chr6.19343_chr6_79271575_79272717_-_2.R fitVsDatCorrelation=0.851483141671229 cont.fitVsDatCorrelation=0.258240515082845 fstatistic=8431.56128466037,62,922 cont.fstatistic=2473.65424456145,62,922 residuals=-0.407256386113025,-0.102390896925613,-0.00648007425017342,0.0767405932863166,1.18299401740258 cont.residuals=-0.588420649084761,-0.223285693229673,-0.089700045630573,0.160558569744745,1.31436401179537 predictedValues: Include Exclude Both chr6.19343_chr6_79271575_79272717_-_2.R.tl.Lung 50.8781084241822 118.523038599407 66.5790288089109 chr6.19343_chr6_79271575_79272717_-_2.R.tl.cerebhem 60.9025511628714 94.4232514424657 66.0116369641412 chr6.19343_chr6_79271575_79272717_-_2.R.tl.cortex 48.3403147334688 99.2466517148837 63.9629864557394 chr6.19343_chr6_79271575_79272717_-_2.R.tl.heart 50.9359817226448 93.7029227637396 66.9586319580958 chr6.19343_chr6_79271575_79272717_-_2.R.tl.kidney 51.0645467354985 125.915411086914 67.6541179237621 chr6.19343_chr6_79271575_79272717_-_2.R.tl.liver 54.0679962953215 111.295626376224 70.0522832076728 chr6.19343_chr6_79271575_79272717_-_2.R.tl.stomach 56.8659231683216 100.803050463580 81.6677639942901 chr6.19343_chr6_79271575_79272717_-_2.R.tl.testicle 53.2410317526769 92.8243118759469 69.4632070079092 diffExp=-67.6449301752243,-33.5207002795944,-50.9063369814149,-42.7669410410948,-74.8508643514159,-57.2276300809024,-43.9371272952581,-39.58328012327 diffExpScore=0.997569499022945 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 64.4368214050865 58.6813977150512 64.8929383654934 cerebhem 62.221352888345 59.4386958145553 51.881885526667 cortex 58.3265732910866 59.9264608457235 60.271602613228 heart 58.9248628141735 61.2816367315535 64.3496784717394 kidney 60.4843684764345 57.8048189593902 57.1653843215426 liver 60.6401845084428 60.4250525807131 64.0700938207134 stomach 61.4805381112073 61.582026327826 51.2883910282624 testicle 60.3530572872746 50.4793638631755 56.554285143841 cont.diffExp=5.75542369003532,2.78265707378974,-1.59988755463685,-2.35677391738008,2.67954951704434,0.215131927729750,-0.101488216618755,9.87369342409903 cont.diffExpScore=1.38997041144999 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=-0.325365738301083 cont.tran.correlation=-0.0262792111676825 tran.covariance=-0.00268386383956107 cont.tran.covariance=-3.98508297407574e-05 tran.mean=78.9394198948841 cont.tran.mean=59.7804507262524 weightedLogRatios: wLogRatio Lung -3.6806043173194 cerebhem -1.89811411325745 cortex -3.04852736120525 heart -2.58169877368375 kidney -3.9569634219343 liver -3.14134973221547 stomach -2.47704916690201 testicle -2.36402587901393 cont.weightedLogRatios: wLogRatio Lung 0.385374784290073 cerebhem 0.187944252390388 cortex -0.110395143010456 heart -0.160628264311013 kidney 0.184863981711853 liver 0.0145826521900686 stomach -0.00679468055121475 testicle 0.716533084808982 varWeightedLogRatios=0.482533974887936 cont.varWeightedLogRatios=0.083745398869508 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.40227239948073 0.094186056508792 46.7401711321228 1.95127402488140e-245 *** df.mm.trans1 -0.570332336611297 0.0852165048529051 -6.69274499811704 3.79819661149924e-11 *** df.mm.trans2 0.428847513866139 0.0775260175000637 5.53165927639436 4.1326628608295e-08 *** df.mm.exp2 -0.0389190849630241 0.105938016135023 -0.367376003279313 0.713422876823038 df.mm.exp3 -0.18858096638895 0.105938016135023 -1.78010664413986 0.0753877715326153 . df.mm.exp4 -0.239526485984291 0.105938016135023 -2.26100596106126 0.0239912480454805 * df.mm.exp5 0.0481421157418543 0.105938016135023 0.454436636613006 0.649621552856207 df.mm.exp6 -0.0529599155924715 0.105938016135023 -0.499914171745217 0.617254797464506 df.mm.exp7 -0.254944925016848 0.105938016135023 -2.40654803929789 0.0162998869791486 * df.mm.exp8 -0.241409713473933 0.105938016135023 -2.27878265311524 0.0229079012894629 * df.mm.trans1:exp2 0.218761408674321 0.102774971833668 2.12854749333687 0.0335563040941598 * df.mm.trans2:exp2 -0.188400924799772 0.0877608396960399 -2.14675389903173 0.0320731722206837 * df.mm.trans1:exp3 0.137414111328886 0.102774971833668 1.33703866687775 0.181539796713021 df.mm.trans2:exp3 0.0110817892782536 0.0877608396960399 0.126272598537520 0.899543675828426 df.mm.trans1:exp4 0.240663328726039 0.102774971833668 2.34165307401428 0.0194106529020187 * df.mm.trans2:exp4 0.00454850728614959 0.0877608396960399 0.0518284385370897 0.958676621707446 df.mm.trans1:exp5 -0.0444844021206938 0.102774971833668 -0.432833026631113 0.66523730497767 df.mm.trans2:exp5 0.0123608649408449 0.0877608396960399 0.140847158979527 0.888021452180045 df.mm.trans1:exp6 0.113769619552075 0.102774971833668 1.10697787138489 0.268592217474759 df.mm.trans2:exp6 -0.00995748294797527 0.0877608396960399 -0.113461573322031 0.909689326423935 df.mm.trans1:exp7 0.366208455763707 0.102774971833668 3.56320657870267 0.000385130511583517 *** df.mm.trans2:exp7 0.0930061824929883 0.0877608396960398 1.05976860311633 0.289527426876028 df.mm.trans1:exp8 0.286806344946392 0.102774971833668 2.79062440815419 0.00536923594520314 ** df.mm.trans2:exp8 -0.00298905986195052 0.0877608396960399 -0.0340591529468399 0.972837352633071 df.mm.trans1:probe2 0.211279977954982 0.0513874859168339 4.11150641416706 4.28152839628331e-05 *** df.mm.trans1:probe3 -0.0431316664567465 0.0513874859168339 -0.839341829770603 0.401495087677997 df.mm.trans1:probe4 -0.0688556191743346 0.0513874859168339 -1.33992971140428 0.180598329514849 df.mm.trans1:probe5 0.0713806349539982 0.0513874859168339 1.38906649509029 0.165147932154695 df.mm.trans1:probe6 0.00207681446413304 0.0513874859168339 0.0404147902369496 0.96777118850568 df.mm.trans1:probe7 -0.00928496968050399 0.0513874859168339 -0.1806854239869 0.856654208137857 df.mm.trans1:probe8 -0.0293023075769377 0.0513874859168339 -0.570222634054541 0.568665629905616 df.mm.trans1:probe9 -0.0424642850004971 0.0513874859168339 -0.826354592813157 0.408816760016594 df.mm.trans1:probe10 0.0643277866457924 0.0513874859168339 1.25181813233481 0.210953622725920 df.mm.trans1:probe11 0.313523010829526 0.0513874859168339 6.10115488694923 1.548294023618e-09 *** df.mm.trans1:probe12 0.357793320474729 0.0513874859168339 6.96265470262129 6.33997777904391e-12 *** df.mm.trans1:probe13 0.289852832600353 0.0513874859168339 5.64053343783843 2.25439857149109e-08 *** df.mm.trans1:probe14 0.280640575377527 0.0513874859168339 5.46126299760449 6.08145634359625e-08 *** df.mm.trans1:probe15 0.238933186204722 0.0513874859168339 4.64963759058798 3.81028093329363e-06 *** df.mm.trans1:probe16 0.289839100550627 0.0513874859168339 5.64026621227794 2.25778313818618e-08 *** df.mm.trans1:probe17 0.103245281338114 0.0513874859168339 2.00915221860060 0.0448120541608629 * df.mm.trans1:probe18 0.553419143163992 0.0513874859168339 10.7695314003034 1.47566114973139e-25 *** df.mm.trans1:probe19 0.133633460756951 0.0513874859168339 2.60050590864135 0.00945763510360785 ** df.mm.trans1:probe20 0.246566354483839 0.0513874859168339 4.79817897460259 1.86679348072222e-06 *** df.mm.trans1:probe21 0.0949139266591187 0.0513874859168339 1.84702413371085 0.0650638918945679 . df.mm.trans1:probe22 0.0860777482703767 0.0513874859168339 1.67507218410502 0.0942591632960248 . df.mm.trans1:probe23 -0.00108960201579732 0.0513874859168339 -0.0212036451357194 0.983087794273644 df.mm.trans1:probe24 0.0439328742516118 0.0513874859168339 0.85493332603804 0.392810153521186 df.mm.trans1:probe25 0.0219773621598036 0.0513874859168339 0.427679264079430 0.668984518006128 df.mm.trans1:probe26 -0.0215473703010184 0.0513874859168339 -0.419311626489977 0.675086074940816 df.mm.trans1:probe27 -0.0147670320212723 0.0513874859168339 -0.287366306364383 0.773896430159187 df.mm.trans1:probe28 0.00903866209931629 0.0513874859168339 0.175892280738244 0.860417193251476 df.mm.trans1:probe29 -0.0030507195315685 0.0513874859168339 -0.0593669738291112 0.95267268235041 df.mm.trans1:probe30 -0.00698634887422004 0.0513874859168339 -0.135954284386024 0.891887098271 df.mm.trans1:probe31 -0.00210243817790499 0.0513874859168339 -0.0409134274696296 0.96737377049574 df.mm.trans1:probe32 0.242374045149676 0.0513874859168339 4.71659667378818 2.76909919295936e-06 *** df.mm.trans2:probe2 -0.0444155396883627 0.0513874859168339 -0.864325990966854 0.387633641701376 df.mm.trans2:probe3 -0.106562565610073 0.0513874859168339 -2.07370653980883 0.038383724401532 * df.mm.trans2:probe4 -0.137518493439695 0.0513874859168339 -2.67610860866508 0.00758038007162331 ** df.mm.trans2:probe5 -0.159020007153347 0.0513874859168339 -3.09452786638963 0.00203072534985154 ** df.mm.trans2:probe6 -0.0565963720816952 0.0513874859168339 -1.10136487652444 0.271025319403309 df.mm.trans3:probe2 0.184756017918972 0.0513874859168339 3.59535039752642 0.000341139406645100 *** df.mm.trans3:probe3 -0.371949812491862 0.0513874859168339 -7.23813990615984 9.57796099746784e-13 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15169946050377 0.173521080237357 23.9261964876241 8.51884698565451e-99 *** df.mm.trans1 0.104792491496186 0.156996274440556 0.667483937880729 0.504630092624993 df.mm.trans2 -0.082094956669158 0.142827917440789 -0.574782284445136 0.56557876882096 df.mm.exp2 0.201604797961868 0.195171978521425 1.03295974908476 0.301893602890815 df.mm.exp3 -0.00475430074791381 0.195171978521425 -0.0243595457910056 0.980571087465042 df.mm.exp4 -0.0376577757270290 0.195171978521425 -0.192946631029285 0.847043257726736 df.mm.exp5 0.0484393655027557 0.195171978521425 0.248188115270032 0.80404412668026 df.mm.exp6 -0.0186853122808834 0.195171978521425 -0.0957376792633793 0.923749718513186 df.mm.exp7 0.236557094932954 0.195171978521425 1.21204435557323 0.225805908549089 df.mm.exp8 -0.0784939470261716 0.195171978521425 -0.402178364029624 0.687645979750535 df.mm.trans1:exp2 -0.236591793769648 0.189344631201086 -1.24952998280888 0.211788394741907 df.mm.trans2:exp2 -0.188782111267775 0.161683759476359 -1.16760095064080 0.243269622286035 df.mm.trans1:exp3 -0.0948731378163922 0.189344631201087 -0.501060617428522 0.616448061401925 df.mm.trans2:exp3 0.0257496864063198 0.161683759476359 0.159259572450038 0.87349924975961 df.mm.trans1:exp4 -0.0517643342623339 0.189344631201087 -0.273386860424680 0.784617078418153 df.mm.trans2:exp4 0.0810152376024985 0.161683759476359 0.501072203323824 0.616439910952108 df.mm.trans1:exp5 -0.111739636806011 0.189344631201086 -0.590138923386437 0.555242117377459 df.mm.trans2:exp5 -0.0634899925659361 0.161683759476359 -0.392680085937879 0.694646545594858 df.mm.trans1:exp6 -0.0420421343700688 0.189344631201086 -0.222040277051317 0.8243317008784 df.mm.trans2:exp6 0.0479663367854774 0.161683759476359 0.296667624137543 0.766787092665548 df.mm.trans1:exp7 -0.283521654344597 0.189344631201087 -1.49738417480395 0.134635573721794 df.mm.trans2:exp7 -0.188309819628103 0.161683759476359 -1.16467986789753 0.244449858109431 df.mm.trans1:exp8 0.0130203218962010 0.189344631201087 0.0687652024438614 0.945191427027347 df.mm.trans2:exp8 -0.0720642088458781 0.161683759476359 -0.445710868421606 0.655910750929947 df.mm.trans1:probe2 -0.112177693073534 0.0946723156005433 -1.1849049256052 0.236360367036762 df.mm.trans1:probe3 -0.156417038260063 0.0946723156005433 -1.65219406822205 0.098835475236033 . df.mm.trans1:probe4 -0.293016018806954 0.0946723156005433 -3.09505494767123 0.00202715750875743 ** df.mm.trans1:probe5 0.0755330508879828 0.0946723156005432 0.797836731982812 0.425170674313932 df.mm.trans1:probe6 -0.122360806777795 0.0946723156005433 -1.29246660971175 0.196519414851542 df.mm.trans1:probe7 -0.108999992393498 0.0946723156005433 -1.15133966780118 0.249891001415748 df.mm.trans1:probe8 -0.125254389521875 0.0946723156005433 -1.32303080079258 0.186153153138836 df.mm.trans1:probe9 -0.0122016187231604 0.0946723156005433 -0.128882647960608 0.897478615179836 df.mm.trans1:probe10 -0.0549125398496298 0.0946723156005433 -0.580027429363042 0.562037833933158 df.mm.trans1:probe11 -0.095234043572248 0.0946723156005433 -1.00593339212357 0.314711533812576 df.mm.trans1:probe12 -0.142995363415702 0.0946723156005433 -1.51042427248797 0.131277823156476 df.mm.trans1:probe13 0.0215993446755832 0.0946723156005433 0.22814847760478 0.819581401122655 df.mm.trans1:probe14 -0.0888861107589671 0.0946723156005433 -0.93888176490802 0.348037339973381 df.mm.trans1:probe15 -0.00787290586291662 0.0946723156005433 -0.0831595362696658 0.933742737299815 df.mm.trans1:probe16 -0.13669541757095 0.0946723156005433 -1.44387951962343 0.149112566832428 df.mm.trans1:probe17 -0.0480394523975949 0.0946723156005433 -0.507428724995919 0.61197538537628 df.mm.trans1:probe18 -0.0547992338091528 0.0946723156005433 -0.578830605985921 0.562844849634167 df.mm.trans1:probe19 -0.195741607781406 0.0946723156005433 -2.06756966426501 0.0389590245977241 * df.mm.trans1:probe20 0.0167506106978547 0.0946723156005433 0.176932512863967 0.859600258410271 df.mm.trans1:probe21 -0.117753200054512 0.0946723156005433 -1.24379761187374 0.213890195045123 df.mm.trans1:probe22 -0.136017215895341 0.0946723156005433 -1.43671584488592 0.151137983758582 df.mm.trans1:probe23 -0.183439317788162 0.0946723156005433 -1.93762365084804 0.0529742364112124 . df.mm.trans1:probe24 -0.0946020268285642 0.0946723156005433 -0.999257557274973 0.317932115177960 df.mm.trans1:probe25 -0.116749470886847 0.0946723156005433 -1.23319547162505 0.217817153418023 df.mm.trans1:probe26 -0.180280085544544 0.0946723156005433 -1.90425347052048 0.0571882145394823 . df.mm.trans1:probe27 -0.0703684470478169 0.0946723156005433 -0.743284312857909 0.457498917549403 df.mm.trans1:probe28 -0.129758714659473 0.0946723156005433 -1.37060886106316 0.170830496051555 df.mm.trans1:probe29 -0.047374812692535 0.0946723156005433 -0.500408301962598 0.616907028883758 df.mm.trans1:probe30 -0.127889993035471 0.0946723156005433 -1.35087002176101 0.177068491760135 df.mm.trans1:probe31 -0.121245047227832 0.0946723156005433 -1.28068112054435 0.200627756203620 df.mm.trans1:probe32 -0.211035688718894 0.0946723156005433 -2.22911721742743 0.0260463949274516 * df.mm.trans2:probe2 -0.0309191676075589 0.0946723156005433 -0.326591437121049 0.744051042274919 df.mm.trans2:probe3 -0.0625791465203115 0.0946723156005433 -0.661007878843437 0.50877239148498 df.mm.trans2:probe4 0.159068559936936 0.0946723156005433 1.68020142876936 0.0932568759022124 . df.mm.trans2:probe5 -0.0381045433654189 0.0946723156005433 -0.402488764785217 0.687417651361185 df.mm.trans2:probe6 -0.00480128656112353 0.0946723156005433 -0.0507147895418751 0.95956377600265 df.mm.trans3:probe2 0.0526542283457123 0.0946723156005433 0.556173449563435 0.578227234046487 df.mm.trans3:probe3 -0.0576287847510661 0.0946723156005433 -0.608718445149507 0.542861017690494