chr7.21361_chr7_16745194_16748868_+_1.R fitVsDatCorrelation=0.987483998324885 cont.fitVsDatCorrelation=0.279249228517646 fstatistic=10580.4896231929,49,623 cont.fstatistic=273.081819367755,49,623 residuals=-0.62423051459702,-0.104448214679106,-0.00462678465866976,0.103106724509737,0.623056687095598 cont.residuals=-1.80216735952695,-0.828845571608535,-0.346359140477978,0.741837731323413,2.84461737868531 predictedValues: Include Exclude Both chr7.21361_chr7_16745194_16748868_+_1.R.tl.Lung 121.979167963796 1241.35440808511 66.966326941711 chr7.21361_chr7_16745194_16748868_+_1.R.tl.cerebhem 90.7395958889244 1024.83532992077 67.3447844737386 chr7.21361_chr7_16745194_16748868_+_1.R.tl.cortex 96.4685684228695 757.693090987615 76.4053686231576 chr7.21361_chr7_16745194_16748868_+_1.R.tl.heart 109.984086230082 759.709682601463 68.6803447723245 chr7.21361_chr7_16745194_16748868_+_1.R.tl.kidney 135.184983542701 1187.50778905000 68.1486646423957 chr7.21361_chr7_16745194_16748868_+_1.R.tl.liver 137.732170824703 913.851642362963 66.4501217455166 chr7.21361_chr7_16745194_16748868_+_1.R.tl.stomach 101.624829095904 741.812171168653 62.6041104987389 chr7.21361_chr7_16745194_16748868_+_1.R.tl.testicle 132.190878483102 1116.23285969204 65.8588773651288 diffExp=-1119.37524012131,-934.095734031847,-661.224522564746,-649.725596371381,-1052.32280550730,-776.11947153826,-640.18734207275,-984.041981208938 diffExpScore=0.999853331416136 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 208.214450101086 175.299275905163 185.798127402445 cerebhem 218.400525083633 129.189684411947 135.286281113340 cortex 205.565579008984 137.981596363205 179.016598688977 heart 252.945144227242 204.072554920495 191.355131387997 kidney 93.4275678437687 91.8745747269612 149.516048372818 liver 130.615053641044 79.474990306712 141.881846502356 stomach 118.203260969826 113.112600381255 148.700773437564 testicle 160.08648262859 210.163167740497 152.199678852196 cont.diffExp=32.9151741959228,89.2108406716861,67.5839826457786,48.8725893067473,1.55299311680754,51.140063334332,5.09066058857061,-50.0766851119068 cont.diffExpScore=1.40096050422917 cont.diffExp1.5=0,1,0,0,0,1,0,0 cont.diffExp1.5Score=0.666666666666667 cont.diffExp1.4=0,1,1,0,0,1,0,0 cont.diffExp1.4Score=0.75 cont.diffExp1.3=0,1,1,0,0,1,0,-1 cont.diffExp1.3Score=1.33333333333333 cont.diffExp1.2=0,1,1,1,0,1,0,-1 cont.diffExp1.2Score=1.25 tran.correlation=0.540120649443565 cont.tran.correlation=0.658356826621231 tran.covariance=0.0185148847062139 cont.tran.covariance=0.0882985394133094 tran.mean=541.806328395043 cont.tran.mean=158.039156766276 weightedLogRatios: wLogRatio Lung -13.8369023871568 cerebhem -13.8672989406399 cortex -11.5414228096347 heart -10.9513446282168 kidney -13.0228756005451 liver -11.1109558608015 stomach -11.1619245846490 testicle -12.6962263215281 cont.weightedLogRatios: wLogRatio Lung 0.903823585148561 cerebhem 2.69024881759758 cortex 2.04362965539131 heart 1.16490844277747 kidney 0.0759124334232574 liver 2.29718380526274 stomach 0.209121435658673 testicle -1.41849555881289 varWeightedLogRatios=1.51368913246588 cont.varWeightedLogRatios=1.85623432293653 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 7.31701911550825 0.0909530212433543 80.4483349259042 0 *** df.mm.trans1 -2.74630861310622 0.0718787137462756 -38.2075369740255 2.01687675778018e-165 *** df.mm.trans2 -0.197390213931297 0.0718787137462756 -2.74615673602708 0.0062039447849319 ** df.mm.exp2 -0.493163107824636 0.0952826960527506 -5.17578876600648 3.06571750951037e-07 *** df.mm.exp3 -0.860176006443188 0.0952826960527505 -9.02762035581966 2.16881651999147e-18 *** df.mm.exp4 -0.619809700343605 0.0952826960527505 -6.50495552729181 1.59672904601877e-10 *** df.mm.exp5 0.0409459277342171 0.0952826960527505 0.429730994508684 0.667539922363054 df.mm.exp6 -0.177091060409097 0.0952826960527505 -1.85858574269409 0.063557275392643 . df.mm.exp7 -0.630065672940942 0.0952826960527505 -6.61259283209329 8.12956721627152e-11 *** df.mm.exp8 -0.00917121979422173 0.0952826960527506 -0.0962527318616629 0.923350804770358 df.mm.trans1:exp2 0.197306652787236 0.0724992609174267 2.721498816546 0.00668031211694349 ** df.mm.trans2:exp2 0.301492005658217 0.0724992609174268 4.15855281616736 3.65190263789600e-05 *** df.mm.trans1:exp3 0.625542970148071 0.0724992609174267 8.62826685723783 5.17616021488033e-17 *** df.mm.trans2:exp3 0.366496089841111 0.0724992609174267 5.0551700141955 5.6606101071644e-07 *** df.mm.trans1:exp4 0.516295109276646 0.0724992609174267 7.1213844492109 2.95735703602619e-12 *** df.mm.trans2:exp4 0.128787736970781 0.0724992609174267 1.77640068796651 0.0761548491459923 . df.mm.trans1:exp5 0.0618478854769901 0.0724992609174267 0.85308297897591 0.393941143903132 df.mm.trans2:exp5 -0.0852921597438312 0.0724992609174267 -1.17645557574683 0.239862191705028 df.mm.trans1:exp6 0.298551792907589 0.0724992609174267 4.11799774410978 4.33716348343347e-05 *** df.mm.trans2:exp6 -0.129199025298003 0.0724992609174268 -1.78207368824289 0.0752241697491699 . df.mm.trans1:exp7 0.447503283336429 0.0724992609174267 6.17252200468794 1.21141096322875e-09 *** df.mm.trans2:exp7 0.115203418379720 0.0724992609174267 1.58902886625191 0.112561200572704 df.mm.trans1:exp8 0.0895678712148617 0.0724992609174267 1.23543150759668 0.217135621322608 df.mm.trans2:exp8 -0.097072330488954 0.0724992609174268 -1.33894234590219 0.181077775020285 df.mm.trans1:probe2 0.469226319862856 0.0535442527273543 8.763336790825 1.79174720695225e-17 *** df.mm.trans1:probe3 1.13090058553810 0.0535442527273543 21.1208585036495 4.34861166834468e-75 *** df.mm.trans1:probe4 1.07581193007529 0.0535442527273543 20.0920150207958 1.33643966823942e-69 *** df.mm.trans1:probe5 1.46265618525791 0.0535442527273543 27.3167727768228 1.28451293976785e-108 *** df.mm.trans1:probe6 0.99047999309617 0.0535442527273543 18.4983437557652 3.19707791225531e-61 *** df.mm.trans2:probe2 0.371856607611731 0.0535442527273543 6.94484634056269 9.55342761782167e-12 *** df.mm.trans2:probe3 -0.173180149803158 0.0535442527273543 -3.23433685189306 0.00128373427737029 ** df.mm.trans2:probe4 0.316981972606129 0.0535442527273543 5.91999993389004 5.31949228527361e-09 *** df.mm.trans2:probe5 -0.6519827608055 0.0535442527273543 -12.1765218038503 9.51383974383138e-31 *** df.mm.trans2:probe6 0.231571691829671 0.0535442527273543 4.3248655090739 1.77707519722385e-05 *** df.mm.trans3:probe2 -0.504137539205022 0.0535442527273543 -9.41534363682458 9.00199858403812e-20 *** df.mm.trans3:probe3 -0.191180748779952 0.0535442527273543 -3.5705185718706 0.000383727634588875 *** df.mm.trans3:probe4 -0.0339496925912545 0.0535442527273543 -0.634049237069854 0.526281417578584 df.mm.trans3:probe5 -0.329276815127267 0.0535442527273543 -6.1496201432474 1.38834504371696e-09 *** df.mm.trans3:probe6 -0.304008515700483 0.0535442527273543 -5.67770582677631 2.09449661229205e-08 *** df.mm.trans3:probe7 -0.222005944202257 0.0535442527273543 -4.14621426005708 3.84864777179598e-05 *** df.mm.trans3:probe8 0.450549282452749 0.0535442527273543 8.41452181146187 2.70350089040081e-16 *** df.mm.trans3:probe9 -0.207685173604889 0.0535442527273543 -3.87875753280964 0.000116156967006466 *** df.mm.trans3:probe10 0.134734569424291 0.0535442527273543 2.51632178172987 0.0121088254153355 * df.mm.trans3:probe11 -0.169723584737470 0.0535442527273543 -3.16978155623345 0.00160032902211232 ** df.mm.trans3:probe12 -0.339140469971571 0.0535442527273543 -6.33383514937567 4.58198034763517e-10 *** df.mm.trans3:probe13 -0.136102758571219 0.0535442527273543 -2.54187427480312 0.0112670841586028 * df.mm.trans3:probe14 -0.618195581677568 0.0535442527273543 -11.5455076910943 4.4494302805561e-28 *** df.mm.trans3:probe15 -0.109249124055392 0.0535442527273543 -2.04035201708175 0.0417364006793119 * df.mm.trans3:probe16 -0.126713957675856 0.0535442527273543 -2.36652770785838 0.0182607350175062 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.86820725704646 0.553735851895105 8.7915695550243 1.43314078286940e-17 *** df.mm.trans1 0.488820764946004 0.437608561489392 1.11702742579421 0.264413198298609 df.mm.trans2 0.303339536265133 0.437608561489392 0.693175506513682 0.488457745930753 df.mm.exp2 0.0598167880386531 0.580095571849818 0.103115401911979 0.91790456395632 df.mm.exp3 -0.214995585005549 0.580095571849818 -0.370620972540729 0.711045837140999 df.mm.exp4 0.317114735462922 0.580095571849818 0.546659465873359 0.584808437468264 df.mm.exp5 -1.23019541696264 0.580095571849818 -2.12067713780296 0.0343434387298963 * df.mm.exp6 -0.987700256309865 0.580095571849818 -1.70265091519363 0.0891322246246822 . df.mm.exp7 -0.781549047718254 0.580095571849817 -1.34727635521512 0.178381043111358 df.mm.exp8 0.118002596278227 0.580095571849818 0.203419232975591 0.83887379119171 df.mm.trans1:exp2 -0.0120546986185164 0.441386547220499 -0.0273109787655001 0.978220446232675 df.mm.trans2:exp2 -0.365029703205403 0.441386547220499 -0.827006861681829 0.408549809064538 df.mm.trans1:exp3 0.202192128622432 0.441386547220498 0.458084030643157 0.64705180458849 df.mm.trans2:exp3 -0.0243787597322717 0.441386547220499 -0.0552322219283522 0.95597116622803 df.mm.trans1:exp4 -0.122510650157533 0.441386547220499 -0.277558640898793 0.781443262983272 df.mm.trans2:exp4 -0.165133804791157 0.441386547220499 -0.374125142306756 0.708438508630645 df.mm.trans1:exp5 0.42881331889666 0.441386547220499 0.97151424663254 0.331669294483729 df.mm.trans2:exp5 0.584125084347826 0.441386547220499 1.32338669591581 0.186192262045006 df.mm.trans1:exp6 0.521386173078215 0.441386547220498 1.18124618061310 0.237955671937663 df.mm.trans2:exp6 0.196647979825306 0.441386547220499 0.44552327447141 0.65609641587886 df.mm.trans1:exp7 0.215386182210086 0.441386547220498 0.487976318187351 0.625738303305873 df.mm.trans2:exp7 0.343438172526369 0.441386547220499 0.778089352040903 0.436811733419166 df.mm.trans1:exp8 -0.380856969318911 0.441386547220499 -0.862864923539797 0.388543773880353 df.mm.trans2:exp8 0.0633869606649199 0.441386547220499 0.143608728141083 0.88585588583209 df.mm.trans1:probe2 -0.426450844268274 0.325985569173545 -1.30818933288806 0.191291547022765 df.mm.trans1:probe3 -0.106844055735858 0.325985569173545 -0.327757010860127 0.743205506127228 df.mm.trans1:probe4 0.214134484277795 0.325985569173545 0.656883324070693 0.511498516467517 df.mm.trans1:probe5 -0.238007243181889 0.325985569173545 -0.730115887599862 0.465593795247345 df.mm.trans1:probe6 0.151059465963981 0.325985569173545 0.463393107697848 0.643244459306654 df.mm.trans2:probe2 -0.146737373720288 0.325985569173545 -0.450134569123117 0.652770000893228 df.mm.trans2:probe3 -0.00856167698652268 0.325985569173545 -0.0262639754521302 0.979055198782262 df.mm.trans2:probe4 -0.308915393436291 0.325985569173545 -0.947635179739608 0.343682630847242 df.mm.trans2:probe5 0.232852897252032 0.325985569173545 0.714304310593787 0.47530656756445 df.mm.trans2:probe6 0.120214642964163 0.325985569173545 0.368772897735739 0.712422292473755 df.mm.trans3:probe2 -0.67553521066631 0.325985569173545 -2.07228563024726 0.0386495866787473 * df.mm.trans3:probe3 -0.320356427452971 0.325985569173545 -0.98273192971442 0.326120872789774 df.mm.trans3:probe4 -0.554982183792185 0.325985569173545 -1.70247469910770 0.0891652474396761 . df.mm.trans3:probe5 -0.585575559261469 0.325985569173545 -1.79632356348181 0.0729274225634834 . df.mm.trans3:probe6 -0.264369440070590 0.325985569173545 -0.810985102011823 0.417683928692089 df.mm.trans3:probe7 -0.693708744478052 0.325985569173545 -2.12803513430603 0.0337260874469369 * df.mm.trans3:probe8 -0.386003901890655 0.325985569173545 -1.18411346511219 0.236819721450946 df.mm.trans3:probe9 -0.214762449182278 0.325985569173545 -0.658809682056645 0.510261397392591 df.mm.trans3:probe10 -0.728656071380587 0.325985569173545 -2.23524026915643 0.025755163430369 * df.mm.trans3:probe11 -0.212517134128805 0.325985569173545 -0.65192190767091 0.514691981784475 df.mm.trans3:probe12 -0.47876558562065 0.325985569173545 -1.46867110355357 0.142426744378073 df.mm.trans3:probe13 -0.322885860642247 0.325985569173545 -0.990491270705153 0.32231858589414 df.mm.trans3:probe14 -0.425229760461287 0.325985569173545 -1.30444351122460 0.192564088688079 df.mm.trans3:probe15 -0.533577086391193 0.325985569173545 -1.63681198448123 0.102174767198411 df.mm.trans3:probe16 -0.574383552494322 0.325985569173545 -1.7619907345915 0.078561246438776 .