chr8.23366_chr8_13174692_13177557_-_2.R fitVsDatCorrelation=0.929406343065397 cont.fitVsDatCorrelation=0.232514295505027 fstatistic=10616.3858113142,52,692 cont.fstatistic=1517.24351477140,52,692 residuals=-0.578714527087999,-0.0873768241989154,-0.00566413341672045,0.0709123628987101,0.656485312188832 cont.residuals=-0.593194441984226,-0.254219352793009,-0.102081181898485,0.145825481341598,1.95893340889647 predictedValues: Include Exclude Both chr8.23366_chr8_13174692_13177557_-_2.R.tl.Lung 58.6534278864094 68.6898631564067 67.7566987705582 chr8.23366_chr8_13174692_13177557_-_2.R.tl.cerebhem 63.0868209666268 65.2408362715658 64.5634326502024 chr8.23366_chr8_13174692_13177557_-_2.R.tl.cortex 56.443416763406 68.9247346672097 57.3155743228316 chr8.23366_chr8_13174692_13177557_-_2.R.tl.heart 57.9121614101054 71.5626025478876 59.5994261537072 chr8.23366_chr8_13174692_13177557_-_2.R.tl.kidney 59.5296294368022 75.8538177503943 93.521281054466 chr8.23366_chr8_13174692_13177557_-_2.R.tl.liver 60.8991192121693 72.3305601465136 76.1002125944717 chr8.23366_chr8_13174692_13177557_-_2.R.tl.stomach 59.229321760831 70.9169249129229 75.4130314821224 chr8.23366_chr8_13174692_13177557_-_2.R.tl.testicle 57.6218329820615 65.2105757965925 58.1281258614164 diffExp=-10.0364352699973,-2.15401530493904,-12.4813179038038,-13.6504411377822,-16.3241883135921,-11.4314409343443,-11.6876031520919,-7.58874281453098 diffExpScore=0.988419785306802 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=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,-1,-1,-1,0,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 64.2423420359058 57.0838740482557 52.7386064977634 cerebhem 63.1061797846823 58.0233073103412 51.4151592888428 cortex 64.5639706794983 59.0417164714049 59.6638233933356 heart 67.1034467957652 64.470119885304 55.806372725927 kidney 59.9893883945182 61.5065838563902 65.2787962412652 liver 60.0505724747756 58.9408400707784 70.429686496227 stomach 65.8858819751035 66.3435880055014 71.0485326110626 testicle 63.7394716839672 69.6728940716885 61.8550325340252 cont.diffExp=7.15846798765013,5.08287247434113,5.52225420809341,2.63332691046121,-1.517195461872,1.10973240399719,-0.457706030397887,-5.9334223877213 cont.diffExpScore=2.01495497456674 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=-0.0646589208408809 cont.tran.correlation=0.354137812565629 tran.covariance=-0.000111860228540958 cont.tran.covariance=0.00100494677583127 tran.mean=64.506602854244 cont.tran.mean=62.7352610964925 weightedLogRatios: wLogRatio Lung -0.655614344173344 cerebhem -0.139710228069098 cortex -0.825701392500803 heart -0.88144944401828 kidney -1.01965397274428 liver -0.72169755027343 stomach -0.751248773079452 testicle -0.509202521378676 cont.weightedLogRatios: wLogRatio Lung 0.484801922680081 cerebhem 0.344530868921633 cortex 0.368642365304254 heart 0.167589200687240 kidney -0.102570045101547 liver 0.0762129524420678 stomach -0.0290166806295079 testicle -0.373769154507782 varWeightedLogRatios=0.0721648120153815 cont.varWeightedLogRatios=0.080711642524834 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.98309442794457 0.0813458669065443 48.964926915348 5.1155076811308e-227 *** df.mm.trans1 -0.0422180267301145 0.0730604844219748 -0.577850353226188 0.563553132826378 df.mm.trans2 0.221165189117233 0.0671890379496967 3.29168560625641 0.00104647000208660 ** df.mm.exp2 0.0696249656405193 0.0920323425777852 0.756527147852101 0.449590677602151 df.mm.exp3 0.132357054268316 0.0920323425777853 1.43815804923632 0.150841289634307 df.mm.exp4 0.156529758951922 0.0920323425777853 1.70081250316566 0.0894274727309626 . df.mm.exp5 -0.208231141694998 0.0920323425777853 -2.26258656318568 0.0239703941739233 * df.mm.exp6 -0.0269099477739499 0.0920323425777853 -0.292396640357228 0.770071021312387 df.mm.exp7 -0.0653785779970236 0.0920323425777853 -0.710386980987319 0.4777035262439 df.mm.exp8 0.0835492350390946 0.0920323425777853 0.907824713561748 0.364286869101791 df.mm.trans1:exp2 0.00324090273188512 0.0881142595742894 0.0367806839385934 0.970670482998767 df.mm.trans2:exp2 -0.121141005615794 0.0766936188148211 -1.57954478466184 0.114668115420384 df.mm.trans1:exp3 -0.170764411016984 0.0881142595742894 -1.93798837829435 0.0530310811119368 . df.mm.trans2:exp3 -0.128943582985857 0.0766936188148211 -1.68128176735532 0.0931594206181576 . df.mm.trans1:exp4 -0.169248374680968 0.0881142595742894 -1.92078303215241 0.0551696614072229 . df.mm.trans2:exp4 -0.115558768253709 0.0766936188148211 -1.50675858095481 0.132328892703359 df.mm.trans1:exp5 0.223059284148931 0.0881142595742895 2.53147771117419 0.0115785708018826 * df.mm.trans2:exp5 0.307437543089049 0.0766936188148211 4.00864567143931 6.77006015392582e-05 *** df.mm.trans1:exp6 0.0644826396532004 0.0881142595742894 0.731807087351563 0.464534074882446 df.mm.trans2:exp6 0.078555036890149 0.0766936188148211 1.02427083379417 0.306065287693422 df.mm.trans1:exp7 0.0751492774028067 0.0881142595742894 0.852861702134012 0.394031090665865 df.mm.trans2:exp7 0.097286062288195 0.0766936188148211 1.26850269672494 0.205045045585366 df.mm.trans1:exp8 -0.101293714206786 0.0881142595742894 -1.14957232457233 0.250717208545439 df.mm.trans2:exp8 -0.135529209824063 0.0766936188148211 -1.76715106052437 0.0776436480863974 . df.mm.trans1:probe2 -0.0920279802819863 0.0440571297871447 -2.08883285694291 0.0370875958668421 * df.mm.trans1:probe3 0.199038421409566 0.0440571297871447 4.51773464070831 7.3487570426053e-06 *** df.mm.trans1:probe4 -0.0597670680374197 0.0440571297871447 -1.35658106477147 0.175356754940094 df.mm.trans1:probe5 0.0539451132302503 0.0440571297871447 1.22443548844144 0.221204659501898 df.mm.trans1:probe6 0.210754504093700 0.0440571297871447 4.78366396340224 2.10471755650939e-06 *** df.mm.trans1:probe7 -0.020238890309297 0.0440571297871447 -0.459378320990907 0.646106784973227 df.mm.trans1:probe8 0.0273855377221385 0.0440571297871447 0.621591507536861 0.534415221813058 df.mm.trans1:probe9 0.0301681427524329 0.0440571297871447 0.684750525015716 0.49373063693809 df.mm.trans1:probe10 0.127530771779598 0.0440571297871447 2.8946681818753 0.00391511567304459 ** df.mm.trans1:probe11 -0.0148335058191754 0.0440571297871447 -0.336687975155013 0.736454207835793 df.mm.trans1:probe12 -0.117722626662547 0.0440571297871447 -2.67204484793507 0.00771650296568706 ** df.mm.trans1:probe13 -0.108159500561681 0.0440571297871447 -2.45498290706266 0.0143341608941469 * df.mm.trans1:probe14 -0.149144993107753 0.0440571297871447 -3.38526349374832 0.000751255987815418 *** df.mm.trans1:probe15 -0.0278320906766426 0.0440571297871447 -0.631727277993574 0.527773730005368 df.mm.trans1:probe16 -0.165861556248950 0.0440571297871447 -3.76469273078580 0.000180914141799233 *** df.mm.trans1:probe17 -0.0509062709276836 0.0440571297871447 -1.15546044814153 0.248300718767024 df.mm.trans1:probe18 -0.0551928116176373 0.0440571297871447 -1.25275549915060 0.210717869904352 df.mm.trans1:probe19 0.663910784061175 0.0440571297871447 15.0693153927357 1.37781741784434e-44 *** df.mm.trans1:probe20 0.364368034877252 0.0440571297871447 8.27035343967345 6.83907955559605e-16 *** df.mm.trans1:probe21 0.83592765690924 0.0440571297871447 18.9737202797344 5.92051515390234e-65 *** df.mm.trans1:probe22 1.61789879399539 0.0440571297871447 36.7227461664438 1.18787229448493e-164 *** df.mm.trans2:probe2 -0.283643273512071 0.0440571297871447 -6.43807880546124 2.26213678292003e-10 *** df.mm.trans2:probe3 0.411009698021039 0.0440571297871447 9.32901666556058 1.41803017283478e-19 *** df.mm.trans2:probe4 -0.212645191101187 0.0440571297871447 -4.82657840237322 1.71023588210209e-06 *** df.mm.trans2:probe5 0.175488896437625 0.0440571297871447 3.98321219029639 7.51911567580588e-05 *** df.mm.trans2:probe6 0.137868040894515 0.0440571297871447 3.12930146744927 0.00182584003469627 ** df.mm.trans3:probe2 0.256105099336389 0.0440571297871447 5.81302278595363 9.36701675777285e-09 *** df.mm.trans3:probe3 -0.094459881188831 0.0440571297871447 -2.14403166173556 0.0323781253326576 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15171730384604 0.214373910815361 19.3667097272013 4.26249052006725e-67 *** df.mm.trans1 0.0159016870635577 0.192539121742932 0.0825893819376032 0.93420192749656 df.mm.trans2 -0.103354661403870 0.177065871653271 -0.583707410348726 0.55960738268078 df.mm.exp2 0.0238939954316859 0.242536393675235 0.0985171547643313 0.92155018017543 df.mm.exp3 -0.0846615136501153 0.242536393675235 -0.349067256947343 0.727145004530563 df.mm.exp4 0.108712771237674 0.242536393675235 0.448232818136334 0.65412548360897 df.mm.exp5 -0.207191789320522 0.242536393675235 -0.85427092479143 0.393250525180341 df.mm.exp6 -0.324729974406403 0.242536393675235 -1.33889174109362 0.181045568824036 df.mm.exp7 -0.122428328690001 0.242536393675235 -0.504783331007788 0.613871729064457 df.mm.exp8 0.031985477034433 0.242536393675235 0.131879082350267 0.895118267785854 df.mm.trans1:exp2 -0.0417378208831578 0.232210917922133 -0.17974099261411 0.857408528020985 df.mm.trans2:exp2 -0.00757087623469318 0.202113661396029 -0.0374585081602105 0.970130225743996 df.mm.trans1:exp3 0.089655512861243 0.232210917922133 0.386095165823801 0.69954486401278 df.mm.trans2:exp3 0.118384105737307 0.202113661396029 0.585730350534497 0.558247706713217 df.mm.trans1:exp4 -0.0651398871041308 0.232210917922133 -0.280520346274046 0.779162160344575 df.mm.trans2:exp4 0.0129674268273535 0.202113661396029 0.0641590812703383 0.948862087286541 df.mm.trans1:exp5 0.138696949240679 0.232210917922133 0.597288665329629 0.550510052603871 df.mm.trans2:exp5 0.281814352170949 0.202113661396029 1.39433599007814 0.163663654873337 df.mm.trans1:exp6 0.257254529619804 0.232210917922133 1.10784855389990 0.268312139065193 df.mm.trans2:exp6 0.356742543789143 0.202113661396029 1.76505903324431 0.0779948736809618 . df.mm.trans1:exp7 0.147689986612879 0.232210917922133 0.636016548810181 0.524975925697569 df.mm.trans2:exp7 0.272753785038306 0.202113661396029 1.34950692177043 0.17761552186202 df.mm.trans1:exp8 -0.0398439832590775 0.232210917922133 -0.171585313970631 0.863813716834651 df.mm.trans2:exp8 0.167304210048613 0.202113661396029 0.827772892208362 0.408084501609937 df.mm.trans1:probe2 0.079706018978063 0.116105458961066 0.686496739182529 0.492629880300528 df.mm.trans1:probe3 0.0398585328320334 0.116105458961066 0.343295941368262 0.731480101444539 df.mm.trans1:probe4 0.00901366624676202 0.116105458961066 0.07763344055842 0.938142082059278 df.mm.trans1:probe5 -0.00974546617711843 0.116105458961066 -0.0839363305078219 0.9331313291733 df.mm.trans1:probe6 -0.0330837220407997 0.116105458961066 -0.284945448188561 0.775771191953595 df.mm.trans1:probe7 0.0723498074332348 0.116105458961066 0.623138723025037 0.533398677045678 df.mm.trans1:probe8 -0.0320442572425312 0.116105458961066 -0.275992683972565 0.782636085855082 df.mm.trans1:probe9 -0.000266541704331159 0.116105458961066 -0.00229568623832354 0.998168970628867 df.mm.trans1:probe10 -0.0307762020665082 0.116105458961066 -0.265071102960183 0.791033625890369 df.mm.trans1:probe11 0.141753889410792 0.116105458961066 1.22090632670706 0.222537314457533 df.mm.trans1:probe12 0.125430785776267 0.116105458961066 1.08031772923208 0.280377081280509 df.mm.trans1:probe13 -0.136645690986085 0.116105458961066 -1.17691013160636 0.239636031820646 df.mm.trans1:probe14 0.0332212985696148 0.116105458961066 0.286130375495565 0.774863902461683 df.mm.trans1:probe15 -0.114286546882106 0.116105458961066 -0.984333965902756 0.32529535995207 df.mm.trans1:probe16 -0.138741312923544 0.116105458961066 -1.19495942882469 0.232512258801629 df.mm.trans1:probe17 -0.0208297449627654 0.116105458961066 -0.179403665849598 0.857673269401139 df.mm.trans1:probe18 -0.060789584508606 0.116105458961066 -0.523572147705738 0.600743782567055 df.mm.trans1:probe19 -0.0619239475376286 0.116105458961066 -0.533342257045756 0.593967930759916 df.mm.trans1:probe20 -0.0542932308722137 0.116105458961066 -0.46761996686495 0.640203606320596 df.mm.trans1:probe21 0.00560969244755771 0.116105458961066 0.0483154926370758 0.961478748424527 df.mm.trans1:probe22 0.0625709476947252 0.116105458961066 0.538914778466248 0.590118984585517 df.mm.trans2:probe2 -0.0770882565407984 0.116105458961066 -0.663950319223564 0.506943256665985 df.mm.trans2:probe3 0.0586472356826912 0.116105458961066 0.505120398364364 0.613635102967542 df.mm.trans2:probe4 0.0597772869931506 0.116105458961066 0.514853371478387 0.606819891465279 df.mm.trans2:probe5 -0.0600882584462813 0.116105458961066 -0.517531724898746 0.604950421128206 df.mm.trans2:probe6 -0.0158168424002087 0.116105458961066 -0.136228240616254 0.89168047076458 df.mm.trans3:probe2 -0.0765405515406081 0.116105458961066 -0.659233013034076 0.509965459643815 df.mm.trans3:probe3 -0.220209161587189 0.116105458961066 -1.89663055947293 0.058292898713803 .