chr18.11215_chr18_74578103_74580724_-_1.R fitVsDatCorrelation=0.917856138385624 cont.fitVsDatCorrelation=0.186324781357152 fstatistic=17474.0993342712,68,1060 cont.fstatistic=2838.83588028191,68,1060 residuals=-0.67340343310163,-0.0728095004314392,-0.00362753714868683,0.0690355447569938,0.649211737457021 cont.residuals=-0.527645624845455,-0.189343801581528,-0.0889350187761001,0.0715093546921605,1.02172446149024 predictedValues: Include Exclude Both chr18.11215_chr18_74578103_74580724_-_1.R.tl.Lung 56.9162117251191 57.7143806252622 45.7671427159693 chr18.11215_chr18_74578103_74580724_-_1.R.tl.cerebhem 51.4136606793065 61.7979889549746 47.1389021806984 chr18.11215_chr18_74578103_74580724_-_1.R.tl.cortex 52.0600011610336 51.9651210850114 46.0079071976159 chr18.11215_chr18_74578103_74580724_-_1.R.tl.heart 53.1658019034223 56.25768905132 47.5167173647028 chr18.11215_chr18_74578103_74580724_-_1.R.tl.kidney 60.3139653263981 57.4166088025474 45.40406198121 chr18.11215_chr18_74578103_74580724_-_1.R.tl.liver 60.3024434765623 60.3814901947646 48.6861395224442 chr18.11215_chr18_74578103_74580724_-_1.R.tl.stomach 53.1070530647563 57.1330969613441 46.8399990399034 chr18.11215_chr18_74578103_74580724_-_1.R.tl.testicle 53.248229082108 57.2425521382976 46.9468235646426 diffExp=-0.798168900143104,-10.3843282756681,0.0948800760221715,-3.09188714789766,2.89735652385067,-0.0790467182022994,-4.02604389658775,-3.99432305618959 diffExpScore=1.24455796605522 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,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 51.9306904027468 57.773502418534 51.503128096562 cerebhem 53.2539768710335 51.3080459145935 52.4921012636158 cortex 54.0540330095205 54.5715374319673 51.2344548546607 heart 53.1195346140818 50.1483704984002 56.1371556959158 kidney 53.3490302940024 57.7208345048369 53.7706879936628 liver 52.4460824364719 53.5802964174181 53.8950831590393 stomach 54.5032123485373 49.5212997434521 54.2514486292868 testicle 52.5934348499587 52.1499784571568 56.3590965421791 cont.diffExp=-5.84281201578714,1.94593095643999,-0.517504422446812,2.97116411568159,-4.37180421083450,-1.13421398094616,4.98191260508523,0.443456392801878 cont.diffExpScore=8.79949988400799 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=0.265394902426153 cont.tran.correlation=-0.436887869986347 tran.covariance=0.000899574596542248 cont.tran.covariance=-0.000414783494585812 tran.mean=56.2772683895143 cont.tran.mean=53.2514912632945 weightedLogRatios: wLogRatio Lung -0.0563806486054564 cerebhem -0.741733874821542 cortex 0.00720820658076914 heart -0.226204342911270 kidney 0.200609970063481 liver -0.00537095144247625 stomach -0.292941798711288 testicle -0.290137113534665 cont.weightedLogRatios: wLogRatio Lung -0.426824456340152 cerebhem 0.147278846894427 cortex -0.0380631475085635 heart 0.226998098349245 kidney -0.316328425998591 liver -0.0849514952961322 stomach 0.378665983952362 testicle 0.0335175145825672 varWeightedLogRatios=0.0811571676712108 cont.varWeightedLogRatios=0.0725492490866837 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.03834910721812 0.0555002877400343 72.7626697384692 0 *** df.mm.trans1 -0.138970162219465 0.0466229381545572 -2.9807251048566 0.00294130617178664 ** df.mm.trans2 0.000992575668485509 0.0413985160050646 0.0239761171237172 0.980876171600625 df.mm.exp2 -0.0628440412001073 0.0521159461327422 -1.20585052874297 0.228144218156737 df.mm.exp3 -0.199363774609192 0.0521159461327421 -3.82538914483873 0.000138189599840392 *** df.mm.exp4 -0.131243663693478 0.0521159461327421 -2.51830146879026 0.0119385913154116 * df.mm.exp5 0.0607755514822911 0.0521159461327421 1.16616037877337 0.243811919583346 df.mm.exp6 0.0411406846481804 0.0521159461327421 0.789406845716527 0.430050792274611 df.mm.exp7 -0.102564356763220 0.0521159461327421 -1.96800335356059 0.0493279938153958 * df.mm.exp8 -0.100273624304922 0.0521159461327421 -1.92404881318895 0.0546159653662223 . df.mm.trans1:exp2 -0.038832266313286 0.0456662348469105 -0.850349638928315 0.395322757859616 df.mm.trans2:exp2 0.131208490551035 0.0321604354976269 4.07981075258501 4.84517531947337e-05 *** df.mm.trans1:exp3 0.110180479843104 0.0456662348469105 2.41273405202922 0.0160022868912894 * df.mm.trans2:exp3 0.094430146371527 0.0321604354976269 2.93622100914942 0.00339413597106181 ** df.mm.trans1:exp4 0.0630788153442542 0.0456662348469105 1.38130098870023 0.167477532589421 df.mm.trans2:exp4 0.105680016280288 0.0321604354976269 3.28602565994748 0.00104942110022645 ** df.mm.trans1:exp5 -0.00279209374533776 0.0456662348469105 -0.0611413170956145 0.951258190072795 df.mm.trans2:exp5 -0.0659483114518207 0.0321604354976269 -2.05060380655252 0.0405510285690337 * df.mm.trans1:exp6 0.0166517236289984 0.0456662348469105 0.364639731846097 0.715453083990922 df.mm.trans2:exp6 0.00403554619538491 0.0321604354976269 0.125481702375662 0.900166003990619 df.mm.trans1:exp7 0.0332938856344412 0.0456662348469105 0.729070083094308 0.46611984692501 df.mm.trans2:exp7 0.0924415636492844 0.0321604354976269 2.87438780659875 0.00412900377853887 ** df.mm.trans1:exp8 0.0336579550330698 0.0456662348469105 0.737042481078267 0.461259616675739 df.mm.trans2:exp8 0.092064791220565 0.0321604354976269 2.86267240464944 0.00428354502997912 ** df.mm.trans1:probe2 -0.0408702114737386 0.0355152597123461 -1.15077889912011 0.250082733708148 df.mm.trans1:probe3 0.00690377743447751 0.0355152597123461 0.194389045452413 0.845908514208382 df.mm.trans1:probe4 -0.0395362081870114 0.0355152597123461 -1.11321748755979 0.265867410072745 df.mm.trans1:probe5 -0.0183430893253909 0.0355152597123461 -0.516484730055748 0.605623630813133 df.mm.trans1:probe6 0.0985762001318405 0.0355152597123461 2.77560127478309 0.00560692233277026 ** df.mm.trans1:probe7 0.0130049400490456 0.0355152597123461 0.36617893700844 0.714304628369168 df.mm.trans1:probe8 0.0153275215912806 0.0355152597123461 0.431575658334615 0.66613761482163 df.mm.trans1:probe9 -0.0687884845789667 0.0355152597123461 -1.93687122482322 0.0530265582294613 . df.mm.trans1:probe10 0.0323178892168275 0.0355152597123461 0.909971924141465 0.363044180950472 df.mm.trans1:probe11 0.825581832833393 0.0355152597123461 23.2458340307842 6.30404673475154e-97 *** df.mm.trans1:probe12 0.688162419194623 0.0355152597123461 19.3765278578379 7.72172646280218e-72 *** df.mm.trans1:probe13 0.76989904743676 0.0355152597123461 21.6779788088983 1.51373305436634e-86 *** df.mm.trans1:probe14 0.805083800724838 0.0355152597123461 22.6686727689892 4.46893521219253e-93 *** df.mm.trans1:probe15 0.647393259839797 0.0355152597123461 18.2285942742169 8.62474516263569e-65 *** df.mm.trans1:probe16 0.815727997454109 0.0355152597123461 22.968380466905 4.51530458351363e-95 *** df.mm.trans2:probe2 0.288962554922785 0.0355152597123461 8.13629288545885 1.13312985748012e-15 *** df.mm.trans2:probe3 0.0576463895913826 0.0355152597123461 1.62314425005720 0.104855906780705 df.mm.trans2:probe4 -0.0854265238299107 0.0355152597123461 -2.40534701201169 0.0163276404509468 * df.mm.trans2:probe5 -0.0363605861646522 0.0355152597123461 -1.02380178152019 0.306162493702548 df.mm.trans2:probe6 0.27628357144518 0.0355152597123461 7.77929187855934 1.72312190278745e-14 *** df.mm.trans3:probe2 -0.0918226798096332 0.0355152597123461 -2.58544300543895 0.00985795502918197 ** df.mm.trans3:probe3 0.184268212822291 0.0355152597123461 5.18842363296119 2.54017555742160e-07 *** df.mm.trans3:probe4 -0.149584615969297 0.0355152597123461 -4.21184068991327 2.74817443003440e-05 *** df.mm.trans3:probe5 -0.186662639210848 0.0355152597123461 -5.25584328321719 1.78106340402334e-07 *** df.mm.trans3:probe6 -0.112773627128704 0.0355152597123461 -3.17535696041948 0.00153968402677184 ** df.mm.trans3:probe7 -0.0813660358524586 0.0355152597123461 -2.29101621419858 0.0221580087565152 * df.mm.trans3:probe8 -0.142594114453100 0.0355152597123461 -4.01500976222708 6.36302283886658e-05 *** df.mm.trans3:probe9 -0.170633784850178 0.0355152597123461 -4.80452026064899 1.77450389045338e-06 *** df.mm.trans3:probe10 -0.138838330553010 0.0355152597123461 -3.90925848994274 9.8458428992382e-05 *** df.mm.trans3:probe11 -0.0577316060205185 0.0355152597123461 -1.62554368145165 0.104344016783288 df.mm.trans3:probe12 -0.098601817494257 0.0355152597123461 -2.77632258057176 0.0055945882090265 ** df.mm.trans3:probe13 -0.0508278285860409 0.0355152597123461 -1.43115463599923 0.152680648050982 df.mm.trans3:probe14 -0.09726781420753 0.0355152597123461 -2.73876116901144 0.00627050420902266 ** df.mm.trans3:probe15 -0.0760746953459093 0.0355152597123461 -2.14202841150739 0.0324185480098227 * df.mm.trans3:probe16 -0.0447266659714728 0.0355152597123461 -1.25936474444321 0.208175853354444 df.mm.trans3:probe17 -0.0424040844292378 0.0355152597123461 -1.19396802311703 0.232757600335622 df.mm.trans3:probe18 -0.126520090599485 0.0355152597123461 -3.56241490627487 0.000383793707412319 *** df.mm.trans3:probe19 -0.025413716803691 0.0355152597123461 -0.715571757310181 0.474413406146237 df.mm.trans3:probe20 -0.0809738279322395 0.0355152597123461 -2.27997285077126 0.02280724236026 * df.mm.trans3:probe21 -0.0525538330782019 0.0355152597123461 -1.47975359053710 0.139236145516328 df.mm.trans3:probe22 0.0135750751884617 0.0355152597123461 0.382232181276788 0.702365773437018 df.mm.trans3:probe23 -0.114723640745690 0.0355152597123461 -3.23026331990495 0.00127478524429122 ** df.mm.trans3:probe24 -0.130461131828014 0.0355152597123461 -3.67338245263240 0.000251321742544800 *** df.mm.trans3:probe25 -0.045414081028718 0.0355152597123461 -1.27872022889729 0.201275478376342 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.12963916195834 0.137381106358717 30.0597314391646 4.49493205434433e-144 *** df.mm.trans1 -0.187320384054825 0.115406803931701 -1.62313119914216 0.104858696486515 df.mm.trans2 -0.0774025200403096 0.102474674672404 -0.755333161952001 0.450216904117962 df.mm.exp2 -0.112540282988549 0.129003769713483 -0.872379801291864 0.383198775926577 df.mm.exp3 -0.0117132821244545 0.129003769713483 -0.090797983271882 0.927670266903838 df.mm.exp4 -0.205064781717260 0.129003769713483 -1.58960301836689 0.112222507372383 df.mm.exp5 -0.0170521349493212 0.129003769713483 -0.132183229894708 0.894864451085925 df.mm.exp6 -0.110869857188881 0.129003769713483 -0.85943114247842 0.390297010579578 df.mm.exp7 -0.157764819905881 0.129003769713483 -1.22294736236218 0.221621398129504 df.mm.exp8 -0.179826231184674 0.129003769713483 -1.39396105698359 0.163621503919571 df.mm.trans1:exp2 0.137702815172852 0.113038654788453 1.21819226733153 0.223421989841301 df.mm.trans2:exp2 -0.00614237159155796 0.0796074469079755 -0.077158253783197 0.93851221222073 df.mm.trans1:exp3 0.0517874870576703 0.113038654788453 0.458139626259603 0.64694603453524 df.mm.trans2:exp3 -0.0453044983987198 0.0796074469079755 -0.569098748400898 0.569409690221553 df.mm.trans1:exp4 0.227699573176277 0.113038654788453 2.01435140574175 0.0442248633353987 * df.mm.trans2:exp4 0.0635205681439036 0.0796074469079755 0.797922438303191 0.425094215611129 df.mm.trans1:exp5 0.0439979835838372 0.113038654788453 0.389229539808107 0.697184532034967 df.mm.trans2:exp5 0.0161400916621620 0.0796074469079755 0.202746002906230 0.839372473026083 df.mm.trans1:exp6 0.120745545208614 0.113038654788453 1.06817924748471 0.285682863867717 df.mm.trans2:exp6 0.0355210186309467 0.0796074469079755 0.446202208594984 0.655542318719567 df.mm.trans1:exp7 0.206114509372359 0.113038654788453 1.82339846274792 0.068524580050318 . df.mm.trans2:exp7 0.00363745987739903 0.0796074469079755 0.0456924574104714 0.963563982012213 df.mm.trans1:exp8 0.192507577759456 0.113038654788453 1.70302431606007 0.0888566664260176 . df.mm.trans2:exp8 0.0774197646384668 0.0796074469079755 0.97251912535221 0.331014221668883 df.mm.trans1:probe2 0.065237832555486 0.0879117184897006 0.742083463686687 0.458201163618415 df.mm.trans1:probe3 -0.0317368872360948 0.0879117184897006 -0.361008609333611 0.718164945926137 df.mm.trans1:probe4 -0.0525950755224283 0.0879117184897006 -0.598271498111939 0.549786598726417 df.mm.trans1:probe5 -0.0247927648049822 0.0879117184897006 -0.282018884750692 0.777984080680959 df.mm.trans1:probe6 -0.00732485941594201 0.0879117184897006 -0.0833206259845798 0.933612331731886 df.mm.trans1:probe7 0.0798419575880486 0.0879117184897006 0.908206084009182 0.363975781886231 df.mm.trans1:probe8 0.0277559652165292 0.0879117184897006 0.315725431073002 0.752273073360452 df.mm.trans1:probe9 -0.0122397675688815 0.0879117184897006 -0.139227941156849 0.889296476733214 df.mm.trans1:probe10 0.0299418681321360 0.0879117184897006 0.340590181224177 0.733479610058422 df.mm.trans1:probe11 0.052638376802956 0.0879117184897006 0.598764052247743 0.549458167744017 df.mm.trans1:probe12 0.0676106906914009 0.0879117184897006 0.769074838405324 0.44202020976634 df.mm.trans1:probe13 0.0567104236082201 0.0879117184897006 0.645083779301437 0.519012486382284 df.mm.trans1:probe14 -0.00864205458526413 0.0879117184897006 -0.0983037839975406 0.921709658907582 df.mm.trans1:probe15 0.0544710539223359 0.0879117184897006 0.619610842082646 0.535647184370019 df.mm.trans1:probe16 -0.0539591677938438 0.0879117184897006 -0.613788112902893 0.53948704712976 df.mm.trans2:probe2 -0.00124529838183048 0.0879117184897006 -0.0141653286185775 0.98870074655529 df.mm.trans2:probe3 0.0808338623312726 0.0879117184897006 0.919489047876397 0.358049020975251 df.mm.trans2:probe4 0.0681563732270621 0.0879117184897006 0.775282003332093 0.438345958786302 df.mm.trans2:probe5 0.0357751758957887 0.0879117184897006 0.406944335867806 0.68413102822122 df.mm.trans2:probe6 -0.0504187306600854 0.0879117184897006 -0.573515471273517 0.566417380655521 df.mm.trans3:probe2 0.127200249802299 0.0879117184897006 1.44690892167238 0.148218080403369 df.mm.trans3:probe3 0.0707154750703084 0.0879117184897006 0.804391909124074 0.421351039275369 df.mm.trans3:probe4 0.0824316595849135 0.0879117184897006 0.93766406801126 0.348630665065389 df.mm.trans3:probe5 0.105931214175621 0.0879117184897006 1.20497262475913 0.228482816330365 df.mm.trans3:probe6 0.0237287839405729 0.0879117184897006 0.269916051559757 0.787277375503234 df.mm.trans3:probe7 0.0661721582680185 0.0879117184897006 0.752711463327508 0.45179044495081 df.mm.trans3:probe8 0.186705014166548 0.0879117184897006 2.12377846064312 0.0339197581592812 * df.mm.trans3:probe9 0.118315982897233 0.0879117184897006 1.34584996095935 0.178638684840854 df.mm.trans3:probe10 0.0107528972375855 0.0879117184897006 0.122314720065963 0.90267295931744 df.mm.trans3:probe11 -0.010062786527119 0.0879117184897006 -0.114464677747119 0.908891122542665 df.mm.trans3:probe12 0.118019985100730 0.0879117184897006 1.34248297187543 0.179726877109361 df.mm.trans3:probe13 0.0776046900546898 0.0879117184897006 0.88275705887585 0.377567692183640 df.mm.trans3:probe14 0.0800898205023501 0.0879117184897006 0.911025536507208 0.362489041233845 df.mm.trans3:probe15 0.0598914921466857 0.0879117184897006 0.681268585981541 0.495850359679407 df.mm.trans3:probe16 0.054469256198749 0.0879117184897006 0.619590392890913 0.535660645649662 df.mm.trans3:probe17 0.105614868652179 0.0879117184897006 1.20137417930868 0.229874442932365 df.mm.trans3:probe18 0.0706139070743453 0.0879117184897006 0.803236568315043 0.422018084939510 df.mm.trans3:probe19 0.0846591857837736 0.0879117184897006 0.963002284999035 0.335766006236693 df.mm.trans3:probe20 0.0253790039129029 0.0879117184897006 0.288687382625516 0.772877062668501 df.mm.trans3:probe21 0.0621567798257252 0.0879117184897006 0.707036341611355 0.479699292897076 df.mm.trans3:probe22 0.112180089232622 0.0879117184897006 1.27605387722872 0.202216003878332 df.mm.trans3:probe23 0.0463541040457664 0.0879117184897006 0.527280149246509 0.598109393290139 df.mm.trans3:probe24 0.0692982785720885 0.0879117184897006 0.788271231214837 0.430714317128074 df.mm.trans3:probe25 0.169935082805545 0.0879117184897006 1.93301968980909 0.053499858462864 .