chr1.1139_chr1_182592162_182598246_-_1.R fitVsDatCorrelation=0.90164759113642 cont.fitVsDatCorrelation=0.267612301874238 fstatistic=5516.86214597318,37,347 cont.fstatistic=1103.93476425584,37,347 residuals=-0.703912880612351,-0.0934009987747932,-0.00319900915644341,0.0800185557077476,0.983877559453508 cont.residuals=-0.589668863570883,-0.267487612764394,-0.110024377943937,0.178284814121795,1.71431229015712 predictedValues: Include Exclude Both chr1.1139_chr1_182592162_182598246_-_1.R.tl.Lung 55.0046836421107 58.8380431746562 118.259300031458 chr1.1139_chr1_182592162_182598246_-_1.R.tl.cerebhem 64.9962318953119 89.1126197304575 106.666866891173 chr1.1139_chr1_182592162_182598246_-_1.R.tl.cortex 56.3379381731265 56.8561583790764 111.541041318678 chr1.1139_chr1_182592162_182598246_-_1.R.tl.heart 60.5762761057876 54.047632205534 122.053167963607 chr1.1139_chr1_182592162_182598246_-_1.R.tl.kidney 51.7996550761223 56.5345792433938 95.5282587679427 chr1.1139_chr1_182592162_182598246_-_1.R.tl.liver 54.9875856533177 56.7508053793385 95.9060501034847 chr1.1139_chr1_182592162_182598246_-_1.R.tl.stomach 57.2696346861625 56.1671837661318 108.52637503892 chr1.1139_chr1_182592162_182598246_-_1.R.tl.testicle 57.289913876282 66.2907592727013 152.245353391894 diffExp=-3.83335953254554,-24.1163878351456,-0.518220205949881,6.52864390025358,-4.73492416727145,-1.76321972602076,1.10245092003075,-9.00084539641927 diffExpScore=1.38199706288064 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,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 70.1091462588543 67.9777061817118 74.1021591434895 cerebhem 59.4460695423622 80.6015284903788 70.4700888905027 cortex 68.482720703321 75.3138646356723 70.6427377216864 heart 64.705972632524 68.7366912402377 71.7995907953653 kidney 70.8503895767997 81.8445817254052 56.9931220614595 liver 73.7944573106818 64.4008962264385 77.3873003928835 stomach 69.7260166321575 62.88194907764 78.0881712174658 testicle 70.6714959531466 61.1706157304614 64.6347329170386 cont.diffExp=2.13144007714253,-21.1554589480166,-6.83114393235131,-4.03071860771369,-10.9941921486055,9.39356108424326,6.8440675545175,9.50088022268514 cont.diffExpScore=4.39123863770312 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,-1,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,-1,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.72989357216684 cont.tran.correlation=-0.499447013680938 tran.covariance=0.00777015817625417 cont.tran.covariance=-0.00381456286085583 tran.mean=59.5537312662194 cont.tran.mean=69.419631369862 weightedLogRatios: wLogRatio Lung -0.272250353247296 cerebhem -1.36709277398446 cortex -0.0369546403333437 heart 0.461496989170915 kidney -0.349099028774905 liver -0.126972035114320 stomach 0.0784911625236565 testicle -0.601373511835167 cont.weightedLogRatios: wLogRatio Lung 0.130737259098981 cerebhem -1.29003599878019 cortex -0.406395492339050 heart -0.253808557456685 kidney -0.624998541513851 liver 0.576376657496781 stomach 0.433188507264211 testicle 0.604334367753136 varWeightedLogRatios=0.292946080614745 cont.varWeightedLogRatios=0.442904187102594 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.86361852650691 0.100272421372783 28.5583861175629 4.01902370091655e-93 *** df.mm.trans1 1.0930197372203 0.0835406875712597 13.0836813652983 4.50674802402012e-32 *** df.mm.trans2 1.26353259716449 0.0835406875712596 15.1247569764937 4.73715743365592e-40 *** df.mm.exp2 0.685192367458442 0.115101517885 5.95293945769707 6.44914264479282e-09 *** df.mm.exp3 0.0481727943038166 0.115101517885 0.418524405142484 0.675822658711431 df.mm.exp4 -0.0200149898312066 0.115101517885 -0.173889886067392 0.862053400808615 df.mm.exp5 0.113486550668906 0.115101517885 0.985969192711186 0.324835210533315 df.mm.exp6 0.173080919083531 0.115101517885 1.50372403651931 0.133562008990575 df.mm.exp7 0.0797826781135908 0.115101517885 0.693150529893994 0.488678759004207 df.mm.exp8 -0.0926455971306545 0.115101517885 -0.804903348218382 0.421426605202241 df.mm.trans1:exp2 -0.518281408820629 0.0972785375662182 -5.32780839214232 1.79221749218068e-07 *** df.mm.trans2:exp2 -0.270080045889170 0.0972785375662182 -2.77635799885792 0.00579541344893257 ** df.mm.trans1:exp3 -0.0242229674074785 0.0972785375662182 -0.249006286622985 0.803503237509639 df.mm.trans2:exp3 -0.0824368916267224 0.0972785375662182 -0.84743144468642 0.397338794800631 df.mm.trans1:exp4 0.116499984117903 0.0972785375662182 1.19759185358436 0.231893368805057 df.mm.trans2:exp4 -0.0649079129875908 0.0972785375662182 -0.667237754714472 0.505063740476958 df.mm.trans1:exp5 -0.173521398923849 0.0972785375662182 -1.78375830131833 0.0753367523911511 . df.mm.trans2:exp5 -0.153422716179887 0.0972785375662182 -1.57714867038838 0.115672500143192 df.mm.trans1:exp6 -0.173391813458931 0.0972785375662182 -1.78242619386524 0.0755539864080855 . df.mm.trans2:exp6 -0.209199709577573 0.0972785375662182 -2.15052276495388 0.0322042167529797 * df.mm.trans1:exp7 -0.0394304692635906 0.0972785375662182 -0.405335752881256 0.685480518267105 df.mm.trans2:exp7 -0.126238648974254 0.0972785375662182 -1.29770298909277 0.195251403967566 df.mm.trans1:exp8 0.133351843519835 0.0972785375662182 1.37082492044107 0.171315536692715 df.mm.trans2:exp8 0.211907468661618 0.0972785375662182 2.17835787793758 0.0300520641322308 * df.mm.trans1:probe2 -0.0226631181306672 0.0532816493861314 -0.425345656370882 0.670848375442934 df.mm.trans1:probe3 -0.099097016457894 0.0532816493861314 -1.85987141163253 0.0637497309334116 . df.mm.trans1:probe4 0.241552810208748 0.0532816493861314 4.53350849667242 7.99761819978864e-06 *** df.mm.trans1:probe5 0.0633153890851385 0.0532816493861314 1.18831511063580 0.235521943702581 df.mm.trans1:probe6 0.324692685383511 0.0532816493861314 6.09389328454281 2.93072710130213e-09 *** df.mm.trans2:probe2 -0.155619242088973 0.0532816493861314 -2.92069115505795 0.00372104852945341 ** df.mm.trans2:probe3 0.0282153304646261 0.0532816493861314 0.529550619954535 0.596762164035074 df.mm.trans2:probe4 -0.0670007393035313 0.0532816493861314 -1.25748245550692 0.209424623913867 df.mm.trans2:probe5 -0.170999911463642 0.0532816493861314 -3.20935844580201 0.00145441203222269 ** df.mm.trans2:probe6 -0.158220289709934 0.0532816493861314 -2.96950810518859 0.00319033761548755 ** df.mm.trans3:probe2 -0.341166023145167 0.0532816493861314 -6.4030679807365 4.94910794245511e-10 *** df.mm.trans3:probe3 -0.952517225195686 0.0532816493861314 -17.8770221299421 3.9596180743427e-51 *** df.mm.trans3:probe4 -0.495481509305942 0.0532816493861314 -9.29928999973695 1.62265336291890e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.16286296994482 0.223402344256811 18.6339269795639 3.37081557227957e-54 *** df.mm.trans1 0.0915370115054355 0.186124810678113 0.491804457299037 0.62316854580309 df.mm.trans2 0.0533662609111695 0.186124810678113 0.286722982909903 0.77449556031399 df.mm.exp2 0.0556103807676215 0.256440889438876 0.21685457763504 0.828449116097483 df.mm.exp3 0.126822001576818 0.256440889438876 0.494546723240274 0.621233069026546 df.mm.exp4 -0.0375305220172164 0.256440889438876 -0.146351551421218 0.883728856590155 df.mm.exp5 0.458673582768983 0.256440889438876 1.78861328929492 0.074549353370786 . df.mm.exp6 -0.0461999063688711 0.256440889438876 -0.180158111563106 0.857133606592853 df.mm.exp7 -0.135794321079629 0.256440889438876 -0.529534589342453 0.596773269456204 df.mm.exp8 0.0391689431523903 0.256440889438876 0.152740630552704 0.8786915940533 df.mm.trans1:exp2 -0.220594133463900 0.216732108795631 -1.01781934707197 0.309472932985037 df.mm.trans2:exp2 0.114727431313116 0.216732108795631 0.529351335852591 0.5969002274765 df.mm.trans1:exp3 -0.150293800614115 0.216732108795631 -0.693454243809513 0.488488437560185 df.mm.trans2:exp3 -0.0243375595647339 0.216732108795631 -0.112293280861689 0.910655799879086 df.mm.trans1:exp4 -0.0426692280017952 0.216732108795631 -0.196875434096525 0.844040278356167 df.mm.trans2:exp4 0.0486338567117985 0.216732108795631 0.224396177299498 0.82258111504478 df.mm.trans1:exp5 -0.448156378011642 0.216732108795631 -2.06778949599126 0.0394005811270765 * df.mm.trans2:exp5 -0.273031279908154 0.216732108795631 -1.25976386897804 0.208601178110651 df.mm.trans1:exp6 0.0974302709853333 0.216732108795631 0.449542393726283 0.65332109874636 df.mm.trans2:exp6 -0.00785234521056493 0.216732108795631 -0.0362306501523932 0.971119279854485 df.mm.trans1:exp7 0.130314575106139 0.216732108795631 0.60127027707288 0.54805256266829 df.mm.trans2:exp7 0.0578736643494822 0.216732108795631 0.267028566607334 0.789605791582257 df.mm.trans1:exp8 -0.0311798805020667 0.216732108795631 -0.143863688104783 0.885691621288441 df.mm.trans2:exp8 -0.144681805211579 0.216732108795631 -0.667560547514479 0.504857861312995 df.mm.trans1:probe2 0.0121072256848440 0.118709064923031 0.101990742599936 0.918822933051595 df.mm.trans1:probe3 0.0608017702660616 0.118709064923031 0.512191468322023 0.608842847712678 df.mm.trans1:probe4 0.0824887674633206 0.118709064923031 0.694881789497752 0.487594408077453 df.mm.trans1:probe5 -0.00725748711335977 0.118709064923031 -0.0611367557992762 0.951285493870657 df.mm.trans1:probe6 -0.191607491021463 0.118709064923031 -1.61409317094442 0.107416156334446 df.mm.trans2:probe2 0.0195295910130773 0.118709064923031 0.164516425310400 0.869420407526896 df.mm.trans2:probe3 -0.0361668159704905 0.118709064923031 -0.304667684763084 0.760801906496628 df.mm.trans2:probe4 -0.0126134344655401 0.118709064923031 -0.106255023352416 0.915441415330464 df.mm.trans2:probe5 0.0460068558949219 0.118709064923031 0.387559753122072 0.698579470722782 df.mm.trans2:probe6 0.0127495064932522 0.118709064923031 0.107401288195798 0.914532702043475 df.mm.trans3:probe2 -0.071010186619626 0.118709064923031 -0.598186723698546 0.550105575865643 df.mm.trans3:probe3 0.0490541669744031 0.118709064923031 0.413230169121533 0.679693226997721 df.mm.trans3:probe4 0.0126697306645393 0.118709064923031 0.106729260084343 0.915065445736352