chr17.10612_chr17_31270502_31271395_+_0.R fitVsDatCorrelation=0.69279049979727 cont.fitVsDatCorrelation=0.297125903913056 fstatistic=9385.34674433826,44,508 cont.fstatistic=5348.42541111082,44,508 residuals=-0.465914461698932,-0.0791576170452672,-0.00590912349915727,0.0699824905261506,1.26312739980445 cont.residuals=-0.617661537899057,-0.116428061287863,-0.0224831883243790,0.100922503844184,1.05014682432288 predictedValues: Include Exclude Both chr17.10612_chr17_31270502_31271395_+_0.R.tl.Lung 58.6689621038957 59.252238557136 58.9292645030722 chr17.10612_chr17_31270502_31271395_+_0.R.tl.cerebhem 57.0842845515876 67.6173268000594 63.0892202987472 chr17.10612_chr17_31270502_31271395_+_0.R.tl.cortex 60.0528002698308 56.3290380256522 59.5129582010348 chr17.10612_chr17_31270502_31271395_+_0.R.tl.heart 57.8437869009113 56.7934570752758 60.5183890211107 chr17.10612_chr17_31270502_31271395_+_0.R.tl.kidney 55.1423760832046 53.5678873893415 54.9661063592241 chr17.10612_chr17_31270502_31271395_+_0.R.tl.liver 57.0385914700343 51.4726409037584 55.2758414444185 chr17.10612_chr17_31270502_31271395_+_0.R.tl.stomach 85.5945219662367 57.5756829967705 61.7458277001693 chr17.10612_chr17_31270502_31271395_+_0.R.tl.testicle 58.3485787645927 57.8763829118222 57.2492273353898 diffExp=-0.583276453240288,-10.5330422484719,3.72376224417861,1.05032982563549,1.57448869386304,5.56595056627583,28.0188389694662,0.472195852770497 diffExpScore=1.70099587116317 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,1,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,0,1,0 diffExp1.3Score=0.5 diffExp1.2=0,0,0,0,0,0,1,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 58.3052044671371 55.1735973368826 58.3033165763846 cerebhem 60.5467917127189 57.164247382675 60.9028556649393 cortex 63.95232698175 56.0370286735917 59.4140441261267 heart 62.9595733798009 57.0017538939288 58.639457371085 kidney 59.4287049469928 69.0882023370464 58.2693097249519 liver 65.656128527517 58.9930668465716 62.0233045204192 stomach 64.0886281287586 58.9780990220617 59.0316113072256 testicle 58.9841438370265 62.4665855766747 62.3463251574908 cont.diffExp=3.13160713025449,3.38254433004386,7.91529830815834,5.95781948587214,-9.65949739005356,6.66306168094539,5.11052910669689,-3.48244173964829 cont.diffExpScore=2.26299905825132 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=0.0219857110745139 cont.tran.correlation=-0.303961234056383 tran.covariance=0.000530151824997469 cont.tran.covariance=-0.000951105485277392 tran.mean=59.3911597981319 cont.tran.mean=60.5515051906959 weightedLogRatios: wLogRatio Lung -0.0403312413019905 cerebhem -0.699219199552943 cortex 0.260102802522205 heart 0.0741899742340196 kidney 0.115742628014042 liver 0.409929186827208 stomach 1.68575373679980 testicle 0.0330091889704614 cont.weightedLogRatios: wLogRatio Lung 0.222930035007917 cerebhem 0.234244066235724 cortex 0.540666335198607 heart 0.406867234464731 kidney -0.626535709948063 liver 0.442054552767766 stomach 0.342268508656875 testicle -0.235530130612374 varWeightedLogRatios=0.452166248600325 cont.varWeightedLogRatios=0.157558005068786 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.13411175803635 0.0747896195673279 55.2765448193609 4.9527521093068e-217 *** df.mm.trans1 -0.0910029778186679 0.0596939165236081 -1.52449333396775 0.128007802656255 df.mm.trans2 -0.0716566256755282 0.0596939165236081 -1.20040080880250 0.230543179481495 df.mm.exp2 0.0364666207606928 0.0797502806731131 0.457260093041743 0.647679591961485 df.mm.exp3 -0.0371362940239344 0.0797502806731131 -0.465657220394642 0.641660450761102 df.mm.exp4 -0.0831566704628678 0.0797502806731131 -1.04271320127031 0.297577097583121 df.mm.exp5 -0.0932250534207383 0.0797502806731131 -1.16896207303466 0.242967142517240 df.mm.exp6 -0.104934020820194 0.0797502806731131 -1.31578246414337 0.188840539710341 df.mm.exp7 0.302318612811996 0.0797502806731131 3.79081565933497 0.000168181347852183 *** df.mm.exp8 -4.63130506608756e-05 0.079750280673113 -0.000580725864159742 0.999536875778476 df.mm.trans1:exp2 -0.063848601123418 0.0621409180660277 -1.02748081474393 0.304683093329969 df.mm.trans2:exp2 0.095594082199278 0.0621409180660277 1.53834357737851 0.124587242565808 df.mm.trans1:exp3 0.0604496413907095 0.0621409180660276 0.972783204240384 0.331124024649543 df.mm.trans2:exp3 -0.0134570915500514 0.0621409180660277 -0.216557655871009 0.828640024271786 df.mm.trans1:exp4 0.0689918858020752 0.0621409180660276 1.11024889797682 0.267417035089564 df.mm.trans2:exp4 0.0407742363615591 0.0621409180660277 0.656157611289787 0.512019803443508 df.mm.trans1:exp5 0.0312327175369337 0.0621409180660277 0.50261113786165 0.615455507607678 df.mm.trans2:exp5 -0.00762873477119892 0.0621409180660277 -0.122765079896197 0.902341679207592 df.mm.trans1:exp6 0.0767512705226784 0.0621409180660277 1.23511645645670 0.217358096325330 df.mm.trans2:exp6 -0.035819118114304 0.0621409180660277 -0.576417588105867 0.564588258783473 df.mm.trans1:exp7 0.0753918401907945 0.0621409180660277 1.21323988343215 0.225601942565712 df.mm.trans2:exp7 -0.331021865300606 0.0621409180660277 -5.32695485684456 1.50324970822357e-07 *** df.mm.trans1:exp8 -0.00542951796536167 0.0621409180660277 -0.0873742798520058 0.930408442914562 df.mm.trans2:exp8 -0.0234478408802880 0.0621409180660277 -0.377333351518457 0.706083474842699 df.mm.trans1:probe2 -0.026922042777391 0.0432892616824735 -0.621910416834181 0.534279781069735 df.mm.trans1:probe3 0.0226641576628893 0.0432892616824735 0.52355149480559 0.600818835702405 df.mm.trans1:probe4 0.220378251451674 0.0432892616824735 5.0908295241473 5.02982595019905e-07 *** df.mm.trans1:probe5 0.0620242085117406 0.0432892616824735 1.43278508574916 0.152534348264884 df.mm.trans1:probe6 0.211490311835062 0.0432892616824735 4.88551441201151 1.38482002787630e-06 *** df.mm.trans2:probe2 0.060011894781202 0.0432892616824735 1.38629979927560 0.166263511842088 df.mm.trans2:probe3 0.0916300730769716 0.0432892616824735 2.11669290525391 0.0347714897961643 * df.mm.trans2:probe4 0.167088575851259 0.0432892616824735 3.85981579165876 0.000128103407057027 *** df.mm.trans2:probe5 -0.0408432764321162 0.0432892616824735 -0.943496720542416 0.34587538389832 df.mm.trans2:probe6 0.0510360171505057 0.0432892616824735 1.17895328233719 0.238968664311883 df.mm.trans3:probe2 -0.0961882494207423 0.0432892616824734 -2.22198867992443 0.0267238204087029 * df.mm.trans3:probe3 0.0690174701264862 0.0432892616824735 1.59433234580735 0.111483555158778 df.mm.trans3:probe4 0.194655379305285 0.0432892616824735 4.49662044904072 8.56178206763477e-06 *** df.mm.trans3:probe5 0.123537231903835 0.0432892616824734 2.85376158202881 0.00449686800678689 ** df.mm.trans3:probe6 0.0600223005072321 0.0432892616824735 1.38654017588693 0.166190196594405 df.mm.trans3:probe7 0.323923294786947 0.0432892616824735 7.48276321187742 3.22896523426891e-13 *** df.mm.trans3:probe8 -0.00109197301711586 0.0432892616824735 -0.0252250321367334 0.979885376107515 df.mm.trans3:probe9 0.305959838585764 0.0432892616824735 7.06779988141119 5.22364065161452e-12 *** df.mm.trans3:probe10 0.100249266039006 0.0432892616824735 2.31579985757979 0.0209664978785726 * df.mm.trans3:probe11 0.073657738279322 0.0432892616824735 1.70152447550621 0.0894564306037926 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.915819325345 0.0990266829403457 39.5430727262057 3.58412255174054e-157 *** df.mm.trans1 0.159737754493716 0.079038917155198 2.02100130218207 0.0438036492128204 * df.mm.trans2 0.0799277359818867 0.079038917155198 1.01124533152375 0.312380431479793 df.mm.exp2 0.0295481656610516 0.105594944917596 0.279825570098174 0.779725233961395 df.mm.exp3 0.089103072171771 0.105594944917596 0.843819486257652 0.399167452586981 df.mm.exp4 0.103650151770244 0.105594944917596 0.98158251657909 0.326772805854738 df.mm.exp5 0.244568896263146 0.105594944917596 2.31610420796187 0.0209497396962687 * df.mm.exp6 0.123823770337589 0.105594944917596 1.17262971664239 0.24149390807227 df.mm.exp7 0.148843116862977 0.105594944917596 1.40956668881385 0.159279555476269 df.mm.exp8 0.0686788010873833 0.105594944917596 0.650398569183201 0.515728833743733 df.mm.trans1:exp2 0.00817695736189883 0.0822789182047765 0.0993809537158468 0.920875009068667 df.mm.trans2:exp2 0.00589596180242513 0.0822789182047765 0.0716582319149021 0.942902099053844 df.mm.trans1:exp3 0.00334348288826519 0.0822789182047765 0.0406359607201435 0.967602080633057 df.mm.trans2:exp3 -0.0735749033785946 0.0822789182047765 -0.894213304986348 0.371631228660043 df.mm.trans1:exp4 -0.026848683555898 0.0822789182047765 -0.326313035485916 0.744321913300047 df.mm.trans2:exp4 -0.0710526440824672 0.0822789182047765 -0.863558316428404 0.388238101752901 df.mm.trans1:exp5 -0.225482898175911 0.0822789182047765 -2.74046989308643 0.00635113286284232 ** df.mm.trans2:exp5 -0.0196694429915223 0.0822789182047765 -0.239058113799806 0.811156933276616 df.mm.trans1:exp6 -0.00508418224267541 0.0822789182047765 -0.061792040459524 0.950752745592462 df.mm.trans2:exp6 -0.0568883741542045 0.0822789182047765 -0.691408873566133 0.489624472895796 df.mm.trans1:exp7 -0.0542675367831598 0.0822789182047765 -0.659555788617666 0.509837820479212 df.mm.trans2:exp7 -0.0821614746283651 0.0822789182047765 -0.998572616425035 0.318477176954261 df.mm.trans1:exp8 -0.0571015016463935 0.0822789182047765 -0.693999178553598 0.487999950688205 df.mm.trans2:exp8 0.0554684521734683 0.0822789182047765 0.674151451960245 0.500521675185698 df.mm.trans1:probe2 -0.00442513234152761 0.0573180077148655 -0.0772031778135224 0.93849231065258 df.mm.trans1:probe3 -0.0342199353766021 0.0573180077148655 -0.597018925480328 0.550760731049367 df.mm.trans1:probe4 -0.0781451947743585 0.0573180077148655 -1.36336201989260 0.173372346388499 df.mm.trans1:probe5 -0.0172402545386597 0.0573180077148655 -0.300782515408128 0.763703403650534 df.mm.trans1:probe6 -0.0336867232802892 0.0573180077148655 -0.587716227819141 0.556983931116034 df.mm.trans2:probe2 -0.00716929653311546 0.0573180077148655 -0.125079304374637 0.900510248294575 df.mm.trans2:probe3 0.0539195656602869 0.0573180077148655 0.940708998967925 0.34730110342444 df.mm.trans2:probe4 0.122925112389900 0.0573180077148655 2.14461592945457 0.0324572726132971 * df.mm.trans2:probe5 -0.00208358370919022 0.0573180077148655 -0.0363512932891043 0.971016530394623 df.mm.trans2:probe6 0.0829451652542869 0.0573180077148655 1.44710482030196 0.148484347986748 df.mm.trans3:probe2 -0.0800809086560788 0.0573180077148655 -1.39713349868072 0.162983379182981 df.mm.trans3:probe3 -0.0966496875921511 0.0573180077148655 -1.68620109884044 0.0923710776954866 . df.mm.trans3:probe4 -0.051797058930141 0.0573180077148655 -0.903678634257683 0.366594152685468 df.mm.trans3:probe5 -0.124330569159349 0.0573180077148655 -2.16913626478165 0.030535437338669 * df.mm.trans3:probe6 -0.115336972258446 0.0573180077148655 -2.01222926016902 0.0447231138533829 * df.mm.trans3:probe7 -0.124646438576175 0.0573180077148655 -2.17464708815843 0.0301172642568634 * df.mm.trans3:probe8 -0.117634946882593 0.0573180077148655 -2.05232092971167 0.0406503468924406 * df.mm.trans3:probe9 -0.119458667832342 0.0573180077148655 -2.08413852111890 0.0376466387054471 * df.mm.trans3:probe10 -0.0617777068517942 0.0573180077148655 -1.07780624824076 0.281631758853117 df.mm.trans3:probe11 -0.096371239036221 0.0573180077148655 -1.68134313941318 0.0933109143032657 .