chr10.2306_chr10_75964158_75969353_+_0.R fitVsDatCorrelation=0.92681479916557 cont.fitVsDatCorrelation=0.260527803285407 fstatistic=8413.33619753855,43,485 cont.fstatistic=1263.21858331012,43,485 residuals=-0.494810235473327,-0.0963490398176743,-0.00622893938310161,0.0846953187453428,0.989248844598174 cont.residuals=-0.923421814401162,-0.318187225673157,0.0367148380800676,0.300620760761295,1.58474263198335 predictedValues: Include Exclude Both chr10.2306_chr10_75964158_75969353_+_0.R.tl.Lung 124.979952847643 139.488486123569 65.2345766350141 chr10.2306_chr10_75964158_75969353_+_0.R.tl.cerebhem 79.7624953365153 101.521916141089 91.7093752721351 chr10.2306_chr10_75964158_75969353_+_0.R.tl.cortex 110.470334434388 133.883523157740 86.3534222061321 chr10.2306_chr10_75964158_75969353_+_0.R.tl.heart 125.220850457261 147.951026746653 71.318121982199 chr10.2306_chr10_75964158_75969353_+_0.R.tl.kidney 138.314637738520 150.264157092811 66.7583163268987 chr10.2306_chr10_75964158_75969353_+_0.R.tl.liver 139.808027134269 137.069002818643 69.127130239476 chr10.2306_chr10_75964158_75969353_+_0.R.tl.stomach 128.039380637613 123.254786607735 67.3145356530727 chr10.2306_chr10_75964158_75969353_+_0.R.tl.testicle 137.278505614644 150.238763666772 69.8763459975341 diffExp=-14.5085332759255,-21.7594208045736,-23.4131887233526,-22.7301762893919,-11.9495193542918,2.73902431562587,4.78459402987799,-12.9602580521284 diffExpScore=1.13936099343204 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,-1,-1,0,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 88.6978860464957 86.8565155879742 92.7207000958996 cerebhem 72.0927744282944 90.7514985111576 97.1377486711137 cortex 79.8026227237288 94.357545529382 101.957295929783 heart 97.9840146684518 124.653094472712 97.3521057621923 kidney 93.2528947372529 84.595646083171 86.4668364679944 liver 85.4561864496639 93.1728615955575 105.195173428341 stomach 88.6317159332456 100.387964763267 85.3145830221973 testicle 95.656620253437 86.3549563165611 91.1903220207067 cont.diffExp=1.84137045852145,-18.6587240828632,-14.5549228056531,-26.6690798042603,8.65724865408195,-7.71667514589362,-11.7562488300211,9.30166393687587 cont.diffExpScore=1.63744251940949 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,-1,0,-1,0,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.834407679135759 cont.tran.correlation=0.322488283015306 tran.covariance=0.0212397556973068 cont.tran.covariance=0.00345575423425443 tran.mean=129.221615409742 cont.tran.mean=91.419049881272 weightedLogRatios: wLogRatio Lung -0.53630104576083 cerebhem -1.08541475431564 cortex -0.922836276717009 heart -0.819579970133756 kidney -0.411912475668881 liver 0.0975513499566598 stomach 0.184072343110533 testicle -0.448103833182023 cont.weightedLogRatios: wLogRatio Lung 0.093873678441703 cerebhem -1.01115098544904 cortex -0.747762383933454 heart -1.13267679388303 kidney 0.437139656067855 liver -0.388278924631365 stomach -0.566311377791596 testicle 0.461327835121875 varWeightedLogRatios=0.208573194061393 cont.varWeightedLogRatios=0.390096799205038 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.60067054620566 0.0873403660153089 64.1246516556157 5.32011542349344e-239 *** df.mm.trans1 -0.711151337621745 0.069920530507197 -10.1708515719649 3.72163587650077e-22 *** df.mm.trans2 -0.605272079163353 0.069920530507197 -8.65657160740581 7.22345064599008e-17 *** df.mm.exp2 -1.10744226755689 0.0936283538128106 -11.8280651368813 1.54380389530115e-28 *** df.mm.exp3 -0.444876989043109 0.0936283538128106 -4.75151992880857 2.66392793779931e-06 *** df.mm.exp4 -0.02833592320677 0.0936283538128106 -0.302642544195762 0.76229204200581 df.mm.exp5 0.152701223944535 0.0936283538128106 1.63092928291602 0.103554404724214 df.mm.exp6 0.0366616760384027 0.0936283538128106 0.391565957804831 0.695550923295251 df.mm.exp7 -0.130930433187818 0.0936283538128106 -1.39840580183205 0.162630317332975 df.mm.exp8 0.0993645786775613 0.0936283538128107 1.06126589469061 0.289097260734455 df.mm.trans1:exp2 0.658342331062577 0.0734481235564755 8.96336487829218 6.82673798556124e-18 *** df.mm.trans2:exp2 0.789734904196651 0.0734481235564755 10.7522815554220 2.49103741185046e-24 *** df.mm.trans1:exp3 0.321470660078522 0.0734481235564755 4.376839659237 1.47563428264215e-05 *** df.mm.trans2:exp3 0.403865119721837 0.0734481235564755 5.49864448764709 6.1995322477684e-08 *** df.mm.trans1:exp4 0.0302615579839149 0.0734481235564755 0.412012676683921 0.680512165910517 df.mm.trans2:exp4 0.0872351807443643 0.0734481235564755 1.18771149649981 0.235528423757603 df.mm.trans1:exp5 -0.0513234976151973 0.0734481235564755 -0.698772073812529 0.485029418328188 df.mm.trans2:exp5 -0.0782884924950017 0.0734481235564755 -1.06590187337876 0.286998327905461 df.mm.trans1:exp6 0.0754552235913364 0.0734481235564755 1.02732677075566 0.304778701130719 df.mm.trans2:exp6 -0.0541592679069318 0.0734481235564755 -0.737381232963533 0.461247123725602 df.mm.trans1:exp7 0.155114963799125 0.0734481235564755 2.11189825264705 0.0352059186378189 * df.mm.trans2:exp7 0.00720202082394367 0.0734481235564755 0.0980558859125368 0.921928427032667 df.mm.trans1:exp8 -0.00550617589302203 0.0734481235564755 -0.0749668694910666 0.94027198268878 df.mm.trans2:exp8 -0.0251208534129658 0.0734481235564755 -0.342021718140286 0.732482701715822 df.mm.trans1:probe2 -0.243906284770469 0.0502864925978853 -4.85033399964599 1.66323032573304e-06 *** df.mm.trans1:probe3 -0.184372619295889 0.0502864925978853 -3.66644420342098 0.000273124905352444 *** df.mm.trans1:probe4 -0.209218425430498 0.0502864925978853 -4.16052929170282 3.75616716602931e-05 *** df.mm.trans1:probe5 -0.174376956035409 0.0502864925978853 -3.46766988562535 0.000571719629057201 *** df.mm.trans1:probe6 -0.169979496264015 0.0502864925978853 -3.38022175503971 0.000782717528930952 *** df.mm.trans2:probe2 -0.428271204903719 0.0502864925978853 -8.5166250970888 2.0783372320263e-16 *** df.mm.trans2:probe3 -0.0770029300162465 0.0502864925978853 -1.53128456645403 0.126351055027201 df.mm.trans2:probe4 -0.186532934565477 0.0502864925978853 -3.70940435351265 0.000231776738658101 *** df.mm.trans2:probe5 0.0722185132250003 0.0502864925978853 1.43614138696238 0.151606788256326 df.mm.trans2:probe6 -0.299073938713004 0.0502864925978853 -5.94740104672921 5.21838086243162e-09 *** df.mm.trans3:probe2 1.03982011769858 0.0502864925978853 20.6779209282595 1.41106899580580e-68 *** df.mm.trans3:probe3 -0.0866086866222074 0.0502864925978853 -1.72230517874396 0.0856517713851358 . df.mm.trans3:probe4 -0.616987353684138 0.0502864925978853 -12.2694449703992 2.50671270288602e-30 *** df.mm.trans3:probe5 0.136853134419429 0.0502864925978853 2.72146907349 0.00673300967049653 ** df.mm.trans3:probe6 -0.200298718241293 0.0502864925978853 -3.98315149642623 7.84419625627572e-05 *** df.mm.trans3:probe7 -0.310463846190515 0.0502864925978853 -6.1739013828849 1.40941318257273e-09 *** df.mm.trans3:probe8 -0.494612822477777 0.0502864925978853 -9.8358982089472 6.14832858833065e-21 *** df.mm.trans3:probe9 -0.344501374911977 0.0502864925978853 -6.85077357983135 2.23515033111311e-11 *** df.mm.trans3:probe10 -0.185775295671124 0.0502864925978853 -3.69433790414997 0.000245556385908962 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.44114606002426 0.224553860567214 19.7776428728774 2.81407919788120e-64 *** df.mm.trans1 0.0263505505664305 0.179767108550320 0.146581600933157 0.883523184617025 df.mm.trans2 0.00890040060162713 0.179767108550320 0.0495107290393767 0.96053266959262 df.mm.exp2 -0.209953069496532 0.240720405311039 -0.872186424018554 0.38353825045345 df.mm.exp3 -0.117808263501653 0.240720405311039 -0.489398741869974 0.624780687671265 df.mm.exp4 0.412102804471176 0.240720405311039 1.71195625870890 0.0875439858819886 . df.mm.exp5 0.0935351365634871 0.240720405311039 0.388563389308973 0.697769666311898 df.mm.exp6 -0.0932589369816993 0.240720405311039 -0.387416001818366 0.698618212514972 df.mm.exp7 0.227284869140558 0.240720405311039 0.944186135142464 0.345544649962101 df.mm.exp8 0.0863805166891538 0.240720405311039 0.358841688462346 0.719869680352474 df.mm.trans1:exp2 0.00267083638146572 0.188836622153993 0.014143635651816 0.988721203797528 df.mm.trans2:exp2 0.253820543989011 0.188836622153993 1.34412774965877 0.179535467605298 df.mm.trans1:exp3 0.0121285772191861 0.188836622153993 0.0642278869471381 0.94881523580779 df.mm.trans2:exp3 0.200641994897011 0.188836622153993 1.06251633082798 0.288530108493847 df.mm.trans1:exp4 -0.312534511143735 0.188836622153993 -1.6550524341029 0.098560770937441 . df.mm.trans2:exp4 -0.0508256805530309 0.188836622153993 -0.26915160827005 0.787927479217585 df.mm.trans1:exp5 -0.0434560921729226 0.188836622153993 -0.230125341563698 0.818091401635876 df.mm.trans2:exp5 -0.119909847049392 0.188836622153993 -0.634992543721774 0.525732721960825 df.mm.trans1:exp6 0.0560266859870764 0.188836622153993 0.296693964062689 0.766827179734424 df.mm.trans2:exp6 0.163457920637961 0.188836622153993 0.865604980503536 0.387134792207732 df.mm.trans1:exp7 -0.228031164351087 0.188836622153993 -1.20755795009472 0.227805885369503 df.mm.trans2:exp7 -0.0825000529798809 0.188836622153993 -0.436885875413528 0.662388558843383 df.mm.trans1:exp8 -0.0108516662948038 0.188836622153993 -0.0574658992044161 0.954197737105986 df.mm.trans2:exp8 -0.0921718267880198 0.188836622153993 -0.488103556061574 0.625697082155879 df.mm.trans1:probe2 0.0560000666962019 0.129287597046027 0.43314337937819 0.665103173541828 df.mm.trans1:probe3 0.0663311729747783 0.129287597046027 0.513051325032857 0.608149022887345 df.mm.trans1:probe4 0.209472438023335 0.129287597046027 1.62020520768718 0.105838174109475 df.mm.trans1:probe5 -0.0888082613260532 0.129287597046027 -0.68690472524164 0.492471067609564 df.mm.trans1:probe6 0.0408357167452169 0.129287597046027 0.315851772932860 0.752250892882234 df.mm.trans2:probe2 0.00115994957354058 0.129287597046027 0.00897185499648222 0.992845280597312 df.mm.trans2:probe3 0.0242296756983104 0.129287597046027 0.187409127030836 0.851418242297707 df.mm.trans2:probe4 0.125024853725810 0.129287597046027 0.967028984855374 0.334011501204988 df.mm.trans2:probe5 0.0547278433519419 0.129287597046027 0.423303121121963 0.672261760333891 df.mm.trans2:probe6 0.0222344842088593 0.129287597046027 0.171976931406217 0.86352739967025 df.mm.trans3:probe2 0.0579551339012755 0.129287597046027 0.448265225941535 0.654161977640683 df.mm.trans3:probe3 0.179618433879875 0.129287597046027 1.38929362122749 0.165380979156278 df.mm.trans3:probe4 -0.00492173128121804 0.129287597046027 -0.0380680853668111 0.96964906099342 df.mm.trans3:probe5 -0.00137560274534579 0.129287597046027 -0.0106398662886129 0.991515150220913 df.mm.trans3:probe6 0.0718857768617108 0.129287597046027 0.55601448634024 0.578457362739618 df.mm.trans3:probe7 0.0874559917752699 0.129287597046027 0.676445334072804 0.49908040225116 df.mm.trans3:probe8 -0.00344919469121044 0.129287597046027 -0.0266784654523551 0.978727162501255 df.mm.trans3:probe9 0.0247629066990169 0.129287597046027 0.191533505647886 0.848187791040598 df.mm.trans3:probe10 0.120015670167572 0.129287597046027 0.92828448288698 0.353721764953066