chr16.9680_chr16_13572401_13579442_+_2.R fitVsDatCorrelation=0.86118230542691 cont.fitVsDatCorrelation=0.291728883508427 fstatistic=4537.04195271812,43,485 cont.fstatistic=1273.16130953265,43,485 residuals=-0.776680859487298,-0.102588656418668,0.00078129282365022,0.0859083890261111,2.07618391464183 cont.residuals=-0.781771173982357,-0.233329117533585,-0.0723049283833203,0.111038255161122,2.03127988566743 predictedValues: Include Exclude Both chr16.9680_chr16_13572401_13579442_+_2.R.tl.Lung 53.2828462876695 65.2703070947207 57.1305113506942 chr16.9680_chr16_13572401_13579442_+_2.R.tl.cerebhem 73.4480635618164 68.8124966392413 106.363328946236 chr16.9680_chr16_13572401_13579442_+_2.R.tl.cortex 140.875194056839 65.1389402161147 184.075795252685 chr16.9680_chr16_13572401_13579442_+_2.R.tl.heart 58.5110821643293 70.2127527175792 65.2723809057066 chr16.9680_chr16_13572401_13579442_+_2.R.tl.kidney 54.494633560784 63.2471941172617 55.8390525127871 chr16.9680_chr16_13572401_13579442_+_2.R.tl.liver 54.737946808273 65.4478799513936 55.4043099202446 chr16.9680_chr16_13572401_13579442_+_2.R.tl.stomach 54.466288606343 70.9572197278519 61.2997844870888 chr16.9680_chr16_13572401_13579442_+_2.R.tl.testicle 54.226351995042 75.3406792772106 58.855117392741 diffExp=-11.9874608070512,4.63556692257508,75.736253840724,-11.7016705532499,-8.75256055647771,-10.7099331431206,-16.4909311215089,-21.1143272821686 diffExpScore=116.333148091101 diffExp1.5=0,0,1,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,1,0,0,0,-1,-1 diffExp1.3Score=1.5 diffExp1.2=-1,0,1,0,0,0,-1,-1 diffExp1.2Score=1.33333333333333 cont.predictedValues: Include Exclude Both Lung 64.4059978812943 85.584993887126 63.209948362953 cerebhem 74.9871349619503 64.5688663021158 61.0325117207137 cortex 67.1940267245314 63.4564900885078 66.2608388400444 heart 65.126845938507 69.0229209238867 61.0811175020396 kidney 67.4854161410195 72.4791703314305 62.9508417036251 liver 66.7696417171823 65.4662657771333 63.6444087790364 stomach 79.2979497052084 65.2724868979252 68.0188566196336 testicle 76.8541077336764 80.5144519436216 61.1997081709377 cont.diffExp=-21.1789960058318,10.4182686598345,3.73753663602355,-3.89607498537964,-4.99375419041098,1.30337594004905,14.0254628072832,-3.66034420994526 cont.diffExpScore=12.0532954339359 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=-1,0,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=-1,0,0,0,0,0,1,0 cont.diffExp1.2Score=2 tran.correlation=-0.268670238957287 cont.tran.correlation=-0.168449997790633 tran.covariance=-0.00483715348280203 cont.tran.covariance=-0.00158112335748787 tran.mean=68.0293672989043 cont.tran.mean=70.5304229346948 weightedLogRatios: wLogRatio Lung -0.827331527444013 cerebhem 0.277982511340712 cortex 3.51906006762265 heart -0.758493348932165 kidney -0.606604302133878 liver -0.731210875194948 stomach -1.0923201096588 testicle -1.36723808118560 cont.weightedLogRatios: wLogRatio Lung -1.22459576251513 cerebhem 0.634614695401815 cortex 0.239161918018491 heart -0.244340565967300 kidney -0.303226766728666 liver 0.0826271196857334 stomach 0.832266607562035 testicle -0.203101781239914 varWeightedLogRatios=2.48164324919582 cont.varWeightedLogRatios=0.406801555839055 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36845376362808 0.110125320427527 39.6680231818525 2.34710745340175e-154 *** df.mm.trans1 -0.424171710779011 0.0933296363814665 -4.54487692468116 6.94844771699524e-06 *** df.mm.trans2 -0.139864396641489 0.0881610780657604 -1.58646422786666 0.113285766238078 df.mm.exp2 -0.247710301256208 0.118053689664300 -2.09828512739079 0.0363966428645812 * df.mm.exp3 -0.199764063649652 0.118053689664300 -1.69214587208333 0.0912604500904774 . df.mm.exp4 0.0333638020030486 0.118053689664300 0.282615495525152 0.777592171801124 df.mm.exp5 0.0138661640722255 0.118053689664300 0.117456422680694 0.906546999274545 df.mm.exp6 0.060340567268893 0.118053689664300 0.511128177700153 0.609493900957008 df.mm.exp7 0.0350694413718099 0.118053689664300 0.297063492649270 0.766545219530279 df.mm.exp8 0.131295080967702 0.118053689664300 1.11216414616989 0.266618549191883 df.mm.trans1:exp2 0.56867439337572 0.103539935228857 5.49231938496737 6.4122709983607e-08 *** df.mm.trans2:exp2 0.300558449273325 0.0926089334230602 3.24545849049250 0.00125366162321948 ** df.mm.trans1:exp3 1.17202396739909 0.103539935228857 11.3195354508242 1.60705942769747e-26 *** df.mm.trans2:exp3 0.197749376413135 0.0926089334230602 2.13531642255037 0.0332355480662114 * df.mm.trans1:exp4 0.0602379268796158 0.103539935228857 0.581784475202443 0.560982172063917 df.mm.trans2:exp4 0.0396289375472181 0.0926089334230602 0.42791700630201 0.66890149154769 df.mm.trans1:exp5 0.00862161985381171 0.103539935228857 0.0832685459456304 0.933672366048433 df.mm.trans2:exp5 -0.0453526168574494 0.0926089334230602 -0.489721835476364 0.624552176932693 df.mm.trans1:exp6 -0.0333978185497364 0.103539935228857 -0.322559778272183 0.747167660817302 df.mm.trans2:exp6 -0.0576236849449527 0.0926089334230602 -0.622225986360448 0.534085686953955 df.mm.trans1:exp7 -0.0131019348905054 0.103539935228857 -0.126539917777095 0.899356994575157 df.mm.trans2:exp7 0.0484704973991473 0.0926089334230602 0.523389003712225 0.600942622423447 df.mm.trans1:exp8 -0.113742537628488 0.103539935228857 -1.09853784800115 0.272514738859466 df.mm.trans2:exp8 0.0121879197853641 0.0926089334230602 0.131606307673221 0.895350271672077 df.mm.trans1:probe2 0.146426661640066 0.0634050023278802 2.3093865825107 0.0213413984006489 * df.mm.trans1:probe3 0.0147205692340578 0.0634050023278802 0.232167316356756 0.81650599254561 df.mm.trans1:probe4 0.204467065059197 0.0634050023278803 3.22477813346423 0.00134575364415335 ** df.mm.trans1:probe5 -0.0273912662406007 0.0634050023278802 -0.432004814051655 0.665929907048548 df.mm.trans1:probe6 0.00831520738063864 0.0634050023278802 0.131144343117267 0.89571550411004 df.mm.trans1:probe7 -0.0571295227515304 0.0634050023278802 -0.9010254814928 0.368021968784693 df.mm.trans1:probe8 -0.141989006155768 0.0634050023278802 -2.23939753872279 0.0255825415019321 * df.mm.trans1:probe9 0.567008150816611 0.0634050023278802 8.94264064346999 8.0205541403552e-18 *** df.mm.trans1:probe10 -0.213109566641582 0.0634050023278803 -3.36108443840991 0.000837673170320425 *** df.mm.trans2:probe2 -0.172101624804750 0.0634050023278802 -2.71432250589279 0.0068777443353771 ** df.mm.trans2:probe3 -0.133436165447512 0.0634050023278802 -2.10450533157441 0.0358483636255043 * df.mm.trans2:probe4 -0.0358596744065216 0.0634050023278802 -0.565565382697786 0.571950810105445 df.mm.trans2:probe5 -0.114757113730506 0.0634050023278802 -1.80990630892297 0.070929159107155 . df.mm.trans2:probe6 -0.144471213932766 0.0634050023278802 -2.27854599209185 0.0231280200481575 * df.mm.trans3:probe2 0.647648793823284 0.0634050023278802 10.2144747266810 2.57158502078066e-22 *** df.mm.trans3:probe3 0.421072734939623 0.0634050023278803 6.64100180553846 8.3745087217634e-11 *** df.mm.trans3:probe4 0.115858366903266 0.0634050023278802 1.82727486238607 0.0682727848228945 . df.mm.trans3:probe5 0.0346822271413870 0.0634050023278803 0.546995124486205 0.584633665517443 df.mm.trans3:probe6 0.226261880031128 0.0634050023278802 3.56851780970028 0.000394706560179942 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.50321921332142 0.207231497300763 21.7303801399733 1.28692449177892e-73 *** df.mm.trans1 -0.295515170424932 0.175625734524833 -1.68264161983133 0.093088091796696 . df.mm.trans2 -0.0358970813306987 0.165899650873118 -0.216378281339201 0.828783873221634 df.mm.exp2 -0.0946128141368355 0.222150934735417 -0.425894287816164 0.670373813166115 df.mm.exp3 -0.303915252206331 0.222150934735417 -1.36805749914263 0.17192755633128 df.mm.exp4 -0.169682321667254 0.222150934735417 -0.763815474687723 0.445348623041092 df.mm.exp5 -0.115398425944143 0.222150934735417 -0.519459556096775 0.60367728433189 df.mm.exp6 -0.238783009056231 0.222150934735417 -1.07486835173853 0.282968110383188 df.mm.exp7 -0.136257109680686 0.222150934735417 -0.613353753577357 0.539929989244961 df.mm.exp8 0.147948167647866 0.222150934735417 0.665980396724746 0.505740259261794 df.mm.trans1:exp2 0.246722615542635 0.19483925880625 1.26628800096175 0.206017839041167 df.mm.trans2:exp2 -0.187164799758518 0.174269530950580 -1.07399611818314 0.283358460961086 df.mm.trans1:exp3 0.346292844180616 0.19483925880625 1.77732581360809 0.0761412491830425 . df.mm.trans2:exp3 0.00475976507168381 0.174269530950580 0.0273126635833639 0.978221591012353 df.mm.trans1:exp4 0.180812402308062 0.19483925880625 0.928008058621612 0.35386498712548 df.mm.trans2:exp4 -0.045389004317798 0.174269530950580 -0.260452897705163 0.794624984997769 df.mm.trans1:exp5 0.162103179732596 0.19483925880625 0.831984173650512 0.405827327894121 df.mm.trans2:exp5 -0.0508123219718412 0.174269530950580 -0.291573183761255 0.770737645193665 df.mm.trans1:exp6 0.274824757410293 0.19483925880625 1.41052044179444 0.159027103392876 df.mm.trans2:exp6 -0.0291919700005032 0.174269530950580 -0.167510464056861 0.867038236536056 df.mm.trans1:exp7 0.344262619495603 0.19483925880625 1.76690581561872 0.0778728859285804 . df.mm.trans2:exp7 -0.134682239421586 0.174269530950580 -0.772838709595082 0.439994308342806 df.mm.trans1:exp8 0.0287539886485744 0.19483925880625 0.147578002630197 0.882737146460776 df.mm.trans2:exp8 -0.209021434809508 0.174269530950580 -1.19941468637328 0.230952364425148 df.mm.trans1:probe2 -0.199245117527147 0.119314191484347 -1.66991968891887 0.0955804767945338 . df.mm.trans1:probe3 -0.148095257865990 0.119314191484346 -1.24122081391651 0.215124179348155 df.mm.trans1:probe4 -0.197020175926749 0.119314191484347 -1.6512719356825 0.0993303616081351 . df.mm.trans1:probe5 -0.114553062367824 0.119314191484347 -0.960095869089078 0.337485351640132 df.mm.trans1:probe6 0.00126684400265325 0.119314191484347 0.0106177143464066 0.991532814805259 df.mm.trans1:probe7 0.0285443939738665 0.119314191484347 0.239237207399687 0.811022716041233 df.mm.trans1:probe8 -0.166615395357288 0.119314191484347 -1.39644239536374 0.163220058525333 df.mm.trans1:probe9 0.0456002159581012 0.119314191484347 0.382186019875797 0.702490799672723 df.mm.trans1:probe10 0.0701610859863997 0.119314191484347 0.588036386229919 0.556781573975061 df.mm.trans2:probe2 0.0740199242281104 0.119314191484347 0.620378207380481 0.535300206163888 df.mm.trans2:probe3 -0.092465141043524 0.119314191484347 -0.77497186121112 0.438733949328723 df.mm.trans2:probe4 0.0598692695920699 0.119314191484347 0.501778278403072 0.616051195862209 df.mm.trans2:probe5 -0.196309014474366 0.119314191484347 -1.64531152608213 0.100553475166826 df.mm.trans2:probe6 -0.0588610695145464 0.119314191484347 -0.49332831897259 0.622003922498514 df.mm.trans3:probe2 0.0331294696542764 0.119314191484347 0.277665793499702 0.781387240466271 df.mm.trans3:probe3 -0.0586355437432873 0.119314191484347 -0.49143813500995 0.623338916795277 df.mm.trans3:probe4 -0.0565036645734707 0.119314191484347 -0.473570359657374 0.636019407062265 df.mm.trans3:probe5 -0.0192377404442289 0.119314191484347 -0.161235978762449 0.871974685732365 df.mm.trans3:probe6 -0.0508240928605135 0.119314191484347 -0.425968547649098 0.670319737485208