chr18.11275_chr18_36842277_36843862_+_1.R fitVsDatCorrelation=0.794935774921146 cont.fitVsDatCorrelation=0.279987139817958 fstatistic=12576.3046122323,56,784 cont.fstatistic=5014.3927634071,56,784 residuals=-0.508926230306783,-0.0842123366055098,-0.000118147235818195,0.0852616756826748,0.72825035310434 cont.residuals=-0.684618190295205,-0.147745409608186,-0.0254376277144648,0.122058460623701,1.14618207282144 predictedValues: Include Exclude Both chr18.11275_chr18_36842277_36843862_+_1.R.tl.Lung 56.459568681035 64.7729363566174 76.3858032813896 chr18.11275_chr18_36842277_36843862_+_1.R.tl.cerebhem 55.1605260070844 51.382906700869 75.8083298297924 chr18.11275_chr18_36842277_36843862_+_1.R.tl.cortex 56.9584222963517 66.4736610514685 72.616720527321 chr18.11275_chr18_36842277_36843862_+_1.R.tl.heart 56.2627378699885 72.3072950924477 72.4010759311436 chr18.11275_chr18_36842277_36843862_+_1.R.tl.kidney 53.73593176692 60.1134259117277 65.4345444182659 chr18.11275_chr18_36842277_36843862_+_1.R.tl.liver 55.1698902045878 53.3551647929398 62.165346424268 chr18.11275_chr18_36842277_36843862_+_1.R.tl.stomach 55.8966672434573 66.1456296826769 67.648360822105 chr18.11275_chr18_36842277_36843862_+_1.R.tl.testicle 55.1550038585303 62.8471352614216 76.0936531560825 diffExp=-8.3133676755824,3.77761930621542,-9.51523875511672,-16.0445572224592,-6.37749414480767,1.81472541164804,-10.2489624392197,-7.69213140289136 diffExpScore=1.19001496510041 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,-1,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 60.2757226147408 58.632622188976 60.2295275152297 cerebhem 62.7841310932296 69.7247707382899 63.9249796231198 cortex 58.0934620008708 61.5227901459246 60.1450127065637 heart 60.3701612880301 62.8778126728484 58.4403641267611 kidney 63.1800746405688 59.746622008255 67.330497667713 liver 65.029864165448 61.4419637915564 62.7415282072911 stomach 63.4298908969414 62.3174091706474 65.7760033708155 testicle 63.2297493324158 57.5249723916337 61.4953569228186 cont.diffExp=1.64310042576478,-6.9406396450603,-3.42932814505376,-2.50765138481823,3.43345263231382,3.58790037389166,1.11248172629403,5.70477694078208 cont.diffExpScore=7.86864597309174 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=0.566153927273013 cont.tran.correlation=0.0511733021459652 tran.covariance=0.00115966794380291 cont.tran.covariance=0.000100668847469595 tran.mean=58.8873064236327 cont.tran.mean=61.8863761962735 weightedLogRatios: wLogRatio Lung -0.563491711552592 cerebhem 0.281978704014562 cortex -0.636407608934445 heart -1.04257869180062 kidney -0.453108967464648 liver 0.133575305938681 stomach -0.691544919644356 testicle -0.53207903284238 cont.weightedLogRatios: wLogRatio Lung 0.112905007474041 cerebhem -0.439558532200337 cortex -0.234622002332648 heart -0.167711753025530 kidney 0.230101537410835 liver 0.235326932691119 stomach 0.0732740177472776 testicle 0.387631023440191 varWeightedLogRatios=0.191294423572471 cont.varWeightedLogRatios=0.0783168775616735 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.67360955029757 0.0661732306439271 55.5150400630275 7.45603022195512e-274 *** df.mm.trans1 0.354952359601405 0.0556717952716369 6.37580228676841 3.10587511880331e-10 *** df.mm.trans2 0.505310213536106 0.0503056750040901 10.0447954131422 2.0498869740741e-22 *** df.mm.exp2 -0.247270819969179 0.0644945379513156 -3.83398079626269 0.000136196635592248 *** df.mm.exp3 0.0853163647103529 0.0644945379513156 1.32284635909408 0.186272185412856 df.mm.exp4 0.16012053790344 0.0644945379513156 2.48269920197442 0.0132473100134174 * df.mm.exp5 0.0306489937759810 0.0644945379513157 0.47521844096498 0.634763647038344 df.mm.exp6 -0.0110253859604375 0.0644945379513156 -0.170950693045668 0.864306645101438 df.mm.exp7 0.13242466321614 0.0644945379513156 2.05326943060050 0.0403777392433574 * df.mm.exp8 -0.049727825956788 0.0644945379513156 -0.771039339708512 0.440915918670488 df.mm.trans1:exp2 0.223993625449048 0.0564327207074012 3.96921542398134 7.87305267842615e-05 *** df.mm.trans2:exp2 0.0156885153629624 0.0434142145034782 0.36136817266855 0.717921598109053 df.mm.trans1:exp3 -0.0765195798628981 0.0564327207074012 -1.35594348285360 0.175507563717002 df.mm.trans2:exp3 -0.0593984372217783 0.0434142145034782 -1.36817947534256 0.171647880134977 df.mm.trans1:exp4 -0.163612854856830 0.0564327207074012 -2.8992551272718 0.00384497703913479 ** df.mm.trans2:exp4 -0.050083380819372 0.0434142145034782 -1.15361711347696 0.249008740911053 df.mm.trans1:exp5 -0.0800918790193873 0.0564327207074012 -1.41924539549771 0.156224934528226 df.mm.trans2:exp5 -0.105303651610325 0.0434142145034782 -2.42555699359454 0.0155096907193629 * df.mm.trans1:exp6 -0.0120820603098928 0.0564327207074012 -0.214096718330084 0.830527303810589 df.mm.trans2:exp6 -0.182891698663970 0.0434142145034782 -4.21271467780023 2.81635238141629e-05 *** df.mm.trans1:exp7 -0.142444688230135 0.0564327207074012 -2.52415064247386 0.0117942174322899 * df.mm.trans2:exp7 -0.111453708916089 0.0434142145034782 -2.56721698620529 0.0104362359168712 * df.mm.trans1:exp8 0.0263505159122963 0.0564327207074012 0.466936833489236 0.640674805083661 df.mm.trans2:exp8 0.0195453120870976 0.0434142145034782 0.450205360401758 0.652686832095573 df.mm.trans1:probe2 -0.0915497846470833 0.0413045221912778 -2.21645911368067 0.0269463942247232 * df.mm.trans1:probe3 0.0692259787510009 0.0413045221912778 1.6759903051395 0.0941387953390647 . df.mm.trans1:probe4 0.0831816287923581 0.0413045221912778 2.01386251140132 0.0443651468469061 * df.mm.trans1:probe5 0.068478720982988 0.0413045221912778 1.65789887765482 0.0977379081286605 . df.mm.trans1:probe6 -0.0713831901600906 0.0413045221912778 -1.72821730825310 0.0843430171123132 . df.mm.trans1:probe7 0.0980268023818746 0.0413045221912778 2.37327046002181 0.0178715077920712 * df.mm.trans1:probe8 0.0781920205766973 0.0413045221912778 1.89306198034677 0.0587176656171721 . df.mm.trans1:probe9 -0.0307231454186174 0.0413045221912778 -0.743820380643578 0.457208006346586 df.mm.trans1:probe10 0.0589714514153463 0.0413045221912778 1.42772384927380 0.153769550631808 df.mm.trans1:probe11 -0.109537050469901 0.0413045221912778 -2.65193844786882 0.0081648206679705 ** df.mm.trans1:probe12 0.035974566717211 0.0413045221912778 0.870959517474039 0.384042866825040 df.mm.trans1:probe13 0.0362090814184336 0.0413045221912778 0.876637217851168 0.380952242801832 df.mm.trans1:probe14 -0.0811437481210706 0.0413045221912778 -1.96452455605952 0.0498221237943718 * df.mm.trans2:probe2 -0.0292071487632131 0.0413045221912778 -0.707117458663661 0.479703607802488 df.mm.trans2:probe3 -0.0791767595604912 0.0413045221912778 -1.91690292878417 0.0556134696114443 . df.mm.trans2:probe4 0.0599885730503472 0.0413045221912778 1.45234879543080 0.146804724193970 df.mm.trans2:probe5 -0.0183936840246839 0.0413045221912778 -0.445318891222231 0.656212248206896 df.mm.trans2:probe6 -0.101880807887427 0.0413045221912778 -2.46657756784174 0.0138539591234864 * df.mm.trans3:probe2 -0.220390851805459 0.0413045221912778 -5.33575599264525 1.24635033510297e-07 *** df.mm.trans3:probe3 -0.353647301207301 0.0413045221912778 -8.56195114834133 5.82580624727421e-17 *** df.mm.trans3:probe4 -0.449054372475084 0.0413045221912778 -10.8717968070312 9.69065237992972e-26 *** df.mm.trans3:probe5 -0.0561596310615492 0.0413045221912778 -1.35964848598124 0.174332083060466 df.mm.trans3:probe6 -0.141622904911417 0.0413045221912778 -3.42875059189823 0.000638025294260643 *** df.mm.trans3:probe7 -0.473314502265572 0.0413045221912778 -11.4591448382744 3.23462932880976e-28 *** df.mm.trans3:probe8 -0.137561011797387 0.0413045221912778 -3.33041043690940 0.000907736873659246 *** df.mm.trans3:probe9 0.0519997239371219 0.0413045221912778 1.25893537023175 0.208428395862481 df.mm.trans3:probe10 -0.246969716146674 0.0413045221912778 -5.97924157076502 3.40383921863907e-09 *** df.mm.trans3:probe11 -0.00838900248650703 0.0413045221912778 -0.203101308076105 0.839108548417408 df.mm.trans3:probe12 -0.414636441125832 0.0413045221912778 -10.0385240920034 2.16869193779746e-22 *** df.mm.trans3:probe13 -0.364477004379611 0.0413045221912778 -8.82414285515152 7.09429660760692e-18 *** df.mm.trans3:probe14 0.090355550607825 0.0413045221912778 2.18754620110108 0.0289973106909599 * df.mm.trans3:probe15 -0.24726151668774 0.0413045221912778 -5.98630618562037 3.26549802676977e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.06669268014518 0.104709445639420 38.8378780473094 7.66889136210145e-185 *** df.mm.trans1 0.0245682502682700 0.0880924622225525 0.278891628732116 0.780401540313277 df.mm.trans2 -0.0106321761441578 0.0796013628311274 -0.133567765249368 0.893778652208902 df.mm.exp2 0.154489870344697 0.102053160317821 1.51381760117545 0.130475121199056 df.mm.exp3 0.0126443557468444 0.102053160317821 0.1238996980345 0.901426449588205 df.mm.exp4 0.101623583991143 0.102053160317821 0.995790661206961 0.319659105240345 df.mm.exp5 -0.0455695880532305 0.102053160317821 -0.446527945938319 0.655339239275234 df.mm.exp6 0.081858179305468 0.102053160317821 0.802113124674825 0.422730583233088 df.mm.exp7 0.0238630372775277 0.102053160317821 0.233829478707096 0.81517840641025 df.mm.exp8 0.0079744642889619 0.102053160317821 0.0781402973129622 0.937736390811606 df.mm.trans1:exp2 -0.113716931026687 0.089296515278093 -1.27347557373927 0.203226768512088 df.mm.trans2:exp2 0.0187745407469444 0.0686966359250479 0.273296362975161 0.784697387117958 df.mm.trans1:exp3 -0.0495206409529525 0.089296515278093 -0.554564092436665 0.579351016227582 df.mm.trans2:exp3 0.0354720875676819 0.0686966359250479 0.516358437207668 0.605749694908962 df.mm.trans1:exp4 -0.100058032154261 0.089296515278093 -1.12051441025054 0.262837736474829 df.mm.trans2:exp4 -0.0317214565441152 0.068696635925048 -0.461761425679202 0.644380512981157 df.mm.trans1:exp5 0.0926291521954977 0.0892965152780931 1.03732101870970 0.29990609804728 df.mm.trans2:exp5 0.0643910075082263 0.0686966359250479 0.93732402818794 0.348880555640309 df.mm.trans1:exp6 -0.00594097885135765 0.089296515278093 -0.0665309148162822 0.946972107676715 df.mm.trans2:exp6 -0.0350563625553949 0.0686966359250479 -0.510306830652427 0.609980157485797 df.mm.trans1:exp7 0.0271427656857810 0.089296515278093 0.303962205033996 0.76123733945588 df.mm.trans2:exp7 0.0370865560891608 0.0686966359250479 0.539859857615509 0.589446984630572 df.mm.trans1:exp8 0.0398710307697913 0.089296515278093 0.446501530833788 0.655358307505033 df.mm.trans2:exp8 -0.0270465426362215 0.0686966359250479 -0.393709855977968 0.693902313953457 df.mm.trans1:probe2 0.0128348233368576 0.0653583568304556 0.196376163038371 0.844366643103804 df.mm.trans1:probe3 0.0259071285657741 0.0653583568304556 0.396385861305833 0.691928255620098 df.mm.trans1:probe4 0.0356313705270403 0.0653583568304556 0.54516931353508 0.585792126723437 df.mm.trans1:probe5 -0.0184153146844584 0.0653583568304557 -0.281759144163141 0.7782025548534 df.mm.trans1:probe6 0.0127112562945248 0.0653583568304556 0.194485554884722 0.845846089077687 df.mm.trans1:probe7 0.0729651710457802 0.0653583568304556 1.11638625241233 0.264598868763941 df.mm.trans1:probe8 0.0642499019265494 0.0653583568304556 0.983040349273442 0.325890843193716 df.mm.trans1:probe9 -0.0471895280792103 0.0653583568304556 -0.722012155256954 0.470502290955877 df.mm.trans1:probe10 0.0672082120375052 0.0653583568304556 1.02830326979988 0.304124333718503 df.mm.trans1:probe11 -0.0319658904942309 0.0653583568304556 -0.489086507746099 0.624917246597133 df.mm.trans1:probe12 -0.0628955101955772 0.0653583568304556 -0.962317800594846 0.336186695823124 df.mm.trans1:probe13 0.153794576828870 0.0653583568304556 2.35309735873294 0.0188638839793236 * df.mm.trans1:probe14 -0.062450212931195 0.0653583568304556 -0.955504635668786 0.339617087125986 df.mm.trans2:probe2 0.0732409150224362 0.0653583568304556 1.12060520757014 0.262799092361305 df.mm.trans2:probe3 0.0682978414563338 0.0653583568304556 1.04497488566769 0.296356657632887 df.mm.trans2:probe4 0.0741348100830729 0.0653583568304556 1.13428203642549 0.257022955570707 df.mm.trans2:probe5 0.0227734692751146 0.0653583568304556 0.34844005234389 0.727603271518796 df.mm.trans2:probe6 0.0813983011081755 0.0653583568304556 1.24541535398952 0.213351197227605 df.mm.trans3:probe2 0.0098173053997895 0.0653583568304556 0.150207347244918 0.8806396638871 df.mm.trans3:probe3 0.081240350973061 0.0653583568304556 1.24299867549921 0.214239924650251 df.mm.trans3:probe4 0.0108259488001464 0.0653583568304556 0.165639855791199 0.868483082840414 df.mm.trans3:probe5 0.0270027449930864 0.0653583568304556 0.41314907997356 0.679610363784328 df.mm.trans3:probe6 0.0123839906395084 0.0653583568304556 0.189478304536226 0.84976700033999 df.mm.trans3:probe7 0.0192594269807892 0.0653583568304556 0.294674283668875 0.768320718021983 df.mm.trans3:probe8 -0.0109061404485290 0.0653583568304556 -0.166866809042037 0.86751787680825 df.mm.trans3:probe9 -0.0614602002724536 0.0653583568304556 -0.940357182355209 0.347324012108887 df.mm.trans3:probe10 0.0311247184219703 0.0653583568304556 0.476216354439725 0.634052929689127 df.mm.trans3:probe11 0.0811268918670664 0.0653583568304556 1.24126272142238 0.214879966152108 df.mm.trans3:probe12 0.00177327594663607 0.0653583568304556 0.0271315870323375 0.97836168567666 df.mm.trans3:probe13 0.0677902383710824 0.0653583568304556 1.03720842534238 0.2999585237311 df.mm.trans3:probe14 -0.0310340866869251 0.0653583568304556 -0.474829665124994 0.635040625879054 df.mm.trans3:probe15 0.0240650562245096 0.0653583568304556 0.368201671393546 0.7128222553967