chr17.9989_chr17_23162543_23163354_-_2.R fitVsDatCorrelation=0.802360347184916 cont.fitVsDatCorrelation=0.28026561689754 fstatistic=14550.5537695385,57,807 cont.fstatistic=5616.32009904335,57,807 residuals=-0.425449255858765,-0.0790128985583086,-0.00274024120415268,0.0680230092293433,0.732914096846979 cont.residuals=-0.507675488500397,-0.151816783807399,-0.0285520073852923,0.115322839289323,0.754703106998071 predictedValues: Include Exclude Both chr17.9989_chr17_23162543_23163354_-_2.R.tl.Lung 62.7058896614696 50.6148426475501 53.7385523116592 chr17.9989_chr17_23162543_23163354_-_2.R.tl.cerebhem 63.8969037744866 53.2985631894867 50.2022852436397 chr17.9989_chr17_23162543_23163354_-_2.R.tl.cortex 57.9923977591141 52.9744974080172 51.2422822844209 chr17.9989_chr17_23162543_23163354_-_2.R.tl.heart 62.6199100422067 51.8148693759127 50.2080121603276 chr17.9989_chr17_23162543_23163354_-_2.R.tl.kidney 61.4124747883767 50.2569727301976 54.444090610028 chr17.9989_chr17_23162543_23163354_-_2.R.tl.liver 62.6601962783451 53.1893303173709 52.4776639574334 chr17.9989_chr17_23162543_23163354_-_2.R.tl.stomach 64.2389914896553 53.9150378221428 54.0599299192168 chr17.9989_chr17_23162543_23163354_-_2.R.tl.testicle 61.836187132484 54.3213577095653 51.2155136125012 diffExp=12.0910470139196,10.5983405850000,5.01790035109696,10.8050406662940,11.1555020581791,9.4708659609742,10.3239536675125,7.51482942291876 diffExpScore=0.987175784553243 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=1,0,0,1,1,0,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 56.9858032302151 58.9123592126053 62.0660445233635 cerebhem 60.710597728011 54.0322652733434 62.7933952447253 cortex 58.6321150617059 60.0912005941621 59.1316244587263 heart 57.3587817637692 57.4353574399696 61.8837532843752 kidney 58.9754838275406 58.230865647884 56.5400216560268 liver 57.7319934651049 53.334069785684 56.0866376162801 stomach 58.3252199116481 58.2453732318872 54.1119867336689 testicle 58.7924717578884 60.2909558460151 59.3731668381322 cont.diffExp=-1.92655598239017,6.67833245466754,-1.45908553245618,-0.0765756762003917,0.74461817965662,4.39792367942091,0.0798466797609052,-1.49848408812667 cont.diffExpScore=2.12359954752288 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=0.120240583501228 cont.tran.correlation=-0.243762881249744 tran.covariance=0.000100845528068509 cont.tran.covariance=-0.000217815688760253 tran.mean=57.3592763828988 cont.tran.mean=58.0053071110896 weightedLogRatios: wLogRatio Lung 0.863557569542317 cerebhem 0.737523018648984 cortex 0.363368313231085 heart 0.765651543703211 kidney 0.805339043360885 liver 0.664616974927344 stomach 0.713945551363658 testicle 0.526020879084705 cont.weightedLogRatios: wLogRatio Lung -0.134970784176253 cerebhem 0.471723876341336 cortex -0.100377626071331 heart -0.00540325495403127 kidney 0.0517243164261628 liver 0.31822776170027 stomach 0.005569238545702 testicle -0.102852687231898 varWeightedLogRatios=0.0264860677623380 cont.varWeightedLogRatios=0.0476703762784672 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.0177002883335 0.062721927013677 64.0557533804312 0 *** df.mm.trans1 0.165093224093451 0.0547368548285955 3.0161255083148 0.00264059979761994 ** df.mm.trans2 -0.133411480345501 0.0487448051028530 -2.7369374041808 0.00633780014800142 ** df.mm.exp2 0.138550151914830 0.063728555953419 2.17406702289159 0.0299900596295839 * df.mm.exp3 0.0149879491156866 0.0637285559534189 0.235184194768853 0.814125437027682 df.mm.exp4 0.0900162428623505 0.0637285559534189 1.41249462686941 0.158189888888968 df.mm.exp5 -0.0409816090508781 0.0637285559534189 -0.64306508185801 0.520364543544531 df.mm.exp6 0.0726270262224807 0.063728555953419 1.13963081598092 0.254778282850016 df.mm.exp7 0.0813569803839995 0.0637285559534189 1.2766173525643 0.202104606670776 df.mm.exp8 0.104794157216094 0.0637285559534189 1.6443830500834 0.100486564853649 df.mm.trans1:exp2 -0.119734623160755 0.0596750075237322 -2.00644504507415 0.0451419883871989 * df.mm.trans2:exp2 -0.0868856443869118 0.0463012953051093 -1.87652729398532 0.060943566938158 . df.mm.trans1:exp3 -0.0931313975009053 0.0596750075237322 -1.56064324690479 0.118999998323217 df.mm.trans2:exp3 0.0305778014156546 0.0463012953051093 0.660409200523605 0.509179726408079 df.mm.trans1:exp4 -0.091388340753692 0.0596750075237322 -1.53143408850594 0.126054037864994 df.mm.trans2:exp4 -0.0665839474351185 0.0463012953051093 -1.43805798512447 0.150805265536631 df.mm.trans1:exp5 0.0201392187942961 0.0596750075237322 0.337481629747377 0.735841617755782 df.mm.trans2:exp5 0.033886040928372 0.0463012953051093 0.731859458900122 0.464466826174385 df.mm.trans1:exp6 -0.0733559854909355 0.0596750075237322 -1.22925808533434 0.219333350518643 df.mm.trans2:exp6 -0.0230140741687258 0.0463012953051093 -0.49705033125037 0.619288941480247 df.mm.trans1:exp7 -0.0572019871933103 0.0596750075237322 -0.958558525033475 0.338068279349838 df.mm.trans2:exp7 -0.0181924127711758 0.0463012953051093 -0.392913689591062 0.694486995772756 df.mm.trans1:exp8 -0.118760789037824 0.0596750075237322 -1.99012608403265 0.0469140478965013 * df.mm.trans2:exp8 -0.0341215458600048 0.0463012953051093 -0.736945816205698 0.461369489258048 df.mm.trans1:probe2 0.268960517954245 0.0379228054438597 7.0923159509496 2.88474633125708e-12 *** df.mm.trans1:probe3 -0.0617407815671653 0.0379228054438597 -1.62806471843349 0.103901477052006 df.mm.trans1:probe4 -0.182057040539917 0.0379228054438597 -4.80072711944878 1.88387291256772e-06 *** df.mm.trans1:probe5 -0.215431830893337 0.0379228054438597 -5.68079888531081 1.87162236264753e-08 *** df.mm.trans1:probe6 -0.215352441637786 0.0379228054438597 -5.67870544167915 1.89377717037532e-08 *** df.mm.trans1:probe7 0.0290830336800954 0.0379228054438597 0.76690090144173 0.443364728702120 df.mm.trans1:probe8 0.0849908897321325 0.0379228054438597 2.24115512387267 0.0252871998236801 * df.mm.trans1:probe9 0.413743964353812 0.0379228054438597 10.9101623551114 6.02884499189524e-26 *** df.mm.trans1:probe10 -0.208376353282678 0.0379228054438597 -5.49475047649507 5.24814831391147e-08 *** df.mm.trans1:probe11 -0.181759911291855 0.0379228054438597 -4.7928920121938 1.95686051706611e-06 *** df.mm.trans1:probe12 -0.346984610693305 0.0379228054438597 -9.1497611168819 4.57154961957964e-19 *** df.mm.trans1:probe13 -0.208323075895886 0.0379228054438597 -5.49334558605596 5.28856747436417e-08 *** df.mm.trans1:probe14 -0.318621678686763 0.0379228054438597 -8.40184883363774 1.97141068125796e-16 *** df.mm.trans1:probe15 -0.235782288042292 0.0379228054438597 -6.21742735756563 8.10041207987017e-10 *** df.mm.trans1:probe16 0.158697141616346 0.0379228054438597 4.18474160228675 3.16835918976773e-05 *** df.mm.trans1:probe17 -0.0325044552890727 0.0379228054438597 -0.857121589730269 0.391632147387236 df.mm.trans1:probe18 -0.048629633939289 0.0379228054438597 -1.28233218429157 0.200094369760029 df.mm.trans1:probe19 -0.0175210618587934 0.0379228054438597 -0.462019137395605 0.64419212251843 df.mm.trans1:probe20 0.0470469492183814 0.0379228054438597 1.2405978056668 0.215114910474898 df.mm.trans1:probe21 -0.122523652400522 0.0379228054438597 -3.23086994663157 0.00128415137964748 ** df.mm.trans1:probe22 0.187455416579984 0.0379228054438597 4.94307882515417 9.35472688480324e-07 *** df.mm.trans1:probe23 -0.124513153262257 0.0379228054438597 -3.28333180535876 0.00107003979169392 ** df.mm.trans2:probe2 0.0152157537463747 0.0379228054438597 0.401229644491885 0.688357273483152 df.mm.trans2:probe3 0.145828889702699 0.0379228054438597 3.84541407197792 0.000129826569089846 *** df.mm.trans2:probe4 0.153367798249819 0.0379228054438597 4.04421024380336 5.75355760945429e-05 *** df.mm.trans2:probe5 0.192546124222803 0.0379228054438597 5.07731751301587 4.75608395048855e-07 *** df.mm.trans2:probe6 0.0124701912926602 0.0379228054438597 0.3288309276359 0.742368798230233 df.mm.trans3:probe2 -0.103551629310677 0.0379228054438597 -2.7305898943572 0.00645999085164544 ** df.mm.trans3:probe3 -0.0512053629663047 0.0379228054438597 -1.35025250286686 0.177313553505608 df.mm.trans3:probe4 -0.127548237479846 0.0379228054438597 -3.36336502500234 0.000806279132465759 *** df.mm.trans3:probe5 -0.123613508164918 0.0379228054438597 -3.25960874249964 0.00116239589395788 ** df.mm.trans3:probe6 -0.0462014543511987 0.0379228054438597 -1.21830264956517 0.22346503358123 df.mm.trans3:probe7 -0.00463938376477587 0.0379228054438597 -0.122337567341739 0.902662138905499 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.96109555630209 0.100878288582853 39.2660860126388 2.11196901791927e-189 *** df.mm.trans1 0.0826095494123949 0.088035564282212 0.938365648995863 0.348337286616076 df.mm.trans2 0.115368232517875 0.0783983010440392 1.47156546738262 0.141528091582499 df.mm.exp2 -0.0348042695871953 0.102497291848760 -0.339562821216299 0.7342741307999 df.mm.exp3 0.0967261002000561 0.102497291848760 0.943694203577404 0.345608370582852 df.mm.exp4 -0.0159256443254793 0.102497291848760 -0.155376244954631 0.876563564917893 df.mm.exp5 0.115934624848844 0.102497291848760 1.13109939548366 0.25834927989864 df.mm.exp6 0.0148352179463286 0.102497291848760 0.144737657734594 0.884954144516229 df.mm.exp7 0.148989499778599 0.102497291848760 1.45359450080341 0.14644752339046 df.mm.exp8 0.0986995135024382 0.102497291848760 0.962947524975339 0.335862305252007 df.mm.trans1:exp2 0.0981203736321491 0.0959778010144725 1.02232362687027 0.306934114364807 df.mm.trans2:exp2 -0.0516652596975035 0.0744683024252458 -0.693788605552908 0.488014437680672 df.mm.trans1:exp3 -0.068245685815583 0.0959778010144725 -0.711056984992731 0.477254384280488 df.mm.trans2:exp3 -0.0769135845968255 0.0744683024252458 -1.03283655047776 0.301989814680436 df.mm.trans1:exp4 0.0224494312874401 0.0959778010144725 0.233902330019574 0.815120127404549 df.mm.trans2:exp4 -0.009465161189906 0.0744683024252458 -0.127103222198565 0.898890363831045 df.mm.trans1:exp5 -0.0816149663215733 0.0959778010144725 -0.85035253422056 0.395381336251733 df.mm.trans2:exp5 -0.127569975574037 0.0744683024252458 -1.71307752989397 0.0870823624815355 . df.mm.trans1:exp6 -0.00182588931050728 0.0959778010144725 -0.0190240794351181 0.984826598691825 df.mm.trans2:exp6 -0.114310785470185 0.0744683024252458 -1.53502606810373 0.125169367272887 df.mm.trans1:exp7 -0.125757082101462 0.0959778010144725 -1.31027259191424 0.190476444644959 df.mm.trans2:exp7 -0.16037574245879 0.0744683024252458 -2.1536108281746 0.0315662671390612 * df.mm.trans1:exp8 -0.0674878687988475 0.0959778010144725 -0.703161231925609 0.482158077631431 df.mm.trans2:exp8 -0.0755683094640313 0.0744683024252458 -1.01477147998492 0.310518912070496 df.mm.trans1:probe2 0.0117327593958679 0.0609928280839148 0.192362934535939 0.847506319483827 df.mm.trans1:probe3 0.0214871871411984 0.0609928280839148 0.352290389152574 0.72471247466259 df.mm.trans1:probe4 -0.0333632420028249 0.0609928280839148 -0.547002705907049 0.584528095423543 df.mm.trans1:probe5 -0.0180885633574250 0.0609928280839148 -0.296568693823125 0.766872122989977 df.mm.trans1:probe6 -0.0476494626802582 0.0609928280839148 -0.781230583613886 0.434896006112301 df.mm.trans1:probe7 -0.0310394126246891 0.0609928280839148 -0.508902662817744 0.610959557375661 df.mm.trans1:probe8 0.0669037635151421 0.0609928280839148 1.09691197501278 0.273007077425251 df.mm.trans1:probe9 -0.0280160922900529 0.0609928280839148 -0.459334206498966 0.646117890024457 df.mm.trans1:probe10 0.0325030329561636 0.0609928280839148 0.532899260080964 0.594250027109176 df.mm.trans1:probe11 -0.0918055752341315 0.0609928280839148 -1.50518639843727 0.132667440046132 df.mm.trans1:probe12 -0.065246883053575 0.0609928280839148 -1.06974680635906 0.285053144727868 df.mm.trans1:probe13 -0.0463760096630695 0.0609928280839148 -0.760351849880854 0.447266379953596 df.mm.trans1:probe14 0.0167120847144842 0.0609928280839148 0.27400081680901 0.784154111024788 df.mm.trans1:probe15 0.0332643128138119 0.0609928280839148 0.545380725223076 0.585642395969395 df.mm.trans1:probe16 -0.0169316628802166 0.0609928280839148 -0.277600882138500 0.781389853804624 df.mm.trans1:probe17 0.0680660344781879 0.0609928280839148 1.11596783780122 0.264768082525369 df.mm.trans1:probe18 -0.0417245912249837 0.0609928280839148 -0.684090122326816 0.49411464866535 df.mm.trans1:probe19 0.0916991907260395 0.0609928280839148 1.50344218503655 0.133116277400925 df.mm.trans1:probe20 0.0379057418634662 0.0609928280839148 0.62147867305505 0.534460234121687 df.mm.trans1:probe21 -0.0308128278633333 0.0609928280839148 -0.505187721758705 0.613564928254033 df.mm.trans1:probe22 0.0565605663601222 0.0609928280839148 0.927331427923056 0.354031736608859 df.mm.trans1:probe23 -0.0128684022489139 0.0609928280839148 -0.210982219601449 0.832954409417765 df.mm.trans2:probe2 0.0272569642942203 0.0609928280839148 0.446888021928083 0.655075817304829 df.mm.trans2:probe3 0.0858996369548583 0.0609928280839148 1.40835635358105 0.159410706815403 df.mm.trans2:probe4 -0.114802106532021 0.0609928280839148 -1.88222304389747 0.0601653368736147 . df.mm.trans2:probe5 0.0439712593104576 0.0609928280839148 0.720925077452734 0.471164398584451 df.mm.trans2:probe6 -0.0476932765014935 0.0609928280839148 -0.781948927435803 0.434473947965437 df.mm.trans3:probe2 -0.0443403645821888 0.0609928280839148 -0.726976695049862 0.467451058596371 df.mm.trans3:probe3 0.0208968039255811 0.0609928280839148 0.342610837733758 0.731980464421263 df.mm.trans3:probe4 -0.0821583597889727 0.0609928280839148 -1.34701672917901 0.178352967674884 df.mm.trans3:probe5 -0.0113874175821316 0.0609928280839148 -0.186700927631436 0.85194205276688 df.mm.trans3:probe6 0.0116321500054656 0.0609928280839148 0.190713406328067 0.848798101034627 df.mm.trans3:probe7 -0.110762813269896 0.0609928280839148 -1.81599733525238 0.0697417316894791 .