chr7.21421_chr7_17472863_17473439_-_0.R fitVsDatCorrelation=0.839829488726028 cont.fitVsDatCorrelation=0.305355708356517 fstatistic=5285.31359994525,36,324 cont.fstatistic=1711.59420513625,36,324 residuals=-0.825868008954537,-0.104698933906987,-0.00708210011016013,0.09521786499962,0.995649935236271 cont.residuals=-0.682508350343408,-0.234017313330106,-0.0278602165954748,0.212183369532730,1.12957438010408 predictedValues: Include Exclude Both chr7.21421_chr7_17472863_17473439_-_0.R.tl.Lung 80.0572502668286 118.656008294700 105.066593925742 chr7.21421_chr7_17472863_17473439_-_0.R.tl.cerebhem 128.293611360377 121.863176029252 126.664581160797 chr7.21421_chr7_17472863_17473439_-_0.R.tl.cortex 147.542732531594 85.8083611348194 206.970217723913 chr7.21421_chr7_17472863_17473439_-_0.R.tl.heart 88.8892548802422 99.6333203657711 126.510993290155 chr7.21421_chr7_17472863_17473439_-_0.R.tl.kidney 78.5802877873446 134.417774859676 86.7456634844709 chr7.21421_chr7_17472863_17473439_-_0.R.tl.liver 69.757007695103 130.819883996884 71.4373833029767 chr7.21421_chr7_17472863_17473439_-_0.R.tl.stomach 80.8762284824468 107.833549653269 104.188084474875 chr7.21421_chr7_17472863_17473439_-_0.R.tl.testicle 86.5707037051003 114.250156369955 104.43301445736 diffExp=-38.5987580278712,6.4304353311243,61.7343713967748,-10.7440654855289,-55.8374870723318,-61.0628763017814,-26.9573211708224,-27.6794526648546 diffExpScore=1.88039214051689 diffExp1.5=0,0,1,0,-1,-1,0,0 diffExp1.5Score=1.5 diffExp1.4=-1,0,1,0,-1,-1,0,0 diffExp1.4Score=1.33333333333333 diffExp1.3=-1,0,1,0,-1,-1,-1,-1 diffExp1.3Score=1.2 diffExp1.2=-1,0,1,0,-1,-1,-1,-1 diffExp1.2Score=1.2 cont.predictedValues: Include Exclude Both Lung 90.0789292005415 95.1270206690288 116.939507610622 cerebhem 102.992025147134 112.023072302337 98.006516187541 cortex 104.363731344582 102.638910285965 112.196266457411 heart 96.337695183814 100.720260045746 101.131783871826 kidney 102.904487189660 97.6544528520893 91.099351712899 liver 122.722070850829 102.875186058657 96.924953162674 stomach 114.326917105454 109.086022664514 105.924763580501 testicle 117.698484388803 105.699734345500 83.54725139035 cont.diffExp=-5.04809146848729,-9.03104715520284,1.72482105861646,-4.38256486193255,5.25003433757115,19.8468847921714,5.24089444093926,11.9987500433034 cont.diffExpScore=2.35052020807041 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=-0.603298463227081 cont.tran.correlation=0.49913252228971 tran.covariance=-0.0242726860879689 cont.tran.covariance=0.00302497085012091 tran.mean=104.615581713335 cont.tran.mean=104.828062477166 weightedLogRatios: wLogRatio Lung -1.80196616694067 cerebhem 0.248299537976841 cortex 2.55993608331574 heart -0.518545517306784 kidney -2.48689303810918 liver -2.86697932329649 stomach -1.30508309294820 testicle -1.27608319264661 cont.weightedLogRatios: wLogRatio Lung -0.246894520318882 cerebhem -0.393090204942268 cortex 0.077318609038725 heart -0.204201278178443 kidney 0.241282504102088 liver 0.83293841541882 stomach 0.221280768546424 testicle 0.506906160220821 varWeightedLogRatios=2.99056035716564 cont.varWeightedLogRatios=0.169576117601186 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.75437119414845 0.114619598203083 41.4795660487719 1.18810311144675e-131 *** df.mm.trans1 -0.345336749132877 0.0970816023456322 -3.55718015348985 0.000430804305489723 *** df.mm.trans2 -0.0542180432919266 0.0970816023456322 -0.558479073088413 0.57690303055772 df.mm.exp2 0.311301633365696 0.135197902182007 2.30256260150113 0.0219368650919247 * df.mm.exp3 -0.390716860908415 0.135197902182007 -2.88996245209798 0.00411272221925987 ** df.mm.exp4 -0.2558175401702 0.135197902182007 -1.8921709289972 0.0593595856435985 . df.mm.exp5 0.297716869816878 0.135197902182007 2.20208202207224 0.0283637205064328 * df.mm.exp6 0.345641771702641 0.135197902182007 2.55656164869577 0.0110267934702513 * df.mm.exp7 -0.0770652469002035 0.135197902182007 -0.570018067265988 0.56906047909628 df.mm.exp8 0.0464297559871171 0.135197902182007 0.343420683588805 0.731504734454846 df.mm.trans1:exp2 0.160277835405624 0.117084817827981 1.36890365786879 0.171977663702649 df.mm.trans2:exp2 -0.284631346358807 0.117084817827981 -2.43098423552216 0.0155999220624236 * df.mm.trans1:exp3 1.00209269973907 0.117084817827981 8.55869034370731 4.65030605045117e-16 *** df.mm.trans2:exp3 0.0666046913694718 0.117084817827981 0.568858478879184 0.569846283836642 df.mm.trans1:exp4 0.360466800826777 0.117084817827981 3.07868097259516 0.00225689301976669 ** df.mm.trans2:exp4 0.0810855702303534 0.117084817827981 0.692537014914117 0.489096200385822 df.mm.trans1:exp5 -0.316338000414157 0.117084817827981 -2.70178496480145 0.00725988857828907 ** df.mm.trans2:exp5 -0.172992817461101 0.117084817827981 -1.47749999248629 0.140513027991229 df.mm.trans1:exp6 -0.483365894370195 0.117084817827981 -4.12833963734177 4.6528462282266e-05 *** df.mm.trans2:exp6 -0.248048946317125 0.117084817827981 -2.1185406521412 0.0348900779290272 * df.mm.trans1:exp7 0.087243182427673 0.117084817827981 0.745128053714436 0.456734475856347 df.mm.trans2:exp7 -0.0185745421795969 0.117084817827981 -0.158641765210638 0.874049956654466 df.mm.trans1:exp8 0.0317897008790541 0.117084817827981 0.27151001700117 0.786171708715882 df.mm.trans2:exp8 -0.0842679779576985 0.117084817827981 -0.719717376863525 0.47221785634681 df.mm.trans1:probe2 0.0471491145742336 0.0585424089139907 0.80538391652971 0.421188819251411 df.mm.trans1:probe3 0.0940783649730996 0.0585424089139907 1.60701219369564 0.109025756229672 df.mm.trans1:probe4 -0.170310644552884 0.0585424089139907 -2.90918408914639 0.00387421892087581 ** df.mm.trans1:probe5 -0.0706048970957767 0.0585424089139907 -1.20604700772577 0.228679317308223 df.mm.trans1:probe6 -0.136943879414462 0.0585424089139907 -2.33922522073967 0.0199310321598713 * df.mm.trans2:probe2 0.500247489091482 0.0585424089139907 8.54504449631439 5.12126328894463e-16 *** df.mm.trans2:probe3 0.109739925047401 0.0585424089139907 1.87453723007245 0.0617559763067813 . df.mm.trans2:probe4 -0.0080978937014547 0.0585424089139907 -0.138325256026823 0.890069295483307 df.mm.trans2:probe5 0.109596555062275 0.0585424089139907 1.87208823646618 0.0620950670473774 . df.mm.trans2:probe6 -0.0268068495295052 0.0585424089139907 -0.457904791189738 0.647327780979999 df.mm.trans3:probe2 0.423893614757667 0.0585424089139907 7.24079556378425 3.26679542549589e-12 *** df.mm.trans3:probe3 0.475440311024109 0.0585424089139907 8.12129736107402 9.76691805850333e-15 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.16285244882011 0.20105944758699 20.7045851303208 2.91739384908652e-61 *** df.mm.trans1 0.284665426648142 0.170295251811027 1.67159931719077 0.0955688261203125 . df.mm.trans2 0.38574130873649 0.170295251811027 2.26513249567604 0.0241645393753859 * df.mm.exp2 0.474079880886885 0.237156786045190 1.999014613044 0.0464417437596141 * df.mm.exp3 0.264607037619829 0.237156786045191 1.11574727433440 0.265357157891625 df.mm.exp4 0.269539628394702 0.237156786045191 1.13654613426639 0.256567977524056 df.mm.exp5 0.409043254356438 0.237156786045190 1.7247798858199 0.0855208067240141 . df.mm.exp6 0.575259116138467 0.237156786045190 2.42564897986453 0.0158268572656086 * df.mm.exp7 0.474227180287775 0.237156786045190 1.99963571861448 0.0463742263680842 * df.mm.exp8 0.709073596951061 0.237156786045191 2.98989376933092 0.00300439747889089 ** df.mm.trans1:exp2 -0.34011459852613 0.205383801395006 -1.65599524507779 0.0986909131679147 . df.mm.trans2:exp2 -0.310588086314208 0.205383801395006 -1.51223263083376 0.131449439932240 df.mm.trans1:exp3 -0.117411100558305 0.205383801395006 -0.571666800209301 0.567944097470483 df.mm.trans2:exp3 -0.188602992430383 0.205383801395006 -0.918295362873582 0.359147343287256 df.mm.trans1:exp4 -0.202366228366088 0.205383801395006 -0.985307638633514 0.325208191002662 df.mm.trans2:exp4 -0.212405715000139 0.205383801395006 -1.03418922795975 0.301819000552858 df.mm.trans1:exp5 -0.275928282297246 0.205383801395006 -1.34347636192868 0.180057950744851 df.mm.trans2:exp5 -0.382821056155813 0.205383801395006 -1.86393013254025 0.0632358316239878 . df.mm.trans1:exp6 -0.266023181177946 0.205383801395006 -1.29524908669071 0.196156443245394 df.mm.trans2:exp6 -0.49695570675946 0.205383801395006 -2.4196441169364 0.0160857588173050 * df.mm.trans1:exp7 -0.235851419091217 0.205383801395006 -1.14834479393832 0.251673366758971 df.mm.trans2:exp7 -0.33730346876142 0.205383801395006 -1.64230804216492 0.101496344335523 df.mm.trans1:exp8 -0.441633736783676 0.205383801395006 -2.15028514315158 0.0322722940205771 * df.mm.trans2:exp8 -0.603684275571846 0.205383801395006 -2.93929838415448 0.00352583826583679 ** df.mm.trans1:probe2 0.124659673038431 0.102691900697503 1.21391923016050 0.225662676840022 df.mm.trans1:probe3 0.145645072128115 0.102691900697503 1.41827224093494 0.157072403154492 df.mm.trans1:probe4 -0.0192180528739385 0.102691900697503 -0.187142829603950 0.85166578149106 df.mm.trans1:probe5 0.132126820088636 0.102691900697503 1.28663330984436 0.199140668101537 df.mm.trans1:probe6 0.0953021024427874 0.102691900697503 0.928039132545774 0.35407809946906 df.mm.trans2:probe2 -0.096185412678306 0.102691900697503 -0.936640689528544 0.349641033347431 df.mm.trans2:probe3 -0.00504775694835832 0.102691900697503 -0.0491543823229777 0.960826551639888 df.mm.trans2:probe4 0.0145292609620565 0.102691900697503 0.141484000815751 0.887575527524428 df.mm.trans2:probe5 0.088333184162844 0.102691900697503 0.860176738017976 0.390327659687689 df.mm.trans2:probe6 0.0579444303482184 0.102691900697503 0.564255116076817 0.57297090974459 df.mm.trans3:probe2 -0.148691164990571 0.102691900697503 -1.44793468599405 0.148602269263120 df.mm.trans3:probe3 -0.0661160712542118 0.102691900697503 -0.643829462743788 0.520141499498852