051438 2.89026978125196e-09 *** df.mm.trans3:probe5 -0.313065858864687 0.0617422714858822 -5.07052706890242 4.84201216541724e-07 *** df.mm.trans3:probe6 -0.264347622841844 0.0617422714858822 -4.28146902405898 2.06164502258486e-05 *** df.mm.trans3:probe7 -0.071854996358715 0.0617422714858822 -1.16378932341589 0.244826278695828 df.mm.trans3:probe8 -0.175367961932165 0.0617422714858822 -2.84032248428475 0.00461123059824379 ** df.mm.trans3:probe9 1.23867793134699 0.0617422714858822 20.0620725726008 5.81185328178364e-74 *** df.mm.trans3:probe10 0.0470836449307779 0.0617422714858822 0.762583620551503 0.445917043761849 df.mm.trans3:probe11 0.118844199772926 0.0617422714858822 1.9248433352521 0.0545725174359384 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.53267819599939 0.27963262938619 16.2094037664663 7.06990628463623e-52 *** df.mm.trans1 0.178415671913806 0.240971875042436 0.740400396861195 0.459255473198245 df.mm.trans2 0.232327457757073 0.212396229075384 1.09383984248899 0.274325913113088 df.mm.exp2 0.403014208400838 0.272084928296029 1.48120739698730 0.138910902907725 df.mm.exp3 0.318975587375830 0.272084928296029 1.17233831867667 0.241379968497823 df.mm.exp4 -0.0455661211118569 0.272084928296029 -0.167470213793985 0.8670387345447 df.mm.exp5 0.404059918176392 0.272084928296029 1.48505071819625 0.137890140710387 df.mm.exp6 0.0846374451420948 0.272084928296029 0.311069950372294 0.755821451020567 df.mm.exp7 0.281837086153018 0.272084928296029 1.03584233025351 0.300561559140843 df.mm.exp8 0.416398867279282 0.272084928296029 1.53040034186032 0.126278681333368 df.mm.trans1:exp2 -0.264987790214285 0.250849909258459 -1.05635992055018 0.291095042477509 df.mm.trans2:exp2 -0.33509162387573 0.182520118596923 -1.83591609764261 0.0667088541021541 . df.mm.trans1:exp3 -0.247663256178047 0.250849909258458 -0.987296574713414 0.323769883605986 df.mm.trans2:exp3 -0.401040926819278 0.182520118596923 -2.19724230896943 0.0282643162582098 * df.mm.trans1:exp4 0.0167263136593013 0.250849909258459 0.0666785716955058 0.946852808271167 df.mm.trans2:exp4 -0.0646806398553372 0.182520118596923 -0.354375399011096 0.723142854727381 df.mm.trans1:exp5 -0.174864948820620 0.250849909258459 -0.697089942498051 0.485931456682343 df.mm.trans2:exp5 -0.507651038635092 0.182520118596923 -2.78134291461966 0.00552967738807646 ** df.mm.trans1:exp6 -0.00503757340764618 0.250849909258458 -0.0200820220447272 0.983982514664212 df.mm.trans2:exp6 -0.250289333370746 0.182520118596923 -1.37129723175057 0.170633481922865 df.mm.trans1:exp7 -0.00815363849261222 0.250849909258458 -0.0325040519915488 0.974077488957083 df.mm.trans2:exp7 -0.386406143545295 0.182520118596923 -2.11706055483469 0.0345360235849009 * df.mm.trans1:exp8 -0.216416294127895 0.250849909258458 -0.862732200173589 0.388520847758851 df.mm.trans2:exp8 -0.530844353749915 0.182520118596923 -2.90841556443556 0.00372444583910067 ** df.mm.trans1:probe2 -0.0650219263208032 0.174749709255596 -0.372086034350418 0.709918785117932 df.mm.trans1:probe3 -0.223550096394177 0.174749709255596 -1.27925876012305 0.201144691813092 df.mm.trans1:probe4 -0.227210239634325 0.174749709255596 -1.30020382066558 0.193873101757831 df.mm.trans1:probe5 0.0826883644714697 0.174749709255596 0.473181699836344 0.636201436914168 df.mm.trans1:probe6 0.23172718176376 0.174749709255596 1.32605188730144 0.185168158214305 df.mm.trans1:probe7 -0.08725429218577 0.174749709255596 -0.499310085020791 0.617686324171997 df.mm.trans1:probe8 -0.17418416131476 0.174749709255596 -0.996763668773786 0.319154485525599 df.mm.trans1:probe9 -0.0348149581798064 0.174749709255596 -0.199227559966264 0.842130974266721 df.mm.trans1:probe10 -0.219221602294643 0.174749709255596 -1.25448908171859 0.209998903784443 df.mm.trans1:probe11 0.116765037268170 0.174749709255596 0.66818444371421 0.50419187574255 df.mm.trans1:probe12 -0.110755111085776 0.174749709255596 -0.63379282035761 0.526381487532318 df.mm.trans1:probe13 -0.0848720547528244 0.174749709255596 -0.485677802351518 0.627317076267451 df.mm.trans1:probe14 -0.147257159893896 0.174749709255596 -0.842674706133627 0.399640410109649 df.mm.trans1:probe15 -0.304525995710401 0.174749709255596 -1.74264092917597 0.0817472651714278 . df.mm.trans1:probe16 -0.0482356216026596 0.174749709255596 -0.276026906185625 0.782592504373298 df.mm.trans1:probe17 0.000272103267770536 0.174749709255596 0.00155710283541901 0.998757966697251 df.mm.trans1:probe18 -0.0316665155882132 0.174749709255596 -0.181210691125652 0.85624412094095 df.mm.trans1:probe19 -0.020267632843787 0.174749709255596 -0.115980924547021 0.907694241415082 df.mm.trans1:probe20 0.0638797726054428 0.174749709255596 0.365550093774461 0.714789069107671 df.mm.trans1:probe21 -0.217132921731676 0.174749709255596 -1.24253666948360 0.214371125175918 df.mm.trans1:probe22 -0.0203268552199958 0.174749709255596 -0.116319822828804 0.907425736247298 df.mm.trans2:probe2 0.0130373212523174 0.174749709255596 0.0746056820801258 0.940545485937203 df.mm.trans2:probe3 -0.0216382208986394 0.174749709255596 -0.123824073818575 0.90148297550538 df.mm.trans2:probe4 -0.0410091171047298 0.174749709255596 -0.234673449697981 0.81451703652875 df.mm.trans2:probe5 0.135085856220330 0.174749709255596 0.773024783822375 0.439716242488265 df.mm.trans2:probe6 -0.058664906449222 0.174749709255596 -0.335708177708132 0.737171323575135 df.mm.trans3:probe2 -8.686890836458e-05 0.174749709255596 -0.000497104737596571 0.99960348102106 df.mm.trans3:probe3 -0.143034104386209 0.174749709255596 -0.818508396926726 0.413289546008258 df.mm.trans3:probe4 -0.0729149655637 0.174749709255596 -0.417253716039388 0.676595074817192 df.mm.trans3:probe5 -0.0087867025033005 0.174749709255596 -0.0502816430466772 0.9599094142672 df.mm.trans3:probe6 -0.00967445131661212 0.174749709255596 -0.055361759157275 0.955862884576937 df.mm.trans3:probe7 -0.144846439220716 0.174749709255596 -0.828879428971512 0.407398422477564 df.mm.trans3:probe8 0.0382227924525440 0.174749709255596 0.2187287899669 0.82691226853928 df.mm.trans3:probe9 -0.0579254328119331 0.174749709255596 -0.331476561870607 0.74036382449108 df.mm.trans3:probe10 0.0585631852138706 0.174749709255596 0.335126080972265 0.737610212766238 df.mm.trans3:probe11 -0.0742326763383332 0.174749709255596 -0.424794276651743 0.671090965082155