chr7.21554_chr7_68877227_68878073_-_0.R fitVsDatCorrelation=0.727579586133076 cont.fitVsDatCorrelation=0.274375155181104 fstatistic=8587.39403992624,43,485 cont.fstatistic=4364.94661475461,43,485 residuals=-0.432809445675884,-0.0859821146678471,-0.00112706095600877,0.0715761867294997,1.2109717225727 cont.residuals=-0.507638228614239,-0.128215772224312,-0.0385974487375419,0.0915945428511513,1.21820418308083 predictedValues: Include Exclude Both chr7.21554_chr7_68877227_68878073_-_0.R.tl.Lung 46.6440152720753 45.8581743825315 51.567659706292 chr7.21554_chr7_68877227_68878073_-_0.R.tl.cerebhem 56.8302819711317 55.4160138861834 55.3258555793165 chr7.21554_chr7_68877227_68878073_-_0.R.tl.cortex 47.3419147838812 45.3732791203365 57.2100578881943 chr7.21554_chr7_68877227_68878073_-_0.R.tl.heart 48.3081445725348 46.6047400827031 50.1842725637791 chr7.21554_chr7_68877227_68878073_-_0.R.tl.kidney 46.3691335304527 45.1045100086071 50.0058827919797 chr7.21554_chr7_68877227_68878073_-_0.R.tl.liver 52.4066549130443 50.3502908104408 50.643071102562 chr7.21554_chr7_68877227_68878073_-_0.R.tl.stomach 47.8865796350189 47.00826321165 51.5109908324359 chr7.21554_chr7_68877227_68878073_-_0.R.tl.testicle 52.4358386238269 51.6207135709471 51.6099536706683 diffExp=0.785840889543813,1.41426808494829,1.96863566354467,1.70340448983166,1.26462352184556,2.05636410260347,0.87831642336895,0.815125052879814 diffExpScore=0.915871499705713 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 49.4463460896129 51.8057056137276 51.9497174435071 cerebhem 50.619508204885 54.7023425244493 48.8189895504232 cortex 49.6969696529791 50.6679329013422 51.9409285423539 heart 47.5217382158323 47.9795620412956 50.7137773815056 kidney 54.2087309386353 49.9438331649065 48.0916615865565 liver 45.3624176400438 47.2839123077042 48.0987778972254 stomach 52.082933887735 53.0880628008219 47.9442394915244 testicle 53.207901424371 52.2255490692274 47.516623833146 cont.diffExp=-2.35935952411472,-4.08283431956431,-0.970963248363113,-0.457823825463244,4.26489777372886,-1.92149466766039,-1.0051289130869,0.982352355143597 cont.diffExpScore=2.44946360491994 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=0.990432349587115 cont.tran.correlation=0.59181542486511 tran.covariance=0.0053277609198165 cont.tran.covariance=0.00181070004031061 tran.mean=49.0974092734603 cont.tran.mean=50.6152154048481 weightedLogRatios: wLogRatio Lung 0.065144944402267 cerebhem 0.101495059901998 cortex 0.162932001404396 heart 0.138553437579936 kidney 0.105707413125743 liver 0.157675741517048 stomach 0.071448113125786 testicle 0.0619132899904684 cont.weightedLogRatios: wLogRatio Lung -0.182915035192707 cerebhem -0.307417350915103 cortex -0.0757643412576441 heart -0.0370665499246494 kidney 0.323827910189967 liver -0.159117141101098 stomach -0.0757402493415353 testicle 0.0738860598963372 varWeightedLogRatios=0.00168039618184167 cont.varWeightedLogRatios=0.0360317527144575 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.41214610421473 0.0744241402152199 45.8473029630907 1.96774968295746e-178 *** df.mm.trans1 0.428733665678152 0.0595804162931728 7.19588234443538 2.37298058532918e-12 *** df.mm.trans2 0.410179091649191 0.0595804162931728 6.88446165986579 1.80249863246257e-11 *** df.mm.exp2 0.316494206733302 0.0797822364410913 3.96697586895788 8.37779863994954e-05 *** df.mm.exp3 -0.099613674340574 0.0797822364410913 -1.24856959122881 0.212424895245084 df.mm.exp4 0.0783973724355746 0.0797822364410913 0.982641950548237 0.326273615474834 df.mm.exp5 0.00827222848662151 0.0797822364410913 0.103685091514443 0.91746211802658 df.mm.exp6 0.228032187463271 0.0797822364410913 2.858182443051 0.00444406146749769 ** df.mm.exp7 0.0521601195737862 0.0797822364410913 0.653781115954296 0.513562682268551 df.mm.exp8 0.23459539289859 0.0797822364410914 2.94044643724431 0.00343395015681166 ** df.mm.trans1:exp2 -0.118969518852507 0.0625863354540305 -1.90088647928413 0.0579094161526614 . df.mm.trans2:exp2 -0.127179063577092 0.0625863354540305 -2.03205799883435 0.0426922425341801 * df.mm.trans1:exp3 0.114465096016373 0.0625863354540305 1.82891513276804 0.0680262159553091 . df.mm.trans2:exp3 0.0889835723389533 0.0625863354540305 1.42177316651350 0.155734833933513 df.mm.trans1:exp4 -0.0433418303782522 0.0625863354540305 -0.692512671717082 0.488946864094246 df.mm.trans2:exp4 -0.0622485861088346 0.0625863354540305 -0.994603465073555 0.32042511575431 df.mm.trans1:exp5 -0.0141828453277776 0.0625863354540305 -0.226612490168799 0.820820557148055 df.mm.trans2:exp5 -0.0248434549316547 0.0625863354540305 -0.396946949384857 0.691581193677683 df.mm.trans1:exp6 -0.111543231118852 0.0625863354540305 -1.78222978402019 0.075337259892546 . df.mm.trans2:exp6 -0.134581260681836 0.0625863354540305 -2.15032977574931 0.0320227751147535 * df.mm.trans1:exp7 -0.0258694581232420 0.0625863354540305 -0.413340355136194 0.679539958906357 df.mm.trans2:exp7 -0.0273901884480196 0.0625863354540305 -0.437638475704296 0.661843196682025 df.mm.trans1:exp8 -0.117549721227172 0.0625863354540305 -1.87820105418238 0.0609533608675236 . df.mm.trans2:exp8 -0.116225843312127 0.0625863354540305 -1.85704822736418 0.0639103165972761 . df.mm.trans1:probe2 -0.0483218786418022 0.0428499346496973 -1.12770017123337 0.260004133784665 df.mm.trans1:probe3 -0.0519632207569567 0.0428499346496973 -1.21267911332331 0.225842878087605 df.mm.trans1:probe4 0.190314514192251 0.0428499346496973 4.44141900677543 1.10789060704717e-05 *** df.mm.trans1:probe5 -0.00841717886949037 0.0428499346496973 -0.196433878798222 0.84435286298002 df.mm.trans1:probe6 -0.0549744889969764 0.0428499346496973 -1.28295385853908 0.200120986111090 df.mm.trans2:probe2 0.294734370084281 0.0428499346496973 6.87829217229304 1.87504854376760e-11 *** df.mm.trans2:probe3 -0.09441709373774 0.0428499346496973 -2.20343611978897 0.0280333275140465 * df.mm.trans2:probe4 0.00985207668151797 0.0428499346496973 0.229920459904075 0.818250515067755 df.mm.trans2:probe5 -0.0632125619752708 0.0428499346496973 -1.47520789686239 0.140805518876096 df.mm.trans2:probe6 -0.0953044345583088 0.0428499346496973 -2.22414422186247 0.0265983856076446 * df.mm.trans3:probe2 -0.357048609764906 0.0428499346496973 -8.3325356895831 8.19018382269867e-16 *** df.mm.trans3:probe3 -0.348079962183789 0.0428499346496973 -8.12323204292794 3.79293401537376e-15 *** df.mm.trans3:probe4 -0.270100613217704 0.0428499346496973 -6.30340782140754 6.55067989375745e-10 *** df.mm.trans3:probe5 -0.534120273802004 0.0428499346496973 -12.4649028795141 3.94336039245594e-31 *** df.mm.trans3:probe6 -0.40545289836068 0.0428499346496973 -9.46215908321215 1.30736779122244e-19 *** df.mm.trans3:probe7 -0.189594771003572 0.0428499346496973 -4.42462217395497 1.19404701363692e-05 *** df.mm.trans3:probe8 -0.422908721991627 0.0428499346496973 -9.86953015095472 4.65201601011375e-21 *** df.mm.trans3:probe9 -0.449563547340822 0.0428499346496973 -10.4915806993885 2.40151399890679e-23 *** df.mm.trans3:probe10 -0.104771956559156 0.0428499346496973 -2.44509022979097 0.0148363113726189 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.89132426769138 0.104322909386257 37.3007644302146 1.37215553533234e-144 *** df.mm.trans1 -0.0069207342426679 0.0835159445869825 -0.0828672210665103 0.933991307396095 df.mm.trans2 0.0496139389375828 0.0835159445869825 0.594065470766598 0.552745309450251 df.mm.exp2 0.140012029804049 0.111833539478025 1.25196815246611 0.211184900153646 df.mm.exp3 -0.0169820774705039 0.111833539478025 -0.151851381524420 0.879367300042077 df.mm.exp4 -0.0923474104690354 0.111833539478025 -0.825757736901292 0.409347530935847 df.mm.exp5 0.132520037926037 0.111833539478025 1.18497580014515 0.236607325828814 df.mm.exp6 -0.100514870996351 0.111833539478025 -0.898790036204674 0.369210502169491 df.mm.exp7 0.156638593159161 0.111833539478025 1.40064057607637 0.161961030093737 df.mm.exp8 0.170586928014481 0.111833539478025 1.52536465188067 0.127819979022904 df.mm.trans1:exp2 -0.116563154495029 0.0877294461650154 -1.32866625278563 0.184582890230038 df.mm.trans2:exp2 -0.085605786531709 0.0877294461650154 -0.975793080588793 0.329653457835281 df.mm.trans1:exp3 0.0220378716072853 0.0877294461650154 0.251202675619688 0.801763810157902 df.mm.trans2:exp3 -0.0052249893726135 0.0877294461650154 -0.0595579888055547 0.952532211038259 df.mm.trans1:exp4 0.0526464991406376 0.0877294461650154 0.600100666788797 0.548719425380307 df.mm.trans2:exp4 0.0156222497130767 0.0877294461650154 0.178073046120591 0.858739961945973 df.mm.trans1:exp5 -0.0405662193547342 0.0877294461650154 -0.462401407144767 0.64400086063262 df.mm.trans2:exp5 -0.169121290798203 0.0877294461650154 -1.92775969974885 0.0544685934792343 . df.mm.trans1:exp6 0.0143106636084668 0.0877294461650154 0.163122694078668 0.870489778053207 df.mm.trans2:exp6 0.00918469811281212 0.0877294461650154 0.104693446890524 0.91666234317566 df.mm.trans1:exp7 -0.104689426925464 0.0877294461650154 -1.19332141603342 0.233326921821773 df.mm.trans2:exp7 -0.132186786377676 0.0877294461650154 -1.50675505381668 0.132524487279759 df.mm.trans1:exp8 -0.0972681840106472 0.0877294461650154 -1.10872903298272 0.268096579298484 df.mm.trans2:exp8 -0.162515397277623 0.0877294461650154 -1.85246122461480 0.0645669706827247 . df.mm.trans1:probe2 0.0645618273060601 0.0600642457775175 1.07487951393250 0.282963117337747 df.mm.trans1:probe3 0.0616555057432051 0.0600642457775175 1.02649263209900 0.305171111236761 df.mm.trans1:probe4 0.0354207414795542 0.0600642457775175 0.589714247153878 0.555656858215673 df.mm.trans1:probe5 0.0438509270938282 0.0600642457775175 0.730067056136113 0.465701672117245 df.mm.trans1:probe6 0.0582650924747882 0.0600642457775175 0.970046185056689 0.332506976836272 df.mm.trans2:probe2 -0.0228673509867266 0.0600642457775175 -0.380714861074406 0.703581535137293 df.mm.trans2:probe3 0.0522401738289444 0.0600642457775175 0.869738280281516 0.384873676499163 df.mm.trans2:probe4 0.0230185292160053 0.0600642457775175 0.383231803180675 0.701715816257416 df.mm.trans2:probe5 0.0130329363778221 0.0600642457775175 0.216983268650322 0.828312628230273 df.mm.trans2:probe6 0.0395690484262797 0.0600642457775175 0.65877874456039 0.510350481890089 df.mm.trans3:probe2 -0.0156610237753768 0.0600642457775175 -0.260737874465059 0.794405325588419 df.mm.trans3:probe3 0.0104959353487688 0.0600642457775175 0.174745145184150 0.861352815199461 df.mm.trans3:probe4 0.00745635033371085 0.0600642457775175 0.124139581496282 0.901256190685269 df.mm.trans3:probe5 0.0141534534567292 0.0600642457775175 0.235638577884664 0.813812602505816 df.mm.trans3:probe6 0.00762067954229014 0.0600642457775175 0.126875472148900 0.899091542757598 df.mm.trans3:probe7 0.0307703616012732 0.0600642457775175 0.512290817989274 0.608680695246973 df.mm.trans3:probe8 0.00373758797845559 0.0600642457775175 0.0622265031396531 0.950408092251883 df.mm.trans3:probe9 0.110053939733645 0.0600642457775175 1.83227040161785 0.0675241328505684 . df.mm.trans3:probe10 -0.00603922286329836 0.0600642457775175 -0.100546053398691 0.91995237201383