chr4.16513_chr4_99424415_99428312_+_1.R fitVsDatCorrelation=0.847894522435952 cont.fitVsDatCorrelation=0.26179457319119 fstatistic=9396.11046257104,45,531 cont.fstatistic=2827.09492744119,45,531 residuals=-0.625365959973902,-0.0940677224414379,-0.0134939262025460,0.0839329197801494,0.950526445268323 cont.residuals=-0.612307918188575,-0.200115581006678,-0.0219546407383483,0.178838229929931,1.12420983537578 predictedValues: Include Exclude Both chr4.16513_chr4_99424415_99428312_+_1.R.tl.Lung 95.121844907416 85.9732045647464 86.281058473473 chr4.16513_chr4_99424415_99428312_+_1.R.tl.cerebhem 72.529048485361 83.4972147548423 80.1577385427572 chr4.16513_chr4_99424415_99428312_+_1.R.tl.cortex 81.9258198239024 78.9212450257323 77.2587198867428 chr4.16513_chr4_99424415_99428312_+_1.R.tl.heart 86.4482384740783 76.7329721823018 85.0153340289075 chr4.16513_chr4_99424415_99428312_+_1.R.tl.kidney 70.3275882389969 107.642810492279 81.1455201452038 chr4.16513_chr4_99424415_99428312_+_1.R.tl.liver 60.8861785216694 96.1520920661394 75.1635284168359 chr4.16513_chr4_99424415_99428312_+_1.R.tl.stomach 84.2470527076601 79.9565875475504 83.1148839294802 chr4.16513_chr4_99424415_99428312_+_1.R.tl.testicle 111.706145615432 76.0847885808377 98.4828836507551 diffExp=9.1486403426696,-10.9681662694813,3.00457479817007,9.71526629177656,-37.315222253282,-35.26591354447,4.29046516010969,35.6213570345941 diffExpScore=6.38278429673021 diffExp1.5=0,0,0,0,-1,-1,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,0,0,-1,-1,0,1 diffExp1.4Score=1.5 diffExp1.3=0,0,0,0,-1,-1,0,1 diffExp1.3Score=1.5 diffExp1.2=0,0,0,0,-1,-1,0,1 diffExp1.2Score=1.5 cont.predictedValues: Include Exclude Both Lung 81.566639436509 77.8159136461244 84.6113552746731 cerebhem 81.1212521727892 82.5150685863487 75.562363299617 cortex 81.8193649432651 84.4985976723739 86.7529509514052 heart 91.2433179732368 84.0541493995312 93.8797085286375 kidney 87.7097482030207 88.6335024303854 94.6725758510532 liver 81.0483386562698 85.9416727121206 83.5757672641473 stomach 76.8084571982462 86.216202305987 85.7297543787322 testicle 81.7578469273156 89.3930856131769 87.5877219895338 cont.diffExp=3.75072579038465,-1.39381641355955,-2.67923272910882,7.18916857370556,-0.92375422736474,-4.89333405585081,-9.40774510774092,-7.63523868586128 cont.diffExpScore=2.22871241029483 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=-0.649462224440666 cont.tran.correlation=0.0865876907990291 tran.covariance=-0.0157590172814406 cont.tran.covariance=0.000197586474065055 tran.mean=84.259551999309 cont.tran.mean=83.8839473672938 weightedLogRatios: wLogRatio Lung 0.455518265566195 cerebhem -0.613213307320847 cortex 0.163919692379209 heart 0.524535760833215 kidney -1.90096830076012 liver -1.98189804278905 stomach 0.230385146494551 testicle 1.7372679594388 cont.weightedLogRatios: wLogRatio Lung 0.206086188031458 cerebhem -0.0750342124373035 cortex -0.142436886271874 heart 0.367051154041532 kidney -0.0469287460743455 liver -0.259369503344939 stomach -0.508285262858021 testicle -0.397160253765282 varWeightedLogRatios=1.60838911258916 cont.varWeightedLogRatios=0.0852961282380077 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.17383646417227 0.0816929518637443 51.0917572318088 4.04383777909947e-207 *** df.mm.trans1 0.357104240060914 0.0650371754243422 5.49077105103271 6.21142080766004e-08 *** df.mm.trans2 0.245472423001324 0.0650371754243421 3.77434015852798 0.000178516082194315 *** df.mm.exp2 -0.226780231011116 0.0867162338991229 -2.61519926332282 0.00917105903335126 ** df.mm.exp3 -0.124479361148875 0.0867162338991228 -1.43547932782323 0.151739328608397 df.mm.exp4 -0.194538535156028 0.0867162338991229 -2.24339234314920 0.0252829999172244 * df.mm.exp5 -0.0158457258375396 0.0867162338991228 -0.182730788977447 0.855079000263315 df.mm.exp6 -0.196312924717361 0.0867162338991228 -2.26385436602023 0.0239857100092646 * df.mm.exp7 -0.156570608145260 0.0867162338991228 -1.80555129189995 0.0715546749902694 . df.mm.exp8 -0.0937469132035398 0.0867162338991228 -1.08107685249103 0.280153664139101 df.mm.trans1:exp2 -0.0443912663837695 0.067170105947426 -0.66087831420892 0.508976970768299 df.mm.trans2:exp2 0.197557833206705 0.067170105947426 2.94115708796609 0.00341252920017404 ** df.mm.trans1:exp3 -0.0248650850839197 0.067170105947426 -0.370180822751442 0.711395369780895 df.mm.trans2:exp3 0.0388941450982821 0.067170105947426 0.579039507972855 0.562808056966569 df.mm.trans1:exp4 0.0989257230652133 0.067170105947426 1.47276413621623 0.141407123290114 df.mm.trans2:exp4 0.0808343633162112 0.067170105947426 1.20342765842115 0.229347108760521 df.mm.trans1:exp5 -0.286148764269358 0.067170105947426 -4.26006123160393 2.4178132396686e-05 *** df.mm.trans2:exp5 0.240628488590284 0.067170105947426 3.58237470666823 0.000371794087561073 *** df.mm.trans1:exp6 -0.249839527764658 0.067170105947426 -3.71950474456907 0.00022085987056346 *** df.mm.trans2:exp6 0.308208481998168 0.067170105947426 4.58847693703808 5.57724095070797e-06 *** df.mm.trans1:exp7 0.0351655462946064 0.067170105947426 0.523529713085914 0.600824092538238 df.mm.trans2:exp7 0.0840187669971975 0.067170105947426 1.25083570752381 0.211545280771457 df.mm.trans1:exp8 0.2544599889309 0.067170105947426 3.78829220740051 0.000169034788497460 *** df.mm.trans2:exp8 -0.0284404020269994 0.067170105947426 -0.423408622419915 0.672168595127472 df.mm.trans1:probe2 0.0789681341483368 0.0474964374084437 1.66261173378655 0.096980299281044 . df.mm.trans1:probe3 -0.0704279096802025 0.0474964374084438 -1.48280404853443 0.138719720054408 df.mm.trans1:probe4 0.201240139313131 0.0474964374084438 4.23695229144397 2.67137803632819e-05 *** df.mm.trans1:probe5 -0.00419299334450368 0.0474964374084438 -0.0882801652773702 0.92968728639666 df.mm.trans1:probe6 0.230335613276658 0.0474964374084438 4.84953453026162 1.62805311185025e-06 *** df.mm.trans2:probe2 0.069610943019102 0.0474964374084438 1.46560346032873 0.143348220609079 df.mm.trans2:probe3 0.0909294284619443 0.0474964374084437 1.91444734433449 0.0561001230274435 . df.mm.trans2:probe4 0.422192880468157 0.0474964374084438 8.88893785522325 9.60993168062055e-18 *** df.mm.trans2:probe5 0.0140072840820262 0.0474964374084437 0.294912310192259 0.768176133940361 df.mm.trans2:probe6 0.0283416066022719 0.0474964374084438 0.596710156564994 0.550955225149232 df.mm.trans3:probe2 0.282002965866426 0.0474964374084437 5.93734985723987 5.23687340989284e-09 *** df.mm.trans3:probe3 -0.588939630046457 0.0474964374084437 -12.3996590519389 3.46221022499249e-31 *** df.mm.trans3:probe4 -0.412410346266912 0.0474964374084437 -8.68297431911378 4.75881169483505e-17 *** df.mm.trans3:probe5 -0.414451063186227 0.0474964374084437 -8.72594000308215 3.41613024927322e-17 *** df.mm.trans3:probe6 -0.518681448021349 0.0474964374084437 -10.9204284852139 3.48878080742999e-25 *** df.mm.trans3:probe7 -0.560551430655461 0.0474964374084437 -11.8019679209837 1.04174235563474e-28 *** df.mm.trans3:probe8 -0.383787322112691 0.0474964374084437 -8.0803391381195 4.37879603861956e-15 *** df.mm.trans3:probe9 -0.630985779867868 0.0474964374084438 -13.2849075487858 5.6276418969026e-35 *** df.mm.trans3:probe10 -0.25870107885948 0.0474964374084438 -5.44674701882986 7.85748073539961e-08 *** df.mm.trans3:probe11 -0.452023535348749 0.0474964374084437 -9.51699874796063 6.21104119572946e-20 *** df.mm.trans3:probe12 0.112892213702563 0.0474964374084438 2.37685645202714 0.0178142847797669 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.21509226440954 0.148715596467904 28.3433100799166 2.52868089638556e-108 *** df.mm.trans1 0.181197065321591 0.118395064875985 1.53044441093376 0.126502259694307 df.mm.trans2 0.129950762201887 0.118395064875985 1.09760286324439 0.272875411960053 df.mm.exp2 0.166269759623708 0.157860086501313 1.05327295397323 0.292694950249562 df.mm.exp3 0.060486631412991 0.157860086501313 0.383166085573429 0.701749967650698 df.mm.exp4 0.0852788996200992 0.157860086501313 0.540218249654828 0.589273023770795 df.mm.exp5 0.09042077298938 0.157860086501313 0.572790595731922 0.567028941202801 df.mm.exp6 0.105263156196640 0.157860086501313 0.666812989461806 0.505181226926558 df.mm.exp7 0.0292750949438074 0.157860086501313 0.185449631966114 0.852947197507557 df.mm.exp8 0.106466477842139 0.157860086501313 0.674435699370106 0.500327864186343 df.mm.trans1:exp2 -0.171745131786052 0.122277897210090 -1.40454763865436 0.160740501111199 df.mm.trans2:exp2 -0.107634789277723 0.122277897210090 -0.880247303343728 0.379123560043168 df.mm.trans1:exp3 -0.0573930285150936 0.122277897210090 -0.469365517600329 0.639001056574453 df.mm.trans2:exp3 0.0219023513714646 0.122277897210090 0.179119463706784 0.857912223054501 df.mm.trans1:exp4 0.0268305145294918 0.122277897210090 0.219422439718548 0.826405310946976 df.mm.trans2:exp4 -0.00816362860418406 0.122277897210090 -0.0667629129257748 0.946795578074645 df.mm.trans1:exp5 -0.0178080737612542 0.122277897210090 -0.145636081152569 0.884263949237054 df.mm.trans2:exp5 0.0397431885687468 0.122277897210090 0.325023487282111 0.74529131139375 df.mm.trans1:exp6 -0.111637753929695 0.122277897210090 -0.912983920044733 0.361665328929936 df.mm.trans2:exp6 -0.00594027016668505 0.122277897210090 -0.0485800811284715 0.961272210552576 df.mm.trans1:exp7 -0.0893806890252639 0.122277897210090 -0.73096357612117 0.465123812318203 df.mm.trans2:exp7 0.0732370710018739 0.122277897210090 0.598939568579945 0.549468609744654 df.mm.trans1:exp8 -0.104125033733920 0.122277897210090 -0.851544196536345 0.394850983626638 df.mm.trans2:exp8 0.0322309033640303 0.122277897210090 0.263587321170999 0.792200184947949 df.mm.trans1:probe2 0.0152036684574485 0.086463530306486 0.175839089654982 0.860487394776824 df.mm.trans1:probe3 -0.00614050570736696 0.086463530306486 -0.0710184477270451 0.943409812301626 df.mm.trans1:probe4 -0.0373418618455557 0.086463530306486 -0.431879911833238 0.666004003565194 df.mm.trans1:probe5 -0.0266992073197067 0.086463530306486 -0.308791547431228 0.75760121181684 df.mm.trans1:probe6 0.147336234196068 0.086463530306486 1.70402750933032 0.088960979554454 . df.mm.trans2:probe2 0.0257205011420096 0.086463530306486 0.297472252761812 0.766222329433503 df.mm.trans2:probe3 -0.04466568056927 0.086463530306486 -0.516584048915701 0.605661622341934 df.mm.trans2:probe4 0.00377236116645862 0.086463530306486 0.0436295066033828 0.965216132423863 df.mm.trans2:probe5 0.131156050748761 0.086463530306486 1.51689446734194 0.129888414231029 df.mm.trans2:probe6 0.0514694937660577 0.086463530306486 0.595274025749521 0.551913914754484 df.mm.trans3:probe2 -0.0794527390884632 0.086463530306486 -0.918916204402344 0.358556623490981 df.mm.trans3:probe3 -0.115580802104860 0.086463530306486 -1.33675784108240 0.181874131945370 df.mm.trans3:probe4 -0.072037236067432 0.086463530306486 -0.833151686174306 0.405133771704802 df.mm.trans3:probe5 -0.123111984791474 0.086463530306486 -1.42386026056397 0.155074267306816 df.mm.trans3:probe6 -0.0674073363392773 0.086463530306486 -0.779604257428993 0.435971177404951 df.mm.trans3:probe7 -0.0413200928330174 0.086463530306486 -0.477890420233255 0.63292486650267 df.mm.trans3:probe8 -0.159518972152704 0.086463530306486 -1.84492781623951 0.0656047164251398 . df.mm.trans3:probe9 -0.109869031970001 0.086463530306486 -1.27069796457014 0.204392543876236 df.mm.trans3:probe10 -0.0448756251233856 0.086463530306486 -0.519012177322804 0.60396848638128 df.mm.trans3:probe11 -0.143933668345709 0.086463530306486 -1.66467489628876 0.0965675362926852 . df.mm.trans3:probe12 -0.100951845860478 0.086463530306486 -1.16756562567634 0.243506023668997