chrX.25701_chrX_55491340_55493423_-_2.R fitVsDatCorrelation=0.660857460267864 cont.fitVsDatCorrelation=0.301689706569197 fstatistic=12213.1850280828,53,715 cont.fstatistic=7562.98286775973,53,715 residuals=-0.348416919003127,-0.0777500104299945,-0.00704510690262856,0.0670963673617789,1.06847699169386 cont.residuals=-0.374040710493406,-0.101258550980035,-0.0213421515014832,0.070767420336738,1.2857633013561 predictedValues: Include Exclude Both chrX.25701_chrX_55491340_55493423_-_2.R.tl.Lung 45.0022212811938 46.0113487203939 47.3655373154578 chrX.25701_chrX_55491340_55493423_-_2.R.tl.cerebhem 50.6290967421952 54.8790086059075 54.5916578689781 chrX.25701_chrX_55491340_55493423_-_2.R.tl.cortex 44.2278040090941 45.7047096253306 46.6650066950067 chrX.25701_chrX_55491340_55493423_-_2.R.tl.heart 45.2863522252761 47.8182302515025 49.4145979227721 chrX.25701_chrX_55491340_55493423_-_2.R.tl.kidney 43.0058432707881 44.0672131309301 47.6313494578533 chrX.25701_chrX_55491340_55493423_-_2.R.tl.liver 48.756264768687 52.2113073695099 50.7742578631229 chrX.25701_chrX_55491340_55493423_-_2.R.tl.stomach 45.9970023094821 48.4533895792222 50.2188781765044 chrX.25701_chrX_55491340_55493423_-_2.R.tl.testicle 50.2933608750917 47.7090658288943 49.9721486355106 diffExp=-1.00912743920018,-4.24991186371233,-1.4769056162365,-2.53187802622636,-1.06136986014196,-3.45504260082295,-2.45638726974006,2.58429504619748 diffExpScore=1.28442255097352 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 50.7289611479155 49.5800339308767 48.338429809612 cerebhem 50.5374070561592 48.075064799981 49.2710098082699 cortex 48.4267142682299 49.5368953729629 47.5397347147871 heart 50.2203097364066 48.0862551207491 49.610600016063 kidney 50.1070828530277 46.7655925006387 46.5703677140781 liver 48.3366348120366 50.5972531229244 48.0363262028992 stomach 52.5196864277767 47.3703266834398 49.115733685766 testicle 49.3956241097541 50.0784721082808 47.8460023770163 cont.diffExp=1.14892721703880,2.46234225617816,-1.11018110473297,2.13405461565751,3.34149035238901,-2.26061831088784,5.14935974433692,-0.682847998526668 cont.diffExpScore=1.63557145657265 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=0.81342348441015 cont.tran.correlation=-0.668771063132985 tran.covariance=0.00362196138687169 cont.tran.covariance=-0.0005088921887124 tran.mean=47.5032636620937 cont.tran.mean=49.3976446281975 weightedLogRatios: wLogRatio Lung -0.0846643987057069 cerebhem -0.319582926596837 cortex -0.125011062355519 heart -0.208911990504007 kidney -0.0919987267544336 liver -0.268457341509557 stomach -0.200539127145829 testicle 0.205282857374389 cont.weightedLogRatios: wLogRatio Lung 0.0896887802704882 cerebhem 0.194692658907051 cortex -0.0882026849756092 heart 0.169120453860641 kidney 0.267753007758769 liver -0.178307067133711 stomach 0.403438878713938 testicle -0.0536369245100252 varWeightedLogRatios=0.0259844244645096 cont.varWeightedLogRatios=0.0386490967251131 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.68208889648943 0.0625362830256127 58.8792412715251 1.96594148435128e-276 *** df.mm.trans1 0.107583968457871 0.0522622529971072 2.05854057734223 0.0399000639626535 * df.mm.trans2 0.151068536081146 0.0479250585119254 3.15218261118148 0.00168823297633724 ** df.mm.exp2 0.152071254831753 0.0618948682953533 2.45692832087615 0.0142495718098767 * df.mm.exp3 -0.00914459766525614 0.0618948682953533 -0.147744036252237 0.882586429921883 df.mm.exp4 0.00246175514723406 0.0618948682953533 0.0397731704587676 0.968285068935735 df.mm.exp5 -0.0941441348258793 0.0618948682953533 -1.52103296151528 0.128693657303988 df.mm.exp6 0.137038269473500 0.0618948682953533 2.21404897122606 0.0271401676296367 * df.mm.exp7 0.0150824838061896 0.0618948682953533 0.243679067773732 0.807549285138544 df.mm.exp8 0.093823669718227 0.0618948682953533 1.51585538998991 0.129997877435134 df.mm.trans1:exp2 -0.0342566597329146 0.0532706209163567 -0.643068527147506 0.520385808242221 df.mm.trans2:exp2 0.0241715862877699 0.0429481476239571 0.562808587215683 0.573741656075856 df.mm.trans1:exp3 -0.00821361139696353 0.0532706209163566 -0.154186515112339 0.877506183978704 df.mm.trans2:exp3 0.00245786814068705 0.0429481476239571 0.0572287345709879 0.954378977767667 df.mm.trans1:exp4 0.00383210621261975 0.0532706209163567 0.0719365786750781 0.942672496972035 df.mm.trans2:exp4 0.0360571202819371 0.0429481476239571 0.839550068553455 0.401441324389659 df.mm.trans1:exp5 0.0487682809491855 0.0532706209163567 0.915481744163625 0.360247427150787 df.mm.trans2:exp5 0.0509720969778687 0.0429481476239571 1.18682876440137 0.235689347244562 df.mm.trans1:exp6 -0.0569164225668406 0.0532706209163567 -1.06843925578056 0.285683003830263 df.mm.trans2:exp6 -0.0106272591424636 0.0429481476239571 -0.247443946488987 0.804635703970178 df.mm.trans1:exp7 0.00678189301788913 0.0532706209163566 0.127310192770942 0.898730688369073 df.mm.trans2:exp7 0.0366317350651619 0.0429481476239571 0.852929336694563 0.393984116311142 df.mm.trans1:exp8 0.0173375577647383 0.0532706209163567 0.325461905014418 0.744926727239853 df.mm.trans2:exp8 -0.0575903079507297 0.0429481476239571 -1.34092646916872 0.180370156418456 df.mm.trans1:probe2 -0.0134335682429734 0.0385982318601182 -0.348035845052626 0.727915703260316 df.mm.trans1:probe3 0.0504601114089725 0.0385982318601182 1.30731665615778 0.191525428133467 df.mm.trans1:probe4 -0.00354014163899971 0.0385982318601182 -0.0917177152525886 0.92694801186641 df.mm.trans1:probe5 0.0114214434837238 0.0385982318601182 0.2959058727124 0.76738788442575 df.mm.trans1:probe6 -0.00866530255594745 0.0385982318601182 -0.224499987132854 0.822432415895734 df.mm.trans1:probe7 0.0295718670190988 0.0385982318601182 0.766145639164732 0.443842504945592 df.mm.trans1:probe8 0.103533928435865 0.0385982318601182 2.68234899492486 0.00747955091788515 ** df.mm.trans1:probe9 0.0761660720593309 0.0385982318601182 1.97330469269578 0.0488455558647149 * df.mm.trans1:probe10 0.182714992121519 0.0385982318601182 4.73376585703943 2.65753965306922e-06 *** df.mm.trans1:probe11 0.0305617022445562 0.0385982318601182 0.791790213482142 0.428745606888979 df.mm.trans1:probe12 -0.0157774836993312 0.0385982318601182 -0.408761825062597 0.682836856588718 df.mm.trans2:probe2 -0.0639266634707126 0.0385982318601182 -1.65620704343105 0.0981186508056002 . df.mm.trans2:probe3 -0.0635597396007262 0.0385982318601182 -1.6467008082409 0.100058972850123 df.mm.trans2:probe4 -0.0493357934702887 0.0385982318601182 -1.27818791412736 0.201597955762231 df.mm.trans2:probe5 -0.0284167698638736 0.0385982318601182 -0.736219471577281 0.461838608791439 df.mm.trans2:probe6 0.119851862469561 0.0385982318601182 3.10511276536993 0.00197717368589107 ** df.mm.trans3:probe2 -0.139553656040353 0.0385982318601182 -3.61554530648197 0.000320765943109106 *** df.mm.trans3:probe3 -0.25425247950237 0.0385982318601182 -6.58715353656076 8.69168123226938e-11 *** df.mm.trans3:probe4 -0.112791784672953 0.0385982318601182 -2.92220081691087 0.00358531777646039 ** df.mm.trans3:probe5 0.0176684450270888 0.0385982318601182 0.457752704608855 0.647269188077472 df.mm.trans3:probe6 -0.0954412559305641 0.0385982318601182 -2.47268466277025 0.0136417537188747 * df.mm.trans3:probe7 -0.201404623509248 0.0385982318601182 -5.21797537874657 2.37248846462818e-07 *** df.mm.trans3:probe8 0.213363199433757 0.0385982318601182 5.52779723711168 4.5472846098361e-08 *** df.mm.trans3:probe9 -0.135537376463231 0.0385982318601182 -3.5114918464251 0.000473579500782716 *** df.mm.trans3:probe10 -0.270691143708795 0.0385982318601182 -7.01304517496534 5.41149332456891e-12 *** df.mm.trans3:probe11 -0.0662559961961681 0.0385982318601182 -1.71655521517885 0.086493584170744 . df.mm.trans3:probe12 0.132100672318616 0.0385982318601182 3.42245398176153 0.000655920756013261 *** df.mm.trans3:probe13 -0.128869274181260 0.0385982318601182 -3.33873516922454 0.000885233498301122 *** df.mm.trans3:probe14 -0.118185693249815 0.0385982318601182 -3.06194578233857 0.00228146556056022 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.03323028173008 0.0794424641731678 50.7691991141978 2.66702424723897e-239 *** df.mm.trans1 -0.0805069694998336 0.0663909327586878 -1.21261994905921 0.225675662624244 df.mm.trans2 -0.128479511561722 0.060881212627736 -2.11033102029885 0.0351765911118374 * df.mm.exp2 -0.053716728267377 0.0786276481293697 -0.683178621583523 0.494715287402815 df.mm.exp3 -0.0306548112661617 0.0786276481293696 -0.389873180687332 0.696746433256704 df.mm.exp4 -0.0666469113976072 0.0786276481293696 -0.847626922376592 0.396929419583275 df.mm.exp5 -0.0335126426940443 0.0786276481293697 -0.426219574047343 0.67007613748226 df.mm.exp6 -0.021728765738288 0.0786276481293696 -0.276350193033075 0.782358968146934 df.mm.exp7 -0.0268536246654577 0.0786276481293696 -0.341529033416772 0.732805698679223 df.mm.exp8 -0.00639280446586685 0.0786276481293697 -0.0813047905915802 0.935222317322053 df.mm.trans1:exp2 0.0499335508192181 0.067671904834779 0.73787712849418 0.460831214240603 df.mm.trans2:exp2 0.0228921566618129 0.0545588339096319 0.419586619093256 0.674913504200074 df.mm.trans1:exp3 -0.0157905528945876 0.067671904834779 -0.233339861396547 0.815564293866693 df.mm.trans2:exp3 0.0297843532941346 0.0545588339096319 0.545912571069016 0.585296317633018 df.mm.trans1:exp4 0.0565694593918252 0.067671904834779 0.83593715190875 0.403469524197824 df.mm.trans2:exp4 0.0360550803748023 0.0545588339096319 0.660847708631783 0.508922848623226 df.mm.trans1:exp5 0.0211780417974524 0.067671904834779 0.312951761135711 0.754408571516694 df.mm.trans2:exp5 -0.0249278387274274 0.0545588339096319 -0.456898304841273 0.647882917356434 df.mm.trans1:exp6 -0.0265784496930328 0.067671904834779 -0.392754567171178 0.694617825450959 df.mm.trans2:exp6 0.0420378434769306 0.0545588339096319 0.770504801230899 0.441255021292841 df.mm.trans1:exp7 0.0615447302455286 0.067671904834779 0.909457630840894 0.36341507365064 df.mm.trans2:exp7 -0.0187385729751678 0.0545588339096319 -0.343456258728061 0.731356207841384 df.mm.trans1:exp8 -0.0202423293449088 0.067671904834779 -0.299124568671894 0.764931923524775 df.mm.trans2:exp8 0.0163958107993268 0.0545588339096319 0.300516151545392 0.763870835708152 df.mm.trans1:probe2 -0.0869234670725565 0.049032953404653 -1.7727560963992 0.0766948408511216 . df.mm.trans1:probe3 -0.0547940941713276 0.0490329534046531 -1.11749528361324 0.264157964686404 df.mm.trans1:probe4 -0.104746625924766 0.049032953404653 -2.13624957608255 0.0329980205029192 * df.mm.trans1:probe5 -0.0528600297693735 0.0490329534046531 -1.07805110846856 0.281374513645811 df.mm.trans1:probe6 -0.0618026462469831 0.049032953404653 -1.26043083183152 0.207925164665604 df.mm.trans1:probe7 0.0448186328411886 0.049032953404653 0.914051260002941 0.360998038615379 df.mm.trans1:probe8 -0.0308483917048299 0.049032953404653 -0.629135908870268 0.529461033957373 df.mm.trans1:probe9 -0.0526153126046428 0.0490329534046531 -1.07306023707007 0.283606113567399 df.mm.trans1:probe10 -0.106153780076479 0.049032953404653 -2.16494770772682 0.0307218358477576 * df.mm.trans1:probe11 -0.0815407644100583 0.049032953404653 -1.66297884888004 0.0967549134693432 . df.mm.trans1:probe12 -0.094418332866248 0.0490329534046531 -1.92560974426819 0.0545493881790253 . df.mm.trans2:probe2 0.00430950587656009 0.0490329534046531 0.0878899918794436 0.92998872057074 df.mm.trans2:probe3 0.000213749520153532 0.0490329534046531 0.00435930339315942 0.996523006105097 df.mm.trans2:probe4 0.000469121977043171 0.0490329534046531 0.00956748358948846 0.992369037815046 df.mm.trans2:probe5 -0.0427176499191315 0.0490329534046531 -0.871202873842753 0.383935811173643 df.mm.trans2:probe6 0.0144740886386310 0.0490329534046531 0.295191042627619 0.767933639880873 df.mm.trans3:probe2 0.0569316467877426 0.0490329534046531 1.16108948848959 0.245992995212416 df.mm.trans3:probe3 0.109783902565000 0.049032953404653 2.23898205068311 0.0254639012291555 * df.mm.trans3:probe4 0.0291547328804354 0.049032953404653 0.594594672685345 0.552302543783874 df.mm.trans3:probe5 -0.00141647633499740 0.0490329534046531 -0.0288882524229711 0.976961776131381 df.mm.trans3:probe6 0.0887480644940936 0.049032953404653 1.80996775294534 0.0707205222149323 . df.mm.trans3:probe7 0.048200938585562 0.049032953404653 0.983031517350694 0.325924442584245 df.mm.trans3:probe8 0.00351490266447314 0.049032953404653 0.0716844982896663 0.942873039241746 df.mm.trans3:probe9 0.0366802687332274 0.0490329534046531 0.748073819468246 0.4546616102348 df.mm.trans3:probe10 0.0161121049881754 0.049032953404653 0.328597481273612 0.742556166305686 df.mm.trans3:probe11 0.222297520727234 0.049032953404653 4.53363514313903 6.79594737359347e-06 *** df.mm.trans3:probe12 0.0528070187888105 0.0490329534046531 1.07696997880204 0.281856909883068 df.mm.trans3:probe13 0.0242633373181336 0.0490329534046531 0.494837362087822 0.62086694606756 df.mm.trans3:probe14 0.06868532386839 0.0490329534046531 1.40079924008559 0.161707955458551