chr16.9180_chr16_20274442_20278513_+_2.R fitVsDatCorrelation=0.911908539638163 cont.fitVsDatCorrelation=0.263167240822771 fstatistic=6792.13950443189,49,623 cont.fstatistic=1218.6596049782,49,623 residuals=-0.843625599060757,-0.109678336292158,0.00728161600828923,0.11031846987858,1.05649606435038 cont.residuals=-0.986341962689802,-0.359913270079789,-0.025451322219871,0.275370652390806,1.80512043166432 predictedValues: Include Exclude Both chr16.9180_chr16_20274442_20278513_+_2.R.tl.Lung 103.396725502611 63.9753261155906 110.659069813012 chr16.9180_chr16_20274442_20278513_+_2.R.tl.cerebhem 141.367384745774 84.8522588636234 154.338930984064 chr16.9180_chr16_20274442_20278513_+_2.R.tl.cortex 194.215293643341 63.0408467314328 314.665551250697 chr16.9180_chr16_20274442_20278513_+_2.R.tl.heart 107.178401938126 62.511403530968 128.542934436626 chr16.9180_chr16_20274442_20278513_+_2.R.tl.kidney 105.111675870361 63.422358701087 116.405334013356 chr16.9180_chr16_20274442_20278513_+_2.R.tl.liver 98.5972813473561 57.946803958881 105.352271482658 chr16.9180_chr16_20274442_20278513_+_2.R.tl.stomach 92.522570999059 70.1952528282826 98.2424882759245 chr16.9180_chr16_20274442_20278513_+_2.R.tl.testicle 115.518406513334 70.7130487022248 134.511307689665 diffExp=39.4213993870203,56.5151258821506,131.174446911908,44.6669984071581,41.6893171692742,40.6504773884752,22.3273181707764,44.8053578111097 diffExpScore=0.997631737228435 diffExp1.5=1,1,1,1,1,1,0,1 diffExp1.5Score=0.875 diffExp1.4=1,1,1,1,1,1,0,1 diffExp1.4Score=0.875 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 122.268448751542 126.351786219957 91.7279537783263 cerebhem 116.958787432261 90.769723211056 92.1739048033883 cortex 110.910043249135 98.6114624606642 109.103662017083 heart 118.165555469426 126.109474246792 101.010524833547 kidney 120.820613376937 91.2104440752247 95.2673806189668 liver 119.914338046356 113.112860186482 94.6093921566842 stomach 112.363994913764 83.8702579155814 97.7911492844742 testicle 103.879988632064 124.413245211831 116.303518987578 cont.diffExp=-4.08333746841532,26.1890642212053,12.2985807884706,-7.94391877736514,29.6101693017126,6.8014778598741,28.4937369981822,-20.5332565797674 cont.diffExpScore=1.89264624037556 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=0,0,0,0,1,0,1,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,1,0,0,1,0,1,0 cont.diffExp1.2Score=0.75 tran.correlation=0.173679808941306 cont.tran.correlation=0.0132248919386697 tran.covariance=0.0063581297650697 cont.tran.covariance=1.18151136687572e-05 tran.mean=93.4103149995033 cont.tran.mean=111.233188962442 weightedLogRatios: wLogRatio Lung 2.11163045491151 cerebhem 2.39714518299395 cortex 5.29553996310341 heart 2.37489494020852 kidney 2.22413293627247 liver 2.29896777185305 stomach 1.21221990606715 testicle 2.21057684675165 cont.weightedLogRatios: wLogRatio Lung -0.158428423455053 cerebhem 1.17497191848493 cortex 0.546518074608258 heart -0.312606350475085 kidney 1.30834032405604 liver 0.277802604135051 stomach 1.33821006069896 testicle -0.853778627940169 varWeightedLogRatios=1.40697262889584 cont.varWeightedLogRatios=0.676011632759704 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05181690669287 0.100058931431851 40.4943051930602 1.21787629849289e-176 *** df.mm.trans1 0.662138192613083 0.0842802813420822 7.85638327339647 1.73973348820772e-14 *** df.mm.trans2 0.143373593573856 0.077663167143907 1.84609511621114 0.0653525892382973 . df.mm.exp2 0.262505904610521 0.101426542906035 2.58813814499934 0.00987468777554103 ** df.mm.exp3 -0.429376892097857 0.101426542906035 -4.23337796789196 2.64899171875261e-05 *** df.mm.exp4 -0.137035959459695 0.101426542906035 -1.35108577630068 0.177158407832264 df.mm.exp5 -0.0428552755316322 0.101426542906035 -0.422525251317447 0.672787459726886 df.mm.exp6 -0.0973573373410846 0.101426542906035 -0.959880269519585 0.3374878499761 df.mm.exp7 0.100677776874391 0.101426542906035 0.992617652044621 0.321281683384435 df.mm.exp8 0.0157949924513293 0.101426542906035 0.155728392181939 0.876297560693066 df.mm.trans1:exp2 0.0502828694198078 0.0885323448275412 0.567960438840264 0.570266461120574 df.mm.trans2:exp2 0.0199082289810517 0.073407142400829 0.271202887483969 0.786324827237154 df.mm.trans1:exp3 1.05977090356015 0.0885323448275412 11.9704375347175 7.24358026671561e-30 *** df.mm.trans2:exp3 0.414662289630523 0.0734071424008289 5.64880032199484 2.45855776014162e-08 *** df.mm.trans1:exp4 0.172957420020314 0.0885323448275412 1.95360712920493 0.0511948580407773 . df.mm.trans2:exp4 0.113887476474185 0.073407142400829 1.55144952860743 0.121301815391854 df.mm.trans1:exp5 0.0593053468870964 0.0885323448275412 0.669872090280923 0.503187533353316 df.mm.trans2:exp5 0.0341742561248065 0.0734071424008289 0.465544019384422 0.641704618491956 df.mm.trans1:exp6 0.0498277327079188 0.0885323448275412 0.5628195300258 0.573760254511861 df.mm.trans2:exp6 -0.00161472567605383 0.073407142400829 -0.0219968469449043 0.982457513905113 df.mm.trans1:exp7 -0.211798444640767 0.0885323448275412 -2.39232842023269 0.0170371801844504 * df.mm.trans2:exp7 -0.00789457123812994 0.0734071424008289 -0.107545001479867 0.91439125026979 df.mm.trans1:exp8 0.0950615952456795 0.0885323448275412 1.07374988690130 0.283350502956898 df.mm.trans2:exp8 0.0843376482289982 0.073407142400829 1.14890248374586 0.251037096255730 df.mm.trans1:probe2 -0.444918230509137 0.0606139529135877 -7.34019494065039 6.68836743404486e-13 *** df.mm.trans1:probe3 -0.561415330929622 0.0606139529135877 -9.26214681510683 3.20230876998772e-19 *** df.mm.trans1:probe4 0.27681842744367 0.0606139529135877 4.56690933584727 5.96707772736994e-06 *** df.mm.trans1:probe5 -0.348657109495439 0.0606139529135877 -5.75209325140848 1.38223304110272e-08 *** df.mm.trans1:probe6 -0.457115535227961 0.0606139529135877 -7.54142426380991 1.65093528665986e-13 *** df.mm.trans1:probe7 0.290740910269434 0.0606139529135877 4.796600391397 2.02133992836395e-06 *** df.mm.trans1:probe8 -0.115968518865585 0.0606139529135877 -1.91323141440573 0.0561764999469534 . df.mm.trans1:probe9 -0.080059731497873 0.0606139529135877 -1.32081356931147 0.187048486955210 df.mm.trans1:probe10 0.022581752467587 0.0606139529135877 0.372550401056666 0.709609796218284 df.mm.trans1:probe11 -0.074435156112844 0.0606139529135877 -1.22802015930161 0.219903144300989 df.mm.trans1:probe12 -0.165971209242062 0.0606139529135877 -2.73816837978994 0.00635480395150166 ** df.mm.trans2:probe2 -0.127049573315245 0.0606139529135877 -2.09604500627717 0.0364810502585519 * df.mm.trans2:probe3 -0.226115267464224 0.0606139529135877 -3.73041612690362 0.000208616539118826 *** df.mm.trans2:probe4 -0.18949659706034 0.0606139529135876 -3.12628673682592 0.00185270884927806 ** df.mm.trans2:probe5 -0.00657282729519278 0.0606139529135877 -0.108437529302257 0.913683547169686 df.mm.trans2:probe6 -0.0378540658495844 0.0606139529135877 -0.624510760807002 0.532520838389595 df.mm.trans3:probe2 -0.0831813675831302 0.0606139529135877 -1.37231385819227 0.170459622722145 df.mm.trans3:probe3 -0.270756085456300 0.0606139529135877 -4.46689371739697 9.42497368178049e-06 *** df.mm.trans3:probe4 0.211062284509974 0.0606139529135877 3.48207424800141 0.000532363955309692 *** df.mm.trans3:probe5 -0.424486521140664 0.0606139529135877 -7.00311563157445 6.50466975482236e-12 *** df.mm.trans3:probe6 -0.269123341013885 0.0606139529135877 -4.43995694188683 1.06439323597206e-05 *** df.mm.trans3:probe7 0.0248704363895778 0.0606139529135877 0.410308768758797 0.681720525457915 df.mm.trans3:probe8 -0.0304079002166753 0.0606139529135877 -0.501665025213342 0.616080400815738 df.mm.trans3:probe9 0.0256894390095998 0.0606139529135877 0.423820552443149 0.671842977096441 df.mm.trans3:probe10 -0.692413557055107 0.0606139529135877 -11.4233361094635 1.42866688815074e-27 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.19400560544832 0.235217857215529 22.0816806467592 2.97935528952944e-80 *** df.mm.trans1 -0.295617564431961 0.198125513626022 -1.49207216688893 0.136186415275063 df.mm.trans2 -0.376485116202538 0.182570046459103 -2.06214066055394 0.0396084478371097 * df.mm.exp2 -0.379991474122326 0.238432828991236 -1.59370450675772 0.111509415059225 df.mm.exp3 -0.518853324964645 0.238432828991236 -2.17609851445296 0.0299228974647602 * df.mm.exp4 -0.132449516663772 0.238432828991236 -0.555500336191708 0.578751894207232 df.mm.exp5 -0.375672976226498 0.238432828991236 -1.57559249628459 0.115627384304968 df.mm.exp6 -0.161054858269632 0.238432828991236 -0.675472664360123 0.499626143797929 df.mm.exp7 -0.558281288012276 0.238432828991236 -2.34146149409986 0.0195226394023576 * df.mm.exp8 -0.41582021770038 0.238432828991236 -1.74397216800906 0.0816571591047381 . df.mm.trans1:exp2 0.335594075696557 0.208121235621867 1.61249319269992 0.107361080000836 df.mm.trans2:exp2 0.0492472884988561 0.17256501236572 0.285383971082652 0.775444838591836 df.mm.trans1:exp3 0.42135374952697 0.208121235621867 2.02455913865764 0.0433387550155208 * df.mm.trans2:exp3 0.270970861139557 0.17256501236572 1.57025376943319 0.116863805231544 df.mm.trans1:exp4 0.0983171432775785 0.208121235621867 0.472403226820207 0.63680443560061 df.mm.trans2:exp4 0.130529918804484 0.17256501236572 0.756410103154917 0.449689284264378 df.mm.trans1:exp5 0.363760860705064 0.208121235621867 1.74783154452330 0.0809858304165129 . df.mm.trans2:exp5 0.0497724143052712 0.17256501236572 0.288427031777378 0.77311581271417 df.mm.trans1:exp6 0.141613469523753 0.208121235621867 0.680437385933306 0.496480342923034 df.mm.trans2:exp6 0.0503709704490955 0.17256501236572 0.291895615215113 0.770463606932663 df.mm.trans1:exp7 0.473805817204279 0.208121235621867 2.27658564388466 0.0231493278896184 * df.mm.trans2:exp7 0.148482373373191 0.17256501236572 0.860443095257978 0.389875840708274 df.mm.trans1:exp8 0.252837468059306 0.208121235621867 1.21485665460243 0.224880999312069 df.mm.trans2:exp8 0.400358894288077 0.17256501236572 2.32004673948384 0.0206605534487978 * df.mm.trans1:probe2 -0.194461351954396 0.142490869307430 -1.36472851137456 0.172831084530835 df.mm.trans1:probe3 -0.171532686516483 0.142490869307430 -1.20381528550011 0.229118173645839 df.mm.trans1:probe4 -0.207368224259962 0.142490869307430 -1.45530885780868 0.146087458867848 df.mm.trans1:probe5 -0.193872644428616 0.142490869307430 -1.36059696576296 0.174133118314003 df.mm.trans1:probe6 -0.162738907273162 0.142490869307430 -1.14210059959734 0.253850960448018 df.mm.trans1:probe7 -0.241935645824990 0.142490869307430 -1.69790279897166 0.0900254779964912 . df.mm.trans1:probe8 -0.246971929900767 0.142490869307430 -1.73324740807016 0.0835464938080597 . df.mm.trans1:probe9 -0.0410898821678633 0.142490869307430 -0.288368527524456 0.77316057008772 df.mm.trans1:probe10 -0.214243611310161 0.142490869307430 -1.50356027969709 0.133201361130998 df.mm.trans1:probe11 -0.248253507778359 0.142490869307430 -1.74224151333333 0.0819596683897834 . df.mm.trans1:probe12 -0.105249916895212 0.142490869307430 -0.738643236628242 0.460401832342491 df.mm.trans2:probe2 0.192067508399168 0.142490869307430 1.34792853277338 0.178171281113115 df.mm.trans2:probe3 -0.0501824440161926 0.142490869307430 -0.352180067818393 0.72482232250805 df.mm.trans2:probe4 0.24537866671969 0.14249086930743 1.72206589736129 0.0855539219786531 . df.mm.trans2:probe5 -0.0395275352349912 0.142490869307430 -0.277403986845703 0.781561944160843 df.mm.trans2:probe6 -0.0029444907845474 0.142490869307430 -0.0206644172981676 0.983519970218914 df.mm.trans3:probe2 -0.0292564508561430 0.142490869307430 -0.205321583048391 0.837387950448291 df.mm.trans3:probe3 0.0401813472726298 0.142490869307430 0.28199243550081 0.778042958912209 df.mm.trans3:probe4 0.0433270383410311 0.142490869307430 0.304068875090874 0.761176949727361 df.mm.trans3:probe5 -0.00134365347218467 0.142490869307430 -0.0094297513848812 0.99247927718901 df.mm.trans3:probe6 -0.108956561936636 0.142490869307430 -0.764656447576003 0.444765679607647 df.mm.trans3:probe7 -0.0859191036247465 0.142490869307430 -0.602979714015024 0.546741619551311 df.mm.trans3:probe8 -0.0110360698007396 0.142490869307430 -0.0774510665446836 0.938289584702963 df.mm.trans3:probe9 0.0432268751387673 0.142490869307430 0.303365930384658 0.761712284790087 df.mm.trans3:probe10 0.0790190534659647 0.142490869307430 0.554555206589958 0.579397954208675