chr4.16695_chr4_44650932_44651848_-_0.R fitVsDatCorrelation=0.755758382437659 cont.fitVsDatCorrelation=0.299110281450605 fstatistic=11645.0103605647,40,416 cont.fstatistic=5478.8912929941,40,416 residuals=-0.345673602676974,-0.0709687791742954,-0.00667314579991017,0.0633450159351364,0.562446626848084 cont.residuals=-0.401005106753117,-0.124714103056658,-0.02160443133032,0.0861191685934699,0.789063916740931 predictedValues: Include Exclude Both chr4.16695_chr4_44650932_44651848_-_0.R.tl.Lung 47.7282074169696 48.9697502732822 60.2083260292002 chr4.16695_chr4_44650932_44651848_-_0.R.tl.cerebhem 50.6527904879651 56.5174110390256 61.3540256522456 chr4.16695_chr4_44650932_44651848_-_0.R.tl.cortex 46.6360628022621 51.1992232379395 55.420901142429 chr4.16695_chr4_44650932_44651848_-_0.R.tl.heart 46.2629655641673 52.9062006644826 56.8989195648307 chr4.16695_chr4_44650932_44651848_-_0.R.tl.kidney 44.9536378042507 51.8604607693781 59.9220369879847 chr4.16695_chr4_44650932_44651848_-_0.R.tl.liver 48.5943802718061 52.2412951139483 58.9598050451144 chr4.16695_chr4_44650932_44651848_-_0.R.tl.stomach 46.7676311324837 49.9484679603765 61.6886710824866 chr4.16695_chr4_44650932_44651848_-_0.R.tl.testicle 46.9916570702575 51.5165493464888 55.2362241754929 diffExp=-1.24154285631256,-5.86462055106054,-4.56316043567747,-6.6432351003153,-6.90682296512746,-3.64691484214226,-3.18083682789276,-4.52489227623132 diffExpScore=0.973384453532912 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 52.9623798810558 50.3543796401138 54.7939965759173 cerebhem 56.8331563616489 49.6605451692621 49.652379731783 cortex 55.7636324827226 50.3563394985509 52.6562093427093 heart 53.0495928957584 53.8799529694278 50.7817629294268 kidney 51.0625474369692 51.3637118845715 53.7839290330061 liver 52.6482726749168 58.5617502071868 49.3460889087963 stomach 51.5630530890346 52.1515152695761 56.4138164956011 testicle 52.1793836884219 53.7614283891779 52.4530242290648 cont.diffExp=2.60800024094208,7.17261119238683,5.40729298417162,-0.830360073669432,-0.301164447602325,-5.91347753226998,-0.588462180541512,-1.58204470075605 cont.diffExpScore=3.50000418264076 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=0.568859342072758 cont.tran.correlation=-0.419069091701281 tran.covariance=0.000825312735187095 cont.tran.covariance=-0.000858775328671063 tran.mean=49.6091681846927 cont.tran.mean=52.8844775961497 weightedLogRatios: wLogRatio Lung -0.0995971000989013 cerebhem -0.436001594047222 cortex -0.363043676459601 heart -0.523489294466527 kidney -0.554133597067001 liver -0.283649915544100 stomach -0.255180411359805 testicle -0.358165238909463 cont.weightedLogRatios: wLogRatio Lung 0.199174056481083 cerebhem 0.53594884725503 cortex 0.404942203627181 heart -0.0617988821064103 kidney -0.0231460987531706 liver -0.427587767716963 stomach -0.0448067367520097 testicle -0.118567830596581 varWeightedLogRatios=0.0221179929012490 cont.varWeightedLogRatios=0.095315924276783 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88994331075350 0.0636663345253041 61.0989047784344 7.11998417464415e-210 *** df.mm.trans1 -0.0282219315591475 0.0526628941448606 -0.535897846432766 0.592315558271688 df.mm.trans2 -0.0122348162263508 0.0516402163244068 -0.236924186170929 0.81283223060229 df.mm.exp2 0.183967595769040 0.0698303176666775 2.63449461374608 0.00874076653200104 ** df.mm.exp3 0.104226992165652 0.0698303176666775 1.49257508269060 0.136306430840147 df.mm.exp4 0.102671264575270 0.0698303176666775 1.47029639855503 0.142237416141685 df.mm.exp5 0.00222932803614972 0.0698303176666775 0.0319249304691841 0.974547228244867 df.mm.exp6 0.103610536107643 0.0698303176666775 1.48374716841772 0.138633247112451 df.mm.exp7 -0.0248318376134363 0.0698303176666775 -0.355602529720209 0.722318492977752 df.mm.exp8 0.121339515467221 0.0698303176666774 1.73763373161802 0.0830154214540211 . df.mm.trans1:exp2 -0.124495846624792 0.0584242354318853 -2.13089389539274 0.0336837182859639 * df.mm.trans2:exp2 -0.040621611220824 0.056299001967432 -0.7215334162464 0.47098671919736 df.mm.trans1:exp3 -0.127375444023905 0.0584242354318853 -2.18018161611045 0.0298038562068146 * df.mm.trans2:exp3 -0.059705397484664 0.056299001967432 -1.06050543345693 0.289530040059398 df.mm.trans1:exp4 -0.133852076957330 0.0584242354318853 -2.29103686112219 0.0224605200595435 * df.mm.trans2:exp4 -0.0253534838659967 0.056299001967432 -0.450336293362061 0.652702571662231 df.mm.trans1:exp5 -0.0621202139525685 0.0584242354318853 -1.06326105071571 0.288280393867972 df.mm.trans2:exp5 0.0551245707898844 0.056299001967432 0.979139396143702 0.328080174333946 df.mm.trans1:exp6 -0.0856252177305759 0.0584242354318853 -1.46557703490024 0.143518954535013 df.mm.trans2:exp6 -0.0389400259475412 0.056299001967432 -0.691664587057287 0.489533819762468 df.mm.trans1:exp7 0.00450058519765419 0.0584242354318853 0.0770328471461344 0.938634449338423 df.mm.trans2:exp7 0.0446209046582876 0.056299001967432 0.792570082931488 0.428480097151952 df.mm.trans1:exp8 -0.136892012233323 0.0584242354318853 -2.34306895454234 0.0195961510016176 * df.mm.trans2:exp8 -0.0706391789999035 0.0562990019674319 -1.25471458696137 0.210286848405019 df.mm.trans1:probe2 0.0047532453792852 0.0357773913550882 0.132856119444528 0.89437141022907 df.mm.trans1:probe3 -0.0531507682286766 0.0357773913550881 -1.48559652382587 0.138143278012769 df.mm.trans1:probe4 0.00500158876708175 0.0357773913550881 0.139797469229697 0.888887670317672 df.mm.trans1:probe5 0.0103396128841026 0.0357773913550881 0.288998512537782 0.772726476449934 df.mm.trans1:probe6 0.107844747764195 0.0357773913550882 3.01432674880746 0.00273312216251427 ** df.mm.trans1:probe7 -0.0291740939851959 0.0357773913550882 -0.815433794365974 0.415290663700278 df.mm.trans2:probe2 -0.000861350435492943 0.0357773913550881 -0.0240752722003709 0.98080411143561 df.mm.trans2:probe3 0.0147572590930174 0.0357773913550881 0.412474429634978 0.680204202362011 df.mm.trans2:probe4 0.066502969007679 0.0357773913550881 1.85879871306551 0.0637616654358253 . df.mm.trans2:probe5 0.0491679307498282 0.0357773913550881 1.37427377702974 0.170096619227005 df.mm.trans2:probe6 0.0323644506438964 0.0357773913550882 0.90460621688936 0.366197614346037 df.mm.trans3:probe2 0.438181670667229 0.0357773913550881 12.2474460565978 1.15681825779161e-29 *** df.mm.trans3:probe3 0.205673221616586 0.0357773913550881 5.74869250737968 1.74226803979948e-08 *** df.mm.trans3:probe4 0.416701535397863 0.0357773913550881 11.6470631204531 2.50224967801891e-27 *** df.mm.trans3:probe5 0.0883875372096584 0.0357773913550881 2.47048579736902 0.0138929770929967 * df.mm.trans3:probe6 0.34100055060617 0.0357773913550881 9.53117423296079 1.31362804245335e-19 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.85633506181719 0.0927716951933354 41.5680133232515 3.30832840736273e-150 *** df.mm.trans1 0.125065451837084 0.0767379809130383 1.62977251093969 0.103906501978831 df.mm.trans2 0.0730915435303955 0.075247781174864 0.971344834215673 0.331941019976702 df.mm.exp2 0.155197521917355 0.101753571870107 1.52522922846848 0.127961800728131 df.mm.exp3 0.0913754504472695 0.101753571870107 0.898007301049978 0.369701093157263 df.mm.exp4 0.145361566218007 0.101753571870107 1.42856475253339 0.153879747354496 df.mm.exp5 0.00192171377132551 0.101753571870107 0.0188859588514364 0.9849411354205 df.mm.exp6 0.249769865506301 0.101753571870107 2.45465452382491 0.0145106308444822 * df.mm.exp7 -0.0208422850361247 0.101753571870107 -0.204830991709371 0.837804353656087 df.mm.exp8 0.094238930694147 0.101753571870107 0.926148625175022 0.354905716453205 df.mm.trans1:exp2 -0.0846594756882377 0.0851331461407804 -0.994436121839555 0.320588658432322 df.mm.trans2:exp2 -0.169072363158267 0.0820363523226821 -2.06094442733433 0.0399293911437193 * df.mm.trans1:exp3 -0.0398353891114889 0.0851331461407804 -0.467918677005253 0.640087788870089 df.mm.trans2:exp3 -0.0913365298943397 0.0820363523226821 -1.11336654188470 0.266193938273197 df.mm.trans1:exp4 -0.143716223110185 0.0851331461407804 -1.68813475861128 0.0921348294742697 . df.mm.trans2:exp4 -0.0776886868490325 0.0820363523226821 -0.947003183947667 0.344186913716067 df.mm.trans1:exp5 -0.0384522601035785 0.0851331461407804 -0.451672020202236 0.651740665825199 df.mm.trans2:exp5 0.0179246155981946 0.082036352322682 0.218496009277568 0.827149814775439 df.mm.trans1:exp6 -0.255718283108604 0.0851331461407804 -3.00374524730633 0.00282786398526611 ** df.mm.trans2:exp6 -0.0987737081786183 0.082036352322682 -1.20402364783482 0.229265069181401 df.mm.trans1:exp7 -0.00593417230150467 0.0851331461407804 -0.0697046047339967 0.944462270636588 df.mm.trans2:exp7 0.0559099227484295 0.082036352322682 0.681526191322028 0.495917825515833 df.mm.trans1:exp8 -0.109133310266134 0.0851331461407804 -1.28191327600728 0.200587035273185 df.mm.trans2:exp8 -0.0287682643896105 0.082036352322682 -0.350677030061665 0.726008167132564 df.mm.trans1:probe2 -0.0207143162852536 0.0521331920606757 -0.397334509291989 0.691324588394708 df.mm.trans1:probe3 -0.00339696255071674 0.0521331920606757 -0.0651593047815517 0.948078462721856 df.mm.trans1:probe4 -0.0414625162641694 0.0521331920606757 -0.79531896331828 0.426881528233938 df.mm.trans1:probe5 0.00200556965193520 0.0521331920606757 0.0384701103588861 0.969331316718296 df.mm.trans1:probe6 -0.00398586828240704 0.0521331920606757 -0.0764554811408449 0.93909348696024 df.mm.trans1:probe7 -0.0742698947180603 0.0521331920606757 -1.42461820929017 0.155017332454671 df.mm.trans2:probe2 -0.0584477966111362 0.0521331920606757 -1.12112445643288 0.26288164900685 df.mm.trans2:probe3 -0.0134684119831148 0.0521331920606757 -0.258346198472547 0.796267501587813 df.mm.trans2:probe4 -0.0120025648141144 0.0521331920606757 -0.230228849216542 0.818027115396194 df.mm.trans2:probe5 0.0224585717482269 0.0521331920606757 0.430792185563628 0.666842600210839 df.mm.trans2:probe6 -0.0626318709521271 0.0521331920606757 -1.20138185437087 0.230286636306568 df.mm.trans3:probe2 -0.00806812794800944 0.0521331920606757 -0.154759906867381 0.877085656845668 df.mm.trans3:probe3 -0.093714697196794 0.0521331920606757 -1.79760136474519 0.0729652522573495 . df.mm.trans3:probe4 -0.0851928028180967 0.0521331920606757 -1.63413747462354 0.102986487606291 df.mm.trans3:probe5 -0.0348378054711651 0.0521331920606757 -0.66824616130581 0.504347067659724 df.mm.trans3:probe6 -0.0836550911634988 0.0521331920606757 -1.60464164684441 0.109331553136196