chr16.9620_chr16_29357155_29369736_+_2.R fitVsDatCorrelation=0.837342201872038 cont.fitVsDatCorrelation=0.217313051179170 fstatistic=7351.68687425875,59,853 cont.fstatistic=2296.08935235326,59,853 residuals=-0.956529913325715,-0.109894946392833,0.00257691366384951,0.108474670833232,1.18402845543984 cont.residuals=-0.748702998370927,-0.252143611397037,-0.0186689333851168,0.175388192255246,1.32797891420289 predictedValues: Include Exclude Both chr16.9620_chr16_29357155_29369736_+_2.R.tl.Lung 78.182376885687 75.6033593697923 59.5529753456616 chr16.9620_chr16_29357155_29369736_+_2.R.tl.cerebhem 78.5881456427177 67.3732162485114 67.3201717750916 chr16.9620_chr16_29357155_29369736_+_2.R.tl.cortex 77.9017723380628 81.5954379787517 94.166024768526 chr16.9620_chr16_29357155_29369736_+_2.R.tl.heart 127.107565535162 76.9755521555746 96.0444636228074 chr16.9620_chr16_29357155_29369736_+_2.R.tl.kidney 93.1874355971201 84.8601805920973 68.0505164715926 chr16.9620_chr16_29357155_29369736_+_2.R.tl.liver 87.6811009747387 74.3873441654243 64.3082883578778 chr16.9620_chr16_29357155_29369736_+_2.R.tl.stomach 84.3833723608488 66.7888090309078 63.523229453654 chr16.9620_chr16_29357155_29369736_+_2.R.tl.testicle 90.8923853109315 73.8951409293327 68.4437449076098 diffExp=2.57901751589472,11.2149293942063,-3.69366564068889,50.1320133795872,8.32725500502282,13.2937568093144,17.594563329941,16.9972443815988 diffExpScore=1.05438567050024 diffExp1.5=0,0,0,1,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,1,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,1,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,0,1,0,0,1,1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 90.899231045341 86.4117823461675 88.9494485902458 cerebhem 81.0029177143495 82.513571592553 84.4266647051229 cortex 87.8182431153868 86.3855773881068 85.8528005752188 heart 81.4034546835099 77.6417626220444 86.8517653998577 kidney 84.2036642804632 72.6828787569405 86.4510002084912 liver 89.4859733270886 74.9417067533754 88.5223109255648 stomach 86.4506938234665 74.4309022768514 75.4028259605134 testicle 87.4350331695747 77.9429624566668 84.7485091364956 cont.diffExp=4.4874486991735,-1.51065387820360,1.43266572728007,3.76169206146545,11.5207855235227,14.5442665737132,12.0197915466152,9.49207071290786 cont.diffExpScore=1.03561897108498 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=0.214560724010867 cont.tran.correlation=0.235842324394188 tran.covariance=0.00314174904122338 cont.tran.covariance=0.000616627462324927 tran.mean=82.4626996947288 cont.tran.mean=82.6031472094929 weightedLogRatios: wLogRatio Lung 0.145655209774145 cerebhem 0.660119665295085 cortex -0.202837619951426 heart 2.30423235520895 kidney 0.420094520423508 liver 0.722052238462688 stomach 1.00980473505286 testicle 0.91220391849013 cont.weightedLogRatios: wLogRatio Lung 0.227035899010863 cerebhem -0.0813702888475545 cortex 0.0734763053996531 heart 0.20702713661162 kidney 0.641449844141167 liver 0.781390954688769 stomach 0.656406273132788 testicle 0.507186541441522 varWeightedLogRatios=0.557167625225979 cont.varWeightedLogRatios=0.0974415261942364 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.63057701532831 0.0945757671539646 48.961559125288 5.25311371558814e-250 *** df.mm.trans1 -0.325569808446164 0.0816733191130393 -3.98624436941961 7.28734204881188e-05 *** df.mm.trans2 -0.347328970337782 0.0721580082795523 -4.81345007462194 1.75436063733959e-06 *** df.mm.exp2 -0.232670220337269 0.0928182704597836 -2.50672867728214 0.0123704165230832 * df.mm.exp3 -0.385516110490723 0.0928182704597836 -4.15345070082683 3.60452778114174e-05 *** df.mm.exp4 0.0260315981178278 0.0928182704597836 0.280457694254353 0.779194382516906 df.mm.exp5 0.157688828079059 0.0928182704597836 1.6988985821211 0.0897029367269948 . df.mm.exp6 0.0216249550548526 0.0928182704597836 0.232981663499346 0.81583153720203 df.mm.exp7 -0.112178470456707 0.0928182704597836 -1.20858177922322 0.227158481486166 df.mm.exp8 -0.0113675866666548 0.0928182704597836 -0.122471433806561 0.902554515810445 df.mm.trans1:exp2 0.237846826967266 0.085793857540376 2.77230601101404 0.00568736763108656 ** df.mm.trans2:exp2 0.117417055933654 0.0633629630490801 1.85308657113627 0.0642151238996959 . df.mm.trans1:exp3 0.381920551924075 0.085793857540376 4.45160717647341 9.65414294384232e-06 *** df.mm.trans2:exp3 0.461788745468185 0.06336296304908 7.287991647589 7.17179088599926e-13 *** df.mm.trans1:exp4 0.459957839943655 0.085793857540376 5.36119779585843 1.06488900898331e-07 *** df.mm.trans2:exp4 -0.00804444945901521 0.06336296304908 -0.126958227202602 0.899003381495222 df.mm.trans1:exp5 0.0178798106990182 0.085793857540376 0.208404321843247 0.834963033445598 df.mm.trans2:exp5 -0.0421845784956957 0.0633629630490801 -0.665760824079835 0.50574397809483 df.mm.trans1:exp6 0.0930371621675334 0.085793857540376 1.08442684400510 0.278481989114742 df.mm.trans2:exp6 -0.0378398512953873 0.06336296304908 -0.59719194738537 0.550537716662059 df.mm.trans1:exp7 0.188504580071136 0.085793857540376 2.19718037485869 0.0282758045502935 * df.mm.trans2:exp7 -0.0117867108193471 0.0633629630490801 -0.186018933650834 0.85247414006734 df.mm.trans1:exp8 0.161999551748514 0.085793857540376 1.8882418437972 0.0593326194413393 . df.mm.trans2:exp8 -0.0114860578186816 0.06336296304908 -0.181274000866794 0.856195575546548 df.mm.trans1:probe2 -0.398974829348060 0.0587390388378108 -6.79232818994029 2.06819742785732e-11 *** df.mm.trans1:probe3 0.28106287643588 0.0587390388378108 4.78494170141166 2.01512176994348e-06 *** df.mm.trans1:probe4 0.529412547304947 0.0587390388378108 9.01295897549076 1.29434859224536e-18 *** df.mm.trans1:probe5 -0.081995738323156 0.0587390388378108 -1.39593258496383 0.16309800307277 df.mm.trans1:probe6 -0.0733099882251693 0.0587390388378107 -1.24806244153214 0.212350668917686 df.mm.trans1:probe7 -0.312598885390534 0.0587390388378108 -5.3218250004682 1.31398321234155e-07 *** df.mm.trans1:probe8 0.180601965104512 0.0587390388378108 3.07464964830608 0.00217463443310001 ** df.mm.trans1:probe9 -0.349914982938487 0.0587390388378108 -5.95711114553076 3.75159780113792e-09 *** df.mm.trans1:probe10 0.68887091330588 0.0587390388378108 11.7276504167523 1.48842871044e-29 *** df.mm.trans1:probe11 0.21278940762339 0.0587390388378107 3.62262324739328 0.000308865680887433 *** df.mm.trans1:probe12 0.115633045618577 0.0587390388378107 1.96858933864855 0.0493236939971107 * df.mm.trans1:probe13 0.0282781353272060 0.0587390388378108 0.481419782936645 0.630341654014722 df.mm.trans1:probe14 0.0952063180934954 0.0587390388378108 1.62083547802642 0.105422502189811 df.mm.trans1:probe15 -0.201052875685337 0.0587390388378107 -3.42281521222165 0.000649270211370465 *** df.mm.trans1:probe16 0.0773798066364254 0.0587390388378108 1.31734887338019 0.188075448545917 df.mm.trans1:probe17 0.244875413289449 0.0587390388378108 4.16886993955749 3.37370792275137e-05 *** df.mm.trans1:probe18 0.114261506109396 0.0587390388378108 1.94523962887600 0.0520743860558939 . df.mm.trans1:probe19 0.0969434258588257 0.0587390388378108 1.65040878735698 0.0992274780019505 . df.mm.trans1:probe20 0.163298546480067 0.0587390388378108 2.78006841295044 0.00555439019728339 ** df.mm.trans1:probe21 0.179351371442029 0.0587390388378107 3.05335897540392 0.00233320580732968 ** df.mm.trans1:probe22 0.139067805401641 0.0587390388378108 2.36755330276398 0.0181283865497924 * df.mm.trans2:probe2 0.212001958323995 0.0587390388378108 3.60921735388573 0.000325017048147559 *** df.mm.trans2:probe3 0.116983934017382 0.0587390388378108 1.99158747456518 0.046734837200829 * df.mm.trans2:probe4 0.0635884186062933 0.0587390388378108 1.08255803745568 0.279310565343738 df.mm.trans2:probe5 0.0872476912478944 0.0587390388378108 1.48534421015641 0.137822127916613 df.mm.trans2:probe6 0.196220770861563 0.0587390388378108 3.34055127124848 0.000872436454606594 *** df.mm.trans3:probe2 0.267736384581517 0.0587390388378107 4.55806546853425 5.91664618450438e-06 *** df.mm.trans3:probe3 0.484953586423946 0.0587390388378108 8.25606949005399 5.70043171686525e-16 *** df.mm.trans3:probe4 0.751514065350896 0.0587390388378107 12.7941158081589 2.06190225737043e-34 *** df.mm.trans3:probe5 0.119211131417691 0.0587390388378107 2.02950429180251 0.042716957610837 * df.mm.trans3:probe6 -0.145131873535495 0.0587390388378107 -2.47079074508234 0.0136759286153319 * df.mm.trans3:probe7 -0.096407649909947 0.0587390388378108 -1.64128749495112 0.101106376390462 df.mm.trans3:probe8 0.124863521044064 0.0587390388378107 2.1257331327609 0.0338122092331122 * df.mm.trans3:probe9 -0.168842564287225 0.0587390388378107 -2.87445228297709 0.00414791467008904 ** df.mm.trans3:probe10 -0.0460163827671439 0.0587390388378107 -0.783403740980569 0.43360751630736 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.53376725021584 0.168866144648589 26.8482901629016 1.42060328935732e-115 *** df.mm.trans1 0.0289807715243258 0.145828671913604 0.198731642714921 0.842520026764819 df.mm.trans2 -0.0864995144164712 0.128838972501828 -0.671376934609158 0.502162202653998 df.mm.exp2 -0.109242688144427 0.165728113629546 -0.65916811428035 0.50996572362489 df.mm.exp3 0.000648421309327879 0.165728113629546 0.0039125607305065 0.99687915097499 df.mm.exp4 -0.193487009065302 0.165728113629546 -1.16749659926623 0.243336123253039 df.mm.exp5 -0.221040854450722 0.165728113629546 -1.33375593078202 0.182639870945264 df.mm.exp6 -0.153269525727156 0.165728113629546 -0.924825139021144 0.355318424100373 df.mm.exp7 -0.0342066703554274 0.165728113629546 -0.206402339387571 0.836525898295281 df.mm.exp8 -0.0936221696933937 0.165728113629546 -0.564914229957801 0.572280635906847 df.mm.trans1:exp2 -0.00602367846754111 0.153185941741169 -0.039322658457256 0.968642342967243 df.mm.trans2:exp2 0.063081435848086 0.113135315795962 0.557575107332996 0.577280846390723 df.mm.trans1:exp3 -0.0351307036945942 0.153185941741169 -0.229333731903107 0.81866451373076 df.mm.trans2:exp3 -0.0009517240716147 0.113135315795962 -0.0084122633584293 0.993290031066448 df.mm.trans1:exp4 0.0831531802021007 0.153185941741169 0.542825139545773 0.587392003682489 df.mm.trans2:exp4 0.0864684333478873 0.113135315795962 0.764292146440213 0.444904531097062 df.mm.trans1:exp5 0.144527751721966 0.153185941741169 0.943479212773766 0.345702986031393 df.mm.trans2:exp5 0.048022669588036 0.113135315795962 0.424471079168985 0.671329328084663 df.mm.trans1:exp6 0.137599874322865 0.153185941741169 0.898253930868939 0.369303658966558 df.mm.trans2:exp6 0.0108560575051602 0.113135315795962 0.0959563990146007 0.923577739593712 df.mm.trans1:exp7 -0.0159706336282653 0.153185941741169 -0.104256522803183 0.916990287432475 df.mm.trans2:exp7 -0.115046157117106 0.113135315795962 -1.01688987481672 0.309494155135860 df.mm.trans1:exp8 0.0547666674211416 0.153185941741169 0.357517581565534 0.720792805138001 df.mm.trans2:exp8 -0.00952455792647718 0.113135315795962 -0.084187310208729 0.932927260753473 df.mm.trans1:probe2 -0.216431561130140 0.104879244730388 -2.06362623688337 0.0393551760023158 * df.mm.trans1:probe3 -0.100635930341462 0.104879244730388 -0.959540952074602 0.337558195633799 df.mm.trans1:probe4 -0.0811342579879682 0.104879244730388 -0.773596894185682 0.439383542222159 df.mm.trans1:probe5 -0.0715033204557875 0.104879244730388 -0.681768071839193 0.495570680178631 df.mm.trans1:probe6 -0.190355188530862 0.104879244730388 -1.81499389150071 0.0698759390805282 . df.mm.trans1:probe7 -0.117070694097121 0.104879244730388 -1.11624272655732 0.264632589509662 df.mm.trans1:probe8 -0.0100001488902709 0.104879244730388 -0.0953491695709495 0.924059883292223 df.mm.trans1:probe9 -0.0711460962706886 0.104879244730388 -0.678362019612013 0.497726167067276 df.mm.trans1:probe10 -0.0408872843514326 0.104879244730388 -0.389851056389099 0.696744007068134 df.mm.trans1:probe11 -0.0116888578480950 0.104879244730388 -0.111450629513432 0.911285232380532 df.mm.trans1:probe12 -0.137795209058802 0.104879244730388 -1.31384631356786 0.189251161561093 df.mm.trans1:probe13 -0.109378487528536 0.104879244730388 -1.04289926772179 0.297290423516167 df.mm.trans1:probe14 -0.150071654306588 0.104879244730388 -1.43089945672641 0.152825267613065 df.mm.trans1:probe15 0.00686726484437614 0.104879244730388 0.0654778251123923 0.947808903663666 df.mm.trans1:probe16 -0.0531394819168943 0.104879244730388 -0.506673003352564 0.612515215660868 df.mm.trans1:probe17 0.0555117387532253 0.104879244730388 0.529291938513943 0.596740659179762 df.mm.trans1:probe18 -0.0420049566586438 0.104879244730388 -0.400507810354904 0.688882819702904 df.mm.trans1:probe19 -0.170448606584766 0.104879244730388 -1.62518911175359 0.104491663205178 df.mm.trans1:probe20 -0.0210825095247019 0.104879244730388 -0.201016984617866 0.840733214493918 df.mm.trans1:probe21 -0.023476965825575 0.104879244730388 -0.223847586678632 0.822929501237455 df.mm.trans1:probe22 -0.140015150359467 0.104879244730388 -1.33501295436864 0.182228287427233 df.mm.trans2:probe2 0.00757897684166059 0.104879244730388 0.072263838866725 0.942408881941707 df.mm.trans2:probe3 -0.0313451547890214 0.104879244730388 -0.298868998051999 0.765112797950436 df.mm.trans2:probe4 0.0665545148857211 0.104879244730388 0.634582324241675 0.525871015588316 df.mm.trans2:probe5 0.0197771316242161 0.104879244730388 0.188570500055154 0.850474308636979 df.mm.trans2:probe6 0.127135338804538 0.104879244730388 1.21220684923278 0.225768858407352 df.mm.trans3:probe2 0.00290136000976544 0.104879244730388 0.0276638148684607 0.977936754814874 df.mm.trans3:probe3 -0.0138162369740736 0.104879244730388 -0.131734710805659 0.89522519444934 df.mm.trans3:probe4 -0.0103319118325190 0.104879244730388 -0.0985124545764904 0.921548527096061 df.mm.trans3:probe5 0.0887528167714984 0.104879244730388 0.846238137961942 0.39765724191262 df.mm.trans3:probe6 0.0165423167482366 0.104879244730388 0.157727268066830 0.874709041337681 df.mm.trans3:probe7 -0.0471242661698519 0.104879244730388 -0.449319274666725 0.653315494691824 df.mm.trans3:probe8 0.0290285151079658 0.104879244730388 0.276780359951954 0.78201581616932 df.mm.trans3:probe9 -0.0284691935923930 0.104879244730388 -0.271447355151903 0.786112663394528 df.mm.trans3:probe10 0.0807136766421904 0.104879244730388 0.769586745687196 0.44175812459167