chr12.5746_chr12_93086387_93087536_+_0.R fitVsDatCorrelation=0.97103360628286 cont.fitVsDatCorrelation=0.260239292268051 fstatistic=6117.96245069304,46,554 cont.fstatistic=363.365864678645,46,554 residuals=-1.05642845564333,-0.114889871768366,0.0023887034423155,0.108118477918211,0.77337294946293 cont.residuals=-1.43899855518789,-0.573313799963347,-0.239159601005978,0.389788103865564,3.15102197265136 predictedValues: Include Exclude Both chr12.5746_chr12_93086387_93087536_+_0.R.tl.Lung 180.720578782072 72.0889410105646 99.6511672605583 chr12.5746_chr12_93086387_93087536_+_0.R.tl.cerebhem 95.0045088095475 68.6578106548294 91.3642733307408 chr12.5746_chr12_93086387_93087536_+_0.R.tl.cortex 116.826402973136 63.3152654056085 101.510816056472 chr12.5746_chr12_93086387_93087536_+_0.R.tl.heart 183.481270508142 65.2743193829394 80.2535030896387 chr12.5746_chr12_93086387_93087536_+_0.R.tl.kidney 196.327531872360 69.9967912236342 120.124705317852 chr12.5746_chr12_93086387_93087536_+_0.R.tl.liver 180.502859159409 73.5403953648799 96.850466531299 chr12.5746_chr12_93086387_93087536_+_0.R.tl.stomach 165.557287967049 72.2140832721375 85.5840306772165 chr12.5746_chr12_93086387_93087536_+_0.R.tl.testicle 179.745232119358 67.5466129858552 93.499378969368 diffExp=108.631637771507,26.3466981547181,53.5111375675274,118.206951125202,126.330740648726,106.962463794530,93.3432046949117,112.198619133503 diffExpScore=0.998660471710699 diffExp1.5=1,0,1,1,1,1,1,1 diffExp1.5Score=0.875 diffExp1.4=1,0,1,1,1,1,1,1 diffExp1.4Score=0.875 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 88.4173748133124 127.263666656069 91.3890165943277 cerebhem 71.0452806310408 85.604690244504 101.522703639701 cortex 48.5939923188172 82.8770227717141 119.419659430437 heart 76.853161322999 140.958608701905 92.377749562481 kidney 119.717674107707 82.3687728195021 100.611460373655 liver 88.5045898052892 98.028446512204 112.662277409935 stomach 83.4688690757278 115.069866978582 130.437169471525 testicle 102.424706905664 68.5734668296292 79.182760315816 cont.diffExp=-38.8462918427563,-14.5594096134633,-34.2830304528969,-64.1054473789055,37.3489012882046,-9.5238567069148,-31.6009979028544,33.8512400760349 cont.diffExpScore=2.15222912959241 cont.diffExp1.5=0,0,-1,-1,0,0,0,0 cont.diffExp1.5Score=0.666666666666667 cont.diffExp1.4=-1,0,-1,-1,1,0,0,1 cont.diffExp1.4Score=2.5 cont.diffExp1.3=-1,0,-1,-1,1,0,-1,1 cont.diffExp1.3Score=2 cont.diffExp1.2=-1,-1,-1,-1,1,0,-1,1 cont.diffExp1.2Score=1.75 tran.correlation=0.397528648269086 cont.tran.correlation=-0.162541290517508 tran.covariance=0.00529345844985986 cont.tran.covariance=-0.0055497218560897 tran.mean=115.674993218220 cont.tran.mean=92.4856369059166 weightedLogRatios: wLogRatio Lung 4.35393845473291 cerebhem 1.42632249576873 cortex 2.72860377955765 heart 4.85271535129962 kidney 4.91340020968708 liver 4.26219925079766 stomach 3.89492332851328 testicle 4.60212880872724 cont.weightedLogRatios: wLogRatio Lung -1.69865407062612 cerebhem -0.812155613315703 cortex -2.21573944332767 heart -2.81762625534922 kidney 1.71939356237250 liver -0.463405685779344 stomach -1.47208856729320 testicle 1.77681962844528 varWeightedLogRatios=1.46467431262523 cont.varWeightedLogRatios=2.9162163289869 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.45553586705136 0.107096021534968 22.9283574857129 2.58005598630445e-82 *** df.mm.trans1 2.720760550587 0.0850725635869966 31.9816452669221 6.16149351092513e-128 *** df.mm.trans2 1.82269952677532 0.0850725635869966 21.4252333528353 1.24309300179035e-74 *** df.mm.exp2 -0.604972230155378 0.113233668904073 -5.34268858379819 1.33951628467774e-07 *** df.mm.exp3 -0.584526760605794 0.113233668904073 -5.16212859887971 3.40861918182488e-07 *** df.mm.exp4 0.132343905467215 0.113233668904074 1.16876814774350 0.242999722359176 df.mm.exp5 -0.133473630403086 0.113233668904073 -1.17874508257928 0.239005712022579 df.mm.exp6 0.0472362999303449 0.113233668904074 0.41715772691567 0.676724644556415 df.mm.exp7 0.0662767005017312 0.113233668904073 0.585309132373675 0.558578202942771 df.mm.exp8 -0.00677336383825386 0.113233668904074 -0.0598175781444647 0.952322496539858 df.mm.trans1:exp2 -0.0380554930171872 0.087252401774978 -0.436154102844430 0.662894835321981 df.mm.trans2:exp2 0.556206482428927 0.0872524017749779 6.3746839183106 3.86449821966491e-10 *** df.mm.trans1:exp3 0.148263783506022 0.087252401774978 1.69925160213229 0.0898331707391034 . df.mm.trans2:exp3 0.454752571843606 0.087252401774978 5.21192038949716 2.64177306758270e-07 *** df.mm.trans1:exp4 -0.117183386057407 0.087252401774978 -1.34303908744679 0.179809261271777 df.mm.trans2:exp4 -0.231645866338472 0.087252401774978 -2.65489386682881 0.00816150539661006 ** df.mm.trans1:exp5 0.216305901112775 0.087252401774978 2.47908248612598 0.0134685469887683 * df.mm.trans2:exp5 0.104022383255139 0.087252401774978 1.19220080065429 0.233692935644283 df.mm.trans1:exp6 -0.0484417568645232 0.087252401774978 -0.555191099374587 0.578988054495324 df.mm.trans2:exp6 -0.0273020963677445 0.087252401774978 -0.312909396329926 0.754467256251568 df.mm.trans1:exp7 -0.153911489522691 0.0872524017749779 -1.76397997524040 0.0782864473195345 . df.mm.trans2:exp7 -0.0645422629379071 0.087252401774978 -0.739719040678791 0.459783823941037 df.mm.trans1:exp8 0.00136176022146263 0.087252401774978 0.0156071373825855 0.987553430429035 df.mm.trans2:exp8 -0.0583093623428647 0.087252401774978 -0.668283751010583 0.504230812307431 df.mm.trans1:probe2 -0.0653404203705372 0.0625032928611075 -1.04539164865656 0.296297902251356 df.mm.trans1:probe3 0.278865585294411 0.0625032928611075 4.46161430109124 9.85600313373209e-06 *** df.mm.trans1:probe4 -0.124799881942532 0.0625032928611075 -1.99669291376149 0.0463473699765377 * df.mm.trans1:probe5 -0.0114158799799815 0.0625032928611075 -0.182644456914445 0.855143815942971 df.mm.trans1:probe6 0.315148083069778 0.0625032928611075 5.04210368196262 6.24725071151307e-07 *** df.mm.trans2:probe2 -0.053434905020933 0.0625032928611075 -0.854913438555534 0.392968623226338 df.mm.trans2:probe3 0.147445858419485 0.0625032928611075 2.35900944846432 0.0186699309897784 * df.mm.trans2:probe4 -0.0825800398165598 0.0625032928611075 -1.32121102803441 0.186976421422137 df.mm.trans2:probe5 0.0207662343511168 0.0625032928611075 0.332242245176791 0.739832013885051 df.mm.trans2:probe6 -0.0385573094388279 0.0625032928611075 -0.616884449984235 0.537564371969848 df.mm.trans3:probe2 -2.95678967406754 0.0625032928611075 -47.3061424241761 1.03995597306334e-196 *** df.mm.trans3:probe3 -3.02223719684101 0.0625032928611075 -48.353247621 5.95304039440384e-201 *** df.mm.trans3:probe4 -1.77905662104332 0.0625032928611075 -28.4634063199947 1.72437644952851e-110 *** df.mm.trans3:probe5 -2.90824432143819 0.0625032928611075 -46.529457702345 1.58687589159483e-193 *** df.mm.trans3:probe6 -2.91755320416362 0.0625032928611075 -46.6783919792338 3.86931052243956e-194 *** df.mm.trans3:probe7 -2.98206919508008 0.0625032928611075 -47.7105934515597 2.35331527179737e-198 *** df.mm.trans3:probe8 -3.0476512928705 0.0625032928611075 -48.7598517352178 1.38881680864556e-202 *** df.mm.trans3:probe9 -3.09321508636237 0.0625032928611075 -49.4888340240888 1.72852303599859e-205 *** df.mm.trans3:probe10 -2.8377881342285 0.0625032928611075 -45.4022180964854 7.5403095669619e-189 *** df.mm.trans3:probe11 -2.68015109787968 0.0625032928611075 -42.8801583915812 3.86101002522465e-178 *** df.mm.trans3:probe12 -0.0290086870774255 0.0625032928611075 -0.464114541003265 0.642747999896818 df.mm.trans3:probe13 -2.91139788381137 0.0625032928611075 -46.5799120420898 9.83507660300827e-194 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.5871386981067 0.432764119856327 10.5996280366995 5.00078471445009e-24 *** df.mm.trans1 -0.193767800144017 0.343769568439355 -0.563656058980684 0.573216306281024 df.mm.trans2 0.255154863083652 0.343769568439355 0.742226440350738 0.458264715125714 df.mm.exp2 -0.720429196313903 0.457565728017018 -1.57448242340193 0.115946829302871 df.mm.exp3 -1.29499032013155 0.457565728017018 -2.83017332994701 0.00482107420101616 ** df.mm.exp4 -0.0487274916715616 0.457565728017018 -0.106492878919789 0.915229855293646 df.mm.exp5 -0.228127766929959 0.457565728017018 -0.498568299506632 0.61828142605747 df.mm.exp6 -0.469286773214198 0.457565728017018 -1.02561609071548 0.305520184854254 df.mm.exp7 -0.514082676122062 0.457565728017018 -1.12351656744480 0.261704784799369 df.mm.exp8 -0.327929206481237 0.457565728017018 -0.716682186627929 0.47387223725762 df.mm.trans1:exp2 0.501678127449918 0.35257807263335 1.42288521717463 0.155332277191237 df.mm.trans2:exp2 0.323908220825173 0.35257807263335 0.918685096909043 0.358660105415368 df.mm.trans1:exp3 0.696421730565365 0.35257807263335 1.97522700536622 0.048738343198857 * df.mm.trans2:exp3 0.8660871261302 0.35257807263335 2.45644069598977 0.0143378059912343 * df.mm.trans1:exp4 -0.0914444009039967 0.35257807263335 -0.259359296569446 0.795454378366091 df.mm.trans2:exp4 0.150932733936945 0.35257807263335 0.42808315562466 0.66875694765629 df.mm.trans1:exp5 0.53119552396596 0.35257807263335 1.50660397000456 0.13248216878595 df.mm.trans2:exp5 -0.206926888330595 0.35257807263335 -0.586896646138797 0.557512204962261 df.mm.trans1:exp6 0.4702726880974 0.35257807263335 1.33381150048557 0.182813720211397 df.mm.trans2:exp6 0.208283430585773 0.352578072633350 0.590744140808692 0.55493278162571 df.mm.trans1:exp7 0.456487915029262 0.35257807263335 1.29471442060995 0.195958111769773 df.mm.trans2:exp7 0.413361109241084 0.35257807263335 1.17239596369041 0.241542010555239 df.mm.trans1:exp8 0.474988670366783 0.35257807263335 1.34718721110240 0.178470702960761 df.mm.trans2:exp8 -0.290426164226498 0.35257807263335 -0.823721571955258 0.410452241072936 df.mm.trans1:probe2 0.341699734375284 0.252569443154595 1.35289419855170 0.176641284080098 df.mm.trans1:probe3 0.399142831279486 0.252569443154595 1.58032906235287 0.114602269210918 df.mm.trans1:probe4 0.419366773068868 0.252569443154595 1.66040185950831 0.0973994765868526 . df.mm.trans1:probe5 0.262973859076057 0.252569443154595 1.04119427826071 0.29823955222437 df.mm.trans1:probe6 0.262071202767529 0.252569443154595 1.03762038469206 0.299899491768159 df.mm.trans2:probe2 -0.0202258413591100 0.252569443154595 -0.0800803181354367 0.936202295174746 df.mm.trans2:probe3 0.0313321606242461 0.252569443154595 0.124053647317376 0.901317794398866 df.mm.trans2:probe4 -0.00427172795989747 0.252569443154595 -0.0169130830180545 0.986512044675172 df.mm.trans2:probe5 0.083987744755219 0.252569443154595 0.332533277605602 0.739612403044392 df.mm.trans2:probe6 -0.0154400541615343 0.252569443154595 -0.0611319167065021 0.951276197760633 df.mm.trans3:probe2 -0.149530881023257 0.252569443154595 -0.592038685106221 0.55406621493008 df.mm.trans3:probe3 -0.187127983958381 0.252569443154595 -0.74089716325598 0.459069706242277 df.mm.trans3:probe4 -0.131561294010731 0.252569443154595 -0.520891570918195 0.602650436958772 df.mm.trans3:probe5 -0.219384352377306 0.252569443154595 -0.868610033095031 0.385436591430131 df.mm.trans3:probe6 -0.238033192372684 0.252569443154595 -0.94244651846893 0.346374867496353 df.mm.trans3:probe7 0.00817469221810399 0.252569443154595 0.0323661172784879 0.974191739900412 df.mm.trans3:probe8 -0.153321855277281 0.252569443154595 -0.607048316543323 0.544067553400835 df.mm.trans3:probe9 -0.327589226428729 0.252569443154595 -1.29702636366908 0.195162017358213 df.mm.trans3:probe10 -0.199967880794662 0.252569443154595 -0.791734258495647 0.428854497705838 df.mm.trans3:probe11 -0.0366771946103806 0.252569443154595 -0.145216278550097 0.88459293027378 df.mm.trans3:probe12 0.078156177784673 0.252569443154595 0.309444312853137 0.757099916888323 df.mm.trans3:probe13 -0.177342967819838 0.252569443154595 -0.70215527897921 0.482877223124688