chr9.24307_chr9_15297795_15299393_-_0.R fitVsDatCorrelation=0.739805657678114 cont.fitVsDatCorrelation=0.367962248642494 fstatistic=6904.3879793357,38,370 cont.fstatistic=3610.34735600996,38,370 residuals=-0.640389971713558,-0.0798716640676317,-0.00554768356958543,0.0712754806482022,0.628987299125529 cont.residuals=-0.45337779357426,-0.131345389020686,-0.02680691483676,0.0916210263361582,1.05722447219836 predictedValues: Include Exclude Both chr9.24307_chr9_15297795_15299393_-_0.R.tl.Lung 47.7261817355583 61.6730798715167 48.8020148487415 chr9.24307_chr9_15297795_15299393_-_0.R.tl.cerebhem 60.1920712973343 93.832108200814 53.9705577525153 chr9.24307_chr9_15297795_15299393_-_0.R.tl.cortex 50.257459918585 57.0768810001283 56.5483082885198 chr9.24307_chr9_15297795_15299393_-_0.R.tl.heart 45.8949354096442 59.3813565068869 49.4075895327901 chr9.24307_chr9_15297795_15299393_-_0.R.tl.kidney 49.1941347851509 61.2229276843812 49.7093693408101 chr9.24307_chr9_15297795_15299393_-_0.R.tl.liver 49.6448268936553 58.0759119418638 55.7452103347326 chr9.24307_chr9_15297795_15299393_-_0.R.tl.stomach 46.7976677922184 60.6211587611327 52.347462827769 chr9.24307_chr9_15297795_15299393_-_0.R.tl.testicle 50.1706640420595 61.9769349828905 50.2849339760941 diffExp=-13.9468981359584,-33.6400369034797,-6.81942108154327,-13.4864210972427,-12.0287928992303,-8.43108504820846,-13.8234909689142,-11.8062709408310 diffExpScore=0.99130301810107 diffExp1.5=0,-1,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,-1,0,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=-1,-1,0,-1,-1,0,-1,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 57.6788651360497 59.251814292507 52.4707029347115 cerebhem 53.8300651104442 55.776702175256 52.1904584328461 cortex 58.760719327172 52.1295186716714 55.5318080692297 heart 56.25148218901 56.9833668647001 55.6051501857534 kidney 60.5320022202577 47.5831566183943 53.9123575485251 liver 67.5942909166347 53.6691633461673 52.1312563984781 stomach 54.0874712324901 54.6412611484899 54.7115957004721 testicle 52.4029935959692 54.4368418459736 62.7071260654449 cont.diffExp=-1.57294915645733,-1.94663706481180,6.63120065550062,-0.731884675690118,12.9488456018634,13.9251275704674,-0.553789915999758,-2.03384825000443 cont.diffExpScore=1.45825881756868 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=0,0,0,0,1,1,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.9119799499323 cont.tran.correlation=-0.332472110451142 tran.covariance=0.0118830940610876 cont.tran.covariance=-0.00182018619763415 tran.mean=57.1086438014888 cont.tran.mean=55.9756071681992 weightedLogRatios: wLogRatio Lung -1.02384520557327 cerebhem -1.91772374189757 cortex -0.506514951888572 heart -1.01895218286596 kidney -0.876115528866223 liver -0.624811185714459 stomach -1.02883398049132 testicle -0.849785638642466 cont.weightedLogRatios: wLogRatio Lung -0.109461156265327 cerebhem -0.142224146109604 cortex 0.480597701195601 heart -0.0521772984307776 kidney 0.958639582014186 liver 0.945388604612793 stomach -0.0407030451379925 testicle -0.151472169450571 varWeightedLogRatios=0.180462825738712 cont.varWeightedLogRatios=0.236772925408056 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.07355710970643 0.0840245858395765 48.4805377973995 2.29335635563999e-162 *** df.mm.trans1 -0.248262493498137 0.0691975916171927 -3.58773315221066 0.000378397964180981 *** df.mm.trans2 0.0447797316969683 0.0691975916171927 0.647128471532561 0.517950069053751 df.mm.exp2 0.551053009213123 0.0945911876699291 5.82562734211556 1.23753923770884e-08 *** df.mm.exp3 -0.173093205955750 0.0945911876699291 -1.82990836905177 0.0680680585034597 . df.mm.exp4 -0.0893250214446065 0.0945911876699291 -0.944327094784996 0.345618752400155 df.mm.exp5 0.00454666648824832 0.0945911876699291 0.0480664911842916 0.961689185743352 df.mm.exp6 -0.153702378098472 0.0945911876699291 -1.62491223426445 0.105032742220533 df.mm.exp7 -0.106982175473905 0.0945911876699291 -1.13099516042883 0.258789674981566 df.mm.exp8 0.0249312050847698 0.0945911876699291 0.263567946432451 0.792259583627804 df.mm.trans1:exp2 -0.318992502254661 0.0784308694938117 -4.0671804904551 5.81776003521534e-05 *** df.mm.trans2:exp2 -0.131393435780470 0.0784308694938117 -1.67527705135077 0.0947247462112363 . df.mm.trans1:exp3 0.224772067309696 0.0784308694938118 2.86586223970692 0.00439638114424303 ** df.mm.trans2:exp3 0.0956448255475095 0.0784308694938118 1.21947934741506 0.223439110005307 df.mm.trans1:exp4 0.0501996621061028 0.0784308694938117 0.640049797102703 0.522536358698168 df.mm.trans2:exp4 0.0514578061080255 0.0784308694938117 0.656091236016268 0.512173208732053 df.mm.trans1:exp5 0.0257476075618442 0.0784308694938117 0.32828410201261 0.742882565126257 df.mm.trans2:exp5 -0.0118724406818621 0.0784308694938118 -0.151374589603381 0.879762731361046 df.mm.trans1:exp6 0.193116441036215 0.0784308694938117 2.46225041597240 0.0142618946945540 * df.mm.trans2:exp6 0.0936058305425619 0.0784308694938118 1.19348199435616 0.233445336681487 df.mm.trans1:exp7 0.0873354129982021 0.0784308694938117 1.11353365788063 0.266202165783799 df.mm.trans2:exp7 0.0897786331637776 0.0784308694938118 1.14468491479444 0.253079632546913 df.mm.trans1:exp8 0.0250191385051680 0.0784308694938117 0.318996061966928 0.7499095079909 df.mm.trans2:exp8 -0.0200164345792338 0.0784308694938118 -0.25521117779796 0.79870180072032 df.mm.trans1:probe2 0.152148312086531 0.045793761817952 3.32246808400191 0.0009812181856799 *** df.mm.trans1:probe3 0.0468690735218872 0.045793761817952 1.02348161979376 0.306748872205937 df.mm.trans1:probe4 0.128585940556656 0.045793761817952 2.80793574172472 0.00525026683440872 ** df.mm.trans1:probe5 0.0259702273597356 0.045793761817952 0.567112775381446 0.570981454978711 df.mm.trans1:probe6 0.0884671054216297 0.045793761817952 1.93185931685021 0.0541401291812115 . df.mm.trans2:probe2 -0.0379299180665563 0.045793761817952 -0.828276965263139 0.408047609191017 df.mm.trans2:probe3 0.0805665355328039 0.045793761817952 1.75933429214851 0.0793472017755132 . df.mm.trans2:probe4 -0.123959235734824 0.045793761817952 -2.70690222453465 0.00710619926737102 ** df.mm.trans2:probe5 -0.0677096438977087 0.045793761817952 -1.47857789379437 0.140103954021957 df.mm.trans2:probe6 0.187649824841156 0.045793761817952 4.09771587639245 5.13036132957596e-05 *** df.mm.trans3:probe2 -0.0584519291468916 0.045793761817952 -1.27641684863674 0.202608782556636 df.mm.trans3:probe3 -0.0430720520527818 0.045793761817952 -0.9405659273857 0.347540995797279 df.mm.trans3:probe4 0.0106709125679456 0.045793761817952 0.233021095981732 0.815873877031862 df.mm.trans3:probe5 0.179339328559562 0.045793761817952 3.91623927452182 0.000107092813122748 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.14176287559576 0.116122306219043 35.6672461170648 8.67902144456877e-122 *** df.mm.trans1 -0.0763829857824808 0.095631342220876 -0.798723347478089 0.424963234680938 df.mm.trans2 -0.0791141504996003 0.095631342220876 -0.827282652970335 0.40861009523823 df.mm.exp2 -0.124143532580716 0.13072539127062 -0.94965126035631 0.342909353732778 df.mm.exp3 -0.166183260057823 0.130725391270620 -1.27123933952357 0.204442095387245 df.mm.exp4 -0.122116263214775 0.13072539127062 -0.934143413363183 0.350839141416253 df.mm.exp5 -0.198140929763629 0.130725391270620 -1.51570347457174 0.130448175553163 df.mm.exp6 0.0661651794226348 0.13072539127062 0.506138698683743 0.613060716094014 df.mm.exp7 -0.187116048078476 0.13072539127062 -1.43136728266599 0.153169083725320 df.mm.exp8 -0.358902454324574 0.130725391270620 -2.74546857986904 0.00633734847385259 ** df.mm.trans1:exp2 0.0550848570990547 0.108391768354262 0.508201480014775 0.611614899133974 df.mm.trans2:exp2 0.0637033905227316 0.108391768354262 0.587714283934613 0.557082592037919 df.mm.trans1:exp3 0.184766035460992 0.108391768354262 1.7046131663533 0.0891060502618882 . df.mm.trans2:exp3 0.0381182250472639 0.108391768354262 0.3516708475747 0.725285313449495 df.mm.trans1:exp4 0.0970578363683664 0.108391768354262 0.895435491477061 0.371136492102676 df.mm.trans2:exp4 0.0830792785564429 0.108391768354262 0.766472212953579 0.443884115482185 df.mm.trans1:exp5 0.246422299656856 0.108391768354262 2.27344108688643 0.0235721051349532 * df.mm.trans2:exp5 -0.0211766246913382 0.108391768354262 -0.195371152375018 0.845209641130547 df.mm.trans1:exp6 0.0924675285532903 0.108391768354262 0.853086262520178 0.394163372367134 df.mm.trans2:exp6 -0.165122982727134 0.108391768354262 -1.5233904311576 0.128515265693545 df.mm.trans1:exp7 0.122827804148872 0.108391768354262 1.13318387561893 0.257870783509461 df.mm.trans2:exp7 0.106108943606132 0.108391768354262 0.978939131792107 0.328249689816025 df.mm.trans1:exp8 0.262975356103174 0.108391768354262 2.42615615646825 0.0157369798317464 * df.mm.trans2:exp8 0.274147218261753 0.108391768354262 2.52922544233935 0.0118460990202813 * df.mm.trans1:probe2 -0.0698717899809829 0.063287157914695 -1.10404373151285 0.270291650215599 df.mm.trans1:probe3 -0.0785995589813905 0.063287157914695 -1.24195115677868 0.215041234496936 df.mm.trans1:probe4 0.0596498472084658 0.063287157914695 0.942526875497681 0.346537951330013 df.mm.trans1:probe5 -0.0385689859830554 0.063287157914695 -0.609428314588604 0.54261461160307 df.mm.trans1:probe6 0.0120106935077497 0.063287157914695 0.189780895579778 0.84958489696211 df.mm.trans2:probe2 0.0927715091183145 0.063287157914695 1.46588205530357 0.143529723063364 df.mm.trans2:probe3 0.00338637619287395 0.063287157914695 0.0535081097722615 0.957355947460832 df.mm.trans2:probe4 -0.00134578095013321 0.063287157914695 -0.0212646766654807 0.983045984128852 df.mm.trans2:probe5 0.127677052199865 0.063287157914695 2.01742433073011 0.04437284823024 * df.mm.trans2:probe6 -0.0118647269315801 0.063287157914695 -0.187474478591259 0.851391395686001 df.mm.trans3:probe2 -0.00612392059817386 0.063287157914695 -0.0967640323875551 0.922966179058 df.mm.trans3:probe3 -0.0364952026928092 0.063287157914695 -0.576660477343622 0.564519485022433 df.mm.trans3:probe4 0.00195356677648469 0.063287157914695 0.0308682968370598 0.975391217047076 df.mm.trans3:probe5 -0.0893882675696696 0.063287157914695 -1.41242347602584 0.158665846233317