chr3.15353_chr3_94734498_94734979_+_1.R fitVsDatCorrelation=0.937135819139964 cont.fitVsDatCorrelation=0.295920912457408 fstatistic=7635.1540383094,41,439 cont.fstatistic=1009.73816633802,41,439 residuals=-0.714618377840331,-0.096557359791509,-0.00332346121037616,0.093439379096281,0.715684129868475 cont.residuals=-0.9626760907245,-0.386590279372951,0.0086248758040114,0.32123983562887,1.62676077675115 predictedValues: Include Exclude Both chr3.15353_chr3_94734498_94734979_+_1.R.tl.Lung 125.561223185262 113.328896904633 52.0182938026835 chr3.15353_chr3_94734498_94734979_+_1.R.tl.cerebhem 104.019682730755 85.5286471715184 57.1882186015343 chr3.15353_chr3_94734498_94734979_+_1.R.tl.cortex 113.481746575273 104.101103921124 48.4871392779782 chr3.15353_chr3_94734498_94734979_+_1.R.tl.heart 131.907686225718 109.538058351913 58.9380447936253 chr3.15353_chr3_94734498_94734979_+_1.R.tl.kidney 134.948688623084 120.603289550733 57.5826953369324 chr3.15353_chr3_94734498_94734979_+_1.R.tl.liver 130.530820894708 108.886612256772 59.283676428568 chr3.15353_chr3_94734498_94734979_+_1.R.tl.stomach 129.090595288177 113.127903003420 51.6672063674319 chr3.15353_chr3_94734498_94734979_+_1.R.tl.testicle 126.265177974712 95.857538337256 52.9546435280808 diffExp=12.2323262806297,18.4910355592364,9.38064265414879,22.3696278738054,14.3453990723515,21.6442086379362,15.9626922847567,30.4076396374558 diffExpScore=0.993142868365058 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=0,0,0,0,0,0,0,1 diffExp1.3Score=0.5 diffExp1.2=0,1,0,1,0,0,0,1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 97.9043682122322 92.033713006802 88.9418048473655 cerebhem 76.6220527873796 87.4627545464625 80.0618159859872 cortex 80.1930218740914 96.2448213355363 79.7902276211386 heart 83.7090429430544 86.7615860875286 81.4979385896557 kidney 77.7689139773083 80.4935397162874 100.155484210381 liver 87.7095516850127 88.9000684160112 86.8203171756074 stomach 93.0811765084106 95.1520958293241 93.5585281475011 testicle 97.9630301684809 80.6947070149185 92.0520048410052 cont.diffExp=5.87065520543013,-10.8407017590829,-16.0517994614449,-3.05254314447426,-2.72462573897919,-1.19051673099845,-2.07091932091349,17.2683231535623 cont.diffExpScore=4.28288407595524 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,-1,0,0,0,0,1 cont.diffExp1.2Score=2 tran.correlation=0.810249462673959 cont.tran.correlation=0.087084809325988 tran.covariance=0.00789658844753841 cont.tran.covariance=0.00065041578981851 tran.mean=115.423604437191 cont.tran.mean=87.6684027568025 weightedLogRatios: wLogRatio Lung 0.490104850145309 cerebhem 0.88992305433773 cortex 0.404521262358151 heart 0.889975984620892 kidney 0.544936060011972 liver 0.866798666233772 stomach 0.632854076984647 testicle 1.29512133575522 cont.weightedLogRatios: wLogRatio Lung 0.281544791158958 cerebhem -0.582911805431988 cortex -0.816624014884258 heart -0.159215518108413 kidney -0.150514602557149 liver -0.060410238866238 stomach -0.099999529905717 testicle 0.870228858048169 varWeightedLogRatios=0.0846601485630987 cont.varWeightedLogRatios=0.262922471152828 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.68265020192526 0.0896790635197168 63.3665203325398 5.20490944876607e-223 *** df.mm.trans1 -0.852693862829151 0.072357540669825 -11.7844505898851 4.84966974760712e-28 *** df.mm.trans2 -1.03691513279509 0.072357540669825 -14.3304363746502 1.75274111874228e-38 *** df.mm.exp2 -0.564408592314633 0.0974662516138371 -5.790810490495 1.33625800185108e-08 *** df.mm.exp3 -0.115786208767600 0.0974662516138371 -1.18796205712668 0.235490669026761 df.mm.exp4 -0.109604615422176 0.0974662516138371 -1.12453914670312 0.261398862355662 df.mm.exp5 0.0326868916187575 0.0974662516138371 0.335366253216174 0.73750902247444 df.mm.exp6 -0.131909732881664 0.097466251613837 -1.35338879558324 0.176627902750235 df.mm.exp7 0.0327180401383712 0.097466251613837 0.335685835831675 0.737268155421301 df.mm.exp8 -0.179680564900094 0.097466251613837 -1.84351569825411 0.06592756956252 . df.mm.trans1:exp2 0.376195256810679 0.077751171534911 4.83845129769864 1.81467586926314e-06 *** df.mm.trans2:exp2 0.28296578355993 0.077751171534911 3.63937646177943 0.000305826769641383 *** df.mm.trans1:exp3 0.0146347358653205 0.077751171534911 0.188225277849986 0.850787032534087 df.mm.trans2:exp3 0.0308546055451339 0.077751171534911 0.396837821681952 0.69167990162767 df.mm.trans1:exp4 0.158913472811693 0.077751171534911 2.04387239027440 0.0415624238847504 * df.mm.trans2:exp4 0.0755824859329198 0.077751171534911 0.97210735788055 0.331532448524615 df.mm.trans1:exp5 0.0394142565309248 0.077751171534911 0.506928136937814 0.612459768945202 df.mm.trans2:exp5 0.0295254856256142 0.077751171534911 0.379743289300239 0.704319618215954 df.mm.trans1:exp6 0.170725633432798 0.077751171534911 2.19579499655694 0.0286291285730472 * df.mm.trans2:exp6 0.0919226359287288 0.077751171534911 1.18226689211306 0.237739775469368 df.mm.trans1:exp7 -0.00499706697558827 0.077751171534911 -0.0642699894669053 0.94878450347066 df.mm.trans2:exp7 -0.0344931597556453 0.077751171534911 -0.443635241433726 0.657524889043138 df.mm.trans1:exp8 0.185271373646441 0.077751171534911 2.38287565304727 0.0176025919884923 * df.mm.trans2:exp8 0.0122494953151440 0.077751171534911 0.157547405052847 0.874885887242087 df.mm.trans1:probe2 -0.0958934024327744 0.0509000898471751 -1.88395350029223 0.0602318641675817 . df.mm.trans1:probe3 0.222442675779131 0.0509000898471751 4.37018237977582 1.55121641475355e-05 *** df.mm.trans1:probe4 -0.162149558346427 0.0509000898471751 -3.18564385314982 0.00154731298446975 ** df.mm.trans1:probe5 0.0737590051881409 0.0509000898471751 1.44909381122113 0.148025312468121 df.mm.trans1:probe6 0.00156116517583984 0.0509000898471751 0.030671167389432 0.975545724244617 df.mm.trans2:probe2 0.0672872196736557 0.0509000898471751 1.32194697250402 0.186874212864188 df.mm.trans2:probe3 0.950483761629408 0.0509000898471751 18.6735183470832 1.06146460531622e-57 *** df.mm.trans2:probe4 0.181062213070704 0.0509000898471751 3.55720812309632 0.000415620184084184 *** df.mm.trans2:probe5 -0.0222286019798043 0.0509000898471751 -0.436710466455847 0.662536050886819 df.mm.trans2:probe6 0.00722300481865089 0.0509000898471751 0.141905541627482 0.887219716132276 df.mm.trans3:probe2 0.365246736614466 0.0509000898471751 7.17575819042954 3.08483767906826e-12 *** df.mm.trans3:probe3 0.171825045634863 0.050900089847175 3.37573167652079 0.000801656683478448 *** df.mm.trans3:probe4 0.225152108841126 0.0509000898471751 4.42341279783855 1.22664439571822e-05 *** df.mm.trans3:probe5 0.15909809312635 0.0509000898471751 3.12569375818459 0.00189160620485161 ** df.mm.trans3:probe6 0.0922057254288948 0.050900089847175 1.81150417819964 0.0707460978112769 . df.mm.trans3:probe7 0.0505724497406705 0.0509000898471751 0.99356307410285 0.320982945122763 df.mm.trans3:probe8 0.204333875091095 0.050900089847175 4.01441089209464 7.00764795378379e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.61663337616557 0.245476187627494 18.8068481133952 2.6384477211446e-58 *** df.mm.trans1 -0.0191022935647090 0.198062429876120 -0.096445820525663 0.923210514623277 df.mm.trans2 -0.129799616528075 0.198062429876119 -0.65534698634799 0.51258763726202 df.mm.exp2 -0.190864910567367 0.266791856755357 -0.715407557369288 0.474737553522574 df.mm.exp3 -0.046233212193004 0.266791856755357 -0.173293191011445 0.862500848405003 df.mm.exp4 -0.128230593408269 0.266791856755357 -0.480639083095605 0.631012465457714 df.mm.exp5 -0.482968948854031 0.266791856755357 -1.8102836972903 0.070935399854686 . df.mm.exp6 -0.120460789972988 0.266791856755357 -0.451515992421943 0.651840673807166 df.mm.exp7 -0.0678024573321874 0.266791856755357 -0.254139905755673 0.79950646596443 df.mm.exp8 -0.165254383901537 0.266791856755357 -0.619413148179679 0.535965545812666 df.mm.trans1:exp2 -0.0542413263884 0.212826276534043 -0.254861980727853 0.798949050207647 df.mm.trans2:exp2 0.139922995334556 0.212826276534043 0.657451690708755 0.511235077127364 df.mm.trans1:exp3 -0.153321453436250 0.212826276534043 -0.720406596089297 0.471658095818236 df.mm.trans2:exp3 0.0909734239569647 0.212826276534043 0.42745390953834 0.669258336157649 df.mm.trans1:exp4 -0.0284135626562687 0.212826276534043 -0.133505895601776 0.893854479625612 df.mm.trans2:exp4 0.0692396048718962 0.212826276534043 0.325333910828538 0.745083364278714 df.mm.trans1:exp5 0.252719569253045 0.212826276534043 1.18744533508117 0.235694104922767 df.mm.trans2:exp5 0.348990922436290 0.212826276534043 1.63979245476517 0.101764658481706 df.mm.trans1:exp6 0.0105004289255738 0.212826276534043 0.049338028633387 0.960672352780088 df.mm.trans2:exp6 0.0858187464261663 0.212826276534043 0.403233791540016 0.686972557607805 df.mm.trans1:exp7 0.0172832677765756 0.212826276534043 0.0812083360101964 0.935313279822328 df.mm.trans2:exp7 0.101124121763944 0.212826276534043 0.475148667781014 0.634917319752668 df.mm.trans1:exp8 0.165853380554249 0.212826276534043 0.779289960127267 0.436228815688859 df.mm.trans2:exp8 0.0337724129612262 0.212826276534043 0.158685353666017 0.873989750455723 df.mm.trans1:probe2 -0.310492206565598 0.139327503156122 -2.22850621400771 0.0263524503976010 * df.mm.trans1:probe3 0.170334330120293 0.139327503156122 1.22254634771877 0.222157031939191 df.mm.trans1:probe4 -0.0617171838379117 0.139327503156122 -0.44296483063186 0.658009366340259 df.mm.trans1:probe5 0.0649207005194975 0.139327503156122 0.465957539242996 0.641476993289737 df.mm.trans1:probe6 -0.0526044493386126 0.139327503156122 -0.377559693147354 0.705940178730371 df.mm.trans2:probe2 -0.03044120078957 0.139327503156122 -0.218486659848195 0.827151482074869 df.mm.trans2:probe3 0.127709535203234 0.139327503156122 0.916613965730301 0.359848217521243 df.mm.trans2:probe4 0.196220452260308 0.139327503156122 1.40833968753774 0.159738303815717 df.mm.trans2:probe5 0.0973569215401845 0.139327503156122 0.698763125261004 0.485070086561512 df.mm.trans2:probe6 0.103651035965219 0.139327503156122 0.743938085570036 0.457311923219816 df.mm.trans3:probe2 -0.0421139581014133 0.139327503156122 -0.302265935636719 0.762592497470528 df.mm.trans3:probe3 0.000717116328025537 0.139327503156122 0.00514698327165152 0.995895657673715 df.mm.trans3:probe4 -0.158872067913321 0.139327503156122 -1.14027786556469 0.254792271920996 df.mm.trans3:probe5 0.140595852146869 0.139327503156122 1.00910336410267 0.313480926735335 df.mm.trans3:probe6 0.233030960124072 0.139327503156122 1.67254099043855 0.095130158471452 . df.mm.trans3:probe7 -0.00366391042937699 0.139327503156122 -0.0262971082261585 0.97903231091059 df.mm.trans3:probe8 -0.00768749880823812 0.139327503156122 -0.0551757451622739 0.956023553483797