chrX.26049_chrX_105884680_105888804_+_2.R fitVsDatCorrelation=0.81398189197313 cont.fitVsDatCorrelation=0.272388390040266 fstatistic=11113.6755597639,51,669 cont.fstatistic=4042.3306515948,51,669 residuals=-0.393999319359154,-0.0783063728595248,-0.00699894147118461,0.0753848618513268,1.00173754225854 cont.residuals=-0.547436913497506,-0.161300562022090,-0.0533692596812859,0.127189135647046,1.42879062808657 predictedValues: Include Exclude Both chrX.26049_chrX_105884680_105888804_+_2.R.tl.Lung 43.0949072100863 44.7978363550226 63.3351196931471 chrX.26049_chrX_105884680_105888804_+_2.R.tl.cerebhem 54.2967169342326 53.6656772006216 61.580443161602 chrX.26049_chrX_105884680_105888804_+_2.R.tl.cortex 43.8137933706998 48.9488750525105 61.2626166879338 chrX.26049_chrX_105884680_105888804_+_2.R.tl.heart 44.9688340844652 46.1116479309734 57.238893548169 chrX.26049_chrX_105884680_105888804_+_2.R.tl.kidney 42.2871271231194 45.4359141023795 68.341863473467 chrX.26049_chrX_105884680_105888804_+_2.R.tl.liver 48.0188473551295 49.4620239287869 63.1309920975018 chrX.26049_chrX_105884680_105888804_+_2.R.tl.stomach 45.3622232150118 49.4077185495728 57.8644042304838 chrX.26049_chrX_105884680_105888804_+_2.R.tl.testicle 47.7551823983366 51.0471073412278 69.7896709806088 diffExp=-1.7029291449363,0.631039733611026,-5.13508168181066,-1.14281384650826,-3.1487869792601,-1.44317657365738,-4.04549533456093,-3.29192494289124 diffExpScore=1.01292358035945 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 51.7675318062432 60.0490302258983 48.5278342041903 cerebhem 55.7709769088405 55.9855238716564 54.1081893752979 cortex 55.864767504489 65.0072667147498 52.0623357385724 heart 54.7484944233178 55.5805466027397 53.4225573470172 kidney 51.7287246379056 61.617121491731 53.0753410502095 liver 51.0240694719209 60.1451817996322 56.358027735369 stomach 49.5973742529903 56.4101746088275 53.6001253920879 testicle 54.1499274113453 49.9517732163896 53.6012389344434 cont.diffExp=-8.28149841965506,-0.214546962815987,-9.14249921026077,-0.832052179421858,-9.8883968538255,-9.12111232771132,-6.81280035583727,4.19815419495569 cont.diffExpScore=1.17998182272253 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=0.8846970386105 cont.tran.correlation=-0.0660187541958868 tran.covariance=0.00437803586506155 cont.tran.covariance=-0.000314546066204876 tran.mean=47.404652009511 cont.tran.mean=55.5874053092923 weightedLogRatios: wLogRatio Lung -0.146601784854843 cerebhem 0.0466274496179194 cortex -0.425064416990103 heart -0.0958291777187574 kidney -0.271508321781583 liver -0.115082483420552 stomach -0.329525475850929 testicle -0.259940237476931 cont.weightedLogRatios: wLogRatio Lung -0.59670374627705 cerebhem -0.0154471674426106 cortex -0.621222598883656 heart -0.0604887900176057 kidney -0.70556212411794 liver -0.660245019163676 stomach -0.510765629086989 testicle 0.318873151823296 varWeightedLogRatios=0.0225535633696665 cont.varWeightedLogRatios=0.146535388972686 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.95634956209903 0.0670009101213897 44.124020953489 2.89816630725469e-200 *** df.mm.trans1 0.779196963046572 0.0561819710946679 13.8691638592318 1.25660330695242e-38 *** df.mm.trans2 0.824912280591572 0.0516276685704951 15.9781044434574 6.41796991816011e-49 *** df.mm.exp2 0.439768381616707 0.0670009101213897 6.56361802876933 1.05340196631615e-10 *** df.mm.exp3 0.138430575360241 0.0670009101213897 2.06609992475383 0.0392034414803546 * df.mm.exp4 0.172676952394846 0.0670009101213897 2.57723293731379 0.0101724824376657 * df.mm.exp5 -0.0808616275251665 0.0670009101213897 -1.20687356901070 0.227907398933551 df.mm.exp6 0.210462279656661 0.0670009101213897 3.14118538502467 0.00175684978176266 ** df.mm.exp7 0.239559231651212 0.0670009101213897 3.57546235143952 0.000374788045613240 *** df.mm.exp8 0.136225772319242 0.0670009101213897 2.03319286368548 0.0424272976124639 * df.mm.trans1:exp2 -0.208709446070067 0.0580244902418014 -3.59691994191292 0.000345738230970511 *** df.mm.trans2:exp2 -0.259154585333671 0.047376797892505 -5.47007389401192 6.36134257420138e-08 *** df.mm.trans1:exp3 -0.121886718363600 0.0580244902418014 -2.10060817175076 0.0360490617545279 * df.mm.trans2:exp3 -0.0498140308182579 0.0473767978925051 -1.05144359758721 0.293434540377042 df.mm.trans1:exp4 -0.130112106415811 0.0580244902418014 -2.24236535079591 0.0252645638588294 * df.mm.trans2:exp4 -0.143771210299451 0.047376797892505 -3.03463333730699 0.00250167580293833 ** df.mm.trans1:exp5 0.0619395159821171 0.0580244902418014 1.06747195406631 0.286143819463804 df.mm.trans2:exp5 0.0950046367657221 0.0473767978925051 2.00529881697116 0.0453334203126431 * df.mm.trans1:exp6 -0.102273520534041 0.0580244902418014 -1.76259231417362 0.0784258456673227 . df.mm.trans2:exp6 -0.111416940491137 0.0473767978925051 -2.3517195219469 0.0189758763959152 * df.mm.trans1:exp7 -0.188284388705199 0.0580244902418014 -3.24491241406129 0.00123338063265386 ** df.mm.trans2:exp7 -0.141612416327543 0.0473767978925051 -2.98906685607694 0.00290091702238363 ** df.mm.trans1:exp8 -0.0335430073283559 0.0580244902418014 -0.578083619323055 0.563402194784502 df.mm.trans2:exp8 -0.00563673516909108 0.047376797892505 -0.118976702095411 0.905329529829411 df.mm.trans1:probe2 0.0924974107236602 0.0410295105248704 2.25441175242858 0.0244929432169294 * df.mm.trans1:probe3 0.0477023857065802 0.0410295105248704 1.16263599288285 0.245391780464485 df.mm.trans1:probe4 0.0583816257384101 0.0410295105248704 1.42291791911633 0.155226080951528 df.mm.trans1:probe5 0.0865014359413738 0.0410295105248704 2.10827365071636 0.0353785857860039 * df.mm.trans1:probe6 0.0344009702529128 0.0410295105248704 0.838444568624828 0.402080532274028 df.mm.trans1:probe7 -0.00849925280172932 0.0410295105248704 -0.207149748875932 0.835955945907585 df.mm.trans1:probe8 0.135883744085263 0.0410295105248704 3.31185389118635 0.000976651210739622 *** df.mm.trans1:probe9 0.0677396339536091 0.0410295105248704 1.65099785708016 0.0992083259492077 . df.mm.trans1:probe10 0.0573631776921638 0.0410295105248704 1.39809558920749 0.162547695637997 df.mm.trans1:probe11 0.0297770333366591 0.0410295105248704 0.72574673584296 0.468247847375282 df.mm.trans1:probe12 0.0668511026532531 0.0410295105248704 1.62934194919851 0.103711390505033 df.mm.trans2:probe2 0.0294973248527180 0.0410295105248704 0.71892948454352 0.472435398416882 df.mm.trans2:probe3 0.0991802654104952 0.0410295105248704 2.41729097280788 0.0159028787003477 * df.mm.trans2:probe4 -0.00400717959949259 0.0410295105248704 -0.0976657909936217 0.92222693320265 df.mm.trans2:probe5 0.106285764650389 0.0410295105248704 2.59047118258851 0.00979323955149181 ** df.mm.trans2:probe6 0.145207823196266 0.0410295105248704 3.53910688523196 0.000429285018060795 *** df.mm.trans3:probe2 -0.594771676876456 0.0410295105248704 -14.496192356863 1.30464421033887e-41 *** df.mm.trans3:probe3 -0.703472856869623 0.0410295105248704 -17.1455337358511 6.79093782998349e-55 *** df.mm.trans3:probe4 -0.68795310953967 0.0410295105248704 -16.7672755716318 6.12095533399523e-53 *** df.mm.trans3:probe5 -0.262866041754754 0.0410295105248704 -6.40675548872111 2.80346794633422e-10 *** df.mm.trans3:probe6 -0.39405603830834 0.0410295105248704 -9.60421007385596 1.50899780181964e-20 *** df.mm.trans3:probe7 -0.392454472198535 0.0410295105248704 -9.56517558162544 2.10413307647350e-20 *** df.mm.trans3:probe8 -0.55118026695166 0.0410295105248704 -13.4337519483094 1.34531814817965e-36 *** df.mm.trans3:probe9 -0.365127915840295 0.0410295105248704 -8.89915358895079 5.23016711073053e-18 *** df.mm.trans3:probe10 -0.336651979574612 0.0410295105248704 -8.20511810323809 1.18244842317878e-15 *** df.mm.trans3:probe11 -0.287997758505246 0.0410295105248704 -7.01928331147586 5.48599200496527e-12 *** df.mm.trans3:probe12 -0.367693687116461 0.0410295105248704 -8.96168836558702 3.1562172295027e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.18656964991132 0.110980397464628 37.7235056420274 8.81504203658978e-168 *** df.mm.trans1 -0.252343011070155 0.0930598923378197 -2.71161941767691 0.00686747356979847 ** df.mm.trans2 -0.0649590864444098 0.0855161395232516 -0.759612007821607 0.447754118966682 df.mm.exp2 -0.104425805299265 0.110980397464628 -0.940939190027214 0.347075648770309 df.mm.exp3 0.0852041805234469 0.110980397464628 0.767740812521452 0.442912068821852 df.mm.exp4 -0.117436969157384 0.110980397464628 -1.05817758667529 0.290356413028860 df.mm.exp5 -0.0645464438128468 0.110980397464628 -0.581602204420105 0.561030569087628 df.mm.exp6 -0.16245290256432 0.110980397464628 -1.46379816864594 0.143718797506918 df.mm.exp7 -0.204750988644103 0.110980397464628 -1.84492931474104 0.0654896406042932 . df.mm.exp8 -0.238544563177464 0.110980397464628 -2.14942970675064 0.0319583359562873 * df.mm.trans1:exp2 0.178916258654326 0.0961118435264616 1.86154226253153 0.0631059824017627 . df.mm.trans2:exp2 0.0343575613733248 0.0784749916260164 0.437815419427638 0.661661406940114 df.mm.trans1:exp3 -0.00903342990860032 0.0961118435264615 -0.0939887278940106 0.925146252570037 df.mm.trans2:exp3 -0.00586652021670206 0.0784749916260165 -0.074756557409522 0.94043075468778 df.mm.trans1:exp4 0.173423684720925 0.0961118435264615 1.80439452993302 0.0716190912694543 . df.mm.trans2:exp4 0.0401088289937450 0.0784749916260164 0.511103323016448 0.609447337150614 df.mm.trans1:exp5 0.0637965196805223 0.0961118435264615 0.663773759192933 0.507063800522152 df.mm.trans2:exp5 0.0903248230749778 0.0784749916260165 1.15100137258292 0.250142881918574 df.mm.trans1:exp6 0.147987220747412 0.0961118435264616 1.53973969614544 0.124096649059508 df.mm.trans2:exp6 0.164052839743063 0.0784749916260165 2.09051108313436 0.0369487697048885 * df.mm.trans1:exp7 0.161925728888439 0.0961118435264616 1.68476353118602 0.092500598598104 . df.mm.trans2:exp7 0.14223913279181 0.0784749916260165 1.81254091073587 0.0703507841422926 . df.mm.trans1:exp8 0.28353804247538 0.0961118435264615 2.95008431918504 0.0032878525684313 ** df.mm.trans2:exp8 0.0544411685177776 0.0784749916260164 0.693739080307579 0.488086652652007 df.mm.trans1:probe2 0.0270658127016866 0.0679613363099013 0.398253097588714 0.69057069168225 df.mm.trans1:probe3 -0.00911374749975575 0.0679613363099014 -0.134101946703893 0.893362291614264 df.mm.trans1:probe4 0.00712601352643187 0.0679613363099013 0.104853934801068 0.916523140197693 df.mm.trans1:probe5 0.076510846043439 0.0679613363099013 1.12579961192276 0.260654047246636 df.mm.trans1:probe6 0.0497178670591826 0.0679613363099013 0.7315610574882 0.464692691890906 df.mm.trans1:probe7 0.0992853592246428 0.0679613363099013 1.46090946140177 0.144509775345844 df.mm.trans1:probe8 -0.0100312776850220 0.0679613363099013 -0.147602713979603 0.88270078597722 df.mm.trans1:probe9 -0.00967943814590573 0.0679613363099013 -0.142425659521582 0.886786677752206 df.mm.trans1:probe10 0.047077690590836 0.0679613363099013 0.692712844493866 0.488730160276844 df.mm.trans1:probe11 0.0135083159172939 0.0679613363099013 0.19876471904108 0.842507184475215 df.mm.trans1:probe12 0.00940891337881354 0.0679613363099013 0.138445090836784 0.889930324027412 df.mm.trans2:probe2 -0.0288847107637853 0.0679613363099013 -0.425016815915332 0.670961081342467 df.mm.trans2:probe3 -0.0956381536515439 0.0679613363099013 -1.40724357177789 0.159819593583636 df.mm.trans2:probe4 -0.171910654631843 0.0679613363099013 -2.52953611518079 0.0116498504765815 * df.mm.trans2:probe5 -0.106400728411207 0.0679613363099013 -1.56560677273948 0.117913570608917 df.mm.trans2:probe6 -0.0732507138141912 0.0679613363099013 -1.07782921572010 0.281498459613104 df.mm.trans3:probe2 -0.0248231126918094 0.0679613363099013 -0.365253452030679 0.715037666453818 df.mm.trans3:probe3 0.055411320875212 0.0679613363099013 0.815335952526571 0.415170205037235 df.mm.trans3:probe4 0.00214994705017376 0.0679613363099013 0.0316348554473691 0.97477268196091 df.mm.trans3:probe5 0.0378081510333308 0.0679613363099013 0.55631853471696 0.578179142616262 df.mm.trans3:probe6 0.0687225565317955 0.0679613363099013 1.01120078361060 0.312285921490036 df.mm.trans3:probe7 0.0163888078532148 0.0679613363099013 0.24114899357603 0.809513529823905 df.mm.trans3:probe8 0.0142441469757938 0.0679613363099013 0.20959192018887 0.834049994527144 df.mm.trans3:probe9 0.0346272040273273 0.0679613363099013 0.509513289577304 0.610560508913381 df.mm.trans3:probe10 -0.0159577358351655 0.0679613363099013 -0.234806092723056 0.814431086525078 df.mm.trans3:probe11 0.0160556063940849 0.0679613363099013 0.236246184460998 0.81331395405966 df.mm.trans3:probe12 -0.0501871076650098 0.0679613363099013 -0.738465580431766 0.460490552168553