chr8.23201_chr8_21140860_21148952_+_2.R fitVsDatCorrelation=0.868338396613657 cont.fitVsDatCorrelation=0.235272479817796 fstatistic=10596.5101939960,59,853 cont.fstatistic=2748.66520109365,59,853 residuals=-0.60533637622144,-0.0842867665867006,-0.000176995548731769,0.0864814844697667,1.29725836654655 cont.residuals=-0.694026921505972,-0.211675788044873,-0.0487342831054552,0.177068178328824,1.38689214662457 predictedValues: Include Exclude Both chr8.23201_chr8_21140860_21148952_+_2.R.tl.Lung 56.7697264443065 64.7814487070695 76.4978193486041 chr8.23201_chr8_21140860_21148952_+_2.R.tl.cerebhem 57.3301957516236 96.1315517189985 80.5886066606871 chr8.23201_chr8_21140860_21148952_+_2.R.tl.cortex 65.400308398038 68.5497456663052 78.3710542367956 chr8.23201_chr8_21140860_21148952_+_2.R.tl.heart 71.9844756394968 73.3237156043384 83.944249522476 chr8.23201_chr8_21140860_21148952_+_2.R.tl.kidney 61.1680274395533 65.3113537062837 81.3451732430208 chr8.23201_chr8_21140860_21148952_+_2.R.tl.liver 60.9944141632668 67.776078551404 80.566721715545 chr8.23201_chr8_21140860_21148952_+_2.R.tl.stomach 62.2917576632145 74.0968119585424 79.4893536703374 chr8.23201_chr8_21140860_21148952_+_2.R.tl.testicle 57.6507906089457 69.6545765716191 78.367005385983 diffExp=-8.01172226276299,-38.8013559673749,-3.14943726826722,-1.33923996484170,-4.14332626673042,-6.78166438813724,-11.8050542953279,-12.0037859626734 diffExpScore=0.988510446799559 diffExp1.5=0,-1,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,-1,0,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,0,0,0,0,-1 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 71.4114324574698 66.043000972321 69.4760297994543 cerebhem 69.676888169202 56.532826233038 63.7759682953826 cortex 67.2797633026833 73.21629940901 65.7534815664192 heart 68.8239212088328 63.9932378397647 67.0633905485594 kidney 67.2279206158766 63.7765004170487 61.2998318955324 liver 66.9335433124619 75.5220774141154 69.3309718099108 stomach 69.8235982454732 68.2167115067499 73.0210198966347 testicle 66.0361283081864 64.6643672882743 67.3133895537624 cont.diffExp=5.36843148514885,13.1440619361641,-5.93653610632666,4.83068336906809,3.45142019882793,-8.58853410165358,1.60688673872329,1.37176101991207 cont.diffExpScore=2.72635641912517 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=0,1,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.143652836392509 cont.tran.correlation=-0.342841224190397 tran.covariance=-0.00120747024948702 cont.tran.covariance=-0.000808549900821753 tran.mean=67.0759361620629 cont.tran.mean=67.4486385437817 weightedLogRatios: wLogRatio Lung -0.541927485729315 cerebhem -2.22638662160448 cortex -0.197727600566585 heart -0.0790003561815818 kidney -0.271759992698064 liver -0.438944955185345 stomach -0.732110292672583 testicle -0.78475580016339 cont.weightedLogRatios: wLogRatio Lung 0.330534304868227 cerebhem 0.865318529357393 cortex -0.359470208326057 heart 0.305298715477293 kidney 0.220393650327101 liver -0.514778080012857 stomach 0.098585657845539 testicle 0.0877390477680835 varWeightedLogRatios=0.462731034810960 cont.varWeightedLogRatios=0.182556517074627 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15447378428409 0.0748355396937024 55.5147166879282 1.99782969081083e-285 *** df.mm.trans1 -0.129068898537059 0.0646261415405718 -1.99716237826191 0.0461248241162743 * df.mm.trans2 0.0529287601454287 0.0570969039461456 0.92699877729538 0.354189376507663 df.mm.exp2 0.352427521948655 0.0734448746472871 4.79853119283215 1.88648045645028e-06 *** df.mm.exp3 0.173871776496253 0.0734448746472871 2.36737794612977 0.018136921615122 * df.mm.exp4 0.268421455232836 0.0734448746472871 3.65473365598222 0.000273188109121661 *** df.mm.exp5 0.0213287776418517 0.0734448746472871 0.290405256245332 0.771576808888331 df.mm.exp6 0.0651456752266098 0.0734448746472871 0.887000972354661 0.375328389865996 df.mm.exp7 0.188818287257231 0.0734448746472871 2.5708844648999 0.0103124160625027 * df.mm.exp8 0.0637891373515794 0.0734448746472871 0.868530822033824 0.385348100668894 df.mm.trans1:exp2 -0.342603259067762 0.0678866249214402 -5.04669748222465 5.49531688697373e-07 *** df.mm.trans2:exp2 0.0422707848935474 0.0501375952748088 0.843095578514632 0.399411336898856 df.mm.trans1:exp3 -0.0323480009824791 0.0678866249214402 -0.476500356585895 0.63383998611488 df.mm.trans2:exp3 -0.117331357673312 0.0501375952748088 -2.34018717950488 0.0195037674866537 * df.mm.trans1:exp4 -0.0309741740879676 0.0678866249214402 -0.456263279015736 0.648316810300065 df.mm.trans2:exp4 -0.144556633985553 0.0501375952748088 -2.88319839021447 0.00403556104908242 ** df.mm.trans1:exp5 0.0532926494007875 0.0678866249214402 0.785024288104746 0.432657300379096 df.mm.trans2:exp5 -0.0131821635313307 0.0501375952748088 -0.262919740348096 0.79267593088902 df.mm.trans1:exp6 0.00663341515523597 0.0678866249214402 0.0977131380873964 0.922183037596057 df.mm.trans2:exp6 -0.0199556432488134 0.0501375952748088 -0.398017558270091 0.690716835449973 df.mm.trans1:exp7 -0.0959923694718998 0.0678866249214402 -1.41401004370131 0.157723818053994 df.mm.trans2:exp7 -0.0544650563681943 0.0501375952748088 -1.08631170022547 0.277647995712244 df.mm.trans1:exp8 -0.0483883752898511 0.0678866249214402 -0.712782162109947 0.476175498008294 df.mm.trans2:exp8 0.00873999186477024 0.0501375952748088 0.174320124785912 0.861655247413451 df.mm.trans1:probe2 0.361122909212175 0.0464787947779565 7.76962722328259 2.25876021782969e-14 *** df.mm.trans1:probe3 -0.110203167365047 0.0464787947779565 -2.37104184588953 0.0179593208266663 * df.mm.trans1:probe4 0.382312745407982 0.0464787947779565 8.22553052923183 7.22090020093067e-16 *** df.mm.trans1:probe5 -0.00203277522720891 0.0464787947779564 -0.0437355408400779 0.965125444486956 df.mm.trans1:probe6 0.56057165932822 0.0464787947779565 12.0608045455190 4.85703871144901e-31 *** df.mm.trans1:probe7 -0.0804455062486319 0.0464787947779564 -1.73080017743457 0.0838490407397304 . df.mm.trans1:probe8 -0.235194788381871 0.0464787947779564 -5.06026004988876 5.12905869771182e-07 *** df.mm.trans1:probe9 -0.0638738988055245 0.0464787947779565 -1.37425893056543 0.169722269891010 df.mm.trans1:probe10 -0.318788919749017 0.0464787947779564 -6.85880348817069 1.33320800773589e-11 *** df.mm.trans1:probe11 0.236353706004864 0.0464787947779564 5.08519438023294 4.51629830535852e-07 *** df.mm.trans1:probe12 0.206386557727768 0.0464787947779564 4.44044555616686 1.01567211083748e-05 *** df.mm.trans1:probe13 0.0989331827646882 0.0464787947779564 2.12856601031336 0.0335761892653049 * df.mm.trans1:probe14 0.221084333706144 0.0464787947779564 4.75667096710085 2.31032446726143e-06 *** df.mm.trans1:probe15 0.332629013202181 0.0464787947779564 7.1565756984718 1.78337030188402e-12 *** df.mm.trans1:probe16 -0.0489549150122681 0.0464787947779564 -1.05327419194368 0.292513604951016 df.mm.trans1:probe17 -0.172516228815691 0.0464787947779564 -3.71171906758457 0.000219212877572312 *** df.mm.trans1:probe18 -0.252263282751464 0.0464787947779564 -5.42749191231407 7.45199645090798e-08 *** df.mm.trans1:probe19 -0.182744503327929 0.0464787947779564 -3.93178231494504 9.11580888002428e-05 *** df.mm.trans1:probe20 -0.19118653223461 0.0464787947779565 -4.11341415258221 4.27605159003522e-05 *** df.mm.trans1:probe21 -0.189581042838088 0.0464787947779565 -4.07887174664867 4.94942076827131e-05 *** df.mm.trans1:probe22 -0.116462538354885 0.0464787947779564 -2.50571338846591 0.0124057245539627 * df.mm.trans2:probe2 -0.133513776043947 0.0464787947779564 -2.87257397016818 0.00417241151745983 ** df.mm.trans2:probe3 -0.0513915094713749 0.0464787947779564 -1.10569797940949 0.269169017999288 df.mm.trans2:probe4 -0.174800872534434 0.0464787947779564 -3.76087360632976 0.000180878935952172 *** df.mm.trans2:probe5 -0.0676909123685756 0.0464787947779564 -1.45638269434386 0.145654829049948 df.mm.trans2:probe6 -0.154735208677789 0.0464787947779564 -3.32915707941668 0.000908441617458491 *** df.mm.trans3:probe2 -0.102865211451259 0.0464787947779564 -2.21316434607820 0.0271503216944121 * df.mm.trans3:probe3 0.726619317352276 0.0464787947779564 15.6333510974964 1.24319911787396e-48 *** df.mm.trans3:probe4 0.456478881326535 0.0464787947779564 9.82122887452818 1.21664756228581e-21 *** df.mm.trans3:probe5 0.279974608035816 0.0464787947779564 6.02370628096836 2.53256751867996e-09 *** df.mm.trans3:probe6 0.706258525348524 0.0464787947779564 15.1952848330629 2.49858917067789e-46 *** df.mm.trans3:probe7 0.157150049450065 0.0464787947779564 3.38111283222424 0.000754744200598984 *** df.mm.trans3:probe8 0.169639257049725 0.0464787947779564 3.64982047964334 0.000278384128582729 *** df.mm.trans3:probe9 -0.193461199645497 0.0464787947779565 -4.16235404918997 3.46947809692048e-05 *** df.mm.trans3:probe10 0.389491665401221 0.0464787947779564 8.37998634134003 2.16789686913286e-16 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27340571018981 0.146651091029376 29.1399517057381 4.14441812725068e-130 *** df.mm.trans1 0.0322465632612283 0.126644295006551 0.254623102126789 0.799075585521589 df.mm.trans2 -0.0891923847341644 0.111889662216285 -0.797145893261828 0.425588131520387 df.mm.exp2 -0.0944685461518582 0.143925881227349 -0.656369412827378 0.511763485246787 df.mm.exp3 0.0985827054031689 0.143925881227349 0.684954676410457 0.493558597904876 df.mm.exp4 -0.0330917407914109 0.143925881227349 -0.229922099550242 0.818207427084236 df.mm.exp5 0.0299140998310176 0.143925881227349 0.207843784425155 0.835400556281089 df.mm.exp6 0.071451299226872 0.143925881227349 0.496445105060747 0.619708312414647 df.mm.exp7 -0.0398679721520595 0.143925881227349 -0.277003495216284 0.78184453337977 df.mm.exp8 -0.0677291035077152 0.143925881227349 -0.470583212207181 0.638058702024585 df.mm.trans1:exp2 0.0698792438383945 0.133033684954757 0.525274811880611 0.599528668359657 df.mm.trans2:exp2 -0.0610160488897756 0.0982518877893351 -0.621016555128202 0.534754634764992 df.mm.trans1:exp3 -0.158181182817863 0.133033684954757 -1.18903105534255 0.234758338365138 df.mm.trans2:exp3 0.00452930054228614 0.0982518877893351 0.0460988653164359 0.96324224030722 df.mm.trans1:exp4 -0.00381485769119997 0.133033684954757 -0.0286758777861212 0.977129802718408 df.mm.trans2:exp4 0.00156310005850123 0.0982518877893351 0.0159091096738286 0.987310622670369 df.mm.trans1:exp5 -0.0902834282166526 0.133033684954757 -0.678650886407881 0.497543166722011 df.mm.trans2:exp5 -0.0648353691089812 0.0982518877893351 -0.659889296458066 0.509503005159043 df.mm.trans1:exp6 -0.136209037923860 0.133033684954757 -1.02386878909791 0.30618752512514 df.mm.trans2:exp6 0.0626676706072304 0.098251887789335 0.637826631296877 0.52375771745924 df.mm.trans1:exp7 0.0173820335490831 0.133033684954757 0.130658889551128 0.896075961896125 df.mm.trans2:exp7 0.0722514839841108 0.0982518877893351 0.735369931405566 0.462316366926861 df.mm.trans1:exp8 -0.0105268806048453 0.133033684954757 -0.0791294370927587 0.936948228576699 df.mm.trans2:exp8 0.0466333561163084 0.098251887789335 0.474630637289092 0.635171750846482 df.mm.trans1:probe2 -0.0693409074750629 0.091081937697195 -0.761302506602232 0.446686816872381 df.mm.trans1:probe3 -0.0710102026059098 0.091081937697195 -0.77962990688654 0.435825001908632 df.mm.trans1:probe4 -0.0110512564627763 0.091081937697195 -0.121333128633216 0.90345575665253 df.mm.trans1:probe5 0.0373196064243688 0.091081937697195 0.409736632398392 0.682102029078211 df.mm.trans1:probe6 -0.0651392883209592 0.091081937697195 -0.715172403748337 0.474698213596958 df.mm.trans1:probe7 0.0458285706273925 0.091081937697195 0.503157615945229 0.614983355611613 df.mm.trans1:probe8 -0.152335779654324 0.091081937697195 -1.67251360155258 0.0947897915876873 . df.mm.trans1:probe9 0.0140738667243637 0.091081937697195 0.154518745211073 0.877237284090228 df.mm.trans1:probe10 -0.115381031803331 0.091081937697195 -1.26678279712186 0.205578742705667 df.mm.trans1:probe11 -0.0582543213708278 0.091081937697195 -0.639581489411175 0.522616447375951 df.mm.trans1:probe12 -0.0760660282568792 0.091081937697195 -0.835138449840223 0.403873604143108 df.mm.trans1:probe13 -0.0240895783001173 0.091081937697195 -0.264482496850296 0.791472040579443 df.mm.trans1:probe14 0.00569864864035677 0.091081937697195 0.0625661770537 0.950126620711262 df.mm.trans1:probe15 -0.153783574531753 0.091081937697195 -1.68840912281655 0.0916982296110578 . df.mm.trans1:probe16 -0.0611302025716624 0.091081937697195 -0.671156149256418 0.502302758175707 df.mm.trans1:probe17 0.0419772870855152 0.091081937697195 0.460873891649847 0.645006563519679 df.mm.trans1:probe18 -0.0560294777938644 0.091081937697195 -0.61515465316665 0.53861666406225 df.mm.trans1:probe19 -0.117814081409915 0.091081937697195 -1.29349555343884 0.196189880713758 df.mm.trans1:probe20 -0.201618268605742 0.091081937697195 -2.21359221930509 0.0271207338161539 * df.mm.trans1:probe21 -0.0330464831573193 0.091081937697195 -0.362821476934136 0.716828129985547 df.mm.trans1:probe22 -0.0690250482578297 0.091081937697195 -0.757834648701763 0.448759283833656 df.mm.trans2:probe2 0.0106600542243552 0.091081937697195 0.117038070268058 0.906857435468371 df.mm.trans2:probe3 -0.0372672708476927 0.091081937697195 -0.409162033548178 0.682523467340822 df.mm.trans2:probe4 0.0559282137542786 0.091081937697195 0.614042862594929 0.539350728840435 df.mm.trans2:probe5 0.0502913252568746 0.091081937697195 0.552154757884816 0.58098696376291 df.mm.trans2:probe6 0.017871426427855 0.091081937697195 0.196212628757078 0.844490478481892 df.mm.trans3:probe2 -0.0686384514492662 0.091081937697195 -0.753590153927742 0.451303310996169 df.mm.trans3:probe3 -0.0410504748818119 0.091081937697195 -0.450698304402412 0.652321541301546 df.mm.trans3:probe4 0.102579587327911 0.091081937697195 1.12623413512502 0.260383176728323 df.mm.trans3:probe5 0.0132817355524302 0.091081937697195 0.145821837877294 0.884096487653525 df.mm.trans3:probe6 0.0775663879181414 0.091081937697195 0.851611086448485 0.394668999379972 df.mm.trans3:probe7 0.00451640470250528 0.091081937697195 0.0495861728098081 0.960463773656551 df.mm.trans3:probe8 0.071578014455617 0.091081937697195 0.785863984290503 0.432165415623029 df.mm.trans3:probe9 0.0680064140897821 0.091081937697195 0.746650936609098 0.455480031204972 df.mm.trans3:probe10 0.0173794762051185 0.091081937697195 0.190811445655638 0.848718725211985