chr3.15314_chr3_135763631_135768060_+_1.R fitVsDatCorrelation=0.865300847975325 cont.fitVsDatCorrelation=0.302841914924140 fstatistic=8553.3413305405,36,324 cont.fstatistic=2359.55402686164,36,324 residuals=-0.433289017374816,-0.0708026537335186,-0.00672378547831297,0.0658863441047929,1.00646455931646 cont.residuals=-0.414262192170584,-0.142938206977862,-0.0412400910701911,0.0749133206432573,1.50460392403616 predictedValues: Include Exclude Both chr3.15314_chr3_135763631_135768060_+_1.R.tl.Lung 46.1171358023291 44.0274597959131 57.0813669766496 chr3.15314_chr3_135763631_135768060_+_1.R.tl.cerebhem 44.235187339809 44.0478204067088 74.8993575476554 chr3.15314_chr3_135763631_135768060_+_1.R.tl.cortex 47.7915837463648 43.9757483830241 57.051464115875 chr3.15314_chr3_135763631_135768060_+_1.R.tl.heart 49.4044053099173 47.0137785915251 57.1287676467732 chr3.15314_chr3_135763631_135768060_+_1.R.tl.kidney 75.3243334777219 48.652937957502 110.031598395388 chr3.15314_chr3_135763631_135768060_+_1.R.tl.liver 51.0053616548372 45.703494282972 62.6518119241721 chr3.15314_chr3_135763631_135768060_+_1.R.tl.stomach 47.0453101271439 43.1754406957929 59.3898452187596 chr3.15314_chr3_135763631_135768060_+_1.R.tl.testicle 45.7415476799751 47.1287133361766 60.6601058007543 diffExp=2.08967600641596,0.187366933100208,3.8158353633407,2.39062671839226,26.6713955202199,5.30186737186525,3.86986943135108,-1.38716565620150 diffExpScore=1.04038126186365 diffExp1.5=0,0,0,0,1,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,1,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,1,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,0,0,1,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 55.7345281855008 50.2740248202886 45.4115662203946 cerebhem 52.8506342728412 47.2911787222468 47.0452510776324 cortex 50.5565600066626 47.7310159587436 48.7078336881441 heart 56.1774040712843 46.4131104852562 51.8618637199741 kidney 48.9568466353001 49.3415957900824 59.4885996560913 liver 59.8319859369785 50.176281750092 50.8502833252812 stomach 51.9100841775418 49.6549942242023 52.1354359288046 testicle 51.5359125286694 49.8068538322736 49.0355979662284 cont.diffExp=5.46050336521218,5.55945555059439,2.82554404791907,9.76429358602807,-0.384749154782291,9.65570418688651,2.25508995333944,1.72905869639583 cont.diffExpScore=0.99391252349734 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=0,0,0,1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.696397721323344 cont.tran.correlation=0.108097572904874 tran.covariance=0.00505967847305063 cont.tran.covariance=0.000179617838428313 tran.mean=48.1493911617321 cont.tran.mean=51.1401882123728 weightedLogRatios: wLogRatio Lung 0.176580971018720 cerebhem 0.0160763497357651 cortex 0.318303301796224 heart 0.192207557108209 kidney 1.79349718215576 liver 0.42552980772492 stomach 0.326892688942687 testicle -0.114660194542721 cont.weightedLogRatios: wLogRatio Lung 0.409253353999005 cerebhem 0.434791409422672 cortex 0.22396836123986 heart 0.750947295335137 kidney -0.0304897849192351 liver 0.704615122273765 stomach 0.174427752239363 testicle 0.133953065023822 varWeightedLogRatios=0.351333425384535 cont.varWeightedLogRatios=0.0764813193559215 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.64863184117064 0.0759865383296369 48.0168187862767 2.29330357358971e-149 *** df.mm.trans1 0.188513284245069 0.0643598041991789 2.92905310373014 0.00364101114070948 ** df.mm.trans2 0.147238667547033 0.0643598041991789 2.28774262723614 0.0227964604520864 * df.mm.exp2 -0.312869250899887 0.0896288308220859 -3.49072109978691 0.000548471582462034 *** df.mm.exp3 0.0350137458008745 0.0896288308220859 0.390652711630001 0.69631068869728 df.mm.exp4 0.13365214024619 0.0896288308220859 1.49117353222526 0.136888872370743 df.mm.exp5 -0.0657725249297678 0.0896288308220859 -0.733832231509601 0.463581673455534 df.mm.exp6 0.044992536612014 0.0896288308220859 0.501987320367088 0.616017398749471 df.mm.exp7 -0.0392606171719874 0.0896288308220859 -0.438035583102943 0.661652374945238 df.mm.exp8 -0.000917156767460767 0.0896288308220859 -0.0102328319922117 0.991841821574296 df.mm.trans1:exp2 0.271205226616038 0.077620844403424 3.4939741856773 0.00054207312486715 *** df.mm.trans2:exp2 0.313331596545827 0.077620844403424 4.03669399571754 6.7703713101686e-05 *** df.mm.trans1:exp3 0.000651215628356954 0.077620844403424 0.00838970038733857 0.993311229433218 df.mm.trans2:exp3 -0.0361889624756026 0.0776208444034241 -0.466227374279971 0.641366051033176 df.mm.trans1:exp4 -0.0647971340280259 0.0776208444034241 -0.834790377842985 0.404450844229134 df.mm.trans2:exp4 -0.0680249455059267 0.077620844403424 -0.876374716466315 0.381475643197621 df.mm.trans1:exp5 0.556391170973435 0.077620844403424 7.16806387832714 5.17476857027969e-12 *** df.mm.trans2:exp5 0.165671195881398 0.0776208444034241 2.13436477217825 0.0335632253964797 * df.mm.trans1:exp6 0.0557536307596356 0.077620844403424 0.718281683072958 0.473101248465672 df.mm.trans2:exp6 -0.00763130582885802 0.0776208444034241 -0.0983151611852522 0.921742834217747 df.mm.trans1:exp7 0.0591872094333797 0.077620844403424 0.76251694874333 0.44630629372776 df.mm.trans2:exp7 0.0197189228229181 0.077620844403424 0.254041591204955 0.799624569218447 df.mm.trans1:exp8 -0.0072604090577596 0.077620844403424 -0.0935368471389539 0.925534892062315 df.mm.trans2:exp8 0.0689860714877552 0.0776208444034241 0.888757034504922 0.374793019406691 df.mm.trans1:probe2 -0.00620382069280413 0.038810422201712 -0.159849348212720 0.87309935519848 df.mm.trans1:probe3 -0.0547324050297636 0.038810422201712 -1.4102501834507 0.159425104859320 df.mm.trans1:probe4 0.0467612317854334 0.038810422201712 1.20486274388869 0.229135612498162 df.mm.trans1:probe5 -0.0025519800243124 0.038810422201712 -0.0657550183568944 0.947613422500272 df.mm.trans1:probe6 -0.0369178419552071 0.038810422201712 -0.951235257460779 0.342194009981636 df.mm.trans2:probe2 0.0230225655244969 0.038810422201712 0.593205747797336 0.553457316086904 df.mm.trans2:probe3 0.0143406332584067 0.038810422201712 0.369504696029152 0.711992953813627 df.mm.trans2:probe4 -0.0519804341997072 0.038810422201712 -1.33934214705385 0.181398079353766 df.mm.trans2:probe5 -0.070596672615672 0.038810422201712 -1.81901326011748 0.0698323771377573 . df.mm.trans2:probe6 -0.0142989406731086 0.038810422201712 -0.368430433423058 0.712792914416418 df.mm.trans3:probe2 0.344057902694245 0.038810422201712 8.86509043643096 5.20318729430241e-17 *** df.mm.trans3:probe3 -0.163776059312900 0.038810422201712 -4.21989893492258 3.17713414145571e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.14746885143447 0.144474526793000 28.7072672497959 5.1161862749076e-91 *** df.mm.trans1 -0.0534137110096547 0.122368414992527 -0.436499165351752 0.66276533410259 df.mm.trans2 -0.282372226905804 0.122368414992527 -2.30755809759446 0.0216535677967975 * df.mm.exp2 -0.149637985925090 0.170412854759305 -0.878090952331282 0.380545045488855 df.mm.exp3 -0.219487301043924 0.170412854759305 -1.28797385240646 0.198674170687589 df.mm.exp4 -0.204808684725699 0.170412854759305 -1.20183823582426 0.230303904600456 df.mm.exp5 -0.418399563182463 0.170412854759305 -2.45521127953299 0.0146052175805671 * df.mm.exp6 -0.0441244083509385 0.170412854759305 -0.25892652530973 0.795856353131058 df.mm.exp7 -0.221554378147444 0.170412854759305 -1.30010367152391 0.194489539521732 df.mm.exp8 -0.164436580318029 0.170412854759305 -0.964930612483917 0.335299219737315 df.mm.trans1:exp2 0.0965078490598367 0.147581861352986 0.653927577380323 0.513622316919147 df.mm.trans2:exp2 0.0884732291125739 0.147581861352986 0.59948579250511 0.549267956182889 df.mm.trans1:exp3 0.121980160237194 0.147581861352986 0.826525422019457 0.409114349869587 df.mm.trans2:exp3 0.16758017881413 0.147581861352986 1.1355066081821 0.257002380834198 df.mm.trans1:exp4 0.212723447500147 0.147581861352986 1.44139290255566 0.150439497837288 df.mm.trans2:exp4 0.124902119049348 0.147581861352986 0.846324324034696 0.397996476187472 df.mm.trans1:exp5 0.288738941865829 0.147581861352986 1.95646632464693 0.0512689880624449 . df.mm.trans2:exp5 0.399678477926908 0.147581861352986 2.70818157639954 0.007124515274972 ** df.mm.trans1:exp6 0.115064957710722 0.147581861352986 0.779668698143807 0.436155052866248 df.mm.trans2:exp6 0.0421783097399479 0.147581861352986 0.285796027731795 0.775216981661185 df.mm.trans1:exp7 0.150467599103024 0.147581861352986 1.01955347170433 0.308700965843768 df.mm.trans2:exp7 0.209164813624831 0.147581861352986 1.41727995369669 0.157361979092537 df.mm.trans1:exp8 0.0861156251316977 0.147581861352986 0.583510902642207 0.559955381616087 df.mm.trans2:exp8 0.155100643473599 0.147581861352986 1.05094651911613 0.294066366151448 df.mm.trans1:probe2 -0.158004320637926 0.0737909306764929 -2.14124309301143 0.033000138569442 * df.mm.trans1:probe3 -0.130258024916482 0.0737909306764929 -1.76523081796525 0.0784669378917071 . df.mm.trans1:probe4 -0.118198050268768 0.0737909306764929 -1.60179644280353 0.110175103695564 df.mm.trans1:probe5 -0.067540661675831 0.0737909306764929 -0.915297598995414 0.360716117390941 df.mm.trans1:probe6 -0.187096551934600 0.0737909306764929 -2.53549521898362 0.0116985748456949 * df.mm.trans2:probe2 0.0951154278589326 0.0737909306764929 1.28898533989127 0.198322713285654 df.mm.trans2:probe3 0.183388637206635 0.0737909306764929 2.48524629687393 0.0134503124347732 * df.mm.trans2:probe4 0.0783461337009702 0.0737909306764929 1.06173120440028 0.289148482190006 df.mm.trans2:probe5 0.0522041641254334 0.0737909306764929 0.7074604378457 0.479788940826655 df.mm.trans2:probe6 0.0624728633844355 0.0737909306764929 0.846619805600814 0.397831948797761 df.mm.trans3:probe2 0.0271417952437511 0.0737909306764929 0.367820204934717 0.713247468745802 df.mm.trans3:probe3 -0.0148900303070151 0.0737909306764929 -0.201786725963582 0.840210091890802