chr19.11737_chr19_46238326_46239167_-_0.R fitVsDatCorrelation=0.935816554760225 cont.fitVsDatCorrelation=0.243488794258849 fstatistic=11146.5469535649,41,439 cont.fstatistic=1462.91881494086,41,439 residuals=-0.400426827797463,-0.0796225881677717,0.00252561101502112,0.0704475457731662,0.508530200949996 cont.residuals=-0.80061371937215,-0.248044276932366,-0.0717437027743243,0.194363007384691,1.38038649423304 predictedValues: Include Exclude Both chr19.11737_chr19_46238326_46239167_-_0.R.tl.Lung 151.375031950655 61.5580253124498 55.6498782841705 chr19.11737_chr19_46238326_46239167_-_0.R.tl.cerebhem 82.7159474094271 57.7503174926179 56.9337484329711 chr19.11737_chr19_46238326_46239167_-_0.R.tl.cortex 107.654695362170 60.3102350531912 55.9339568022688 chr19.11737_chr19_46238326_46239167_-_0.R.tl.heart 109.066871943091 61.7495096608356 55.6200392749991 chr19.11737_chr19_46238326_46239167_-_0.R.tl.kidney 149.266972732131 66.0961562059219 56.3536283774766 chr19.11737_chr19_46238326_46239167_-_0.R.tl.liver 141.619914804349 74.2521787002623 62.9522699222758 chr19.11737_chr19_46238326_46239167_-_0.R.tl.stomach 115.579714202586 71.171272732059 55.3927074227566 chr19.11737_chr19_46238326_46239167_-_0.R.tl.testicle 116.962216427788 58.9879913474655 54.6501086302597 diffExp=89.8170066382053,24.9656299168091,47.3444603089783,47.317362282255,83.1708165262089,67.3677361040869,44.4084414705273,57.9742250803221 diffExpScore=0.997841877275827 diffExp1.5=1,0,1,1,1,1,1,1 diffExp1.5Score=0.875 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 67.2607595640007 75.0284882131857 77.8084833669558 cerebhem 69.7196966211011 64.5979942488722 72.2826391208257 cortex 65.2137478961968 76.9972224187321 70.4763912813661 heart 73.7081173978269 59.8279725768164 70.2807673832578 kidney 64.1584671550692 78.0145660346574 69.6050264403111 liver 62.2139676406444 72.5791412903853 76.853608359573 stomach 69.356727885506 65.953148085664 84.5839833091784 testicle 62.7680123702865 70.5045919495921 71.819846329878 cont.diffExp=-7.76772864918505,5.12170237222897,-11.7834745225352,13.8801448210104,-13.8560988795882,-10.3651736497409,3.40357979984199,-7.73657957930564 cont.diffExpScore=2.45533467155798 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=0,0,0,1,-1,0,0,0 cont.diffExp1.2Score=2 tran.correlation=0.49517192992874 cont.tran.correlation=-0.781908465953672 tran.covariance=0.00981776716688651 cont.tran.covariance=-0.0043289119056174 tran.mean=92.8823157085625 cont.tran.mean=68.6189138342836 weightedLogRatios: wLogRatio Lung 4.11187874971473 cerebhem 1.52184279793236 cortex 2.54322993782672 heart 2.50733071757966 kidney 3.74599477785187 liver 2.98969517879088 stomach 2.18556896152881 testicle 3.02528640473017 cont.weightedLogRatios: wLogRatio Lung -0.465931353845738 cerebhem 0.320941146031873 cortex -0.707701492936421 heart 0.87540865759947 kidney -0.832827250908653 liver -0.648387295435938 stomach 0.212047567876195 testicle -0.487892064321878 varWeightedLogRatios=0.694115362611658 cont.varWeightedLogRatios=0.372613472849040 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.11478849100344 0.0705623117257908 72.4861241916195 2.07676268475430e-246 *** df.mm.trans1 -0.0221519460419759 0.056933191985588 -0.389086669294485 0.697400690144919 df.mm.trans2 -0.992269900927753 0.056933191985588 -17.4286715064024 4.39646773303991e-52 *** df.mm.exp2 -0.691007772049095 0.0766895165849705 -9.01045935376933 6.34341239970338e-18 *** df.mm.exp3 -0.366401740689708 0.0766895165849705 -4.77772917350108 2.42140524054482e-06 *** df.mm.exp4 -0.324157075398570 0.0766895165849705 -4.22687597775388 2.88425287795155e-05 *** df.mm.exp5 0.0445396780946451 0.0766895165849705 0.580779226131853 0.561687278439137 df.mm.exp6 -0.00242363971088897 0.0766895165849704 -0.0316032727654975 0.974802796240429 df.mm.exp7 -0.120058981221971 0.0766895165849704 -1.56552012019724 0.118181525261700 df.mm.exp8 -0.282427122932857 0.0766895165849704 -3.68273442720079 0.000259515963727951 *** df.mm.trans1:exp2 0.0866597770007523 0.0611770706290394 1.4165401531929 0.157326602955028 df.mm.trans2:exp2 0.627156388132953 0.0611770706290394 10.2514942556804 2.97970728488905e-22 *** df.mm.trans1:exp3 0.0255701675328883 0.0611770706290394 0.417969792766617 0.676173568906984 df.mm.trans2:exp3 0.345923334550941 0.0611770706290394 5.65446058456318 2.82042103405401e-08 *** df.mm.trans1:exp4 -0.00364213936671597 0.0611770706290394 -0.0595343864828194 0.952553568648165 df.mm.trans2:exp4 0.327262879149313 0.0611770706290394 5.34943690151076 1.42320212012259e-07 *** df.mm.trans1:exp5 -0.058563625082097 0.0611770706290394 -0.957280636028004 0.338952500751233 df.mm.trans2:exp5 0.0265906848652068 0.0611770706290394 0.434651162466494 0.664029222661175 df.mm.trans1:exp6 -0.0641899605876928 0.0611770706290394 -1.04924867973693 0.294640959605019 df.mm.trans2:exp6 0.189910528717082 0.0611770706290394 3.10427627155681 0.00203083312564639 ** df.mm.trans1:exp7 -0.149740973558024 0.0611770706290394 -2.44766498327472 0.0147692606156234 * df.mm.trans2:exp7 0.265168014347884 0.0611770706290394 4.3344346439173 1.81369377049688e-05 *** df.mm.trans1:exp8 0.0245176561179548 0.0611770706290394 0.400765447999675 0.688787793177036 df.mm.trans2:exp8 0.239780778627977 0.0611770706290394 3.91945505338006 0.000102923140076702 *** df.mm.trans1:probe2 -0.123815688620366 0.0400497938504618 -3.0915437188683 0.00211801312444524 ** df.mm.trans1:probe3 -0.286638383417242 0.0400497938504618 -7.1570501582978 3.48605644607280e-12 *** df.mm.trans1:probe4 -0.296994063806756 0.0400497938504618 -7.41562028797636 6.29779944136549e-13 *** df.mm.trans1:probe5 0.245882587404448 0.0400497938504618 6.13942204852603 1.85482805980038e-09 *** df.mm.trans1:probe6 -0.558700299821464 0.0400497938504618 -13.9501417137762 7.18057771672692e-37 *** df.mm.trans2:probe2 -0.0174259272441774 0.0400497938504618 -0.4351065403543 0.663698919149433 df.mm.trans2:probe3 0.0392818357539499 0.0400497938504618 0.980824917616822 0.327219302747265 df.mm.trans2:probe4 -0.0487586586567827 0.0400497938504618 -1.21745092718425 0.224086703772824 df.mm.trans2:probe5 0.0574038562021181 0.0400497938504618 1.43331215177918 0.152480436199325 df.mm.trans2:probe6 -0.0660381329663553 0.0400497938504618 -1.64890069629144 0.0998833582858136 . df.mm.trans3:probe2 0.0720998514745955 0.0400497938504618 1.80025524585226 0.0725066889907425 . df.mm.trans3:probe3 -0.311001079509839 0.0400497938504618 -7.76536030799702 5.79462399594114e-14 *** df.mm.trans3:probe4 -0.246441292968787 0.0400497938504618 -6.15337232168913 1.71062480347811e-09 *** df.mm.trans3:probe5 0.0050455279335804 0.0400497938504618 0.125981370900920 0.899804346097027 df.mm.trans3:probe6 0.217212428042353 0.0400497938504618 5.42355920365989 9.66870175416543e-08 *** df.mm.trans3:probe7 -0.27486877801909 0.0400497938504618 -6.86317585167597 2.30475259999144e-11 *** df.mm.trans3:probe8 -0.11234068480322 0.0400497938504618 -2.80502529482869 0.00525471288905085 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.06717977371546 0.194159126123384 20.9476621311679 4.73350957998411e-68 *** df.mm.trans1 0.123755924811802 0.156657265514394 0.789978839509563 0.42996662161028 df.mm.trans2 0.245686477286616 0.156657265514394 1.56830566702342 0.117530295422592 df.mm.exp2 -0.0401121642521121 0.211018731654171 -0.190088168655331 0.849327905470248 df.mm.exp3 0.09396745692308 0.211018731654171 0.445303865616437 0.65631967257041 df.mm.exp4 -0.0331063450698492 0.211018731654171 -0.156888181491422 0.875405100839296 df.mm.exp5 0.103220424373091 0.211018731654171 0.489152899195008 0.624977749326997 df.mm.exp6 -0.0988397345002536 0.211018731654171 -0.46839317877352 0.639735922129278 df.mm.exp7 -0.181731575614359 0.211018731654171 -0.861210633718483 0.389592175053941 df.mm.exp8 -0.0512318383703455 0.211018731654171 -0.242783367944354 0.80828661155214 df.mm.trans1:exp2 0.0760180343035459 0.168334714121637 0.451588578744465 0.651788412439067 df.mm.trans2:exp2 -0.109572358477181 0.168334714121637 -0.650919562544927 0.51543895995327 df.mm.trans1:exp3 -0.124874152115207 0.168334714121637 -0.741820561295362 0.458592738728643 df.mm.trans2:exp3 -0.0680659924607799 0.168334714121637 -0.404349113704474 0.686152935487413 df.mm.trans1:exp4 0.124642280082100 0.168334714121637 0.740443114971724 0.45942698944075 df.mm.trans2:exp4 -0.193288218756978 0.168334714121637 -1.14823742545052 0.251495854580729 df.mm.trans1:exp5 -0.150441351275923 0.168334714121637 -0.893703666893186 0.371970134285995 df.mm.trans2:exp5 -0.0641927553406847 0.168334714121637 -0.381339973015309 0.703135487482173 df.mm.trans1:exp6 0.0208422699734212 0.168334714121637 0.123814449575509 0.90151885900684 df.mm.trans2:exp6 0.0656494207430176 0.168334714121637 0.389993359869788 0.696730601516838 df.mm.trans1:exp7 0.212417731958239 0.168334714121637 1.26187716578024 0.207663010937506 df.mm.trans2:exp7 0.0528083038117587 0.168334714121637 0.313710122640538 0.753890218637591 df.mm.trans1:exp8 -0.0178995742840601 0.168334714121637 -0.10633323243788 0.91536653751811 df.mm.trans2:exp8 -0.0109582041525507 0.168334714121637 -0.0650977085132445 0.948125837740285 df.mm.trans1:probe2 0.0647665983771631 0.110200938507308 0.587713673353773 0.557026674993364 df.mm.trans1:probe3 0.0197831662328831 0.110200938507308 0.179519035870744 0.857612999507395 df.mm.trans1:probe4 0.0137053305408619 0.110200938507308 0.124366731595059 0.901081837967187 df.mm.trans1:probe5 0.0393244431763658 0.110200938507308 0.356843087808713 0.72138073584776 df.mm.trans1:probe6 0.109398671106351 0.110200938507308 0.99271995854279 0.321393294619772 df.mm.trans2:probe2 0.0494563453364683 0.110200938507308 0.448783340744314 0.653809399837937 df.mm.trans2:probe3 -0.0741968735199505 0.110200938507308 -0.67328712917477 0.501118927959949 df.mm.trans2:probe4 -0.00665860866167138 0.110200938507308 -0.0604224315315593 0.951846690135082 df.mm.trans2:probe5 0.0688212117792862 0.110200938507308 0.624506585075249 0.532619366886934 df.mm.trans2:probe6 0.0326007906314628 0.110200938507308 0.295830426428728 0.76749946611829 df.mm.trans3:probe2 -0.183445565959164 0.110200938507308 -1.66464613136665 0.0966968846012513 . df.mm.trans3:probe3 -0.0951680595328554 0.110200938507308 -0.86358665200065 0.38828656842362 df.mm.trans3:probe4 -0.130339350413388 0.110200938507308 -1.18274265336447 0.237551309471131 df.mm.trans3:probe5 -0.0677828870261986 0.110200938507308 -0.615084480625396 0.538817627883435 df.mm.trans3:probe6 -0.086436325527531 0.110200938507308 -0.784351990993245 0.433256618786601 df.mm.trans3:probe7 -0.099096246202205 0.110200938507308 -0.899232325463668 0.369021890777603 df.mm.trans3:probe8 0.00329475572003382 0.110200938507308 0.029897710170729 0.97616221904485