fitVsDatCorrelation=0.896892880070694 cont.fitVsDatCorrelation=0.28071837662899 fstatistic=7085.12108558825,54,738 cont.fstatistic=1493.50613323598,54,738 residuals=-0.975333299122902,-0.0959021330700557,-0.0081278231154571,0.0945648515398566,0.760448284827544 cont.residuals=-0.928641738709102,-0.29214568825841,-0.0519326738778206,0.215516044016729,1.62394449764853 predictedValues: Include Exclude Both Lung 71.8435890618248 87.1311409290302 70.4077223126049 cerebhem 83.2884111485091 69.427290910418 69.8385034088001 cortex 133.215741068163 77.809954153622 104.362708157351 heart 65.5939288991785 77.0476484120722 69.2195517610365 kidney 70.8156198974199 83.242059123634 76.8672044320119 liver 68.5416465658765 75.5703667837197 69.1294141076501 stomach 71.7214533628334 90.5051339481888 75.9548616009361 testicle 67.664678071397 84.0798772889352 69.2639765089833 diffExp=-15.2875518672054,13.8611202380911,55.4057869145414,-11.4537195128936,-12.4264392262141,-7.02872021784326,-18.7836805853554,-16.4151992175382 diffExpScore=11.4760502351461 diffExp1.5=0,0,1,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=-1,0,1,0,0,0,-1,-1 diffExp1.2Score=1.33333333333333 cont.predictedValues: Include Exclude Both Lung 72.3830895775968 83.740089371244 83.8810868869574 cerebhem 84.682179335835 81.5023484601823 93.216899560301 cortex 82.0284854191922 98.6645588575958 76.665713600066 heart 81.4471881385193 88.6911581100491 77.384460923588 kidney 82.496803605725 85.4216591281375 73.7175041624838 liver 76.6673387058175 88.8368427423132 88.2499660526373 stomach 77.4757354666207 81.1891533121111 69.912653134717 testicle 75.0773464559461 88.3959295895382 85.027884367578 cont.diffExp=-11.3569997936472,3.17983087565281,-16.6360734384036,-7.24396997152978,-2.92485552241246,-12.1695040364957,-3.71341784549035,-13.3185831335921 cont.diffExpScore=1.08222411745257 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,-1,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.25761674246099 cont.tran.correlation=0.139549981721393 tran.covariance=-0.00544389462432597 cont.tran.covariance=0.00046232654108994 tran.mean=79.8436587265514 cont.tran.mean=83.0437441422765 weightedLogRatios: wLogRatio Lung -0.843257203756257 cerebhem 0.788422921979687 cortex 2.48585401848232 heart -0.686244514344084 kidney -0.701809556037388 liver -0.417459322670286 stomach -1.02097739564044 testicle -0.939004427772354 cont.weightedLogRatios: wLogRatio Lung -0.63469746796888 cerebhem 0.169159332800527 cortex -0.830855046009474 heart -0.37852984207375 kidney -0.154348078081228 liver -0.650168620980296 stomach -0.204747609824051 testicle -0.7185791954835 varWeightedLogRatios=1.47877502090239 cont.varWeightedLogRatios=0.117504058311091 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.41983378760334 0.0981820954480278 45.0166984869757 7.78176743358292e-214 *** df.mm.trans1 -0.519077159111543 0.0858577518050688 -6.04578093647332 2.36018716532413e-09 *** df.mm.trans2 -0.00253387733483361 0.0771618240715916 -0.0328384841250338 0.973812267917126 df.mm.exp2 -0.0711988172975484 0.101827199768905 -0.699212169824298 0.484639750653891 df.mm.exp3 0.110764102175725 0.101827199768905 1.08776537533293 0.277053813445959 df.mm.exp4 -0.196978960677804 0.101827199768905 -1.93444346034109 0.0534405363751838 . df.mm.exp5 -0.147849763903005 0.101827199768905 -1.45196729595380 0.146935678788230 df.mm.exp6 -0.17107732600068 0.101827199768905 -1.68007493468284 0.0933659421310382 . df.mm.exp7 -0.0395455302762361 0.101827199768905 -0.388359204279249 0.697862233409564 df.mm.exp8 -0.0791961660778192 0.101827199768905 -0.777750603547515 0.43696530545268 df.mm.trans1:exp2 0.219016853078210 0.0952506235474573 2.29937448093543 0.0217620705620135 * df.mm.trans2:exp2 -0.155935502422126 0.0763703998266788 -2.04183168840308 0.0415232821221631 * df.mm.trans1:exp3 0.506714443288568 0.0952506235474573 5.31980184923518 1.37876533752943e-07 *** df.mm.trans2:exp3 -0.223909084474052 0.0763703998266788 -2.93188309845450 0.00347333991267267 ** df.mm.trans1:exp4 0.105970723229311 0.0952506235474573 1.11254624151109 0.266265622098948 df.mm.trans2:exp4 0.073988650975921 0.0763703998266787 0.968813193905452 0.33295579266025 df.mm.trans1:exp5 0.133437978442563 0.0952506235474574 1.40091448720101 0.161659981753223 df.mm.trans2:exp5 0.102188151594078 0.0763703998266788 1.33805966481768 0.181289117724163 df.mm.trans1:exp6 0.124027483741203 0.0952506235474573 1.30211728933625 0.193282535848121 df.mm.trans2:exp6 0.0287272078663900 0.0763703998266787 0.3761563109737 0.706908801057751 df.mm.trans1:exp7 0.037844061361606 0.0952506235474573 0.397310379209756 0.691253474952216 df.mm.trans2:exp7 0.0775377573647096 0.0763703998266787 1.01528547108147 0.310302500377486 df.mm.trans1:exp8 0.0192690860650709 0.0952506235474573 0.202298791833844 0.839738898135363 df.mm.trans2:exp8 0.043549082471737 0.0763703998266788 0.570235098553508 0.568691810366173 df.mm.trans1:probe2 0.340564931785169 0.0583288563432996 5.83870408465998 7.88185180474132e-09 *** df.mm.trans1:probe3 0.341855063947814 0.0583288563432996 5.86082233355985 6.94130811936307e-09 *** df.mm.trans1:probe4 0.094826078012285 0.0583288563432996 1.62571467978350 0.104437285595801 df.mm.trans1:probe5 0.292344975058075 0.0583288563432996 5.01201280781939 6.74892159872092e-07 *** df.mm.trans1:probe6 -0.0113523557562155 0.0583288563432996 -0.194626750255486 0.845738723708922 df.mm.trans1:probe7 0.0515937352227242 0.0583288563432996 0.884531918799586 0.376697219132414 df.mm.trans1:probe8 1.59790380976207 0.0583288563432996 27.3947392411993 1.53961219051507e-114 *** df.mm.trans1:probe9 0.103282361848848 0.0583288563432996 1.77069067222869 0.0770248934453935 . df.mm.trans1:probe10 0.415608713038428 0.0583288563432996 7.12526764784014 2.47462442582469e-12 *** df.mm.trans1:probe11 0.421761968141118 0.0583288563432996 7.23076011740742 1.20329499812972e-12 *** df.mm.trans1:probe12 0.318107541294383 0.0583288563432996 5.45369069851349 6.7355084528804e-08 *** df.mm.trans1:probe13 0.512115162754298 0.0583288563432996 8.77979091069776 1.13087415513217e-17 *** df.mm.trans1:probe14 0.282335955979884 0.0583288563432996 4.84041645387612 1.57856996580332e-06 *** df.mm.trans1:probe15 0.318480686454371 0.0583288563432996 5.46008796366459 6.50635307958846e-08 *** df.mm.trans1:probe16 0.682737921138754 0.0583288563432996 11.7049769863554 3.78383265549625e-29 *** df.mm.trans1:probe17 0.743372590059565 0.0583288563432996 12.7445082359301 9.10202678320689e-34 *** df.mm.trans1:probe18 0.76236151936987 0.0583288563432996 13.0700577237950 2.89440567372261e-35 *** df.mm.trans1:probe19 0.985819242469054 0.0583288563432996 16.9010555713098 2.04642702278037e-54 *** df.mm.trans1:probe20 0.92595704612487 0.0583288563432996 15.8747677251731 4.84451097789057e-49 *** df.mm.trans1:probe21 0.911161393889309 0.0583288563432996 15.621108504624 9.74784236207139e-48 *** df.mm.trans2:probe2 0.229678410417918 0.0583288563432996 3.93764638665509 9.00780412926501e-05 *** df.mm.trans2:probe3 -0.105340753335647 0.0583288563432996 -1.80598009183748 0.0713287589666104 . df.mm.trans2:probe4 0.224604854236089 0.0583288563432996 3.85066446210016 0.000128020908857277 *** df.mm.trans2:probe5 0.133332936934128 0.0583288563432996 2.28588292815798 0.0225437258533181 * df.mm.trans2:probe6 0.119097837137393 0.0583288563432996 2.04183391555686 0.0415230605256416 * df.mm.trans3:probe2 0.255503482945038 0.0583288563432996 4.38039589600814 1.35612868302672e-05 *** df.mm.trans3:probe3 0.191236004930609 0.0583288563432996 3.27858313910824 0.00109219693094882 ** df.mm.trans3:probe4 1.05264485572658 0.0583288563432996 18.0467254411976 1.38786131311279e-60 *** df.mm.trans3:probe5 0.350784124431204 0.0583288563432996 6.01390369059585 2.84827069573753e-09 *** df.mm.trans3:probe6 0.286312719556909 0.0583288563432996 4.90859477634518 1.12973541695010e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.9538043307942 0.213080088534425 18.5554847381032 2.25340668096040e-63 *** df.mm.trans1 0.177265444716498 0.186333132049263 0.951336151370184 0.341745214004361 df.mm.trans2 0.441532067140211 0.167460759822097 2.63663002370988 0.00854949049309437 ** df.mm.exp2 0.0243176193693396 0.220990890884542 0.110039012341212 0.912408350722882 df.mm.exp3 0.379047416739283 0.220990890884542 1.71521737942275 0.0867251416663761 . df.mm.exp4 0.256038648998284 0.220990890884542 1.15859367765548 0.246996614406956 df.mm.exp5 0.279828593244872 0.220990890884542 1.26624492133963 0.205824942494278 df.mm.exp6 0.0658135675175607 0.220990890884542 0.297811223141977 0.765931065101624 df.mm.exp7 0.219209437419771 0.220990890884542 0.99193879232922 0.321552494632589 df.mm.exp8 0.0770751703592461 0.220990890884542 0.348770802501151 0.727360861846748 df.mm.trans1:exp2 0.132614859960363 0.206718049821975 0.641525304996686 0.52138063305079 df.mm.trans2:exp2 -0.0514036119125124 0.165743168163407 -0.310140155290343 0.756541936086898 df.mm.trans1:exp3 -0.253953549377598 0.206718049821976 -1.22850205676912 0.219650171001992 df.mm.trans2:exp3 -0.215039442078487 0.165743168163407 -1.29742567649291 0.194890173930684 df.mm.trans1:exp4 -0.138056539833371 0.206718049821976 -0.667849469130852 0.504438478843244 df.mm.trans2:exp4 -0.198596275577141 0.165743168163407 -1.19821696289372 0.231217212980346 df.mm.trans1:exp5 -0.149041747567729 0.206718049821976 -0.720990487749295 0.471143692685607 df.mm.trans2:exp5 -0.259946732676178 0.165743168163407 -1.56837072415495 0.117223280053588 df.mm.trans1:exp6 -0.00831048431272666 0.206718049821976 -0.0402020255119648 0.967942935784037 df.mm.trans2:exp6 -0.00672993565529799 0.165743168163407 -0.0406046036761101 0.967622096013302 df.mm.trans1:exp7 -0.151217343576781 0.206718049821976 -0.731514948535013 0.464696883312979 df.mm.trans2:exp7 -0.250145606912030 0.165743168163407 -1.50923630629173 0.131666393196471 df.mm.trans1:exp8 -0.0405290048937041 0.206718049821976 -0.196059342319683 0.844617690872694 df.mm.trans2:exp8 -0.0229670749801754 0.165743168163407 -0.138570266483214 0.889827542048112 df.mm.trans1:probe2 0.350697843254319 0.126588435671768 2.77037820550723 0.00573991203591583 ** df.mm.trans1:probe3 0.0882911188721361 0.126588435671768 0.697465913087566 0.485730926469237 df.mm.trans1:probe4 0.158744877029485 0.126588435671768 1.25402353056243 0.210230516111597 df.mm.trans1:probe5 0.313984994268792 0.126588435671768 2.48036080549196 0.0133468773074968 * df.mm.trans1:probe6 0.063243185724606 0.126588435671768 0.499596865930075 0.617507874809176 df.mm.trans1:probe7 0.278528758909225 0.126588435671768 2.20027017026598 0.0280968852593235 * df.mm.trans1:probe8 0.0597752337594281 0.126588435671768 0.472201377971207 0.636922648444859 df.mm.trans1:probe9 0.136344576086364 0.126588435671768 1.0770697604629 0.281801046935645 df.mm.trans1:probe10 0.295688392800466 0.126588435671768 2.33582468439028 0.0197673124976964 * df.mm.trans1:probe11 0.200555491293353 0.126588435671768 1.58431131745221 0.113551284518892 df.mm.trans1:probe12 0.142914785249562 0.126588435671768 1.12897188823888 0.259276535430659 df.mm.trans1:probe13 0.349806622752985 0.126588435671768 2.76333790599958 0.0058638888685798 ** df.mm.trans1:probe14 0.155050920218503 0.126588435671768 1.22484269116443 0.221025296448914 df.mm.trans1:probe15 0.176327138484457 0.126588435671768 1.39291664004489 0.164064442409916 df.mm.trans1:probe16 0.106003029861182 0.126588435671768 0.837383204071173 0.402648292426948 df.mm.trans1:probe17 0.191371186312126 0.126588435671768 1.51175883718426 0.131023291298005 df.mm.trans1:probe18 0.220516406408669 0.126588435671768 1.74199487684995 0.0819259497824407 . df.mm.trans1:probe19 0.243170753154333 0.126588435671768 1.92095551117207 0.0551222700192971 . df.mm.trans1:probe20 0.321923997329898 0.126588435671768 2.54307587910019 0.0111911759335324 * df.mm.trans1:probe21 0.221439723418150 0.126588435671768 1.74928872643883 0.080656796564199 . df.mm.trans2:probe2 0.325911254869915 0.126588435671768 2.57457368155629 0.0102301532727228 * df.mm.trans2:probe3 0.0639624480164648 0.126588435671768 0.505278761658083 0.613513917595312 df.mm.trans2:probe4 0.06754847043151 0.126588435671768 0.533606960802146 0.593774161570112 df.mm.trans2:probe5 -0.0101611883414026 0.126588435671768 -0.080269483444362 0.936044690133394 df.mm.trans2:probe6 -0.0586838261431344 0.126588435671768 -0.463579677177592 0.643085607376797 df.mm.trans3:probe2 -0.0816806005034108 0.126588435671768 -0.645245358076793 0.518968737911377 df.mm.trans3:probe3 -0.155824338558577 0.126588435671768 -1.23095239886379 0.218732821496621 df.mm.trans3:probe4 -0.278566713895810 0.126588435671768 -2.20057000007574 0.0280755406765443 * df.mm.trans3:probe5 -0.166143081605126 0.126588435671768 -1.31246650393816 0.189770805119607 df.mm.trans3:probe6 -0.176995339795755 0.126588435671768 -1.39819517364673 0.162474502728844