chr9.24466_chr9_44641219_44641807_+_0.R fitVsDatCorrelation=0.851152483717784 cont.fitVsDatCorrelation=0.264496221390039 fstatistic=7750.68711930166,37,347 cont.fstatistic=2289.66268898171,37,347 residuals=-0.468720673715056,-0.086718057559935,0.00266831185171506,0.0802130066412286,0.7037152991182 cont.residuals=-0.499731502608392,-0.195400382403216,-0.026200171265629,0.140710065993341,0.917901604626658 predictedValues: Include Exclude Both chr9.24466_chr9_44641219_44641807_+_0.R.tl.Lung 78.1158132698482 57.7889210757596 87.588243798042 chr9.24466_chr9_44641219_44641807_+_0.R.tl.cerebhem 86.9887419100508 75.0632557522541 92.0560520435781 chr9.24466_chr9_44641219_44641807_+_0.R.tl.cortex 75.5725956579318 57.5035830431425 84.2594594950162 chr9.24466_chr9_44641219_44641807_+_0.R.tl.heart 68.636265840178 54.6201718397671 77.0637623257495 chr9.24466_chr9_44641219_44641807_+_0.R.tl.kidney 70.3481180911944 59.5840566501985 76.8753414319289 chr9.24466_chr9_44641219_44641807_+_0.R.tl.liver 89.5022178261716 59.3641259663957 93.7017772514858 chr9.24466_chr9_44641219_44641807_+_0.R.tl.stomach 74.5302810916425 54.5239046613146 88.6638736569645 chr9.24466_chr9_44641219_44641807_+_0.R.tl.testicle 74.6312656541958 63.0852854398712 73.2401639554297 diffExp=20.3268921940887,11.9254861577967,18.0690126147893,14.0160940004109,10.7640614409959,30.1380918597759,20.0063764303279,11.5459802143245 diffExpScore=0.992742684358152 diffExp1.5=0,0,0,0,0,1,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,1,0,0 diffExp1.4Score=0.5 diffExp1.3=1,0,1,0,0,1,1,0 diffExp1.3Score=0.8 diffExp1.2=1,0,1,1,0,1,1,0 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 68.9995987687681 74.399957627282 73.3728648807863 cerebhem 70.2649419897137 70.2953791533583 73.333858962399 cortex 69.775403526268 66.4387923517488 67.7321615765066 heart 72.0056856004986 71.3915877185551 82.3712838280542 kidney 69.3459324093342 66.1424523708393 68.7117332749788 liver 62.8119118233908 65.5389020794062 72.8943588162306 stomach 66.2805923470008 74.7109278925148 76.0968995917411 testicle 69.5886179737459 65.7522840854479 75.8567183918605 cont.diffExp=-5.40035885851383,-0.0304371636445921,3.33661117451925,0.614097881943479,3.20348003849489,-2.72699025601543,-8.43033554551396,3.836333888298 cont.diffExpScore=4.18010331848804 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=0.573958544169182 cont.tran.correlation=0.140127960207605 tran.covariance=0.00556486978234095 cont.tran.covariance=0.000364788645870604 tran.mean=68.7411627356198 cont.tran.mean=68.983935482367 weightedLogRatios: wLogRatio Lung 1.26811970561650 cerebhem 0.647597691042703 cortex 1.14448476229853 heart 0.939850613390029 kidney 0.692573646624995 liver 1.76093934484969 stomach 1.29868861495202 testicle 0.710696332130335 cont.weightedLogRatios: wLogRatio Lung -0.321898477995501 cerebhem -0.00184168221199241 cortex 0.206820204198377 heart 0.0365938094837868 kidney 0.199377334141500 liver -0.176855750277288 stomach -0.509299671234426 testicle 0.238975700355254 varWeightedLogRatios=0.148604298469707 cont.varWeightedLogRatios=0.0745431431608948 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.35885294776352 0.0847148527359352 51.4532317178253 1.8389643649556e-164 *** df.mm.trans1 0.00691469753595811 0.0705790979031745 0.0979708970698973 0.922011968748356 df.mm.trans2 -0.244207583116588 0.0705790979031744 -3.46005531909192 0.000607390867264399 *** df.mm.exp2 0.319369416481778 0.0972431701939237 3.28423493233393 0.00112678684007920 ** df.mm.exp3 0.000697338331069874 0.0972431701939236 0.00717107771866377 0.994282477715077 df.mm.exp4 -0.0577516532889478 0.0972431701939236 -0.593889043043113 0.552973280221432 df.mm.exp5 0.0563160917579191 0.0972431701939236 0.579126448115716 0.562879600482561 df.mm.exp6 0.0954935468937815 0.0972431701939236 0.982007751324304 0.326780207493651 df.mm.exp7 -0.117350601649457 0.0972431701939236 -1.20677474228201 0.228340939133549 df.mm.exp8 0.220960314006211 0.0972431701939236 2.27224506940250 0.0236830874028759 * df.mm.trans1:exp2 -0.211783220529637 0.0821854790326838 -2.57689342475469 0.0103817723332836 * df.mm.trans2:exp2 -0.057835328801791 0.0821854790326838 -0.70371712232511 0.482080887113249 df.mm.trans1:exp3 -0.0337961231549919 0.0821854790326838 -0.411217693840437 0.681166763378465 df.mm.trans2:exp3 -0.00564715909140169 0.0821854790326838 -0.0687123705777259 0.945258148686006 df.mm.trans1:exp4 -0.0716198061028608 0.0821854790326838 -0.87144112251726 0.384115857712701 df.mm.trans2:exp4 0.00135783500962782 0.0821854790326838 0.0165215926902103 0.986827771174936 df.mm.trans1:exp5 -0.161052570209904 0.0821854790326838 -1.95962318533005 0.0508400166391408 . df.mm.trans2:exp5 -0.0257251397578413 0.0821854790326838 -0.313013199662812 0.754458678804329 df.mm.trans1:exp6 0.0405773472643333 0.0821854790326838 0.493728913451929 0.621810000257613 df.mm.trans2:exp6 -0.0686005233460443 0.0821854790326838 -0.834703698919404 0.404458846595912 df.mm.trans1:exp7 0.0703635910630344 0.0821854790326838 0.856156000928728 0.392502315527731 df.mm.trans2:exp7 0.0591927443768607 0.0821854790326838 0.72023361150357 0.471866038129854 df.mm.trans1:exp8 -0.266593294949135 0.0821854790326838 -3.24380046313431 0.00129406589773187 ** df.mm.trans2:exp8 -0.133269846365869 0.0821854790326838 -1.62157412640826 0.105802805463545 df.mm.trans1:probe2 -0.0596974732287895 0.0450148407655688 -1.32617315119886 0.185654582935938 df.mm.trans1:probe3 -0.0450277874568869 0.0450148407655688 -1.00028760939943 0.317868361394116 df.mm.trans1:probe4 0.0814315616799805 0.0450148407655688 1.80899366286921 0.071317332311785 . df.mm.trans1:probe5 -0.0330502399802941 0.0450148407655688 -0.734207639485282 0.463318017362571 df.mm.trans1:probe6 -0.0194074040451862 0.0450148407655688 -0.431133459879539 0.666639037447969 df.mm.trans2:probe2 -0.117602176677953 0.0450148407655688 -2.61252010843289 0.00937850941076858 ** df.mm.trans2:probe3 -0.0273500542716523 0.0450148407655688 -0.607578607554954 0.543864393446864 df.mm.trans2:probe4 -0.187042915932520 0.0450148407655688 -4.15513889978229 4.09847084099986e-05 *** df.mm.trans2:probe5 -0.0890427576838248 0.0450148407655688 -1.97807558950497 0.0487108502877127 * df.mm.trans2:probe6 -0.157444937743395 0.0450148407655688 -3.49762289648755 0.00053046594257579 *** df.mm.trans3:probe2 0.168610755289709 0.0450148407655688 3.74567037052981 0.000210596579911054 *** df.mm.trans3:probe3 0.775137161439241 0.0450148407655688 17.219591322694 1.82216291444537e-48 *** df.mm.trans3:probe4 0.460598977409856 0.0450148407655688 10.2321583188219 1.19138043210967e-21 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.20965724345489 0.155639163912773 27.0475447029141 1.83303465737878e-87 *** df.mm.trans1 0.00245508931668099 0.129668782186386 0.0189335418694072 0.984905003651717 df.mm.trans2 0.101381690815721 0.129668782186386 0.781851183502246 0.43483518715991 df.mm.exp2 -0.0380452590569253 0.178656342027608 -0.212952188683265 0.831489305327257 df.mm.exp3 -0.0220003395673452 0.178656342027608 -0.123143345025756 0.902064881000081 df.mm.exp4 -0.114313635987207 0.178656342027608 -0.639852102029165 0.522690979508093 df.mm.exp5 -0.0470035803101965 0.178656342027608 -0.263094944051484 0.792633547793684 df.mm.exp6 -0.214224511060951 0.178656342027608 -1.19908707762440 0.231312266099287 df.mm.exp7 -0.0724859043581376 0.178656342027608 -0.405728134447844 0.685192426973759 df.mm.exp8 -0.148352731031480 0.178656342027608 -0.830380435129221 0.406894666340565 df.mm.trans1:exp2 0.0562175523814129 0.150992167598866 0.37232098376628 0.709881162191131 df.mm.trans2:exp2 -0.0187040469930925 0.150992167598866 -0.123874286266177 0.901486535053426 df.mm.trans1:exp3 0.0331812125880355 0.150992167598866 0.219754528434789 0.82619155254846 df.mm.trans2:exp3 -0.0911739246898083 0.150992167598866 -0.603832146658267 0.54634978071714 df.mm.trans1:exp4 0.156958028924187 0.150992167598866 1.03951106484655 0.299290880938916 df.mm.trans2:exp4 0.073038307010575 0.150992167598866 0.483722488206225 0.628887941303188 df.mm.trans1:exp5 0.0520103826421475 0.150992167598866 0.344457487227556 0.73071092442554 df.mm.trans2:exp5 -0.0706410066540485 0.150992167598866 -0.467845503362248 0.640188772168235 df.mm.trans1:exp6 0.120268555629821 0.150992167598866 0.796521816610598 0.426273413008372 df.mm.trans2:exp6 0.087413029961908 0.150992167598866 0.57892426707943 0.563015867263108 df.mm.trans1:exp7 0.0322823442814446 0.150992167598866 0.213801449405029 0.8308274642583 df.mm.trans2:exp7 0.07665690387952 0.150992167598866 0.507687948974749 0.611994735641378 df.mm.trans1:exp8 0.156853060503636 0.150992167598866 1.03881587368386 0.299613679830950 df.mm.trans2:exp8 0.0247917681150418 0.150992167598866 0.164192411495839 0.869675271446093 df.mm.trans1:probe2 0.0182928982822206 0.0827018162004997 0.221191010338537 0.825073808230669 df.mm.trans1:probe3 -0.0346606741463515 0.0827018162004998 -0.41910414714861 0.675399334585119 df.mm.trans1:probe4 0.095505208786978 0.0827018162004998 1.15481392277333 0.248961266552647 df.mm.trans1:probe5 0.0150532195757177 0.0827018162004997 0.182018004770574 0.855674879500695 df.mm.trans1:probe6 0.125692916135408 0.0827018162004997 1.51983259751735 0.129463675076684 df.mm.trans2:probe2 0.0728417724551912 0.0827018162004998 0.880775970851665 0.379048711122489 df.mm.trans2:probe3 -0.102350343009893 0.0827018162004998 -1.23758277281067 0.216707565003771 df.mm.trans2:probe4 -0.0117794771385677 0.0827018162004997 -0.142433113077105 0.886820573130288 df.mm.trans2:probe5 0.0160910328774513 0.0827018162004998 0.194566862213047 0.845845908341899 df.mm.trans2:probe6 0.00936139524078467 0.0827018162004998 0.113194554495504 0.909941771098217 df.mm.trans3:probe2 -0.0731132391586864 0.0827018162004997 -0.884058446569455 0.377276777496276 df.mm.trans3:probe3 -0.0168223954263373 0.0827018162004998 -0.203410229656306 0.838933634538648 df.mm.trans3:probe4 0.0181762562208616 0.0827018162004998 0.219780617354227 0.826171249276207