chr4.16204_chr4_141188813_141192818_+_2.R fitVsDatCorrelation=0.910067373521307 cont.fitVsDatCorrelation=0.241625469996276 fstatistic=15036.0938480741,55,761 cont.fstatistic=2731.69303145749,55,761 residuals=-0.361387343978439,-0.079047465564475,-0.00917937911351569,0.0686915391918328,0.6982200283641 cont.residuals=-0.649753330522098,-0.163788315190149,-0.0535993093603957,0.078498334470337,1.76037459702630 predictedValues: Include Exclude Both chr4.16204_chr4_141188813_141192818_+_2.R.tl.Lung 46.1183965908005 48.5698953416249 72.8614552470145 chr4.16204_chr4_141188813_141192818_+_2.R.tl.cerebhem 52.8602744199464 62.2049708569751 74.7254028535573 chr4.16204_chr4_141188813_141192818_+_2.R.tl.cortex 46.443854022359 45.8316774237662 65.9561405597441 chr4.16204_chr4_141188813_141192818_+_2.R.tl.heart 47.7397290224528 49.0130331936028 67.0349242165336 chr4.16204_chr4_141188813_141192818_+_2.R.tl.kidney 46.2753787074028 47.0727664296675 73.5691668632393 chr4.16204_chr4_141188813_141192818_+_2.R.tl.liver 52.1529256409242 50.4633161785711 72.4338344804015 chr4.16204_chr4_141188813_141192818_+_2.R.tl.stomach 46.9386975344022 49.1926228147018 68.7472180328 chr4.16204_chr4_141188813_141192818_+_2.R.tl.testicle 50.4235814719462 50.971285667264 69.6847922173802 diffExp=-2.45149875082436,-9.34469643702868,0.612176598592754,-1.27330417115000,-0.797387722264688,1.68960946235316,-2.25392528029962,-0.547704195317792 diffExpScore=1.23450480392326 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 50.4912946788298 53.3033391925272 48.8512361838622 cerebhem 52.8320315032905 49.7106860557899 49.480192159728 cortex 50.8425679691225 47.7683047981992 52.2108248776437 heart 51.7227493662424 52.2573952219063 65.7577988934876 kidney 52.9956141453588 55.5731768542253 50.4008651844977 liver 49.3847190494112 50.1525991168562 50.2841035921943 stomach 49.894089840171 49.7515102629868 55.1505936407978 testicle 53.524439485159 58.317412547867 51.3596295570866 cont.diffExp=-2.81204451369739,3.12134544750057,3.07426317092332,-0.534645855663925,-2.57756270886649,-0.76788006744497,0.142579577184215,-4.79297306270797 cont.diffExpScore=2.89954972019375 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=0.784803215716781 cont.tran.correlation=0.638569184707729 tran.covariance=0.00415671039194659 cont.tran.covariance=0.00124482516148435 tran.mean=49.5170253322755 cont.tran.mean=51.7826206304965 weightedLogRatios: wLogRatio Lung -0.199767228197786 cerebhem -0.6591146931037 cortex 0.0508402659713538 heart -0.102102235871381 kidney -0.0656586592162293 liver 0.129683160512858 stomach -0.181615295179859 testicle -0.0424130204977298 cont.weightedLogRatios: wLogRatio Lung -0.214022635541358 cerebhem 0.239734243839096 cortex 0.243096375720171 heart -0.0406313253940362 kidney -0.189678893515620 liver -0.0602877419477944 stomach 0.0111850120916121 testicle -0.345023641547004 varWeightedLogRatios=0.0571583529674628 cont.varWeightedLogRatios=0.0438564425255915 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.80157206023912 0.060316240283914 63.0273379498586 2.89021512149627e-304 *** df.mm.trans1 0.0485295001130952 0.0528156784953886 0.918846476947795 0.358467015946738 df.mm.trans2 0.098658371947298 0.0473634974903957 2.08300436358831 0.0375841362571252 * df.mm.exp2 0.358610849420471 0.0624611307937945 5.74134417457743 1.35698276389156e-08 *** df.mm.exp3 0.0485735916168966 0.0624611307937946 0.777661098984175 0.437010519807201 df.mm.exp4 0.126980384769516 0.0624611307937945 2.03295046304432 0.0424041489617913 * df.mm.exp5 -0.0375774065958508 0.0624611307937946 -0.601612652833754 0.547611179905238 df.mm.exp6 0.167097357321955 0.0624611307937945 2.67522145690254 0.00762843740337456 ** df.mm.exp7 0.0884937755430938 0.0624611307937945 1.41678151545546 0.156956041667496 df.mm.exp8 0.182083229455094 0.0624611307937946 2.91514462099978 0.00365963098224461 ** df.mm.trans1:exp2 -0.22217067757847 0.0585937344503213 -3.79171390358875 0.000161458969239275 *** df.mm.trans2:exp2 -0.111179837114548 0.0467416302841709 -2.37860417872927 0.0176241375141549 * df.mm.trans1:exp3 -0.0415413777151743 0.0585937344503213 -0.708973034487078 0.478558342871144 df.mm.trans2:exp3 -0.106601994327759 0.0467416302841709 -2.28066487368243 0.0228439303070114 * df.mm.trans1:exp4 -0.0924283687548745 0.0585937344503213 -1.5774445787073 0.115108837653139 df.mm.trans2:exp4 -0.117898040044031 0.0467416302841709 -2.52233478651165 0.0118607488070613 * df.mm.trans1:exp5 0.0409755200976294 0.0585937344503213 0.699315728584776 0.484568424341114 df.mm.trans2:exp5 0.0062681313930768 0.046741630284171 0.134101685263629 0.893357623293701 df.mm.trans1:exp6 -0.0441290057204031 0.0585937344503213 -0.753135230829465 0.451601578858577 df.mm.trans2:exp6 -0.128854598872126 0.0467416302841709 -2.75674164740811 0.00597785379283235 ** df.mm.trans1:exp7 -0.0708632617302282 0.0585937344503213 -1.20939998781456 0.226884794784712 df.mm.trans2:exp7 -0.0757540076386921 0.0467416302841709 -1.62069673603888 0.105496947305113 df.mm.trans1:exp8 -0.092836206223083 0.0585937344503213 -1.58440500667856 0.113517029291285 df.mm.trans2:exp8 -0.133824682982281 0.0467416302841709 -2.86307264356590 0.00431086640662886 ** df.mm.trans1:probe2 -0.0424382202856765 0.0358811878818558 -1.18274290208592 0.237280439489060 df.mm.trans1:probe3 0.181504815740179 0.0358811878818558 5.05849517406755 5.30095457927612e-07 *** df.mm.trans1:probe4 -0.0379882174260179 0.0358811878818558 -1.05872240214286 0.29006215304279 df.mm.trans1:probe5 0.0231026475512425 0.0358811878818558 0.643865181590737 0.519856856235046 df.mm.trans1:probe6 -0.0409321584688218 0.0358811878818558 -1.14076932468337 0.254324800358199 df.mm.trans1:probe7 0.378768091532047 0.0358811878818558 10.5561748061184 2.10134354038796e-24 *** df.mm.trans1:probe8 0.00750104798612527 0.0358811878818558 0.209052387307343 0.834463288146702 df.mm.trans1:probe9 0.0446743196087161 0.0358811878818558 1.24506244764842 0.213492050746903 df.mm.trans1:probe10 -0.0151429770410369 0.0358811878818558 -0.42203109581816 0.673121517858857 df.mm.trans1:probe11 -0.0656998309824851 0.0358811878818558 -1.83103834797253 0.0674856612669264 . df.mm.trans1:probe12 0.0216469510161566 0.0358811878818558 0.603295272370368 0.546492038996054 df.mm.trans1:probe13 -0.111411416058428 0.0358811878818558 -3.10500913250884 0.00197327017969014 ** df.mm.trans1:probe14 -0.0549459433047446 0.0358811878818558 -1.53133010773396 0.126103402764377 df.mm.trans1:probe15 -0.0677235542802425 0.0358811878818558 -1.88743902524165 0.0594816015232183 . df.mm.trans1:probe16 -0.0493627085716025 0.0358811878818558 -1.37572671044606 0.169311021454278 df.mm.trans1:probe17 -0.150659331105676 0.0358811878818558 -4.19883900170039 3.00018889557409e-05 *** df.mm.trans1:probe18 -0.167354479762781 0.0358811878818558 -4.66412874383704 3.66045438636564e-06 *** df.mm.trans1:probe19 -0.152567010466947 0.0358811878818558 -4.25200556261673 2.38214654682579e-05 *** df.mm.trans1:probe20 -0.0865903218627682 0.0358811878818558 -2.41325125990477 0.0160462178567229 * df.mm.trans1:probe21 -0.0811763045378533 0.0358811878818558 -2.26236391072498 0.0239561198810958 * df.mm.trans1:probe22 -0.062115083615949 0.0358811878818558 -1.73113230867585 0.0838334797728452 . df.mm.trans2:probe2 0.0104951403889978 0.0358811878818558 0.292497016084157 0.769986369628488 df.mm.trans2:probe3 -0.0916314283149959 0.0358811878818558 -2.55374567354643 0.0108509287080367 * df.mm.trans2:probe4 -0.0463813467741279 0.0358811878818558 -1.29263688055271 0.19652900381866 df.mm.trans2:probe5 -0.0564799078635329 0.0358811878818558 -1.57408132778384 0.115884270963846 df.mm.trans2:probe6 -0.0227208249448804 0.0358811878818558 -0.633223878197458 0.526777698875711 df.mm.trans3:probe2 0.312063633116988 0.0358811878818558 8.69713773536439 2.08124596971896e-17 *** df.mm.trans3:probe3 0.118607667234537 0.0358811878818558 3.30556690667739 0.000992260528660567 *** df.mm.trans3:probe4 1.32184581242187 0.0358811878818558 36.8395220574705 2.49892815826106e-171 *** df.mm.trans3:probe5 -0.00673639782636752 0.0358811878818558 -0.187741772890801 0.85112912888704 df.mm.trans3:probe6 0.293018278713851 0.0358811878818558 8.16634832934343 1.31853401638971e-15 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.02411065556114 0.141217479816048 28.4958396142106 4.11517474888965e-122 *** df.mm.trans1 -0.106143275027584 0.123656530592517 -0.858371769925805 0.390957481642527 df.mm.trans2 -0.0528976679243078 0.110891423593112 -0.477022173675059 0.633483286153696 df.mm.exp2 -0.0372550790694113 0.146239278768717 -0.254754258794804 0.798981732813778 df.mm.exp3 -0.169213783822811 0.146239278768717 -1.15710214962444 0.247593696400163 df.mm.exp4 -0.292919384210746 0.146239278768717 -2.00301442045545 0.0455299507538779 * df.mm.exp5 0.0588812378624755 0.146239278768718 0.402636270899546 0.687328899447161 df.mm.exp6 -0.111997843085706 0.146239278768717 -0.76585336052453 0.444001030401886 df.mm.exp7 -0.202144367071103 0.146239278768717 -1.38228503841845 0.167289749518063 df.mm.exp8 0.0981664028002136 0.146239278768718 0.671272476360247 0.502250665105634 df.mm.trans1:exp2 0.0825718042744935 0.137184603568401 0.601902852992704 0.547418081482256 df.mm.trans2:exp2 -0.0325239779816527 0.109435455528293 -0.29719781239677 0.766396596225158 df.mm.trans1:exp3 0.176146800845132 0.137184603568401 1.28401290132609 0.199528224980087 df.mm.trans2:exp3 0.0595771456252242 0.109435455528293 0.544404419368651 0.586322682540249 df.mm.trans1:exp4 0.317016156543946 0.137184603568401 2.31087270945737 0.0211063421768139 * df.mm.trans2:exp4 0.273101822163022 0.109435455528293 2.49555156365585 0.0127868620740356 * df.mm.trans1:exp5 -0.0104730185331471 0.137184603568401 -0.07634252139618 0.93916666277486 df.mm.trans2:exp5 -0.0171795619174126 0.109435455528293 -0.15698350990983 0.875299482958925 df.mm.trans1:exp6 0.0898379496190095 0.137184603568401 0.654869039835188 0.512749958870516 df.mm.trans2:exp6 0.051069204782288 0.109435455528293 0.466660503542974 0.64087635551041 df.mm.trans1:exp7 0.190245983912965 0.137184603568401 1.38678815963562 0.165912445296196 df.mm.trans2:exp7 0.133186209034948 0.109435455528293 1.21702978611456 0.223970166863997 df.mm.trans1:exp8 -0.0398289792230449 0.137184603568401 -0.290331263035549 0.77164192008899 df.mm.trans2:exp8 -0.00826466077217003 0.109435455528293 -0.0755208696511827 0.93982014161503 df.mm.trans1:probe2 -0.0444018577289419 0.0840080698271436 -0.528542767621062 0.5972767759358 df.mm.trans1:probe3 -0.0189689933376289 0.0840080698271436 -0.225799656826539 0.821417871528914 df.mm.trans1:probe4 0.0220409097110602 0.0840080698271436 0.262366576882577 0.793109843661011 df.mm.trans1:probe5 -0.0634007614485237 0.0840080698271436 -0.754698466218521 0.450663409514012 df.mm.trans1:probe6 0.0442499111943821 0.0840080698271436 0.526734054066846 0.598531778113502 df.mm.trans1:probe7 -0.0250667186371858 0.0840080698271436 -0.298384651483643 0.765491054447501 df.mm.trans1:probe8 0.0541373164748315 0.0840080698271436 0.644429952815549 0.519490860452277 df.mm.trans1:probe9 0.0786542348802048 0.0840080698271436 0.93626999218105 0.349431210861073 df.mm.trans1:probe10 0.0880598914404168 0.0840080698271436 1.04823133803229 0.294864829380954 df.mm.trans1:probe11 0.0916776225257489 0.0840080698271436 1.09129542809859 0.275488298382865 df.mm.trans1:probe12 -0.0705737703866762 0.0840080698271436 -0.840083226907723 0.401125573232435 df.mm.trans1:probe13 -0.0353092075241695 0.0840080698271436 -0.420307329960352 0.67437960626322 df.mm.trans1:probe14 0.0162934540703600 0.0840080698271436 0.193951058557656 0.846265962376831 df.mm.trans1:probe15 0.0149808193642165 0.0840080698271436 0.178325956006861 0.858514455491584 df.mm.trans1:probe16 0.0130784726140747 0.0840080698271436 0.155681146358739 0.876325644347958 df.mm.trans1:probe17 0.0152831505952784 0.0840080698271436 0.181924791591157 0.855690195423183 df.mm.trans1:probe18 -0.020845901379902 0.0840080698271436 -0.248141653805341 0.804091750342694 df.mm.trans1:probe19 -0.00667324129303237 0.0840080698271436 -0.0794357173871908 0.936706943415327 df.mm.trans1:probe20 -0.037920364807171 0.0840080698271436 -0.451389549661081 0.651837418033801 df.mm.trans1:probe21 -0.00120620288451637 0.0840080698271436 -0.014358178767805 0.988547987750341 df.mm.trans1:probe22 -0.00674913130210903 0.0840080698271436 -0.0803390830904239 0.935988697235652 df.mm.trans2:probe2 -0.0545206459550803 0.0840080698271436 -0.648992960643697 0.516538731711896 df.mm.trans2:probe3 0.0944221062111595 0.0840080698271436 1.12396471440713 0.261382413995151 df.mm.trans2:probe4 -0.0616457073421033 0.0840080698271436 -0.733806971984317 0.463292400404588 df.mm.trans2:probe5 0.0778307688358187 0.0840080698271436 0.926467766679613 0.354496658244379 df.mm.trans2:probe6 0.0013453653076164 0.0840080698271436 0.0160147151384938 0.987226849796155 df.mm.trans3:probe2 0.000467757301187562 0.0840080698271436 0.00556800438517427 0.9955588574757 df.mm.trans3:probe3 0.0570004948078617 0.0840080698271436 0.678512134907359 0.497653305547405 df.mm.trans3:probe4 -0.0399275988345486 0.0840080698271436 -0.475282897425262 0.634721731001154 df.mm.trans3:probe5 0.0714432036811555 0.0840080698271436 0.850432629010025 0.395352090337366 df.mm.trans3:probe6 0.0532759706517318 0.0840080698271436 0.634176820885819 0.526156012350049