chr10.2591_chr10_60720091_60725895_-_2.R fitVsDatCorrelation=0.75368189051584 cont.fitVsDatCorrelation=0.248171064559163 fstatistic=6777.81832372527,47,577 cont.fstatistic=3113.29792959502,47,577 residuals=-0.485000659693744,-0.101909866165984,-0.0089189740655837,0.0743100151641528,1.33368614727493 cont.residuals=-0.531790176074392,-0.174633831608134,-0.0448442536091875,0.134027207195787,1.47061124011015 predictedValues: Include Exclude Both chr10.2591_chr10_60720091_60725895_-_2.R.tl.Lung 53.7975667161202 51.4865295968648 71.0149351311302 chr10.2591_chr10_60720091_60725895_-_2.R.tl.cerebhem 57.905363048022 59.3269736252524 66.5422501722225 chr10.2591_chr10_60720091_60725895_-_2.R.tl.cortex 50.9267399701878 50.5835451202451 67.939808549727 chr10.2591_chr10_60720091_60725895_-_2.R.tl.heart 49.60229559505 48.7029591260036 80.5749532765014 chr10.2591_chr10_60720091_60725895_-_2.R.tl.kidney 52.374702554363 59.7144277236833 87.9482860897999 chr10.2591_chr10_60720091_60725895_-_2.R.tl.liver 51.7098431821597 49.7900772028412 69.1837278298433 chr10.2591_chr10_60720091_60725895_-_2.R.tl.stomach 54.3285871435892 50.8291606912065 66.9889892392947 chr10.2591_chr10_60720091_60725895_-_2.R.tl.testicle 52.1340095556227 50.8285714397067 69.854461283136 diffExp=2.31103711925543,-1.42161057723045,0.343194849942705,0.899336469046453,-7.33972516932026,1.91976597931848,3.49942645238278,1.30543811591598 diffExpScore=7.56478716643492 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 57.4615325811843 58.4100037757368 59.6391508871105 cerebhem 58.2372815629193 60.8844936544916 53.1270770764438 cortex 63.374234726187 56.8801041404614 59.4846393102804 heart 60.2205647304343 59.9507605902594 61.4389707419495 kidney 63.2806841989651 62.6906761625763 65.1482142820571 liver 57.8206128816443 64.3431216639261 62.913325736306 stomach 61.760554492254 58.6617531639618 59.4795314368786 testicle 60.653820795587 53.5107773278899 60.5248549821069 cont.diffExp=-0.948471194552504,-2.64721209157228,6.49413058572568,0.269804140174855,0.590008036388774,-6.52250878228187,3.09880132829218,7.14304346769707 cont.diffExpScore=3.2690849262382 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=0.626359957541009 cont.tran.correlation=-0.243913684785693 tran.covariance=0.00235776126179897 cont.tran.covariance=-0.000554253791314351 tran.mean=52.7525845181824 cont.tran.mean=59.8838110280299 weightedLogRatios: wLogRatio Lung 0.174019546089998 cerebhem -0.0987367652350304 cortex 0.0265536401630235 heart 0.0712660368768175 kidney -0.527747276193007 liver 0.148557860209646 stomach 0.263775337297342 testicle 0.0999428630224315 cont.weightedLogRatios: wLogRatio Lung -0.0664566399843442 cerebhem -0.181667559150018 cortex 0.442717861041077 heart 0.0183913627586356 kidney 0.0388081871380513 liver -0.439380319141097 stomach 0.210928096428297 testicle 0.506527555118549 varWeightedLogRatios=0.0604089105396023 cont.varWeightedLogRatios=0.0991834022401949 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.46850806528583 0.0875608133400852 39.6125610644363 1.0089875360974e-166 *** df.mm.trans1 0.424926419246312 0.0742213364141781 5.72512487346078 1.66342179839314e-08 *** df.mm.trans2 0.498696671270238 0.068656545679173 7.26364349293963 1.22704143973896e-12 *** df.mm.exp2 0.280378748097649 0.0904324191590736 3.10042295345935 0.00202671368035390 ** df.mm.exp3 -0.0282659289762944 0.0904324191590736 -0.312564114054868 0.754724775782492 df.mm.exp4 -0.263069183594835 0.0904324191590737 -2.90901411287127 0.00376536058451596 ** df.mm.exp5 -0.092409864152507 0.0904324191590736 -1.02186654975972 0.307272304744006 df.mm.exp6 -0.0469600641905874 0.0904324191590737 -0.519283511679402 0.603762172534813 df.mm.exp7 0.055334267039974 0.0904324191590736 0.61188529019266 0.540854677140959 df.mm.exp8 -0.0277960575436094 0.0904324191590736 -0.307368284538703 0.75867397120731 df.mm.trans1:exp2 -0.206796979582948 0.0797538971842872 -2.59293886423007 0.00975729419427252 ** df.mm.trans2:exp2 -0.138634890540426 0.067404345536481 -2.05676487824354 0.0401578858102909 * df.mm.trans1:exp3 -0.0265741800059811 0.0797538971842872 -0.333202275301684 0.73910263941241 df.mm.trans2:exp3 0.0105720448995391 0.067404345536481 0.156845153163266 0.875421750716954 df.mm.trans1:exp4 0.181878060592993 0.0797538971842872 2.28049119872761 0.0229423939510437 * df.mm.trans2:exp4 0.207488761947698 0.067404345536481 3.07826981029583 0.00218094962830657 ** df.mm.trans1:exp5 0.065605325405705 0.0797538971842872 0.822597110886141 0.41107695497083 df.mm.trans2:exp5 0.240663313536268 0.067404345536481 3.57044210756434 0.000386083390315553 *** df.mm.trans1:exp6 0.00737998013361307 0.0797538971842872 0.0925344139178574 0.926305568721657 df.mm.trans2:exp6 0.0134555631820305 0.067404345536481 0.199624565373875 0.84184454374505 df.mm.trans1:exp7 -0.0455119497223113 0.0797538971842872 -0.57065486865359 0.568455778109453 df.mm.trans2:exp7 -0.0681842600235684 0.067404345536481 -1.01157068555269 0.312167366588919 df.mm.trans1:exp8 -0.00361466990163662 0.0797538971842872 -0.0453227996280132 0.963865695559615 df.mm.trans2:exp8 0.0149344717084448 0.067404345536481 0.221565413766415 0.824730599294549 df.mm.trans1:probe2 0.0107274916545964 0.0522111815449602 0.205463491481394 0.837282484815656 df.mm.trans1:probe3 -0.0442018142151262 0.0522111815449602 -0.846596704138233 0.397570930387432 df.mm.trans1:probe4 0.0844712224535854 0.0522111815449602 1.61787609385636 0.106235794961444 df.mm.trans1:probe5 0.0684073112626098 0.0522111815449602 1.31020423668640 0.190648085892559 df.mm.trans1:probe6 0.123228751469437 0.0522111815449602 2.36019848283498 0.0185969858031312 * df.mm.trans1:probe7 0.244235716471372 0.0522111815449602 4.67784312180438 3.61486735251993e-06 *** df.mm.trans1:probe8 0.367938383968375 0.0522111815449602 7.0471185114157 5.22966348974634e-12 *** df.mm.trans1:probe9 0.118466329066761 0.0522111815449602 2.26898387589154 0.0236377260721836 * df.mm.trans1:probe10 0.150381987061656 0.0522111815449602 2.88026400881502 0.00412098372945646 ** df.mm.trans1:probe11 0.425108170452689 0.0522111815449602 8.14209059962795 2.40704189587034e-15 *** df.mm.trans1:probe12 0.287111516111315 0.0522111815449602 5.49904268808927 5.74285659434942e-08 *** df.mm.trans2:probe2 -0.0320476721743599 0.0522111815449602 -0.613808598580802 0.539583723970798 df.mm.trans2:probe3 -0.155678775049544 0.0522111815449602 -2.98171331203231 0.00298734304871550 ** df.mm.trans2:probe4 -0.0901676834962441 0.0522111815449602 -1.72698032927293 0.0847063820515391 . df.mm.trans2:probe5 -0.0551814527873953 0.0522111815449602 -1.05688956186286 0.29100428204031 df.mm.trans2:probe6 -0.0293077570481527 0.0522111815449602 -0.561331044058353 0.574789821096155 df.mm.trans3:probe2 0.00661199731179118 0.0522111815449602 0.126639488250183 0.899269858369304 df.mm.trans3:probe3 -0.194550941168579 0.0522111815449602 -3.72623134377923 0.000213484755091879 *** df.mm.trans3:probe4 -0.166455138752736 0.0522111815449602 -3.18811284914895 0.00150959693769259 ** df.mm.trans3:probe5 -0.356275164365913 0.0522111815449602 -6.82373303617188 2.24861599715504e-11 *** df.mm.trans3:probe6 -0.239853216374072 0.0522111815449602 -4.59390516124461 5.34562301322074e-06 *** df.mm.trans3:probe7 -0.17968221402961 0.0522111815449602 -3.44145082935695 0.000620472109693622 *** df.mm.trans3:probe8 0.0962771019969085 0.0522111815449602 1.84399393287053 0.0656965299170379 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.14195278354292 0.129057361038833 32.0938902686583 1.90365043794772e-130 *** df.mm.trans1 -0.0394171317439621 0.109396080792275 -0.360315757735497 0.718742873713805 df.mm.trans2 -0.083215389250252 0.101194041779643 -0.822334870579227 0.411226026130502 df.mm.exp2 0.170526878908118 0.133289869335700 1.27936864037756 0.201281494786208 df.mm.exp3 0.0739942409759405 0.133289869335701 0.555137771120328 0.579015573094629 df.mm.exp4 0.0432024872759402 0.133289869335701 0.324124312607221 0.745961379606704 df.mm.exp5 0.0788373272837302 0.133289869335701 0.591472762908729 0.554435346803102 df.mm.exp6 0.0495267325211705 0.133289869335701 0.371571618818484 0.710348227714785 df.mm.exp7 0.079129944386827 0.133289869335701 0.593668106820124 0.552966770678983 df.mm.exp8 -0.0482790425059512 0.133289869335701 -0.362210892294874 0.717327060907656 df.mm.trans1:exp2 -0.157116877905724 0.117550615515519 -1.33658915537521 0.181883569177070 df.mm.trans2:exp2 -0.129035529102447 0.099348402848897 -1.29881835441987 0.194525072907923 df.mm.trans1:exp3 0.0239474202002683 0.117550615515519 0.203720074924718 0.838644080850143 df.mm.trans2:exp3 -0.100535797296683 0.099348402848897 -1.01195182221089 0.311985245795840 df.mm.trans1:exp4 0.00369568818656147 0.117550615515519 0.0314391223759570 0.97493021304835 df.mm.trans2:exp4 -0.0171660915001838 0.099348402848897 -0.172786788795110 0.862879613363283 df.mm.trans1:exp5 0.0176270830280261 0.117550615515519 0.149953132535482 0.880853992764195 df.mm.trans2:exp5 -0.00811176893784153 0.099348402848897 -0.0816497166057017 0.934953579177997 df.mm.trans1:exp6 -0.0432971215040448 0.117550615515519 -0.368327475906144 0.712764157891294 df.mm.trans2:exp6 0.0472161336642292 0.099348402848897 0.4752581049143 0.634782869240427 df.mm.trans1:exp7 -0.00698078586871759 0.117550615515519 -0.0593853621106388 0.952665733205158 df.mm.trans2:exp7 -0.0748291671054435 0.099348402848897 -0.753199497522413 0.451637246276025 df.mm.trans1:exp8 0.102345948029992 0.117550615515519 0.870654292886111 0.384305020706045 df.mm.trans2:exp8 -0.039325051192538 0.099348402848897 -0.395829727150712 0.692376962603461 df.mm.trans1:probe2 -0.157920771860073 0.0769549419412167 -2.05211995326699 0.0406086655985712 * df.mm.trans1:probe3 -0.0706832179457223 0.0769549419412167 -0.918501348486687 0.358740239326045 df.mm.trans1:probe4 -0.090106275336015 0.0769549419412167 -1.17089654105443 0.242123695143936 df.mm.trans1:probe5 -0.157768988435158 0.0769549419412167 -2.05014758578692 0.0408013763464959 * df.mm.trans1:probe6 -0.128018803458118 0.0769549419412167 -1.66355532508760 0.096744314436993 . df.mm.trans1:probe7 -0.07213611981699 0.0769549419412167 -0.937381251903125 0.34895466595714 df.mm.trans1:probe8 -0.0783293568272275 0.0769549419412167 -1.01785999510026 0.309171089747051 df.mm.trans1:probe9 -0.0461386222136529 0.0769549419412167 -0.599553726502667 0.549039044384347 df.mm.trans1:probe10 -0.127103064400213 0.0769549419412167 -1.6516556467199 0.0991488443924902 . df.mm.trans1:probe11 -0.05672066247039 0.0769549419412167 -0.737063287159868 0.461383457277779 df.mm.trans1:probe12 -0.0434726464996331 0.0769549419412167 -0.564910393056243 0.572354271797295 df.mm.trans2:probe2 0.031472500266369 0.0769549419412167 0.408973088309388 0.682711164105168 df.mm.trans2:probe3 0.0455281432077272 0.0769549419412167 0.591620785608605 0.55433626685183 df.mm.trans2:probe4 0.0436067375460800 0.0769549419412167 0.566652854853555 0.57117040400316 df.mm.trans2:probe5 0.0546129233842306 0.0769549419412167 0.709674024911195 0.478192984169857 df.mm.trans2:probe6 -0.0527234067832832 0.0769549419412167 -0.685120480287762 0.493543029504496 df.mm.trans3:probe2 0.0581158510451576 0.0769549419412167 0.755193228390066 0.450441196412715 df.mm.trans3:probe3 0.129201580104961 0.0769549419412167 1.67892505465930 0.0937080567558451 . df.mm.trans3:probe4 0.0178686640065136 0.0769549419412167 0.232196445813225 0.816467768491052 df.mm.trans3:probe5 0.0882356747382553 0.0769549419412167 1.14658880264838 0.252026857219248 df.mm.trans3:probe6 0.0994009008799104 0.0769549419412167 1.29167664054427 0.196986304849267 df.mm.trans3:probe7 0.0547423343428086 0.0769549419412167 0.711355670758929 0.4771513344645 df.mm.trans3:probe8 0.104370914411660 0.0769549419412167 1.35626006308192 0.175546906890887