chr2.14293_chr2_38989264_38996668_+_1.R fitVsDatCorrelation=0.911123530397044 cont.fitVsDatCorrelation=0.265751099429812 fstatistic=8305.56538312405,43,485 cont.fstatistic=1508.71717717385,43,485 residuals=-0.802604962595188,-0.0887932654439321,-0.00345023771895332,0.0817958496120384,0.588862507268265 cont.residuals=-0.858820032337631,-0.266235176604476,-0.0220993575881969,0.18880397164014,1.53176535102661 predictedValues: Include Exclude Both chr2.14293_chr2_38989264_38996668_+_1.R.tl.Lung 101.569547748309 53.8271880453737 101.004504224156 chr2.14293_chr2_38989264_38996668_+_1.R.tl.cerebhem 66.1972018999103 45.1577072694632 76.8934210052958 chr2.14293_chr2_38989264_38996668_+_1.R.tl.cortex 105.696420221522 56.1210231365505 90.010454111386 chr2.14293_chr2_38989264_38996668_+_1.R.tl.heart 97.1287443610828 61.4379220880621 80.925476190023 chr2.14293_chr2_38989264_38996668_+_1.R.tl.kidney 88.5391840764302 54.9491214198077 97.1702605983127 chr2.14293_chr2_38989264_38996668_+_1.R.tl.liver 104.536399282982 56.2714124433461 98.028963665628 chr2.14293_chr2_38989264_38996668_+_1.R.tl.stomach 124.322871401361 65.6378277398934 95.9485794082434 chr2.14293_chr2_38989264_38996668_+_1.R.tl.testicle 92.3217106849299 52.2525152480633 88.6477635960688 diffExp=47.7423597029349,21.0394946304471,49.5753970849719,35.6908222730207,33.5900626566226,48.2649868396357,58.6850436614674,40.0691954368665 diffExpScore=0.997020771440288 diffExp1.5=1,0,1,1,1,1,1,1 diffExp1.5Score=0.875 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 75.2265824137097 80.4838600376541 74.2737031077262 cerebhem 85.428987911173 71.8331619707257 87.470410231432 cortex 79.2472383336758 68.9947581949388 86.7022763559963 heart 81.7245277748844 77.0402307013561 87.1681289023971 kidney 83.2796285431651 68.3475938491383 79.5349571504325 liver 94.8518369823366 87.9898613140559 76.0724981990863 stomach 93.5466543167913 87.8192377213322 75.0358754365739 testicle 85.8948356595888 78.6926928390353 80.9665602317842 cont.diffExp=-5.25727762394436,13.5958259404472,10.2524801387370,4.6842970735283,14.9320346940268,6.86197566828072,5.72741659545905,7.20214282055346 cont.diffExpScore=1.16126666776328 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=0,0,0,0,1,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.872620808159305 cont.tran.correlation=0.630319692453813 tran.covariance=0.0178466193230782 cont.tran.covariance=0.00456255272487085 tran.mean=76.622924816693 cont.tran.mean=81.2751055352226 weightedLogRatios: wLogRatio Lung 2.73242056630969 cerebhem 1.53044421112409 cortex 2.75004078071771 heart 1.99098466936368 kidney 2.02498918702087 liver 2.68788723530584 stomach 2.87653061220870 testicle 2.41376027450015 cont.weightedLogRatios: wLogRatio Lung -0.294140410272115 cerebhem 0.75593519526291 cortex 0.596188137058967 heart 0.258172140417256 kidney 0.854294504451294 liver 0.339034163071202 stomach 0.284742737520045 testicle 0.386140650849415 varWeightedLogRatios=0.228940340298379 cont.varWeightedLogRatios=0.127236309454459 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.25256963379675 0.0851667452834105 49.93227837515 2.84894519313673e-193 *** df.mm.trans1 0.388376455766323 0.068180433440634 5.69630370719906 2.12552537528803e-08 *** df.mm.trans2 -0.317321063326794 0.068180433440634 -4.65413678549128 4.20410584212583e-06 *** df.mm.exp2 -0.330978501368298 0.0912982452933963 -3.62524493548222 0.000319211875359655 *** df.mm.exp3 0.196798346148001 0.0912982452933963 2.15555452917599 0.0316097415308288 * df.mm.exp4 0.309178599150994 0.0912982452933962 3.38646814248639 0.00076551955192101 *** df.mm.exp5 -0.0779691445030791 0.0912982452933963 -0.854004852475722 0.393523905547719 df.mm.exp6 0.103101636221730 0.0912982452933963 1.12928387495731 0.259336332297726 df.mm.exp7 0.451864393812889 0.0912982452933963 4.94932177897589 1.02919276635851e-06 *** df.mm.exp8 0.00533921135587654 0.0912982452933963 0.0584809854638327 0.953389594856652 df.mm.trans1:exp2 -0.0971270672855607 0.0716202360473551 -1.35614000519826 0.175685642848630 df.mm.trans2:exp2 0.155360776783042 0.0716202360473551 2.16923016953363 0.0305502720842661 * df.mm.trans1:exp3 -0.156971084521945 0.0716202360473551 -2.19171414651798 0.0288748861008832 * df.mm.trans2:exp3 -0.155066553373938 0.0716202360473551 -2.16512206510194 0.0308652698271148 * df.mm.trans1:exp4 -0.353885002556604 0.0716202360473551 -4.94113147466613 1.07121828379585e-06 *** df.mm.trans2:exp4 -0.176930024587409 0.0716202360473551 -2.47039153110894 0.0138394061754743 * df.mm.trans1:exp5 -0.0593294068762132 0.0716202360473551 -0.828388876531833 0.407857761599263 df.mm.trans2:exp5 0.098598142981737 0.0716202360473551 1.37667995001502 0.169246386570020 df.mm.trans1:exp6 -0.0743100702988088 0.0716202360473551 -1.03755690290765 0.299993394805313 df.mm.trans2:exp6 -0.0586936954351404 0.0716202360473551 -0.819512733752124 0.412896469693352 df.mm.trans1:exp7 -0.249726173933061 0.0716202360473551 -3.48681026083107 0.000533259238579042 *** df.mm.trans2:exp7 -0.253490915203388 0.0716202360473551 -3.53937558982325 0.000439709987221906 *** df.mm.trans1:exp8 -0.100803642148924 0.0716202360473551 -1.40747430771204 0.159927350146576 df.mm.trans2:exp8 -0.0350298764733075 0.0716202360473551 -0.489105850616659 0.624987868811073 df.mm.trans1:probe2 -0.0218447949971075 0.0490350235712263 -0.445493718696325 0.656161779922177 df.mm.trans1:probe3 0.0317329807598948 0.0490350235712263 0.647149291440653 0.517841496056602 df.mm.trans1:probe4 -0.147154060047018 0.0490350235712263 -3.00099906821229 0.00282958742515335 ** df.mm.trans1:probe5 -0.184792131912551 0.0490350235712263 -3.76857434654088 0.000184392893189001 *** df.mm.trans1:probe6 -0.00117921352313215 0.0490350235712263 -0.0240483931127161 0.980823898672463 df.mm.trans2:probe2 0.051316796110734 0.0490350235712263 1.04653352590304 0.295836049151499 df.mm.trans2:probe3 0.0314455686412327 0.0490350235712263 0.641287927506676 0.521638555484291 df.mm.trans2:probe4 0.0733465916245016 0.0490350235712263 1.4958000686583 0.135356164264809 df.mm.trans2:probe5 0.352214303510538 0.0490350235712263 7.18291290303809 2.58564713410042e-12 *** df.mm.trans2:probe6 0.300158708955965 0.0490350235712263 6.12131262708514 1.9167516401363e-09 *** df.mm.trans3:probe2 0.936945013604025 0.0490350235712263 19.1076692813874 4.33226072364326e-61 *** df.mm.trans3:probe3 0.307754319757471 0.0490350235712263 6.27621437380242 7.70225825761657e-10 *** df.mm.trans3:probe4 -0.0885320803503834 0.0490350235712263 -1.80548664816660 0.0716185111616503 . df.mm.trans3:probe5 0.422286348678633 0.0490350235712263 8.61193322493741 1.01325458189979e-16 *** df.mm.trans3:probe6 0.173025498449041 0.0490350235712263 3.52861048792424 0.000457516996015067 *** df.mm.trans3:probe7 1.11482335301809 0.0490350235712263 22.7352466018242 1.97280202208558e-78 *** df.mm.trans3:probe8 0.0843975275652966 0.0490350235712263 1.7211682878607 0.0858580074885775 . df.mm.trans3:probe9 0.080465351540433 0.0490350235712263 1.64097711554176 0.101450462471477 df.mm.trans3:probe10 -0.115764473368843 0.0490350235712263 -2.36085281371769 0.0186276436822262 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.38292779154436 0.199217773665859 22.0006865396241 6.52020495788587e-75 *** df.mm.trans1 -0.0414337398800098 0.159484246021342 -0.259798324371582 0.795129590394552 df.mm.trans2 -0.0393721030428917 0.159484246021342 -0.246871424765196 0.805112191854904 df.mm.exp2 -0.150073217395862 0.213560270577732 -0.702720674542496 0.482566985992060 df.mm.exp3 -0.256681406262028 0.213560270577732 -1.20191553217105 0.229982783092731 df.mm.exp4 -0.120961198863489 0.213560270577732 -0.56640309799318 0.571381784679257 df.mm.exp5 -0.13019067988652 0.213560270577732 -0.609620317179422 0.542398834217959 df.mm.exp6 0.297046484247287 0.213560270577732 1.39092577211906 0.164885722402424 df.mm.exp7 0.294970169837957 0.213560270577732 1.38120339068681 0.167852453614184 df.mm.exp8 0.0238334114138867 0.213560270577732 0.111600399032140 0.911186425841579 df.mm.trans1:exp2 0.277254039456597 0.167530459539078 1.65494704795413 0.0985821593471873 . df.mm.trans2:exp2 0.0363627848146005 0.167530459539078 0.217051782193187 0.828259264628112 df.mm.trans1:exp3 0.308749312850535 0.167530459539078 1.84294434397177 0.0659471856022132 . df.mm.trans2:exp3 0.102655271888500 0.167530459539078 0.612755866431291 0.540324980077101 df.mm.trans1:exp4 0.203810715198918 0.167530459539078 1.21655916040377 0.224363692035883 df.mm.trans2:exp4 0.0772322930189994 0.167530459539078 0.461004483790508 0.645002045565513 df.mm.trans1:exp5 0.231889985809555 0.167530459539078 1.38416611789609 0.166944164743922 df.mm.trans2:exp5 -0.0332596288365807 0.167530459539078 -0.198528846205561 0.842714508880735 df.mm.trans1:exp6 -0.0652350788548834 0.167530459539078 -0.38939234712519 0.697156849301644 df.mm.trans2:exp6 -0.207881556642572 0.167530459539078 -1.24085827266582 0.215257983443345 df.mm.trans1:exp7 -0.0770145393776914 0.167530459539078 -0.459704698414719 0.645934191233833 df.mm.trans2:exp7 -0.207746252649980 0.167530459539078 -1.24005063450280 0.215556276909154 df.mm.trans1:exp8 0.108785637354610 0.167530459539078 0.649348408963415 0.516420591226479 df.mm.trans2:exp8 -0.0463397764938465 0.167530459539078 -0.27660508197339 0.782201199628408 df.mm.trans1:probe2 -0.0693750856457803 0.114700264698450 -0.604838060558704 0.545569468101085 df.mm.trans1:probe3 -0.0849527531273522 0.114700264698450 -0.740650018120668 0.459264085243885 df.mm.trans1:probe4 -0.101024020274758 0.114700264698450 -0.880765363012484 0.378881069735427 df.mm.trans1:probe5 -0.0185553003749706 0.114700264698450 -0.161772079809520 0.871552710120185 df.mm.trans1:probe6 -0.0619231386131356 0.114700264698450 -0.539869186666075 0.589535033563337 df.mm.trans2:probe2 0.0546018418870857 0.114700264698450 0.476039371231056 0.634260689498993 df.mm.trans2:probe3 0.178070021848538 0.114700264698450 1.55248135055912 0.121199359458241 df.mm.trans2:probe4 0.223149282247606 0.114700264698450 1.94549927878782 0.0522923019780495 . df.mm.trans2:probe5 -0.00868348473536032 0.114700264698450 -0.075705882267922 0.939684313167016 df.mm.trans2:probe6 0.264878009060911 0.114700264698450 2.30930599643587 0.0213459050998225 * df.mm.trans3:probe2 0.105443547489798 0.114700264698450 0.919296461669134 0.358397502660823 df.mm.trans3:probe3 -0.0794022787083813 0.114700264698450 -0.692258896848513 0.48910604894042 df.mm.trans3:probe4 -0.0138988724283736 0.114700264698450 -0.121175591572645 0.903602142698737 df.mm.trans3:probe5 -0.0391225062972657 0.114700264698450 -0.341084708044222 0.733187523280936 df.mm.trans3:probe6 0.0510575060359767 0.114700264698450 0.445138519690503 0.656418255651175 df.mm.trans3:probe7 0.0425882274644065 0.114700264698450 0.371300167234768 0.710576113970109 df.mm.trans3:probe8 -0.000900717983139857 0.114700264698450 -0.00785279777259338 0.99373766726385 df.mm.trans3:probe9 -0.0829868926964034 0.114700264698450 -0.723510908318985 0.469714851918873 df.mm.trans3:probe10 0.0735721060352288 0.114700264698450 0.641429261115063 0.521546829182953