chr13.6563_chr13_38373786_38375614_-_1.R fitVsDatCorrelation=0.81071376124098 cont.fitVsDatCorrelation=0.233494648530316 fstatistic=11551.1940562167,65,991 cont.fstatistic=4177.66924879354,65,991 residuals=-0.954098255399216,-0.080681239924364,-0.0074780831379432,0.0742172618592188,0.72999338244383 cont.residuals=-0.488588853343133,-0.162258499056194,-0.0518611218412211,0.105242896158320,1.15411900132446 predictedValues: Include Exclude Both chr13.6563_chr13_38373786_38375614_-_1.R.tl.Lung 88.1524703259552 48.044717288051 57.3926005749757 chr13.6563_chr13_38373786_38375614_-_1.R.tl.cerebhem 89.7315307842916 54.2491238937399 55.0555304646833 chr13.6563_chr13_38373786_38375614_-_1.R.tl.cortex 86.2470785349424 50.7553483465589 59.9353156339403 chr13.6563_chr13_38373786_38375614_-_1.R.tl.heart 84.6857234506152 51.5428699523432 57.3999733510693 chr13.6563_chr13_38373786_38375614_-_1.R.tl.kidney 88.5921598227825 50.3981605458943 58.568887728693 chr13.6563_chr13_38373786_38375614_-_1.R.tl.liver 91.7711027669132 58.0393039930251 59.9071946613483 chr13.6563_chr13_38373786_38375614_-_1.R.tl.stomach 86.113128040295 50.3958923691536 59.3234684104365 chr13.6563_chr13_38373786_38375614_-_1.R.tl.testicle 85.364931094876 55.1144963345519 57.2854583045593 diffExp=40.1077530379042,35.4824068905517,35.4917301883835,33.1428534982719,38.1939992768882,33.7317987738881,35.7172356711414,30.2504347603241 diffExpScore=0.996467906488276 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 58.5550196461947 54.0737934500034 57.0699054343702 cerebhem 57.144116479575 57.8199918004888 55.0703965119663 cortex 57.3293901537465 54.7581111293964 58.50035338178 heart 53.7388604950502 57.9325346989728 57.167505171527 kidney 58.1555257838817 53.9707183077176 55.3557676341504 liver 54.9051687389165 52.0694880198258 56.669292433245 stomach 57.6781168226947 51.4716565227983 55.6462364995751 testicle 53.6545851010239 56.9921581682988 58.4861547150766 cont.diffExp=4.48122619619136,-0.675875320913711,2.57127902435013,-4.19367420392260,4.18480747616416,2.83568071909063,6.2064602998964,-3.33757306727496 cont.diffExpScore=2.17915045438350 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=0.452838877451624 cont.tran.correlation=-0.442715640954869 tran.covariance=0.00071529546520999 cont.tran.covariance=-0.000706984328439853 tran.mean=69.9498773464993 cont.tran.mean=55.6405772074116 weightedLogRatios: wLogRatio Lung 2.53432093394773 cerebhem 2.13633710814639 cortex 2.22265679895641 heart 2.08081175076714 kidney 2.37030010954619 liver 1.96567518310375 stomach 2.24361545339637 testicle 1.84992171636543 cont.weightedLogRatios: wLogRatio Lung 0.320869753139655 cerebhem -0.0476376564099305 cortex 0.184738707919610 heart -0.302202140178588 kidney 0.300642345874729 liver 0.211004783721685 stomach 0.455153320333145 testicle -0.242155851054843 varWeightedLogRatios=0.0477271824162012 cont.varWeightedLogRatios=0.0763575956190015 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.79467555149673 0.06873971264864 69.751172455499 0 *** df.mm.trans1 0.0153663065191632 0.0578380289203651 0.265678253668023 0.790542271125943 df.mm.trans2 -0.914454225089451 0.0514323145997791 -17.7797602967178 1.35167074983534e-61 *** df.mm.exp2 0.180782008435543 0.0649454252806679 2.78359880860393 0.00547821560866037 ** df.mm.exp3 -0.0103174609211520 0.0649454252806679 -0.158863551613745 0.873808745788957 df.mm.exp4 0.0300323469523654 0.0649454252806679 0.462424363572611 0.643878540787273 df.mm.exp5 0.0325096771639327 0.0649454252806679 0.500569162853873 0.616785517052081 df.mm.exp6 0.186336555582248 0.0649454252806679 2.86912518898717 0.00420350476847184 ** df.mm.exp7 -0.0087181745577238 0.0649454252806679 -0.134238470531948 0.893241262678937 df.mm.exp8 0.10701660193319 0.0649454252806679 1.64779276555211 0.0997121978952187 . df.mm.trans1:exp2 -0.163027719945745 0.0570786345478202 -2.85619516369407 0.00437708370095811 ** df.mm.trans2:exp2 -0.0593273529657202 0.0407194582900886 -1.45697795248324 0.145439239359942 df.mm.trans1:exp3 -0.0115342883683732 0.0570786345478202 -0.202077160039802 0.83989787773394 df.mm.trans2:exp3 0.0652022720332167 0.0407194582900886 1.60125588038796 0.109638954710798 df.mm.trans1:exp4 -0.0701532463719778 0.0570786345478202 -1.22906314994630 0.219339884782513 df.mm.trans2:exp4 0.0402493533056367 0.0407194582900886 0.988455028524623 0.323171121654895 df.mm.trans1:exp5 -0.0275342456218633 0.0570786345478202 -0.482391455927265 0.629634418233735 df.mm.trans2:exp5 0.015312812745889 0.0407194582900886 0.376056396349856 0.706955458233644 df.mm.trans1:exp6 -0.146107024753731 0.0570786345478202 -2.5597498242766 0.0106221511058880 * df.mm.trans2:exp6 0.00265169311562694 0.0407194582900886 0.065121031245948 0.948090769237154 df.mm.trans1:exp7 -0.0146878838647061 0.0570786345478202 -0.257327176465665 0.796979634537737 df.mm.trans2:exp7 0.0564956583054663 0.0407194582900886 1.38743639227680 0.165620645684754 df.mm.trans1:exp8 -0.139149160961384 0.0570786345478202 -2.43785020548811 0.0149497805576200 * df.mm.trans2:exp8 0.0302639835981971 0.0407194582900886 0.743231488557489 0.457517697140051 df.mm.trans1:probe2 -0.789637684017945 0.0438163837632273 -18.0215165241602 5.08460438649885e-63 *** df.mm.trans1:probe3 -0.4419217608406 0.0438163837632273 -10.0857652523 7.72670557271998e-23 *** df.mm.trans1:probe4 -0.852378864281014 0.0438163837632273 -19.4534279434618 1.20230901044558e-71 *** df.mm.trans1:probe5 -0.953789952464871 0.0438163837632273 -21.7678838495416 3.57524953844346e-86 *** df.mm.trans1:probe6 -0.692029930848106 0.0438163837632273 -15.7938622819186 2.73541707175671e-50 *** df.mm.trans1:probe7 -0.970835301066348 0.0438163837632273 -22.1569015442373 1.12835079998643e-88 *** df.mm.trans1:probe8 -0.598656749915036 0.0438163837632273 -13.6628516207551 4.60391220920610e-39 *** df.mm.trans1:probe9 -0.896972098943658 0.0438163837632273 -20.4711576334247 5.91896541989924e-78 *** df.mm.trans1:probe10 -0.49658787128258 0.0438163837632273 -11.3333832834315 4.40698105599039e-28 *** df.mm.trans1:probe11 -0.896346416388891 0.0438163837632273 -20.456877985014 7.27223592926072e-78 *** df.mm.trans1:probe12 -0.927769514133123 0.0438163837632273 -21.1740320503526 2.19128480421799e-82 *** df.mm.trans1:probe13 -0.935025034532772 0.0438163837632273 -21.3396212609742 1.94288954324386e-83 *** df.mm.trans1:probe14 -0.749070687812097 0.0438163837632273 -17.0956757148168 1.28180153411825e-57 *** df.mm.trans1:probe15 -0.851357139340682 0.0438163837632273 -19.43010961245 1.67076820285523e-71 *** df.mm.trans1:probe16 -0.862682310231859 0.0438163837632273 -19.68857847543 4.31282554945311e-73 *** df.mm.trans2:probe2 -0.0494354740421083 0.0438163837632273 -1.12824176247052 0.259490895249461 df.mm.trans2:probe3 -0.0515035397723143 0.0438163837632273 -1.17544022004706 0.240100962792075 df.mm.trans2:probe4 -0.0765078031272253 0.0438163837632273 -1.74610035233976 0.0811033742009309 . df.mm.trans2:probe5 0.0139126068101367 0.0438163837632273 0.317520653582846 0.750915457484016 df.mm.trans2:probe6 -0.0629616821789414 0.0438163837632273 -1.43694382720332 0.151049588209268 df.mm.trans3:probe2 0.00317934423566941 0.0438163837632273 0.0725606260171945 0.942170401614417 df.mm.trans3:probe3 0.03404008120295 0.0438163837632273 0.776880205059687 0.437414843978742 df.mm.trans3:probe4 0.0137461041479577 0.0438163837632273 0.313720644365316 0.753799241642174 df.mm.trans3:probe5 0.0183620846803677 0.0438163837632273 0.419068921332983 0.67525657864544 df.mm.trans3:probe6 -0.0339741835206083 0.0438163837632273 -0.7753762543298 0.438302342841483 df.mm.trans3:probe7 -0.000446194250638854 0.0438163837632273 -0.0101832742074283 0.991877112719866 df.mm.trans3:probe8 0.0824129013198485 0.0438163837632273 1.88086953421777 0.0602826908623799 . df.mm.trans3:probe9 0.179617873945191 0.0438163837632273 4.09933131213658 4.48264032705612e-05 *** df.mm.trans3:probe10 0.0230120529707665 0.0438163837632273 0.525192884358458 0.599566587904543 df.mm.trans3:probe11 0.0432193082911482 0.0438163837632273 0.986373237113643 0.324190751626284 df.mm.trans3:probe12 -0.0581917798927092 0.0438163837632273 -1.32808266896609 0.184456586152392 df.mm.trans3:probe13 0.449171929399719 0.0438163837632273 10.2512323204701 1.65975611451937e-23 *** df.mm.trans3:probe14 0.393007109432437 0.0438163837632273 8.96940997130544 1.45927522077774e-18 *** df.mm.trans3:probe15 0.194581630625517 0.0438163837632273 4.44084184758348 9.96780356600522e-06 *** df.mm.trans3:probe16 0.0373181084516018 0.0438163837632273 0.851693025450468 0.394590253643578 df.mm.trans3:probe17 0.418352399109576 0.0438163837632273 9.54785318136354 9.98098646554557e-21 *** df.mm.trans3:probe18 0.401971350645804 0.0438163837632273 9.1739964853776 2.5769096216833e-19 *** df.mm.trans3:probe19 0.463544666221015 0.0438163837632273 10.5792542973398 7.42398813153717e-25 *** df.mm.trans3:probe20 0.408230309066443 0.0438163837632273 9.3168416470062 7.53457673339794e-20 *** df.mm.trans3:probe21 0.219061636134714 0.0438163837632273 4.99953709823403 6.79317957814668e-07 *** df.mm.trans3:probe22 0.104993817192142 0.0438163837632273 2.39622278642397 0.0167495646475526 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.06953327035299 0.114169348005725 35.6447097354792 9.44782119764565e-180 *** df.mm.trans1 -0.0143048642693464 0.0960628113988053 -0.148911572137522 0.881653706048992 df.mm.trans2 -0.0874527312861902 0.085423601554702 -1.02375373660860 0.306201440008175 df.mm.exp2 0.0782591770501482 0.107867440443761 0.725512506166774 0.468308962948533 df.mm.exp3 -0.0333334298722428 0.107867440443761 -0.309022164010853 0.757369621721436 df.mm.exp4 -0.0186096584996960 0.107867440443761 -0.172523408575719 0.86306128666979 df.mm.exp5 0.0217421395309346 0.107867440443761 0.201563506480626 0.840299342406139 df.mm.exp6 -0.0950854079785508 0.107867440443761 -0.881502403203176 0.378259698601417 df.mm.exp7 -0.0391448568062245 0.107867440443761 -0.362897799791899 0.716758616310216 df.mm.exp8 -0.0593489942191557 0.107867440443761 -0.550203045284073 0.582304101736178 df.mm.trans1:exp2 -0.102649559858747 0.0948015381543879 -1.08278369588876 0.279167828007020 df.mm.trans2:exp2 -0.0112742413343065 0.0676306871966205 -0.166703042681338 0.867637717977535 df.mm.trans1:exp3 0.0121800202532494 0.0948015381543879 0.128479141692973 0.897795872036014 df.mm.trans2:exp3 0.0459092770167143 0.0676306871966205 0.678823163266754 0.497408408699004 df.mm.trans1:exp4 -0.0672207615857846 0.0948015381543879 -0.709068258748218 0.478448893229705 df.mm.trans2:exp4 0.0875391381032652 0.0676306871966205 1.29437008156912 0.195839149375547 df.mm.trans1:exp5 -0.0285880577139533 0.0948015381543879 -0.301556897393337 0.763053135910268 df.mm.trans2:exp5 -0.0236501526614389 0.0676306871966205 -0.34969558408717 0.726641467561388 df.mm.trans1:exp6 0.0307260813012217 0.0948015381543879 0.324109522898068 0.745923510068962 df.mm.trans2:exp6 0.0573148835071839 0.0676306871966205 0.847468595736049 0.396938606081164 df.mm.trans1:exp7 0.0240558815339324 0.0948015381543879 0.253749907462013 0.799741409834429 df.mm.trans2:exp7 -0.0101735048690453 0.0676306871966205 -0.150427347270155 0.880458078036266 df.mm.trans1:exp8 -0.0280508961604893 0.0948015381543879 -0.295890728215900 0.767375521400155 df.mm.trans2:exp8 0.111913017533142 0.0676306871966205 1.65476682512157 0.098288449327333 . df.mm.trans1:probe2 -0.0685455304386357 0.0727743508586705 -0.94189133437621 0.346477821507125 df.mm.trans1:probe3 0.120514168878735 0.0727743508586705 1.65599785441956 0.0980388324506233 . df.mm.trans1:probe4 0.0792766569476139 0.0727743508586705 1.08934887102698 0.276264894327764 df.mm.trans1:probe5 0.0620091479285838 0.0727743508586705 0.852074215667097 0.394378765527212 df.mm.trans1:probe6 0.0268690491470439 0.0727743508586705 0.369210426888235 0.712049755618797 df.mm.trans1:probe7 0.0318151729048107 0.0727743508586705 0.437175633027582 0.662079227107717 df.mm.trans1:probe8 0.0299789546836219 0.0727743508586705 0.411943965557889 0.680469579984383 df.mm.trans1:probe9 0.0295695883559611 0.0727743508586705 0.406318819846101 0.684596083752063 df.mm.trans1:probe10 0.0743162014210916 0.0727743508586705 1.02118673054763 0.30741519918165 df.mm.trans1:probe11 0.0598576819600267 0.0727743508586705 0.822510695784449 0.41098416186751 df.mm.trans1:probe12 0.00923224185689766 0.0727743508586705 0.126861205190643 0.899075986948829 df.mm.trans1:probe13 -0.0156525557496953 0.0727743508586705 -0.215083412837209 0.829746539774902 df.mm.trans1:probe14 0.00643891702356356 0.0727743508586705 0.0884778352206547 0.92951477625705 df.mm.trans1:probe15 0.0487835979143415 0.0727743508586705 0.670340543594547 0.50279690525121 df.mm.trans1:probe16 0.036119572550527 0.0727743508586705 0.496322840730961 0.619776718300423 df.mm.trans2:probe2 -0.00165123262215685 0.0727743508586705 -0.0226897609208989 0.981902310989279 df.mm.trans2:probe3 0.0685643103677916 0.0727743508586705 0.942149391355549 0.346345770717943 df.mm.trans2:probe4 0.0372411250695186 0.0727743508586705 0.511734211712059 0.608951046307232 df.mm.trans2:probe5 0.105198365095634 0.0727743508586705 1.44554178573069 0.148621912988834 df.mm.trans2:probe6 0.0221827919369521 0.0727743508586705 0.304816074279132 0.760570240503617 df.mm.trans3:probe2 0.0748560440786763 0.0727743508586705 1.02860476521527 0.303916422256164 df.mm.trans3:probe3 0.111758587821018 0.0727743508586705 1.53568649534306 0.12493447856023 df.mm.trans3:probe4 0.00765829765588575 0.0727743508586705 0.105233472583745 0.916211846167853 df.mm.trans3:probe5 0.111600275244985 0.0727743508586705 1.53351110560526 0.125469139676708 df.mm.trans3:probe6 0.103916155535770 0.0727743508586705 1.42792280947415 0.153629127857035 df.mm.trans3:probe7 -0.0219967854858257 0.0727743508586705 -0.302260140094468 0.762517186292787 df.mm.trans3:probe8 0.111373601203363 0.0727743508586705 1.53039635378752 0.126237781539136 df.mm.trans3:probe9 0.0514694039395115 0.0727743508586705 0.707246486326841 0.479579589064664 df.mm.trans3:probe10 0.111952595275629 0.0727743508586705 1.53835237215710 0.124281694826455 df.mm.trans3:probe11 0.243776414904321 0.0727743508586705 3.34975732559594 0.00083932030946732 *** df.mm.trans3:probe12 0.112892483141887 0.0727743508586705 1.55126747006135 0.121156877076028 df.mm.trans3:probe13 0.0485558730621399 0.0727743508586705 0.667211352478245 0.504792465621567 df.mm.trans3:probe14 0.0553699978993943 0.0727743508586705 0.760844957681919 0.446930670753673 df.mm.trans3:probe15 0.0975800620426865 0.0727743508586705 1.34085788318730 0.180273854566563 df.mm.trans3:probe16 0.094017834230205 0.0727743508586705 1.29190893660858 0.196689879749761 df.mm.trans3:probe17 0.119784755049737 0.0727743508586705 1.64597490237133 0.100086007373314 df.mm.trans3:probe18 -0.00633907482053116 0.0727743508586705 -0.0871058930204927 0.930604938051244 df.mm.trans3:probe19 0.0887113412037117 0.0727743508586705 1.21899185849134 0.223137358853128 df.mm.trans3:probe20 0.00993233439417021 0.0727743508586705 0.136481250289117 0.891468566016851 df.mm.trans3:probe21 0.0447027311028611 0.0727743508586705 0.614264924048238 0.539181266702244 df.mm.trans3:probe22 0.111454632769137 0.0727743508586705 1.53150981704508 0.125962586311002