chrX.25821_chrX_57354613_57361914_+_0.R fitVsDatCorrelation=0.877076819360443 cont.fitVsDatCorrelation=0.259850348607088 fstatistic=14795.6601743611,53,715 cont.fstatistic=3650.9483777362,53,715 residuals=-0.415465785621083,-0.0742185551761735,-0.00171649572604662,0.0715533322315649,0.636008843304968 cont.residuals=-0.440909678200297,-0.157091720087209,-0.0478131633850499,0.0858031505784042,0.959536411929453 predictedValues: Include Exclude Both chrX.25821_chrX_57354613_57361914_+_0.R.tl.Lung 51.2740452235913 50.9706746391409 49.9347634321257 chrX.25821_chrX_57354613_57361914_+_0.R.tl.cerebhem 55.0606238671559 55.2724005139574 50.8822611642106 chrX.25821_chrX_57354613_57361914_+_0.R.tl.cortex 49.9721327622228 51.410359027515 46.2228889033279 chrX.25821_chrX_57354613_57361914_+_0.R.tl.heart 50.3189463768802 50.8699990203101 50.0535958895322 chrX.25821_chrX_57354613_57361914_+_0.R.tl.kidney 51.0519254943082 50.1217009530303 50.207361425198 chrX.25821_chrX_57354613_57361914_+_0.R.tl.liver 54.9285015900056 52.1970723942033 50.6579776998498 chrX.25821_chrX_57354613_57361914_+_0.R.tl.stomach 54.9884914278609 48.6107498083611 49.821875462563 chrX.25821_chrX_57354613_57361914_+_0.R.tl.testicle 52.1824105086157 49.3572507691859 48.9169036784111 diffExp=0.30337058445037,-0.211776646801454,-1.43822626529218,-0.551052643429877,0.93022454127783,2.73142919580227,6.37774161949982,2.82515973942984 diffExpScore=1.28429414504614 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 55.3835978209231 58.6806553901156 52.145947879605 cerebhem 50.4736199555563 56.1436907487492 56.5838369807262 cortex 53.1012410617318 50.599833975689 49.2300316227372 heart 56.8868908823061 54.6868682497339 51.7986704291515 kidney 54.8684425850065 55.1913366701313 48.5761083795638 liver 54.9518641308861 61.4681601307935 53.9820905920868 stomach 50.4350772242143 50.444795368266 53.7946879140156 testicle 53.3923934868219 56.0867142684886 51.4127574124249 cont.diffExp=-3.29705756919245,-5.67007079319288,2.50140708604275,2.20002263257217,-0.322894085124801,-6.51629599990738,-0.00971814405167493,-2.69432078166675 cont.diffExpScore=1.56741849465815 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=0.288945276258468 cont.tran.correlation=0.449344894357035 tran.covariance=0.000440364279969099 cont.tran.covariance=0.00133211673665929 tran.mean=51.7867052735215 cont.tran.mean=54.5496988718383 weightedLogRatios: wLogRatio Lung 0.0233465236772325 cerebhem -0.0153952169126835 cortex -0.111387190523041 heart -0.042737018091842 kidney 0.0721527390242609 liver 0.203030720917638 stomach 0.486396230364842 testicle 0.218575211841036 cont.weightedLogRatios: wLogRatio Lung -0.233804487507392 cerebhem -0.423158569615004 cortex 0.190502500593980 heart 0.158607101333879 kidney -0.0235167096122733 liver -0.455249423775150 stomach -0.000755408100631917 testicle -0.197035106167246 varWeightedLogRatios=0.0369798274609435 cont.varWeightedLogRatios=0.0600958142287902 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.89007974417549 0.0584118627473185 66.597426639233 0 *** df.mm.trans1 -0.0270468439376348 0.0498724659328603 -0.542320164678562 0.587766947772126 df.mm.trans2 0.0207445076346864 0.0449246127658136 0.461762636504513 0.644392004205462 df.mm.exp2 0.133476447374277 0.0582050130853954 2.29321222174536 0.0221251449732454 * df.mm.exp3 0.0601123675575733 0.0582050130853953 1.03276959098660 0.302060933834528 df.mm.exp4 -0.0231570570182899 0.0582050130853953 -0.397853308345023 0.690857075343593 df.mm.exp5 -0.0265820502950244 0.0582050130853953 -0.456696921552523 0.64802760920388 df.mm.exp6 0.0782443314685054 0.0582050130853954 1.34428852981631 0.17928144886542 df.mm.exp7 0.0247967374075025 0.0582050130853954 0.426024084405274 0.670218512002114 df.mm.exp8 0.00598941873956046 0.0582050130853953 0.102902111382968 0.918069515348554 df.mm.trans1:exp2 -0.0622263001303094 0.0523886047964954 -1.18778311375210 0.235313286677302 df.mm.trans2:exp2 -0.0524532102467052 0.0408958415116248 -1.2826049864213 0.200046145335150 df.mm.trans1:exp3 -0.085831545344054 0.0523886047964954 -1.63836287829135 0.101785961074707 df.mm.trans2:exp3 -0.0515231381708056 0.0408958415116248 -1.25986252553722 0.208130020371529 df.mm.trans1:exp4 0.00435404765022640 0.0523886047964954 0.083110586111995 0.933786870282685 df.mm.trans2:exp4 0.0211799363255774 0.0408958415116248 0.517899511116722 0.604688587836221 df.mm.trans1:exp5 0.0222406289217494 0.0523886047964954 0.424531804352178 0.67130572595586 df.mm.trans2:exp5 0.00978565708867777 0.0408958415116248 0.239282448458633 0.810955147190618 df.mm.trans1:exp6 -0.00939664610702037 0.0523886047964954 -0.179364312974584 0.857702466789949 df.mm.trans2:exp6 -0.0544683828236256 0.0408958415116248 -1.33188071966052 0.18332378792407 df.mm.trans1:exp7 0.0451424960788844 0.0523886047964954 0.861685403805682 0.389149546837450 df.mm.trans2:exp7 -0.072202501756077 0.0408958415116248 -1.7655218498329 0.0779028754773727 . df.mm.trans1:exp8 0.0115713728428560 0.0523886047964954 0.220875758913704 0.82525223031873 df.mm.trans2:exp8 -0.0381551985071398 0.0408958415116248 -0.932984799843133 0.351142761336904 df.mm.trans1:probe2 -0.0542862900440405 0.0358680257540798 -1.51350092185840 0.130594353524861 df.mm.trans1:probe3 0.0398008732680519 0.0358680257540798 1.10964772750351 0.267523919455635 df.mm.trans1:probe4 -0.0803297499257311 0.0358680257540798 -2.23959217818377 0.025424032985983 * df.mm.trans1:probe5 -0.104780432900415 0.0358680257540798 -2.92127683912172 0.00359584130050078 ** df.mm.trans1:probe6 0.304151865440245 0.0358680257540798 8.4797492765726 1.28927470750381e-16 *** df.mm.trans1:probe7 0.536001354677578 0.0358680257540798 14.9437094294662 3.83961644696098e-44 *** df.mm.trans1:probe8 0.095137999201767 0.0358680257540798 2.65244593761745 0.00816833493316151 ** df.mm.trans1:probe9 0.698536044948865 0.0358680257540798 19.4751740655648 4.25821651227326e-68 *** df.mm.trans1:probe10 0.0420844776871223 0.0358680257540798 1.17331458317957 0.241060385998387 df.mm.trans1:probe11 0.0149279285354275 0.0358680257540798 0.416190415323584 0.677395567078018 df.mm.trans1:probe12 0.0989231501391528 0.0358680257540798 2.75797588686354 0.0059645843187206 ** df.mm.trans1:probe13 -0.0366900988813186 0.0358680257540798 -1.02291938599786 0.306691925289823 df.mm.trans1:probe14 0.00341427147876108 0.0358680257540798 0.0951898357096702 0.924190714172029 df.mm.trans1:probe15 0.284006865652398 0.0358680257540798 7.91810699589712 9.21648601043594e-15 *** df.mm.trans1:probe16 0.0128963118946956 0.0358680257540798 0.359548974987248 0.71929061065748 df.mm.trans2:probe2 0.0117315915927271 0.0358680257540798 0.327076591088727 0.743705686975489 df.mm.trans2:probe3 -0.109012804842784 0.0358680257540798 -3.03927530302902 0.00245796105054577 ** df.mm.trans2:probe4 0.129485333808328 0.0358680257540798 3.61004909208308 0.000327514601728176 *** df.mm.trans2:probe5 -0.0158797514904948 0.0358680257540798 -0.44272722450269 0.65809695172439 df.mm.trans2:probe6 0.310494966268373 0.0358680257540798 8.65659482897675 3.20378842703538e-17 *** df.mm.trans3:probe2 -0.0354810134194639 0.0358680257540798 -0.989210102131927 0.322894990250467 df.mm.trans3:probe3 -0.0665315755701545 0.0358680257540798 -1.85489929181806 0.0640220630352302 . df.mm.trans3:probe4 -0.143935567985809 0.0358680257540798 -4.01292139613894 6.62950332096727e-05 *** df.mm.trans3:probe5 -0.0631788916942148 0.0358680257540798 -1.76142651751689 0.0785936025349268 . df.mm.trans3:probe6 -0.0403531881067230 0.0358680257540798 -1.12504625661291 0.260946954498861 df.mm.trans3:probe7 0.707521142075441 0.0358680257540798 19.7256784336663 1.73208530540305e-69 *** df.mm.trans3:probe8 -0.0880317390380507 0.0358680257540798 -2.45432351481005 0.0143523289555464 * df.mm.trans3:probe9 -0.0876733400062437 0.0358680257540798 -2.44433135537914 0.0147526110329336 * df.mm.trans3:probe10 0.087064316695953 0.0358680257540798 2.42735179496323 0.0154554519125036 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.2566713303415 0.117425491855041 36.2499765859721 4.21766024080559e-164 *** df.mm.trans1 -0.203620055600246 0.100258724285569 -2.03094600545954 0.042629960978489 * df.mm.trans2 -0.171802425568506 0.090312044545525 -1.90232018811071 0.057530850666726 . df.mm.exp2 -0.218705372017516 0.117009661539948 -1.86912233690077 0.0620143988951329 . df.mm.exp3 -0.132702367430732 0.117009661539948 -1.13411461655605 0.257126564576601 df.mm.exp4 -0.0370230702463782 0.117009661539948 -0.316410369529512 0.751783398102247 df.mm.exp5 0.00026542399811704 0.117009661539948 0.00226839386272749 0.998190717842976 df.mm.exp6 0.00397746122429374 0.117009661539948 0.0339925880644975 0.972892548239245 df.mm.exp7 -0.275955364270350 0.117009661539948 -2.35839810694722 0.0186222109469364 * df.mm.exp8 -0.0676662130485229 0.117009661539948 -0.578295947172029 0.563246424990861 df.mm.trans1:exp2 0.125872712992666 0.105316923592036 1.19518030625597 0.232412859187281 df.mm.trans2:exp2 0.174509560566880 0.0822129971287177 2.12265172979471 0.0341260989861503 * df.mm.trans1:exp3 0.0906191858641667 0.105316923592036 0.860442773805247 0.389833440568138 df.mm.trans2:exp3 -0.0154594594905483 0.0822129971287177 -0.188041550976958 0.850897432645711 df.mm.trans1:exp4 0.0638045144214312 0.105316923592036 0.605833442957273 0.544817625441237 df.mm.trans2:exp4 -0.0334634398038306 0.0822129971287177 -0.407033449363708 0.684105241107714 df.mm.trans1:exp5 -0.00961053875711742 0.105316923592036 -0.09125350826184 0.927316717618035 df.mm.trans2:exp5 -0.061569549510257 0.0822129971287177 -0.748902870112616 0.454162043649314 df.mm.trans1:exp6 -0.0118033385231258 0.105316923592036 -0.112074471229791 0.910795814455047 df.mm.trans2:exp6 0.0424317359771909 0.0822129971287177 0.516119560885941 0.605930476035025 df.mm.trans1:exp7 0.182358792237136 0.105316923592036 1.73152410854247 0.0837896670013044 . df.mm.trans2:exp7 0.124724819494463 0.0822129971287177 1.51709369382540 0.129685019559376 df.mm.trans1:exp8 0.0310510230995389 0.105316923592036 0.294834125803187 0.76820618041167 df.mm.trans2:exp8 0.0224550531253104 0.0822129971287177 0.273132642155758 0.784830126123851 df.mm.trans1:probe2 -0.0816291154112115 0.0721055684230076 -1.13207782972230 0.25798123244336 df.mm.trans1:probe3 -0.0471432440861051 0.0721055684230076 -0.653808646366104 0.51344530733694 df.mm.trans1:probe4 -0.00255588230946886 0.0721055684230076 -0.0354463929120531 0.971733685070832 df.mm.trans1:probe5 -0.100060041969599 0.0721055684230076 -1.38768813779536 0.165664332611759 df.mm.trans1:probe6 -0.151634770701978 0.0721055684230076 -2.10295507016063 0.0358186233618346 * df.mm.trans1:probe7 -0.0935412467547889 0.0721055684230076 -1.29728187157514 0.194952640778989 df.mm.trans1:probe8 -0.0384204424477487 0.0721055684230076 -0.532835997108504 0.594312673114449 df.mm.trans1:probe9 -0.0331445822963508 0.0721055684230076 -0.459667443461619 0.645894672982981 df.mm.trans1:probe10 -0.0723295237681151 0.0721055684230076 -1.00310593689233 0.316149101612558 df.mm.trans1:probe11 -0.105724522184481 0.0721055684230076 -1.46624628994320 0.143020848340571 df.mm.trans1:probe12 -0.0332104928519919 0.0721055684230076 -0.460581527589691 0.64523891489749 df.mm.trans1:probe13 -0.0716877267354769 0.0721055684230076 -0.994205139815563 0.320459342971453 df.mm.trans1:probe14 -0.0620623034523957 0.0721055684230076 -0.860714433153165 0.389683867634019 df.mm.trans1:probe15 -0.0864242121680107 0.0721055684230076 -1.19857889006578 0.231088838020736 df.mm.trans1:probe16 0.0103732811564888 0.0721055684230076 0.143862414281708 0.885649652474711 df.mm.trans2:probe2 -0.0240343869800561 0.0721055684230076 -0.333322203897738 0.738988800628469 df.mm.trans2:probe3 0.00848618963914221 0.0721055684230076 0.117691182868956 0.906345406029885 df.mm.trans2:probe4 -0.0306703010766571 0.0721055684230076 -0.42535273970423 0.670707540693864 df.mm.trans2:probe5 -0.0439982390030022 0.0721055684230076 -0.610191972205064 0.541928532007985 df.mm.trans2:probe6 -0.113923785404142 0.0721055684230076 -1.5799582181477 0.114558693918637 df.mm.trans3:probe2 0.071626819649098 0.0721055684230076 0.993360446573266 0.320870379043657 df.mm.trans3:probe3 0.0426186484647897 0.0721055684230076 0.591059045742033 0.554667654704154 df.mm.trans3:probe4 0.0495198490471446 0.0721055684230076 0.686768721614345 0.492451151345755 df.mm.trans3:probe5 0.127899477596297 0.0721055684230076 1.77378086593777 0.0765249604608345 . df.mm.trans3:probe6 0.0580902704540376 0.0721055684230076 0.805628077338643 0.420725170230984 df.mm.trans3:probe7 0.133685926078520 0.0721055684230076 1.85403054163932 0.0641464151971122 . df.mm.trans3:probe8 0.103553456497758 0.0721055684230076 1.43613674730724 0.151400796209899 df.mm.trans3:probe9 0.0251161659869459 0.0721055684230076 0.348324914930312 0.727698717219124 df.mm.trans3:probe10 0.115865666236942 0.0721055684230076 1.60688929816370 0.108520148532192