fitVsDatCorrelation=0.89672909423804 cont.fitVsDatCorrelation=0.284029643675066 fstatistic=13589.8778190939,61,899 cont.fstatistic=2883.93695115590,61,899 residuals=-0.645745312760279,-0.0828017410623446,-0.00824963424520065,0.0830769455736756,0.523304331079707 cont.residuals=-0.552540938708307,-0.214219591981615,-0.0648662861264682,0.135590647113385,1.16700957173027 predictedValues: Include Exclude Both Lung 77.7645940114602 45.9617615714792 66.5151169705898 cerebhem 75.5911455738556 53.2385047986554 64.2053284150098 cortex 74.8528775188158 46.6245682886737 64.1664590330225 heart 80.004147816137 45.9487421539766 71.4397670129195 kidney 87.353984069066 47.1105631646625 74.7664481898545 liver 87.5982074059566 45.864097662593 80.6902228006996 stomach 76.1303291971413 48.3362884521182 67.2038251221243 testicle 96.135346913519 51.1006819291206 89.8252784699809 diffExp=31.8028324399811,22.3526407752002,28.2283092301421,34.0554056621604,40.2434209044035,41.7341097433636,27.7940407450231,45.0346649843983 diffExpScore=0.996326843685646 diffExp1.5=1,0,1,1,1,1,1,1 diffExp1.5Score=0.875 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 62.1130652405078 57.5609422819659 63.4793739297017 cerebhem 62.0396222652098 66.982715542017 62.0981430217959 cortex 62.770098716852 71.9062465990512 64.3753129798684 heart 61.9527456848445 60.0251695984044 62.0899368622309 kidney 64.5865782794849 59.2428776913201 65.2908121435666 liver 58.670556492737 67.5012123284117 67.9274481380627 stomach 63.2099830813478 69.1928771382593 64.9607265653181 testicle 63.0674029942955 61.0323369858636 66.7432071331943 cont.diffExp=4.55212295854188,-4.9430932768072,-9.1361478821992,1.92757608644013,5.34370058816479,-8.83065583567468,-5.9828940569115,2.03506600843190 cont.diffExpScore=2.66623357079132 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=0.0962851281310893 cont.tran.correlation=-0.245859576630895 tran.covariance=0.000402043457451151 cont.tran.covariance=-0.000581374369381812 tran.mean=64.9759900329519 cont.tran.mean=63.2409019325358 weightedLogRatios: wLogRatio Lung 2.15122807022897 cerebhem 1.45483378790707 cortex 1.93090376861736 heart 2.27632645163327 kidney 2.56944232923018 liver 2.68486962512224 stomach 1.86489722227509 testicle 2.68568604432460 cont.weightedLogRatios: wLogRatio Lung 0.311367032889765 cerebhem -0.319379190643998 cortex -0.571722517845313 heart 0.129926669364435 kidney 0.356224343807344 liver -0.58074553086993 stomach -0.379077426343755 testicle 0.135392815589125 varWeightedLogRatios=0.193659934256209 cont.varWeightedLogRatios=0.151935495682801 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.03712652328306 0.0650918446587389 62.0220020564597 0 *** df.mm.trans1 0.764950492941186 0.0559907287239533 13.6620921065800 9.4815578673578e-39 *** df.mm.trans2 -0.220539012226664 0.0492507566934448 -4.47788068718176 8.50848069389628e-06 *** df.mm.exp2 0.153968085191001 0.0628645506038123 2.44920362449333 0.0145071882933532 * df.mm.exp3 0.0121047697374408 0.0628645506038123 0.192553189693950 0.847352395902294 df.mm.exp4 -0.0433164937262818 0.0628645506038123 -0.689044832266008 0.490972845291344 df.mm.exp5 0.0240298884119745 0.0628645506038123 0.382248631083306 0.702367296883928 df.mm.exp6 -0.0762410274060112 0.0628645506038123 -1.21278250896122 0.225531572163462 df.mm.exp7 0.0188323806704701 0.0628645506038123 0.299570751553707 0.764573842452801 df.mm.exp8 0.0176216975192719 0.0628645506038123 0.280312152874967 0.779302516813575 df.mm.trans1:exp2 -0.182315168676337 0.0578269759706369 -3.15277023596931 0.00167092299585103 ** df.mm.trans2:exp2 -0.00699595683817907 0.0415047662646135 -0.168557914374855 0.866182300761514 df.mm.trans1:exp3 -0.050266453578147 0.0578269759706369 -0.86925613408647 0.384939061242968 df.mm.trans2:exp3 0.00221306835230762 0.0415047662646135 0.0533208243650431 0.957488134372746 df.mm.trans1:exp4 0.0717087369852774 0.0578269759706369 1.24005683820799 0.215277938323487 df.mm.trans2:exp4 0.0430331873127183 0.0415047662646135 1.03682519348165 0.30009612837323 df.mm.trans1:exp5 0.092252519762581 0.0578269759706369 1.59531980038909 0.110992198694745 df.mm.trans2:exp5 0.00065757787014876 0.0415047662646135 0.0158434302691015 0.987362815741763 df.mm.trans1:exp6 0.195315323964103 0.0578269759706369 3.37758149523986 0.000762655478501092 *** df.mm.trans2:exp6 0.0741138717980703 0.0415047662646135 1.78567134496211 0.07448948500141 . df.mm.trans1:exp7 -0.0400718889894826 0.0578269759706369 -0.692961862813475 0.488512457066751 df.mm.trans2:exp7 0.0315404309944422 0.0415047662646135 0.759923108429435 0.447499779385882 df.mm.trans1:exp8 0.194449126933848 0.0578269759706369 3.36260237838797 0.000804710241059046 *** df.mm.trans2:exp8 0.088366363986712 0.0415047662646135 2.12906545294901 0.0335200838918917 * df.mm.trans1:probe2 -0.832317453924206 0.0408898468443489 -20.3551130209047 4.78905075280998e-76 *** df.mm.trans1:probe3 -0.814132269006244 0.0408898468443489 -19.9103770700173 2.32770386976401e-73 *** df.mm.trans1:probe4 -0.438610724786916 0.0408898468443489 -10.7266414192386 2.41561982204191e-25 *** df.mm.trans1:probe5 -0.631552042811704 0.0408898468443489 -15.4452044101747 6.57405463641151e-48 *** df.mm.trans1:probe6 -0.74034548259572 0.0408898468443489 -18.1058512010064 1.04206740633863e-62 *** df.mm.trans1:probe7 -0.711477986635966 0.0408898468443489 -17.3998691984413 1.14954620238074e-58 *** df.mm.trans1:probe8 -0.219969635948018 0.0408898468443489 -5.37956614964477 9.52650292830357e-08 *** df.mm.trans1:probe9 -0.293389002539671 0.0408898468443489 -7.1751064183851 1.51080075104245e-12 *** df.mm.trans1:probe10 -0.552378518632056 0.0408898468443489 -13.5089407581970 5.37753856843172e-38 *** df.mm.trans1:probe11 -0.824424436737573 0.0408898468443489 -20.1620817968779 7.06452992687137e-75 *** df.mm.trans1:probe12 -0.956725430159281 0.0408898468443489 -23.3976281153889 6.22096119266113e-95 *** df.mm.trans1:probe13 -0.919723305579511 0.0408898468443489 -22.4927060519577 3.0988979900916e-89 *** df.mm.trans1:probe14 -0.861049354182508 0.0408898468443489 -21.0577789019405 2.46705322167632e-80 *** df.mm.trans1:probe15 -0.86786865985555 0.0408898468443489 -21.2245514921877 2.32894560373171e-81 *** df.mm.trans1:probe16 -0.964666640184722 0.0408898468443489 -23.5918379410130 3.66335082262694e-96 *** df.mm.trans1:probe17 -0.718083801515405 0.0408898468443489 -17.5614206687753 1.38747991731872e-59 *** df.mm.trans1:probe18 -0.695565275965067 0.0408898468443489 -17.0107087613413 1.80035893058627e-56 *** df.mm.trans1:probe19 -0.848196096759333 0.0408898468443489 -20.7434402967581 2.07362548503737e-78 *** df.mm.trans1:probe20 -0.702022527966006 0.0408898468443489 -17.1686269855283 2.33183582097100e-57 *** df.mm.trans1:probe21 -0.851972904556135 0.0408898468443489 -20.8358057147842 5.65259478338614e-79 *** df.mm.trans1:probe22 -0.800814917015787 0.0408898468443489 -19.5846885918689 2.08926824528345e-71 *** df.mm.trans2:probe2 -0.0116763089972605 0.0408898468443489 -0.285555214762910 0.775284587433697 df.mm.trans2:probe3 0.00982534536916125 0.0408898468443489 0.240288143082618 0.810161667263346 df.mm.trans2:probe4 -0.00895392918571381 0.0408898468443489 -0.218976833535176 0.82671779720185 df.mm.trans2:probe5 0.0275681405984691 0.0408898468443489 0.674205034404013 0.500354289038392 df.mm.trans2:probe6 0.185237604130014 0.0408898468443489 4.53016135851863 6.68835164435901e-06 *** df.mm.trans3:probe2 -0.568720339540539 0.0408898468443489 -13.9085954932878 5.65320309149208e-40 *** df.mm.trans3:probe3 0.0899040017517456 0.0408898468443489 2.19868766185341 0.0281542580171773 * df.mm.trans3:probe4 -0.489240143341158 0.0408898468443489 -11.9648318861036 1.01628629607385e-30 *** df.mm.trans3:probe5 -0.40554229971763 0.0408898468443489 -9.91792170954725 4.50470276741805e-22 *** df.mm.trans3:probe6 -0.42189572109017 0.0408898468443489 -10.3178601449929 1.15053365935886e-23 *** df.mm.trans3:probe7 -0.560071729952572 0.0408898468443489 -13.6970855401963 6.36642051756824e-39 *** df.mm.trans3:probe8 -0.45827364005824 0.0408898468443489 -11.2075166679568 2.22085017454126e-27 *** df.mm.trans3:probe9 0.0163840426930512 0.0408898468443489 0.400687308891585 0.688745560316579 df.mm.trans3:probe10 -0.606704788866786 0.0408898468443489 -14.8375412404029 1.03587620561256e-44 *** df.mm.trans3:probe11 -0.780822992544199 0.0408898468443489 -19.0957671110013 1.68899537423624e-68 *** df.mm.trans3:probe12 -0.424321500977719 0.0408898468443489 -10.3771848936715 6.6144515516088e-24 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.14684326183533 0.141016515480014 29.4067914507719 1.04643229808897e-133 *** df.mm.trans1 -0.0383577987446459 0.121299642147699 -0.31622351117854 0.751906268520609 df.mm.trans2 -0.0951380188735206 0.106698006948112 -0.891656944630526 0.372815421560863 df.mm.exp2 0.172406197040226 0.136191252834259 1.26591240958800 0.20587239166552 df.mm.exp3 0.219026167803491 0.136191252834259 1.608224928146 0.108137029942671 df.mm.exp4 0.0614663907962439 0.136191252834259 0.451324071972864 0.651864846218916 df.mm.exp5 0.039715287194745 0.136191252834259 0.291614082169266 0.770648980641247 df.mm.exp6 0.0345577933746416 0.136191252834259 0.253744588257055 0.799750903849399 df.mm.exp7 0.178491717678532 0.136191252834259 1.31059604757253 0.190328988498962 df.mm.exp8 0.0236697788505581 0.136191252834259 0.173798084370099 0.862063276206138 df.mm.trans1:exp2 -0.173589304475760 0.125277731717056 -1.38563575582465 0.166201800979781 df.mm.trans2:exp2 -0.0208158402313033 0.0899169096395008 -0.231500841329614 0.816978411845073 df.mm.trans1:exp3 -0.208503699956443 0.125277731717055 -1.66433169804955 0.0963946326770849 . df.mm.trans2:exp3 0.00349271981972796 0.0899169096395008 0.0388438596670097 0.96902350025859 df.mm.trans1:exp4 -0.0640508198779122 0.125277731717056 -0.511270590551347 0.609287136356333 df.mm.trans2:exp4 -0.0195466755455571 0.0899169096395008 -0.217385980278065 0.827956882648092 df.mm.trans1:exp5 -0.000665021720922926 0.125277731717056 -0.00530837932494581 0.995765723669672 df.mm.trans2:exp5 -0.0109139744619019 0.0899169096395009 -0.121378442671781 0.903418414855773 df.mm.trans1:exp6 -0.09157614250596 0.125277731717056 -0.730984998297927 0.464978785570467 df.mm.trans2:exp6 0.124743512448035 0.0899169096395008 1.38731983726045 0.165688067794679 df.mm.trans1:exp7 -0.160985826009031 0.125277731717056 -1.28503145613000 0.199112262747902 df.mm.trans2:exp7 0.00556195580379867 0.0899169096395008 0.0618566165818858 0.9506907653322 df.mm.trans1:exp8 -0.00842209274064121 0.125277731717056 -0.0672273725362686 0.946415642271107 df.mm.trans2:exp8 0.0348898070686051 0.0899169096395008 0.388022755769599 0.698091053419848 df.mm.trans1:probe2 -0.0641841666394894 0.088584733628799 -0.72455110502949 0.468915985977522 df.mm.trans1:probe3 -0.0786635751446293 0.088584733628799 -0.888003744237209 0.374776253982025 df.mm.trans1:probe4 0.0126976990426603 0.088584733628799 0.143339585981803 0.886054115052352 df.mm.trans1:probe5 0.0624427467738836 0.088584733628799 0.704892866027464 0.481059485307403 df.mm.trans1:probe6 0.0729849208737236 0.088584733628799 0.823899535325759 0.410215164066169 df.mm.trans1:probe7 -0.0968552418683216 0.088584733628799 -1.09336268113848 0.274527473936991 df.mm.trans1:probe8 0.0115579724500415 0.088584733628799 0.130473637799414 0.896220898630006 df.mm.trans1:probe9 0.0343837919110306 0.088584733628799 0.388145795584945 0.698000035499691 df.mm.trans1:probe10 0.151297243382096 0.088584733628799 1.70793811963227 0.0879930545673791 . df.mm.trans1:probe11 0.0266384113055493 0.088584733628799 0.300711084340713 0.763704344656301 df.mm.trans1:probe12 0.116558691158081 0.088584733628799 1.31578756726303 0.188580784083100 df.mm.trans1:probe13 0.0329518893144564 0.088584733628799 0.371981581527764 0.709994224694694 df.mm.trans1:probe14 0.232286220964178 0.088584733628799 2.62219246419523 0.00888436496272165 ** df.mm.trans1:probe15 0.0138334807647168 0.088584733628799 0.156161001992554 0.875941174474632 df.mm.trans1:probe16 -0.0753340672950295 0.088584733628799 -0.85041817262448 0.395319070027608 df.mm.trans1:probe17 0.099606522291433 0.088584733628799 1.12442085911574 0.261134660909311 df.mm.trans1:probe18 0.0603000401280225 0.088584733628799 0.680704650314813 0.496233669487574 df.mm.trans1:probe19 0.0366219438510568 0.088584733628799 0.413411457605275 0.679403774061063 df.mm.trans1:probe20 0.097945572849229 0.088584733628799 1.10567102069365 0.269164737212896 df.mm.trans1:probe21 -0.0840155489624545 0.088584733628799 -0.94842017942402 0.343170461261729 df.mm.trans1:probe22 0.032955851098714 0.088584733628799 0.372026304631795 0.709960938068784 df.mm.trans2:probe2 0.143457827651293 0.088584733628799 1.61944188095018 0.105702941431611 df.mm.trans2:probe3 0.00284026018831451 0.088584733628799 0.0320626373412850 0.974429215919019 df.mm.trans2:probe4 -0.0321172279649297 0.088584733628799 -0.362559401030793 0.717019259243199 df.mm.trans2:probe5 0.0126877482661914 0.088584733628799 0.143227255379663 0.886142800917214 df.mm.trans2:probe6 -0.106366439775609 0.088584733628799 -1.20073104493740 0.230171822643234 df.mm.trans3:probe2 0.163955224047770 0.088584733628799 1.85082933967832 0.0645219667202125 . df.mm.trans3:probe3 0.221104222151452 0.088584733628799 2.49596305248211 0.0127397487580668 * df.mm.trans3:probe4 0.0341363872194519 0.088584733628799 0.385352936347873 0.700067104523596 df.mm.trans3:probe5 0.257135688384818 0.088584733628799 2.90270882861494 0.00378992754938369 ** df.mm.trans3:probe6 0.18030931917525 0.088584733628799 2.03544461657253 0.0420989897746372 * df.mm.trans3:probe7 0.0850806367574456 0.088584733628799 0.960443558073598 0.337090264024618 df.mm.trans3:probe8 0.180365137686477 0.088584733628799 2.03607473091548 0.0420355472013866 * df.mm.trans3:probe9 0.192926466954283 0.088584733628799 2.17787488939925 0.0296746615148216 * df.mm.trans3:probe10 0.0599332015291577 0.088584733628799 0.676563546268579 0.498856944053602 df.mm.trans3:probe11 0.0964246226114954 0.088584733628799 1.0885015810461 0.276665453210641 df.mm.trans3:probe12 0.177040160418481 0.088584733628799 1.99854030334776 0.0459588619985714 *