chr7.22061_chr7_127528275_127536069_+_2.R fitVsDatCorrelation=0.928276293701208 cont.fitVsDatCorrelation=0.278898187618209 fstatistic=10544.0277118952,49,623 cont.fstatistic=1570.46221240637,49,623 residuals=-0.601232060529433,-0.0935385689738253,0.00740773010119431,0.101175586917450,0.625771412507727 cont.residuals=-0.98904666058506,-0.341382697153831,0.0337194306416151,0.287943138519716,1.10300384745731 predictedValues: Include Exclude Both chr7.22061_chr7_127528275_127536069_+_2.R.tl.Lung 64.980965204066 94.9746206975745 71.303275631938 chr7.22061_chr7_127528275_127536069_+_2.R.tl.cerebhem 56.2228673522155 97.2619975375221 73.0751256224854 chr7.22061_chr7_127528275_127536069_+_2.R.tl.cortex 61.4173692311044 138.011876747047 99.8519402112119 chr7.22061_chr7_127528275_127536069_+_2.R.tl.heart 66.1310799124697 162.674122100851 113.728127867502 chr7.22061_chr7_127528275_127536069_+_2.R.tl.kidney 66.2589399574158 118.790109506768 81.1744020053667 chr7.22061_chr7_127528275_127536069_+_2.R.tl.liver 65.8333600870574 106.675116822772 77.3034152001254 chr7.22061_chr7_127528275_127536069_+_2.R.tl.stomach 62.0996055898879 98.6456468913244 78.2169179601345 chr7.22061_chr7_127528275_127536069_+_2.R.tl.testicle 62.4213491516088 183.657725399856 117.546785184803 diffExp=-29.9936554935085,-41.0391301853066,-76.5945075159424,-96.5430421883817,-52.5311695493518,-40.8417567357151,-36.5460413014365,-121.236376248247 diffExpScore=0.99798519391224 diffExp1.5=0,-1,-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 90.878854433222 75.5805014263527 84.425158102494 cerebhem 88.1621760700488 86.8525859333115 84.519365246186 cortex 92.4495598476669 87.3424647381394 83.3928415040362 heart 83.2570204797285 86.9500813337413 80.2424848078554 kidney 92.4040928964516 83.403854760626 84.7221733734338 liver 93.0560355455808 73.216606066208 90.0722827560139 stomach 84.9172791893662 89.1814285624926 100.074346883802 testicle 83.6675775213316 92.641113559485 88.1268826517779 cont.diffExp=15.2983530068692,1.30959013673731,5.10709510952748,-3.69306085401284,9.0002381358257,19.8394294793728,-4.2641493731264,-8.97353603815341 cont.diffExpScore=1.94909689438582 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=1,0,0,0,0,1,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.179944191102442 cont.tran.correlation=-0.692493588919294 tran.covariance=0.00299042653366253 cont.tran.covariance=-0.00264456265395770 tran.mean=94.1285470118463 cont.tran.mean=86.4975770227345 weightedLogRatios: wLogRatio Lung -1.65614871111688 cerebhem -2.35860936685876 cortex -3.66164197028179 heart -4.17803563070652 kidney -2.61855910376141 liver -2.13744461896226 stomach -2.01784721929768 testicle -5.04347442430623 cont.weightedLogRatios: wLogRatio Lung 0.81424815173766 cerebhem 0.0669223223610985 cortex 0.25561948390404 heart -0.192861332582071 kidney 0.458576556129579 liver 1.05822215541160 stomach -0.218821242678410 testicle -0.456204180982472 varWeightedLogRatios=1.43704277480836 cont.varWeightedLogRatios=0.279650803946869 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91939480984508 0.076999509612808 50.9015554716355 3.89685360087879e-224 *** df.mm.trans1 0.0643684459698719 0.0648571820676473 0.9924644259557 0.321356329020317 df.mm.trans2 0.651155116212248 0.0597650374582632 10.8952515367698 2.01631300480871e-25 *** df.mm.exp2 -0.145517942116738 0.078051943526963 -1.86437307696853 0.0627393982084024 . df.mm.exp3 -0.0194178858904895 0.078051943526963 -0.248781580740288 0.803611729432572 df.mm.exp4 0.0888152021158605 0.078051943526963 1.1378986621285 0.255600213043630 df.mm.exp5 0.113566775995687 0.078051943526963 1.45501535085357 0.146168670540008 df.mm.exp6 0.0484146690833446 0.078051943526963 0.620287809574144 0.53529514290895 df.mm.exp7 -0.0999740453928018 0.078051943526963 -1.28086554767552 0.200717503165979 df.mm.exp8 0.119382940401619 0.078051943526963 1.52953193741265 0.126640192831847 df.mm.trans1:exp2 0.000747124651922976 0.0681293217810907 0.0109662716784940 0.99125386717766 df.mm.trans2:exp2 0.169316579643546 0.0564898493923164 2.99729210583762 0.00283232216972387 ** df.mm.trans1:exp3 -0.0369838164555627 0.0681293217810907 -0.542847271757629 0.587429109918914 df.mm.trans2:exp3 0.393147925371222 0.0564898493923164 6.95962070355064 8.66843227536405e-12 *** df.mm.trans1:exp4 -0.0712707544122947 0.0681293217810907 -1.04610984740605 0.295916018506327 df.mm.trans2:exp4 0.449324041248469 0.0564898493923164 7.95406690019579 8.53044862645786e-15 *** df.mm.trans1:exp5 -0.0940907614742431 0.0681293217810907 -1.38106117915822 0.167755322502357 df.mm.trans2:exp5 0.110181668799443 0.0564898493923164 1.95046844671584 0.0515683494962221 . df.mm.trans1:exp6 -0.0353823510190576 0.0681293217810907 -0.5193410134442 0.603707405167341 df.mm.trans2:exp6 0.0677635497604265 0.0564898493923164 1.19957037395896 0.230762241795028 df.mm.trans1:exp7 0.0546192991081533 0.0681293217810907 0.80170032051182 0.423032002538421 df.mm.trans2:exp7 0.137898444731646 0.0564898493923164 2.44111900129092 0.0149191644109363 * df.mm.trans1:exp8 -0.159569973695167 0.0681293217810907 -2.34216295603071 0.0194863128815633 * df.mm.trans2:exp8 0.540081191507095 0.0564898493923164 9.5606767820584 2.66329720703977e-20 *** df.mm.trans1:probe2 -0.004997076612625 0.0466449579587893 -0.107130048590459 0.914720297890966 df.mm.trans1:probe3 -0.0790295744903197 0.0466449579587893 -1.69427903783603 0.0907120568913788 . df.mm.trans1:probe4 -0.0597658346545292 0.0466449579587893 -1.28129249697968 0.200567650222616 df.mm.trans1:probe5 -0.108176380315385 0.0466449579587893 -2.31914412723791 0.0207097631168938 * df.mm.trans1:probe6 0.0191963125939707 0.0466449579587893 0.411540998942064 0.680817424290749 df.mm.trans1:probe7 0.780498449629467 0.0466449579587893 16.7327506291041 3.51406654887554e-52 *** df.mm.trans1:probe8 0.667662050117362 0.0466449579587893 14.3137024736358 2.19538492675533e-40 *** df.mm.trans1:probe9 1.05527032315629 0.0466449579587893 22.6234596264107 3.54606127854605e-83 *** df.mm.trans1:probe10 0.75946909924799 0.0466449579587893 16.2819119682556 6.34575600469545e-50 *** df.mm.trans1:probe11 0.492594910623675 0.0466449579587893 10.5605178390102 4.28649579287566e-24 *** df.mm.trans1:probe12 0.664562540651392 0.0466449579587893 14.2472535024800 4.50291257197193e-40 *** df.mm.trans2:probe2 -0.178116591849760 0.0466449579587893 -3.81856045421085 0.000147657966691117 *** df.mm.trans2:probe3 0.124456185286602 0.0466449579587893 2.6681594481564 0.00782526596046234 ** df.mm.trans2:probe4 -0.0932633946847326 0.0466449579587893 -1.99943142337336 0.0459955091954226 * df.mm.trans2:probe5 -0.113636809268913 0.0466449579587893 -2.43620777553944 0.0151212930009332 * df.mm.trans2:probe6 -0.0104829206784511 0.0466449579587893 -0.224738559904217 0.822256355709737 df.mm.trans3:probe2 -0.25530638420727 0.0466449579587893 -5.47339724119449 6.40547024412568e-08 *** df.mm.trans3:probe3 -0.649537688721781 0.0466449579587893 -13.9251425480037 1.43031249897392e-38 *** df.mm.trans3:probe4 -0.360810163786652 0.0466449579587893 -7.7352446990182 4.16972553012116e-14 *** df.mm.trans3:probe5 -0.678965965282268 0.0466449579587893 -14.5560419602507 1.57640964343924e-41 *** df.mm.trans3:probe6 -0.047729741197387 0.0466449579587893 -1.02325617357306 0.306583898049890 df.mm.trans3:probe7 -0.488798814878531 0.0466449579587893 -10.4791350720132 8.92536323070682e-24 *** df.mm.trans3:probe8 -0.583512484883728 0.0466449579587893 -12.5096582871671 3.42160611275263e-32 *** df.mm.trans3:probe9 -0.102728248298239 0.0466449579587893 -2.20234410735238 0.0280065768912423 * df.mm.trans3:probe10 -0.512371549947398 0.0466449579587893 -10.9845001982867 8.82964063597483e-26 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.45033304421485 0.198832899645016 22.382277038459 7.12045764887408e-82 *** df.mm.trans1 0.0771904940502205 0.167478229902518 0.460898673786735 0.645032153233163 df.mm.trans2 -0.138268026514495 0.154328978911351 -0.89593041753952 0.370635966089061 df.mm.exp2 0.107549369687547 0.201550559639065 0.533609878732885 0.593801794162525 df.mm.exp3 0.174077249210437 0.201550559639065 0.863690229995754 0.388090469878284 df.mm.exp4 0.103353278293169 0.201550559639065 0.512790827662565 0.60827941183767 df.mm.exp5 0.111628204025567 0.201550559639065 0.553847154904801 0.579882177608649 df.mm.exp6 -0.0728486341357341 0.201550559639065 -0.36144099161119 0.717892352351589 df.mm.exp7 -0.0724232069587285 0.201550559639065 -0.359330220111635 0.719469825624643 df.mm.exp8 0.0779467038895232 0.201550559639065 0.386735239183207 0.699084282583271 df.mm.trans1:exp2 -0.137898691022508 0.175927751601282 -0.783837056788168 0.433433626092149 df.mm.trans2:exp2 0.0314645649852325 0.145871585568074 0.215700438592606 0.829291858539527 df.mm.trans1:exp3 -0.156941401990889 0.175927751601282 -0.892078711643953 0.372695163665827 df.mm.trans2:exp3 -0.0294388137911399 0.145871585568074 -0.201813215894617 0.840128628053485 df.mm.trans1:exp4 -0.190948172526519 0.175927751601282 -1.08537834871714 0.278173923162439 df.mm.trans2:exp4 0.0367825656366908 0.145871585568074 0.252157166136549 0.801002749127919 df.mm.trans1:exp5 -0.0949842805679481 0.175927751601282 -0.539905044561805 0.589455445789606 df.mm.trans2:exp5 -0.0131320078557687 0.145871585568074 -0.090024440363955 0.928296727202218 df.mm.trans1:exp6 0.0965231289620477 0.175927751601282 0.548652092029263 0.583440789163243 df.mm.trans2:exp6 0.0410725559242308 0.145871585568074 0.281566528287741 0.778369405988362 df.mm.trans1:exp7 0.00457345396347989 0.175927751601282 0.0259962053846118 0.979268689653945 df.mm.trans2:exp7 0.237897692617838 0.145871585568074 1.63087068459140 0.103423004684604 df.mm.trans1:exp8 -0.160622516401117 0.175927751601282 -0.913002723783726 0.361594284920177 df.mm.trans2:exp8 0.125587997872794 0.145871585568074 0.860949014735876 0.389597342426886 df.mm.trans1:probe2 -0.0483446328764403 0.120449497553984 -0.401368489351918 0.688286478324802 df.mm.trans1:probe3 0.0733537987282145 0.120449497553984 0.609000454280334 0.542746138060734 df.mm.trans1:probe4 -0.101987726691240 0.120449497553984 -0.846726045042483 0.397472966531783 df.mm.trans1:probe5 -0.143236716896745 0.120449497553984 -1.18918484348635 0.234819979827062 df.mm.trans1:probe6 -0.0329524593200083 0.120449497553984 -0.273579051712020 0.784498806920677 df.mm.trans1:probe7 -0.123188887778781 0.120449497553984 -1.02274306062230 0.306826310120993 df.mm.trans1:probe8 0.0998748190474756 0.120449497553984 0.829184189852785 0.407317766729232 df.mm.trans1:probe9 -0.0597917218079362 0.120449497553984 -0.496404908464961 0.619783858242622 df.mm.trans1:probe10 0.0042486041447575 0.120449497553984 0.0352729088210045 0.971873423881281 df.mm.trans1:probe11 -0.0678990229145117 0.120449497553984 -0.563713625157132 0.573151892136518 df.mm.trans1:probe12 0.00400779628688796 0.120449497553984 0.0332736654637492 0.97346701130009 df.mm.trans2:probe2 -0.0146116596204033 0.120449497553984 -0.121309427744640 0.9034850682689 df.mm.trans2:probe3 -0.042104408331806 0.120449497553984 -0.349560680507905 0.726786551561098 df.mm.trans2:probe4 -0.0661016316621955 0.120449497553984 -0.548791261105671 0.583345325663611 df.mm.trans2:probe5 0.187007816873650 0.120449497553984 1.55258278922944 0.121030663956720 df.mm.trans2:probe6 0.145942915985795 0.120449497553984 1.21165234350923 0.226104846810431 df.mm.trans3:probe2 0.0470165569796513 0.120449497553984 0.390342491537409 0.696416733671045 df.mm.trans3:probe3 0.183450289570876 0.120449497553984 1.5230473625567 0.128254267857778 df.mm.trans3:probe4 0.0270075784126582 0.120449497553984 0.224223255066329 0.822657102882188 df.mm.trans3:probe5 -0.0841453710932103 0.120449497553984 -0.698594620998706 0.485066095340689 df.mm.trans3:probe6 0.0647922241463159 0.120449497553984 0.53792025257122 0.590824210617221 df.mm.trans3:probe7 0.32115096868058 0.120449497553984 2.66627072094379 0.00786886028216528 ** df.mm.trans3:probe8 0.0831874160747055 0.120449497553984 0.690641453588643 0.49004811541742 df.mm.trans3:probe9 -0.175007414628574 0.120449497553984 -1.45295263311611 0.146740389827173 df.mm.trans3:probe10 -0.0013529759741312 0.120449497553984 -0.0112327241010267 0.991041367310116