fitVsDatCorrelation=0.950333068501605 cont.fitVsDatCorrelation=0.261437136478317 fstatistic=8127.54274986758,62,922 cont.fstatistic=831.72500851058,62,922 residuals=-0.576110426247416,-0.0891876801578814,-0.00603825933598601,0.0814364406902454,1.46343596752215 cont.residuals=-0.916414816962815,-0.402956493525751,-0.217887822708304,0.411586625239372,2.30110834360877 predictedValues: Include Exclude Both Lung 56.0679508571951 190.189439651686 49.4875693328037 cerebhem 62.4962596116245 111.065049650917 50.4357491118489 cortex 52.6685131249549 150.346686387916 52.8569846210911 heart 55.6478174120804 148.644024873784 50.4619848821051 kidney 54.7798430920366 183.999711873943 52.0666430318802 liver 56.0001806871853 159.320976932753 50.2976934458235 stomach 53.7019629769409 147.629818329125 48.4823841705784 testicle 54.4886845215264 128.608066748541 51.2159875919966 diffExp=-134.121488794491,-48.5687900392928,-97.6781732629615,-92.9962074617033,-129.219868781906,-103.320796245568,-93.9278553521845,-74.1193822270149 diffExpScore=0.998709598433734 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 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 72.3814554129865 69.2566649956412 69.7130102434965 cerebhem 72.8177373592492 82.0216641867166 69.4249772141474 cortex 65.9067286653524 62.789528762853 75.4071245999069 heart 72.6841694878492 55.3133574711729 63.1144574670809 kidney 69.0010622801114 74.5882128373016 68.8722526573103 liver 72.7464642339298 72.269349970972 65.7722555920678 stomach 81.2972515910743 48.1151200928438 72.4958055744184 testicle 67.4907616675333 58.4980917111004 77.3401077240395 cont.diffExp=3.12479041734532,-9.2039268274674,3.11719990249937,17.3708120166763,-5.58715055719013,0.477114262957883,33.1821314982305,8.9926699564329 cont.diffExpScore=1.54469547766554 cont.diffExp1.5=0,0,0,0,0,0,1,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=0,0,0,0,0,0,1,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,0,0,1,0,0,1,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,0,0,1,0,0,1,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=-0.457744074880663 cont.tran.correlation=-0.347754258070293 tran.covariance=-0.00460773887750853 cont.tran.covariance=-0.00432493208652059 tran.mean=104.103436670763 cont.tran.mean=68.573601295418 weightedLogRatios: wLogRatio Lung -5.66424996073014 cerebhem -2.54304291503823 cortex -4.7080845496681 heart -4.4314202281191 kidney -5.58447459506558 liver -4.75537823219412 stomach -4.53961838788941 testicle -3.80213092857031 cont.weightedLogRatios: wLogRatio Lung 0.187991866663023 cerebhem -0.517452747566531 cortex 0.201755744708969 heart 1.13328541107643 kidney -0.332702365081956 liver 0.0281875060542434 stomach 2.16932068218389 testicle 0.592076302157131 varWeightedLogRatios=0.994707799448812 cont.varWeightedLogRatios=0.756792960891028 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.1870528319617 0.0859414133442233 60.3556845311104 0 *** df.mm.trans1 -1.36862010360542 0.0735958295030496 -18.5964355976001 8.52203720204678e-66 *** df.mm.trans2 0.00030441710669585 0.0646943457006986 0.00470546696776566 0.99624661228827 df.mm.exp2 -0.448340936222197 0.0821757087645187 -5.45588158548112 6.26261202564171e-08 *** df.mm.exp3 -0.36349166607575 0.0821757087645187 -4.42334689339115 1.08773495659826e-05 *** df.mm.exp4 -0.273486544551435 0.0821757087645187 -3.32807040746230 0.000909111145796768 *** df.mm.exp5 -0.107131479698204 0.0821757087645187 -1.30368793051969 0.192665419384974 df.mm.exp6 -0.194546888735444 0.0821757087645187 -2.36745008543746 0.0181167442620267 * df.mm.exp7 -0.275906526773569 0.0821757087645187 -3.35751928303049 0.000818630822358732 *** df.mm.exp8 -0.454152623131505 0.0821757087645187 -5.52660427222985 4.24952782307429e-08 *** df.mm.trans1:exp2 0.556883281645043 0.0750158156155031 7.42354498282566 2.59120423049125e-13 *** df.mm.trans2:exp2 -0.0895636274583718 0.053044191917962 -1.68847189899491 0.0916588311668771 . df.mm.trans1:exp3 0.300945105839984 0.0750158156155031 4.01175543278089 6.51516177860126e-05 *** df.mm.trans2:exp3 0.128414909711447 0.053044191917962 2.42090425112052 0.0156741718670543 * df.mm.trans1:exp4 0.265965038280516 0.0750158156155031 3.54545286348324 0.000411650172532763 *** df.mm.trans2:exp4 0.0270202710156122 0.053044191917962 0.509391698480424 0.610599585523717 df.mm.trans1:exp5 0.0838894158850107 0.0750158156155031 1.11828972592912 0.263734555114725 df.mm.trans2:exp5 0.074045045169647 0.053044191917962 1.39591239855562 0.163076906514936 df.mm.trans1:exp6 0.193337442707623 0.0750158156155031 2.57728908392574 0.0101122203898873 * df.mm.trans2:exp6 0.0174471526864939 0.053044191917962 0.328917305658603 0.742292888542131 df.mm.trans1:exp7 0.232791718811060 0.0750158156155031 3.10323518981977 0.00197251852520071 ** df.mm.trans2:exp7 0.0225938135026102 0.053044191917962 0.425943212360625 0.67024863911287 df.mm.trans1:exp8 0.425581316471094 0.0750158156155031 5.67322121314297 1.87555536866167e-08 *** df.mm.trans2:exp8 0.0629015341720205 0.053044191917962 1.18583264062734 0.235993908303571 df.mm.trans1:probe2 -0.0313948998057734 0.0543541223003468 -0.577599241365601 0.563675740546465 df.mm.trans1:probe3 0.06800767619455 0.0543541223003468 1.2511962904811 0.211180249371154 df.mm.trans1:probe4 0.0759106932563982 0.0543541223003468 1.39659495993579 0.162871497992449 df.mm.trans1:probe5 0.0544755560934839 0.0543541223003468 1.00223412296985 0.316493486267798 df.mm.trans1:probe6 0.563702446642363 0.0543541223003468 10.3709235433421 6.5362768040983e-24 *** df.mm.trans1:probe7 -0.0391316596112041 0.0543541223003468 -0.719939131662776 0.471744931925039 df.mm.trans1:probe8 -0.0250875548179065 0.0543541223003468 -0.461557537058168 0.644507523920779 df.mm.trans1:probe9 0.0677627768743735 0.0543541223003468 1.24669066496804 0.212827567533960 df.mm.trans1:probe10 -0.0909994724312087 0.0543541223003468 -1.67419633654222 0.094431171011364 . df.mm.trans1:probe11 0.0826870090887085 0.0543541223003468 1.52126472821696 0.128536278999621 df.mm.trans1:probe12 0.0637957286957153 0.0543541223003468 1.17370543384357 0.240816128153138 df.mm.trans1:probe13 -0.0320680528662914 0.0543541223003468 -0.589983822921316 0.55534605266161 df.mm.trans1:probe14 -0.00848814167013422 0.0543541223003468 -0.156163715113105 0.875938162107408 df.mm.trans1:probe15 0.0100978568046677 0.0543541223003468 0.18577904264316 0.852658902675184 df.mm.trans1:probe16 0.90815874017064 0.0543541223003468 16.7081851704345 6.01412039546919e-55 *** df.mm.trans1:probe17 0.926910530986493 0.0543541223003468 17.0531781538965 6.9248811336301e-57 *** df.mm.trans1:probe18 1.22103842073766 0.0543541223003468 22.4645044214038 1.75518805841547e-89 *** df.mm.trans1:probe19 1.23160248908575 0.0543541223003468 22.6588607627629 1.03503206666997e-90 *** df.mm.trans1:probe20 1.16481730539404 0.0543541223003468 21.4301557287147 5.40542155069187e-83 *** df.mm.trans1:probe21 1.07280977472028 0.0543541223003468 19.7374132690840 1.32352066945964e-72 *** df.mm.trans2:probe2 0.124306816109770 0.0543541223003468 2.28698046898600 0.0224228198831475 * df.mm.trans2:probe3 -0.0659628244909474 0.0543541223003468 -1.21357537752986 0.225220702862782 df.mm.trans2:probe4 0.286859969832761 0.0543541223003468 5.27761203184639 1.63228848860019e-07 *** df.mm.trans2:probe5 0.0510961574762597 0.0543541223003468 0.940060391260032 0.347432797366697 df.mm.trans2:probe6 0.816967424380811 0.0543541223003468 15.0304593249885 7.8191856242877e-46 *** df.mm.trans3:probe2 -0.0656344732470757 0.0543541223003468 -1.20753441449016 0.227536068643445 df.mm.trans3:probe3 -0.0773814539118886 0.0543541223003468 -1.42365382121891 0.154885017108927 df.mm.trans3:probe4 0.0218669977065949 0.0543541223003468 0.402306150502505 0.687551977555509 df.mm.trans3:probe5 -0.00295815502098789 0.0543541223003468 -0.0544237473772805 0.956609349380779 df.mm.trans3:probe6 -0.0505174404082737 0.0543541223003468 -0.929413230686118 0.352918235498602 df.mm.trans3:probe7 0.0493516028728345 0.0543541223003468 0.907964304899089 0.364134334129768 df.mm.trans3:probe8 0.443080667450028 0.0543541223003468 8.1517398993526 1.16426246329814e-15 *** df.mm.trans3:probe9 -0.0585697977518003 0.0543541223003468 -1.07755944301996 0.281512255810196 df.mm.trans3:probe10 0.282401947495165 0.0543541223003468 5.19559392265935 2.51257732059391e-07 *** df.mm.trans3:probe11 0.332418107355258 0.0543541223003468 6.11578465968784 1.41775694372930e-09 *** df.mm.trans3:probe12 -0.0275313828998766 0.0543541223003468 -0.506518764993487 0.612613619686584 df.mm.trans3:probe13 0.0792529725766948 0.0543541223003468 1.45808577569818 0.145157436601044 df.mm.trans3:probe14 0.236001559570241 0.0543541223003468 4.34192568258497 1.56877099120005e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.28107065560865 0.266526854683403 16.0624364126233 2.24365494769555e-51 *** df.mm.trans1 0.0847175455978611 0.228239962457893 0.371177530374376 0.710590579204367 df.mm.trans2 -0.0546273228630701 0.200634127418233 -0.272273334382422 0.785472819050247 df.mm.exp2 0.179313724560162 0.254848417498812 0.703609331068334 0.481853742216847 df.mm.exp3 -0.270255451458887 0.254848417498812 -1.06045567836476 0.289215054638843 df.mm.exp4 -0.121194379439196 0.254848417498811 -0.475554765568678 0.634504253695698 df.mm.exp5 0.0384684610568994 0.254848417498811 0.150946438806428 0.880050985122621 df.mm.exp6 0.105799761406648 0.254848417498812 0.415147806076296 0.678130291028032 df.mm.exp7 -0.287202571862053 0.254848417498812 -1.12695450370373 0.260054927028479 df.mm.exp8 -0.342610372126744 0.254848417498812 -1.3443692352076 0.179159679103716 df.mm.trans1:exp2 -0.173304279866955 0.232643711680989 -0.744934297233865 0.456501277906187 df.mm.trans2:exp2 -0.0101497006514888 0.164503946130035 -0.0616988278412835 0.950816045257271 df.mm.trans1:exp3 0.176545866055977 0.232643711680989 0.758867990801592 0.448125479837486 df.mm.trans2:exp3 0.172224385724883 0.164503946130035 1.04693163766871 0.295405483259364 df.mm.trans1:exp4 0.125367863191230 0.232643711680989 0.538883524017791 0.590097313085976 df.mm.trans2:exp4 -0.103610581469700 0.164503946130035 -0.629836450171227 0.528957641408938 df.mm.trans1:exp5 -0.086296687091645 0.232643711680989 -0.370939263597972 0.710767980923891 df.mm.trans2:exp5 0.0356946429462481 0.164503946130035 0.216983505781877 0.82826920258417 df.mm.trans1:exp6 -0.100769584009039 0.232643711680989 -0.433149829328799 0.665007234945613 df.mm.trans2:exp6 -0.0632190364645343 0.164503946130035 -0.384301033207809 0.700843957437292 df.mm.trans1:exp7 0.403364656199115 0.232643711680989 1.7338300411585 0.0832825192474679 . df.mm.trans2:exp7 -0.0770203392389174 0.164503946130035 -0.46819751775459 0.639754005638604 df.mm.trans1:exp8 0.272650970576349 0.232643711680989 1.17196793588910 0.241512673667795 df.mm.trans2:exp8 0.173785119648856 0.164503946130035 1.05641915429484 0.291053480157357 df.mm.trans1:probe2 -0.0965832961503785 0.168566383680054 -0.572968904249008 0.566805452410241 df.mm.trans1:probe3 0.221385698583479 0.168566383680054 1.31334429647419 0.189393696528025 df.mm.trans1:probe4 -0.330170048015923 0.168566383680054 -1.95869449654089 0.0504496971502669 . df.mm.trans1:probe5 -0.349831363393068 0.168566383680054 -2.07533290894501 0.0382324787823319 * df.mm.trans1:probe6 -0.247988456520697 0.168566383680054 -1.47116199034909 0.141588517293195 df.mm.trans1:probe7 -0.252376445428522 0.168566383680054 -1.49719321206738 0.134685234993983 df.mm.trans1:probe8 -0.108065482724826 0.168566383680054 -0.641085608919149 0.521626379855682 df.mm.trans1:probe9 -0.137100590213324 0.168566383680054 -0.813332926887408 0.416237182147663 df.mm.trans1:probe10 -0.189465785333828 0.168566383680054 -1.12398321182141 0.261312699862185 df.mm.trans1:probe11 -0.107795662979381 0.168566383680054 -0.639484935406703 0.522666361008234 df.mm.trans1:probe12 -0.0142983322664257 0.168566383680054 -0.084823153669622 0.932420400220621 df.mm.trans1:probe13 0.249536010770938 0.168566383680054 1.48034267167152 0.139123435529422 df.mm.trans1:probe14 -0.280020097124314 0.168566383680054 -1.66118588422591 0.0970160881563285 . df.mm.trans1:probe15 -0.192004867200928 0.168566383680054 -1.13904601266977 0.254979814704149 df.mm.trans1:probe16 -0.104415009467122 0.168566383680054 -0.619429610979292 0.535786410697511 df.mm.trans1:probe17 -0.19000362535769 0.168566383680054 -1.12717388372242 0.259962228291186 df.mm.trans1:probe18 -0.213296851298434 0.168566383680054 -1.26535817309387 0.206062643746114 df.mm.trans1:probe19 -0.261206385931771 0.168566383680054 -1.54957578272279 0.121586533144280 df.mm.trans1:probe20 -0.148038297009705 0.168566383680054 -0.878219570105317 0.380053370982284 df.mm.trans1:probe21 -0.182593749455916 0.168566383680054 -1.08321567722830 0.278995894476143 df.mm.trans2:probe2 0.050088051512463 0.168566383680054 0.297141401618558 0.766425487320296 df.mm.trans2:probe3 0.119661172390709 0.168566383680054 0.709875657164422 0.477960737563964 df.mm.trans2:probe4 0.039229804208508 0.168566383680054 0.232726142378232 0.816025718881275 df.mm.trans2:probe5 -0.0797749819016337 0.168566383680054 -0.473255581332574 0.63614287180394 df.mm.trans2:probe6 0.0983170160898598 0.168566383680054 0.583253991356126 0.559864963614121 df.mm.trans3:probe2 0.0902062075042436 0.168566383680054 0.5351375851751 0.59268376074184 df.mm.trans3:probe3 -0.0537817121500521 0.168566383680054 -0.319053603547264 0.749758133262903 df.mm.trans3:probe4 -0.173091689849214 0.168566383680054 -1.02684584002080 0.304762374733444 df.mm.trans3:probe5 -0.172794058708274 0.168566383680054 -1.02508017871609 0.305594221068496 df.mm.trans3:probe6 -0.0948737713061128 0.168566383680054 -0.562827351663348 0.573689280368245 df.mm.trans3:probe7 0.154882551714148 0.168566383680054 0.918822296194723 0.358428833379861 df.mm.trans3:probe8 -0.094065811621152 0.168566383680054 -0.558034227035995 0.576956484040472 df.mm.trans3:probe9 -0.0918899515070275 0.168566383680054 -0.54512619598839 0.585798563792823 df.mm.trans3:probe10 -0.0385986765111192 0.168566383680054 -0.228982052461783 0.818933646245616 df.mm.trans3:probe11 0.0189541662239024 0.168566383680054 0.112443334252683 0.91049637306644 df.mm.trans3:probe12 -0.0852642606502624 0.168566383680054 -0.505820073900959 0.613103872563166 df.mm.trans3:probe13 -0.158916360143749 0.168566383680054 -0.942752384398175 0.346054527784057 df.mm.trans3:probe14 -0.235601476602677 0.168566383680054 -1.39767770690186 0.162546059523761