chr5.18886_chr5_33736063_33737618_+_2.R fitVsDatCorrelation=0.742524655384379 cont.fitVsDatCorrelation=0.262153439395622 fstatistic=12935.7698350354,46,554 cont.fstatistic=6225.77591844072,46,554 residuals=-0.415041166443038,-0.0746796871736291,-9.51823743157385e-05,0.0763335736833303,0.553973589517168 cont.residuals=-0.523830740136168,-0.123097269136233,-0.00384067957046435,0.120876198037627,0.71096436301721 predictedValues: Include Exclude Both chr5.18886_chr5_33736063_33737618_+_2.R.tl.Lung 53.3663194263697 72.2009306856552 71.4811025742267 chr5.18886_chr5_33736063_33737618_+_2.R.tl.cerebhem 53.3385783907401 59.7879476072919 57.6496541216373 chr5.18886_chr5_33736063_33737618_+_2.R.tl.cortex 54.6287658047053 71.9194769492635 67.1850031201845 chr5.18886_chr5_33736063_33737618_+_2.R.tl.heart 49.3213323177871 63.842492120924 60.4437026250648 chr5.18886_chr5_33736063_33737618_+_2.R.tl.kidney 52.9417306494336 65.853319996403 52.9705097373826 chr5.18886_chr5_33736063_33737618_+_2.R.tl.liver 50.1666464178628 63.6054075663542 54.7357895598056 chr5.18886_chr5_33736063_33737618_+_2.R.tl.stomach 53.5803858694021 67.6550052076482 56.1421454737916 chr5.18886_chr5_33736063_33737618_+_2.R.tl.testicle 50.045992785444 65.3839607541557 59.9413530245023 diffExp=-18.8346112592855,-6.44936921655178,-17.2907111445582,-14.5211598031369,-12.9115893469695,-13.4387611484914,-14.0746193382462,-15.3379679687117 diffExpScore=0.991217190988958 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=-1,0,-1,0,0,0,0,-1 diffExp1.3Score=0.75 diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 56.8555771716322 55.8413692305224 57.7508857798681 cerebhem 56.904808761002 56.671507100385 61.3184225204032 cortex 55.268207181714 57.267809497611 61.8976723428534 heart 58.5388241351937 60.8488150214024 59.6976271827197 kidney 56.3845424024058 60.3653258915359 60.5812481262694 liver 54.0351238827018 57.143355059173 60.2663763163708 stomach 53.8216102182463 60.1538610726533 55.656108877496 testicle 57.1853839469871 61.8713673598383 58.6088779325343 cont.diffExp=1.01420794110985,0.233301660616952,-1.99960231589692,-2.30999088620869,-3.9807834891301,-3.1082311764711,-6.33225085440699,-4.68598341285126 cont.diffExpScore=1.06743636513242 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=0.492654421418601 cont.tran.correlation=0.248254418941287 tran.covariance=0.00116347710472492 cont.tran.covariance=0.000267594565966947 tran.mean=59.22739328434 cont.tran.mean=57.4473429958128 weightedLogRatios: wLogRatio Lung -1.24787903786989 cerebhem -0.460427227209798 cortex -1.13790905866617 heart -1.03931695980311 kidney -0.890042233162992 liver -0.957468034434417 stomach -0.955764308158677 testicle -1.08179855182650 cont.weightedLogRatios: wLogRatio Lung 0.072564602515202 cerebhem 0.0165947048652379 cortex -0.143228493635524 heart -0.158254692414227 kidney -0.277402573549767 liver -0.224699611102833 stomach -0.449515019398609 testicle -0.321784600216447 varWeightedLogRatios=0.0555881813947349 cont.varWeightedLogRatios=0.0297469341219787 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.98839091022413 0.0641418961390987 62.1807453520686 5.21287281824467e-252 *** df.mm.trans1 -0.00161846143059657 0.0509516176201028 -0.0317646721771206 0.974671160172752 df.mm.trans2 0.283087478531873 0.0509516176201028 5.55600571197924 4.29188974555653e-08 *** df.mm.exp2 0.0258800693815078 0.0678178528594799 0.381611453183749 0.702895907076037 df.mm.exp3 0.0814580072319276 0.0678178528594799 1.20112925722834 0.230214329044520 df.mm.exp4 -0.0341363613796334 0.0678178528594799 -0.503353614723908 0.614915747506375 df.mm.exp5 0.199686747154071 0.0678178528594799 2.94445693478126 0.00337098996020904 ** df.mm.exp6 0.0783314539440064 0.0678178528594799 1.15502704142390 0.248577291957385 df.mm.exp7 0.180517972110928 0.0678178528594799 2.66180606580048 0.00799790797497986 ** df.mm.exp8 0.0126531379736062 0.0678178528594799 0.186575325524147 0.85206191516244 df.mm.trans1:exp2 -0.0264000274494524 0.0522571652272835 -0.505194404147835 0.613623212548564 df.mm.trans2:exp2 -0.214528909965150 0.0522571652272834 -4.10525349073365 4.64707208058225e-05 *** df.mm.trans1:exp3 -0.0580772416003016 0.0522571652272834 -1.11137374841717 0.266889588180622 df.mm.trans2:exp3 -0.0853638259680564 0.0522571652272835 -1.63353342258007 0.102924995350568 df.mm.trans1:exp4 -0.0446867715709496 0.0522571652272835 -0.855131949400475 0.392847762032986 df.mm.trans2:exp4 -0.0888975854575805 0.0522571652272835 -1.70115590983430 0.0894748456417881 . df.mm.trans1:exp5 -0.207674684742633 0.0522571652272835 -3.97409013365704 7.99934154107e-05 *** df.mm.trans2:exp5 -0.29170983887327 0.0522571652272835 -5.58219791686994 3.7225251652188e-08 *** df.mm.trans1:exp6 -0.140160886678413 0.0522571652272835 -2.68213719723999 0.00753364232403087 ** df.mm.trans2:exp6 -0.205085898782845 0.0522571652272835 -3.92455078439214 9.78257303201805e-05 *** df.mm.trans1:exp7 -0.176514730901675 0.0522571652272834 -3.37780915083998 0.000781980462309923 *** df.mm.trans2:exp7 -0.24554956962047 0.0522571652272835 -4.69886892165877 3.30420431736119e-06 *** df.mm.trans1:exp8 -0.0768905242690845 0.0522571652272835 -1.47138720469552 0.141754213534521 df.mm.trans2:exp8 -0.111829094194903 0.0522571652272835 -2.13997628284124 0.0327928180628420 * df.mm.trans1:probe2 0.0845594692775982 0.0374344411826709 2.25886821349806 0.0242795343211732 * df.mm.trans1:probe3 -0.113458470822722 0.0374344411826709 -3.03085787414516 0.00255251426531064 ** df.mm.trans1:probe4 -0.0520485053903181 0.0374344411826709 -1.39039087391032 0.164968518722504 df.mm.trans1:probe5 0.00629447620044252 0.0374344411826709 0.168146658573772 0.86652926957796 df.mm.trans1:probe6 -0.107606828476561 0.0374344411826709 -2.87454079924596 0.00420157436059561 ** df.mm.trans2:probe2 -0.00913831386344974 0.0374344411826709 -0.244115140355829 0.807232024514725 df.mm.trans2:probe3 0.0162856436239375 0.0374344411826709 0.435044389856589 0.663699628806414 df.mm.trans2:probe4 -0.0456564503748966 0.0374344411826709 -1.21963755655131 0.223121277246594 df.mm.trans2:probe5 0.0809122914505913 0.0374344411826709 2.16143981035430 0.0310890315357087 * df.mm.trans2:probe6 0.109113230575748 0.0374344411826709 2.91478187274927 0.00370326768074363 ** df.mm.trans3:probe2 0.0384503657264278 0.0374344411826709 1.02713876611112 0.304803377897626 df.mm.trans3:probe3 -0.0886412490819769 0.0374344411826709 -2.36790629915989 0.0182314884137160 * df.mm.trans3:probe4 -0.0411702506498775 0.0374344411826709 -1.09979605275732 0.271898663772776 df.mm.trans3:probe5 -0.0190849726595014 0.0374344411826709 -0.509823896298368 0.610377877324572 df.mm.trans3:probe6 -0.0548414255420441 0.0374344411826709 -1.46499917748021 0.143488400887989 df.mm.trans3:probe7 -0.00335102085964441 0.0374344411826709 -0.0895170531140628 0.928703353742991 df.mm.trans3:probe8 0.0561365122988766 0.0374344411826709 1.49959530649714 0.134288892329031 df.mm.trans3:probe9 -0.0906884385211379 0.0374344411826709 -2.42259362383962 0.0157298137308127 * df.mm.trans3:probe10 -0.0518871745771104 0.0374344411826709 -1.38608118454109 0.166279741506613 df.mm.trans3:probe11 0.0671249406978601 0.0374344411826709 1.79313323712532 0.073496975271919 . df.mm.trans3:probe12 0.121431026152837 0.0374344411826709 3.24383167790011 0.00125038276165650 ** df.mm.trans3:probe13 0.0609731281563207 0.0374344411826709 1.62879760535991 0.103924347174533 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.93039854860970 0.0924109873593659 42.5317233471952 1.23934309706145e-176 *** df.mm.trans1 0.0898019399631584 0.073407391662069 1.22333647783811 0.221722719355735 df.mm.trans2 0.087041635879168 0.073407391662069 1.18573394188782 0.236235702830477 df.mm.exp2 -0.0443195240557921 0.0977070389335826 -0.453596020711657 0.650297242276194 df.mm.exp3 -0.0724366805895358 0.0977070389335826 -0.741366040565157 0.458785670224059 df.mm.exp4 0.0818996561354336 0.0977070389335827 0.838216540275115 0.402270592569296 df.mm.exp5 0.0217338972230006 0.0977070389335826 0.222439421562805 0.82405380680147 df.mm.exp6 -0.0704675625393695 0.0977070389335827 -0.721212753026632 0.471082868248666 df.mm.exp7 0.0564982396921862 0.0977070389335826 0.578241243505409 0.563336229299761 df.mm.exp8 0.0935790545363738 0.0977070389335827 0.957751412362266 0.338605891558702 df.mm.trans1:exp2 0.0451850554451173 0.0752883298738515 0.600160151258855 0.548644916158656 df.mm.trans2:exp2 0.0590761094510702 0.0752883298738515 0.784664894945266 0.432985464295924 df.mm.trans1:exp3 0.044120189783302 0.0752883298738515 0.586016316967411 0.55810321364138 df.mm.trans2:exp3 0.0976603788179232 0.0752883298738515 1.29715161674534 0.195118955726614 df.mm.trans1:exp4 -0.0527237802597797 0.0752883298738515 -0.700291537189368 0.484039233762716 df.mm.trans2:exp4 0.00397771058612712 0.0752883298738515 0.0528330299369361 0.957883989196873 df.mm.trans1:exp5 -0.0300531658302257 0.0752883298738515 -0.399174292756673 0.689918610464332 df.mm.trans2:exp5 0.0561659894729813 0.0752883298738515 0.746011892773947 0.455976654352192 df.mm.trans1:exp6 0.0195875212623883 0.0752883298738515 0.260166765489524 0.794831803015306 df.mm.trans2:exp6 0.0935156953321999 0.0752883298738515 1.24210080750747 0.214724920246718 df.mm.trans1:exp7 -0.111337494923569 0.0752883298738515 -1.47881477926419 0.139758165988572 df.mm.trans2:exp7 0.0178924126450158 0.0752883298738515 0.237651873470899 0.812238976596067 df.mm.trans1:exp8 -0.0877950328727446 0.0752883298738515 -1.16611741846112 0.244068739087545 df.mm.trans2:exp8 0.0089634765356722 0.0752883298738515 0.119055324386805 0.905274682233385 df.mm.trans1:probe2 0.0557572536703646 0.0539328251761491 1.03382779389466 0.301667751743796 df.mm.trans1:probe3 0.0399816542604668 0.0539328251761492 0.741323194731286 0.458811621228512 df.mm.trans1:probe4 0.117845723014019 0.0539328251761491 2.18504635403624 0.0293036379910394 * df.mm.trans1:probe5 0.107084951585595 0.0539328251761492 1.98552460835951 0.0475786929711599 * df.mm.trans1:probe6 0.0652931892699728 0.0539328251761491 1.21063914335509 0.226549966001615 df.mm.trans2:probe2 -0.0439125876111365 0.0539328251761491 -0.81420892504174 0.415875105416795 df.mm.trans2:probe3 0.0645813036219177 0.0539328251761492 1.19743965592364 0.231647328724870 df.mm.trans2:probe4 0.0264092290197403 0.0539328251761492 0.489668934150687 0.624562021793975 df.mm.trans2:probe5 0.0115065544055408 0.0539328251761492 0.213349743277113 0.831132665966685 df.mm.trans2:probe6 0.0378365916660278 0.0539328251761492 0.701550336783773 0.483254227447371 df.mm.trans3:probe2 -0.0672278897903203 0.0539328251761491 -1.24651155526803 0.213103443237554 df.mm.trans3:probe3 -0.0352122581748696 0.0539328251761491 -0.652891037320285 0.514097364220934 df.mm.trans3:probe4 -0.102738310706225 0.0539328251761491 -1.90493100205066 0.0573065865358581 . df.mm.trans3:probe5 -0.0333374712847138 0.0539328251761491 -0.618129519005 0.536743991920724 df.mm.trans3:probe6 -0.0061174920346878 0.0539328251761491 -0.113427991482136 0.90973234019286 df.mm.trans3:probe7 -0.0267390324311746 0.0539328251761491 -0.495784011756156 0.620243420827047 df.mm.trans3:probe8 -0.0308631722171697 0.0539328251761492 -0.572252095386585 0.567383335202235 df.mm.trans3:probe9 -0.0716379304420836 0.0539328251761492 -1.32828069377245 0.184632320621640 df.mm.trans3:probe10 -0.080221668438571 0.0539328251761491 -1.48743679153763 0.137468410788488 df.mm.trans3:probe11 -0.125700416211216 0.0539328251761491 -2.33068480652863 0.0201279791726625 * df.mm.trans3:probe12 -0.0423336834070062 0.0539328251761491 -0.784933540357673 0.432828061152197 df.mm.trans3:probe13 -0.04221535570884 0.0539328251761492 -0.782739557420942 0.434114518295096