chr9.25028_chr9_37007356_37019223_+_1.R fitVsDatCorrelation=0.906656546032671 cont.fitVsDatCorrelation=0.26351076987921 fstatistic=3749.70259130933,45,531 cont.fstatistic=707.603178974909,45,531 residuals=-0.99265708478591,-0.125491452310365,-0.00798917358465081,0.126764996551431,0.849125019209674 cont.residuals=-1.00406003065230,-0.396992048017248,-0.160330198909300,0.229379018015852,1.76883425155176 predictedValues: Include Exclude Both chr9.25028_chr9_37007356_37019223_+_1.R.tl.Lung 68.5943080993508 54.0659718974262 56.1449924242436 chr9.25028_chr9_37007356_37019223_+_1.R.tl.cerebhem 194.791561132467 69.2256501330409 179.358566959846 chr9.25028_chr9_37007356_37019223_+_1.R.tl.cortex 209.370492670492 69.3325214577428 203.400853093585 chr9.25028_chr9_37007356_37019223_+_1.R.tl.heart 65.306978605859 55.2049055004411 58.5904291198633 chr9.25028_chr9_37007356_37019223_+_1.R.tl.kidney 75.6693510781787 61.3682657424151 58.1024453148986 chr9.25028_chr9_37007356_37019223_+_1.R.tl.liver 73.7330222615823 60.1137972225356 53.5732673173463 chr9.25028_chr9_37007356_37019223_+_1.R.tl.stomach 61.9852477360619 56.7056055125942 60.2540099496423 chr9.25028_chr9_37007356_37019223_+_1.R.tl.testicle 69.1354579197471 59.8355567918676 53.325612618721 diffExp=14.5283362019247,125.565910999426,140.037971212750,10.1020731054179,14.3010853357636,13.6192250390467,5.27964222346768,9.29990112787947 diffExpScore=0.997003602974865 diffExp1.5=0,1,1,0,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,1,1,0,0,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,1,1,0,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=1,1,1,0,1,1,0,0 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 122.653111875326 78.4233122354976 72.0511611091436 cerebhem 121.145910119835 82.3935905114029 73.1864080907665 cortex 82.7244764494397 79.8580883665828 80.1237651732176 heart 65.516876741595 82.9587051195052 83.532400703223 kidney 88.217282953319 80.2994843855038 79.0578553841376 liver 89.1747192713574 74.3279815092114 100.945857207379 stomach 78.8575699707398 72.2540999132201 84.9371747865523 testicle 69.7458694468204 72.8636973038051 84.9356377887928 cont.diffExp=44.2297996398281,38.7523196084323,2.86638808285693,-17.4418283779103,7.91779856781511,14.846737762146,6.6034700575197,-3.11782785698469 cont.diffExpScore=1.41940863964327 cont.diffExp1.5=1,0,0,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=1,1,0,0,0,0,0,0 cont.diffExp1.4Score=0.666666666666667 cont.diffExp1.3=1,1,0,0,0,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=1,1,0,-1,0,0,0,0 cont.diffExp1.2Score=1.5 tran.correlation=0.92064183681194 cont.tran.correlation=0.286754463181165 tran.covariance=0.0435521681904373 cont.tran.covariance=0.00332341200904486 tran.mean=81.5274183601127 cont.tran.mean=83.8384235108226 weightedLogRatios: wLogRatio Lung 0.978010055532918 cerebhem 4.91896488176942 cortex 5.29553483451791 heart 0.688165470337683 kidney 0.884349156588583 liver 0.857349823731805 stomach 0.363427559619402 testicle 0.601539337038706 cont.weightedLogRatios: wLogRatio Lung 2.05092166342331 cerebhem 1.77488394690796 cortex 0.155088545633123 heart -1.01502809540577 kidney 0.416857569527714 liver 0.801201133296779 stomach 0.378144812729253 testicle -0.186593823929103 varWeightedLogRatios=4.15445946790196 cont.varWeightedLogRatios=1.00160470321264 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.75604569911354 0.128911969397892 29.1365163115331 3.34325050249051e-112 *** df.mm.trans1 0.350520204412881 0.102629053997362 3.41540909479580 0.000685595497020858 *** df.mm.trans2 0.175290255909398 0.102629053997362 1.70799835993717 0.0882211090206824 . df.mm.exp2 0.129437494068881 0.136838738663150 0.945912651149995 0.344623368872599 df.mm.exp3 0.077363711669156 0.136838738663150 0.56536410979057 0.572064925854727 df.mm.exp4 -0.070897706636998 0.136838738663150 -0.518111372040077 0.604596369898788 df.mm.exp5 0.190581244354298 0.136838738663150 1.39274335773763 0.164280263781483 df.mm.exp6 0.225162882214356 0.136838738663150 1.64546154410726 0.100466428617276 df.mm.exp7 -0.124276706424097 0.136838738663150 -0.908198275124589 0.364185466776552 df.mm.exp8 0.160773998544378 0.136838738663150 1.17491581780908 0.240554985175835 df.mm.trans1:exp2 0.914283016549188 0.105994831191682 8.6257320877826 7.38747013508268e-17 *** df.mm.trans2:exp2 0.117728963839193 0.105994831191682 1.11070476282274 0.267198139665954 df.mm.trans1:exp3 1.03853210415776 0.105994831191682 9.79795045175056 6.03093598535407e-21 *** df.mm.trans2:exp3 0.171345366853325 0.105994831191681 1.61654455153066 0.106570533485319 df.mm.trans1:exp4 0.0217870481536956 0.105994831191682 0.205548213141600 0.837222608994932 df.mm.trans2:exp4 0.0917445210350816 0.105994831191682 0.865556555952907 0.387124274329165 df.mm.trans1:exp5 -0.0924175983429439 0.105994831191682 -0.871906651521671 0.383653361002163 df.mm.trans2:exp5 -0.0638933900955505 0.105994831191682 -0.602797224894914 0.546900943660957 df.mm.trans1:exp6 -0.152921679095428 0.105994831191682 -1.44272770073933 0.149686793747748 df.mm.trans2:exp6 -0.119128498628235 0.105994831191681 -1.12390856505826 0.261559652014607 df.mm.trans1:exp7 0.0229635644393238 0.105994831191682 0.216647964633261 0.828565910448155 df.mm.trans2:exp7 0.17194477222912 0.105994831191682 1.62219959498001 0.105354166318086 df.mm.trans1:exp8 -0.152915819143572 0.105994831191681 -1.44267241547881 0.149702368181582 df.mm.trans2:exp8 -0.0593789218058071 0.105994831191681 -0.560205824550312 0.575575314974147 df.mm.trans1:probe2 0.510711095953154 0.0749496639063614 6.81405451785898 2.58177740373206e-11 *** df.mm.trans1:probe3 0.0422757285240153 0.0749496639063614 0.564054944620334 0.57295489070972 df.mm.trans1:probe4 0.609778647313312 0.0749496639063614 8.13584231778731 2.91663146008080e-15 *** df.mm.trans1:probe5 0.667676170896967 0.0749496639063614 8.90832775089065 8.25484709030862e-18 *** df.mm.trans1:probe6 0.359144155418985 0.0749496639063614 4.79180474868684 2.14819472076622e-06 *** df.mm.trans2:probe2 0.193622030427892 0.0749496639063614 2.58336088964714 0.0100504804939850 * df.mm.trans2:probe3 0.0779289378171943 0.0749496639063614 1.03975033049588 0.2989291057366 df.mm.trans2:probe4 0.0440841818937665 0.0749496639063614 0.588183850281747 0.556658970131089 df.mm.trans2:probe5 0.0762828548165038 0.0749496639063614 1.01778781705823 0.309242302724255 df.mm.trans2:probe6 0.667724853197149 0.0749496639063614 8.90897728415931 8.21288927273138e-18 *** df.mm.trans3:probe2 0.0778647466203452 0.0749496639063614 1.03889387306160 0.299326917823791 df.mm.trans3:probe3 -0.60285343376042 0.0749496639063614 -8.04344412422712 5.73006770440552e-15 *** df.mm.trans3:probe4 -0.455213097743082 0.0749496639063614 -6.07358424331034 2.38566980152015e-09 *** df.mm.trans3:probe5 -0.514290636897563 0.0749496639063614 -6.86181378398299 1.90062725420521e-11 *** df.mm.trans3:probe6 -0.338278376881909 0.0749496639063614 -4.51340752247453 7.86230943206863e-06 *** df.mm.trans3:probe7 -0.0278647324757863 0.0749496639063614 -0.371779285236011 0.710205497223052 df.mm.trans3:probe8 -0.158533819121712 0.0749496639063614 -2.11520386962344 0.0348776813237935 * df.mm.trans3:probe9 -0.21078399416071 0.0749496639063614 -2.81234075210869 0.00510022195828242 ** df.mm.trans3:probe10 -0.329052355247642 0.0749496639063614 -4.39031128490108 1.36639504096549e-05 *** df.mm.trans3:probe11 -0.324944086843855 0.0749496639063614 -4.33549758475001 1.74039628537487e-05 *** df.mm.trans3:probe12 -0.163074185607400 0.0749496639063614 -2.17578274681974 0.0300117347813742 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.99488843369926 0.294773032692229 16.9448622490321 8.38626233405622e-52 *** df.mm.trans1 -0.170228264220955 0.234673922293143 -0.725382107042613 0.468537197952817 df.mm.trans2 -0.650958417686221 0.234673922293143 -2.77388476455034 0.00573404026912916 ** df.mm.exp2 0.0213886769704275 0.312898563057524 0.0683565841959309 0.945527513711838 df.mm.exp3 -0.481910752683785 0.312898563057524 -1.54015009840486 0.124119450995474 df.mm.exp4 -0.718688801866227 0.312898563057524 -2.29687472784623 0.0220142090308030 * df.mm.exp5 -0.398718773405927 0.312898563057524 -1.27427486246597 0.203123412411652 df.mm.exp6 -0.709604177948034 0.312898563057524 -2.26784096102601 0.0237398112614445 * df.mm.exp7 -0.688184415796738 0.312898563057524 -2.19938503095720 0.0282808401736227 * df.mm.exp8 -0.802549969862982 0.312898563057524 -2.56488864001411 0.0105945527925372 * df.mm.trans1:exp2 -0.0337531314692533 0.242370184754805 -0.139262721210531 0.889295348565182 df.mm.trans2:exp2 0.0279977387645931 0.242370184754805 0.115516431168781 0.908079467669829 df.mm.trans1:exp3 0.088066135285651 0.242370184754805 0.363353831556236 0.716485192177279 df.mm.trans2:exp3 0.500040683625153 0.242370184754804 2.06312787247748 0.0395860355396888 * df.mm.trans1:exp4 0.0916364291170045 0.242370184754805 0.378084578388671 0.705518857482857 df.mm.trans2:exp4 0.774910524074502 0.242370184754805 3.19721885288178 0.00147023893283766 ** df.mm.trans1:exp5 0.0691615266955362 0.242370184754805 0.285354928311434 0.77548350443815 df.mm.trans2:exp5 0.422360740136757 0.242370184754805 1.7426266376949 0.0819777623734829 . df.mm.trans1:exp6 0.390841618744869 0.242370184754805 1.61258126341021 0.107429647645998 df.mm.trans2:exp6 0.65597042741496 0.242370184754804 2.70648152568179 0.00701884846517885 ** df.mm.trans1:exp7 0.246467586920697 0.242370184754805 1.01690555366799 0.309661466367661 df.mm.trans2:exp7 0.606252254283811 0.242370184754805 2.50134831929567 0.0126720984573083 * df.mm.trans1:exp8 0.238048027031728 0.242370184754804 0.982167122876732 0.326464805229739 df.mm.trans2:exp8 0.729019272373815 0.242370184754804 3.00787521827956 0.00275551731548045 ** df.mm.trans1:probe2 -0.0756206577495944 0.171381601197559 -0.441241400600659 0.659217850088673 df.mm.trans1:probe3 0.00673605843495092 0.171381601197559 0.039304443346786 0.968662436765898 df.mm.trans1:probe4 0.085369835933814 0.171381601197559 0.498127192984996 0.61860063775636 df.mm.trans1:probe5 -0.220150982456833 0.171381601197559 -1.28456602644911 0.199504085934394 df.mm.trans1:probe6 -0.0717347419539284 0.171381601197559 -0.418567345926689 0.675701597167582 df.mm.trans2:probe2 0.0807591122047233 0.171381601197559 0.471223933260076 0.637674365014498 df.mm.trans2:probe3 0.0282961142235859 0.171381601197559 0.165105904168603 0.868923461371812 df.mm.trans2:probe4 0.127930549897925 0.171381601197559 0.746466067559107 0.455716278926957 df.mm.trans2:probe5 0.0364312769221758 0.171381601197559 0.212574025844116 0.83174081053135 df.mm.trans2:probe6 0.0540248543025948 0.171381601197559 0.315231354620839 0.752709843391897 df.mm.trans3:probe2 0.0680344945476029 0.171381601197559 0.396976653690945 0.691544179662224 df.mm.trans3:probe3 0.170623435722537 0.171381601197559 0.99557615596001 0.319909580197845 df.mm.trans3:probe4 0.170225286341656 0.171381601197559 0.993252981371261 0.321039076542126 df.mm.trans3:probe5 -0.0524895318882615 0.171381601197559 -0.306272852636933 0.759516954868252 df.mm.trans3:probe6 0.123021697372232 0.171381601197559 0.717823246559703 0.473181956732877 df.mm.trans3:probe7 0.107393979047429 0.171381601197559 0.626636571819817 0.531166791384298 df.mm.trans3:probe8 0.17094545857262 0.171381601197559 0.997455137413286 0.318997953341709 df.mm.trans3:probe9 0.0219001485149532 0.171381601197559 0.127785878775330 0.898366804149043 df.mm.trans3:probe10 0.111232371786030 0.171381601197559 0.649033332684341 0.516597440773538 df.mm.trans3:probe11 0.0496575651510734 0.171381601197559 0.289748519118053 0.772121738837822 df.mm.trans3:probe12 0.303551325011561 0.171381601197559 1.77120135936672 0.0771007732488187 .