chr7.21516_chr7_11886215_11887703_+_0.R fitVsDatCorrelation=0.907598994118215 cont.fitVsDatCorrelation=0.252046924158196 fstatistic=7280.24683495823,37,347 cont.fstatistic=1362.68453573268,37,347 residuals=-0.472748097905584,-0.0883513058084524,-0.00377237915733247,0.075739799844129,0.800906995376872 cont.residuals=-0.601435974255456,-0.232264006914604,-0.110831532868467,0.173362041876323,1.17499555611217 predictedValues: Include Exclude Both chr7.21516_chr7_11886215_11887703_+_0.R.tl.Lung 54.6723248897528 43.2741669944670 93.703041795744 chr7.21516_chr7_11886215_11887703_+_0.R.tl.cerebhem 73.3475399117675 53.965267679385 75.65665397563 chr7.21516_chr7_11886215_11887703_+_0.R.tl.cortex 55.9036305018097 44.6255524392264 132.875997916454 chr7.21516_chr7_11886215_11887703_+_0.R.tl.heart 57.9977986666706 44.6061991161454 85.0203899655571 chr7.21516_chr7_11886215_11887703_+_0.R.tl.kidney 51.5922652539768 43.3469109861521 104.714508792475 chr7.21516_chr7_11886215_11887703_+_0.R.tl.liver 54.8703672759707 45.0228293566007 90.334243407358 chr7.21516_chr7_11886215_11887703_+_0.R.tl.stomach 60.4508571242472 46.4404840600643 91.3430734891797 chr7.21516_chr7_11886215_11887703_+_0.R.tl.testicle 58.1447522008857 45.7509172247487 112.287534742002 diffExp=11.3981578952859,19.3822722323826,11.2780780625834,13.3915995505252,8.24535426782471,9.84753791936997,14.0103730641830,12.3938349761370 diffExpScore=0.99009383201253 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=0,1,0,1,0,0,1,0 diffExp1.3Score=0.75 diffExp1.2=1,1,1,1,0,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 62.2329002904936 65.0976987149872 54.4150202585789 cerebhem 68.9020007774215 58.2130959235496 57.663209573766 cortex 60.286558557581 59.3385235530675 56.9167793820169 heart 56.9920925289816 54.9256259463426 64.5308200390119 kidney 62.3896658293592 60.9106103109134 56.3428393784787 liver 60.6104101536424 65.6432736357879 61.731160508676 stomach 60.6747953611912 58.0179988716134 64.5085591119421 testicle 49.6203503710495 58.8878999756298 56.3919183294178 cont.diffExp=-2.86479842449359,10.6889048538720,0.948035004513493,2.06646658263906,1.47905551844575,-5.03286348214543,2.65679648957781,-9.26754960458029 cont.diffExpScore=20.9100887013763 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=0.97645308248942 cont.tran.correlation=0.188308254118142 tran.covariance=0.00725652535140762 cont.tran.covariance=0.00113797161071345 tran.mean=52.1257414801169 cont.tran.mean=60.1714688001007 weightedLogRatios: wLogRatio Lung 0.908192925047348 cerebhem 1.27097946220984 cortex 0.88122988185461 heart 1.03152554278694 kidney 0.671522517915878 liver 0.772635659257321 stomach 1.04672723630628 testicle 0.945248575337805 cont.weightedLogRatios: wLogRatio Lung -0.186924800517994 cerebhem 0.699315621447523 cortex 0.0648470493064302 heart 0.148632926597784 kidney 0.098881806483238 liver -0.330588549180546 stomach 0.182823002888944 testicle -0.683229181603969 varWeightedLogRatios=0.0334130076518989 cont.varWeightedLogRatios=0.167081143012906 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.46012850043713 0.0842741787500684 41.0579913296909 2.92500276704020e-135 *** df.mm.trans1 0.604857893759513 0.0702119559984506 8.61474210706879 2.50497037198231e-16 *** df.mm.trans2 0.260908309749300 0.0702119559984506 3.71600970289244 0.000235831258663527 *** df.mm.exp2 0.72856138196435 0.0967373257755753 7.53133680431241 4.38903746884901e-13 *** df.mm.exp3 -0.296263240581146 0.0967373257755753 -3.06255355113349 0.00236601531167486 ** df.mm.exp4 0.186603951346619 0.0967373257755752 1.92897570664222 0.0545491567351055 . df.mm.exp5 -0.167413314736281 0.0967373257755752 -1.73059688588735 0.0844126973886496 . df.mm.exp6 0.079843680208136 0.0967373257755752 0.825365799271405 0.409731002422491 df.mm.exp7 0.196597042275659 0.0967373257755752 2.03227700062490 0.0428868232935865 * df.mm.exp8 -0.0636982246052702 0.0967373257755752 -0.658465841334567 0.510675483471307 df.mm.trans1:exp2 -0.434710054853767 0.081757962470286 -5.31703630715794 1.89308725997351e-07 *** df.mm.trans2:exp2 -0.507776585218849 0.081757962470286 -6.21072944917621 1.50818105034770e-09 *** df.mm.trans1:exp3 0.318534927117631 0.081757962470286 3.89607222945899 0.000117310761144316 *** df.mm.trans2:exp3 0.327014008434704 0.081757962470286 3.9997817772618 7.75140068228251e-05 *** df.mm.trans1:exp4 -0.127556533424148 0.081757962470286 -1.56017260668044 0.119630626103229 df.mm.trans2:exp4 -0.156286960103899 0.081757962470286 -1.91158090761740 0.0567534365151376 . df.mm.trans1:exp5 0.109427439928444 0.081757962470286 1.33843159274198 0.181632080623076 df.mm.trans2:exp5 0.169092906135559 0.081757962470286 2.06821330946223 0.0393604797065429 * df.mm.trans1:exp6 -0.0762278734743317 0.081757962470286 -0.932360239555087 0.35179864581376 df.mm.trans2:exp6 -0.0402298518051646 0.081757962470286 -0.492060352161854 0.622987825653048 df.mm.trans1:exp7 -0.0961239241349725 0.081757962470286 -1.1757133033973 0.240515487091622 df.mm.trans2:exp7 -0.125981313917085 0.081757962470286 -1.54090574313018 0.124251129232402 df.mm.trans1:exp8 0.125276215716278 0.081757962470286 1.53228152868668 0.126364140298739 df.mm.trans2:exp8 0.119354212994091 0.081757962470286 1.45984818344108 0.145236523194867 df.mm.trans1:probe2 -0.125917049595473 0.0447806803006364 -2.81186102466789 0.00520559058051102 ** df.mm.trans1:probe3 -0.138067499364820 0.0447806803006365 -3.08319343158477 0.00221200684550662 ** df.mm.trans1:probe4 -0.174629895007966 0.0447806803006365 -3.89967043456201 0.000115653496051067 *** df.mm.trans1:probe5 -0.0775520890863153 0.0447806803006365 -1.73182025296773 0.0841942722160235 . df.mm.trans1:probe6 -0.120121030104334 0.0447806803006365 -2.68242977323920 0.0076586492817823 ** df.mm.trans2:probe2 0.150958172262116 0.0447806803006365 3.37105580461606 0.000833170855263073 *** df.mm.trans2:probe3 0.0535565300376717 0.0447806803006364 1.19597401553791 0.232523295782653 df.mm.trans2:probe4 0.138398077312258 0.0447806803006365 3.09057558713085 0.00215920225005183 ** df.mm.trans2:probe5 0.0402635841120862 0.0447806803006364 0.899128459902248 0.369207740062672 df.mm.trans2:probe6 0.0820140520127963 0.0447806803006364 1.83146060895441 0.0678889942683008 . df.mm.trans3:probe2 0.410116335649772 0.0447806803006365 9.15833196138253 4.66684259208876e-18 *** df.mm.trans3:probe3 0.31344875817187 0.0447806803006364 6.99964261524216 1.33298349362467e-11 *** df.mm.trans3:probe4 0.133378697804183 0.0447806803006365 2.97848752874545 0.00310061627096291 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.33424134188172 0.194249592415497 22.3127435583538 4.96112163224102e-69 *** df.mm.trans1 -0.251811630618974 0.161836567708858 -1.55596250083591 0.120628558318308 df.mm.trans2 -0.134301332682633 0.161836567708858 -0.829857766906179 0.407189743218058 df.mm.exp2 -0.0679565713182709 0.222976792915414 -0.304769704639397 0.76072426503844 df.mm.exp3 -0.169355065959108 0.222976792915414 -0.759518798996056 0.448057936183139 df.mm.exp4 -0.42838317312609 0.222976792915414 -1.92120071118162 0.0555253827495589 . df.mm.exp5 -0.098780902377308 0.222976792915414 -0.443009790775762 0.658034702469312 df.mm.exp6 -0.144219814017266 0.222976792915414 -0.646792933612491 0.518193559096158 df.mm.exp7 -0.310649026351419 0.222976792915414 -1.39318994721241 0.164454164787308 df.mm.exp8 -0.362421959303593 0.222976792915414 -1.62537972927558 0.104989524659312 df.mm.trans1:exp2 0.169757984294935 0.188449785238182 0.900812829690297 0.368312611339939 df.mm.trans2:exp2 -0.0438222819775628 0.188449785238182 -0.232540896356957 0.816255020124119 df.mm.trans1:exp3 0.137580431870523 0.188449785238182 0.730064147839887 0.465843545535295 df.mm.trans2:exp3 0.0767246007813105 0.188449785238182 0.407135517211326 0.684159487934368 df.mm.trans1:exp4 0.340411900294499 0.188449785238182 1.80637987920364 0.0717252787499996 . df.mm.trans2:exp4 0.25847398912544 0.188449785238182 1.37158017345976 0.171080373743072 df.mm.trans1:exp5 0.101296748847712 0.188449785238182 0.53752647539334 0.591248541524942 df.mm.trans2:exp5 0.0322990884663742 0.188449785238182 0.171393607191175 0.864014184462594 df.mm.trans1:exp6 0.117802673571944 0.188449785238182 0.625114395450507 0.532306837852158 df.mm.trans2:exp6 0.152565752980524 0.188449785238182 0.809583055707366 0.418734643587510 df.mm.trans1:exp7 0.285293601773644 0.188449785238182 1.51389719767025 0.130962194944645 df.mm.trans2:exp7 0.195513115540023 0.188449785238182 1.03748123296035 0.300234048525585 df.mm.trans1:exp8 0.1359391951467 0.188449785238182 0.72135500167796 0.471176869089755 df.mm.trans2:exp8 0.262168396916375 0.188449785238182 1.39118437617225 0.165060838631761 df.mm.trans1:probe2 0.147475892217344 0.103218198331956 1.42877801202315 0.153967574880704 df.mm.trans1:probe3 0.0752384174352837 0.103218198331956 0.728925893409922 0.466538673146564 df.mm.trans1:probe4 0.107169401534940 0.103218198331956 1.03828010241253 0.299862614186502 df.mm.trans1:probe5 0.0614612677299208 0.103218198331956 0.595449917971417 0.551930907599761 df.mm.trans1:probe6 0.0931959432636644 0.103218198331956 0.902902247566271 0.367204113933487 df.mm.trans2:probe2 -0.0144094192331083 0.103218198331956 -0.139601537964911 0.889055815536185 df.mm.trans2:probe3 -0.0529973079045581 0.103218198331956 -0.51344926341492 0.607963849977372 df.mm.trans2:probe4 -0.146237587082907 0.103218198331956 -1.41678104681306 0.157444087794151 df.mm.trans2:probe5 -0.0565161240677247 0.103218198331956 -0.547540307630301 0.584359583787577 df.mm.trans2:probe6 0.0296523321924645 0.103218198331956 0.287278141564734 0.774070834564586 df.mm.trans3:probe2 0.0990180422730707 0.103218198331956 0.959307989029438 0.338071544550733 df.mm.trans3:probe3 0.0652347313701146 0.103218198331956 0.632008041453268 0.527797790436134 df.mm.trans3:probe4 0.0297946058189662 0.103218198331956 0.288656518912924 0.773016596834931