chr10.2574_chr10_7584845_7586850_+_0.R fitVsDatCorrelation=0.776627250572986 cont.fitVsDatCorrelation=0.325413566643983 fstatistic=8790.1226453461,40,416 cont.fstatistic=3895.72340210398,40,416 residuals=-0.453636077630931,-0.0865718844020922,-0.00327494929673578,0.079655507534983,0.64475697932699 cont.residuals=-0.455044823988618,-0.153680980419981,-0.00804920450134481,0.116244499114800,0.98366593722126 predictedValues: Include Exclude Both chr10.2574_chr10_7584845_7586850_+_0.R.tl.Lung 65.088414007702 48.5772926402036 55.4359869721 chr10.2574_chr10_7584845_7586850_+_0.R.tl.cerebhem 83.2693886676127 52.4839750608317 63.9859560462974 chr10.2574_chr10_7584845_7586850_+_0.R.tl.cortex 62.9768710305495 44.143172612475 54.536958633704 chr10.2574_chr10_7584845_7586850_+_0.R.tl.heart 60.1212373395818 46.7441603839193 55.0764290139969 chr10.2574_chr10_7584845_7586850_+_0.R.tl.kidney 70.0816421740567 47.1826185149253 56.7061724575177 chr10.2574_chr10_7584845_7586850_+_0.R.tl.liver 68.6691898462404 50.4469552315676 59.0853986080873 chr10.2574_chr10_7584845_7586850_+_0.R.tl.stomach 70.3141326138388 48.4823384417245 56.3672819154429 chr10.2574_chr10_7584845_7586850_+_0.R.tl.testicle 71.5666063516354 50.1775236716409 61.1667404257324 diffExp=16.5111213674984,30.785413606781,18.8336984180745,13.3770769556625,22.8990236591314,18.2222346146728,21.8317941721143,21.3890826799945 diffExpScore=0.993933858878778 diffExp1.5=0,1,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,1,1,0,1,0,1,1 diffExp1.4Score=0.833333333333333 diffExp1.3=1,1,1,0,1,1,1,1 diffExp1.3Score=0.875 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 51.0109225454724 55.5609990756349 56.5310994801556 cerebhem 54.9140480955507 51.8821515309009 62.4513741708532 cortex 54.024344211359 60.0050016019656 59.2899424493768 heart 56.7881738291561 54.511409291054 50.9856367313539 kidney 58.2023329706034 61.6019855494188 60.3244852317113 liver 56.5345323921168 57.4491077509416 55.0817363483683 stomach 50.4756788961122 56.2800347355134 59.1094499098401 testicle 56.0038021847241 56.0410716880121 55.544800979922 cont.diffExp=-4.5500765301625,3.0318965646498,-5.98065739060657,2.27676453810211,-3.39965257881543,-0.914575358824798,-5.80435583940118,-0.037269503288023 cont.diffExpScore=1.58721245582337 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=0.797683532760672 cont.tran.correlation=0.267372914611718 tran.covariance=0.00414567794446527 cont.tran.covariance=0.000683938761873705 tran.mean=58.7703449117816 cont.tran.mean=55.7053497717835 weightedLogRatios: wLogRatio Lung 1.17897866534780 cerebhem 1.93458882152341 cortex 1.40891707299445 heart 0.999277464730817 kidney 1.60305223556776 liver 1.25667568228232 stomach 1.51203379426498 testicle 1.45330099324882 cont.weightedLogRatios: wLogRatio Lung -0.339610394665114 cerebhem 0.225892342094157 cortex -0.424375124116406 heart 0.164444374653356 kidney -0.232314956850157 liver -0.0648793758905334 stomach -0.432771410646775 testicle -0.00267817323099141 varWeightedLogRatios=0.0813948075176583 cont.varWeightedLogRatios=0.0665701657663834 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00667525537031 0.0748786262320148 53.5089311461918 1.29709186800095e-188 *** df.mm.trans1 0.198067245723956 0.0607345857983832 3.26119365301128 0.00120079657672747 ** df.mm.trans2 -0.121180360713233 0.0607345857983832 -1.99524470481297 0.0466679994557992 * df.mm.exp2 0.180251476175697 0.0821281497546843 2.19475876071854 0.0287329346473806 * df.mm.exp3 -0.112346591081691 0.0821281497546843 -1.36794255583827 0.172068517301115 df.mm.exp4 -0.111343142887229 0.0821281497546843 -1.35572447717147 0.175922361868310 df.mm.exp5 0.0221295996366809 0.0821281497546843 0.269452066103787 0.787715423358108 df.mm.exp6 0.0275654001009777 0.0821281497546843 0.335638878792657 0.737312407160855 df.mm.exp7 0.058609718973433 0.0821281497546843 0.713637396538208 0.475851596601021 df.mm.exp8 0.0289183935083048 0.0821281497546843 0.352113052524422 0.724931784227716 df.mm.trans1:exp2 0.0660829613331752 0.0662138311713127 0.998023527172157 0.318848111314557 df.mm.trans2:exp2 -0.102899782204168 0.0662138311713127 -1.55405268633285 0.120932265236425 df.mm.trans1:exp3 0.0793675624229109 0.0662138311713127 1.19865534162441 0.231344365611616 df.mm.trans2:exp3 0.0166286733642737 0.0662138311713127 0.251135949545812 0.801833003468585 df.mm.trans1:exp4 0.0319597275805834 0.0662138311713127 0.482674495875266 0.629580765186957 df.mm.trans2:exp4 0.072876287150672 0.0662138311713127 1.10062030638465 0.271698394437667 df.mm.trans1:exp5 0.0517847184222267 0.0662138311713127 0.782083101161235 0.434610651823192 df.mm.trans2:exp5 -0.0512602187641807 0.0662138311713127 -0.774161800599589 0.439274858155043 df.mm.trans1:exp6 0.0259886635353334 0.0662138311713127 0.39249599480347 0.694892793804474 df.mm.trans2:exp6 0.0102008005832801 0.0662138311713127 0.154058455806430 0.877638353545664 df.mm.trans1:exp7 0.0186165314088928 0.0662138311713127 0.281157744229101 0.778729202370986 df.mm.trans2:exp7 -0.0605663352868018 0.0662138311713127 -0.914708214513379 0.360874708300432 df.mm.trans1:exp8 0.0659636187832384 0.0662138311713127 0.996221146191845 0.319721817882771 df.mm.trans2:exp8 0.00349260520593815 0.0662138311713127 0.0527473662851777 0.957958518408801 df.mm.trans1:probe2 -0.200045756925882 0.0420781553517803 -4.75414749656838 2.75080459453178e-06 *** df.mm.trans1:probe3 -0.0999582166777565 0.0420781553517803 -2.37553704153828 0.017975680301748 * df.mm.trans1:probe4 0.0779868505734515 0.0420781553517804 1.85338092702659 0.0645356006733418 . df.mm.trans1:probe5 -0.0406755098994562 0.0420781553517803 -0.96666570954459 0.334272806764455 df.mm.trans1:probe6 -0.114254586428170 0.0420781553517804 -2.71529456253446 0.00689743469410396 ** df.mm.trans2:probe2 0.0709803189208662 0.0420781553517803 1.68686859790927 0.0923782916398375 . df.mm.trans2:probe3 -0.0852292199695723 0.0420781553517803 -2.02549801095229 0.0434542109589841 * df.mm.trans2:probe4 0.00229772873860828 0.0420781553517803 0.0546062135898992 0.956478403016104 df.mm.trans2:probe5 -0.0308849838356144 0.0420781553517803 -0.733990917078253 0.463367751012835 df.mm.trans2:probe6 0.0124330228417456 0.0420781553517803 0.295474522060283 0.767778833719111 df.mm.trans3:probe2 -0.210683803934414 0.0420781553517803 -5.006963878836 8.1832164064761e-07 *** df.mm.trans3:probe3 -0.180857683206290 0.0420781553517803 -4.29813716153406 2.14738184559013e-05 *** df.mm.trans3:probe4 -0.215328480605250 0.0420781553517803 -5.11734601493502 4.74283505706459e-07 *** df.mm.trans3:probe5 -0.0175636519037559 0.0420781553517803 -0.417405462690103 0.67659719410821 df.mm.trans3:probe6 0.0120989838899141 0.0420781553517803 0.287535986042272 0.77384513282432 df.mm.trans3:probe7 0.13400242348269 0.0420781553517803 3.18460784134683 0.00155856189759377 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88944277826819 0.112392963018319 34.6057499848478 1.61711159803527e-124 *** df.mm.trans1 0.0253933698139249 0.0911627309296447 0.278549902520169 0.780728669787766 df.mm.trans2 0.106691324338025 0.0911627309296447 1.17033927406545 0.242534478630141 df.mm.exp2 -0.0943745668364224 0.123274511868576 -0.76556431176167 0.444369712556991 df.mm.exp3 0.0866926384272483 0.123274511868576 0.703248685500145 0.482294168424266 df.mm.exp4 0.191463804022887 0.123274511868576 1.55314996685615 0.121147725390186 df.mm.exp5 0.170151107076989 0.123274511868576 1.38026186028129 0.168247280475678 df.mm.exp6 0.162202563041754 0.123274511868576 1.31578345420406 0.188971374192607 df.mm.exp7 -0.0422897018928325 0.123274511868576 -0.343053087388558 0.73173181089979 df.mm.exp8 0.119584152510462 0.123274511868576 0.97006388991385 0.332578312354488 df.mm.trans1:exp2 0.168103990431730 0.099387088848015 1.69140672475877 0.0915080784388436 . df.mm.trans2:exp2 0.0258678970746529 0.099387088848015 0.260274220469529 0.794781033108468 df.mm.trans1:exp3 -0.0292976520743461 0.099387088848015 -0.294783280342870 0.768306490789916 df.mm.trans2:exp3 -0.00974621929398614 0.099387088848015 -0.0980632334335728 0.921929313484974 df.mm.trans1:exp4 -0.084175484559094 0.099387088848015 -0.846945871287336 0.397512259407201 df.mm.trans2:exp4 -0.210535279120614 0.099387088848015 -2.11833631069091 0.0347389092842765 * df.mm.trans1:exp5 -0.0382654450894475 0.099387088848015 -0.385014246145834 0.700423630382868 df.mm.trans2:exp5 -0.0669385037592176 0.099387088848015 -0.673513074334851 0.500994948496945 df.mm.trans1:exp6 -0.0593906961627781 0.099387088848015 -0.597569531929843 0.55045233478742 df.mm.trans2:exp6 -0.128784589394980 0.099387088848015 -1.29578792263370 0.195767056258075 df.mm.trans1:exp7 0.0317415387951295 0.099387088848015 0.319372859825580 0.74960410425257 df.mm.trans2:exp7 0.0551480516825555 0.099387088848015 0.554881447095096 0.579273968554495 df.mm.trans1:exp8 -0.0262043452763756 0.099387088848015 -0.263659450941841 0.792172895849432 df.mm.trans2:exp8 -0.110980807245656 0.099387088848015 -1.11665215806221 0.264787621514609 df.mm.trans1:probe2 0.0814874160217273 0.0631593926907501 1.29018682020452 0.197702494252363 df.mm.trans1:probe3 0.103952025392663 0.0631593926907501 1.64586803267168 0.100546198607553 df.mm.trans1:probe4 -0.0207320464771767 0.0631593926907502 -0.328249617261012 0.742888090298817 df.mm.trans1:probe5 0.0401631055144506 0.0631593926907501 0.635900755270128 0.52519078539193 df.mm.trans1:probe6 0.0187766800569022 0.0631593926907502 0.297290383218838 0.76639321721187 df.mm.trans2:probe2 0.0721756663051496 0.0631593926907501 1.14275427977191 0.253797873526894 df.mm.trans2:probe3 0.0508634088143112 0.0631593926907501 0.805318206008657 0.421096127177686 df.mm.trans2:probe4 0.0907186710302866 0.0631593926907501 1.43634489132085 0.151655801748718 df.mm.trans2:probe5 -0.0117985919324957 0.0631593926907501 -0.186806608326107 0.851903333071463 df.mm.trans2:probe6 0.0755570073173649 0.0631593926907501 1.19629090937143 0.232264428835794 df.mm.trans3:probe2 0.0078623652215625 0.0631593926907501 0.124484496867462 0.900991803482751 df.mm.trans3:probe3 0.126011919817637 0.0631593926907501 1.99514141047294 0.0466793072664518 * df.mm.trans3:probe4 0.0129621560478614 0.0631593926907501 0.205229269878013 0.837493385592037 df.mm.trans3:probe5 -0.0614548460606162 0.0631593926907501 -0.973011985113917 0.331112770031943 df.mm.trans3:probe6 0.0143776232180444 0.0631593926907501 0.227640301869939 0.820037723891593 df.mm.trans3:probe7 -0.00691508520892306 0.0631593926907501 -0.109486252389754 0.912869619441758