chr4.17373_chr4_41950539_42001563_+_2.R fitVsDatCorrelation=0.958940456052492 cont.fitVsDatCorrelation=0.223063973679361 fstatistic=7546.80711342332,63,945 cont.fstatistic=625.068593423162,63,945 residuals=-0.97055269444712,-0.0964715438170874,-0.00318976604907058,0.0856776991426933,1.14288969650971 cont.residuals=-0.926460123848183,-0.369074798816079,-0.168581147261142,0.129381790404613,3.76489180615724 predictedValues: Include Exclude Both chr4.17373_chr4_41950539_42001563_+_2.R.tl.Lung 61.2902913628053 84.5005819434259 113.289513991495 chr4.17373_chr4_41950539_42001563_+_2.R.tl.cerebhem 61.6409964514779 75.7820337482938 80.5529529499211 chr4.17373_chr4_41950539_42001563_+_2.R.tl.cortex 58.9638581733426 78.6760167195841 83.4045675890217 chr4.17373_chr4_41950539_42001563_+_2.R.tl.heart 64.5781872458169 86.2827295177113 83.9738482845653 chr4.17373_chr4_41950539_42001563_+_2.R.tl.kidney 60.8603131372373 89.3633990113168 87.5496732593802 chr4.17373_chr4_41950539_42001563_+_2.R.tl.liver 62.4522136898091 89.170640268969 82.2380244120807 chr4.17373_chr4_41950539_42001563_+_2.R.tl.stomach 64.8139412070122 88.3679035168289 104.958795546351 chr4.17373_chr4_41950539_42001563_+_2.R.tl.testicle 64.2965623892232 85.7468672481432 95.3457497427896 diffExp=-23.2102905806206,-14.1410372968159,-19.7121585462416,-21.7045422718944,-28.5030858740794,-26.7184265791600,-23.5539623098167,-21.45030485892 diffExpScore=0.99444425333656 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,-1,-1,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 79.6886533635623 77.2766443427855 83.9127009501773 cerebhem 79.077081330755 70.261369046997 86.1083972129453 cortex 89.6743799792252 74.9406841889765 87.9040697879187 heart 69.7383090801994 80.5147108592037 73.483981065102 kidney 84.8205740477526 71.340750685457 80.080465978023 liver 62.7056074925141 93.5351651722166 82.5138422643933 stomach 70.8355732053934 81.8766401347658 80.732986633861 testicle 63.7640935349372 102.916774454658 84.9847296889508 cont.diffExp=2.41200902077681,8.815712283758,14.7336957902486,-10.7764017790043,13.4798233622955,-30.8295576797025,-11.0410669293724,-39.1526809197204 cont.diffExpScore=2.45960867151688 cont.diffExp1.5=0,0,0,0,0,0,0,-1 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,0,0,0,0,-1,0,-1 cont.diffExp1.4Score=0.666666666666667 cont.diffExp1.3=0,0,0,0,0,-1,0,-1 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,0,0,0,0,-1,0,-1 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.482944362959733 cont.tran.correlation=-0.833353485417613 tran.covariance=0.00098409408977262 cont.tran.covariance=-0.0149639452571460 tran.mean=73.5491584769374 cont.tran.mean=78.3104381824624 weightedLogRatios: wLogRatio Lung -1.37324267893364 cerebhem -0.872522622555085 cortex -1.21743208948814 heart -1.24963211365961 kidney -1.6520068174151 liver -1.53588927624086 stomach -1.34116808950672 testicle -1.24008718056926 cont.weightedLogRatios: wLogRatio Lung 0.134091340910655 cerebhem 0.509602461690556 cortex 0.790904105422677 heart -0.6202520169896 kidney 0.753549154219173 liver -1.73486563510181 stomach -0.627614693189665 testicle -2.10380733640287 varWeightedLogRatios=0.0544932259851234 cont.varWeightedLogRatios=1.23361895209435 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95349222960567 0.093706290699861 42.1902542516447 1.53416446209619e-219 *** df.mm.trans1 0.100099510277206 0.0776095397608246 1.28978358312252 0.197441360629655 df.mm.trans2 0.547598759395396 0.0707620827666367 7.73859018821279 2.57594469349369e-14 *** df.mm.exp2 0.237840390315836 0.0901540150815116 2.63815638272788 0.00847285689964065 ** df.mm.exp3 0.196126714009019 0.0901540150815116 2.17546288794452 0.0298426886618003 * df.mm.exp4 0.372567471566302 0.0901540150815116 4.13256659982864 3.90568059028347e-05 *** df.mm.exp5 0.306652894656054 0.0901540150815116 3.40143358428128 0.000698429402357567 *** df.mm.exp6 0.392902484941856 0.0901540150815116 4.35812519926726 1.45550138213974e-05 *** df.mm.exp7 0.177028434211355 0.0901540150815116 1.96362229736853 0.049866775154066 * df.mm.exp8 0.234962704206754 0.0901540150815116 2.60623671607211 0.00929839323370954 ** df.mm.trans1:exp2 -0.232134665656965 0.0761940494269984 -3.04662460392493 0.00237853974510325 ** df.mm.trans2:exp2 -0.346737568795795 0.0590196569021739 -5.874950601128 5.85641557207391e-09 *** df.mm.trans1:exp3 -0.234823482461732 0.0761940494269984 -3.08191366947515 0.00211635332133680 ** df.mm.trans2:exp3 -0.267546769353385 0.0590196569021739 -4.53318069599842 6.55551657245065e-06 *** df.mm.trans1:exp4 -0.320312227856329 0.0761940494269984 -4.20390083300691 2.87260241333941e-05 *** df.mm.trans2:exp4 -0.351696436211122 0.0590196569021739 -5.95897120842408 3.57932055414221e-09 *** df.mm.trans1:exp5 -0.313693056320016 0.0761940494269984 -4.11702828080512 4.17344549075802e-05 *** df.mm.trans2:exp5 -0.250700124582639 0.0590196569021739 -4.24773944379546 2.37297357035913e-05 *** df.mm.trans1:exp6 -0.374122252929205 0.0761940494269984 -4.91012429110559 1.07197095178124e-06 *** df.mm.trans2:exp6 -0.339109065746563 0.0590196569021739 -5.74569700241806 1.23414114440827e-08 *** df.mm.trans1:exp7 -0.121129163234897 0.0761940494269984 -1.58974571040420 0.112226606313267 df.mm.trans2:exp7 -0.132278034130803 0.0590196569021739 -2.24125386479382 0.0252411327797129 * df.mm.trans1:exp8 -0.187077987782838 0.0761940494269984 -2.45528343997621 0.0142565622249092 * df.mm.trans2:exp8 -0.220321573621284 0.059019656902174 -3.73302023741802 0.000200501155050257 *** df.mm.trans1:probe2 0.425621229379501 0.0590196569021739 7.2115164966983 1.13318884716833e-12 *** df.mm.trans1:probe3 0.0105485541309526 0.0590196569021739 0.178729506144656 0.858188409058146 df.mm.trans1:probe4 0.029408199493377 0.0590196569021739 0.498278048991738 0.618404021319844 df.mm.trans1:probe5 0.257895123200641 0.0590196569021739 4.36964795691893 1.38222548153308e-05 *** df.mm.trans1:probe6 -0.1656822517216 0.0590196569021739 -2.80723847643203 0.00509963705276466 ** df.mm.trans1:probe7 1.39730398498561 0.0590196569021739 23.6752305643129 1.15612231699066e-97 *** df.mm.trans1:probe8 0.706920551753663 0.0590196569021739 11.9777136780954 7.0428404624664e-31 *** df.mm.trans1:probe9 -0.115233540125084 0.0590196569021739 -1.95246035259212 0.0511785220323614 . df.mm.trans1:probe10 -0.163670134287588 0.0590196569021739 -2.77314614957648 0.00566099221877261 ** df.mm.trans1:probe11 -0.129482216081761 0.0590196569021739 -2.19388290068137 0.0284867090058898 * df.mm.trans1:probe12 -0.0205598887913875 0.0590196569021739 -0.348356630155711 0.727649958124385 df.mm.trans2:probe2 -0.288601409095806 0.0590196569021739 -4.88992014260889 1.18504441403301e-06 *** df.mm.trans2:probe3 -0.305122200304915 0.0590196569021739 -5.16984029254287 2.85948139280149e-07 *** df.mm.trans2:probe4 -0.366664211814353 0.0590196569021739 -6.21257782677566 7.80280352848457e-10 *** df.mm.trans2:probe5 -0.4762535789652 0.0590196569021739 -8.06940609218719 2.13445821026887e-15 *** df.mm.trans2:probe6 -0.493335632535854 0.0590196569021739 -8.35883599515948 2.24777736726393e-16 *** df.mm.trans3:probe2 0.548152226036435 0.0590196569021739 9.28762135884671 1.05459984112976e-19 *** df.mm.trans3:probe3 0.00156198101984895 0.0590196569021739 0.0264654371413571 0.97889168865678 df.mm.trans3:probe4 0.104786918529742 0.0590196569021739 1.77545794113016 0.0761440889310832 . df.mm.trans3:probe5 0.114649208531369 0.0590196569021739 1.94255972584528 0.0523661387869848 . df.mm.trans3:probe6 0.127666092681528 0.0590196569021739 2.16311140020921 0.0307827557774632 * df.mm.trans3:probe7 -0.353626435727064 0.0590196569021739 -5.99167216971806 2.95023545878472e-09 *** df.mm.trans3:probe8 -0.0319192542653603 0.0590196569021739 -0.540824124380578 0.58875626439543 df.mm.trans3:probe9 0.189004267256583 0.0590196569021739 3.20239522181331 0.00140845070435351 ** df.mm.trans3:probe10 -0.390984077592883 0.0590196569021739 -6.62464165525303 5.83596621619555e-11 *** df.mm.trans3:probe11 -0.0668208028921535 0.0590196569021739 -1.13217877567316 0.257846477236837 df.mm.trans3:probe12 -0.279944504706812 0.0590196569021739 -4.7432418180747 2.42753031298689e-06 *** df.mm.trans3:probe13 -0.0296105576805121 0.0590196569021739 -0.501706706455311 0.615990708078437 df.mm.trans3:probe14 2.63466553638751 0.0590196569021739 44.6404753039231 5.63836423071895e-235 *** df.mm.trans3:probe15 -0.304322779474648 0.0590196569021739 -5.15629529970105 3.06804186521916e-07 *** df.mm.trans3:probe16 -0.478013077552073 0.0590196569021739 -8.09921816970891 1.69799570052101e-15 *** df.mm.trans3:probe17 -0.140524127160458 0.0590196569021739 -2.38097160397559 0.0174643224814578 * df.mm.trans3:probe18 -0.39754163727383 0.0590196569021739 -6.73574971695891 2.82943427732670e-11 *** df.mm.trans3:probe19 -0.024379664128841 0.0590196569021739 -0.413077022274980 0.6796438926318 df.mm.trans3:probe20 -0.0928125144453346 0.0590196569021739 -1.57256953559003 0.116153252281636 df.mm.trans3:probe21 -0.280280278575921 0.0590196569021739 -4.74893100514783 2.36178830611320e-06 *** df.mm.trans3:probe22 -0.0235526353168544 0.0590196569021739 -0.399064253387534 0.689936022505549 df.mm.trans3:probe23 2.66118572689512 0.0590196569021739 45.0898203509736 8.96533266429224e-238 *** df.mm.trans3:probe24 -0.397192883009916 0.0590196569021739 -6.7298405964689 2.94127489643395e-11 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.48771407674736 0.322083358079923 13.9333932169006 2.85157595073486e-40 *** df.mm.trans1 -0.137449337899113 0.266756276430445 -0.515261870267381 0.606490547011468 df.mm.trans2 -0.129412168176492 0.243220482552316 -0.532077589923603 0.594797323169505 df.mm.exp2 -0.128703705085637 0.309873624331652 -0.415342562192025 0.677985433121902 df.mm.exp3 0.040893936925936 0.309873624331652 0.131969724800353 0.8950363302748 df.mm.exp4 0.0403802767427725 0.309873624331652 0.130312080706663 0.896347255764998 df.mm.exp5 0.0292318887763472 0.309873624331652 0.0943348722867127 0.924863146944725 df.mm.exp6 -0.0319197149763659 0.309873624331652 -0.103008815433103 0.917977829155858 df.mm.exp7 -0.0213141929141620 0.309873624331652 -0.068783501532708 0.945176502001763 df.mm.exp8 0.0508972999306539 0.309873624331652 0.164251797939987 0.86956804982042 df.mm.trans1:exp2 0.120999586071657 0.261891012032042 0.462022675512259 0.64417140438403 df.mm.trans2:exp2 0.0335340702771396 0.202859905624301 0.165306545785569 0.868738062186436 df.mm.trans1:exp3 0.0771639635512872 0.261891012032042 0.294641511186517 0.768332459712622 df.mm.trans2:exp3 -0.0715887806746181 0.202859905624301 -0.352897633735478 0.724243882974966 df.mm.trans1:exp4 -0.173757690673093 0.261891012032042 -0.663473287322417 0.507189298506472 df.mm.trans2:exp4 0.00066786768266843 0.202859905624301 0.00329226063974085 0.99737385565563 df.mm.trans1:exp5 0.0331790342520902 0.261891012032042 0.126690236502010 0.89921252398563 df.mm.trans2:exp5 -0.109155953152579 0.202859905624301 -0.53808539847562 0.5906447956611 df.mm.trans1:exp6 -0.207756616521266 0.261891012032042 -0.793294183367573 0.427805575311416 df.mm.trans2:exp6 0.222865411766325 0.202859905624301 1.09861734915265 0.272214827857244 df.mm.trans1:exp7 -0.0964516949961943 0.261891012032042 -0.368289443184074 0.712739905431735 df.mm.trans2:exp7 0.0791361519531894 0.202859905624301 0.390102478405716 0.696548679921835 df.mm.trans1:exp8 -0.273834274097732 0.261891012032042 -1.0456039402537 0.296011304968938 df.mm.trans2:exp8 0.235631579779488 0.202859905624301 1.16154830622805 0.245712287244519 df.mm.trans1:probe2 0.0337182219605655 0.202859905624301 0.166214323411015 0.8680238422796 df.mm.trans1:probe3 0.202114094018226 0.202859905624301 0.99632351398479 0.319348025676019 df.mm.trans1:probe4 0.0911194618028707 0.202859905624301 0.449174328078537 0.653408896480723 df.mm.trans1:probe5 0.060624137493143 0.202859905624301 0.298847311924811 0.765122254178284 df.mm.trans1:probe6 0.305272759319954 0.202859905624301 1.50484522005705 0.132698017120830 df.mm.trans1:probe7 -0.092266048528955 0.202859905624301 -0.454826439187214 0.649338571100024 df.mm.trans1:probe8 -0.0329193021963808 0.202859905624301 -0.162276040181877 0.871123172552151 df.mm.trans1:probe9 0.190411406276756 0.202859905624301 0.938634993892783 0.348158017461604 df.mm.trans1:probe10 0.172786417510008 0.202859905624301 0.851752429728576 0.394567305350695 df.mm.trans1:probe11 -0.00949717708135726 0.202859905624301 -0.0468164325135109 0.962669423680318 df.mm.trans1:probe12 0.0816849487670934 0.202859905624301 0.402666798624934 0.687284432455051 df.mm.trans2:probe2 -0.140033997318739 0.202859905624301 -0.690299036114528 0.49017565130684 df.mm.trans2:probe3 0.0788315563678895 0.202859905624301 0.388600971321985 0.697658881762622 df.mm.trans2:probe4 -0.186842071457452 0.202859905624301 -0.921039921035389 0.357264648437570 df.mm.trans2:probe5 -0.0980315354621123 0.202859905624301 -0.48324746657266 0.629031976278974 df.mm.trans2:probe6 0.0187717976272664 0.202859905624301 0.0925357702868699 0.926291985530322 df.mm.trans3:probe2 0.249194130275997 0.202859905624301 1.22840503898048 0.219600796156122 df.mm.trans3:probe3 0.151059994665044 0.202859905624301 0.744651803914417 0.456667379142397 df.mm.trans3:probe4 0.170113869504745 0.202859905624301 0.8385780767334 0.401918177695868 df.mm.trans3:probe5 0.243722501225649 0.202859905624301 1.20143258706344 0.229884455690868 df.mm.trans3:probe6 0.189300735491975 0.202859905624301 0.933159930787717 0.350975757980400 df.mm.trans3:probe7 0.104660120980401 0.202859905624301 0.515923147347969 0.606028772242113 df.mm.trans3:probe8 0.0840746034305663 0.202859905624301 0.414446625969912 0.67864110554245 df.mm.trans3:probe9 0.335105142159832 0.202859905624301 1.65190425938801 0.0988862789298364 . df.mm.trans3:probe10 0.283193382472071 0.202859905624301 1.39600470384004 0.163040940317252 df.mm.trans3:probe11 0.132426323472468 0.202859905624301 0.652796929313981 0.514045974381467 df.mm.trans3:probe12 0.0218771135620315 0.202859905624301 0.107843457260343 0.914142761804568 df.mm.trans3:probe13 0.43354959385651 0.202859905624301 2.13718720080374 0.0328389290886686 * df.mm.trans3:probe14 0.0673916152419122 0.202859905624301 0.332207663384811 0.739806128246091 df.mm.trans3:probe15 0.110658471142471 0.202859905624301 0.545492076425452 0.585543857437644 df.mm.trans3:probe16 0.194748622435228 0.202859905624301 0.960015345742619 0.337293050219812 df.mm.trans3:probe17 0.180976290622464 0.202859905624301 0.892124493824989 0.372553310328169 df.mm.trans3:probe18 0.52155093824864 0.202859905624301 2.57099073690076 0.0102928258933995 * df.mm.trans3:probe19 0.202551971851249 0.202859905624301 0.99848203728527 0.318301267612849 df.mm.trans3:probe20 0.166965033658698 0.202859905624301 0.823055857907768 0.410683874028187 df.mm.trans3:probe21 0.0977151010274423 0.202859905624301 0.481687599758683 0.630139413113444 df.mm.trans3:probe22 0.526145161104609 0.202859905624301 2.59363800592039 0.00964353710713623 ** df.mm.trans3:probe23 0.194332867400129 0.202859905624301 0.957965877002998 0.338324979815956 df.mm.trans3:probe24 0.352872336541232 0.202859905624301 1.7394878275984 0.0822745879658404 .