chr6.20432_chr6_34774936_34863742_+_2.R fitVsDatCorrelation=0.873946142760195 cont.fitVsDatCorrelation=0.265878964635860 fstatistic=10183.346439233,68,1060 cont.fstatistic=2576.84871679666,68,1060 residuals=-0.57291920490574,-0.0885603621547032,-0.0068911744833604,0.083592024265814,0.933382690934626 cont.residuals=-0.545203971781606,-0.211971622629233,-0.0780619988551603,0.140274189657361,1.71953700513754 predictedValues: Include Exclude Both chr6.20432_chr6_34774936_34863742_+_2.R.tl.Lung 59.1137723051934 46.2241017970231 77.8372761712176 chr6.20432_chr6_34774936_34863742_+_2.R.tl.cerebhem 52.1046672399977 47.2152252443283 62.2626823976666 chr6.20432_chr6_34774936_34863742_+_2.R.tl.cortex 53.0096000283568 45.9209174910071 62.0456721027442 chr6.20432_chr6_34774936_34863742_+_2.R.tl.heart 59.1569731480114 49.8637196815329 74.9976239995387 chr6.20432_chr6_34774936_34863742_+_2.R.tl.kidney 54.8762629183032 47.5091721977852 67.2454433627792 chr6.20432_chr6_34774936_34863742_+_2.R.tl.liver 54.2545828423789 49.4246540260657 59.5142956403189 chr6.20432_chr6_34774936_34863742_+_2.R.tl.stomach 67.6480451879175 48.896605934454 102.124036139392 chr6.20432_chr6_34774936_34863742_+_2.R.tl.testicle 54.2306008277938 48.1389686798549 63.9028027579958 diffExp=12.8896705081704,4.88944199566937,7.08868253734963,9.29325346647853,7.36709072051799,4.82992881631316,18.7514392534635,6.0916321479389 diffExpScore=0.986149803068561 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=0,0,0,0,0,0,1,0 diffExp1.3Score=0.5 diffExp1.2=1,0,0,0,0,0,1,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 68.6283259979385 52.7098427755879 64.7439031280253 cerebhem 60.7245978017588 54.5358996490568 55.8744760557862 cortex 59.9904376887298 60.0273747431931 59.2018231245565 heart 59.81268450143 55.0437790840308 60.6476820723543 kidney 59.523203569813 57.2349223712332 62.3530138962811 liver 62.7008042292336 55.5064603633581 59.1304581978095 stomach 61.918342546429 52.2260642079869 57.4562976383487 testicle 62.9163895477123 58.700554924944 60.763463445368 cont.diffExp=15.9184832223506,6.18869815270206,-0.0369370544633369,4.76890541739918,2.28828119857974,7.19434386587546,9.69227833844215,4.21583462276832 cont.diffExpScore=0.98192215654764 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=1,0,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=1,0,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.360064840546571 cont.tran.correlation=-0.463462811921737 tran.covariance=0.000938791044677998 cont.tran.covariance=-0.00107122407969942 tran.mean=52.3492418468753 cont.tran.mean=58.8874802501523 weightedLogRatios: wLogRatio Lung 0.973146698150402 cerebhem 0.384691379859421 cortex 0.559666895457045 heart 0.682704937791531 kidney 0.566974049856956 liver 0.368017144611294 stomach 1.31532592638919 testicle 0.468709726664091 cont.weightedLogRatios: wLogRatio Lung 1.08114625723134 cerebhem 0.435613018882214 cortex -0.00252026779131860 heart 0.336482907873968 kidney 0.159425206182101 liver 0.496937033528643 stomach 0.687867504231898 testicle 0.284860185675948 varWeightedLogRatios=0.106313907180049 cont.varWeightedLogRatios=0.112128080131709 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.28756459662421 0.0744928728597221 44.1326058509655 2.70481126507167e-242 *** df.mm.trans1 0.837860426688284 0.0632816619324614 13.2401773452554 3.82847445466972e-37 *** df.mm.trans2 0.534175327553275 0.0554091090433964 9.64056879411125 3.85372865657887e-21 *** df.mm.exp2 0.118263855946951 0.069556975504671 1.70024436929419 0.08937831316131 . df.mm.exp3 0.111178168539180 0.069556975504671 1.59837554368237 0.110257443766615 df.mm.exp4 0.113686884944088 0.069556975504671 1.63444261512569 0.102462870547553 df.mm.exp5 0.0993095848231063 0.069556975504671 1.42774443688164 0.153659966451628 df.mm.exp6 0.249575455142978 0.069556975504671 3.58807227215074 0.000348350678068747 *** df.mm.exp7 -0.080506503640946 0.069556975504671 -1.15741811740419 0.247362314092035 df.mm.exp8 0.151629294202573 0.069556975504671 2.17992937591703 0.0294821514693157 * df.mm.trans1:exp2 -0.244473260050969 0.0626326020882403 -3.90329080861978 0.00010088350513481 *** df.mm.trans2:exp2 -0.0970487926099744 0.0422737844451168 -2.29572047745030 0.0218863735760953 * df.mm.trans1:exp3 -0.22016906997177 0.0626326020882403 -3.51524705394777 0.000457911245055802 *** df.mm.trans2:exp3 -0.117758782498805 0.0422737844451167 -2.78562196511384 0.00543775224834297 ** df.mm.trans1:exp4 -0.112956343429806 0.0626326020882403 -1.80347518167403 0.0715974731365588 . df.mm.trans2:exp4 -0.0378945530897913 0.0422737844451168 -0.896407870437744 0.370238493654429 df.mm.trans1:exp5 -0.173692630428736 0.0626326020882403 -2.77319837652647 0.00564818865784743 ** df.mm.trans2:exp5 -0.0718881394991556 0.0422737844451168 -1.70053711638915 0.0893232641647036 . df.mm.trans1:exp6 -0.335351921125809 0.0626326020882403 -5.35427093789504 1.05296268058690e-07 *** df.mm.trans2:exp6 -0.182627432065429 0.0422737844451168 -4.32011078408489 1.70596929606778e-05 *** df.mm.trans1:exp7 0.215361030681817 0.0626326020882403 3.43848129410949 0.000607725733187363 *** df.mm.trans2:exp7 0.136713143443337 0.0422737844451167 3.23399348409011 0.00125841400467097 ** df.mm.trans1:exp8 -0.237847885352444 0.0626326020882403 -3.79750924314705 0.000154454448928088 *** df.mm.trans2:exp8 -0.111038631404905 0.0422737844451167 -2.62665462442959 0.0087472527587768 ** df.mm.trans1:probe2 -0.0507359086957073 0.0478364400069017 -1.06061213351970 0.289107804590972 df.mm.trans1:probe3 -0.141819134510559 0.0478364400069017 -2.96466740606320 0.0030979074534499 ** df.mm.trans1:probe4 -0.130506263325567 0.0478364400069017 -2.72817674782526 0.00647382975683487 ** df.mm.trans1:probe5 -0.107445793104498 0.0478364400069017 -2.24610763445181 0.0249024181516420 * df.mm.trans1:probe6 -0.161572562591774 0.0478364400069017 -3.3776042399573 0.000757773197947069 *** df.mm.trans1:probe7 0.408271458576297 0.0478364400069017 8.53473750382329 4.80538699556633e-17 *** df.mm.trans1:probe8 -0.0834845975173485 0.0478364400069017 -1.74520924854156 0.0812382199565379 . df.mm.trans1:probe9 0.126954852476308 0.0478364400069017 2.65393604662035 0.00807497493311842 ** df.mm.trans1:probe10 -0.0715441593085752 0.0478364400069017 -1.49559957426290 0.135055684544996 df.mm.trans1:probe11 -0.243348516225379 0.0478364400069017 -5.08709503027962 4.29804246346976e-07 *** df.mm.trans1:probe12 0.180674988256248 0.0478364400069017 3.77693215110029 0.000167598667893727 *** df.mm.trans1:probe13 -0.00728437210162809 0.0478364400069017 -0.152276634728193 0.87899773126059 df.mm.trans1:probe14 -0.150342547387654 0.0478364400069017 -3.14284565000997 0.00171958440716177 ** df.mm.trans1:probe15 -0.248696718842917 0.0478364400069017 -5.19889688294186 2.40451773035822e-07 *** df.mm.trans1:probe16 -0.209479188872431 0.0478364400069017 -4.37907145352389 1.30997948865859e-05 *** df.mm.trans1:probe17 -0.216979972038592 0.0478364400069017 -4.53587206755534 6.39086252028339e-06 *** df.mm.trans1:probe18 -0.272335729813694 0.0478364400069017 -5.69306013939169 1.61459869838063e-08 *** df.mm.trans1:probe19 -0.263807525244306 0.0478364400069017 -5.51478172719887 4.3864962385612e-08 *** df.mm.trans1:probe20 -0.240923086640137 0.0478364400069017 -5.03639247831522 5.57262036362213e-07 *** df.mm.trans2:probe2 0.013063937094597 0.0478364400069017 0.273095930481285 0.784832670925365 df.mm.trans2:probe3 0.0838033027595732 0.0478364400069017 1.75187164319674 0.0800850449160084 . df.mm.trans2:probe4 -0.0207600839546297 0.0478364400069017 -0.433980537674511 0.664390887690625 df.mm.trans2:probe5 -0.0382038815974771 0.0478364400069017 -0.798635550470838 0.424680664100896 df.mm.trans2:probe6 0.279655114263200 0.0478364400069017 5.84606869204422 6.69554396359651e-09 *** df.mm.trans3:probe2 0.61622506272147 0.0478364400069017 12.8819172712803 2.20585872398546e-35 *** df.mm.trans3:probe3 -0.628707380611406 0.0478364400069017 -13.1428547049216 1.16058790111942e-36 *** df.mm.trans3:probe4 -0.349370995573502 0.0478364400069017 -7.3034489088882 5.50154553115181e-13 *** df.mm.trans3:probe5 -0.175131940221808 0.0478364400069017 -3.66105713963123 0.000263567809109604 *** df.mm.trans3:probe6 -0.456883843599606 0.0478364400069017 -9.55095829735006 8.57281289410705e-21 *** df.mm.trans3:probe7 -0.244144191298897 0.0478364400069017 -5.1037282720803 3.94504129302735e-07 *** df.mm.trans3:probe8 -0.344203648041305 0.0478364400069017 -7.19542775322838 1.17612093158170e-12 *** df.mm.trans3:probe9 -0.268273797261615 0.0478364400069017 -5.60814720374069 2.60813022364214e-08 *** df.mm.trans3:probe10 -0.00681592135384861 0.0478364400069017 -0.142483875323189 0.886724892519539 df.mm.trans3:probe11 -0.617027060190441 0.0478364400069017 -12.8986826800117 1.82792652582462e-35 *** df.mm.trans3:probe12 -0.607209088374529 0.0478364400069017 -12.6934422437565 1.80246140554746e-34 *** df.mm.trans3:probe13 -0.627760683714273 0.0478364400069017 -13.1230644174964 1.45315817066935e-36 *** df.mm.trans3:probe14 -0.529954147319429 0.0478364400069017 -11.0784612576306 4.57057624742132e-27 *** df.mm.trans3:probe15 -0.274969866412429 0.0478364400069017 -5.7481256208187 1.17902674266064e-08 *** df.mm.trans3:probe16 -0.418078776053624 0.0478364400069017 -8.7397552157582 8.99258443816341e-18 *** df.mm.trans3:probe17 -0.538424680780645 0.0478364400069017 -11.2555340803572 7.68336222756044e-28 *** df.mm.trans3:probe18 -0.271925624693556 0.0478364400069017 -5.6844870699894 1.69518690715956e-08 *** df.mm.trans3:probe19 -0.204429123798756 0.0478364400069017 -4.27350203671639 2.09738972682914e-05 *** df.mm.trans3:probe20 -0.190829780561892 0.0478364400069017 -3.98921367339124 7.08463001791729e-05 *** df.mm.trans3:probe21 -0.266662544360125 0.0478364400069017 -5.5744646617025 3.14899885806858e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11120178521047 0.147753584909487 27.8247176725287 2.28777226563889e-128 *** df.mm.trans1 0.136683535149754 0.125516603811999 1.08896776202199 0.276415615253745 df.mm.trans2 -0.159538350333015 0.109901715204611 -1.4516456821078 0.14689604268206 df.mm.exp2 0.0590323327444847 0.137963433168026 0.427883906546376 0.668822560561786 df.mm.exp3 0.084965507111974 0.137963433168026 0.615855268029569 0.538122259848626 df.mm.exp4 -0.0288028477475717 0.137963433168026 -0.208771607709216 0.834666647028574 df.mm.exp5 -0.0223495907726504 0.137963433168026 -0.161996481672290 0.871339497516293 df.mm.exp6 0.0520594952979575 0.137963433168026 0.377342706705147 0.70599447328798 df.mm.exp7 0.00730549110119769 0.137963433168026 0.0529523724761207 0.95777983304827 df.mm.exp8 0.084199160101783 0.137963433168026 0.610300556954369 0.541793546517785 df.mm.trans1:exp2 -0.18138884607856 0.124229219997644 -1.46011418313663 0.144555001348635 df.mm.trans2:exp2 -0.0249753468151730 0.0838483328629166 -0.297863367850201 0.765865817505327 df.mm.trans1:exp3 -0.219485694372992 0.124229219997644 -1.76677994418024 0.077552831043343 . df.mm.trans2:exp3 0.0450329887383428 0.0838483328629166 0.537076733677784 0.591327324923974 df.mm.trans1:exp4 -0.108684763308178 0.124229219997644 -0.874872782025353 0.38184131253894 df.mm.trans2:exp4 0.072129491656084 0.0838483328629166 0.86023763613772 0.389852581061266 df.mm.trans1:exp5 -0.119989561660957 0.124229219997644 -0.965872293678029 0.334328394010053 df.mm.trans2:exp5 0.104711625782573 0.0838483328629166 1.24882179773051 0.212005984576218 df.mm.trans1:exp6 -0.142390586046369 0.124229219997644 -1.14619238572913 0.251974236745088 df.mm.trans2:exp6 -0.000362286419714074 0.0838483328629166 -0.00432073491915904 0.996553376037057 df.mm.trans1:exp7 -0.110194394766587 0.124229219997644 -0.887024765740921 0.37526677311231 df.mm.trans2:exp7 -0.0165260145425120 0.0838483328629166 -0.197094133875391 0.843791658155796 df.mm.trans1:exp8 -0.171097830117702 0.124229219997644 -1.37727525070951 0.168717915094907 df.mm.trans2:exp8 0.0234478122209039 0.0838483328629166 0.279645538799665 0.779804021960569 df.mm.trans1:probe2 -0.0853863415070882 0.0948816340274207 -0.899924863039476 0.368364633478578 df.mm.trans1:probe3 -0.0706653597998526 0.0948816340274207 -0.744773849272352 0.456573532772635 df.mm.trans1:probe4 0.0402402878145306 0.0948816340274206 0.42411040057448 0.671571354460964 df.mm.trans1:probe5 -0.0833026614454462 0.0948816340274207 -0.877964026434998 0.380162194421042 df.mm.trans1:probe6 -0.196512575194185 0.0948816340274206 -2.07113396821763 0.0385878241593839 * df.mm.trans1:probe7 -0.0555074260507324 0.0948816340274207 -0.585017602402282 0.558660386502734 df.mm.trans1:probe8 0.076330030635674 0.0948816340274206 0.804476350118662 0.421302310864781 df.mm.trans1:probe9 0.0177083717487611 0.0948816340274207 0.186636454254607 0.851981395412087 df.mm.trans1:probe10 -0.0601802315970827 0.0948816340274206 -0.634266391108849 0.526043839572393 df.mm.trans1:probe11 -0.0277771195634160 0.0948816340274207 -0.292755493179939 0.769766345928436 df.mm.trans1:probe12 0.0265252726583681 0.0948816340274207 0.279561718453355 0.77986831967237 df.mm.trans1:probe13 -0.0415929749574756 0.0948816340274207 -0.438366975693687 0.661209604315067 df.mm.trans1:probe14 -0.0272328315927446 0.0948816340274207 -0.287018998691299 0.774153867781326 df.mm.trans1:probe15 -0.0447549885614731 0.0948816340274206 -0.471692852049102 0.637243114252716 df.mm.trans1:probe16 -0.0830972749954294 0.0948816340274206 -0.875799366729018 0.381337527967655 df.mm.trans1:probe17 -0.0670540148454386 0.0948816340274206 -0.706712268741705 0.479900619560323 df.mm.trans1:probe18 -0.0866960417517274 0.0948816340274206 -0.913728380001048 0.361067371432039 df.mm.trans1:probe19 0.0173650937452134 0.0948816340274206 0.183018493760288 0.854818506474648 df.mm.trans1:probe20 -0.0347873874791377 0.0948816340274206 -0.366639843798266 0.713960855344507 df.mm.trans2:probe2 -0.0500563741650355 0.0948816340274206 -0.527566527264584 0.597910635919839 df.mm.trans2:probe3 0.224996535861021 0.0948816340274207 2.37133917609384 0.0179017039543070 * df.mm.trans2:probe4 -0.00706255027180767 0.0948816340274207 -0.0744353777651701 0.94067800373809 df.mm.trans2:probe5 -0.0315169938060938 0.0948816340274206 -0.332171701395715 0.73982528219383 df.mm.trans2:probe6 0.21838625785811 0.0948816340274206 2.30167049815981 0.0215469646742286 * df.mm.trans3:probe2 -0.000867460168557175 0.0948816340274206 -0.00914255089985572 0.992707121726595 df.mm.trans3:probe3 0.0808905790652925 0.0948816340274206 0.85254201083758 0.394105916050657 df.mm.trans3:probe4 0.0491858990977107 0.0948816340274206 0.518392200997467 0.60429284409946 df.mm.trans3:probe5 -0.00655120383632481 0.0948816340274206 -0.06904606885703 0.94496597175644 df.mm.trans3:probe6 0.0308220890721812 0.0948816340274207 0.324847789439141 0.74536037275082 df.mm.trans3:probe7 0.0625883881501551 0.0948816340274206 0.659647030657874 0.509623655835598 df.mm.trans3:probe8 0.203653368573537 0.0948816340274206 2.14639398510655 0.0320680108400152 * df.mm.trans3:probe9 0.092344573533212 0.0948816340274207 0.97326078413157 0.330645747896308 df.mm.trans3:probe10 0.143474841579947 0.0948816340274207 1.51214555957671 0.130794899978174 df.mm.trans3:probe11 -0.0534700634727928 0.0948816340274206 -0.563544926485352 0.573183069773486 df.mm.trans3:probe12 0.138501136439302 0.0948816340274206 1.45972545539503 0.144661830910214 df.mm.trans3:probe13 0.0529433059017715 0.0948816340274206 0.557993192723378 0.576966860051689 df.mm.trans3:probe14 0.225932997862607 0.0948816340274206 2.38120896818991 0.0174317083519744 * df.mm.trans3:probe15 0.166996999111920 0.0948816340274206 1.76005610383626 0.0786866892818886 . df.mm.trans3:probe16 0.176159828529492 0.0948816340274206 1.85662726338147 0.0636414871713297 . df.mm.trans3:probe17 0.0366025489280872 0.0948816340274207 0.385770642583043 0.69974394654451 df.mm.trans3:probe18 0.123655652829302 0.0948816340274207 1.3032622603608 0.192768247506694 df.mm.trans3:probe19 0.0593649295477787 0.0948816340274207 0.62567355796826 0.531663764653942 df.mm.trans3:probe20 0.138045706396462 0.0948816340274206 1.45492547437122 0.145985951694311 df.mm.trans3:probe21 0.00366976747619169 0.0948816340274206 0.0386773216314037 0.9691549374816