chr15.8907_chr15_53301291_53303928_-_0.R fitVsDatCorrelation=0.864926191337156 cont.fitVsDatCorrelation=0.318534273254855 fstatistic=10108.0696466406,40,416 cont.fstatistic=2826.29201169153,40,416 residuals=-0.472104238805131,-0.0770176628111075,-0.00631577857024754,0.065437142150928,0.725802275807175 cont.residuals=-0.435997784255956,-0.165346770527144,-0.0426401837431894,0.11564050900643,1.19569123287592 predictedValues: Include Exclude Both chr15.8907_chr15_53301291_53303928_-_0.R.tl.Lung 45.4275189373013 46.5447836226545 68.4992846885854 chr15.8907_chr15_53301291_53303928_-_0.R.tl.cerebhem 39.6477394966494 43.8468817215725 73.8270267964616 chr15.8907_chr15_53301291_53303928_-_0.R.tl.cortex 46.7507582985644 44.6403299401313 76.8819700316341 chr15.8907_chr15_53301291_53303928_-_0.R.tl.heart 46.2365471592718 46.4953897686046 66.8669841829958 chr15.8907_chr15_53301291_53303928_-_0.R.tl.kidney 46.7444998104928 43.8391488246682 66.7672475408529 chr15.8907_chr15_53301291_53303928_-_0.R.tl.liver 46.9702925970386 50.4332637704772 67.1984800577988 chr15.8907_chr15_53301291_53303928_-_0.R.tl.stomach 44.1482334360052 47.6644145369196 65.8450788549416 chr15.8907_chr15_53301291_53303928_-_0.R.tl.testicle 41.7642551733929 46.7592750257893 57.7204207961841 diffExp=-1.11726468535316,-4.19914222492302,2.11042835843311,-0.258842609332810,2.90535098582464,-3.46297117343858,-3.51618110091442,-4.99501985239648 diffExpScore=1.66734131779984 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 53.4003550460212 50.9111230406094 52.7275223242249 cerebhem 52.2135417883406 52.5711946158125 51.0476380067701 cortex 52.2580958383275 53.8412487366944 53.3518691492761 heart 55.1243474234246 51.9949910458809 55.010627534735 kidney 50.7252862102354 52.207082472337 61.6948557483433 liver 52.1487758931519 50.0913532247863 56.5404204453658 stomach 49.6402790305895 50.3348323072694 51.6473687745552 testicle 48.6583723558596 48.2447626375935 54.7314415719898 cont.diffExp=2.48923200541176,-0.357652827471917,-1.58315289836690,3.12935637754369,-1.48179626210152,2.05742266836552,-0.694553276679926,0.413609718266017 cont.diffExpScore=2.45487394975683 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=0.281508959946272 cont.tran.correlation=0.542759744183592 tran.covariance=0.00083791478742033 cont.tran.covariance=0.000762708203179196 tran.mean=45.4945832574709 cont.tran.mean=51.5228526041833 weightedLogRatios: wLogRatio Lung -0.0930149025897535 cerebhem -0.375534988802652 cortex 0.176536471385478 heart -0.0214180420195372 kidney 0.244652907309901 liver -0.276367811040707 stomach -0.2931846802893 testicle -0.42799667695631 cont.weightedLogRatios: wLogRatio Lung 0.188745601533619 cerebhem -0.0270242690967975 cortex -0.118518217438691 heart 0.232629140227609 kidney -0.113470579911438 liver 0.158351545615342 stomach -0.0543527768844765 testicle 0.0331267971454974 varWeightedLogRatios=0.0635061329775734 cont.varWeightedLogRatios=0.0193298450800378 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.42845615873011 0.0686992712783468 49.9052769401168 1.03453593402912e-177 *** df.mm.trans1 0.365358897237295 0.055722467087106 6.5567609679981 1.63281316572938e-10 *** df.mm.trans2 0.399550723562933 0.055722467087106 7.1703703093108 3.44208085747896e-12 *** df.mm.exp2 -0.270697042879733 0.0753505282282197 -3.59250358617066 0.000366693469748966 *** df.mm.exp3 -0.128512904502446 0.0753505282282197 -1.7055342215148 0.0888413077731046 . df.mm.exp4 0.0407086765053874 0.0753505282282197 0.540257347394965 0.589308676904629 df.mm.exp5 -0.00569852985075607 0.0753505282282197 -0.0756269396479412 0.939752258312686 df.mm.exp6 0.132805952722328 0.0753505282282197 1.76250858282093 0.078717698132157 . df.mm.exp7 0.0347236132488766 0.0753505282282197 0.460827734925847 0.645163018795974 df.mm.exp8 0.0917327419693595 0.0753505282282197 1.21741338947912 0.224137098901264 df.mm.trans1:exp2 0.134612912491912 0.0607495380046353 2.21586726275418 0.0272411927026247 * df.mm.trans2:exp2 0.210985708996228 0.0607495380046353 3.47304219795266 0.000568554859309937 *** df.mm.trans1:exp3 0.157225314986088 0.0607495380046353 2.58809071065019 0.00998882324508076 ** df.mm.trans2:exp3 0.0867356758999432 0.0607495380046353 1.42775860934655 0.154111597825665 df.mm.trans1:exp4 -0.0230561925001003 0.0607495380046353 -0.379528688734079 0.704488968076415 df.mm.trans2:exp4 -0.0417704513686508 0.0607495380046353 -0.687584675384093 0.49209753156704 df.mm.trans1:exp5 0.03427706214191 0.0607495380046353 0.564235766522119 0.572897763038232 df.mm.trans2:exp5 -0.0541891809738913 0.0607495380046353 -0.892009762605216 0.372903362228196 df.mm.trans1:exp6 -0.0994086886673326 0.0607495380046353 -1.63636945946400 0.102518565061405 df.mm.trans2:exp6 -0.0525699376804575 0.0607495380046353 -0.86535534931058 0.387342536568516 df.mm.trans1:exp7 -0.0632887651836085 0.0607495380046353 -1.04179829612498 0.298110267419932 df.mm.trans2:exp7 -0.0109534580307044 0.0607495380046353 -0.180305207092581 0.857000768270933 df.mm.trans1:exp8 -0.175809973081087 0.0607495380046353 -2.89401333500959 0.00400361634039414 ** df.mm.trans2:exp8 -0.0871350474192498 0.0607495380046353 -1.43433267612012 0.152228622526551 df.mm.trans1:probe2 0.0383438422768573 0.0386056576471808 0.993218212399946 0.321180981477754 df.mm.trans1:probe3 0.0127681524033457 0.0386056576471808 0.330732674470527 0.741012933261756 df.mm.trans1:probe4 0.154428062854912 0.0386056576471808 4.00014071166041 7.49236255284503e-05 *** df.mm.trans1:probe5 0.0491571116038738 0.0386056576471808 1.27331366954355 0.203617806029623 df.mm.trans1:probe6 0.0352419536102929 0.0386056576471808 0.912870179090614 0.361839556453301 df.mm.trans2:probe2 0.00430263754749948 0.0386056576471808 0.111450958479234 0.911312505680826 df.mm.trans2:probe3 0.0505531950784242 0.0386056576471808 1.30947633480130 0.191095800669386 df.mm.trans2:probe4 -0.0170075478014345 0.0386056576471808 -0.440545475403305 0.659770913538701 df.mm.trans2:probe5 0.0705962568490176 0.0386056576471808 1.82865054376746 0.0681678074174401 . df.mm.trans2:probe6 0.0528601796597904 0.0386056576471808 1.36923401597979 0.171664896680440 df.mm.trans3:probe2 0.415299089097273 0.0386056576471808 10.7574670244635 5.603125868189e-24 *** df.mm.trans3:probe3 -0.00663473302009701 0.0386056576471808 -0.171859085544720 0.863631883503735 df.mm.trans3:probe4 -0.106285343469437 0.0386056576471808 -2.75310278200113 0.00616187812771842 ** df.mm.trans3:probe5 -0.0620302927939037 0.0386056576471808 -1.60676689828216 0.108864240058523 df.mm.trans3:probe6 0.0500441293686406 0.0386056576471808 1.29629003670904 0.195594235890542 df.mm.trans3:probe7 -0.056190186916055 0.0386056576471808 -1.45549099123191 0.146287586406381 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.86268187142425 0.129748831133961 29.7704560238865 3.87358261296943e-105 *** df.mm.trans1 0.0945108491975723 0.105240198883032 0.898048941380425 0.369678920240093 df.mm.trans2 0.0507182511928233 0.105240198883032 0.481928500051521 0.630110180546179 df.mm.exp2 0.0419896887467537 0.142310722967152 0.295056394003745 0.768097997492642 df.mm.exp3 0.0225644316416582 0.142310722967152 0.158557494271648 0.874094461880363 df.mm.exp4 0.0104512317204777 0.142310722967152 0.073439523758797 0.941491665078401 df.mm.exp5 -0.183319116171368 0.142310722967152 -1.28816095055383 0.198405974251050 df.mm.exp6 -0.10976793453036 0.142310722967152 -0.77132581608553 0.440951725017363 df.mm.exp7 -0.0637005981268807 0.142310722967152 -0.447616292003408 0.654663135592779 df.mm.exp8 -0.184088438802273 0.142310722967152 -1.29356688634604 0.196532852047806 df.mm.trans1:exp2 -0.0644652010153922 0.114734572890789 -0.561863781693371 0.574511417666345 df.mm.trans2:exp2 -0.00990277688901625 0.114734572890789 -0.0863103129206077 0.931261266871422 df.mm.trans1:exp3 -0.0441870032710919 0.114734572890789 -0.385123700361458 0.700342601149805 df.mm.trans2:exp3 0.0333940198975764 0.114734572890789 0.29105455362058 0.771154654662807 df.mm.trans1:exp4 0.0213228691067031 0.114734572890789 0.185845195301328 0.852656744963686 df.mm.trans2:exp4 0.0106147291772654 0.114734572890789 0.0925155243953287 0.926332993562417 df.mm.trans1:exp5 0.131926249511441 0.114734572890789 1.14983867710927 0.250870914668961 df.mm.trans2:exp5 0.208455854399728 0.114734572890789 1.81685301254531 0.0699590696019964 . df.mm.trans1:exp6 0.0860512481494181 0.114734572890789 0.75000277580958 0.453676983971375 df.mm.trans2:exp6 0.0935349103988487 0.114734572890789 0.815228645056106 0.41540792797817 df.mm.trans1:exp7 -0.00931421521485966 0.114734572890789 -0.0811805454989186 0.935337412602154 df.mm.trans2:exp7 0.0523164997660988 0.114734572890789 0.455978511515416 0.648643368568241 df.mm.trans1:exp8 0.0910949315275568 0.114734572890789 0.793962353564226 0.427670007260579 df.mm.trans2:exp8 0.130294287321285 0.114734572890789 1.13561487212147 0.256771612376091 df.mm.trans1:probe2 0.0212794464500644 0.072912548585626 0.291848891073593 0.77054764407506 df.mm.trans1:probe3 -0.008111777515192 0.072912548585626 -0.111253517707803 0.911468970605617 df.mm.trans1:probe4 0.0420080639308937 0.072912548585626 0.576143129622755 0.564830005755925 df.mm.trans1:probe5 0.20510060414185 0.072912548585626 2.81296715202579 0.00514125655719697 ** df.mm.trans1:probe6 0.00784442660594568 0.072912548585626 0.107586783867985 0.914375348938338 df.mm.trans2:probe2 0.0367603177866101 0.072912548585626 0.504169974849255 0.614408849376817 df.mm.trans2:probe3 0.111908926700120 0.072912548585626 1.53483767706594 0.125583872166564 df.mm.trans2:probe4 -0.00340192233765695 0.072912548585626 -0.0466575699745546 0.96280853750724 df.mm.trans2:probe5 0.0961233927927174 0.072912548585626 1.31833812776183 0.188115874478841 df.mm.trans2:probe6 -0.0245337579210678 0.072912548585626 -0.336481969112027 0.73667711474552 df.mm.trans3:probe2 -0.115554523801460 0.072912548585626 -1.58483726111641 0.113763163546213 df.mm.trans3:probe3 0.037371501125561 0.072912548585626 0.512552391193311 0.608536494109374 df.mm.trans3:probe4 0.045961128526701 0.072912548585626 0.630359648898102 0.528805414028437 df.mm.trans3:probe5 -0.100892714435735 0.072912548585626 -1.38374966165460 0.167177123930254 df.mm.trans3:probe6 -0.0302432862047388 0.072912548585626 -0.414788493769657 0.678510563573994 df.mm.trans3:probe7 -0.136055956649507 0.072912548585626 -1.86601564872922 0.0627426858643599 .