chr16.9300_chr16_13199696_13209853_-_1.R fitVsDatCorrelation=0.757346840014154 cont.fitVsDatCorrelation=0.294890932495269 fstatistic=8906.49171888772,39,393 cont.fstatistic=4154.3160408798,39,393 residuals=-0.402665641285759,-0.078975484297007,-0.00587798536207614,0.0728759077542844,0.923139695430774 cont.residuals=-0.389208496453802,-0.134878101991017,-0.0335569757538457,0.0865277869950858,1.04201491862994 predictedValues: Include Exclude Both chr16.9300_chr16_13199696_13209853_-_1.R.tl.Lung 55.2404277274889 60.5237715389857 55.6038334268988 chr16.9300_chr16_13199696_13209853_-_1.R.tl.cerebhem 57.615591421413 60.8009572372029 65.4877292195883 chr16.9300_chr16_13199696_13209853_-_1.R.tl.cortex 52.3610115618215 55.2309824212191 55.1328722017777 chr16.9300_chr16_13199696_13209853_-_1.R.tl.heart 52.9881399686698 52.0679655286947 53.0286838575012 chr16.9300_chr16_13199696_13209853_-_1.R.tl.kidney 53.3222940236696 63.0667966752864 57.5431573851666 chr16.9300_chr16_13199696_13209853_-_1.R.tl.liver 52.7291342472857 55.3451595917162 58.6288179277668 chr16.9300_chr16_13199696_13209853_-_1.R.tl.stomach 56.1870060022821 92.690867030342 83.4579051306526 chr16.9300_chr16_13199696_13209853_-_1.R.tl.testicle 53.4663154215135 59.2634598987471 56.0108202846957 diffExp=-5.28334381149683,-3.18536581578981,-2.86997085939763,0.920174439975064,-9.74450265161688,-2.61602534443045,-36.5038610280598,-5.79714447723368 diffExpScore=1.01271713645600 diffExp1.5=0,0,0,0,0,0,-1,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,0,-1,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,0,-1,0 diffExp1.3Score=0.5 diffExp1.2=0,0,0,0,0,0,-1,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 67.1380591172658 53.5699228029271 58.1252680137691 cerebhem 59.5389060829631 54.0897393816897 59.6163834153736 cortex 56.0695646249268 59.6086048699556 59.9817039870669 heart 60.3672465819683 58.5824077940943 58.2970276930913 kidney 61.7731635698249 61.1043225570306 57.7650532622336 liver 62.3381533187178 57.1158371046285 62.7337811178259 stomach 55.6990999635962 53.6292507238758 61.3226203774502 testicle 56.6127339154563 53.4833034192819 59.3753056890714 cont.diffExp=13.5681363143387,5.44916670127348,-3.53904024502881,1.78483878787397,0.668841012794267,5.22231621408925,2.06984923972042,3.12943049617445 cont.diffExpScore=1.20706466055738 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=1,0,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.536043702472313 cont.tran.correlation=0.0102792507453560 tran.covariance=0.00346290196418390 cont.tran.covariance=9.09982363080412e-05 tran.mean=58.3062425185211 cont.tran.mean=58.1700197392627 weightedLogRatios: wLogRatio Lung -0.370604379621424 cerebhem -0.219591889970502 cortex -0.212638449301182 heart 0.0693950817361043 kidney -0.681476107567807 liver -0.193169982239091 stomach -2.14198961105113 testicle -0.414906359888245 cont.weightedLogRatios: wLogRatio Lung 0.924245529916588 cerebhem 0.387650514216513 cortex -0.248327998726889 heart 0.122613220654959 kidney 0.0448305356263233 liver 0.35774058102058 stomach 0.151516365383878 testicle 0.227901325553491 varWeightedLogRatios=0.4756008617704 cont.varWeightedLogRatios=0.114553737303812 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.82691631542252 0.0748606344026818 51.1205434733188 9.98221134017165e-176 *** df.mm.trans1 0.179023515182022 0.0611234520358702 2.9288842370513 0.00359999164327136 ** df.mm.trans2 0.259536521295967 0.0611234520358702 4.24610378915868 2.71767504473297e-05 *** df.mm.exp2 -0.116943168622084 0.0830504171854255 -1.40809850913797 0.159892446009985 df.mm.exp3 -0.136538930205834 0.0830504171854255 -1.64404869756386 0.100965815031901 df.mm.exp4 -0.14469409516146 0.0830504171854255 -1.74224404964040 0.082248034071394 . df.mm.exp5 -0.0284654140564238 0.0830504171854255 -0.342748598033764 0.731970847088766 df.mm.exp6 -0.188948142035782 0.0830504171854254 -2.27510166040370 0.0234377355218932 * df.mm.exp7 0.0371339595609556 0.0830504171854255 0.447125503030854 0.655030762726652 df.mm.exp8 -0.0609792606622618 0.0830504171854255 -0.734243881353592 0.463237952163663 df.mm.trans1:exp2 0.159041312508467 0.0678103816765211 2.34538294249911 0.0195041231637011 * df.mm.trans2:exp2 0.121512495638714 0.0678103816765212 1.79194531330572 0.0739108856020997 . df.mm.trans1:exp3 0.083006118836853 0.0678103816765212 1.22409160344828 0.22165099191708 df.mm.trans2:exp3 0.0450267959147935 0.0678103816765212 0.664010359499033 0.507073043910515 df.mm.trans1:exp4 0.103067137909781 0.0678103816765212 1.51993154088774 0.129332272464815 df.mm.trans2:exp4 -0.00579221614300777 0.0678103816765212 -0.0854178372072676 0.931972710983563 df.mm.trans1:exp5 -0.00687513944541468 0.0678103816765211 -0.101387711961444 0.919294396845481 df.mm.trans2:exp5 0.0696236375869296 0.0678103816765212 1.02674009297069 0.30517439351746 df.mm.trans1:exp6 0.142421205257999 0.0678103816765211 2.10028614700028 0.0363404596018302 * df.mm.trans2:exp6 0.0995011405843734 0.0678103816765211 1.46734376247914 0.143082361051072 df.mm.trans1:exp7 -0.0201435109048342 0.0678103816765212 -0.297056444851257 0.76658035930015 df.mm.trans2:exp7 0.389099780505732 0.0678103816765211 5.73805619266195 1.91526282956493e-08 *** df.mm.trans1:exp8 0.0283360263953394 0.0678103816765212 0.417871507205372 0.676269283175341 df.mm.trans2:exp8 0.0399359803003879 0.0678103816765211 0.588936079004779 0.55624250715159 df.mm.trans1:probe2 0.0613564632415077 0.0415252085927127 1.47757146371747 0.140323438383468 df.mm.trans1:probe3 0.0580388181485883 0.0415252085927127 1.39767673939568 0.162998599176670 df.mm.trans1:probe4 0.00494307302900956 0.0415252085927127 0.119037885576739 0.905306163630897 df.mm.trans1:probe5 -0.0267205894719598 0.0415252085927127 -0.643478753690091 0.520288798228132 df.mm.trans1:probe6 -0.0285548731626339 0.0415252085927127 -0.687651528561979 0.492077881135125 df.mm.trans2:probe2 0.0508262990687225 0.0415252085927127 1.22398660455235 0.221690555469293 df.mm.trans2:probe3 0.0436078523095637 0.0415252085927127 1.05015372077424 0.2942927751542 df.mm.trans2:probe4 -0.0347227362808236 0.0415252085927127 -0.836184511952509 0.403559227126361 df.mm.trans2:probe5 0.119149421851761 0.0415252085927127 2.86932747335244 0.00433555274187825 ** df.mm.trans2:probe6 0.0201395928028945 0.0415252085927127 0.484996788346748 0.627948848087988 df.mm.trans3:probe2 -0.217628684428079 0.0415252085927127 -5.24088118527285 2.61372524990468e-07 *** df.mm.trans3:probe3 -0.347791355150995 0.0415252085927127 -8.37542704630817 9.83108978119695e-16 *** df.mm.trans3:probe4 -0.315011234073194 0.0415252085927127 -7.5860241224285 2.41087788466619e-13 *** df.mm.trans3:probe5 -0.241355605217803 0.0415252085927127 -5.81226713597191 1.27587242969730e-08 *** df.mm.trans3:probe6 -0.361558360092231 0.0415252085927127 -8.70696071965501 8.74208406718686e-17 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.13278959350296 0.109540956745932 37.7282590573726 1.04513446760653e-132 *** df.mm.trans1 0.0808700654898801 0.0894398166546052 0.904184159971846 0.366451675666498 df.mm.trans2 -0.175298329669196 0.0894398166546052 -1.95995850870486 0.0507069096723338 . df.mm.exp2 -0.135794312663346 0.121524780403335 -1.11742076153235 0.264497103226712 df.mm.exp3 -0.104784873346984 0.121524780403335 -0.862251081624734 0.38907519659656 df.mm.exp4 -0.0198083602426372 0.121524780403335 -0.162998527352974 0.870603375771661 df.mm.exp5 0.0545292779183129 0.121524780403335 0.448709125310349 0.653888806608642 df.mm.exp6 -0.0863833667358528 0.121524780403335 -0.710829235396684 0.477611697587373 df.mm.exp7 -0.239228533175756 0.121524780403335 -1.9685576257103 0.0497063001673407 * df.mm.exp8 -0.193413327568365 0.121524780403335 -1.59155463541210 0.112288816315504 df.mm.trans1:exp2 0.0156732121335889 0.099224567697316 0.157956970711124 0.874571833262023 df.mm.trans2:exp2 0.145451051459085 0.099224567697316 1.46587740147966 0.143481313296644 df.mm.trans1:exp3 -0.0753730645361889 0.099224567697316 -0.759620991911136 0.447936575205829 df.mm.trans2:exp3 0.211597045168234 0.099224567697316 2.13250659668994 0.0335845282958253 * df.mm.trans1:exp4 -0.0864960401736954 0.099224567697316 -0.87172000020752 0.383893368542247 df.mm.trans2:exp4 0.109255034620794 0.099224567697316 1.10108854244723 0.271532049635414 df.mm.trans1:exp5 -0.137811337349712 0.099224567697316 -1.38888322265213 0.165654812009472 df.mm.trans2:exp5 0.077065562529757 0.099224567697316 0.776678239252653 0.437815833410418 df.mm.trans1:exp6 0.0122059347617745 0.099224567697316 0.123013231954899 0.9021595033642 df.mm.trans2:exp6 0.150477033449014 0.099224567697316 1.51652999797433 0.130189315285007 df.mm.trans1:exp7 0.0524414381958549 0.099224567697316 0.528512639690477 0.597441904092063 df.mm.trans2:exp7 0.240335406019158 0.099224567697316 2.42213608581596 0.0158811158503364 * df.mm.trans1:exp8 0.0228961851717695 0.099224567697316 0.230751170835173 0.817628193162916 df.mm.trans2:exp8 0.191795078299200 0.099224567697316 1.93293941964323 0.0539617525755211 . df.mm.trans1:probe2 -0.0477375417611787 0.0607623902016677 -0.785642921595742 0.432550108376505 df.mm.trans1:probe3 0.0240204647971665 0.0607623902016677 0.395317970827738 0.692822726648739 df.mm.trans1:probe4 -0.0135851413466305 0.0607623902016677 -0.223578126231408 0.823201757853238 df.mm.trans1:probe5 -0.0166795636422256 0.0607623902016677 -0.274504732069737 0.783841005349284 df.mm.trans1:probe6 -0.0289211282837116 0.0607623902016677 -0.475970879152774 0.634359603069515 df.mm.trans2:probe2 0.032365055269832 0.0607623902016677 0.532649475480043 0.594577279172544 df.mm.trans2:probe3 0.0894031035685109 0.0607623902016677 1.47135593698316 0.141995137521112 df.mm.trans2:probe4 0.103651281103432 0.0607623902016677 1.70584601361826 0.0888268485673389 . df.mm.trans2:probe5 -0.0101403181579193 0.0607623902016677 -0.166884780606293 0.867546539734737 df.mm.trans2:probe6 0.0666789383074922 0.0607623902016677 1.09737187898942 0.273150772343161 df.mm.trans3:probe2 0.0744939473747269 0.0607623902016677 1.22598777183526 0.220937389959971 df.mm.trans3:probe3 -0.0266200543126694 0.0607623902016677 -0.438100842055729 0.661553882392635 df.mm.trans3:probe4 0.0399716442557673 0.0607623902016677 0.65783528467368 0.511029105515045 df.mm.trans3:probe5 0.0721986003749655 0.0607623902016677 1.18821198664736 0.23546745720913 df.mm.trans3:probe6 -0.0146092643158499 0.0607623902016677 -0.240432679941694 0.810120215226894