chr15.8627_chr15_84876042_84880035_-_2.R fitVsDatCorrelation=0.914818570959203 cont.fitVsDatCorrelation=0.235867650218860 fstatistic=8187.53898282256,53,715 cont.fstatistic=1402.95659665029,53,715 residuals=-0.87492023934546,-0.0976855286980537,-0.0048992348656701,0.0915469929157947,1.41826553719939 cont.residuals=-0.90860459764349,-0.334603994826887,-0.0899998622265763,0.308778816043851,1.99652352002921 predictedValues: Include Exclude Both chr15.8627_chr15_84876042_84880035_-_2.R.tl.Lung 61.1238267217996 123.792865008471 58.100231259258 chr15.8627_chr15_84876042_84880035_-_2.R.tl.cerebhem 63.5690802803748 112.927415768422 123.599135587933 chr15.8627_chr15_84876042_84880035_-_2.R.tl.cortex 59.8549217458418 107.473174255604 273.953638936548 chr15.8627_chr15_84876042_84880035_-_2.R.tl.heart 58.730344681717 106.733732833719 58.8775598583032 chr15.8627_chr15_84876042_84880035_-_2.R.tl.kidney 62.2159119910306 125.977915277451 62.1999051222188 chr15.8627_chr15_84876042_84880035_-_2.R.tl.liver 62.4867402587453 117.388413071047 57.7979804048984 chr15.8627_chr15_84876042_84880035_-_2.R.tl.stomach 63.4689123763352 110.695941528831 57.5323354162983 chr15.8627_chr15_84876042_84880035_-_2.R.tl.testicle 64.3825834799909 101.756055743336 66.5920465964333 diffExp=-62.669038286671,-49.358335488047,-47.6182525097618,-48.0033881520017,-63.7620032864201,-54.9016728123014,-47.2270291524956,-37.3734722633453 diffExpScore=0.997572304020506 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 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 71.6404280703008 74.3123466758676 65.2816740747161 cerebhem 80.1807979190233 61.9437776644206 72.888392434044 cortex 77.4421825938766 79.1113816312659 73.4388175015434 heart 69.6134234862915 73.3830016862255 74.3802418405749 kidney 75.4863949644771 64.8831878095072 71.050173370701 liver 83.9097143907225 76.67981377148 61.5140493935561 stomach 73.5471574596668 80.2046238416115 80.7631032737637 testicle 70.6689890791232 62.963558498856 80.2405342832432 cont.diffExp=-2.6719186055668,18.2370202546027,-1.66919903738929,-3.76957819993407,10.6032071549700,7.2299006192424,-6.65746638194469,7.70543058026709 cont.diffExpScore=1.95097635543783 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=0,1,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.0207387667209256 cont.tran.correlation=0.0635717813487497 tran.covariance=-3.04027200334257e-05 cont.tran.covariance=0.000361480616426067 tran.mean=87.6611146889196 cont.tran.mean=73.4981737214197 weightedLogRatios: wLogRatio Lung -3.15151942835361 cerebhem -2.55098032910035 cortex -2.56637333733234 heart -2.61153755508414 kidney -3.1629908646248 liver -2.80601506688545 stomach -2.46338921298870 testicle -2.01115600022059 cont.weightedLogRatios: wLogRatio Lung -0.157088167393147 cerebhem 1.09809805865679 cortex -0.0929816934270234 heart -0.225142459416427 kidney 0.643034782822729 liver 0.395073290623839 stomach -0.376188622478509 testicle 0.484925552375947 varWeightedLogRatios=0.142617737853029 cont.varWeightedLogRatios=0.263512100960834 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.55823639706832 0.0927694660953652 49.1350935703623 2.11316475968961e-231 *** df.mm.trans1 -0.736203003047378 0.0823810812866918 -8.93655426159485 3.37645919728639e-18 *** df.mm.trans2 0.248940942734114 0.0749363063026115 3.32203380466644 0.000939092321366601 *** df.mm.exp2 -0.80751291840122 0.101025276648137 -7.99317700671806 5.28007391163819e-15 *** df.mm.exp3 -1.71313578722303 0.101025276648137 -16.9574966192842 1.82935721152422e-54 *** df.mm.exp4 -0.201508071673869 0.101025276648137 -1.99463023868477 0.0464624397314854 * df.mm.exp5 -0.0329779055824376 0.101025276648137 -0.326432222475341 0.744192887129086 df.mm.exp6 -0.0258530832751116 0.101025276648137 -0.255907077247170 0.798096194232559 df.mm.exp7 -0.0643515845481057 0.101025276648137 -0.636984987155611 0.52433851262658 df.mm.exp8 -0.280505491845865 0.101025276648137 -2.77658721809635 0.00563705149066844 ** df.mm.trans1:exp2 0.846738358544606 0.0959282398035965 8.82678927788333 8.21274497038998e-18 *** df.mm.trans2:exp2 0.715648467014781 0.080778806905686 8.8593592110161 6.31439886932635e-18 *** df.mm.trans1:exp3 1.69215769765457 0.0959282398035965 17.6398284917882 4.33360645524350e-58 *** df.mm.trans2:exp3 1.57176733646310 0.080778806905686 19.457669612504 5.3235851763971e-68 *** df.mm.trans1:exp4 0.161562857236419 0.0959282398035965 1.68420537651063 0.0925784275446463 . df.mm.trans2:exp4 0.0532356011595443 0.080778806905686 0.659029307299623 0.510089128459801 df.mm.trans1:exp5 0.0506869395273281 0.0959282398035965 0.528383921471973 0.597396854361336 df.mm.trans2:exp5 0.0504747962169913 0.080778806905686 0.624851964896233 0.532267570264311 df.mm.trans1:exp6 0.0479057087737247 0.0959282398035965 0.499391095591943 0.617657516937275 df.mm.trans2:exp6 -0.0272684357382906 0.080778806905686 -0.337569181606359 0.735786947417076 df.mm.trans1:exp7 0.102000048816740 0.0959282398035965 1.06329532393772 0.288007021226307 df.mm.trans2:exp7 -0.0474709636774582 0.080778806905686 -0.587666066086905 0.556942000373016 df.mm.trans1:exp8 0.332446892647195 0.0959282398035965 3.46557899246194 0.000560686606653307 *** df.mm.trans2:exp8 0.084474104924015 0.080778806905686 1.04574588508894 0.296031770995859 df.mm.trans1:probe2 0.168170509172772 0.0525420608421947 3.20068353766817 0.00143164769186097 ** df.mm.trans1:probe3 0.016478042996389 0.0525420608421947 0.313616229212617 0.753904001242735 df.mm.trans1:probe4 0.021051693680208 0.0525420608421947 0.400663646282068 0.68878747148725 df.mm.trans1:probe5 0.0388813520034475 0.0525420608421947 0.740004319971844 0.45954027858522 df.mm.trans1:probe6 0.573569285063098 0.0525420608421947 10.9163834815266 9.12016219979181e-26 *** df.mm.trans1:probe7 -0.0195442998040328 0.0525420608421947 -0.371974366645653 0.710022148908772 df.mm.trans1:probe8 -0.0637281100604 0.0525420608421947 -1.21289703979830 0.225569757852350 df.mm.trans1:probe9 0.291133858474675 0.0525420608421947 5.54096763256145 4.2312056027898e-08 *** df.mm.trans1:probe10 0.320135636693951 0.0525420608421947 6.09294023802092 1.81104768811157e-09 *** df.mm.trans1:probe11 0.187872933207361 0.0525420608421947 3.57566738334874 0.00037284677306188 *** df.mm.trans1:probe12 0.233474003573703 0.0525420608421947 4.44356387685137 1.0251621602763e-05 *** df.mm.trans1:probe13 0.0795231569830353 0.0525420608421948 1.51351423427939 0.130590975012780 df.mm.trans1:probe14 0.369969946325833 0.0525420608421947 7.0414053121556 4.47488540324837e-12 *** df.mm.trans1:probe15 0.298840085269018 0.0525420608421947 5.68763540064705 1.87806278940076e-08 *** df.mm.trans1:probe16 0.255983495208969 0.0525420608421947 4.87197287479438 1.36146000852624e-06 *** df.mm.trans1:probe17 0.64613109858097 0.0525420608421947 12.2974068436631 1.15353589200901e-31 *** df.mm.trans1:probe18 1.20679599393872 0.0525420608421947 22.968189191574 7.22073301898636e-88 *** df.mm.trans1:probe19 0.831336502070595 0.0525420608421947 15.8223048115193 1.43330453206609e-48 *** df.mm.trans1:probe20 0.65339446978211 0.0525420608421947 12.4356460197578 2.79558260326443e-32 *** df.mm.trans1:probe21 0.837888212899426 0.0525420608421947 15.9469994033151 3.29618413275711e-49 *** df.mm.trans1:probe22 0.615219464156757 0.0525420608421947 11.7090851461748 4.28351327407132e-29 *** df.mm.trans2:probe2 0.020923974661951 0.0525420608421947 0.398232850530821 0.690577435195883 df.mm.trans2:probe3 0.0940514039802256 0.0525420608421947 1.79002122247737 0.0738735397007273 . df.mm.trans2:probe4 0.00994206314556388 0.0525420608421947 0.189221035227833 0.849973281009966 df.mm.trans2:probe5 -0.0105746476114347 0.0525420608421947 -0.201260617530681 0.840552035593513 df.mm.trans2:probe6 -1.89384148935644e-05 0.0525420608421947 -0.000360442940189275 0.999712508654872 df.mm.trans3:probe2 0.0284114803181919 0.0525420608421947 0.540737836749936 0.588856704253621 df.mm.trans3:probe3 0.0306851088881125 0.0525420608421947 0.584010379422924 0.559397525381021 df.mm.trans3:probe4 -0.0943153593632149 0.0525420608421947 -1.79504491927872 0.0730687729303442 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.3509912825423 0.223223059407922 19.4916748031448 3.44975206782931e-68 *** df.mm.trans1 -0.0273665913578486 0.198226396853941 -0.138057250659776 0.890234041429562 df.mm.trans2 -0.120610414954690 0.180312685387265 -0.668895894349589 0.503777792941002 df.mm.exp2 -0.179643488758705 0.243088294889470 -0.739005096236276 0.460146427882452 df.mm.exp3 0.0227104270028813 0.243088294889471 0.0934246011853736 0.925592419108464 df.mm.exp4 -0.171765909672504 0.243088294889471 -0.706598850226844 0.48004599900884 df.mm.exp5 -0.168070620091494 0.243088294889470 -0.691397420710504 0.489540247876284 df.mm.exp6 0.248888978669431 0.243088294889471 1.02386245616062 0.306246519172753 df.mm.exp7 -0.110237561745363 0.243088294889471 -0.453487741133264 0.650335167405308 df.mm.exp8 -0.385691138730944 0.243088294889470 -1.58662982479808 0.113038626490626 df.mm.trans1:exp2 0.292267995048258 0.230823740545872 1.26619555838181 0.205855447658234 df.mm.trans2:exp2 -0.00240646067711595 0.194370984029086 -0.0123807608894744 0.990125288047286 df.mm.trans1:exp3 0.0551616474180335 0.230823740545872 0.238977356867983 0.811191621748274 df.mm.trans2:exp3 0.0398692153492466 0.194370984029086 0.205119172228302 0.837537397634472 df.mm.trans1:exp4 0.143063772126946 0.230823740545872 0.61979661099251 0.535589162028815 df.mm.trans2:exp4 0.159181122602658 0.194370984029086 0.818955171718624 0.413084863740381 df.mm.trans1:exp5 0.220363508490669 0.230823740545872 0.95468303203793 0.34006066190993 df.mm.trans2:exp5 0.0323820514094843 0.194370984029086 0.166599204975156 0.867732520511228 df.mm.trans1:exp6 -0.0908071390225655 0.230823740545872 -0.393404676693208 0.694137893492982 df.mm.trans2:exp6 -0.217527600354336 0.194370984029086 -1.11913617889482 0.263457871322164 df.mm.trans1:exp7 0.136504807846809 0.230823740545872 0.591381144435104 0.554451985725949 df.mm.trans2:exp7 0.186541617627219 0.194370984029086 0.959719469235723 0.337520840712348 df.mm.trans1:exp8 0.372038436050051 0.230823740545872 1.61178583784416 0.107449850081114 df.mm.trans2:exp8 0.219970150023313 0.194370984029086 1.13170261045958 0.258138895456841 df.mm.trans1:probe2 -0.0521663881984121 0.126427369504694 -0.412619422541061 0.680009162209605 df.mm.trans1:probe3 0.0142473746289352 0.126427369504694 0.112692170095386 0.910306240141598 df.mm.trans1:probe4 -0.175840859838245 0.126427369504694 -1.39084488214173 0.164705153858632 df.mm.trans1:probe5 -0.0818329389300698 0.126427369504694 -0.647272337079129 0.517663454298111 df.mm.trans1:probe6 -0.0232065743708123 0.126427369504694 -0.183556570556906 0.854413376277592 df.mm.trans1:probe7 -0.00842092015417216 0.126427369504694 -0.0666067813256173 0.946913366831899 df.mm.trans1:probe8 0.00470241911473161 0.126427369504694 0.0371946290835152 0.97034020295163 df.mm.trans1:probe9 -0.0952057188825394 0.126427369504694 -0.753046743403174 0.451669696344696 df.mm.trans1:probe10 0.0109228442040421 0.126427369504694 0.0863961992315013 0.931175656268511 df.mm.trans1:probe11 -0.0247724421853329 0.126427369504694 -0.195942083445888 0.844711171323257 df.mm.trans1:probe12 -0.0512476875955534 0.126427369504694 -0.405352795018414 0.685339461898117 df.mm.trans1:probe13 -0.0577405343352654 0.126427369504694 -0.456709133168524 0.648018834902285 df.mm.trans1:probe14 -0.136818414584789 0.126427369504694 -1.08218983848833 0.279533017818360 df.mm.trans1:probe15 -0.269916808568792 0.126427369504694 -2.13495550549101 0.0331039815109316 * df.mm.trans1:probe16 -0.0986624669975385 0.126427369504694 -0.780388513848463 0.435420496240105 df.mm.trans1:probe17 -0.0477805417163525 0.126427369504694 -0.377928781588536 0.705595667770964 df.mm.trans1:probe18 -0.0619691107058788 0.126427369504694 -0.490155817910758 0.624174010260854 df.mm.trans1:probe19 -0.145169306568515 0.126427369504694 -1.14824271941469 0.251252390347663 df.mm.trans1:probe20 -0.0586000494096825 0.126427369504694 -0.463507622117432 0.643141613657284 df.mm.trans1:probe21 -0.00553154372054697 0.126427369504694 -0.0437527391593923 0.965113715356307 df.mm.trans1:probe22 0.0139160622908731 0.126427369504694 0.110071595615666 0.912383481392041 df.mm.trans2:probe2 0.277847827435821 0.126427369504694 2.19768732454333 0.0282913212111283 * df.mm.trans2:probe3 0.164946206668540 0.126427369504694 1.30467166496268 0.192424439871904 df.mm.trans2:probe4 0.065385119074764 0.126427369504694 0.517175350012614 0.605193703874199 df.mm.trans2:probe5 0.0833107299848824 0.126427369504694 0.658961191008482 0.510132843855982 df.mm.trans2:probe6 0.187472553101148 0.126427369504694 1.48284785039515 0.138555460302602 df.mm.trans3:probe2 -0.0896894028049759 0.126427369504694 -0.709414450022596 0.478298588759961 df.mm.trans3:probe3 -0.029614856668833 0.126427369504694 -0.234244031057955 0.81486259140373 df.mm.trans3:probe4 0.0220925739705272 0.126427369504694 0.174745184188199 0.86132933120673