chr19.12178_chr19_12242200_12245822_+_2.R fitVsDatCorrelation=0.916199740472067 cont.fitVsDatCorrelation=0.215734194978010 fstatistic=3676.21468196203,65,991 cont.fstatistic=606.456174578626,65,991 residuals=-1.35806680856373,-0.152486795075901,0.00459803920097933,0.154209378143131,1.3236097163836 cont.residuals=-1.13483815070901,-0.457965262493529,-0.207668598230572,0.224095011853995,3.10510896939053 predictedValues: Include Exclude Both chr19.12178_chr19_12242200_12245822_+_2.R.tl.Lung 62.7584551619337 63.4449468547077 49.392968831918 chr19.12178_chr19_12242200_12245822_+_2.R.tl.cerebhem 53.1534428353032 60.1781551315619 48.5855233403922 chr19.12178_chr19_12242200_12245822_+_2.R.tl.cortex 54.2864292240188 101.152430745895 70.6374788376894 chr19.12178_chr19_12242200_12245822_+_2.R.tl.heart 65.230630386165 109.784192945510 68.6894322772129 chr19.12178_chr19_12242200_12245822_+_2.R.tl.kidney 66.4713879071959 874.054089685011 439.964032640956 chr19.12178_chr19_12242200_12245822_+_2.R.tl.liver 67.6231660674041 174.729892173844 114.404701116383 chr19.12178_chr19_12242200_12245822_+_2.R.tl.stomach 66.7184727854231 67.3277717555953 49.8910335328754 chr19.12178_chr19_12242200_12245822_+_2.R.tl.testicle 65.903466202541 64.4220405088701 48.5668751796013 diffExp=-0.686491692773991,-7.02471229625876,-46.8660015218764,-44.5535625593445,-807.582701777815,-107.106726106440,-0.609298970172134,1.48142569367094 diffExpScore=1.00193585001728 diffExp1.5=0,0,-1,-1,-1,-1,0,0 diffExp1.5Score=0.8 diffExp1.4=0,0,-1,-1,-1,-1,0,0 diffExp1.4Score=0.8 diffExp1.3=0,0,-1,-1,-1,-1,0,0 diffExp1.3Score=0.8 diffExp1.2=0,0,-1,-1,-1,-1,0,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 70.2734797307303 86.5841854177315 81.192675260999 cerebhem 76.9740224614242 105.261164096837 82.1321991996246 cortex 92.0887101727676 78.3200859498186 75.2187253836693 heart 72.9106565523561 89.7555898207565 84.5192683368895 kidney 71.7953732144626 67.0705547503208 77.7354893544835 liver 76.8315907153945 75.3984400615357 78.9368129043978 stomach 67.6778690826805 99.3565324457883 84.1840744392884 testicle 86.1450480982241 76.0336054612235 85.5689817582594 cont.diffExp=-16.3107056870012,-28.2871416354132,13.768624222949,-16.8449332684004,4.72481846414179,1.43315065385877,-31.6786633631078,10.1114426370006 cont.diffExpScore=1.92186220773481 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,-1,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,-1,0,0,0,0,-1,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=-1,-1,0,-1,0,0,-1,0 cont.diffExp1.2Score=0.8 tran.correlation=0.300066275877676 cont.tran.correlation=-0.333325500783737 tran.covariance=0.0301116803994838 cont.tran.covariance=-0.00513443148386465 tran.mean=126.077435648186 cont.tran.mean=80.7798067520033 weightedLogRatios: wLogRatio Lung -0.045091538656583 cerebhem -0.500881127604179 cortex -2.67951630348714 heart -2.31048292526383 kidney -14.1312821225214 liver -4.45084049961839 stomach -0.0382276501778203 testicle 0.0949609976823322 cont.weightedLogRatios: wLogRatio Lung -0.909353836575195 cerebhem -1.40838076417595 cortex 0.719338245158681 heart -0.913142994863199 kidney 0.288622829166609 liver 0.0815723473700263 stomach -1.69199085008511 testicle 0.54857247930585 varWeightedLogRatios=22.8451444251525 cont.varWeightedLogRatios=0.866678897474865 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36415773982277 0.13357218499399 32.672653666773 1.72099153222655e-159 *** df.mm.trans1 -0.424185072208188 0.113412950608491 -3.74018196275048 0.000194431497635425 *** df.mm.trans2 -0.204934982226667 0.0997900920767662 -2.05366061862148 0.0402705153150165 * df.mm.exp2 -0.202491100482039 0.125820371914024 -1.60936657078399 0.107854691541047 df.mm.exp3 -0.0363157784591738 0.125820371914024 -0.288631943354843 0.772923408818779 df.mm.exp4 0.257192562074136 0.125820371914024 2.04412495497853 0.0412050991860791 * df.mm.exp5 0.493563098704421 0.125820371914024 3.92275981382160 9.35751798250946e-05 *** df.mm.exp6 0.247791973106509 0.125820371914024 1.96941059175879 0.0491841814324919 * df.mm.exp7 0.110555596608335 0.125820371914024 0.878678030644196 0.379788866559551 df.mm.exp8 0.081047357651628 0.125820371914024 0.644151312054693 0.519626375613503 df.mm.trans1:exp2 0.0363806664497187 0.112990029493697 0.321981210313323 0.747534834715218 df.mm.trans2:exp2 0.149627964184998 0.0782817887327157 1.91140195704886 0.0562411622584468 . df.mm.trans1:exp3 -0.108703260597555 0.112990029493697 -0.962060644506855 0.336253821936019 df.mm.trans2:exp3 0.502771821696215 0.0782817887327157 6.42258984925438 2.0758252947446e-10 *** df.mm.trans1:exp4 -0.218556724811277 0.112990029493697 -1.93430098027781 0.0533605335755165 . df.mm.trans2:exp4 0.291151443203573 0.0782817887327157 3.71927427715885 0.000211008519662738 *** df.mm.trans1:exp5 -0.43608481321564 0.112990029493697 -3.85949818023515 0.000120974271509728 *** df.mm.trans2:exp5 2.12940661135836 0.0782817887327157 27.201813420858 3.74695171060193e-122 *** df.mm.trans1:exp6 -0.173134668009998 0.112990029493697 -1.53230040549424 0.125767474692385 df.mm.trans2:exp6 0.765276783953425 0.0782817887327157 9.775923574848 1.30437751482736e-21 *** df.mm.trans1:exp7 -0.0493670412778114 0.112990029493697 -0.436915022493780 0.662268160020044 df.mm.trans2:exp7 -0.0511553401650542 0.0782817887327157 -0.653476894092422 0.51360046783308 df.mm.trans1:exp8 -0.0321496321635222 0.112990029493697 -0.284535124980347 0.776059764783291 df.mm.trans2:exp8 -0.0657640903561175 0.0782817887327157 -0.84009437470395 0.401058103337558 df.mm.trans1:probe2 0.085783010177484 0.0853056015513771 1.00559645108205 0.314855200446952 df.mm.trans1:probe3 0.0104403736958654 0.0853056015513771 0.122387903091891 0.902616632256248 df.mm.trans1:probe4 -0.00699492002864276 0.0853056015513771 -0.0819983670642065 0.93466456993707 df.mm.trans1:probe5 -0.0496364955378687 0.0853056015513771 -0.581866778208861 0.560788755445602 df.mm.trans1:probe6 0.0578814792079894 0.0853056015513771 0.678519090837535 0.497601033877389 df.mm.trans1:probe7 0.0444155234573696 0.0853056015513771 0.520663621727343 0.602717427135238 df.mm.trans1:probe8 0.517146569879726 0.0853056015513771 6.06228149705108 1.90546332706214e-09 *** df.mm.trans1:probe9 0.483878862243171 0.0853056015513771 5.67229881090218 1.84827043311507e-08 *** df.mm.trans1:probe10 0.391076903694843 0.0853056015513771 4.58442231908193 5.13383534399497e-06 *** df.mm.trans1:probe11 0.527049499115716 0.0853056015513771 6.17836917542031 9.4433994575663e-10 *** df.mm.trans1:probe12 0.401096815944463 0.0853056015513771 4.70188133780281 2.9424983026021e-06 *** df.mm.trans1:probe13 0.397135802567768 0.0853056015513771 4.65544812234381 3.67212718419601e-06 *** df.mm.trans1:probe14 1.23000484506502 0.0853056015513771 14.4188051276354 6.30066605272537e-43 *** df.mm.trans1:probe15 0.390220585485718 0.0853056015513771 4.57438407782284 5.3808419756873e-06 *** df.mm.trans1:probe16 0.197997550832410 0.0853056015513771 2.32103809400092 0.020486928427969 * df.mm.trans1:probe17 1.19070529883035 0.0853056015513771 13.9581138539094 1.480311546071e-40 *** df.mm.trans1:probe18 0.649550502067321 0.0853056015513771 7.6143944858781 6.16469513022933e-14 *** df.mm.trans1:probe19 1.05643230089525 0.0853056015513771 12.3840906304259 7.46537548141373e-33 *** df.mm.trans2:probe2 -0.0478517791426902 0.0853056015513771 -0.560945333863808 0.57496161049598 df.mm.trans2:probe3 -0.213661890598856 0.0853056015513771 -2.50466425080155 0.0124164033392702 * df.mm.trans2:probe4 0.177492466611726 0.0853056015513771 2.08066602173630 0.037720875509742 * df.mm.trans2:probe5 -0.150468489741023 0.0853056015513771 -1.76387584173355 0.0780609665428856 . df.mm.trans2:probe6 0.0082345326998476 0.0853056015513771 0.0965298005065726 0.923119324239042 df.mm.trans3:probe2 0.0194303374160359 0.0853056015513771 0.227773288772057 0.81986947768649 df.mm.trans3:probe3 0.120709516640017 0.0853056015513771 1.41502450536401 0.157375454324465 df.mm.trans3:probe4 0.423580697068626 0.0853056015513771 4.96544997474187 8.06729119511784e-07 *** df.mm.trans3:probe5 0.0499853701407437 0.0853056015513771 0.585956481540535 0.558038182375714 df.mm.trans3:probe6 0.345499978737657 0.0853056015513771 4.05014409903167 5.51827270549519e-05 *** df.mm.trans3:probe7 -0.172490401080089 0.0853056015513771 -2.02202901032476 0.0434416388243429 * df.mm.trans3:probe8 0.0808235454114827 0.0853056015513771 0.947458829685469 0.343635995674123 df.mm.trans3:probe9 0.270035678948401 0.0853056015513771 3.16550934566432 0.00159535264998722 ** df.mm.trans3:probe10 0.198740594677927 0.0853056015513771 2.32974846977935 0.0200195485357065 * df.mm.trans3:probe11 0.654281449076723 0.0853056015513771 7.669853294249 4.10406851442845e-14 *** df.mm.trans3:probe12 -0.222909641348319 0.0853056015513771 -2.61307156030155 0.00910916984930605 ** df.mm.trans3:probe13 -0.0229913055199014 0.0853056015513771 -0.269516949670115 0.787588007393466 df.mm.trans3:probe14 -0.0682870747752255 0.0853056015513771 -0.80049930524314 0.423613425174616 df.mm.trans3:probe15 0.559121609644521 0.0853056015513771 6.55433640319361 8.97405241896828e-11 *** df.mm.trans3:probe16 -0.0178215371534601 0.0853056015513771 -0.20891403177934 0.834558283235893 df.mm.trans3:probe17 0.341047789765059 0.0853056015513771 3.99795304836642 6.86349666186256e-05 *** df.mm.trans3:probe18 0.578020995792574 0.0853056015513771 6.7758855840721 2.11824933757140e-11 *** df.mm.trans3:probe19 -0.00614005401680847 0.0853056015513771 -0.0719771492744294 0.942634616207628 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.47961370557136 0.325479706664806 13.7631121505977 1.44086816519150e-39 *** df.mm.trans1 -0.275716076747915 0.276357041682762 -0.997680663640979 0.318677772405595 df.mm.trans2 -0.0206775675742990 0.243161777271677 -0.0850362577799246 0.932249741248245 df.mm.exp2 0.274895770038375 0.306590610499304 0.896621620573079 0.370138609596066 df.mm.exp3 0.246469612467149 0.306590610499304 0.803904633823442 0.421644843175722 df.mm.exp4 0.0326589513491873 0.306590610499304 0.106522999174697 0.915188967627337 df.mm.exp5 -0.190433375114082 0.306590610499304 -0.621132443697309 0.534655320882867 df.mm.exp6 -0.0209318457239831 0.306590610499304 -0.0682729509879451 0.945582128193051 df.mm.exp7 0.0637815556866508 0.306590610499304 0.208034928345582 0.83524444036562 df.mm.exp8 0.0211984172307613 0.306590610499304 0.0691424215380837 0.944890202852655 df.mm.trans1:exp2 -0.183822259151348 0.275326495986504 -0.66765190357981 0.504511262947178 df.mm.trans2:exp2 -0.0795684133907186 0.190751791887413 -0.417130620915383 0.67667321255639 df.mm.trans1:exp3 0.0238882575949687 0.275326495986504 0.0867633807250415 0.93087712286912 df.mm.trans2:exp3 -0.34678269929553 0.190751791887413 -1.81797872441592 0.0693692012615065 . df.mm.trans1:exp4 0.00418137398498914 0.275326495986504 0.0151869654607964 0.987886077360498 df.mm.trans2:exp4 0.00331417373661365 0.190751791887413 0.0173742731526725 0.986141530333569 df.mm.trans1:exp5 0.211858925768401 0.275326495986504 0.769482519324206 0.441790281663634 df.mm.trans2:exp5 -0.0649386860630677 0.190751791887413 -0.3404355231504 0.733600721272903 df.mm.trans1:exp6 0.110153255328061 0.275326495986504 0.400082291148109 0.689182129016006 df.mm.trans2:exp6 -0.117398750783729 0.190751791887413 -0.615452938198456 0.53839694886829 df.mm.trans1:exp7 -0.10141680948286 0.275326495986504 -0.368351070315555 0.712690145205968 df.mm.trans2:exp7 0.073815980525936 0.190751791887413 0.386973982239203 0.698858552365662 df.mm.trans1:exp8 0.182439580680213 0.275326495986504 0.662629944228674 0.507721664201415 df.mm.trans2:exp8 -0.151140180023119 0.190751791887413 -0.792339503223782 0.428352371743766 df.mm.trans1:probe2 0.331252075733865 0.207866946034134 1.59357744005856 0.111349582871063 df.mm.trans1:probe3 0.301400938357282 0.207866946034134 1.44997049366275 0.147383145966977 df.mm.trans1:probe4 0.0795464578403234 0.207866946034134 0.382679686972748 0.702039337789026 df.mm.trans1:probe5 0.0806303617809382 0.207866946034134 0.387894099178702 0.698177707481155 df.mm.trans1:probe6 -0.0383307954792073 0.207866946034134 -0.184400628433310 0.853736903733398 df.mm.trans1:probe7 0.0862364256176643 0.207866946034134 0.414863581069322 0.678331564995228 df.mm.trans1:probe8 0.295649813450947 0.207866946034134 1.42230315637773 0.155252928776654 df.mm.trans1:probe9 0.072361083703623 0.207866946034134 0.348112506986756 0.72782962302706 df.mm.trans1:probe10 0.125047073659080 0.207866946034134 0.601572669656416 0.547596213073142 df.mm.trans1:probe11 0.0382320220049725 0.207866946034134 0.183925451998965 0.854109564193084 df.mm.trans1:probe12 -0.0314159496730727 0.207866946034134 -0.151134897935691 0.879900062512695 df.mm.trans1:probe13 0.0121775726395808 0.207866946034134 0.0585834971452418 0.953295666006492 df.mm.trans1:probe14 0.0097598686179746 0.207866946034134 0.0469524799598101 0.962560561148848 df.mm.trans1:probe15 -0.188209511283839 0.207866946034134 -0.905432609054317 0.365456320030195 df.mm.trans1:probe16 0.252458123118832 0.207866946034134 1.21451788240241 0.224839341900915 df.mm.trans1:probe17 0.0862089528538922 0.207866946034134 0.414731415930534 0.67842829278581 df.mm.trans1:probe18 0.0437392468558471 0.207866946034134 0.210419442293940 0.833383574593729 df.mm.trans1:probe19 0.286136714624474 0.207866946034134 1.37653782904708 0.168966104993830 df.mm.trans2:probe2 0.140441283660124 0.207866946034134 0.67563066826922 0.499432786597005 df.mm.trans2:probe3 -0.00891275919051219 0.207866946034134 -0.0428772316164621 0.965808035867246 df.mm.trans2:probe4 0.237112758652041 0.207866946034134 1.14069486840445 0.254272511633976 df.mm.trans2:probe5 -0.219417182900705 0.207866946034134 -1.05556552923366 0.291424020304356 df.mm.trans2:probe6 -0.0946979884336001 0.207866946034134 -0.455570210850405 0.648798868779234 df.mm.trans3:probe2 0.083418155036448 0.207866946034134 0.401305530426901 0.688281708596349 df.mm.trans3:probe3 0.228082792585287 0.207866946034134 1.09725378150230 0.272797010472827 df.mm.trans3:probe4 0.267364322458438 0.207866946034134 1.28622817412507 0.198663860070523 df.mm.trans3:probe5 0.170261486733529 0.207866946034134 0.81908879685744 0.412932682425383 df.mm.trans3:probe6 0.350766247124065 0.207866946034134 1.68745562397624 0.0918304545529612 . df.mm.trans3:probe7 0.251846832879097 0.207866946034134 1.21157710585569 0.225963115080116 df.mm.trans3:probe8 0.255110735056162 0.207866946034134 1.22727898746475 0.220009211076891 df.mm.trans3:probe9 0.077454773596432 0.207866946034134 0.372617075846745 0.709513137059405 df.mm.trans3:probe10 0.238392420795382 0.207866946034134 1.14685102823532 0.251720011004272 df.mm.trans3:probe11 0.102118271963806 0.207866946034134 0.491267485822575 0.623346051455097 df.mm.trans3:probe12 0.0961069193901387 0.207866946034134 0.462348253167471 0.643933092247668 df.mm.trans3:probe13 0.148549562660321 0.207866946034134 0.714637730983586 0.475001225959758 df.mm.trans3:probe14 0.402259695344453 0.207866946034134 1.93517874303305 0.0532526163552572 . df.mm.trans3:probe15 0.39472568396175 0.207866946034134 1.89893434955710 0.0578633275399978 . df.mm.trans3:probe16 0.199851774960758 0.207866946034134 0.9614408580763 0.336565072514782 df.mm.trans3:probe17 0.275627687115987 0.207866946034134 1.32598131821654 0.185151424263815 df.mm.trans3:probe18 0.288062427443597 0.207866946034134 1.38580198987623 0.166119141327796 df.mm.trans3:probe19 0.228494749276657 0.207866946034134 1.09923561025973 0.271932278553134