chr5.19007_chr5_135455551_135466504_-_0.R fitVsDatCorrelation=0.77186640506091 cont.fitVsDatCorrelation=0.288972061802476 fstatistic=10351.1993008242,38,370 cont.fstatistic=4559.97742163987,38,370 residuals=-0.327782974873814,-0.0735813619335369,-0.00465028798486983,0.0708871915733953,0.837646786599246 cont.residuals=-0.444289009955602,-0.135757117554295,-0.0231784850966402,0.115565029921137,0.826763132700313 predictedValues: Include Exclude Both chr5.19007_chr5_135455551_135466504_-_0.R.tl.Lung 43.9659688708563 51.7894149611395 60.0039654235044 chr5.19007_chr5_135455551_135466504_-_0.R.tl.cerebhem 51.8966332908147 52.8181061720029 56.9333970275907 chr5.19007_chr5_135455551_135466504_-_0.R.tl.cortex 45.4573043881156 52.292041421899 60.4560597826245 chr5.19007_chr5_135455551_135466504_-_0.R.tl.heart 45.5808283152327 52.0974336559083 57.1482271217669 chr5.19007_chr5_135455551_135466504_-_0.R.tl.kidney 47.9437337912195 56.1919953069737 62.597062625324 chr5.19007_chr5_135455551_135466504_-_0.R.tl.liver 50.4231846159498 58.417918492122 62.6060657027787 chr5.19007_chr5_135455551_135466504_-_0.R.tl.stomach 45.5022562837778 49.6748183347293 61.5841878657683 chr5.19007_chr5_135455551_135466504_-_0.R.tl.testicle 52.4635643328583 59.9996995986374 59.5716297690484 diffExp=-7.82344609028318,-0.921472881188208,-6.8347370337834,-6.51660534067561,-8.2482615157542,-7.99473387617227,-4.17256205095158,-7.53613526577908 diffExpScore=0.980410576319461 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 53.5897977267382 54.5430755156565 50.8260340775226 cerebhem 57.1659247723956 52.9676365659322 56.6007741675388 cortex 53.6690985434175 55.9042170977625 57.1045942077748 heart 53.8192450891912 57.2300314996128 50.7515910440862 kidney 56.6040529951265 56.3827559315745 56.8255055472499 liver 52.6247456772529 58.0301715728322 53.8784390830208 stomach 56.5274473951914 56.072059584089 52.9266133815583 testicle 59.5464138586726 53.2143795650571 54.5588814759824 cont.diffExp=-0.953277788918221,4.19828820646336,-2.23511855434498,-3.41078641042163,0.221297063551965,-5.40542589557936,0.455387811102412,6.33203429361549 cont.diffExpScore=12.9125498256303 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=0.738742606416726 cont.tran.correlation=-0.693098567537448 tran.covariance=0.00331937238497117 cont.tran.covariance=-0.000969587453795168 tran.mean=51.0321813645148 cont.tran.mean=55.4931908369064 weightedLogRatios: wLogRatio Lung -0.633019870878544 cerebhem -0.0696622763301969 cortex -0.544427910657421 heart -0.519320605445004 kidney -0.626952449970861 liver -0.587808736254826 stomach -0.3388048133732 testicle -0.540537283708943 cont.weightedLogRatios: wLogRatio Lung -0.0703549879313172 cerebhem 0.305704066530009 cortex -0.163341853222334 heart -0.246795646545952 kidney 0.0158025613398162 liver -0.392287092120948 stomach 0.0326028683475124 testicle 0.453143482721853 varWeightedLogRatios=0.0363221540392443 cont.varWeightedLogRatios=0.0779996546296344 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.51646032011645 0.068335348918377 51.4588770786358 1.03422943774843e-170 *** df.mm.trans1 0.233207281959823 0.0562768803942735 4.14392696123137 4.23516600470795e-05 *** df.mm.trans2 0.378336780898425 0.0562768803942736 6.7227745789712 6.76990226168548e-11 *** df.mm.exp2 0.238034825842762 0.0769289339476125 3.09421713818185 0.00212342074068051 ** df.mm.exp3 0.0355098580821408 0.0769289339476126 0.461593008767319 0.64464446522383 df.mm.exp4 0.0907634841797436 0.0769289339476125 1.17983546010858 0.238823674729093 df.mm.exp5 0.125893037569471 0.0769289339476125 1.63648488428557 0.102588409133962 df.mm.exp6 0.215020544686855 0.0769289339476125 2.79505426181105 0.00545950701743596 ** df.mm.exp7 -0.0333361358461759 0.0769289339476125 -0.433336771166975 0.665022645944149 df.mm.exp8 0.331087987831356 0.0769289339476126 4.30381614356988 2.15201512011923e-05 *** df.mm.trans1:exp2 -0.0721968068408228 0.0637861023565662 -1.13185794669253 0.25842717851187 df.mm.trans2:exp2 -0.218366557994580 0.0637861023565662 -3.42341905096983 0.000687598627077357 *** df.mm.trans1:exp3 -0.00215223767176066 0.0637861023565662 -0.0337414827407008 0.973101493930395 df.mm.trans2:exp3 -0.0258514542606906 0.0637861023565662 -0.405283491318848 0.685503373663233 df.mm.trans1:exp4 -0.0546921876591011 0.0637861023565662 -0.85743109609316 0.391761712613325 df.mm.trans2:exp4 -0.08483357868236 0.0637861023565662 -1.32996962579933 0.184347530930173 df.mm.trans1:exp5 -0.0392808268331955 0.0637861023565662 -0.615821086129617 0.538391241140617 df.mm.trans2:exp5 -0.044304507079251 0.0637861023565662 -0.694579311831714 0.487754889448046 df.mm.trans1:exp6 -0.0779853630715144 0.0637861023565662 -1.22260743626525 0.222256209914845 df.mm.trans2:exp6 -0.094583662422868 0.0637861023565663 -1.48282555178152 0.138972001598997 df.mm.trans1:exp7 0.0676821493180371 0.0637861023565662 1.06107987190833 0.289345720418618 df.mm.trans2:exp7 -0.00835151680377313 0.0637861023565662 -0.130930037974227 0.8959018110576 df.mm.trans1:exp8 -0.154384971789563 0.0637861023565662 -2.42035437322297 0.0159862555115921 * df.mm.trans2:exp8 -0.183934216315897 0.0637861023565662 -2.88360958767631 0.00416128653545841 ** df.mm.trans1:probe2 0.117040573839853 0.0372430600025742 3.14261432416571 0.00180950849344987 ** df.mm.trans1:probe3 0.0625517141793924 0.0372430600025742 1.67955356447802 0.0938883512063051 . df.mm.trans1:probe4 0.133132613196778 0.0372430600025742 3.57469588126154 0.000397092422009865 *** df.mm.trans1:probe5 0.0488683155229187 0.0372430600025742 1.31214555193749 0.190284477511980 df.mm.trans1:probe6 0.00963805943234284 0.0372430600025742 0.258788064989200 0.795942672704455 df.mm.trans2:probe2 0.154476230909528 0.0372430600025742 4.14778567869694 4.16756127011773e-05 *** df.mm.trans2:probe3 -0.0548142815352193 0.0372430600025742 -1.47179854532443 0.141925309994872 df.mm.trans2:probe4 -0.0672824322192199 0.0372430600025742 -1.80657637193532 0.0716406862926347 . df.mm.trans2:probe5 0.219891314653285 0.0372430600025742 5.90422254879396 8.02358764227018e-09 *** df.mm.trans2:probe6 0.324004681080088 0.0372430600025742 8.69973307933593 1.10686237773254e-16 *** df.mm.trans3:probe2 -0.106923005202318 0.0372430600025742 -2.87095113008778 0.00432776321151392 ** df.mm.trans3:probe3 0.0470430971144459 0.0372430600025742 1.26313726936493 0.207335237315227 df.mm.trans3:probe4 -0.210366792297796 0.0372430600025742 -5.6484830269923 3.23198846776321e-08 *** df.mm.trans3:probe5 0.102278037662030 0.0372430600025742 2.74623077843123 0.00632294160951709 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.12245811074838 0.102896450142555 40.0641431753676 1.24225364823674e-136 *** df.mm.trans1 -0.143623988307972 0.0847393231954453 -1.6948918505841 0.0909373094120336 . df.mm.trans2 -0.102438513591644 0.0847393231954454 -1.20886631765251 0.227486151823715 df.mm.exp2 -0.072324336475658 0.115836303490823 -0.624366751148857 0.5327715455651 df.mm.exp3 -0.0903481351045724 0.115836303490823 -0.77996390062404 0.435910766398385 df.mm.exp4 0.0538261675777718 0.115836303490823 0.464674423783181 0.642437945569687 df.mm.exp5 -0.0236820798791199 0.115836303490823 -0.204444368176822 0.83811867251488 df.mm.exp6 -0.0145216166078755 0.115836303490823 -0.125363259792090 0.900304059289061 df.mm.exp7 0.0405168859650353 0.115836303490823 0.349777097024210 0.72670499486713 df.mm.exp8 0.00986344338449101 0.115836303490823 0.085149845836305 0.932188317976931 df.mm.trans1:exp2 0.136923627546419 0.0960463889451987 1.42559891163158 0.154827221857337 df.mm.trans2:exp2 0.0430146667650842 0.0960463889451987 0.447853034741652 0.654521369672916 df.mm.trans1:exp3 0.0918268158273118 0.0960463889451987 0.956067342414149 0.339662453705873 df.mm.trans2:exp3 0.114997186531407 0.0960463889451987 1.19730879832474 0.231952725616347 df.mm.trans1:exp4 -0.0495537580030241 0.0960463889451987 -0.515935669703294 0.606207506851766 df.mm.trans2:exp4 -0.00573814667334614 0.0960463889451987 -0.0597434920392496 0.952392186688193 df.mm.trans1:exp5 0.0784039611717344 0.0960463889451987 0.816313471362983 0.414846108023141 df.mm.trans2:exp5 0.056854679818967 0.0960463889451987 0.591950207013057 0.554245457638865 df.mm.trans1:exp6 -0.00365063317810635 0.0960463889451987 -0.038009062268747 0.969700955543537 df.mm.trans2:exp6 0.0764939255087212 0.0960463889451987 0.796426876104279 0.42629467038298 df.mm.trans1:exp7 0.0128507197919572 0.0960463889451987 0.133797011351352 0.893635834047638 df.mm.trans2:exp7 -0.0128700100525248 0.0960463889451987 -0.133997854514531 0.89347712557404 df.mm.trans1:exp8 0.095533920885715 0.0960463889451987 0.994664369320787 0.320549567508567 df.mm.trans2:exp8 -0.0345255569033810 0.0960463889451987 -0.359467516504761 0.719450439901645 df.mm.trans1:probe2 -0.0293310542611346 0.0560790092882731 -0.523030892189247 0.601265822144203 df.mm.trans1:probe3 -0.0281612922154018 0.0560790092882731 -0.502171714030097 0.615845451308935 df.mm.trans1:probe4 0.0501054513944311 0.0560790092882731 0.893479610826664 0.372181159150346 df.mm.trans1:probe5 0.00629040263744846 0.0560790092882731 0.112170359592352 0.910749158792006 df.mm.trans1:probe6 0.0288669450463192 0.0560790092882731 0.514754904066319 0.60703165455885 df.mm.trans2:probe2 -0.0675760737471408 0.0560790092882731 -1.20501547022286 0.228967479409338 df.mm.trans2:probe3 -0.0600126352673803 0.0560790092882731 -1.07014435577645 0.285251934969337 df.mm.trans2:probe4 0.0389404555644831 0.0560790092882731 0.694385583103125 0.487876198884734 df.mm.trans2:probe5 -0.0484010648444671 0.0560790092882731 -0.863087017027393 0.388648723952095 df.mm.trans2:probe6 -0.0942678253064188 0.0560790092882731 -1.68098235869034 0.0936102383824202 . df.mm.trans3:probe2 0.0270805916293513 0.0560790092882731 0.48290067840079 0.629451855190525 df.mm.trans3:probe3 0.0812991911927557 0.0560790092882731 1.44972588183287 0.147982053293511 df.mm.trans3:probe4 0.0324326295547879 0.0560790092882731 0.578338133401546 0.563387692800181 df.mm.trans3:probe5 0.119253069136582 0.0560790092882731 2.12651882852572 0.0341214558717735 *