chr5.18025_chr5_29316137_29317567_+_0.R fitVsDatCorrelation=0.80312889666847 cont.fitVsDatCorrelation=0.281529369628176 fstatistic=8768.01045880811,41,439 cont.fstatistic=3373.85295559372,41,439 residuals=-0.414649201665891,-0.0950902965523199,-0.00677834469840114,0.0890886480122449,0.672500437502069 cont.residuals=-0.562425920949663,-0.157519380053594,-0.00897728156757738,0.156112878429520,0.703347427711911 predictedValues: Include Exclude Both chr5.18025_chr5_29316137_29317567_+_0.R.tl.Lung 84.7314082139378 81.6326463924022 75.3487318529193 chr5.18025_chr5_29316137_29317567_+_0.R.tl.cerebhem 86.6301026286186 77.243988878085 68.7337813752074 chr5.18025_chr5_29316137_29317567_+_0.R.tl.cortex 84.275541442255 65.2061838433081 74.598496096318 chr5.18025_chr5_29316137_29317567_+_0.R.tl.heart 108.472828756999 77.9809207945747 94.6645202204842 chr5.18025_chr5_29316137_29317567_+_0.R.tl.kidney 82.8672874598004 78.9942291267826 66.4847365602324 chr5.18025_chr5_29316137_29317567_+_0.R.tl.liver 79.0465101416475 84.1738266476455 67.417505855254 chr5.18025_chr5_29316137_29317567_+_0.R.tl.stomach 88.3815965963818 78.7597938211807 80.2606106485215 chr5.18025_chr5_29316137_29317567_+_0.R.tl.testicle 95.4199430934256 87.2254921656729 85.3794100450344 diffExp=3.09876182153560,9.38611375053367,19.0693575989468,30.4919079624244,3.87305833301777,-5.12731650599807,9.62180277520108,8.19445092775273 diffExpScore=1.11625235057473 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=0,0,0,1,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,1,1,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 79.483135136057 87.1568166351831 79.4196393306258 cerebhem 72.5903775539455 71.2627926054544 73.5525495762261 cortex 83.2646619780328 81.0012757052613 73.3088014981091 heart 79.8324131512893 80.7896712132082 81.9793857417247 kidney 70.5821505568651 83.3718173638391 79.6286269213776 liver 75.4912044199852 78.3505821521266 80.0859294426114 stomach 79.3119914912736 79.1914972636063 74.9119814654788 testicle 74.0590572128414 95.3159264216468 84.923356793668 cont.diffExp=-7.6736814991261,1.32758494849105,2.26338627277146,-0.957258061918907,-12.7896668069740,-2.85937773214148,0.120494227667365,-21.2568692088054 cont.diffExpScore=1.14997951492819 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,-1 cont.diffExp1.2Score=0.5 tran.correlation=0.0936030129122131 cont.tran.correlation=0.00751285009296278 tran.covariance=0.000861482944616513 cont.tran.covariance=0.000172971198111784 tran.mean=83.8151437501698 cont.tran.mean=79.4409606787885 weightedLogRatios: wLogRatio Lung 0.164708296195694 cerebhem 0.505078411796532 cortex 1.10460453818068 heart 1.49224966507586 kidney 0.210288220719199 liver -0.276621256723235 stomach 0.509919060142655 testicle 0.405261874948687 cont.weightedLogRatios: wLogRatio Lung -0.40751537070248 cerebhem 0.0789191898475992 cortex 0.121488263319455 heart -0.0522777135704866 kidney -0.722760704043398 liver -0.161446091236062 stomach 0.00664813974868445 testicle -1.11809980463310 varWeightedLogRatios=0.308147477959268 cont.varWeightedLogRatios=0.193253662602367 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.47137744861679 0.0812492842213904 55.0328226453423 3.92636987830275e-199 *** df.mm.trans1 -0.0425338109367704 0.0655559743456829 -0.648816699946912 0.516796121005941 df.mm.trans2 -0.00934232140574262 0.0655559743456829 -0.142509077151072 0.886743291377011 df.mm.exp2 0.0587871424638954 0.0883044812084274 0.665732267031144 0.505931885026848 df.mm.exp3 -0.220062868517516 0.0883044812084275 -2.49209174331816 0.0130671984237263 * df.mm.exp4 -0.0269638840646315 0.0883044812084274 -0.305351253930001 0.760243371001835 df.mm.exp5 0.0700542711714022 0.0883044812084274 0.793326343269615 0.428016274853887 df.mm.exp6 0.072427210183273 0.0883044812084274 0.820198581002034 0.412548019564442 df.mm.exp7 -0.0568010881041662 0.0883044812084274 -0.643241286589942 0.520403394273848 df.mm.exp8 0.0600907461560279 0.0883044812084274 0.68049486655376 0.496549900972353 df.mm.trans1:exp2 -0.0366261319730198 0.0704426070773716 -0.519942879638043 0.603365466261037 df.mm.trans2:exp2 -0.114047303887944 0.0704426070773716 -1.61901026409597 0.106163204861726 df.mm.trans1:exp3 0.214668204236812 0.0704426070773716 3.04741992301659 0.00244746039460917 ** df.mm.trans2:exp3 -0.00461208312311246 0.0704426070773716 -0.0654729192241098 0.947827271744529 df.mm.trans1:exp4 0.273977249392445 0.0704426070773716 3.88936839165419 0.000116068307652116 *** df.mm.trans2:exp4 -0.0188011846625915 0.0704426070773716 -0.266900750023931 0.789670907981621 df.mm.trans1:exp5 -0.092300239409178 0.0704426070773716 -1.31028994011818 0.190782910693416 df.mm.trans2:exp5 -0.102908730698985 0.0704426070773716 -1.46088759301531 0.144761624685472 df.mm.trans1:exp6 -0.141877145071111 0.0704426070773716 -2.01408140552320 0.044610497638788 * df.mm.trans2:exp6 -0.0417724449715241 0.0704426070773716 -0.592999701524998 0.553486708026885 df.mm.trans1:exp7 0.0989785026877134 0.0704426070773716 1.40509425749957 0.1607004736522 df.mm.trans2:exp7 0.0209744637620234 0.0704426070773716 0.297752519849043 0.766032912075448 df.mm.trans1:exp8 0.058710507463759 0.0704426070773716 0.833451655178995 0.405043282865904 df.mm.trans2:exp8 0.00617662308663437 0.0704426070773716 0.0876830563617583 0.930168568699166 df.mm.trans1:probe2 0.225805592679774 0.0461155112974411 4.89652150278346 1.37333970860548e-06 *** df.mm.trans1:probe3 0.156839595858482 0.0461155112974411 3.40101608864054 0.00073278918116451 *** df.mm.trans1:probe4 -0.159245917273476 0.0461155112974411 -3.4531963929957 0.000607738693271412 *** df.mm.trans1:probe5 -0.0716311440327627 0.0461155112974411 -1.55329827247817 0.121072483063534 df.mm.trans1:probe6 -0.00277015351956565 0.0461155112974411 -0.0600698862839967 0.952127309510781 df.mm.trans2:probe2 -0.225164836505626 0.0461155112974411 -4.88262691165521 1.46839749016692e-06 *** df.mm.trans2:probe3 -0.249425555491211 0.0461155112974411 -5.40871278391425 1.04507790460164e-07 *** df.mm.trans2:probe4 0.0633673577880362 0.0461155112974411 1.37410073108205 0.170111547069820 df.mm.trans2:probe5 -0.106562732119898 0.0461155112974411 -2.31077850210915 0.0213076968601409 * df.mm.trans2:probe6 -0.319496370449396 0.0461155112974411 -6.92817582328584 1.52563913772905e-11 *** df.mm.trans3:probe2 -0.288078369189136 0.0461155112974411 -6.24688659160809 9.90615272829809e-10 *** df.mm.trans3:probe3 0.062922512510031 0.0461155112974411 1.36445440459689 0.173123607974543 df.mm.trans3:probe4 -0.151666250843823 0.0461155112974411 -3.28883376930571 0.00108699709736488 ** df.mm.trans3:probe5 -0.100180571439438 0.0461155112974411 -2.17238340464842 0.0303611587752625 * df.mm.trans3:probe6 -0.424403802840611 0.0461155112974411 -9.20305968426205 1.42598247262727e-18 *** df.mm.trans3:probe7 -0.102328508888721 0.0461155112974411 -2.21896073598124 0.0270000046039389 * df.mm.trans3:probe8 0.224741219245827 0.0461155112974411 4.87344090790332 1.53469484381210e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.52929762100863 0.130853083501162 34.6136101635573 1.51087426678828e-127 *** df.mm.trans1 -0.169184977421132 0.105578793305816 -1.60245227401942 0.109774875562139 df.mm.trans2 -0.043636496243393 0.105578793305816 -0.413307397035662 0.679583221449616 df.mm.exp2 -0.215301554417804 0.142215574744117 -1.51390981476669 0.130768313839766 df.mm.exp3 0.0533002962332245 0.142215574744117 0.374785225381437 0.708001186821514 df.mm.exp4 -0.103197258881947 0.142215574744117 -0.72563964296896 0.468446359961713 df.mm.exp5 -0.165794208648319 0.142215574744117 -1.16579501891144 0.2443303180256 df.mm.exp6 -0.166398789891526 0.142215574744117 -1.17004617947732 0.242617194343928 df.mm.exp7 -0.0395637389002312 0.142215574744117 -0.278195542024259 0.780993236094934 df.mm.exp8 -0.0481975683710554 0.142215574744117 -0.338904993055616 0.734843348032896 df.mm.trans1:exp2 0.12458906438226 0.113448782155648 1.09819657835840 0.272720609528121 df.mm.trans2:exp2 0.0139669166115437 0.113448782155648 0.123112089404199 0.90207468021283 df.mm.trans1:exp3 -0.00682092552645564 0.113448782155648 -0.0601233913388117 0.952084720110884 df.mm.trans2:exp3 -0.126544378415533 0.113448782155648 -1.11543179231240 0.265275647767432 df.mm.trans1:exp4 0.107581998206975 0.113448782155648 0.948286937618914 0.343505109160490 df.mm.trans2:exp4 0.0273373985397555 0.113448782155648 0.240966875274602 0.809693288450099 df.mm.trans1:exp5 0.0470266337055588 0.113448782155648 0.414518629570124 0.678696800019248 df.mm.trans2:exp5 0.121395553429463 0.113448782155648 1.07004721534086 0.285186025851668 df.mm.trans1:exp6 0.114870079108822 0.113448782155648 1.01252809352527 0.311843348182968 df.mm.trans2:exp6 0.0598832026359503 0.113448782155648 0.52784350345685 0.597874647692897 df.mm.trans1:exp7 0.0374082103939884 0.113448782155648 0.329736553210995 0.741756307477232 df.mm.trans2:exp7 -0.0562763120037926 0.113448782155648 -0.496050384450873 0.620107117858814 df.mm.trans1:exp8 -0.0224844487726082 0.113448782155648 -0.198190305311169 0.842987915362928 df.mm.trans2:exp8 0.137685497690096 0.113448782155648 1.21363574887209 0.225539390126603 df.mm.trans1:probe2 0.107780657542335 0.0742696616755447 1.45120706235592 0.147436401465196 df.mm.trans1:probe3 0.070259918733558 0.0742696616755447 0.94601102453511 0.344663361025649 df.mm.trans1:probe4 0.0240547460950734 0.0742696616755447 0.323883878725060 0.746180195863844 df.mm.trans1:probe5 0.0247406210099138 0.0742696616755447 0.333118805872523 0.739203636310587 df.mm.trans1:probe6 -0.0107848785673241 0.0742696616755447 -0.145212437003403 0.884609793185175 df.mm.trans2:probe2 -0.081910500830832 0.0742696616755447 -1.10287968172882 0.270683637158821 df.mm.trans2:probe3 -0.0732902084143486 0.0742696616755447 -0.986812202464649 0.324278274627439 df.mm.trans2:probe4 -0.0723054377554919 0.0742696616755447 -0.97355280910483 0.330814750151254 df.mm.trans2:probe5 -0.0550909158142673 0.0742696616755447 -0.741768773027917 0.458624088879918 df.mm.trans2:probe6 0.0312671225991446 0.0742696616755447 0.420994547352841 0.673965091274256 df.mm.trans3:probe2 0.103555538648727 0.0742696616755447 1.39431816858303 0.163926779269116 df.mm.trans3:probe3 0.0316104393820011 0.0742696616755447 0.425617118334197 0.670595438173731 df.mm.trans3:probe4 0.0865631941580071 0.0742696616755447 1.16552562924237 0.244439162512774 df.mm.trans3:probe5 0.070771275860296 0.0742696616755447 0.952896165993971 0.341167044223121 df.mm.trans3:probe6 0.0758636923012388 0.0742696616755447 1.02146274252140 0.307597812734196 df.mm.trans3:probe7 0.0599128196311463 0.0742696616755447 0.806693046386587 0.420280155720044 df.mm.trans3:probe8 0.0378793364757596 0.0742696616755447 0.510024357472365 0.61029071591715