chr5.18032_chr5_120450371_120451696_+_0.R fitVsDatCorrelation=0.831204436778389 cont.fitVsDatCorrelation=0.254106329150693 fstatistic=9306.56672814332,45,531 cont.fstatistic=3067.31830509109,45,531 residuals=-0.394829405072051,-0.08475672037221,-0.00706921215811883,0.0702338308599273,0.911202970307572 cont.residuals=-0.572302600865975,-0.177579596440025,-0.0587843138558681,0.124314931023757,0.972700731043877 predictedValues: Include Exclude Both chr5.18032_chr5_120450371_120451696_+_0.R.tl.Lung 54.3721531532563 46.5904254456873 60.9710327486928 chr5.18032_chr5_120450371_120451696_+_0.R.tl.cerebhem 63.8530958443404 54.0972143290089 68.354665103368 chr5.18032_chr5_120450371_120451696_+_0.R.tl.cortex 53.5500943487583 47.0333540508606 62.2629166435022 chr5.18032_chr5_120450371_120451696_+_0.R.tl.heart 55.9777324575857 46.9036793869182 62.7038334727577 chr5.18032_chr5_120450371_120451696_+_0.R.tl.kidney 55.0912937638065 48.2048851741284 61.9178940588944 chr5.18032_chr5_120450371_120451696_+_0.R.tl.liver 51.8923922789622 50.9160379663456 58.7242054602462 chr5.18032_chr5_120450371_120451696_+_0.R.tl.stomach 53.5282186768287 47.5971414511499 62.5600924894121 chr5.18032_chr5_120450371_120451696_+_0.R.tl.testicle 62.8932880397329 51.3170855746009 69.5693315301491 diffExp=7.78172770756903,9.75588151533152,6.51674029789775,9.07405307066746,6.8864085896781,0.976354312616564,5.93107722567879,11.5762024651319 diffExpScore=0.983192838117065 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=0,0,0,0,0,0,0,1 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 61.3568731418962 52.8130931619148 62.3433675197191 cerebhem 59.1624071857693 58.9617981081491 57.3085765998444 cortex 58.2723732247396 59.7566990305805 56.2367705426183 heart 54.7741140165224 58.1941372085075 53.4969450872896 kidney 61.5103785760974 58.623388529767 61.0256317032406 liver 56.6754217495758 56.8378999826291 61.2709531504225 stomach 59.8604183333217 56.6009985160329 54.1110124597653 testicle 57.1611129689511 59.5521254229031 57.2176038710397 cont.diffExp=8.54377997998135,0.200609077620186,-1.48432580584089,-3.42002319198506,2.88699004633040,-0.162478233053292,3.25941981728881,-2.39101245395201 cont.diffExpScore=2.65015375736838 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=0.714631558042745 cont.tran.correlation=-0.402821713804748 tran.covariance=0.00290455995107259 cont.tran.covariance=-0.000652019826209525 tran.mean=52.7386307463732 cont.tran.mean=58.1320774473349 weightedLogRatios: wLogRatio Lung 0.605259137522669 cerebhem 0.675426458831529 cortex 0.508108216270459 heart 0.696205368129248 kidney 0.52641067604423 liver 0.0748311084829706 stomach 0.460525220832201 testicle 0.821744833971346 cont.weightedLogRatios: wLogRatio Lung 0.606050047407371 cerebhem 0.0138532584133536 cortex -0.102567296695882 heart -0.244296677785211 kidney 0.196863738191013 liver -0.0115618673815280 stomach 0.227539923367193 testicle -0.166632080231626 varWeightedLogRatios=0.0499978968791511 cont.varWeightedLogRatios=0.074630959794437 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.62446797662305 0.0748104812957646 48.4486653988182 4.96975384854434e-197 *** df.mm.trans1 0.383233702610071 0.0595579212724155 6.43463865800782 2.76727190511351e-10 *** df.mm.trans2 0.199828001209951 0.0595579212724154 3.35518763819754 0.000849815197132258 *** df.mm.exp2 0.195809635587019 0.079410561696554 2.4657883208943 0.0139862238396301 * df.mm.exp3 -0.0267397519564554 0.0794105616965539 -0.336727903507775 0.736455064933319 df.mm.exp4 0.00777920410171239 0.079410561696554 0.0979618319719047 0.92199956728755 df.mm.exp5 0.0317945345074411 0.0794105616965539 0.400381685108026 0.689036441274809 df.mm.exp6 0.0796498449607588 0.0794105616965539 1.00301324230799 0.316311316100273 df.mm.exp7 -0.0199942133142734 0.0794105616965539 -0.251782796735224 0.801306371782038 df.mm.exp8 0.110291055844849 0.0794105616965539 1.38887137288232 0.165454094322559 df.mm.trans1:exp2 -0.0350767003125734 0.061511156592746 -0.570249402800369 0.56874975354247 df.mm.trans2:exp2 -0.0464219996885399 0.061511156592746 -0.754692355988221 0.450768166911824 df.mm.trans1:exp3 0.0115051784941838 0.061511156592746 0.187042142132971 0.851699033886302 df.mm.trans2:exp3 0.0362017051506869 0.061511156592746 0.588538846544078 0.55642091766437 df.mm.trans1:exp4 0.0213226406337796 0.061511156592746 0.346646719309033 0.728994067287813 df.mm.trans2:exp4 -0.00107813746238018 0.061511156592746 -0.0175275108143118 0.986022369458352 df.mm.trans1:exp5 -0.0186549709855884 0.061511156592746 -0.303277844523385 0.761796912902341 df.mm.trans2:exp5 0.00227077609128874 0.061511156592746 0.0369164915288965 0.970565464283156 df.mm.trans1:exp6 -0.126329781989867 0.061511156592746 -2.05377022620910 0.0404870547381014 * df.mm.trans2:exp6 0.00913305923107596 0.061511156592746 0.148478093031875 0.882021840818396 df.mm.trans1:exp7 0.00435104782966056 0.061511156592746 0.0707359131363444 0.943634569863052 df.mm.trans2:exp7 0.0413718617187086 0.061511156592746 0.672591185248297 0.501499983330605 df.mm.trans1:exp8 0.0352962615014249 0.061511156592746 0.573818855904709 0.566333348980941 df.mm.trans2:exp8 -0.0136623644635876 0.061511156592746 -0.222111974808791 0.82431211502317 df.mm.trans1:probe2 -0.0538144730408767 0.0434949559453583 -1.23725778935108 0.216538251435433 df.mm.trans1:probe3 0.0564048373006754 0.0434949559453583 1.29681329880034 0.195258656236312 df.mm.trans1:probe4 -0.0672634669764703 0.0434949559453583 -1.54646591804741 0.122587835489584 df.mm.trans1:probe5 -0.100601542238561 0.0434949559453583 -2.31294733037423 0.0211068210596064 * df.mm.trans1:probe6 -0.0480172009919796 0.0434949559453583 -1.10397171231309 0.270105428923436 df.mm.trans2:probe2 0.0190255305836899 0.0434949559453583 0.437419240235379 0.661985181342784 df.mm.trans2:probe3 0.194334522508728 0.0434949559453583 4.46797837323633 9.65576052816442e-06 *** df.mm.trans2:probe4 0.138370396533329 0.0434949559453583 3.1812975441833 0.00155186671233824 ** df.mm.trans2:probe5 -0.057801242385953 0.0434949559453583 -1.32891828787153 0.184445673500926 df.mm.trans2:probe6 0.0138542264490782 0.0434949559453583 0.318524898990195 0.75021205408458 df.mm.trans3:probe2 -0.402879209771979 0.0434949559453583 -9.26266508415612 4.9255253448831e-19 *** df.mm.trans3:probe3 -0.132156702814107 0.0434949559453583 -3.03843744502539 0.00249512813227927 ** df.mm.trans3:probe4 0.258387452336054 0.0434949559453583 5.94063028045506 5.13954904754589e-09 *** df.mm.trans3:probe5 -0.343770706888723 0.0434949559453583 -7.9036913457412 1.57373481171546e-14 *** df.mm.trans3:probe6 -0.0392441843616645 0.0434949559453583 -0.902269780683674 0.36732265947763 df.mm.trans3:probe7 -0.392775863198664 0.0434949559453583 -9.03037730839639 3.15387713141504e-18 *** df.mm.trans3:probe8 -0.252393134504511 0.0434949559453583 -5.80281388999651 1.12198862188021e-08 *** df.mm.trans3:probe9 0.184808869485344 0.0434949559453583 4.24897244907007 2.53647796399315e-05 *** df.mm.trans3:probe10 0.351776498850218 0.0434949559453583 8.08775388328125 4.14793546725298e-15 *** df.mm.trans3:probe11 -0.367055150646994 0.0434949559453583 -8.43902799000686 3.05512778116715e-16 *** df.mm.trans3:probe12 -0.0302674928435952 0.0434949559453583 -0.695885124739972 0.48680522831432 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.93804370172545 0.130142504496821 30.2594737741629 1.18922643217058e-117 *** df.mm.trans1 0.183777694992849 0.103608704325439 1.77376694544501 0.0766747432277831 . df.mm.trans2 0.0284706684094016 0.103608704325439 0.274790314141698 0.783584219861608 df.mm.exp2 0.157916737956749 0.138144939100585 1.14312358443886 0.253502398272655 df.mm.exp3 0.175029545947629 0.138144939100585 1.26699933480870 0.205710940545266 df.mm.exp4 0.136568732130939 0.138144939100585 0.988590193894121 0.323313935622478 df.mm.exp5 0.128236647185361 0.138144939100585 0.928276113625779 0.353686118571828 df.mm.exp6 0.0114290695889174 0.138144939100585 0.0827324523310677 0.934095457113237 df.mm.exp7 0.186195353415302 0.138144939100585 1.34782609212872 0.178289054499695 df.mm.exp8 0.135055157958032 0.138144939100585 0.977633772451822 0.328700667299479 df.mm.trans1:exp2 -0.194337608061908 0.107006609699881 -1.81612714024828 0.0699146433007538 . df.mm.trans2:exp2 -0.0477861299779257 0.107006609699881 -0.446571759557194 0.655366353851317 df.mm.trans1:exp3 -0.226608634084877 0.107006609699881 -2.11770688484049 0.0346639986271461 * df.mm.trans2:exp3 -0.0515073802766338 0.107006609699881 -0.481347651524476 0.630467729980403 df.mm.trans1:exp4 -0.250058218275614 0.107006609699881 -2.33684834027493 0.0198179257482004 * df.mm.trans2:exp4 -0.0395432542392235 0.107006609699881 -0.369540296156748 0.711872366715044 df.mm.trans1:exp5 -0.125737925810483 0.107006609699881 -1.1750482158358 0.240502061186213 df.mm.trans2:exp5 -0.0238620450844103 0.107006609699881 -0.222995992035778 0.823624379921263 df.mm.trans1:exp6 -0.090795628212378 0.107006609699881 -0.848504858410435 0.396539199589098 df.mm.trans2:exp6 0.0620151503165105 0.107006609699881 0.579545043903765 0.562467254073148 df.mm.trans1:exp7 -0.210887059021166 0.107006609699881 -1.97078535253698 0.0492673905507659 * df.mm.trans2:exp7 -0.116927863258282 0.107006609699881 -1.09271626852049 0.275013890575047 df.mm.trans1:exp8 -0.205888530505208 0.107006609699881 -1.92407301831783 0.0548795694403354 . df.mm.trans2:exp8 -0.0149623079156181 0.107006609699881 -0.139826015959038 0.888850476665125 df.mm.trans1:probe2 -0.0704077204864308 0.075665099350568 -0.930517782844916 0.352525901116755 df.mm.trans1:probe3 0.0431178093854316 0.075665099350568 0.56985069411804 0.569019972920058 df.mm.trans1:probe4 -0.0802486473870587 0.075665099350568 -1.06057677946413 0.289364434412984 df.mm.trans1:probe5 0.0144937927409582 0.075665099350568 0.191551889383060 0.848166454482235 df.mm.trans1:probe6 0.000989162440416917 0.075665099350568 0.0130729021557743 0.989574540419887 df.mm.trans2:probe2 -0.0489506056393522 0.075665099350568 -0.646937703901724 0.517951810913695 df.mm.trans2:probe3 -0.0367185453809335 0.075665099350568 -0.485277171325857 0.62767989609692 df.mm.trans2:probe4 -0.0119504368423495 0.075665099350568 -0.157938560114502 0.874565254524755 df.mm.trans2:probe5 0.0581472224212475 0.075665099350568 0.768481412438812 0.442542865658036 df.mm.trans2:probe6 0.0438781604196305 0.075665099350568 0.579899594347141 0.562228296437778 df.mm.trans3:probe2 -0.091681304833018 0.075665099350568 -1.21167229832402 0.226176758639631 df.mm.trans3:probe3 -0.0371375055750754 0.075665099350568 -0.490814204882117 0.623760631880479 df.mm.trans3:probe4 -0.0253612644604864 0.075665099350568 -0.335177838635799 0.737623309321114 df.mm.trans3:probe5 0.00332242608392300 0.075665099350568 0.0439096242843705 0.964992950341954 df.mm.trans3:probe6 0.0779048170297677 0.075665099350568 1.02960040624308 0.303666369317832 df.mm.trans3:probe7 -0.0445490688056895 0.075665099350568 -0.588766408662029 0.556268345841432 df.mm.trans3:probe8 0.0617923368408012 0.075665099350568 0.816655728614164 0.414491420132697 df.mm.trans3:probe9 -0.0266142744158519 0.075665099350568 -0.351737784583404 0.725174456359214 df.mm.trans3:probe10 -0.0575830666300823 0.075665099350568 -0.761025454592891 0.446979681285249 df.mm.trans3:probe11 -0.0921827320723199 0.075665099350568 -1.21829922729928 0.223651304280886 df.mm.trans3:probe12 0.0204723531060669 0.075665099350568 0.270565337015093 0.786830490989053