chr11.4827_chr11_105992561_106004121_+_2.R fitVsDatCorrelation=0.842871002902255 cont.fitVsDatCorrelation=0.260470391045199 fstatistic=7463.33958418736,44,508 cont.fstatistic=2310.48321587006,44,508 residuals=-0.590074494807502,-0.108527426595779,-0.000979566503879112,0.0934404095384586,0.64958371470099 cont.residuals=-0.671941884070455,-0.251911590118588,0.00240650477276384,0.241502003215965,0.925317751062064 predictedValues: Include Exclude Both chr11.4827_chr11_105992561_106004121_+_2.R.tl.Lung 96.7203479863683 52.1741311369256 74.8818353050117 chr11.4827_chr11_105992561_106004121_+_2.R.tl.cerebhem 87.9582233906212 58.0204459157993 75.0761696375656 chr11.4827_chr11_105992561_106004121_+_2.R.tl.cortex 90.7390599516364 50.473142049622 95.6386993263094 chr11.4827_chr11_105992561_106004121_+_2.R.tl.heart 82.3922725280623 53.5879402036224 93.6855647145785 chr11.4827_chr11_105992561_106004121_+_2.R.tl.kidney 88.318166112938 51.6188875665476 68.381402151996 chr11.4827_chr11_105992561_106004121_+_2.R.tl.liver 93.396702404531 51.0180077608375 66.0719745705393 chr11.4827_chr11_105992561_106004121_+_2.R.tl.stomach 77.2969079433146 48.3523367810879 80.0599970795166 chr11.4827_chr11_105992561_106004121_+_2.R.tl.testicle 84.4771214999816 53.8917946666857 77.2091397146053 diffExp=44.5462168494426,29.9377774748219,40.2659179020144,28.8043323244399,36.6992785463904,42.3786946436935,28.9445711622267,30.5853268332959 diffExpScore=0.996468454131303 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 75.701285486579 68.756211369133 77.9053855565027 cerebhem 83.2727861322246 70.9312653567474 86.930700619206 cortex 78.6077236888723 78.384366379763 67.907924868489 heart 77.0227791648268 80.9658201058212 71.365242107626 kidney 77.3142502227108 88.5917023290256 87.0208096752458 liver 77.420154927097 75.731626919663 84.0879732583635 stomach 73.8508630333862 78.003641997258 73.7661935289821 testicle 78.2375305390225 84.9548966670245 78.5456159354107 cont.diffExp=6.94507411744598,12.3415207754772,0.223357309109289,-3.94304094099436,-11.2774521063148,1.68852800743403,-4.15277896387190,-6.71736612800201 cont.diffExpScore=8.02577237622076 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=0.129469542558728 cont.tran.correlation=-0.17601621649638 tran.covariance=0.000640347898767722 cont.tran.covariance=-0.000499959241870537 tran.mean=70.0272179936614 cont.tran.mean=77.9841815199472 weightedLogRatios: wLogRatio Lung 2.6314080689868 cerebhem 1.77611663718450 cortex 2.47212637087789 heart 1.80515871826108 kidney 2.26231284003016 liver 2.56051683498048 stomach 1.92960988995654 testicle 1.89318298742029 cont.weightedLogRatios: wLogRatio Lung 0.411729191085397 cerebhem 0.696488936921152 cortex 0.0124148821236347 heart -0.218129373419260 kidney -0.601276485335055 liver 0.0956633380806003 stomach -0.236852135319786 testicle -0.362509527623066 varWeightedLogRatios=0.126823122287107 cont.varWeightedLogRatios=0.179346424938977 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.03748650007987 0.0904976109175925 44.6142882573599 7.57960990820617e-178 *** df.mm.trans1 0.384499159690065 0.0779828696603517 4.93055925441986 1.11223622607087e-06 *** df.mm.trans2 -0.112909882427664 0.0728321082676506 -1.55027617781887 0.121697981102684 df.mm.exp2 0.00865497612150322 0.0979187373292093 0.0883893763090982 0.929602037817788 df.mm.exp3 -0.341647536549486 0.0979187373292092 -3.48909254620844 0.000526785312523739 *** df.mm.exp4 -0.357627682073196 0.0979187373292093 -3.65229058122786 0.000286879325951086 *** df.mm.exp5 -0.0107666897963657 0.0979187373292093 -0.109955357779659 0.91248820237098 df.mm.exp6 0.0677907682498852 0.0979187373292092 0.692316609659377 0.489054849779416 df.mm.exp7 -0.367107074083336 0.0979187373292092 -3.74909934601278 0.000197870499883512 *** df.mm.exp8 -0.133558122405781 0.0979187373292092 -1.36396900173201 0.173181342403362 df.mm.trans1:exp2 -0.103616812736752 0.0883883980662174 -1.17228974620771 0.241630203034495 df.mm.trans2:exp2 0.0975536881328545 0.077697739383315 1.25555375107610 0.209855006269944 df.mm.trans1:exp3 0.277811646756399 0.0883883980662174 3.14307819617086 0.00176948681053764 ** df.mm.trans2:exp3 0.308502090789265 0.077697739383315 3.97054139847361 8.20576427171978e-05 *** df.mm.trans1:exp4 0.197295530417746 0.0883883980662175 2.23214284605475 0.0260407269573181 * df.mm.trans2:exp4 0.384364928670822 0.077697739383315 4.94692550544606 1.02666197651050e-06 *** df.mm.trans1:exp5 -0.080111296184254 0.0883883980662174 -0.90635533550724 0.365177498281849 df.mm.trans2:exp5 6.75334570506558e-05 0.077697739383315 0.000869181749516359 0.99930683459562 df.mm.trans1:exp6 -0.102758533973717 0.0883883980662174 -1.16257943601075 0.245546032910121 df.mm.trans2:exp6 -0.0901989044612039 0.077697739383315 -1.16089483654364 0.246229891595696 df.mm.trans1:exp7 0.142937223969332 0.0883883980662174 1.61714916319954 0.106466835787150 df.mm.trans2:exp7 0.291034825381366 0.077697739383315 3.74573092719688 0.000200471838360395 *** df.mm.trans1:exp8 -0.00178493554441186 0.0883883980662174 -0.0201942289198935 0.98389636072302 df.mm.trans2:exp8 0.165949556240548 0.077697739383315 2.13583506492835 0.0331703223379093 * df.mm.trans1:probe2 0.232029321968371 0.051607705928009 4.49602085184805 8.58501866889673e-06 *** df.mm.trans1:probe3 0.256012575279672 0.051607705928009 4.96074318119857 9.59393978896115e-07 *** df.mm.trans1:probe4 0.297389510084140 0.051607705928009 5.76250202826277 1.43841920176010e-08 *** df.mm.trans1:probe5 -0.0176768074384777 0.051607705928009 -0.342522635343183 0.732099286539657 df.mm.trans1:probe6 0.0252560638534573 0.051607705928009 0.489385517129723 0.624780027371944 df.mm.trans1:probe7 0.309002657785012 0.051607705928009 5.987529424696 4.03216998065109e-09 *** df.mm.trans1:probe8 0.354488563644194 0.051607705928009 6.86890760342444 1.89763505520343e-11 *** df.mm.trans1:probe9 0.200351484265233 0.051607705928009 3.88220093612989 0.000117172056511412 *** df.mm.trans1:probe10 0.366980904269569 0.051607705928009 7.1109710782629 3.93287480912187e-12 *** df.mm.trans1:probe11 0.261603991062100 0.051607705928009 5.06908777202824 5.60851446239071e-07 *** df.mm.trans1:probe12 0.261810190096044 0.051607705928009 5.07308328064921 5.49754920310253e-07 *** df.mm.trans2:probe2 0.113780461186545 0.051607705928009 2.20471844544426 0.0279212994978435 * df.mm.trans2:probe3 -0.00491440593775929 0.051607705928009 -0.0952262040985646 0.924172707255285 df.mm.trans2:probe4 0.0182490792205332 0.051607705928009 0.353611517744851 0.723776744979321 df.mm.trans2:probe5 0.0739456181035863 0.051607705928009 1.43284063443428 0.152518476336778 df.mm.trans2:probe6 0.129051252841574 0.051607705928009 2.50061983033302 0.0127115394390428 * df.mm.trans3:probe2 0.216891061055497 0.051607705928009 4.20268750868432 3.11646271326921e-05 *** df.mm.trans3:probe3 -0.0243531228154164 0.051607705928009 -0.471889272687071 0.637208446917302 df.mm.trans3:probe4 0.018407127063996 0.051607705928009 0.356674003097005 0.721483968578584 df.mm.trans3:probe5 -0.176767242923528 0.051607705928009 -3.42521024224778 0.00066405427679353 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22766674393343 0.162369922821280 26.0372529005068 1.40340575688669e-95 *** df.mm.trans1 0.107067150913192 0.139916097228946 0.76522396660333 0.444493544886723 df.mm.trans2 0.00739152362998188 0.130674651832501 0.056564326182069 0.954914493087833 df.mm.exp2 0.0168547200711242 0.175684834789491 0.0959372508806457 0.92360824255907 df.mm.exp3 0.306074581279822 0.175684834789491 1.74217986228901 0.0820821778111184 . df.mm.exp4 0.268450220861113 0.175684834789491 1.52802159152083 0.127129562130719 df.mm.exp5 0.163902069953070 0.175684834789491 0.932932373755885 0.351298078093649 df.mm.exp6 0.0427123242615116 0.175684834789491 0.243119016576988 0.808011335983037 df.mm.exp7 0.156035532127360 0.175684834789491 0.888155954464283 0.374877193669834 df.mm.exp8 0.236323263498291 0.175684834789491 1.34515459903787 0.179175485583990 df.mm.trans1:exp2 0.078471937004537 0.158585593882439 0.49482386819265 0.620938390555132 df.mm.trans2:exp2 0.0142895148450157 0.139404519292169 0.102503956956138 0.918397121111482 df.mm.trans1:exp3 -0.268399762538564 0.158585593882439 -1.69245992632554 0.091171480661985 . df.mm.trans2:exp3 -0.175017161882360 0.139404519292169 -1.25546261176478 0.209888043217342 df.mm.trans1:exp4 -0.251144151075909 0.158585593882439 -1.58365047497369 0.113895595280687 df.mm.trans2:exp4 -0.104990208899973 0.139404519292169 -0.753133466784747 0.451718595192622 df.mm.trans1:exp5 -0.142818923395577 0.158585593882439 -0.900579427797523 0.368238706935905 df.mm.trans2:exp5 0.0895690504479567 0.139404519292169 0.642511813123034 0.520830782164448 df.mm.trans1:exp6 -0.0202603196168922 0.158585593882439 -0.127756368790417 0.898392336256504 df.mm.trans2:exp6 0.0539164621398732 0.139404519292169 0.386762656000219 0.699093882769137 df.mm.trans1:exp7 -0.180782978544352 0.158585593882439 -1.13997100315662 0.254835576186629 df.mm.trans2:exp7 -0.0298470939082869 0.139404519292169 -0.214104205945664 0.830551705667916 df.mm.trans1:exp8 -0.203368942538020 0.158585593882439 -1.28239228771801 0.200289931116815 df.mm.trans2:exp8 -0.0247698549403183 0.139404519292169 -0.177683299408713 0.859042528385925 df.mm.trans1:probe2 0.0158947461068915 0.09259403804753 0.171660578176021 0.86377273805563 df.mm.trans1:probe3 -0.00508408867870537 0.09259403804753 -0.0549073005769078 0.95623390261052 df.mm.trans1:probe4 0.0246899979384316 0.09259403804753 0.266647815119131 0.789848538205725 df.mm.trans1:probe5 0.0203225411630982 0.09259403804753 0.219480018277919 0.82636430369678 df.mm.trans1:probe6 -0.0510579558086061 0.09259403804753 -0.551417314605043 0.581590088160562 df.mm.trans1:probe7 -0.0846862752924122 0.09259403804753 -0.914597495455824 0.360836877381775 df.mm.trans1:probe8 -0.0554647118354341 0.09259403804753 -0.59900953673673 0.549433547976776 df.mm.trans1:probe9 -0.00820051572803672 0.09259403804753 -0.088564187294945 0.929463173116762 df.mm.trans1:probe10 0.0385840397971280 0.09259403804753 0.416701124723843 0.677073045457779 df.mm.trans1:probe11 0.0419022350452092 0.09259403804753 0.452537073971222 0.651075309491593 df.mm.trans1:probe12 -0.0718588174263275 0.09259403804753 -0.776063113150344 0.438072875886748 df.mm.trans2:probe2 -0.0508851574941359 0.09259403804753 -0.549551121941736 0.582868792501299 df.mm.trans2:probe3 0.130227045869554 0.09259403804753 1.40643013973218 0.160207848351499 df.mm.trans2:probe4 0.000179466206959859 0.09259403804753 0.00193820477801969 0.998454298165282 df.mm.trans2:probe5 -0.0837960653787641 0.09259403804753 -0.904983378473572 0.365903182949967 df.mm.trans2:probe6 -0.0451283562181072 0.09259403804753 -0.487378638729872 0.626200275549929 df.mm.trans3:probe2 0.0221528092865682 0.09259403804753 0.239246605436916 0.81101085851519 df.mm.trans3:probe3 -0.102513275396408 0.09259403804753 -1.10712609103176 0.268763373856253 df.mm.trans3:probe4 0.0787986373170423 0.09259403804753 0.85101199794952 0.395163640691247 df.mm.trans3:probe5 0.068410159994054 0.09259403804753 0.73881819430899 0.460358612842798