chr8.22884_chr8_70762717_70765978_-_0.R fitVsDatCorrelation=0.861060832816291 cont.fitVsDatCorrelation=0.268497464556768 fstatistic=6905.14034488881,36,324 cont.fstatistic=1917.71736688915,36,324 residuals=-0.612005872781552,-0.0935568940849072,-0.0135232680443814,0.104292305610404,0.541682112207959 cont.residuals=-0.68404714318636,-0.235382267908756,-0.0386022330223476,0.188465753748674,0.864214658984345 predictedValues: Include Exclude Both chr8.22884_chr8_70762717_70765978_-_0.R.tl.Lung 92.666838148659 117.087268097183 94.3128952202593 chr8.22884_chr8_70762717_70765978_-_0.R.tl.cerebhem 74.6257392451835 84.9987577986873 62.949940031642 chr8.22884_chr8_70762717_70765978_-_0.R.tl.cortex 72.9171529823668 88.2588506102238 74.8642404984632 chr8.22884_chr8_70762717_70765978_-_0.R.tl.heart 79.8856778158202 133.280955302689 95.713918906933 chr8.22884_chr8_70762717_70765978_-_0.R.tl.kidney 99.4108577170815 138.633196399716 119.468375515801 chr8.22884_chr8_70762717_70765978_-_0.R.tl.liver 92.8217882357705 138.390352501187 109.444170517878 chr8.22884_chr8_70762717_70765978_-_0.R.tl.stomach 76.7251804523468 114.223104546716 87.7299162364505 chr8.22884_chr8_70762717_70765978_-_0.R.tl.testicle 83.661789791007 138.948569073413 100.804750960301 diffExp=-24.4204299485244,-10.3730185535038,-15.3416976278569,-53.3952774868687,-39.2223386826341,-45.5685642654164,-37.4979240943693,-55.2867792824058 diffExpScore=0.996455233515543 diffExp1.5=0,0,0,-1,0,0,0,-1 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,0,-1,0,-1,-1,-1 diffExp1.4Score=0.8 diffExp1.3=0,0,0,-1,-1,-1,-1,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 102.177311764802 98.7427972728576 95.8145058661521 cerebhem 85.0336831014778 93.473429465397 98.425701871459 cortex 96.8372803877716 89.090637303463 88.8900363067818 heart 105.460900979037 93.8380552892317 100.672299934695 kidney 98.9611955427489 98.8942801400326 97.7652996763535 liver 93.3878092062465 93.0057461058293 81.0779208909573 stomach 98.116960939415 91.3557959391646 109.214384233140 testicle 96.2013224571121 106.360873058378 88.8640327219817 cont.diffExp=3.43451449194421,-8.4397463639192,7.74664308430852,11.6228456898053,0.0669154027162904,0.382063100417241,6.76116500025043,-10.1595506012658 cont.diffExpScore=3.9157496466818 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=0.709312774900978 cont.tran.correlation=0.129474055855843 tran.covariance=0.0168754462527794 cont.tran.covariance=0.00050124500181474 tran.mean=101.658504919878 cont.tran.mean=96.3086299345603 weightedLogRatios: wLogRatio Lung -1.08673241283680 cerebhem -0.569744669428922 cortex -0.837277686971285 heart -2.37326600453957 kidney -1.58487888108473 liver -1.88929921590482 stomach -1.80625171672762 testicle -2.37448941193107 cont.weightedLogRatios: wLogRatio Lung 0.157608202197766 cerebhem -0.42492195173761 cortex 0.377813422404523 heart 0.537135855561288 kidney 0.00310767686723726 liver 0.018590215581415 stomach 0.324896168852648 testicle -0.463486094977092 varWeightedLogRatios=0.460169562383964 cont.varWeightedLogRatios=0.131739710333964 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.60029541932722 0.097952918656046 46.9643526956129 1.22571075228297e-146 *** df.mm.trans1 0.0254827596664907 0.0829650988717614 0.307150356150112 0.758926250683324 df.mm.trans2 0.234464087505529 0.0829650988717614 2.82605686841811 0.00500505964209114 ** df.mm.exp2 -0.132529974234137 0.115538959501831 -1.14705874802374 0.252203676652019 df.mm.exp3 -0.291390746011051 0.115538959501832 -2.52201289735894 0.0121474223590777 * df.mm.exp4 -0.0336200860645759 0.115538959501831 -0.290984843636600 0.77124909344116 df.mm.exp5 0.00272892786475917 0.115538959501831 0.0236191140765459 0.981170965141216 df.mm.exp6 0.020032885273273 0.115538959501831 0.173386408875834 0.86245597890712 df.mm.exp7 -0.14119170205848 0.115538959501831 -1.22202677492731 0.222585805901986 df.mm.exp8 0.00238844391403033 0.115538959501831 0.0206721951134800 0.983519874022962 df.mm.trans1:exp2 -0.0839952235149626 0.100059674055408 -0.839451300515438 0.401835063373049 df.mm.trans2:exp2 -0.187752921454571 0.100059674055407 -1.87640948491001 0.0614977813260937 . df.mm.trans1:exp3 0.0517039764849721 0.100059674055408 0.516731410261654 0.605696190140013 df.mm.trans2:exp3 0.0087451890574213 0.100059674055408 0.0873997356075604 0.930407770633707 df.mm.trans1:exp4 -0.114794004143008 0.100059674055408 -1.14725542759056 0.252122523825224 df.mm.trans2:exp4 0.163159894155498 0.100059674055407 1.63062588096328 0.103940953408530 df.mm.trans1:exp5 0.0675217368526265 0.100059674055408 0.674814679240677 0.500274754627184 df.mm.trans2:exp5 0.166183104536874 0.100059674055408 1.66083995481388 0.097712937919119 . df.mm.trans1:exp6 -0.0183621615618008 0.100059674055407 -0.183512106501895 0.854511009238595 df.mm.trans2:exp6 0.147125910180113 0.100059674055408 1.47038166543139 0.142428847801731 df.mm.trans1:exp7 -0.0475890210476528 0.100059674055408 -0.475606396851749 0.634675312853435 df.mm.trans2:exp7 0.116425757399173 0.100059674055407 1.16356322862598 0.245457226956426 df.mm.trans1:exp8 -0.104616760045673 0.100059674055408 -1.04554368214054 0.296551158651882 df.mm.trans2:exp8 0.168795876142064 0.100059674055408 1.68695208869653 0.0925750514449482 . df.mm.trans1:probe2 -0.155889325933388 0.0500298370277038 -3.11592711859255 0.00199768815607785 ** df.mm.trans1:probe3 0.00263590002200894 0.0500298370277038 0.052686560233033 0.958014116425085 df.mm.trans1:probe4 -0.308313818061061 0.0500298370277037 -6.16259888854592 2.12350953048871e-09 *** df.mm.trans1:probe5 -0.210420334872975 0.0500298370277038 -4.20589686823196 3.36948233356041e-05 *** df.mm.trans1:probe6 -0.19891995218478 0.0500298370277038 -3.9760263875061 8.64601925568407e-05 *** df.mm.trans2:probe2 -0.246963393721499 0.0500298370277038 -4.93632217080268 1.27640098714287e-06 *** df.mm.trans2:probe3 -0.0545657361102941 0.0500298370277038 -1.09066387883851 0.276231250555729 df.mm.trans2:probe4 0.0389680988208297 0.0500298370277037 0.778897176883693 0.436608785555217 df.mm.trans2:probe5 -0.405016616088919 0.0500298370277038 -8.09550140778278 1.16515133378715e-14 *** df.mm.trans2:probe6 0.0210179270574598 0.0500298370277038 0.420107845760545 0.674685133066765 df.mm.trans3:probe2 -0.545149723769781 0.0500298370277038 -10.8964920966644 9.15742265789053e-24 *** df.mm.trans3:probe3 -0.395723294551493 0.0500298370277038 -7.9097458249237 4.10718334001505e-14 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.67091321054799 0.185557054650200 25.172382798128 3.077547292314e-78 *** df.mm.trans1 0.00092538807855185 0.157164886933734 0.00588800778981903 0.995305700255207 df.mm.trans2 -0.0367004943982443 0.157164886933734 -0.233515864225567 0.815508318951085 df.mm.exp2 -0.265391324094684 0.218871160927733 -1.21254587845089 0.226186880538325 df.mm.exp3 -0.081527787508457 0.218871160927733 -0.37249214178279 0.709769997861613 df.mm.exp4 -0.0687739540304768 0.218871160927733 -0.314221178061849 0.753555287198273 df.mm.exp5 -0.0506045188977225 0.218871160927733 -0.231206883004706 0.817300021408955 df.mm.exp6 0.0171973879518626 0.218871160927733 0.078573110678299 0.937420679396442 df.mm.exp7 -0.249204829054758 0.218871160927733 -1.13859143433264 0.255714771385698 df.mm.exp8 0.0893593749382215 0.218871160927733 0.408273865590388 0.683342306441614 df.mm.trans1:exp2 0.0817291191353087 0.189547985519209 0.431179043720442 0.666624924327291 df.mm.trans2:exp2 0.210550081050746 0.189547985519209 1.11080094295916 0.267477690068788 df.mm.trans1:exp3 0.0278501808842361 0.189547985519209 0.146929448012592 0.88327909013047 df.mm.trans2:exp3 -0.0213364264173798 0.189547985519209 -0.112564775399407 0.910445321006623 df.mm.trans1:exp4 0.100404576684284 0.189547985519209 0.52970532189649 0.596679031510765 df.mm.trans2:exp4 0.0178259723621541 0.189547985519209 0.0940446415894384 0.925131825422948 df.mm.trans1:exp5 0.0186226732427467 0.189547985519209 0.0982478035402783 0.92179627686103 df.mm.trans2:exp5 0.0521374589595996 0.189547985519209 0.275062057857196 0.783443881731365 df.mm.trans1:exp6 -0.107146228389776 0.189547985519209 -0.56527231400683 0.572279763491211 df.mm.trans2:exp6 -0.0770545727351187 0.189547985519209 -0.406517497529985 0.684630788176284 df.mm.trans1:exp7 0.20865542033222 0.189547985519209 1.10080526448578 0.271798565155207 df.mm.trans2:exp7 0.171448095363947 0.189547985519209 0.904510247863184 0.366396961824271 df.mm.trans1:exp8 -0.149625925142479 0.189547985519209 -0.789382829538517 0.430465566963618 df.mm.trans2:exp8 -0.0150400621873151 0.189547985519209 -0.079346990399911 0.936805615262129 df.mm.trans1:probe2 -0.136459305639355 0.0947739927596044 -1.4398391548775 0.150878406714055 df.mm.trans1:probe3 -0.0732134755798528 0.0947739927596044 -0.77250597392852 0.440377951433581 df.mm.trans1:probe4 -0.076726120767821 0.0947739927596044 -0.809569361105613 0.418781863291358 df.mm.trans1:probe5 -0.0258894565885219 0.0947739927596044 -0.273170474670101 0.78489621236294 df.mm.trans1:probe6 -0.0938721364316284 0.0947739927596044 -0.990484137032576 0.322676654689194 df.mm.trans2:probe2 -0.133574124285933 0.0947739927596044 -1.40939640081162 0.159677070013919 df.mm.trans2:probe3 -0.129557308089594 0.0947739927596044 -1.36701329465160 0.172568842424545 df.mm.trans2:probe4 0.0253988710777760 0.0947739927596044 0.267994101949472 0.788874392034925 df.mm.trans2:probe5 -0.0885957446004944 0.0947739927596044 -0.934810722021798 0.350582030691204 df.mm.trans2:probe6 -0.0489199804413396 0.0947739927596044 -0.516175155408149 0.606084166735823 df.mm.trans3:probe2 -0.0380615713752662 0.0947739927596044 -0.401603544041981 0.68824058406302 df.mm.trans3:probe3 -0.180110466085762 0.0947739927596044 -1.90042078888261 0.0582653489180784 .