chrX.25671_chrX_17879239_17880087_+_1.R fitVsDatCorrelation=0.829464126994215 cont.fitVsDatCorrelation=0.253310682818521 fstatistic=4222.81042119421,53,715 cont.fstatistic=1398.81209159176,53,715 residuals=-0.830611059516574,-0.104187515176292,-0.0105175252377331,0.094710540821135,3.80552744848959 cont.residuals=-0.93967876216868,-0.320751301658629,-0.0995837430394202,0.246221228165988,3.15857264491537 predictedValues: Include Exclude Both chrX.25671_chrX_17879239_17880087_+_1.R.tl.Lung 56.4311277030054 60.0200065972456 86.7986836649679 chrX.25671_chrX_17879239_17880087_+_1.R.tl.cerebhem 60.3379956282338 70.9844139100345 132.332325600325 chrX.25671_chrX_17879239_17880087_+_1.R.tl.cortex 53.0989859901141 67.967368848597 144.385760693306 chrX.25671_chrX_17879239_17880087_+_1.R.tl.heart 57.2641674286243 64.937931612781 111.924082442256 chrX.25671_chrX_17879239_17880087_+_1.R.tl.kidney 58.7027085025518 61.5742507386348 85.9398157706874 chrX.25671_chrX_17879239_17880087_+_1.R.tl.liver 62.3512513460004 60.8372592718622 69.3413779645233 chrX.25671_chrX_17879239_17880087_+_1.R.tl.stomach 55.4630976877852 58.7108583741416 78.2411451234258 chrX.25671_chrX_17879239_17880087_+_1.R.tl.testicle 63.1537334480263 66.4745856380246 92.661137918662 diffExp=-3.5888788942402,-10.6464182818007,-14.8683828584828,-7.67376418415677,-2.87154223608299,1.51399207413817,-3.24776068635644,-3.32085218999825 diffExpScore=1.04437251827571 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=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,-1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 79.8475426064305 73.7621799469678 80.9805548856826 cerebhem 82.5092448338877 72.8107436639494 83.0224572067073 cortex 68.2482244464157 94.5773600606175 78.487123096438 heart 74.6878950611742 87.5265830774582 75.4389099358418 kidney 100.502762747695 86.6533271448666 76.8548999646397 liver 78.7202201823247 85.946734275269 82.333038103856 stomach 74.0963622612304 84.5324630362229 78.7531363417779 testicle 65.5936360621678 86.8911344121649 75.658661272288 cont.diffExp=6.08536265946275,9.69850116993831,-26.3291356142017,-12.8386880162840,13.8494356028289,-7.22651409294431,-10.4361007749925,-21.2974983499971 cont.diffExpScore=2.17723054266483 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,-1,0,0,0,0,-1 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,0,-1,0,0,0,0,-1 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.126907234091251 cont.tran.correlation=-0.293465837625340 tran.covariance=0.000488526208415941 cont.tran.covariance=-0.00384791014104158 tran.mean=61.1443589203539 cont.tran.mean=81.0566508636777 weightedLogRatios: wLogRatio Lung -0.250564902834497 cerebhem -0.679439771635357 cortex -1.01107853110398 heart -0.516930208533639 kidney -0.195633816121218 liver 0.101287172883206 stomach -0.230140836636033 testicle -0.213764344624725 cont.weightedLogRatios: wLogRatio Lung 0.344082929373072 cerebhem 0.544002067250144 cortex -1.43109868526947 heart -0.696778881338629 kidney 0.672559292082283 liver -0.387303748563293 stomach -0.57599625115467 testicle -1.21582969516539 varWeightedLogRatios=0.119844116996795 cont.varWeightedLogRatios=0.631059370662755 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81636421857348 0.125425075878015 30.4274419756793 4.24312200925988e-131 *** df.mm.trans1 0.205798788322826 0.098661658254519 2.08590441275499 0.0373412137529009 * df.mm.trans2 0.317951858681511 0.0986616582545189 3.22264863885914 0.00132773636475564 ** df.mm.exp2 -0.187001440526288 0.130296002672216 -1.43520473914096 0.151666045119287 df.mm.exp3 -0.445410468891103 0.130296002672216 -3.41845075640283 0.00066548663876129 *** df.mm.exp4 -0.160821222291223 0.130296002672216 -1.23427594855537 0.217505608733171 df.mm.exp5 0.0749749988084648 0.130296002672216 0.575420559885311 0.565187941698238 df.mm.exp6 0.337836937824767 0.130296002672216 2.59284192067394 0.00971323388868421 ** df.mm.exp7 0.064439512555732 0.130296002672216 0.494562467260346 0.621060921865482 df.mm.exp8 0.149335101216683 0.130296002672216 1.14612189287466 0.252128150258000 df.mm.trans1:exp2 0.253942539858817 0.0979577610971127 2.59236774110288 0.00972650600918114 ** df.mm.trans2:exp2 0.354783821145485 0.0979577610971127 3.62180410385005 0.000313239838599960 *** df.mm.trans1:exp3 0.384547384839623 0.0979577610971127 3.92564489564429 9.48613606107672e-05 *** df.mm.trans2:exp3 0.56976023908957 0.0979577610971127 5.81638690705401 9.06835300790548e-09 *** df.mm.trans1:exp4 0.175475384227416 0.0979577610971127 1.79133722802683 0.0736620245854383 . df.mm.trans2:exp4 0.239575187786491 0.0979577610971127 2.44569889208659 0.0146972520111383 * df.mm.trans1:exp5 -0.0355100474841573 0.0979577610971127 -0.362503665727451 0.717082808618205 df.mm.trans2:exp5 -0.0494091730778396 0.0979577610971127 -0.504392633360175 0.614140891048887 df.mm.trans1:exp6 -0.238074112082488 0.0979577610971127 -2.43037518840868 0.0153281831904034 * df.mm.trans2:exp6 -0.324312469506541 0.0979577610971127 -3.31073787185710 0.000977227430063854 *** df.mm.trans1:exp7 -0.0817425353637806 0.0979577610971127 -0.834467166748975 0.404296493595032 df.mm.trans2:exp7 -0.0864927719380633 0.0979577610971127 -0.882959869328952 0.377554718528066 df.mm.trans1:exp8 -0.0367840497362625 0.0979577610971127 -0.375509294253835 0.707393108168826 df.mm.trans2:exp8 -0.0471933473872105 0.0979577610971127 -0.481772417607874 0.630115050075569 df.mm.trans1:probe2 0.0452168924244407 0.0744042607308172 0.607719127645502 0.543566740644647 df.mm.trans1:probe3 -0.0266260154163728 0.0744042607308172 -0.357856057634945 0.720556651075216 df.mm.trans1:probe4 0.128682134041383 0.0744042607308172 1.72949953104076 0.0841513171221816 . df.mm.trans1:probe5 0.0553739389876228 0.0744042607308172 0.744230752966646 0.456981400705606 df.mm.trans1:probe6 0.0796586836863018 0.0744042607308172 1.07061992020180 0.28470162859695 df.mm.trans2:probe2 -0.290409416379068 0.0744042607308172 -3.90312884674338 0.000103933927864679 *** df.mm.trans2:probe3 -0.199373358023184 0.0744042607308172 -2.67959598099476 0.00754070813930204 ** df.mm.trans2:probe4 -0.258039059856522 0.0744042607308172 -3.46806832460935 0.000555604059526706 *** df.mm.trans2:probe5 -0.164123669571452 0.0744042607308172 -2.20583697706809 0.0277127721614134 * df.mm.trans2:probe6 -0.118645806628094 0.0744042607308172 -1.59461038202282 0.111241297059764 df.mm.trans3:probe2 0.444957645894669 0.0744042607308172 5.98027104260139 3.5180769015292e-09 *** df.mm.trans3:probe3 0.330102120255422 0.0744042607308172 4.43660238020077 1.05794457330070e-05 *** df.mm.trans3:probe4 0.46220427047445 0.0744042607308172 6.21206723828131 8.8722973480535e-10 *** df.mm.trans3:probe5 0.317181080277936 0.0744042607308172 4.26294243316854 2.28812060967424e-05 *** df.mm.trans3:probe6 -0.0281896672128779 0.0744042607308172 -0.378871679336532 0.704895632661976 df.mm.trans3:probe7 0.143593169193980 0.0744042607308172 1.92990519337969 0.0540138521459874 . df.mm.trans3:probe8 -0.311871136312593 0.0744042607308172 -4.19157630556795 3.11747710099693e-05 *** df.mm.trans3:probe9 0.181028172819007 0.0744042607308172 2.43303503107084 0.0152169833959031 * df.mm.trans3:probe10 -0.296318438118965 0.0744042607308172 -3.98254663386817 7.51571615672247e-05 *** df.mm.trans3:probe11 -0.00595516573084204 0.0744042607308172 -0.0800379665404766 0.936229456163013 df.mm.trans3:probe12 0.230371728899509 0.0744042607308172 3.09621689183846 0.00203664665195661 ** df.mm.trans3:probe13 0.104252036768563 0.0744042607308172 1.40115681205045 0.161601065510825 df.mm.trans3:probe14 -0.189522310603383 0.0744042607308172 -2.54719701186259 0.0110675461625466 * df.mm.trans3:probe15 0.445007099757676 0.0744042607308172 5.98093570699722 3.50443190282671e-09 *** df.mm.trans3:probe16 0.345690494776377 0.0744042607308172 4.64611154496958 4.026726462757e-06 *** df.mm.trans3:probe17 -0.0318766572877073 0.0744042607308172 -0.428425159723474 0.668470639957252 df.mm.trans3:probe18 -0.101316604458441 0.0744042607308172 -1.36170433605931 0.173720107922680 df.mm.trans3:probe19 0.145762197096577 0.0744042607308172 1.95905712475151 0.0504943534041737 . df.mm.trans3:probe20 0.284431779977766 0.0744042607308172 3.82278887235766 0.000143450743296611 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.34461105429406 0.217227042159339 20.0003232153169 5.10512851505928e-71 *** df.mm.trans1 0.0114723608119747 0.170874763655785 0.0671390003212234 0.946489815411923 df.mm.trans2 -0.0333628008006762 0.170874763655785 -0.195247092589302 0.845254982891463 df.mm.exp2 -0.0050934419429474 0.225663130498709 -0.0225709974495658 0.981998775984964 df.mm.exp3 0.122878798775164 0.225663130498709 0.544523150519117 0.586251299933695 df.mm.exp4 0.175181226896399 0.225663130498709 0.776295296928893 0.437831363741313 df.mm.exp5 0.443425232266638 0.225663130498709 1.96498750720458 0.0498024685826275 * df.mm.exp6 0.122099153315781 0.225663130498709 0.54106824205596 0.588629075412836 df.mm.exp7 0.0894278041013913 0.225663130498709 0.396288945844891 0.69201011658695 df.mm.exp8 0.0351465916787502 0.225663130498709 0.155748046218615 0.876275584175364 df.mm.trans1:exp2 0.0378846885335826 0.169655665350144 0.223303409617322 0.823363155429926 df.mm.trans2:exp2 -0.00788916934860161 0.169655665350144 -0.0465010663352711 0.962923868816267 df.mm.trans1:exp3 -0.279846479550117 0.169655665350144 -1.64949681445978 0.099485122099551 . df.mm.trans2:exp3 0.125693192406778 0.169655665350144 0.740872355469918 0.459014074608315 df.mm.trans1:exp4 -0.241982294327045 0.169655665350144 -1.42631425733782 0.154214125465087 df.mm.trans2:exp4 -0.00408480648034846 0.169655665350144 -0.0240770414116028 0.9807978741094 df.mm.trans1:exp5 -0.213359114183014 0.169655665350144 -1.25760088083515 0.208946721850057 df.mm.trans2:exp5 -0.282355952732135 0.169655665350144 -1.66428838170181 0.0964929549446863 . df.mm.trans1:exp6 -0.136318202608615 0.169655665350144 -0.803499266159335 0.421953254227057 df.mm.trans2:exp6 0.0307824487501963 0.169655665350144 0.181440735778943 0.856073068975804 df.mm.trans1:exp7 -0.164180464359988 0.169655665350144 -0.967727567606679 0.333507664845836 df.mm.trans2:exp7 0.0468617010205919 0.169655665350144 0.276216540861611 0.782461572678347 df.mm.trans1:exp8 -0.231787010746529 0.169655665350144 -1.36622028075605 0.172299355903097 df.mm.trans2:exp8 0.128663281407507 0.169655665350144 0.758378926763009 0.44847407000987 df.mm.trans1:probe2 -0.0351410882571923 0.128862728361648 -0.272701724571362 0.785161247102677 df.mm.trans1:probe3 0.108249707393064 0.128862728361648 0.84003892179953 0.401167366396346 df.mm.trans1:probe4 0.289659309750399 0.128862728361648 2.24781295129405 0.0248921087945275 * df.mm.trans1:probe5 0.188993419779065 0.128862728361648 1.46662593739800 0.142917511208300 df.mm.trans1:probe6 0.0731664342543116 0.128862728361648 0.567785853865931 0.57035862873162 df.mm.trans2:probe2 -0.139690307036665 0.128862728361648 -1.08402413027163 0.27871949549749 df.mm.trans2:probe3 -0.105345055879234 0.128862728361648 -0.817498257398273 0.413916068489497 df.mm.trans2:probe4 0.0571203153140723 0.128862728361648 0.443264829484027 0.657708279432924 df.mm.trans2:probe5 -0.0334534862313177 0.128862728361648 -0.259605602462738 0.795242744958754 df.mm.trans2:probe6 -0.0490865884087064 0.128862728361648 -0.380921535907163 0.703374623063341 df.mm.trans3:probe2 0.125662043101637 0.128862728361648 0.97516205577281 0.329809681171722 df.mm.trans3:probe3 0.164035661356958 0.128862728361648 1.27294884597351 0.203449932825412 df.mm.trans3:probe4 0.0103383223842729 0.128862728361648 0.0802274056720172 0.936078843459493 df.mm.trans3:probe5 0.054151299339943 0.128862728361648 0.420224684269991 0.674447578293138 df.mm.trans3:probe6 -0.0815849885905433 0.128862728361648 -0.633115483645345 0.526860671769383 df.mm.trans3:probe7 0.132866545925211 0.128862728361648 1.03107040813482 0.302856444522183 df.mm.trans3:probe8 -0.00225848034978054 0.128862728361648 -0.0175262496650095 0.986021681234297 df.mm.trans3:probe9 0.072599715367154 0.128862728361648 0.56338800435302 0.573347339032888 df.mm.trans3:probe10 0.0882921450852135 0.128862728361648 0.685164331127814 0.49346228917972 df.mm.trans3:probe11 0.204987911568645 0.128862728361648 1.59074632498355 0.112108692106878 df.mm.trans3:probe12 0.0232012778230499 0.128862728361648 0.180046458103359 0.857167110664023 df.mm.trans3:probe13 0.00766954613362692 0.128862728361648 0.0595171794912077 0.952556813026894 df.mm.trans3:probe14 -0.0277478584969419 0.128862728361648 -0.215328814232993 0.829572372295376 df.mm.trans3:probe15 0.26329632961369 0.128862728361648 2.04323106425902 0.0413956634925484 * df.mm.trans3:probe16 0.0534076095333412 0.128862728361648 0.414453505775969 0.678666320451182 df.mm.trans3:probe17 0.151178904371810 0.128862728361648 1.17317789475583 0.241115148718723 df.mm.trans3:probe18 0.086135866936099 0.128862728361648 0.66843119054846 0.504074119190922 df.mm.trans3:probe19 0.091626485633472 0.128862728361648 0.711039466557981 0.477291662812445 df.mm.trans3:probe20 0.0119106314149171 0.128862728361648 0.0924288315663353 0.926383223924046