fitVsDatCorrelation=0.85654725110296 cont.fitVsDatCorrelation=0.208145947521617 fstatistic=11305.0819035491,52,692 cont.fstatistic=3137.59488560685,52,692 residuals=-0.499080707716959,-0.0948132245649386,-0.00533322427951814,0.0935416473991634,0.54177113294279 cont.residuals=-0.733310153701377,-0.211885784757109,-0.00554456443969542,0.228081125565673,0.743860486858264 predictedValues: Include Exclude Both Lung 82.8364774382695 110.757389451656 89.1714874315924 cerebhem 62.3574381228412 67.1703353981244 60.1303577258445 cortex 60.0391628791176 80.2615632925244 65.7374049584162 heart 71.0981589381473 104.452352957708 80.1469358687927 kidney 71.5663717389629 101.512697074021 68.655182239523 liver 68.7117635213502 104.388384350843 68.4708800690907 stomach 68.3271162226853 103.041050124318 69.2113313881038 testicle 65.640790448889 101.157050372621 66.866252745472 diffExp=-27.9209120133865,-4.81289727528328,-20.2224004134068,-33.3541940195606,-29.9463253350577,-35.6766208294924,-34.7139339016331,-35.5162599237322 diffExpScore=0.995518981356146 diffExp1.5=0,0,0,0,0,-1,-1,-1 diffExp1.5Score=0.75 diffExp1.4=0,0,0,-1,-1,-1,-1,-1 diffExp1.4Score=0.833333333333333 diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 76.8824678398803 72.6202919361739 79.0348084641996 cerebhem 71.3220146984142 79.7304982112352 73.9603323435538 cortex 76.8550951367753 84.999100583606 72.8765688535037 heart 74.1707254602961 76.8352518087515 76.1495033954535 kidney 77.5281702443723 78.3912150667325 80.371888433919 liver 74.5751063441024 73.4505793403196 68.6566847446404 stomach 74.2639867119469 76.121871556094 70.2036339082014 testicle 68.7694077454208 75.8202466446389 73.6971688344101 cont.diffExp=4.26217590370645,-8.40848351282096,-8.14400544683079,-2.66452634845544,-0.863044822360209,1.12452700378275,-1.85788484414709,-7.05083889921816 cont.diffExpScore=1.39725931429739 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=0.754348861881957 cont.tran.correlation=0.135065669304661 tran.covariance=0.0125407308692065 cont.tran.covariance=0.000245621544167764 tran.mean=82.7073813957549 cont.tran.mean=75.7710018330475 weightedLogRatios: wLogRatio Lung -1.32517122667027 cerebhem -0.310038555647199 cortex -1.23088740979521 heart -1.71424003161355 kidney -1.55392935958269 liver -1.85638901051109 stomach -1.81981969047508 testicle -1.90308706934737 cont.weightedLogRatios: wLogRatio Lung 0.246028800270095 cerebhem -0.481778003501949 cortex -0.442385766109899 heart -0.152611865784770 kidney -0.0482251011962176 liver 0.0653980180975275 stomach -0.106744549760240 testicle -0.417712537533468 varWeightedLogRatios=0.278474746179291 cont.varWeightedLogRatios=0.0686179450660009 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.81144209015805 0.0732402086921503 65.6939975469203 1.30538808334828e-299 *** df.mm.trans1 -0.433147596104581 0.0630514231049716 -6.86975130416092 1.43491935535799e-11 *** df.mm.trans2 -0.115768903341050 0.0568589655752543 -2.03607121884459 0.042123526437788 * df.mm.exp2 -0.390049461315077 0.0742698549136458 -5.25178703753482 2.00703857957467e-07 *** df.mm.exp3 -0.339029487589206 0.0742698549136458 -4.56483304004564 5.91554897810257e-06 *** df.mm.exp4 -0.104718491901221 0.0742698549136458 -1.40997302368475 0.158996937505870 df.mm.exp5 0.0280632620761556 0.0742698549136458 0.377855350717798 0.705653922041947 df.mm.exp6 0.0179809758139465 0.0742698549136458 0.242103284500193 0.808771863489184 df.mm.exp7 -0.0113797405493198 0.0742698549136458 -0.153221526587754 0.878268250086887 df.mm.exp8 -0.0354721258250447 0.0742698549136458 -0.47761135209283 0.633077669046741 df.mm.trans1:exp2 0.106063909452944 0.0677513631333147 1.56548746103073 0.117925810286998 df.mm.trans2:exp2 -0.110060954756510 0.0537867357740189 -2.04624714946309 0.0411094630598534 * df.mm.trans1:exp3 0.0171580383882413 0.0677513631333147 0.253250083758158 0.800150182520875 df.mm.trans2:exp3 0.0169782014812650 0.0537867357740189 0.315657777646105 0.752357452071112 df.mm.trans1:exp4 -0.0480885787525425 0.0677513631333147 -0.709780239519586 0.478079518444577 df.mm.trans2:exp4 0.046107378159741 0.0537867357740189 0.857225810345841 0.391616871013232 df.mm.trans1:exp5 -0.174306480038034 0.0677513631333147 -2.57273760964854 0.0102970548378937 * df.mm.trans2:exp5 -0.115221505703183 0.0537867357740189 -2.14219182564411 0.0325264043147269 * df.mm.trans1:exp6 -0.204929074097123 0.0677513631333147 -3.02472252394219 0.00258075862784577 ** df.mm.trans2:exp6 -0.0772046961731793 0.0537867357740189 -1.43538541728112 0.151629126189756 df.mm.trans1:exp7 -0.181182068376015 0.0677513631333147 -2.67422026652811 0.00766714051006052 ** df.mm.trans2:exp7 -0.0608349343332237 0.0537867357740189 -1.13103971560604 0.258430199615884 df.mm.trans1:exp8 -0.197199078992085 0.0677513631333147 -2.91062895080141 0.00372291928795699 ** df.mm.trans2:exp8 -0.0551957394493652 0.0537867357740189 -1.02619611796608 0.30515772354916 df.mm.trans1:probe2 -0.177242787172957 0.0443536785706936 -3.99612372377315 7.12954273300952e-05 *** df.mm.trans1:probe3 -0.136425866950535 0.0443536785706936 -3.07586363401834 0.00218164264061308 ** df.mm.trans1:probe4 0.0115986072289989 0.0443536785706936 0.261502711900488 0.79378267025092 df.mm.trans1:probe5 -0.0869154433705664 0.0443536785706936 -1.95959943281898 0.0504436570768077 . df.mm.trans1:probe6 0.144366386018224 0.0443536785706936 3.25489092833921 0.00118958516427158 ** df.mm.trans1:probe7 -0.0263622040250275 0.0443536785706936 -0.594363418651054 0.552463333938678 df.mm.trans1:probe8 0.00337332628637537 0.0443536785706936 0.0760551637447333 0.93939719319453 df.mm.trans1:probe9 0.30031459207753 0.0443536785706936 6.7709060838972 2.73414004907988e-11 *** df.mm.trans1:probe10 0.158527702086460 0.0443536785706936 3.57417258714604 0.000375746917583245 *** df.mm.trans1:probe11 -0.140852987737169 0.0443536785706936 -3.17567769520332 0.00156122117634261 ** df.mm.trans1:probe12 0.301687229916688 0.0443536785706936 6.8018536373672 2.23625798308914e-11 *** df.mm.trans1:probe13 0.141379801143651 0.0443536785706936 3.18755525358085 0.0014993926462786 ** df.mm.trans1:probe14 0.330808710185846 0.0443536785706936 7.45842782033475 2.62989135425804e-13 *** df.mm.trans1:probe15 0.0619483797033072 0.0443536785706936 1.39669091041840 0.162954308940078 df.mm.trans1:probe16 -0.0304273379606108 0.0443536785706936 -0.68601610827192 0.492932722838988 df.mm.trans1:probe17 0.108572369969625 0.0443536785706936 2.44787745838434 0.0146174218589773 * df.mm.trans2:probe2 0.0200182888987839 0.0443536785706936 0.451333227454348 0.65189080494397 df.mm.trans2:probe3 0.0611596491582339 0.0443536785706936 1.37890815664261 0.168368605026414 df.mm.trans2:probe4 0.134764632372402 0.0443536785706936 3.03840936569908 0.00246785280655218 ** df.mm.trans2:probe5 -0.0291987236814018 0.0443536785706936 -0.658315716358523 0.510554232821985 df.mm.trans2:probe6 -0.0233786616383101 0.0443536785706936 -0.52709633995854 0.598295618526346 df.mm.trans3:probe2 0.195513109552209 0.0443536785706936 4.40804722071899 1.20883736958495e-05 *** df.mm.trans3:probe3 0.544459030226746 0.0443536785706936 12.2753973914239 1.77483892141391e-31 *** df.mm.trans3:probe4 -0.154648515144267 0.0443536785706936 -3.48671226666755 0.000519895940326002 *** df.mm.trans3:probe5 0.0809326805219508 0.0443536785706936 1.82471179685706 0.06847554109462 . df.mm.trans3:probe6 0.0740478090965967 0.0443536785706936 1.66948518099970 0.0954735367541707 . df.mm.trans3:probe7 0.565819855957225 0.0443536785706936 12.7569995137018 1.26249585461441e-33 *** df.mm.trans3:probe8 0.518162030969228 0.0443536785706936 11.6825040823469 6.62493298524805e-29 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.24260838196652 0.138811166815823 30.5638838667476 1.64697655343250e-130 *** df.mm.trans1 0.085515941568912 0.119500500707029 0.715611575373778 0.474472608797508 df.mm.trans2 0.0684896275685258 0.107764020561668 0.635551895814174 0.525278641520828 df.mm.exp2 0.0846944352946822 0.140762641230840 0.601682623699776 0.547582482146353 df.mm.exp3 0.238161388993857 0.140762641230840 1.69193606280299 0.09110835664775 . df.mm.exp4 0.0577006404205809 0.140762641230840 0.409914448294247 0.681995571791161 df.mm.exp5 0.0680548758206823 0.140762641230840 0.483472569323826 0.628913171107132 df.mm.exp6 0.121667163604448 0.140762641230840 0.864342715798595 0.38769915755979 df.mm.exp7 0.130927806268889 0.140762641230840 0.930131781586688 0.35262721050959 df.mm.exp8 0.00152608939697979 0.140762641230840 0.0108415797233949 0.991352965118571 df.mm.trans1:exp2 -0.159767258024115 0.128408232825064 -1.24421350959461 0.213842255666234 df.mm.trans2:exp2 0.00871335390002468 0.101941265396833 0.0854742568292212 0.931909083865782 df.mm.trans1:exp3 -0.238517485485363 0.128408232825064 -1.85749371545597 0.0636655483687415 . df.mm.trans2:exp3 -0.0807650999589413 0.101941265396833 -0.79227091840132 0.428474235805705 df.mm.trans1:exp4 -0.0936089677136392 0.128408232825064 -0.728995062498576 0.466251329081219 df.mm.trans2:exp4 -0.00128148368249295 0.101941265396833 -0.0125708041537884 0.989973836812814 df.mm.trans1:exp5 -0.0596913825238586 0.128408232825064 -0.464856350800954 0.642180562016095 df.mm.trans2:exp5 0.008412606506979 0.101941265396833 0.0825240541622743 0.934253855175383 df.mm.trans1:exp6 -0.152138271243840 0.128408232825064 -1.18480153411273 0.236502668294667 df.mm.trans2:exp6 -0.110298759498847 0.101941265396833 -1.08198342515644 0.279636784183406 df.mm.trans1:exp7 -0.165579537142446 0.128408232825064 -1.28947757865356 0.197662921110522 df.mm.trans2:exp7 -0.0838365632566397 0.101941265396833 -0.82240065326131 0.411132189816558 df.mm.trans1:exp8 -0.113044962250273 0.128408232825064 -0.88035603141022 0.378972100128057 df.mm.trans2:exp8 0.0415948876845791 0.101941265396833 0.408027971034696 0.68337935195302 df.mm.trans1:probe2 0.0621346776342759 0.0840629209680527 0.73914488003444 0.460069665514597 df.mm.trans1:probe3 -0.0287617170310522 0.0840629209680527 -0.342145106306535 0.732345579370242 df.mm.trans1:probe4 0.0213646643083936 0.0840629209680527 0.254150867735288 0.799454505264058 df.mm.trans1:probe5 0.0636520587551006 0.0840629209680527 0.757195420074576 0.449190532102691 df.mm.trans1:probe6 0.0109336164854349 0.0840629209680527 0.130064674883116 0.896553055762053 df.mm.trans1:probe7 0.0820770372017142 0.0840629209680527 0.976376222197974 0.329219252582827 df.mm.trans1:probe8 -0.0375729989748318 0.0840629209680527 -0.446962805267153 0.655041768649428 df.mm.trans1:probe9 0.0101847571470191 0.0840629209680527 0.121156355617118 0.903602363333027 df.mm.trans1:probe10 0.0414886363067283 0.0840629209680527 0.493542644354408 0.62178581290248 df.mm.trans1:probe11 0.0187585481354573 0.0840629209680527 0.223148897509596 0.823485476474503 df.mm.trans1:probe12 0.0724574529071267 0.0840629209680527 0.861943078740548 0.389017426342606 df.mm.trans1:probe13 -0.0337300896462123 0.0840629209680527 -0.401248127685583 0.6883613209254 df.mm.trans1:probe14 -0.0304812073697734 0.0840629209680527 -0.362599907530664 0.7170144953866 df.mm.trans1:probe15 0.052650898407664 0.0840629209680527 0.62632725345903 0.531306856661202 df.mm.trans1:probe16 0.00228654177850868 0.0840629209680527 0.0272003607794887 0.978307770019807 df.mm.trans1:probe17 0.0463956365993129 0.0840629209680527 0.551915589715769 0.581184375509019 df.mm.trans2:probe2 -0.0982155525804107 0.0840629209680527 -1.16835759987137 0.243064900646735 df.mm.trans2:probe3 -0.08360440269161 0.0840629209680527 -0.994545534806994 0.320304979586435 df.mm.trans2:probe4 -0.0461668951883709 0.0840629209680527 -0.549194515925947 0.583049135820869 df.mm.trans2:probe5 -0.0204678029410600 0.0840629209680527 -0.243481938354706 0.807704235457491 df.mm.trans2:probe6 -0.113496075668171 0.0840629209680527 -1.35013242891363 0.177414927246797 df.mm.trans3:probe2 0.00773319766591595 0.0840629209680527 0.0919929688007734 0.92673024989307 df.mm.trans3:probe3 0.0468224584100207 0.0840629209680527 0.556992998468553 0.577712309002994 df.mm.trans3:probe4 -0.0460121864766516 0.0840629209680527 -0.547354124110654 0.584311945178167 df.mm.trans3:probe5 -0.0357445121178265 0.0840629209680527 -0.425211397679256 0.670814767971655 df.mm.trans3:probe6 -0.0543679192354106 0.0840629209680527 -0.646752677748048 0.518006477881486 df.mm.trans3:probe7 -0.0132253936861088 0.0840629209680527 -0.157327315465697 0.875032786227275 df.mm.trans3:probe8 -0.119010302221306 0.0840629209680527 -1.41572884752047 0.157304815123008