chr4.16975_chr4_131565076_131567707_-_2.R fitVsDatCorrelation=0.808770991566673 cont.fitVsDatCorrelation=0.297969906916774 fstatistic=8359.8154991244,44,508 cont.fstatistic=3166.15585585839,44,508 residuals=-0.618191023038417,-0.0960760944359724,-0.00242519055645221,0.0843163567121202,0.891167011420102 cont.residuals=-0.543349534622097,-0.176381910764095,-0.0344515054919019,0.145613727862665,1.41761955405088 predictedValues: Include Exclude Both chr4.16975_chr4_131565076_131567707_-_2.R.tl.Lung 78.6155886055274 79.3475166216466 66.3299455158785 chr4.16975_chr4_131565076_131567707_-_2.R.tl.cerebhem 77.3173445811905 102.831596891314 73.1126047122491 chr4.16975_chr4_131565076_131567707_-_2.R.tl.cortex 65.7193700071392 87.7927153215507 89.6100956669132 chr4.16975_chr4_131565076_131567707_-_2.R.tl.heart 69.5290005184661 70.0480276232348 67.3092847679496 chr4.16975_chr4_131565076_131567707_-_2.R.tl.kidney 85.3142921079072 82.4856165098689 66.3769459041649 chr4.16975_chr4_131565076_131567707_-_2.R.tl.liver 78.0724178807251 69.5688124975097 63.6600469317591 chr4.16975_chr4_131565076_131567707_-_2.R.tl.stomach 70.8979676497396 67.6314311609612 64.9612251648604 chr4.16975_chr4_131565076_131567707_-_2.R.tl.testicle 75.9205563706236 88.206306351191 75.239681535026 diffExp=-0.731928016119156,-25.5142523101236,-22.0733453144115,-0.519027104768654,2.82867559803837,8.50360538321536,3.26653648877833,-12.2857499805674 diffExpScore=1.59331608689948 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,-1,-1,0,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,-1,-1,0,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 76.5591438264012 81.5869110551353 69.7375125392105 cerebhem 78.0723839762937 73.2599455955958 74.2412112864283 cortex 73.9665160458399 79.3236653183345 75.3921058710185 heart 74.6095609992585 69.2417531673368 69.8562326128943 kidney 69.342954754508 86.9204108332187 83.9238118169402 liver 80.5260779660582 76.7740069410138 76.1552659459785 stomach 83.4383876633707 74.4929882317755 77.5739013858003 testicle 71.249953223678 81.7775234153447 73.2028530877116 cont.diffExp=-5.02776722873408,4.81243838069784,-5.3571492724946,5.36780783192175,-17.5774560787107,3.75207102504444,8.94539943159518,-10.5275701916667 cont.diffExpScore=3.69412618530371 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,-1,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.160038003654992 cont.tran.correlation=-0.582384145910246 tran.covariance=0.00200365069325587 cont.tran.covariance=-0.00255122436433112 tran.mean=78.0811600436622 cont.tran.mean=76.9463864383227 weightedLogRatios: wLogRatio Lung -0.0404900294859748 cerebhem -1.28057702022523 cortex -1.25395617215513 heart -0.031574254158935 kidney 0.149353566732714 liver 0.495875278326656 stomach 0.199885782230368 testicle -0.660662708410136 cont.weightedLogRatios: wLogRatio Lung -0.277946569309351 cerebhem 0.275219300787242 cortex -0.303370513892517 heart 0.319186466023716 kidney -0.983246580305256 liver 0.208262569862127 stomach 0.495279140684484 testicle -0.597412389361923 varWeightedLogRatios=0.460885663528751 cont.varWeightedLogRatios=0.266563654449152 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.57285537388352 0.0848393281749478 53.9001837031737 2.81175031281881e-212 *** df.mm.trans1 -0.138934406864744 0.0731070599992228 -1.90042393807411 0.0579433428453901 . df.mm.trans2 -0.266842556946773 0.0682783453876941 -3.90815792959837 0.000105601700988378 *** df.mm.exp2 0.145244500165214 0.0917964552491227 1.58224519422936 0.114215962104993 df.mm.exp3 -0.378861449109769 0.0917964552491227 -4.12719040274041 4.29147770030077e-05 *** df.mm.exp4 -0.262138829082022 0.0917964552491227 -2.85565306819979 0.00447059507894115 ** df.mm.exp5 0.119850432474352 0.0917964552491227 1.30561068125228 0.192275947010625 df.mm.exp6 -0.0973696540617833 0.0917964552491227 -1.06071257106318 0.289324578622703 df.mm.exp7 -0.242241650805033 0.0917964552491227 -2.63889983711924 0.00857295218380228 ** df.mm.exp8 -0.0550785240038111 0.0917964552491226 -0.60000709019031 0.548769053187421 df.mm.trans1:exp2 -0.161896197733564 0.0828619919837021 -1.95380528343329 0.0512726423114279 . df.mm.trans2:exp2 0.114011018416488 0.0728397572395052 1.56523062043723 0.118151533862133 df.mm.trans1:exp3 0.199685148133276 0.0828619919837021 2.40985213308113 0.0163134655799718 * df.mm.trans2:exp3 0.480002827336801 0.0728397572395051 6.58984661025845 1.10362890768360e-10 *** df.mm.trans1:exp4 0.139312760180027 0.0828619919837021 1.68126250461645 0.0933265788588421 . df.mm.trans2:exp4 0.137482794800546 0.0728397572395051 1.887469151613 0.0596670328897371 . df.mm.trans1:exp5 -0.0380784490079511 0.0828619919837021 -0.459540593900284 0.646042599077198 df.mm.trans2:exp5 -0.0810636496376355 0.0728397572395052 -1.11290389630335 0.266276046552101 df.mm.trans1:exp6 0.0904364763057394 0.0828619919837021 1.09141084037090 0.275609508927598 df.mm.trans2:exp6 -0.0341511252922208 0.0728397572395051 -0.468852815913816 0.639375979429114 df.mm.trans1:exp7 0.138913410822137 0.0828619919837021 1.67644305304004 0.0942666814660598 . df.mm.trans2:exp7 0.0824773338277776 0.0728397572395051 1.13231203608468 0.258037447148464 df.mm.trans1:exp8 0.0201959985735154 0.0828619919837021 0.243730546297846 0.807537909962505 df.mm.trans2:exp8 0.160919835071826 0.0728397572395051 2.20923079881642 0.0276040288060359 * df.mm.trans1:probe2 0.0238040591204298 0.0483809799528245 0.492012752607342 0.622922869649451 df.mm.trans1:probe3 -0.0632668633586762 0.0483809799528245 -1.30768048560337 0.191573184949522 df.mm.trans1:probe4 -0.267663066806278 0.0483809799528245 -5.53240275553063 5.0649666800878e-08 *** df.mm.trans1:probe5 0.27034143775567 0.0483809799528245 5.58776275344723 3.75606135832649e-08 *** df.mm.trans1:probe6 -0.142042229980620 0.0483809799528245 -2.93591056070223 0.00347642543940949 ** df.mm.trans1:probe7 -0.051339517098491 0.0483809799528245 -1.06115083135049 0.289125588055398 df.mm.trans1:probe8 -0.290604654704956 0.0483809799528245 -6.00658884934368 3.6137608026309e-09 *** df.mm.trans1:probe9 -0.187847334371708 0.0483809799528245 -3.8826690686066 0.000116953159386616 *** df.mm.trans1:probe10 -0.229343502688276 0.0483809799528245 -4.74036497218339 2.77422802886692e-06 *** df.mm.trans1:probe11 -0.0395951986447457 0.0483809799528245 -0.818404229996051 0.413510131619904 df.mm.trans1:probe12 -0.201409431091513 0.0483809799528245 -4.16298783711913 3.68968979570548e-05 *** df.mm.trans2:probe2 0.00239204723730833 0.0483809799528245 0.049441893067085 0.96058657585025 df.mm.trans2:probe3 0.427527504932937 0.0483809799528245 8.83668551876817 1.61481369328172e-17 *** df.mm.trans2:probe4 -0.196992252823861 0.0483809799528245 -4.07168794464158 5.41250732112576e-05 *** df.mm.trans2:probe5 0.0504104973010176 0.0483809799528245 1.04194866144034 0.297931090048723 df.mm.trans2:probe6 0.462729866708255 0.0483809799528245 9.56429297545141 4.87327420855588e-20 *** df.mm.trans3:probe2 -0.0499498571650842 0.0483809799528245 -1.03242756169448 0.302363124003672 df.mm.trans3:probe3 0.0151999594674675 0.0483809799528245 0.314172211523799 0.753519225001587 df.mm.trans3:probe4 -0.171951049154228 0.0483809799528245 -3.55410430549142 0.000414666804217349 *** df.mm.trans3:probe5 0.344516207127736 0.0483809799528245 7.1209017978484 3.68360111959326e-12 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.46593703897820 0.137701514959566 32.4320109353158 7.60902727326131e-126 *** df.mm.trans1 -0.126543018837629 0.11865903623581 -1.06644232796693 0.286730301362986 df.mm.trans2 -0.0117365883140042 0.110821617769415 -0.105905224542240 0.91569935437056 df.mm.exp2 -0.150663029806372 0.148993529624093 -1.01120518579895 0.31239962269554 df.mm.exp3 -0.140547592517486 0.148993529624093 -0.943313396709802 0.345969025830292 df.mm.exp4 -0.191560634019224 0.148993529624093 -1.28569767091582 0.199134355917771 df.mm.exp5 -0.220845987673121 0.148993529624093 -1.48225220404074 0.138893247815925 df.mm.exp6 -0.098321052230063 0.148993529624093 -0.659901490206485 0.509616117957379 df.mm.exp7 -0.111411594242085 0.148993529624093 -0.747761292206273 0.454950155582488 df.mm.exp8 -0.118031839631253 0.148993529624093 -0.792194398837614 0.428617106172980 df.mm.trans1:exp2 0.170235861825095 0.134492128523155 1.26576821777183 0.206176268587804 df.mm.trans2:exp2 0.0430081991679864 0.118225180902938 0.363782054208028 0.716172112842586 df.mm.trans1:exp3 0.106096533410573 0.134492128523155 0.788867977446776 0.430557170170792 df.mm.trans2:exp3 0.11241525899142 0.118225180902938 0.950857153551005 0.342129055075293 df.mm.trans1:exp4 0.165765732504199 0.134492128523155 1.2325311103665 0.218320864710206 df.mm.trans2:exp4 0.0274958387909415 0.118225180902938 0.232571763315934 0.816187624305414 df.mm.trans1:exp5 0.121846975534874 0.134492128523155 0.90597849013815 0.365376737486966 df.mm.trans2:exp5 0.284170024249434 0.118225180902938 2.40363365975930 0.0165903268594587 * df.mm.trans1:exp6 0.148838570041635 0.134492128523155 1.10667123552893 0.268959864947773 df.mm.trans2:exp6 0.0375183384393245 0.118225180902938 0.317346424448501 0.751111142854578 df.mm.trans1:exp7 0.197456517400753 0.134492128523155 1.46816411911242 0.142678538882698 df.mm.trans2:exp7 0.0204477521360439 0.118225180902938 0.172955980949874 0.862754916856564 df.mm.trans1:exp8 0.0461624382285836 0.134492128523155 0.343235241612195 0.731563490443987 df.mm.trans2:exp8 0.120365425240967 0.118225180902938 1.01810311747195 0.309113575775672 df.mm.trans1:probe2 0.0278841115546315 0.0785264850399842 0.355091808075115 0.722668191144494 df.mm.trans1:probe3 0.0525105229105664 0.0785264850399842 0.668698247270701 0.503991728895276 df.mm.trans1:probe4 0.040904354521361 0.0785264850399842 0.520898834330013 0.602664220971385 df.mm.trans1:probe5 -0.00149206868982678 0.0785264850399842 -0.0190008337832394 0.984847900634734 df.mm.trans1:probe6 -0.0786867194299351 0.0785264850399842 -1.00204051397270 0.316801082051753 df.mm.trans1:probe7 0.0561863426320772 0.0785264850399842 0.715508183047645 0.47462381302066 df.mm.trans1:probe8 -0.0840121256220237 0.0785264850399842 -1.06985720269087 0.285191654466325 df.mm.trans1:probe9 -0.0840666214123973 0.0785264850399842 -1.07055118243981 0.284879652538411 df.mm.trans1:probe10 0.088378142551865 0.0785264850399842 1.12545649416072 0.260927080858529 df.mm.trans1:probe11 -0.00716651893321418 0.0785264850399842 -0.0912624438692897 0.927320033328876 df.mm.trans1:probe12 -0.0330571737685509 0.0785264850399842 -0.4209684637192 0.673956149688654 df.mm.trans2:probe2 -0.181117208810805 0.0785264850399842 -2.30644741985565 0.0214872066350975 * df.mm.trans2:probe3 -0.0695914929690516 0.0785264850399842 -0.886216834149866 0.375920016743178 df.mm.trans2:probe4 -0.134840764126418 0.0785264850399842 -1.71713739711843 0.086563568385719 . df.mm.trans2:probe5 -0.08273494919484 0.0785264850399842 -1.05359292667579 0.29257018946974 df.mm.trans2:probe6 -0.109563243157984 0.0785264850399842 -1.39523936544716 0.163553310184158 df.mm.trans3:probe2 -0.140083376566728 0.0785264850399842 -1.78389974408507 0.07503676162061 . df.mm.trans3:probe3 -0.0635741456446121 0.0785264850399842 -0.809588581638938 0.418555712899760 df.mm.trans3:probe4 -0.112964647532604 0.0785264850399842 -1.43855474334658 0.15089250890786 df.mm.trans3:probe5 -0.0979731245015011 0.0785264850399842 -1.24764433874272 0.212736149332667