fitVsDatCorrelation=0.941606483231903 cont.fitVsDatCorrelation=0.258725929758806 fstatistic=5323.68826991209,52,692 cont.fstatistic=635.196416060752,52,692 residuals=-1.26654115116870,-0.117166086969332,0.00436910734377493,0.136799632387704,0.854303032042151 cont.residuals=-1.15799219857881,-0.478493512944894,-0.198660489533093,0.369757900825727,1.80242926185302 predictedValues: Include Exclude Both Lung 61.4312451043792 51.4698929638077 83.6120591116551 cerebhem 52.4530241534921 52.658158368163 65.6843504757035 cortex 67.5910025534592 48.9461159361482 83.0194891137132 heart 103.013407487689 50.2152553421775 122.830037143154 kidney 115.242374834663 54.8614032518023 175.433651632574 liver 96.628204306346 53.0096842887392 127.343005643753 stomach 87.383129619025 47.2401789505282 103.368215801770 testicle 78.8391585175031 51.8321154974477 100.554243069503 diffExp=9.96135214057152,-0.205134214670863,18.6448866173111,52.7981521455115,60.3809715828609,43.6185200176067,40.1429506684968,27.0070430200554 diffExpScore=0.99767225380298 diffExp1.5=0,0,0,1,1,1,1,1 diffExp1.5Score=0.833333333333333 diffExp1.4=0,0,0,1,1,1,1,1 diffExp1.4Score=0.833333333333333 diffExp1.3=0,0,1,1,1,1,1,1 diffExp1.3Score=0.857142857142857 diffExp1.2=0,0,1,1,1,1,1,1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 82.3081311650678 74.2923648128368 83.6722332164918 cerebhem 78.0794247627929 84.9858233069838 117.491927329046 cortex 88.5139980468126 89.0119607068845 74.4434663423275 heart 96.6521852549974 96.1590947686246 92.8190536025823 kidney 89.3888671837742 107.355689424562 79.7667168400745 liver 115.844160396449 114.833475074185 79.562948859341 stomach 83.0644587037374 95.1501482557514 81.5643168292545 testicle 80.6414491160892 77.6280191261456 103.981292222802 cont.diffExp=8.01576635223097,-6.90639854419092,-0.497962660071806,0.493090486372779,-17.9668222407879,1.01068532226462,-12.085689552014,3.0134299899436 cont.diffExpScore=1.92833036371927 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=0.238073192762209 cont.tran.correlation=0.793474956312662 tran.covariance=0.00198678519468145 cont.tran.covariance=0.0148993449409712 tran.mean=67.0508969484607 cont.tran.mean=90.869328131606 weightedLogRatios: wLogRatio Lung 0.712897919436856 cerebhem -0.0154639310203413 cortex 1.30783413457520 heart 3.07218291519547 kidney 3.24793077317924 liver 2.56409498354474 stomach 2.56034489965710 testicle 1.74374294700623 cont.weightedLogRatios: wLogRatio Lung 0.446655117426318 cerebhem -0.37294362924261 cortex -0.0251664523020842 heart 0.0233670881844760 kidney -0.839670637352066 liver 0.0416046193387713 stomach -0.609583528045686 testicle 0.166465535497355 varWeightedLogRatios=1.36018465891489 cont.varWeightedLogRatios=0.182135442619065 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.98028133138008 0.112531197594531 26.4840452699930 2.85855834365158e-107 *** df.mm.trans1 0.639236924047523 0.09745487583515 6.55931187197678 1.05863852177314e-10 *** df.mm.trans2 0.963429031310216 0.0878896749542072 10.9617999134960 6.81801452664031e-26 *** df.mm.exp2 0.106150611668801 0.115388579764177 0.919940360525665 0.357924491170379 df.mm.exp3 0.0523916450052837 0.115388579764177 0.454045323309795 0.649938575199486 df.mm.exp4 0.107648573122904 0.115388579764177 0.932922247096797 0.351185500826544 df.mm.exp5 -0.0481411966397714 0.115388579764177 -0.417209369750100 0.67665467152271 df.mm.exp6 0.0617331500477814 0.115388579764177 0.535002252163492 0.592820167296368 df.mm.exp7 0.0545216341071939 0.115388579764177 0.472504594636847 0.63671564150154 df.mm.exp8 0.071994582168094 0.115388579764177 0.62393160844194 0.532878118806033 df.mm.trans1:exp2 -0.264151204292882 0.106246887346709 -2.48620181625550 0.0131463014568984 * df.mm.trans2:exp2 -0.083326464410562 0.0850655895918281 -0.979555479605667 0.327647916927956 df.mm.trans1:exp3 0.0431646472235352 0.106246887346709 0.406267405111629 0.68467173536193 df.mm.trans2:exp3 -0.102668660831513 0.0850655895918281 -1.20693527575780 0.227869475349810 df.mm.trans1:exp4 0.409291992778647 0.106246887346709 3.85227278652436 0.000127896358812898 *** df.mm.trans2:exp4 -0.132326735390813 0.0850655895918281 -1.55558476730437 0.120264064515215 df.mm.trans1:exp5 0.677260130732408 0.106246887346709 6.37439973674094 3.3547063746878e-10 *** df.mm.trans2:exp5 0.11195422652242 0.0850655895918281 1.31609299435426 0.188578617667677 df.mm.trans1:exp6 0.391218934960599 0.106246887346709 3.6821684355228 0.000249278795510046 *** df.mm.trans2:exp6 -0.0322555648367555 0.0850655895918281 -0.379184638483411 0.70466707891791 df.mm.trans1:exp7 0.297862021328513 0.106246887346709 2.80348938935516 0.00519689750582199 ** df.mm.trans2:exp7 -0.140273888749151 0.0850655895918281 -1.64900859939054 0.0995997308064614 . df.mm.trans1:exp8 0.177496643125369 0.106246887346709 1.67060558250666 0.0952517856404654 . df.mm.trans2:exp8 -0.0649816687974485 0.0850655895918281 -0.76390076303769 0.445186757385006 df.mm.trans1:probe2 0.0523605330061581 0.0675187185567185 0.77549654563087 0.438311236504037 df.mm.trans1:probe3 0.220273059668482 0.0675187185567185 3.26239988520286 0.00115898374423659 ** df.mm.trans1:probe4 0.0129318902285242 0.0675187185567185 0.191530445259575 0.84816621403615 df.mm.trans1:probe5 -0.0059151032517446 0.0675187185567185 -0.0876068648544576 0.930214490486357 df.mm.trans1:probe6 0.0153730365641444 0.0675187185567185 0.227685549915028 0.819957961314072 df.mm.trans1:probe7 1.56668863027672 0.0675187185567185 23.2037672480504 1.51101452333770e-88 *** df.mm.trans1:probe8 1.65885021817693 0.0675187185567185 24.5687455810262 2.54646699393911e-96 *** df.mm.trans1:probe9 1.70447813148511 0.0675187185567185 25.2445272647359 3.51082458299374e-100 *** df.mm.trans1:probe10 1.68100472244606 0.0675187185567185 24.8968694664125 3.40521188108391e-98 *** df.mm.trans1:probe11 1.50117451647066 0.0675187185567185 22.2334568629233 4.70960968742422e-83 *** df.mm.trans1:probe12 1.47198580132827 0.0675187185567185 21.8011513368955 1.28458841707052e-80 *** df.mm.trans1:probe13 0.579018961874095 0.0675187185567185 8.57568055572167 6.42855270239592e-17 *** df.mm.trans1:probe14 0.513903513637016 0.0675187185567185 7.6112746897783 8.90924235710174e-14 *** df.mm.trans1:probe15 0.354388655978925 0.0675187185567185 5.24874677058961 2.03914794776727e-07 *** df.mm.trans1:probe16 0.464446443064067 0.0675187185567185 6.87878047735626 1.35232887864802e-11 *** df.mm.trans1:probe17 0.329866826694849 0.0675187185567185 4.88556112654518 1.28243405065894e-06 *** df.mm.trans1:probe18 0.339178367664756 0.0675187185567185 5.02347163742203 6.46561292300528e-07 *** df.mm.trans2:probe2 0.0379272986628233 0.0675187185567185 0.561730131636945 0.574481781219819 df.mm.trans2:probe3 -0.110706658722101 0.0675187185567185 -1.63964395487012 0.101533700930116 df.mm.trans2:probe4 0.105678319105944 0.0675187185567185 1.56517068695801 0.118000050446149 df.mm.trans2:probe5 -0.0650939628029037 0.0675187185567185 -0.964087651459529 0.335338761090205 df.mm.trans2:probe6 -0.00307826819803004 0.0675187185567185 -0.0455913302833817 0.963649132514735 df.mm.trans3:probe2 -0.661802876646997 0.0675187185567185 -9.80176891377249 2.50444411948632e-21 *** df.mm.trans3:probe3 0.119037293202002 0.0675187185567185 1.76302654651252 0.0783373437005812 . df.mm.trans3:probe4 -0.166670279398040 0.0675187185567185 -2.46850477853826 0.0138084827192828 * df.mm.trans3:probe5 -0.401723764351245 0.0675187185567185 -5.94981322125923 4.2631475537272e-09 *** df.mm.trans3:probe6 0.0289327255874819 0.0675187185567185 0.428514139574158 0.668410189907562 df.mm.trans3:probe7 -0.0150898035127660 0.0675187185567185 -0.223490668000311 0.823219604257642 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.31393858051003 0.322823403583584 13.3631531438615 2.12468559344126e-36 *** df.mm.trans1 0.187034978392762 0.27957326843954 0.669001651827131 0.503717543174797 df.mm.trans2 0.00233371081278667 0.252133138321377 0.00925586707215 0.99261765967678 df.mm.exp2 -0.257729500900327 0.331020506760855 -0.778590738749982 0.436486981788898 df.mm.exp3 0.370320557903804 0.331020506760855 1.11872391691836 0.263646125965205 df.mm.exp4 0.314900028598231 0.331020506760855 0.951300666172112 0.3417839054544 df.mm.exp5 0.498466426769905 0.331020506760855 1.50584757315359 0.132562617300271 df.mm.exp6 0.827609455210696 0.331020506760855 2.50017578460358 0.0126434724089388 * df.mm.exp7 0.282110290562992 0.331020506760855 0.852244150441114 0.394373447213022 df.mm.exp8 -0.193840698917751 0.331020506760855 -0.585585167561206 0.558345234704002 df.mm.trans1:exp2 0.204986173805982 0.304795314780275 0.672537154823905 0.501466294592966 df.mm.trans2:exp2 0.392205774017255 0.244031555221029 1.60719286348857 0.108468220406178 df.mm.trans1:exp3 -0.297629750269235 0.304795314780275 -0.976490568707708 0.329162652561045 df.mm.trans2:exp3 -0.189557992075759 0.244031555221029 -0.776776560326673 0.437556041371703 df.mm.trans1:exp4 -0.154251115107279 0.304795314780275 -0.50608099149581 0.612960974170587 df.mm.trans2:exp4 -0.056904156499031 0.244031555221029 -0.233183599750002 0.815687797279762 df.mm.trans1:exp5 -0.415940182382651 0.304795314780275 -1.36465412102053 0.172805355981159 df.mm.trans2:exp5 -0.130327089857565 0.244031555221029 -0.534058350525703 0.59347268030264 df.mm.trans1:exp6 -0.48583351408586 0.304795314780275 -1.59396647693254 0.111400155115451 df.mm.trans2:exp6 -0.392134603947411 0.244031555221029 -1.60690122059108 0.108532203472306 df.mm.trans1:exp7 -0.272963275176427 0.304795314780275 -0.895562569172707 0.370797841702608 df.mm.trans2:exp7 -0.0346623235349568 0.244031555221029 -0.142040333691935 0.88708948976287 df.mm.trans1:exp8 0.173383571376705 0.304795314780275 0.568852482203333 0.569640823118433 df.mm.trans2:exp8 0.237760947310049 0.244031555221029 0.974304110362694 0.330246015652932 df.mm.trans1:probe2 -0.0171419121391892 0.193694042150150 -0.0884999453204704 0.929504932837878 df.mm.trans1:probe3 -0.265947102562616 0.193694042150150 -1.37302675709796 0.170188769224706 df.mm.trans1:probe4 -0.0382779343920689 0.193694042150150 -0.197620608084559 0.843399943930915 df.mm.trans1:probe5 -0.182976850241246 0.193694042150150 -0.944669480847548 0.345157353977567 df.mm.trans1:probe6 -0.0765842002404064 0.193694042150150 -0.395387485284853 0.69267879844878 df.mm.trans1:probe7 0.00602576512543335 0.193694042150150 0.0311097081693520 0.975191017600019 df.mm.trans1:probe8 -0.254199827872029 0.193694042150150 -1.31237814570969 0.189827698208051 df.mm.trans1:probe9 -0.2812367176739 0.193694042150150 -1.45196369775735 0.146964920084188 df.mm.trans1:probe10 -0.116561141245262 0.193694042150150 -0.601779693125017 0.547517894741217 df.mm.trans1:probe11 -0.158324042457095 0.193694042150150 -0.817392423120395 0.413985535207020 df.mm.trans1:probe12 -0.273598775613241 0.193694042150150 -1.41253067247752 0.158243332222271 df.mm.trans1:probe13 -0.245460919759126 0.193694042150150 -1.26726107336252 0.205488250451883 df.mm.trans1:probe14 0.0202415345450000 0.193694042150150 0.104502618254561 0.91680077141413 df.mm.trans1:probe15 -0.0434062798817803 0.193694042150150 -0.224097134841825 0.822747867892474 df.mm.trans1:probe16 -0.192719203049880 0.193694042150150 -0.994967118815586 0.320100035886113 df.mm.trans1:probe17 -0.0113048920640891 0.193694042150150 -0.0583646865881686 0.95347499214824 df.mm.trans1:probe18 -0.131118925742410 0.193694042150150 -0.676938352294635 0.49867128907977 df.mm.trans2:probe2 0.108131159105787 0.193694042150150 0.558257538050472 0.57684910761097 df.mm.trans2:probe3 0.0077753139256497 0.193694042150150 0.0401422461906305 0.967991299678865 df.mm.trans2:probe4 -0.0639360120031574 0.193694042150150 -0.330087654186052 0.741433633138772 df.mm.trans2:probe5 -0.168558631715337 0.193694042150150 -0.870231370279688 0.384475739822861 df.mm.trans2:probe6 0.00915478640476462 0.193694042150150 0.0472641610611228 0.96231632823528 df.mm.trans3:probe2 -0.130048809935353 0.193694042150150 -0.671413578299637 0.502181158063405 df.mm.trans3:probe3 0.0519552213767078 0.193694042150150 0.268233451065224 0.788599566130275 df.mm.trans3:probe4 -0.238887244698329 0.193694042150150 -1.23332262596464 0.217874184473614 df.mm.trans3:probe5 -0.0447167308207317 0.193694042150150 -0.230862706587885 0.81748970155658 df.mm.trans3:probe6 -0.30389605892094 0.193694042150150 -1.56894892350567 0.117116962368703 df.mm.trans3:probe7 0.130793000019197 0.193694042150150 0.675255669029856 0.499738905705428