chr2.14098_chr2_167918205_167919884_-_1.R fitVsDatCorrelation=0.90462864415307 cont.fitVsDatCorrelation=0.290175846260924 fstatistic=10032.4568131079,41,439 cont.fstatistic=1981.33769783681,41,439 residuals=-0.595584210676484,-0.0795408139014289,-0.00707807968233365,0.0742438612187,1.07297582930976 cont.residuals=-0.822646190536175,-0.268813135703091,0.0283358512730743,0.252044566260091,1.75718404354027 predictedValues: Include Exclude Both chr2.14098_chr2_167918205_167919884_-_1.R.tl.Lung 134.585247144388 144.933477351052 84.64321765122 chr2.14098_chr2_167918205_167919884_-_1.R.tl.cerebhem 134.738876210375 128.728605856890 76.4284284640244 chr2.14098_chr2_167918205_167919884_-_1.R.tl.cortex 114.601813216624 115.704514531611 86.954609232626 chr2.14098_chr2_167918205_167919884_-_1.R.tl.heart 122.032816976044 119.592512549349 81.284525691724 chr2.14098_chr2_167918205_167919884_-_1.R.tl.kidney 128.517575162016 131.871591614868 73.505264716455 chr2.14098_chr2_167918205_167919884_-_1.R.tl.liver 134.967869269033 121.313145519566 78.3514902503167 chr2.14098_chr2_167918205_167919884_-_1.R.tl.stomach 149.239410287633 112.504363359956 79.222547439598 chr2.14098_chr2_167918205_167919884_-_1.R.tl.testicle 122.549171819994 114.571054854161 75.2976171025745 diffExp=-10.3482302066635,6.0102703534844,-1.10270131498648,2.44030442669558,-3.35401645285197,13.6547237494673,36.7350469276767,7.97811696583292 diffExpScore=1.53967174684699 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,1,0 diffExp1.3Score=0.5 diffExp1.2=0,0,0,0,0,0,1,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 101.081295228332 94.9475202515419 112.867421358220 cerebhem 101.559858356209 114.615998874221 97.425814099027 cortex 112.167917533435 110.498584460078 125.894265130261 heart 102.513942085706 107.604918202133 110.933305710468 kidney 106.440565861146 98.827425995028 109.325664193264 liver 104.127863173933 108.843762685109 102.734628059080 stomach 110.424548903112 94.3152002807543 106.116641674568 testicle 117.771652413278 111.287622427356 93.5386049456348 cont.diffExp=6.13377497679036,-13.0561405180126,1.66933307335735,-5.09097611642711,7.61313986611755,-4.71589951117565,16.1093486223581,6.48402998592276 cont.diffExpScore=3.76999514087443 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=0.129427053599414 cont.tran.correlation=0.144218539211187 tran.covariance=0.00105629693261657 cont.tran.covariance=0.000576459991859371 tran.mean=126.903252857723 cont.tran.mean=106.064292295711 weightedLogRatios: wLogRatio Lung -0.365884077682742 cerebhem 0.222709454635869 cortex -0.0454502311729856 heart 0.0968415123741843 kidney -0.125438645092412 liver 0.517490539576986 stomach 1.37444764991447 testicle 0.321429572222731 cont.weightedLogRatios: wLogRatio Lung 0.287001063384515 cerebhem -0.566129896224098 cortex 0.0706606444615222 heart -0.225579228073749 kidney 0.343635424251510 liver -0.206753521521624 stomach 0.72939359714893 testicle 0.268448613562834 varWeightedLogRatios=0.281827791676854 cont.varWeightedLogRatios=0.166042158075902 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.09681306765484 0.0810585946463554 62.8781326630613 1.11040410347671e-221 *** df.mm.trans1 -0.232844338757345 0.0654021164870104 -3.56019577445312 0.000411052058463758 *** df.mm.trans2 -0.0840242533485303 0.0654021164870103 -1.28473293926533 0.199563094532525 df.mm.exp2 -0.0153373977541903 0.0880972333027175 -0.174096247738999 0.861870075606922 df.mm.exp3 -0.412910653710970 0.0880972333027175 -4.68698775467938 3.70525671609043e-06 *** df.mm.exp4 -0.249603110100712 0.0880972333027175 -2.83326843242662 0.00481975637181853 ** df.mm.exp5 0.000509609363641029 0.0880972333027175 0.00578462392672336 0.99538719130778 df.mm.exp6 -0.0978207454864384 0.0880972333027174 -1.11037250341687 0.267446355052779 df.mm.exp7 -0.0837448281559594 0.0880972333027175 -0.950595438885096 0.34233282413958 df.mm.exp8 -0.211768623111083 0.0880972333027175 -2.40380560401266 0.0166390048219288 * df.mm.trans1:exp2 0.0164782467857269 0.0702772804417383 0.234474736104619 0.814725761216041 df.mm.trans2:exp2 -0.103231104872196 0.0702772804417383 -1.46891149206858 0.142572991661459 df.mm.trans1:exp3 0.252176473965484 0.0702772804417383 3.58830723642678 0.000370314964808521 *** df.mm.trans2:exp3 0.187675446176315 0.0702772804417383 2.67049955542749 0.00785508243018373 ** df.mm.trans1:exp4 0.151695304233584 0.0702772804417383 2.15852553314643 0.0314283671290967 * df.mm.trans2:exp4 0.0574184852941294 0.0702772804417383 0.817027707008822 0.414355755804981 df.mm.trans1:exp5 -0.0466417487274673 0.0702772804417383 -0.663681753680474 0.507242399342705 df.mm.trans2:exp5 -0.0949558113027315 0.0702772804417383 -1.35115944592438 0.177340276351584 df.mm.trans1:exp6 0.100659684146967 0.0702772804417383 1.43232184732043 0.152763374621079 df.mm.trans2:exp6 -0.080078932729591 0.0702772804417383 -1.13947113812946 0.255128049823210 df.mm.trans1:exp7 0.187098819029168 0.0702772804417383 2.66229452609906 0.00804598585616567 ** df.mm.trans2:exp7 -0.169538025790828 0.0702772804417383 -2.41241585794401 0.0162562915052473 * df.mm.trans1:exp8 0.118083169132038 0.0702772804417383 1.68024670832179 0.0936207074906461 . df.mm.trans2:exp8 -0.0233110402154042 0.0702772804417383 -0.331700943304567 0.740273383721658 df.mm.trans1:probe2 0.0928152498875735 0.0460072796085579 2.01740356476780 0.0442614739938208 * df.mm.trans1:probe3 0.152942959030747 0.0460072796085579 3.32432085383065 0.000960653908665416 *** df.mm.trans1:probe4 0.0374560629538631 0.0460072796085579 0.814133399595655 0.416009916538432 df.mm.trans1:probe5 0.137752636027602 0.0460072796085579 2.99414869124274 0.00290764887238087 ** df.mm.trans1:probe6 0.114240172032905 0.0460072796085579 2.48308904601382 0.0133973289435061 * df.mm.trans2:probe2 -0.330380892180712 0.0460072796085579 -7.18105688907669 2.97970406056323e-12 *** df.mm.trans2:probe3 0.423245848090905 0.0460072796085579 9.19954084857858 1.46567554481872e-18 *** df.mm.trans2:probe4 -0.206593953874964 0.0460072796085579 -4.49046228407157 9.09572225756744e-06 *** df.mm.trans2:probe5 -0.0610515115455576 0.0460072796085579 -1.32699677235863 0.185199520446469 df.mm.trans2:probe6 -0.336414847124631 0.0460072796085579 -7.31220906749838 1.25536371871398e-12 *** df.mm.trans3:probe2 0.00508982501111892 0.0460072796085579 0.110630862211904 0.91195963146668 df.mm.trans3:probe3 -0.446045552999469 0.0460072796085579 -9.69510818276025 2.87076346164485e-20 *** df.mm.trans3:probe4 -0.317186044782933 0.0460072796085579 -6.89425776706721 1.89281640486328e-11 *** df.mm.trans3:probe5 -0.6315619413518 0.0460072796085579 -13.727435021703 6.20699597268383e-36 *** df.mm.trans3:probe6 -0.547048886672205 0.0460072796085579 -11.8904854041935 1.86035606310487e-28 *** df.mm.trans3:probe7 -0.202151815144446 0.0460072796085579 -4.39390933053219 1.39750827539226e-05 *** df.mm.trans3:probe8 -0.59309152352853 0.0460072796085579 -12.8912539183953 1.79948270501636e-32 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.52569088284721 0.182005610583135 24.8656668788790 8.04615315213279e-86 *** df.mm.trans1 0.130768192182195 0.146851203090564 0.890480904685197 0.37369546410334 df.mm.trans2 0.0170908546490697 0.146851203090564 0.116382122103077 0.907402924028844 df.mm.exp2 0.340109006269249 0.197809878247956 1.71937321473360 0.0862513112243111 . df.mm.exp3 0.146521784447449 0.197809878247956 0.740720260005334 0.459259067977609 df.mm.exp4 0.156500443980558 0.197809878247956 0.791165968892536 0.42927437582387 df.mm.exp5 0.12359519763101 0.197809878247956 0.624818127010234 0.532415041445764 df.mm.exp6 0.260348295465284 0.197809878247956 1.3161541666738 0.188809104722245 df.mm.exp7 0.143400408119881 0.197809878247956 0.724940581279405 0.468874698543115 df.mm.exp8 0.499455856102356 0.197809878247956 2.52492878781456 0.0119234193601602 * df.mm.trans1:exp2 -0.335385740445806 0.157797694281835 -2.12541597627396 0.0341096887205186 * df.mm.trans2:exp2 -0.151845926056810 0.157797694281835 -0.96228228649276 0.336437554820897 df.mm.trans1:exp3 -0.0424498685658677 0.157797694281835 -0.269014504673623 0.788044904197018 df.mm.trans2:exp3 0.00515660555581141 0.157797694281835 0.0326785862067252 0.973945752548808 df.mm.trans1:exp4 -0.142426730634843 0.157797694281835 -0.90259069552982 0.367238127760817 df.mm.trans2:exp4 -0.0313584096556536 0.157797694281835 -0.198725398354971 0.842569555041831 df.mm.trans1:exp5 -0.0719335316215635 0.157797694281835 -0.455859205984889 0.64871663518131 df.mm.trans2:exp5 -0.0835443609121146 0.157797694281835 -0.529439680930319 0.596768098779881 df.mm.trans1:exp6 -0.230653794218748 0.157797694281835 -1.46170573194047 0.144537289894721 df.mm.trans2:exp6 -0.123759131911259 0.157797694281835 -0.784289862247412 0.433293026601459 df.mm.trans1:exp7 -0.0549930320742723 0.157797694281835 -0.348503394327498 0.727629233467797 df.mm.trans2:exp7 -0.150082361350667 0.157797694281835 -0.95110617448321 0.342073813598924 df.mm.trans1:exp8 -0.346633351827875 0.157797694281835 -2.19669465644263 0.0285643095021875 * df.mm.trans2:exp8 -0.340662133659835 0.157797694281835 -2.1588536842078 0.0314027274085606 * df.mm.trans1:probe2 -0.0748627954985257 0.103302839790858 -0.724692522007033 0.469026745047375 df.mm.trans1:probe3 -0.0781722951006929 0.103302839790858 -0.756729391553577 0.449617920915008 df.mm.trans1:probe4 -0.171963422524847 0.103302839790858 -1.66465339068118 0.096695434543274 . df.mm.trans1:probe5 -0.129018720222196 0.103302839790858 -1.24893681996933 0.212353745560616 df.mm.trans1:probe6 -0.113458468428981 0.103302839790858 -1.09830928809589 0.272671461814094 df.mm.trans2:probe2 -0.0688351711581169 0.103302839790858 -0.666343454811862 0.505541611754978 df.mm.trans2:probe3 0.116094596275406 0.103302839790858 1.12382773320119 0.261700271700171 df.mm.trans2:probe4 0.0905130796613366 0.103302839790858 0.87619159206644 0.381405062934946 df.mm.trans2:probe5 0.0192418642386771 0.103302839790858 0.186266556443494 0.8523217728416 df.mm.trans2:probe6 -0.00941820617482352 0.103302839790858 -0.0911708351279707 0.927398433621505 df.mm.trans3:probe2 0.106010515495820 0.103302839790858 1.02621104812262 0.305357224705405 df.mm.trans3:probe3 0.0174775144740195 0.103302839790858 0.169187163774041 0.865727354780995 df.mm.trans3:probe4 0.0741979214470802 0.103302839790858 0.71825635768869 0.472981308031692 df.mm.trans3:probe5 0.118362314151148 0.103302839790858 1.14577986811184 0.252510440199061 df.mm.trans3:probe6 0.0858147079719059 0.103302839790858 0.830710057396698 0.406588996092152 df.mm.trans3:probe7 -0.133337925102196 0.103302839790858 -1.29074791527653 0.197470443400694 df.mm.trans3:probe8 0.152786454811672 0.103302839790858 1.47901505051551 0.139853462517923