chrX.25780_chrX_143329936_143331954_+_1.R fitVsDatCorrelation=0.741289914774236 cont.fitVsDatCorrelation=0.300914898222437 fstatistic=10167.0502544705,43,485 cont.fstatistic=5030.47906840439,43,485 residuals=-0.319535293112217,-0.0788934456567819,-0.00927475404861964,0.066555932100339,1.27781234252829 cont.residuals=-0.543229865051762,-0.137048307944621,-0.00803854054592008,0.125246961984702,1.33410827305853 predictedValues: Include Exclude Both chrX.25780_chrX_143329936_143331954_+_1.R.tl.Lung 56.3065615616175 66.7041831307466 45.7602925038027 chrX.25780_chrX_143329936_143331954_+_1.R.tl.cerebhem 60.095638025411 55.0726348128551 52.0445102184273 chrX.25780_chrX_143329936_143331954_+_1.R.tl.cortex 59.0358363412928 59.4631083804444 49.3489534633347 chrX.25780_chrX_143329936_143331954_+_1.R.tl.heart 60.7029534938489 59.7373664297577 46.6902176423879 chrX.25780_chrX_143329936_143331954_+_1.R.tl.kidney 62.8398013155005 68.929812986381 45.9247102544333 chrX.25780_chrX_143329936_143331954_+_1.R.tl.liver 57.8126558812882 74.0979086561775 48.5604904047712 chrX.25780_chrX_143329936_143331954_+_1.R.tl.stomach 59.4295490736937 66.503886876614 48.4403797757331 chrX.25780_chrX_143329936_143331954_+_1.R.tl.testicle 55.3443778944616 60.1984986285809 49.3276879191957 diffExp=-10.3976215691291,5.02300321255588,-0.427272039151539,0.965587064091217,-6.09001167088046,-16.2852527748892,-7.07433780292031,-4.85412073411933 diffExpScore=1.27347218129113 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=0,0,0,0,0,-1,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 53.566947744699 49.0272953490768 50.6700876892345 cerebhem 52.4205940983946 51.8128283547773 53.8553561197207 cortex 56.1875322815136 54.226241402486 53.5346004998967 heart 53.9227778666294 54.0893719613781 52.6202340690114 kidney 53.2461073794957 59.1641435757314 56.9330583099393 liver 51.2468674033009 53.3365885262336 57.7200768735312 stomach 56.6515761736674 49.5787877538792 51.6001284243662 testicle 52.7539038723199 51.5781221262038 54.7557771128942 cont.diffExp=4.53965239562224,0.607765743617321,1.9612908790276,-0.166594094748646,-5.91803619623575,-2.08972112293268,7.07278841978821,1.17578174611601 cont.diffExpScore=2.87569819247679 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=-0.0250019673512785 cont.tran.correlation=-0.169428549447931 tran.covariance=-0.000128122687245318 cont.tran.covariance=-0.00034900088138296 tran.mean=61.392173343042 cont.tran.mean=53.3006053668617 weightedLogRatios: wLogRatio Lung -0.697405275236076 cerebhem 0.353701795692474 cortex -0.0294352893664055 heart 0.0657094978072044 kidney -0.387283134758060 liver -1.03771273119157 stomach -0.465736360868406 testicle -0.340964695184138 cont.weightedLogRatios: wLogRatio Lung 0.348610616830509 cerebhem 0.046104406603 cortex 0.142508086379224 heart -0.0123052870736455 kidney -0.424474816499499 liver -0.158139194719212 stomach 0.529458432419803 testicle 0.0891321748211908 varWeightedLogRatios=0.195824463520491 cont.varWeightedLogRatios=0.0854638979459148 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.46289747950652 0.0694818193450449 64.2311545894313 2.58895745327159e-239 *** df.mm.trans1 -0.413636903480456 0.0556238299752399 -7.43632546814162 4.73047668264444e-13 *** df.mm.trans2 -0.235212930391418 0.0556238299752399 -4.22863600899325 2.81093338979574e-05 *** df.mm.exp2 -0.255171113091744 0.0744840978117196 -3.42584686649174 0.000664949560519776 *** df.mm.exp3 -0.143077813192245 0.0744840978117195 -1.92091758369574 0.0553279988405968 . df.mm.exp4 -0.05524659046734 0.0744840978117195 -0.741723295178953 0.458614018091151 df.mm.exp5 0.139012111330556 0.0744840978117195 1.86633275309248 0.0625981133419766 . df.mm.exp6 0.0721227724680707 0.0744840978117195 0.968297590854658 0.333378376143922 df.mm.exp7 -0.00594380384568385 0.0744840978117196 -0.0797996353625517 0.936429529846058 df.mm.exp8 -0.194925054046071 0.0744840978117196 -2.61700228334377 0.00914751569764626 ** df.mm.trans1:exp2 0.320297298644657 0.0584301335683556 5.48171429849551 6.78498223036762e-08 *** df.mm.trans2:exp2 0.0635563934580736 0.0584301335683557 1.08773315371119 0.277253172719089 df.mm.trans1:exp3 0.190411393580879 0.0584301335683556 3.25878758018108 0.0011974366277561 ** df.mm.trans2:exp3 0.0281662396696063 0.0584301335683557 0.482049893599086 0.629987961848206 df.mm.trans1:exp4 0.130427869836706 0.0584301335683556 2.23220215103774 0.0260574690629758 * df.mm.trans2:exp4 -0.0550633481423152 0.0584301335683556 -0.942379296085268 0.346467648258475 df.mm.trans1:exp5 -0.0292345344423577 0.0584301335683557 -0.500333178395992 0.617067443389916 df.mm.trans2:exp5 -0.106190994089829 0.0584301335683556 -1.81740118676264 0.0697726328955482 . df.mm.trans1:exp6 -0.0457261356162398 0.0584301335683556 -0.782577975159789 0.434256900731046 df.mm.trans2:exp6 0.0329968696137901 0.0584301335683556 0.564723501362325 0.57252293740431 df.mm.trans1:exp7 0.0599242909334275 0.0584301335683557 1.02557169175942 0.305604746495996 df.mm.trans2:exp7 0.0029365325133579 0.0584301335683556 0.0502571590037962 0.959938153260133 df.mm.trans1:exp8 0.177689059577129 0.0584301335683557 3.04105174377628 0.00248513490012405 ** df.mm.trans2:exp8 0.092304799766843 0.0584301335683557 1.57974651313878 0.114816859829647 df.mm.trans1:probe2 -0.130890054992414 0.0400043777417853 -3.27189328721135 0.00114444029696000 ** df.mm.trans1:probe3 0.0839715178951781 0.0400043777417853 2.09905821900758 0.0363281110139298 * df.mm.trans1:probe4 -0.169108443902659 0.0400043777417853 -4.2272484525118 2.82769496526177e-05 *** df.mm.trans1:probe5 -0.0104888099333202 0.0400043777417853 -0.262191553160056 0.793285088315517 df.mm.trans1:probe6 -0.0686762296091233 0.0400043777417853 -1.71671785654073 0.0866692067073235 . df.mm.trans2:probe2 -0.20385866740999 0.0400043777417853 -5.09590897090885 4.98145598408801e-07 *** df.mm.trans2:probe3 0.0498502857939315 0.0400043777417853 1.24612076497473 0.213321635411832 df.mm.trans2:probe4 -0.0817665486317062 0.0400043777417853 -2.04394001975188 0.0414990933592743 * df.mm.trans2:probe5 -0.00467103214653961 0.0400043777417853 -0.116763024704185 0.90709617907535 df.mm.trans2:probe6 -0.198224158512348 0.0400043777417853 -4.95506166329641 1.00069414937141e-06 *** df.mm.trans3:probe2 -0.0438336699940729 0.0400043777417853 -1.0957218301708 0.273744323360108 df.mm.trans3:probe3 0.03714635218087 0.0400043777417853 0.928557179932585 0.353580509960880 df.mm.trans3:probe4 0.0312769523038981 0.0400043777417853 0.781838240449088 0.434691151626692 df.mm.trans3:probe5 -0.00790393668030889 0.0400043777417853 -0.197576793503104 0.843458970603436 df.mm.trans3:probe6 0.218039389279181 0.0400043777417853 5.45038822217288 8.01306342497943e-08 *** df.mm.trans3:probe7 -0.0906848068614015 0.0400043777417853 -2.26687207701970 0.0238375367650183 * df.mm.trans3:probe8 -0.0515136458202007 0.0400043777417853 -1.28770021502906 0.198464428557888 df.mm.trans3:probe9 0.0553902965229823 0.0400043777417853 1.38460587689947 0.166809663397611 df.mm.trans3:probe10 -0.0542259954806846 0.0400043777417853 -1.35550153612425 0.175888699981668 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.86901011327853 0.0987230188049703 39.190557178178 2.06975448451553e-152 *** df.mm.trans1 0.118999937041435 0.0790329393273391 1.50570050986666 0.132795050914397 df.mm.trans2 0.00670244092603443 0.0790329393273391 0.0848056643607068 0.93245088485158 df.mm.exp2 -0.0273382330383584 0.105830490022453 -0.258320952993399 0.79626880000999 df.mm.exp3 0.0935579813927056 0.105830490022453 0.884036173061815 0.377114702001701 df.mm.exp4 0.0671163023114256 0.105830490022453 0.634186823638315 0.526257904599763 df.mm.exp5 0.0653905423633461 0.105830490022453 0.617879992329933 0.536944465085963 df.mm.exp6 -0.0903016860519168 0.105830490022453 -0.853267201472456 0.393932343303263 df.mm.exp7 0.0489850525797868 0.105830490022453 0.462863325771186 0.643669942645391 df.mm.exp8 -0.0421211839158094 0.105830490022453 -0.398006131379275 0.690800797628552 df.mm.trans1:exp2 0.0057055329813317 0.0830202667319342 0.0687245802251444 0.945237176529606 df.mm.trans2:exp2 0.0825988123224602 0.0830202667319343 0.994923475603435 0.320269598785462 df.mm.trans1:exp3 -0.0457953259470376 0.0830202667319343 -0.551616222758077 0.581465373843478 df.mm.trans2:exp3 0.00722977764597635 0.0830202667319343 0.0870844906981656 0.930640295049393 df.mm.trans1:exp4 -0.0604955500543226 0.0830202667319343 -0.728684120585373 0.466546607484698 df.mm.trans2:exp4 0.0311442215539478 0.0830202667319343 0.375139984246377 0.707720356352323 df.mm.trans1:exp5 -0.0713980726183012 0.0830202667319343 -0.860007747853182 0.390209673353731 df.mm.trans2:exp5 0.122547942226929 0.0830202667319343 1.47612079617409 0.140560404914682 df.mm.trans1:exp6 0.0460239470463145 0.0830202667319343 0.554370021418049 0.579581167713789 df.mm.trans2:exp6 0.174547054664603 0.0830202667319342 2.10246318803337 0.0360275839245038 * df.mm.trans1:exp7 0.0070025262018891 0.0830202667319343 0.084347189879547 0.93281519802855 df.mm.trans2:exp7 -0.0377991675339921 0.0830202667319343 -0.455300482905489 0.649096831567994 df.mm.trans1:exp8 0.0268267292738443 0.0830202667319343 0.323134703487109 0.746732499794864 df.mm.trans2:exp8 0.092841585734626 0.0830202667319343 1.11830025835022 0.263992372845655 df.mm.trans1:probe2 0.0239524655328255 0.0568400910239701 0.421400900338539 0.673649057015307 df.mm.trans1:probe3 -0.0153245770048467 0.0568400910239701 -0.26960859366647 0.787576056542611 df.mm.trans1:probe4 -0.0122235218816842 0.0568400910239701 -0.215051060993717 0.829817906169315 df.mm.trans1:probe5 -0.0460119386921253 0.0568400910239701 -0.8094979769248 0.418625722847560 df.mm.trans1:probe6 -0.0636375319864025 0.0568400910239701 -1.11958884723752 0.263443154919875 df.mm.trans2:probe2 0.0365197639014519 0.0568400910239701 0.64250009532974 0.520852122367083 df.mm.trans2:probe3 0.00709122048535525 0.0568400910239701 0.124757373846653 0.900767325864052 df.mm.trans2:probe4 0.078411310149073 0.0568400910239701 1.37950711788984 0.168374156196585 df.mm.trans2:probe5 0.140453925016632 0.0568400910239701 2.47103624372103 0.0138147979928681 * df.mm.trans2:probe6 0.00415796849219768 0.0568400910239701 0.0731520378889649 0.941715290670964 df.mm.trans3:probe2 -0.0296845930562713 0.0568400910239701 -0.522247458114608 0.601736491763274 df.mm.trans3:probe3 -0.115060214456875 0.0568400910239701 -2.02427920828545 0.043489018325823 * df.mm.trans3:probe4 -0.029489931014392 0.0568400910239701 -0.518822726760866 0.604121007454914 df.mm.trans3:probe5 -0.0712151119023961 0.0568400910239701 -1.25290284761092 0.210844790744154 df.mm.trans3:probe6 -0.0707125345891442 0.0568400910239701 -1.24406089637196 0.214078062153358 df.mm.trans3:probe7 -0.0884723917051058 0.0568400910239701 -1.55651389910329 0.120238208454143 df.mm.trans3:probe8 -0.112874971706295 0.0568400910239701 -1.98583376051764 0.0476143644719281 * df.mm.trans3:probe9 -0.105365299193374 0.0568400910239701 -1.85371446975587 0.0643870097594074 . df.mm.trans3:probe10 -0.0708923640257162 0.0568400910239701 -1.24722467449639 0.212917054071819