chr5.18258_chr5_20081533_20084479_+_1.R fitVsDatCorrelation=0.820879549262628 cont.fitVsDatCorrelation=0.252331201151637 fstatistic=8008.76996009956,39,393 cont.fstatistic=2783.17337809907,39,393 residuals=-0.61577724539166,-0.083587325385258,-0.00183658674357589,0.0801864065865101,0.932431261316005 cont.residuals=-0.572540470029707,-0.178061110928467,-0.0220646394822776,0.136927684842625,0.794890954869436 predictedValues: Include Exclude Both chr5.18258_chr5_20081533_20084479_+_1.R.tl.Lung 69.5076141612722 63.2218914225836 79.64174579752 chr5.18258_chr5_20081533_20084479_+_1.R.tl.cerebhem 55.1115370149898 59.7002569100887 73.1435979428414 chr5.18258_chr5_20081533_20084479_+_1.R.tl.cortex 70.2260747900516 69.9229309314894 71.3452610951899 chr5.18258_chr5_20081533_20084479_+_1.R.tl.heart 69.1888049996083 72.6777387623169 73.9838880928561 chr5.18258_chr5_20081533_20084479_+_1.R.tl.kidney 70.4956942062477 77.0771337719152 80.1635342599819 chr5.18258_chr5_20081533_20084479_+_1.R.tl.liver 70.1853557747682 64.6099719721382 76.313136774169 chr5.18258_chr5_20081533_20084479_+_1.R.tl.stomach 63.2199186606799 58.0347705829243 84.5962674631673 chr5.18258_chr5_20081533_20084479_+_1.R.tl.testicle 71.2250764586931 64.5310417223759 76.7385134178108 diffExp=6.28572273868865,-4.58871989509884,0.303143858562237,-3.48893376270856,-6.58143956566748,5.57538380262997,5.18514807775566,6.69403473631719 diffExpScore=3.72700879140263 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 74.7696898763543 68.6861727846907 66.8760158255953 cerebhem 71.356123586667 72.4900790732148 74.3004818075671 cortex 69.6743887161305 68.7090754634325 71.495717163548 heart 66.7892780479172 71.4593620084786 75.6203189142735 kidney 81.2115187832941 72.6546224732974 71.323657024861 liver 73.629959292919 69.6255959447253 67.0286279052804 stomach 69.8010520890352 68.7289071413084 69.1133087528172 testicle 75.462719523209 72.4091670527538 73.5329817893605 cont.diffExp=6.08351709166361,-1.13395548654780,0.965313252698024,-4.67008396056141,8.55689630999673,4.00436334819379,1.07214494772685,3.0535524704552 cont.diffExpScore=1.56033277587454 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=0.602521582750041 cont.tran.correlation=0.369601397715355 tran.covariance=0.00526294654105961 cont.tran.covariance=0.000557233410646804 tran.mean=66.808488258884 cont.tran.mean=71.7161069910892 weightedLogRatios: wLogRatio Lung 0.397535254951209 cerebhem -0.323855678354161 cortex 0.0183837053500661 heart -0.209645909912064 kidney -0.383812596615287 liver 0.348445222865002 stomach 0.351193930334824 testicle 0.416162129686685 cont.weightedLogRatios: wLogRatio Lung 0.362540227519364 cerebhem -0.0674109203463006 cortex 0.0591105392624355 heart -0.286250948234477 kidney 0.483370379511179 liver 0.238838365416033 stomach 0.0655994413073648 testicle 0.177738465878931 varWeightedLogRatios=0.117896224926224 cont.varWeightedLogRatios=0.0592105401120691 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.02357891729441 0.0826271854907273 48.6955823727767 1.46378630137831e-168 *** df.mm.trans1 0.226360406620697 0.0674648144448598 3.35523648116909 0.00087014357655715 *** df.mm.trans2 0.0621432781423096 0.0674648144448598 0.921121308250249 0.35755218078565 df.mm.exp2 -0.204277730085558 0.0916666320104897 -2.22848517072365 0.026413225317996 * df.mm.exp3 0.221033875435171 0.0916666320104896 2.41127955273711 0.0163545393804072 * df.mm.exp4 0.208478334631686 0.0916666320104897 2.27430996491536 0.0234857234451870 * df.mm.exp5 0.205741036955274 0.0916666320104897 2.24444852442850 0.0253593485772548 * df.mm.exp6 0.074114822487207 0.0916666320104896 0.808525641901253 0.419277215220976 df.mm.exp7 -0.240776929129636 0.0916666320104897 -2.62665840174081 0.00895996446262592 ** df.mm.exp8 0.0820390887277157 0.0916666320104896 0.894972215389432 0.371349738900325 df.mm.trans1:exp2 -0.0277994953082153 0.0748454916217248 -0.371425114671118 0.710521080440374 df.mm.trans2:exp2 0.146963429381720 0.0748454916217249 1.96355753963759 0.0502860772023596 . df.mm.trans1:exp3 -0.210750500460062 0.0748454916217249 -2.81580755091050 0.00511058200461816 ** df.mm.trans2:exp3 -0.120290851054604 0.0748454916217249 -1.60718900294708 0.108816001940117 df.mm.trans1:exp4 -0.213075565404947 0.0748454916217249 -2.8468724139304 0.0046466807381021 ** df.mm.trans2:exp4 -0.069093828273514 0.0748454916217249 -0.923152841626317 0.356493976304003 df.mm.trans1:exp5 -0.191625706938089 0.0748454916217249 -2.56028389667852 0.0108316383623450 * df.mm.trans2:exp5 -0.00758500365267905 0.0748454916217249 -0.101342158202584 0.91933053377174 df.mm.trans1:exp6 -0.0644114432611781 0.0748454916217248 -0.860592159467917 0.38998741450421 df.mm.trans2:exp6 -0.0523966831626086 0.0748454916217248 -0.700064653558904 0.484301125493092 df.mm.trans1:exp7 0.145960046461682 0.0748454916217249 1.95015148272893 0.0518686940968598 . df.mm.trans2:exp7 0.155168628416362 0.0748454916217249 2.073186040391 0.0388060758118087 * df.mm.trans1:exp8 -0.0576304377103897 0.0748454916217249 -0.769992105892745 0.441767157973959 df.mm.trans2:exp8 -0.0615433381289395 0.0748454916217248 -0.822271813511286 0.411420815668421 df.mm.trans1:probe2 0.0621466947559655 0.0458333160052448 1.35592839821701 0.175900535587262 df.mm.trans1:probe3 -0.114157219544289 0.0458333160052448 -2.49070391352932 0.0131610151292618 * df.mm.trans1:probe4 -0.062559028346087 0.0458333160052448 -1.36492477085726 0.173057593070915 df.mm.trans1:probe5 -0.0627713916263435 0.0458333160052448 -1.36955815326913 0.171606917900086 df.mm.trans1:probe6 0.0753046919457912 0.0458333160052448 1.64301208180473 0.101180224715877 df.mm.trans2:probe2 0.229809975273477 0.0458333160052448 5.01403771979272 8.0814184464221e-07 *** df.mm.trans2:probe3 0.261930163496541 0.0458333160052448 5.71484209142903 2.17293236343161e-08 *** df.mm.trans2:probe4 0.169356948354464 0.0458333160052448 3.69506208835259 0.000251001452295948 *** df.mm.trans2:probe5 -0.0402874923701309 0.0458333160052448 -0.879000165851422 0.379938303388927 df.mm.trans2:probe6 0.110331553341462 0.0458333160052448 2.40723480118341 0.0165340769179031 * df.mm.trans3:probe2 0.383918666581934 0.0458333160052448 8.37641043772616 9.76167631098788e-16 *** df.mm.trans3:probe3 -0.256344350734303 0.0458333160052448 -5.59296976690425 4.18731149401321e-08 *** df.mm.trans3:probe4 0.486404780793184 0.0458333160052448 10.6124719568081 2.64674178407334e-23 *** df.mm.trans3:probe5 -0.366123451567410 0.0458333160052448 -7.98815105425743 1.53231575686350e-14 *** df.mm.trans3:probe6 0.144879143000556 0.0458333160052448 3.16100067871976 0.00169417043956618 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.33831798281717 0.139998696815056 30.9882740447811 1.44346964355443e-107 *** df.mm.trans1 -0.0276551222524538 0.114308457283830 -0.241934174509814 0.808957361787778 df.mm.trans2 -0.0886283857396172 0.114308457283830 -0.775344080793174 0.43860265121674 df.mm.exp2 -0.098104661085212 0.155314609189175 -0.63165121167526 0.527982049892668 df.mm.exp3 -0.137043556553607 0.155314609189175 -0.882361017222057 0.378120981447815 df.mm.exp4 -0.196173633530199 0.155314609189176 -1.26307264045751 0.207311918624569 df.mm.exp5 0.0744259389322649 0.155314609189176 0.479194708860987 0.632066645493724 df.mm.exp6 -0.00405565596595708 0.155314609189175 -0.0261125208190637 0.97918084425841 df.mm.exp7 -0.101048448499748 0.155314609189175 -0.650604917510815 0.5156817293719 df.mm.exp8 -0.0328824778609401 0.155314609189175 -0.211715292158311 0.83243890754126 df.mm.trans1:exp2 0.0513752386440929 0.126813847371088 0.405123255142292 0.685607346304872 df.mm.trans2:exp2 0.152006463751610 0.126813847371088 1.19865824515838 0.231383145556019 df.mm.trans1:exp3 0.0664637692213061 0.126813847371088 0.524104982217101 0.600500975182586 df.mm.trans2:exp3 0.137376940411432 0.126813847371088 1.08329605369857 0.279341210513701 df.mm.trans1:exp4 0.0833036056235942 0.126813847371088 0.656896761280556 0.5116317843561 df.mm.trans2:exp4 0.235754648671327 0.126813847371088 1.85906076945566 0.0637657577250906 . df.mm.trans1:exp5 0.00821856781008115 0.126813847371088 0.064808126087616 0.948359723708908 df.mm.trans2:exp5 -0.0182568337902281 0.126813847371088 -0.14396561707338 0.885601413202775 df.mm.trans1:exp6 -0.0113049328152204 0.126813847371088 -0.0891458862701284 0.929011381610407 df.mm.trans2:exp6 0.0176400040652272 0.126813847371088 0.139101560522869 0.889441147651365 df.mm.trans1:exp7 0.0322849437616315 0.126813847371088 0.254585318803222 0.79917655990101 df.mm.trans2:exp7 0.101670423293465 0.126813847371088 0.801729664395032 0.423193840717381 df.mm.trans1:exp8 0.042108643714649 0.126813847371088 0.332050833466388 0.740027895050601 df.mm.trans2:exp8 0.085667476526848 0.126813847371088 0.675537240630863 0.499731785235163 df.mm.trans1:probe2 0.0285282108337404 0.0776573045945877 0.367360301554023 0.71354802123935 df.mm.trans1:probe3 -0.0287982548151336 0.0776573045945877 -0.370837681857177 0.710958241630364 df.mm.trans1:probe4 0.0210855381812751 0.0776573045945877 0.271520345592121 0.78613342334172 df.mm.trans1:probe5 -0.00173398179400617 0.0776573045945877 -0.0223286373774943 0.982197137582367 df.mm.trans1:probe6 0.0259152084374445 0.0776573045945878 0.333712437905688 0.738774547720376 df.mm.trans2:probe2 -0.0707161284739474 0.0776573045945878 -0.91061785936459 0.363054929935014 df.mm.trans2:probe3 -0.0504695952567766 0.0776573045945877 -0.649901455120734 0.516135571000871 df.mm.trans2:probe4 -0.03956197635934 0.0776573045945878 -0.509443078997841 0.610727619121955 df.mm.trans2:probe5 -0.00174492273706898 0.0776573045945877 -0.0224695248718507 0.982084825361928 df.mm.trans2:probe6 -0.0792076285681642 0.0776573045945878 -1.01996365933211 0.308373158714358 df.mm.trans3:probe2 0.0930136659727876 0.0776573045945877 1.19774522768165 0.231738092603802 df.mm.trans3:probe3 0.020484923820969 0.0776573045945877 0.263786181195847 0.792082943924849 df.mm.trans3:probe4 -0.136083366362369 0.0776573045945877 -1.75235758017609 0.0804921806594292 . df.mm.trans3:probe5 -0.0563716120332635 0.0776573045945877 -0.72590224870092 0.468330761862847 df.mm.trans3:probe6 -0.0362080902512453 0.0776573045945878 -0.466254790071208 0.641291461936999