chr1.1082_chr1_163909933_163910992_-_1.R fitVsDatCorrelation=0.890982645477704 cont.fitVsDatCorrelation=0.283704555430661 fstatistic=9330.3766484942,36,324 cont.fstatistic=2084.84148900322,36,324 residuals=-0.350260136557012,-0.0714564319217039,0.00545492677317034,0.070051218863322,0.52555755012114 cont.residuals=-0.626117845841212,-0.205338463711171,-0.0558645922638678,0.181803099639339,0.899072915931515 predictedValues: Include Exclude Both chr1.1082_chr1_163909933_163910992_-_1.R.tl.Lung 84.5611961641294 58.5488418559539 83.0326073797376 chr1.1082_chr1_163909933_163910992_-_1.R.tl.cerebhem 66.7031420944616 53.2414423877767 71.9797419314694 chr1.1082_chr1_163909933_163910992_-_1.R.tl.cortex 103.846412440919 69.033827655272 101.064773304183 chr1.1082_chr1_163909933_163910992_-_1.R.tl.heart 85.1138753993922 56.3137498605881 75.1178289096668 chr1.1082_chr1_163909933_163910992_-_1.R.tl.kidney 67.4180884465732 52.0229762920342 63.3473013606739 chr1.1082_chr1_163909933_163910992_-_1.R.tl.liver 64.5174694546443 52.8968792244425 62.003864101043 chr1.1082_chr1_163909933_163910992_-_1.R.tl.stomach 76.2545215548068 52.2420965889253 68.3360657862529 chr1.1082_chr1_163909933_163910992_-_1.R.tl.testicle 71.694866809232 53.5460182221886 71.1569320483333 diffExp=26.0123543081755,13.4616997066849,34.8125847856475,28.8001255388040,15.395112154539,11.6205902302018,24.0124249658816,18.1488485870434 diffExpScore=0.994228451963455 diffExp1.5=0,0,1,1,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=1,0,1,1,0,0,1,0 diffExp1.4Score=0.8 diffExp1.3=1,0,1,1,0,0,1,1 diffExp1.3Score=0.833333333333333 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 62.5228841491686 65.1838449668042 75.2130498207497 cerebhem 71.1434550880239 64.574060400479 65.2535319380681 cortex 70.7617578049691 71.179771832744 66.5141689638997 heart 69.6118036528658 67.2427930569714 65.8116235627328 kidney 69.2646336263336 63.7093787872325 77.7865885143228 liver 74.1296614632516 65.7225999708993 71.9658257230638 stomach 64.880965031494 70.8021320762184 57.4845016634622 testicle 60.8439023808619 66.1402657419519 65.2141932040105 cont.diffExp=-2.66096081763560,6.56939468754494,-0.418014027774902,2.36901059589449,5.5552548391011,8.40706149235223,-5.92116704472441,-5.29636336109001 cont.diffExpScore=3.87301008823922 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=0.929600418802638 cont.tran.correlation=-0.0398735018758111 tran.covariance=0.0140181650252725 cont.tran.covariance=-0.000101385399367358 tran.mean=66.7472127782087 cont.tran.mean=67.3571193768918 weightedLogRatios: wLogRatio Lung 1.56370881081941 cerebhem 0.9213937548969 cortex 1.81241622883794 heart 1.75029034984037 kidney 1.05798661010966 liver 0.807799531509567 stomach 1.56758309745153 testicle 1.20442763770898 cont.weightedLogRatios: wLogRatio Lung -0.173233540923421 cerebhem 0.408494215420854 cortex -0.0251045788919504 heart 0.146309201033037 kidney 0.350807861340475 liver 0.511058660542817 stomach -0.368223007531724 testicle -0.34638811941265 varWeightedLogRatios=0.149941005585483 cont.varWeightedLogRatios=0.118145315953009 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.87797623349174 0.0779981614928999 49.7188159216387 1.10449399259534e-153 *** df.mm.trans1 0.542451230624516 0.0660636280047653 8.21104209695216 5.2720437394638e-15 *** df.mm.trans2 0.189602462122518 0.0660636280047653 2.86999772565990 0.00437455030071168 ** df.mm.exp2 -0.189398978328611 0.0920016120559899 -2.05864847469572 0.0403270884489954 * df.mm.exp3 0.173644607365012 0.09200161205599 1.88740831257757 0.0599990480804332 . df.mm.exp4 0.0677674618285864 0.09200161205599 0.736589939177856 0.461904750164085 df.mm.exp5 -0.0741368837036465 0.0920016120559899 -0.805821572545149 0.420936751872841 df.mm.exp6 -0.0800197386504173 0.0920016120559899 -0.869764527622834 0.385072988832622 df.mm.exp7 -0.0225757520124178 0.0920016120559899 -0.24538430912144 0.806314231796472 df.mm.exp8 -0.100030564129247 0.09200161205599 -1.08726968901775 0.277725813244039 df.mm.trans1:exp2 -0.047824449202491 0.079675733229608 -0.600238582865268 0.548766833307471 df.mm.trans2:exp2 0.0943747542503472 0.079675733229608 1.18448554440509 0.237089075825026 df.mm.trans1:exp3 0.031792909568512 0.079675733229608 0.399028766724892 0.690134877508642 df.mm.trans2:exp3 -0.0089092764507114 0.079675733229608 -0.111819196254358 0.911036007548253 df.mm.trans1:exp4 -0.0612528785748261 0.079675733229608 -0.768777092999054 0.442585664026985 df.mm.trans2:exp4 -0.106690041044069 0.079675733229608 -1.33905314352880 0.181492038816875 df.mm.trans1:exp5 -0.152425247012001 0.079675733229608 -1.91306990012562 0.0566203386283668 . df.mm.trans2:exp5 -0.0440389529531055 0.079675733229608 -0.552727300622323 0.580831280546273 df.mm.trans1:exp6 -0.190519717167620 0.0796757332296079 -2.39118875277349 0.017364140864782 * df.mm.trans2:exp6 -0.0214972275127267 0.079675733229608 -0.269808969950441 0.78747898229011 df.mm.trans1:exp7 -0.0808230223848792 0.079675733229608 -1.01439948035326 0.311149080097129 df.mm.trans2:exp7 -0.0913969394601249 0.079675733229608 -1.14711136948987 0.252181962483591 df.mm.trans1:exp8 -0.065025770652983 0.079675733229608 -0.816130181891053 0.415025288523859 df.mm.trans2:exp8 0.0107106925764714 0.079675733229608 0.134428541066644 0.893147180068709 df.mm.trans1:probe2 0.157231052075655 0.039837866614804 3.94677389720532 9.71761997233884e-05 *** df.mm.trans1:probe3 -0.348202952458543 0.039837866614804 -8.74050199086586 1.27483076253351e-16 *** df.mm.trans1:probe4 0.193419352466843 0.039837866614804 4.85516341366952 1.87439882881056e-06 *** df.mm.trans1:probe5 -0.171617585155159 0.039837866614804 -4.30790099315673 2.18792401001863e-05 *** df.mm.trans1:probe6 0.322602340880114 0.039837866614804 8.09788194732881 1.14635100861945e-14 *** df.mm.trans2:probe2 0.0836101387419173 0.039837866614804 2.09876044694741 0.0366117092174122 * df.mm.trans2:probe3 -0.0855084510451097 0.039837866614804 -2.1464113997845 0.0325824035816987 * df.mm.trans2:probe4 -0.0121181406000973 0.039837866614804 -0.304186484614469 0.761181090764324 df.mm.trans2:probe5 0.00917904260526799 0.039837866614804 0.230409993939209 0.817918607401079 df.mm.trans2:probe6 0.0253809345525922 0.039837866614804 0.637105766681806 0.524505859977304 df.mm.trans3:probe2 -0.350066511981853 0.039837866614804 -8.78728058825735 9.11400284014796e-17 *** df.mm.trans3:probe3 -0.222323094802002 0.039837866614804 -5.58069780572501 5.06608656812548e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95107624372956 0.164729511563493 23.9852361985950 8.29850365842798e-74 *** df.mm.trans1 0.215439841231699 0.139524175506725 1.54410402677000 0.123539262367746 df.mm.trans2 0.209497188537485 0.139524175506725 1.50151174716949 0.134197163472999 df.mm.exp2 0.261811400078434 0.194304331370897 1.34742956181803 0.178783424631952 df.mm.exp3 0.33469284624817 0.194304331370897 1.72251871014287 0.0859298412539174 . df.mm.exp4 0.272027972343574 0.194304331370897 1.40000982183106 0.16246718383001 df.mm.exp5 0.0458776448948871 0.194304331370897 0.236112311914004 0.813494708307687 df.mm.exp6 0.222647684967470 0.194304331370897 1.14587093039357 0.2526941762175 df.mm.exp7 0.388508475916435 0.194304331370897 1.99948438192473 0.0463906697954721 * df.mm.exp8 0.129992653015909 0.194304331370897 0.669015724450184 0.503961853187707 df.mm.trans1:exp2 -0.132645703406496 0.168272487032546 -0.788279211567368 0.431109759723043 df.mm.trans2:exp2 -0.271210273365781 0.168272487032546 -1.61173272082990 0.107993768381923 df.mm.trans1:exp3 -0.210906771590251 0.168272487032546 -1.25336455952813 0.210976756820804 df.mm.trans2:exp3 -0.246695833316134 0.168272487032546 -1.46604972486334 0.143604565766228 df.mm.trans1:exp4 -0.164626462782651 0.168272487032546 -0.978332618039988 0.328639758204912 df.mm.trans2:exp4 -0.240929787691607 0.168272487032546 -1.43178360253870 0.153169704777929 df.mm.trans1:exp5 0.0565241576384109 0.168272487032546 0.335908493629611 0.737157292700581 df.mm.trans2:exp5 -0.0687575210942324 0.168272487032546 -0.408608218174928 0.683097128273216 df.mm.trans1:exp6 -0.0523645792212649 0.168272487032546 -0.311189191677763 0.755856920277247 df.mm.trans2:exp6 -0.214416492894654 0.168272487032546 -1.27422192822991 0.203497961216270 df.mm.trans1:exp7 -0.351486828235477 0.168272487032546 -2.08879558645552 0.0375062358801301 * df.mm.trans2:exp7 -0.305831023247460 0.168272487032546 -1.81747491013375 0.0700679917910231 . df.mm.trans1:exp8 -0.157213682098362 0.168272487032546 -0.934280373879268 0.350855045090861 df.mm.trans2:exp8 -0.115426589248040 0.168272487032546 -0.685950456212814 0.493234893387303 df.mm.trans1:probe2 -0.132990607727542 0.0841362435162732 -1.58065777802190 0.114932004690395 df.mm.trans1:probe3 -0.105403423375511 0.0841362435162732 -1.25277073197503 0.211192570562231 df.mm.trans1:probe4 -0.0542695961750339 0.0841362435162732 -0.645020432419678 0.519370405056713 df.mm.trans1:probe5 0.0720937589621315 0.0841362435162732 0.856869239095366 0.392150472400452 df.mm.trans1:probe6 -0.0582811713109877 0.0841362435162732 -0.692699945650832 0.488994060870106 df.mm.trans2:probe2 0.0301070799013161 0.0841362435162732 0.357837224994398 0.720698131481122 df.mm.trans2:probe3 -0.0326101912132091 0.0841362435162732 -0.387587915152187 0.698575507239363 df.mm.trans2:probe4 0.0143928712345562 0.0841362435162732 0.171066244855255 0.86427845972498 df.mm.trans2:probe5 0.0427388066362932 0.0841362435162732 0.507971414578628 0.611819031854335 df.mm.trans2:probe6 0.0951154978710465 0.0841362435162732 1.13049375508011 0.259104380125430 df.mm.trans3:probe2 -0.092894758672215 0.0841362435162732 -1.10409919423426 0.270369410882391 df.mm.trans3:probe3 -0.074170809571751 0.0841362435162732 -0.881555991472394 0.378670465449913