chr5.18645_chr5_91661255_91672399_-_2.R fitVsDatCorrelation=0.828565113279189 cont.fitVsDatCorrelation=0.226651857428223 fstatistic=11070.0267101610,53,715 cont.fstatistic=3649.12105899468,53,715 residuals=-0.403610329231343,-0.0814484447976373,-0.00728290744384743,0.0748946270082013,0.830179677591763 cont.residuals=-0.561467709299695,-0.177450645126305,-0.0317491625733942,0.164281364285021,1.29677618884537 predictedValues: Include Exclude Both chr5.18645_chr5_91661255_91672399_-_2.R.tl.Lung 58.624065068122 72.2076392926264 56.3478068038327 chr5.18645_chr5_91661255_91672399_-_2.R.tl.cerebhem 56.519351568157 59.7242700170038 56.5623096226398 chr5.18645_chr5_91661255_91672399_-_2.R.tl.cortex 57.4964192808877 65.2060661989432 66.7814560627097 chr5.18645_chr5_91661255_91672399_-_2.R.tl.heart 55.4605319759636 68.9380334837466 48.9295191452238 chr5.18645_chr5_91661255_91672399_-_2.R.tl.kidney 57.8888057685521 68.2238919569109 53.122257191841 chr5.18645_chr5_91661255_91672399_-_2.R.tl.liver 55.6505013563562 68.3284953131617 50.8013034447912 chr5.18645_chr5_91661255_91672399_-_2.R.tl.stomach 64.493162300218 75.9489858757556 56.6886343456786 chr5.18645_chr5_91661255_91672399_-_2.R.tl.testicle 58.8080864730303 65.704162006095 57.6841847668285 diffExp=-13.5835742245044,-3.20491844884678,-7.70964691805547,-13.4775015077830,-10.3350861883588,-12.6779939568055,-11.4558235755375,-6.89607553306468 diffExpScore=0.987552996284983 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=-1,0,0,-1,0,-1,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 60.8235622395681 59.4615065690302 52.3033906856186 cerebhem 58.890495162239 62.6210505460385 61.4046926878426 cortex 60.5294564384991 64.009156050152 61.3774224835127 heart 61.4166902415913 60.3645736925724 63.703982139407 kidney 63.1913118448759 59.5109343978827 61.3786036240053 liver 61.2352356832886 58.9488338753956 62.1945196019251 stomach 57.7839040767845 65.1225818014883 67.0355648685634 testicle 60.8177888270398 62.7684784273606 57.498009700514 cont.diffExp=1.36205567053786,-3.7305553837995,-3.47969961165293,1.05211654901894,3.68037744699321,2.28640180789302,-7.3386777247038,-1.95068960032089 cont.diffExpScore=2.72853074916519 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=0.647950443942189 cont.tran.correlation=-0.754565774329472 tran.covariance=0.00211647942851915 cont.tran.covariance=-0.000771608537268315 tran.mean=63.0764042459707 cont.tran.mean=61.0934724921129 weightedLogRatios: wLogRatio Lung -0.87014438993581 cerebhem -0.224050044046864 cortex -0.517744156405913 heart -0.89721580765439 kidney -0.680189423739102 liver -0.845922245023246 stomach -0.694610112742621 testicle -0.457915427591428 cont.weightedLogRatios: wLogRatio Lung 0.0927813948656287 cerebhem -0.252221849126675 cortex -0.230910770520252 heart 0.0710010477799472 kidney 0.246997606520094 liver 0.155853224826958 stomach -0.492172198789416 testicle -0.130187017219343 varWeightedLogRatios=0.0552384318607993 cont.varWeightedLogRatios=0.062681327211828 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11414827634972 0.0759311528865383 54.182613063934 2.42322765105526e-255 *** df.mm.trans1 -0.305172138716262 0.0674283332805626 -4.52587397417153 7.04297877883873e-06 *** df.mm.trans2 0.21436849713643 0.0613348375290513 3.49505282434137 0.000503200932249244 *** df.mm.exp2 -0.230169107676079 0.082688475523713 -2.78356936947123 0.00551843110122227 ** df.mm.exp3 -0.291298069083308 0.082688475523713 -3.5228375809126 0.000454095145275376 *** df.mm.exp4 0.0393509821128667 0.082688475523713 0.475894395967934 0.634294984203938 df.mm.exp5 -0.0104249504612415 0.082688475523713 -0.126075011000195 0.899707990368862 df.mm.exp6 -0.00365184605431051 0.082688475523713 -0.0441639059274137 0.964786083572263 df.mm.exp7 0.139899537692036 0.082688475523713 1.69188676905666 0.0911032808571284 . df.mm.exp8 -0.114689207035196 0.082688475523713 -1.38700352508381 0.16587290724138 df.mm.trans1:exp2 0.193606913843925 0.0785165868603323 2.46580909315785 0.0139041059424479 * df.mm.trans2:exp2 0.0403617307319706 0.0661168830145314 0.610460277189711 0.541750934034426 df.mm.trans1:exp3 0.271875462597697 0.0785165868603323 3.46265003955556 0.000566722297970019 *** df.mm.trans2:exp3 0.189304725870397 0.0661168830145315 2.86318285495689 0.00431685419739352 ** df.mm.trans1:exp4 -0.0948246288795229 0.0785165868603323 -1.20770187130268 0.227561285717878 df.mm.trans2:exp4 -0.0856887941496049 0.0661168830145314 -1.29601987030713 0.195386827134145 df.mm.trans1:exp5 -0.00219629995656849 0.0785165868603323 -0.0279724328882932 0.977691943358551 df.mm.trans2:exp5 -0.0463260717675796 0.0661168830145315 -0.7006693246171 0.483737346518994 df.mm.trans1:exp6 -0.0484023461397564 0.0785165868603323 -0.616460140146641 0.537787089568409 df.mm.trans2:exp6 -0.051567113955716 0.0661168830145315 -0.779938672311311 0.435685072827648 df.mm.trans1:exp7 -0.0444856093331704 0.0785165868603323 -0.566575944167093 0.57118012232561 df.mm.trans2:exp7 -0.0893835089426855 0.0661168830145315 -1.35190143375392 0.176834335204617 df.mm.trans1:exp8 0.117823298561629 0.0785165868603323 1.50061666296342 0.133896177748824 df.mm.trans2:exp8 0.0203056314874524 0.0661168830145314 0.307117192487576 0.758843597084078 df.mm.trans1:probe2 0.100736628752372 0.0430053057617177 2.34242326541125 0.0194323693728226 * df.mm.trans1:probe3 0.125457941375744 0.0430053057617177 2.91726658266023 0.0036418426395585 ** df.mm.trans1:probe4 0.400079613034157 0.0430053057617177 9.30302914833124 1.63315653889921e-19 *** df.mm.trans1:probe5 0.287606926878692 0.0430053057617177 6.68770798822485 4.57236630081985e-11 *** df.mm.trans1:probe6 0.315032337807146 0.0430053057617177 7.32542955403378 6.44109231643025e-13 *** df.mm.trans1:probe7 0.0588496910546976 0.0430053057617177 1.36842861624493 0.171607775262655 df.mm.trans1:probe8 -0.0103144797890239 0.0430053057617177 -0.239842028938800 0.81052146484984 df.mm.trans1:probe9 0.267471594367086 0.0430053057617177 6.21950221326371 8.48253196721201e-10 *** df.mm.trans1:probe10 0.102137553577276 0.0430053057617177 2.37499889300162 0.0178118360228299 * df.mm.trans1:probe11 0.480624719132671 0.0430053057617177 11.1759400525065 7.72345420743814e-27 *** df.mm.trans1:probe12 0.338372825410980 0.0430053057617177 7.86816462335656 1.33195372210015e-14 *** df.mm.trans1:probe13 0.152457179428746 0.0430053057617177 3.54507837413075 0.000418056448877761 *** df.mm.trans1:probe14 0.097025845557283 0.0430053057617177 2.25613662869601 0.0243633928622293 * df.mm.trans1:probe15 0.334349388328654 0.0430053057617177 7.77460786306706 2.64157099186791e-14 *** df.mm.trans1:probe16 0.330526445502265 0.0430053057617177 7.685713184639 5.03191171502633e-14 *** df.mm.trans1:probe17 0.500103099784555 0.0430053057617177 11.6288697621523 9.45685329446043e-29 *** df.mm.trans1:probe18 0.607956269509677 0.0430053057617177 14.136773561807 3.42704086625534e-40 *** df.mm.trans1:probe19 0.756844252925864 0.0430053057617177 17.598857618159 7.18032885563851e-58 *** df.mm.trans1:probe20 0.689352242205615 0.0430053057617177 16.0294696199849 1.24322349481627e-49 *** df.mm.trans1:probe21 0.461740836690056 0.0430053057617177 10.7368341768910 4.91347924453688e-25 *** df.mm.trans1:probe22 0.419986763319097 0.0430053057617177 9.76592901457658 3.12105116551365e-21 *** df.mm.trans2:probe2 -0.194764790695299 0.0430053057617177 -4.52885492256339 6.94709651208341e-06 *** df.mm.trans2:probe3 -0.179185214662743 0.0430053057617177 -4.16658390142757 3.47041369081352e-05 *** df.mm.trans2:probe4 -0.350184680303647 0.0430053057617177 -8.14282503289101 1.71748267123678e-15 *** df.mm.trans2:probe5 0.220404437790994 0.0430053057617177 5.1250522205842 3.83183246699769e-07 *** df.mm.trans2:probe6 0.0140209897959069 0.0430053057617177 0.326029301444661 0.74449758352772 df.mm.trans3:probe2 -0.192791798307969 0.0430053057617177 -4.48297703953514 8.57131527328839e-06 *** df.mm.trans3:probe3 0.0201418960969902 0.0430053057617177 0.468358397649624 0.639671057957029 df.mm.trans3:probe4 0.205444638199743 0.0430053057617177 4.77719282681218 2.15768081695831e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.17789624568664 0.132087998810633 31.6296429903238 5.2115587907549e-138 *** df.mm.trans1 -0.116551835422046 0.117296699280658 -0.993649745788418 0.320729564338378 df.mm.trans2 -0.0858243199388523 0.106696601310580 -0.804377261174688 0.421446495128196 df.mm.exp2 -0.140950267982509 0.143842873990731 -0.979890515755351 0.327471599241766 df.mm.exp3 -0.0911309943664894 0.143842873990731 -0.633545422433381 0.526580129635548 df.mm.exp4 -0.172408248806254 0.143842873990731 -1.19858734758987 0.231085549848815 df.mm.exp5 -0.120979616358081 0.143842873990731 -0.841053943109315 0.400598898231691 df.mm.exp6 -0.175119476140992 0.143842873990731 -1.21743588182391 0.223840060655442 df.mm.exp7 -0.208486874106872 0.143842873990731 -1.44940703924135 0.147662395189913 df.mm.exp8 -0.0406602012070049 0.143842873990731 -0.282670945587648 0.777510900698183 df.mm.trans1:exp2 0.108652722426254 0.136585557278707 0.79549203144916 0.426591345166825 df.mm.trans2:exp2 0.192722605495238 0.115015332086898 1.67562534488559 0.094248809252978 . df.mm.trans1:exp3 0.0862838734609373 0.136585557278707 0.631720331051345 0.527771558103945 df.mm.trans2:exp3 0.164827976006718 0.115015332086898 1.43309568399268 0.152267590020006 df.mm.trans1:exp4 0.182112624325631 0.136585557278707 1.33332270229732 0.182850558550041 df.mm.trans2:exp4 0.187481498632868 0.115015332086898 1.63005657794579 0.103529960340401 df.mm.trans1:exp5 0.159169186517871 0.136585557278707 1.16534419662747 0.244268313107932 df.mm.trans2:exp5 0.121810528648264 0.115015332086898 1.05908078895284 0.289920636706508 df.mm.trans1:exp6 0.181864995705714 0.136585557278707 1.33150970958528 0.183445692840543 df.mm.trans2:exp6 0.16646016657036 0.115015332086898 1.44728675342687 0.148254913528908 df.mm.trans1:exp7 0.157219884089195 0.136585557278707 1.15107253813361 0.250087179487980 df.mm.trans2:exp7 0.299429087073500 0.115015332086898 2.60338410228014 0.00942230949063341 ** df.mm.trans1:exp8 0.0405652760441049 0.136585557278707 0.296995354796776 0.766556313638779 df.mm.trans2:exp8 0.0947840580286345 0.115015332086898 0.824099329270486 0.41015793351524 df.mm.trans1:probe2 0.0153810804521979 0.0748109907509618 0.205599208054869 0.837162518127067 df.mm.trans1:probe3 0.0367667503530508 0.0748109907509618 0.491461882592139 0.623250632919577 df.mm.trans1:probe4 -0.0154528717713508 0.0748109907509618 -0.206558844044612 0.83641321047757 df.mm.trans1:probe5 0.0553416019688204 0.0748109907509618 0.739752293256574 0.459693120812902 df.mm.trans1:probe6 0.0252582894455237 0.0748109907509618 0.337628056946953 0.735742592668803 df.mm.trans1:probe7 0.0910159925080752 0.0748109907509618 1.21661258051051 0.224153103035003 df.mm.trans1:probe8 0.00189752580409697 0.0748109907509618 0.0253642651306897 0.979771491399283 df.mm.trans1:probe9 0.0951038272401967 0.0748109907509618 1.27125474860756 0.204051432999317 df.mm.trans1:probe10 0.124284735796676 0.0748109907509618 1.66131653316031 0.0970882632432471 . df.mm.trans1:probe11 0.0689140656171689 0.0748109907509618 0.921175684553848 0.357269413317573 df.mm.trans1:probe12 0.0218360999003348 0.0748109907509618 0.291883581291216 0.77046030367832 df.mm.trans1:probe13 0.107865216752927 0.0748109907509618 1.4418364958165 0.149786350584509 df.mm.trans1:probe14 0.0660483471389291 0.0748109907509618 0.882869568708123 0.377603478104545 df.mm.trans1:probe15 0.0622026057534104 0.0748109907509618 0.831463467185945 0.405989441347666 df.mm.trans1:probe16 0.131406516802293 0.0748109907509618 1.75651352138527 0.0794288191355568 . df.mm.trans1:probe17 -0.00693226237488693 0.0748109907509618 -0.092663688921909 0.926196701968213 df.mm.trans1:probe18 -0.0173861768588993 0.0748109907509618 -0.232401371568198 0.816292788111628 df.mm.trans1:probe19 0.0870283059853189 0.0748109907509618 1.16330909551816 0.245092194217842 df.mm.trans1:probe20 0.116473715302663 0.0748109907509618 1.55690646699751 0.119935189503965 df.mm.trans1:probe21 0.127400074128713 0.0748109907509618 1.70295932255214 0.0890102785622387 . df.mm.trans1:probe22 0.0180004122623015 0.0748109907509618 0.240611868411462 0.809924924247632 df.mm.trans2:probe2 -0.0327232025212508 0.0748109907509618 -0.43741169837174 0.661944888803313 df.mm.trans2:probe3 0.0296495938296899 0.0748109907509618 0.396326709913392 0.691982273465875 df.mm.trans2:probe4 0.0446945582900819 0.0748109907509618 0.59743304882655 0.550407446105835 df.mm.trans2:probe5 -0.0264403584968407 0.0748109907509618 -0.353428797445792 0.723871180275419 df.mm.trans2:probe6 -0.0826083008746562 0.0748109907509618 -1.10422679937031 0.269866233857084 df.mm.trans3:probe2 0.0271962877243259 0.0748109907509618 0.36353331845129 0.716313987340713 df.mm.trans3:probe3 -0.0624844484109103 0.0748109907509618 -0.835230863589478 0.403866733970747 df.mm.trans3:probe4 -0.0385473988788298 0.0748109907509618 -0.515263846820986 0.606527922737143