chr14.7080_chr14_47239379_47240113_-_0.R fitVsDatCorrelation=0.6625769298913 cont.fitVsDatCorrelation=0.31436252520562 fstatistic=7764.49627827103,38,370 cont.fstatistic=4829.80631452854,38,370 residuals=-0.404388508865462,-0.0750597435370574,-0.00200234252934028,0.0751096021632224,1.38886750591118 cont.residuals=-0.471163020418382,-0.100411936555189,-0.0107395370863106,0.0679609818267396,1.76867047922977 predictedValues: Include Exclude Both chr14.7080_chr14_47239379_47240113_-_0.R.tl.Lung 45.6932709522421 43.2332191450022 48.179063654842 chr14.7080_chr14_47239379_47240113_-_0.R.tl.cerebhem 45.4804806314064 41.6696498880934 68.9008377924505 chr14.7080_chr14_47239379_47240113_-_0.R.tl.cortex 43.7383804011423 42.8900630499632 46.6299543970512 chr14.7080_chr14_47239379_47240113_-_0.R.tl.heart 46.2561473809157 44.2225633407258 49.0563194279857 chr14.7080_chr14_47239379_47240113_-_0.R.tl.kidney 45.573009648321 42.2686194776999 47.7471451455529 chr14.7080_chr14_47239379_47240113_-_0.R.tl.liver 46.7151320992297 45.3819768718121 51.0476871625285 chr14.7080_chr14_47239379_47240113_-_0.R.tl.stomach 44.9009953613129 43.1695089885134 52.3454594000715 chr14.7080_chr14_47239379_47240113_-_0.R.tl.testicle 48.1822057180688 41.9599915928387 54.4039795299467 diffExp=2.46005180723995,3.81083074331298,0.848317351179183,2.03358404018983,3.30439017062106,1.33315522741751,1.73148637279957,6.22221412523011 diffExpScore=0.95603241786424 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 44.9375752806626 47.5378621892283 44.8913657348313 cerebhem 46.7676629726147 54.463990032475 46.7456204045199 cortex 49.5068642557718 49.7840356350855 45.1316948820845 heart 45.729324382248 47.9432038567253 44.9682668159978 kidney 46.3765252013886 47.7295689029897 46.6333220018257 liver 46.2774325953595 47.6661815762326 45.806441881379 stomach 44.6467772593318 45.8527781630409 45.1189662916408 testicle 45.785746119501 50.4213633065805 45.9139127773569 cont.diffExp=-2.60028690856569,-7.69632705986034,-0.277171379313629,-2.21387947447728,-1.35304370160111,-1.38874898087310,-1.20600090370910,-4.63561718707947 cont.diffExpScore=0.95529942242911 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=0.0789072876043219 cont.tran.correlation=0.434561649615658 tran.covariance=6.21653353384896e-05 cont.tran.covariance=0.000761860394101167 tran.mean=44.4584509092055 cont.tran.mean=47.5891807330772 weightedLogRatios: wLogRatio Lung 0.209982600499940 cerebhem 0.330222177392292 cortex 0.0738078448958882 heart 0.171371759735157 kidney 0.284649697655434 liver 0.110878544390459 stomach 0.148839145615841 testicle 0.526247474163092 cont.weightedLogRatios: wLogRatio Lung -0.215637135286200 cerebhem -0.597411260145028 cortex -0.0218011946887805 heart -0.181846362272783 kidney -0.110750818422554 liver -0.113819188724384 stomach -0.101606665294318 testicle -0.37344265693077 varWeightedLogRatios=0.0213960864131281 cont.varWeightedLogRatios=0.0348185135818098 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.8591972131225 0.0757796253117974 50.9265808222689 3.01504753442193e-169 *** df.mm.trans1 -0.0466829132319322 0.062407538375033 -0.7480332416157 0.454915288800515 df.mm.trans2 -0.103274750346897 0.0624075383750331 -1.65484415883023 0.0988040706028144 . df.mm.exp2 -0.399247699182830 0.0853093732959394 -4.67999803254718 4.03106228013332e-06 *** df.mm.exp3 -0.0190126194615935 0.0853093732959394 -0.222866711206967 0.823762226370576 df.mm.exp4 0.016824857078577 0.0853093732959394 0.197221670123063 0.843762368134363 df.mm.exp5 -0.0161943174651213 0.0853093732959394 -0.189830458711061 0.849546085356873 df.mm.exp6 0.0127873009084857 0.0853093732959394 0.149893269806665 0.880930495260734 df.mm.exp7 -0.101906441498064 0.0853093732959394 -1.19455151949773 0.233027491091405 df.mm.exp8 -0.0983665861028402 0.0853093732959394 -1.15305718823657 0.249631265164013 df.mm.trans1:exp2 0.394579892856926 0.0707347955807489 5.57829975498387 4.69725628051704e-08 *** df.mm.trans2:exp2 0.362411581127932 0.0707347955807489 5.12352623842975 4.8386473491369e-07 *** df.mm.trans1:exp3 -0.0247124369665799 0.0707347955807489 -0.349367475563972 0.72701219855896 df.mm.trans2:exp3 0.0110436265877144 0.0707347955807489 0.156127214294516 0.876017895276147 df.mm.trans1:exp4 -0.00458152873388847 0.0707347955807489 -0.0647705092843356 0.948391701937882 df.mm.trans2:exp4 0.00580112313453177 0.0707347955807489 0.0820122980055746 0.934681280591475 df.mm.trans1:exp5 0.0135589219448021 0.0707347955807489 0.191686733996758 0.848092748662765 df.mm.trans2:exp5 -0.00636988955373106 0.0707347955807489 -0.0900531273389964 0.928293724471808 df.mm.trans1:exp6 0.00932979597617933 0.0707347955807489 0.131898253180483 0.895136464044458 df.mm.trans2:exp6 0.0357185786765898 0.0707347955807489 0.504964754380529 0.61388421734354 df.mm.trans1:exp7 0.0844153612786887 0.0707347955807489 1.19340644990375 0.23347487079049 df.mm.trans2:exp7 0.100431715655221 0.0707347955807489 1.41983467727097 0.156497809729192 df.mm.trans1:exp8 0.151405319898218 0.0707347955807489 2.14046451474336 0.0329704801154835 * df.mm.trans2:exp8 0.0684740078292236 0.0707347955807489 0.96803853417595 0.333657460189256 df.mm.trans1:probe2 0.00457281393273638 0.0413002227563206 0.110721289803129 0.911897396048958 df.mm.trans1:probe3 0.0807128928738061 0.0413002227563206 1.95429679278071 0.0514190260027641 . df.mm.trans1:probe4 -0.0172201061916072 0.0413002227563206 -0.416949474902573 0.676957162517189 df.mm.trans1:probe5 0.0201695908370414 0.0413002227563206 0.48836518282349 0.625580499509905 df.mm.trans1:probe6 0.0155689841297712 0.0413002227563206 0.376970948114039 0.706411302476158 df.mm.trans2:probe2 0.038557398977131 0.0413002227563206 0.933588160156597 0.351125212553614 df.mm.trans2:probe3 0.0220192152916166 0.0413002227563206 0.533150037023632 0.594249809283503 df.mm.trans2:probe4 0.0275520787730714 0.0413002227563206 0.66711695323374 0.50511325278188 df.mm.trans2:probe5 0.0106031066367427 0.0413002227563206 0.256732432154256 0.797528029659882 df.mm.trans2:probe6 0.0188218856338048 0.0413002227563206 0.455733271582034 0.64884913746051 df.mm.trans3:probe2 0.0965100835231943 0.0413002227563206 2.33679329268083 0.0199828871299179 * df.mm.trans3:probe3 0.353822019656757 0.0413002227563206 8.56707291252099 2.90722893661511e-16 *** df.mm.trans3:probe4 0.129946076016081 0.0413002227563206 3.14637712205060 0.00178699196617172 ** df.mm.trans3:probe5 0.248146886514387 0.0413002227563206 6.00836678239006 4.48734342167459e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91006015940921 0.0960459484996904 40.7103081440423 9.93966259799972e-139 *** df.mm.trans1 -0.134605664033304 0.0790976623610691 -1.70176538743773 0.089639386915695 . df.mm.trans2 -0.00507104641826108 0.0790976623610691 -0.064111204641074 0.948916302296415 df.mm.exp2 0.135455855162585 0.108124309673079 1.25277891319857 0.211077360610563 df.mm.exp3 0.137665599575657 0.108124309673079 1.27321598622824 0.20374075661387 df.mm.exp4 0.0242444350885552 0.108124309673079 0.224227420844211 0.822704113754927 df.mm.exp5 -0.00252616879402271 0.108124309673079 -0.0233635599770371 0.981372866712286 df.mm.exp6 0.0118965298822202 0.108124309673079 0.110026412359905 0.912448079908193 df.mm.exp7 -0.0476401071428449 0.108124309673079 -0.440604960039865 0.659756318602784 df.mm.exp8 0.055064274632441 0.108124309673079 0.509268219135286 0.610867809849256 df.mm.trans1:exp2 -0.0955381638971329 0.0896519414754492 -1.06565638540349 0.287273892820498 df.mm.trans2:exp2 0.000557402530331018 0.0896519414754492 0.00621740612816132 0.995042610403421 df.mm.trans1:exp3 -0.0408285784914720 0.0896519414754493 -0.455412095037035 0.649079925427048 df.mm.trans2:exp3 -0.0914977287617783 0.0896519414754493 -1.02058836937552 0.308116341877466 df.mm.trans1:exp4 -0.00677898229765385 0.0896519414754492 -0.0756144505750636 0.939766706206521 df.mm.trans2:exp4 -0.0157538700087976 0.0896519414754492 -0.175722574988649 0.8606080287221 df.mm.trans1:exp5 0.0340452669102719 0.0896519414754492 0.379749354559099 0.704349351803778 df.mm.trans2:exp5 0.00655077553164562 0.0896519414754493 0.0730689756834716 0.941790713066777 df.mm.trans1:exp6 0.0174835847266524 0.0896519414754492 0.195016242135037 0.845487273294502 df.mm.trans2:exp6 -0.00920085731131902 0.0896519414754493 -0.102628645402382 0.918313305476807 df.mm.trans1:exp7 0.041147923307709 0.0896519414754492 0.458974146354401 0.646522238018399 df.mm.trans2:exp7 0.0115494041956839 0.0896519414754493 0.128824920081030 0.897566182567267 df.mm.trans1:exp8 -0.0363657627597424 0.0896519414754492 -0.405632741034403 0.685246927652607 df.mm.trans2:exp8 0.00382419351904007 0.0896519414754493 0.042656003384905 0.965998761245751 df.mm.trans1:probe2 0.0322929391434049 0.0523454563355009 0.616919622143092 0.537667166317198 df.mm.trans1:probe3 0.154873951663620 0.0523454563355009 2.95868949295191 0.00328819268785491 ** df.mm.trans1:probe4 0.0775624240701636 0.0523454563355009 1.48174129141292 0.139260270271236 df.mm.trans1:probe5 0.0544549310510556 0.0523454563355009 1.04029909878012 0.298880261888032 df.mm.trans1:probe6 0.00883372274881122 0.0523454563355009 0.168758157196925 0.866079087476175 df.mm.trans2:probe2 -0.071330969727473 0.0523454563355009 -1.36269649213271 0.173806992182644 df.mm.trans2:probe3 -0.110796127837930 0.0523454563355009 -2.11663314438979 0.0349580849355552 * df.mm.trans2:probe4 -0.0750516849322451 0.0523454563355009 -1.43377649535065 0.152480575641842 df.mm.trans2:probe5 -0.094782481151486 0.0523454563355009 -1.81071076243926 0.0709966076972928 . df.mm.trans2:probe6 -0.126127564537367 0.0523454563355009 -2.40952268576989 0.0164609852419049 * df.mm.trans3:probe2 0.0859385753925583 0.0523454563355009 1.64175807049511 0.101489798460749 df.mm.trans3:probe3 0.0308945955341843 0.0523454563355009 0.590205868799188 0.555412919476035 df.mm.trans3:probe4 0.0377909866108033 0.0523454563355009 0.721953522930189 0.470779058064508 df.mm.trans3:probe5 0.0146859794933059 0.0523454563355009 0.280558820600935 0.77920560916806