chr5.18903_chr5_113620526_113623487_-_2.R fitVsDatCorrelation=0.813830063854769 cont.fitVsDatCorrelation=0.258960458955908 fstatistic=10809.0041620614,69,1083 cont.fstatistic=3902.34136714486,69,1083 residuals=-0.462620371187363,-0.09477739116305,-0.006674089915812,0.0792330379340884,1.51882068279909 cont.residuals=-0.613970187896021,-0.191423816356646,-0.0294722622945653,0.158538127707267,1.35322711339234 predictedValues: Include Exclude Both chr5.18903_chr5_113620526_113623487_-_2.R.tl.Lung 56.5733327534097 68.8433805470538 61.7251891471839 chr5.18903_chr5_113620526_113623487_-_2.R.tl.cerebhem 73.2675402451019 72.5668182409832 67.9327494417743 chr5.18903_chr5_113620526_113623487_-_2.R.tl.cortex 70.005243160753 66.6350230003661 80.5074734817388 chr5.18903_chr5_113620526_113623487_-_2.R.tl.heart 55.718813745379 69.4187893971522 63.6693030150191 chr5.18903_chr5_113620526_113623487_-_2.R.tl.kidney 57.1744238872191 72.0543628101167 65.7366709695071 chr5.18903_chr5_113620526_113623487_-_2.R.tl.liver 56.3416406372197 62.4308344215708 57.6362969289718 chr5.18903_chr5_113620526_113623487_-_2.R.tl.stomach 57.0035451800503 88.476623743877 72.0208299359315 chr5.18903_chr5_113620526_113623487_-_2.R.tl.testicle 58.0321538628926 98.9468916459263 75.8384512460864 diffExp=-12.2700477936441,0.700722004118646,3.37022016038691,-13.6999756517732,-14.8799389228976,-6.08919378435102,-31.4730785638267,-40.9147377830337 diffExpScore=1.06143237738662 diffExp1.5=0,0,0,0,0,0,-1,-1 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,0,0,0,0,-1,-1 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,0,0,0,0,-1,-1 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,0,0,-1,-1,0,-1,-1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 63.2572979427484 69.7002376267047 73.2920132317398 cerebhem 69.5350973353455 69.8083986165455 62.7667563088095 cortex 63.1513353349943 70.6020835026562 62.0231588724652 heart 65.9293954234376 67.2303942732095 64.4629462266983 kidney 66.4652228505745 66.6900717226209 61.2792538359083 liver 63.4322284668368 70.3502130562472 62.3486957393826 stomach 68.4737594157143 66.6711549657157 67.2815664534157 testicle 64.6681782524124 76.1305586211518 66.1033474895175 cont.diffExp=-6.44293968395639,-0.273301281200006,-7.45074816766187,-1.30099884977193,-0.224848872046351,-6.91798458941039,1.80260444999858,-11.4623803687395 cont.diffExpScore=1.07830364064671 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=-0.172901412074340 cont.tran.correlation=-0.403488269615101 tran.covariance=-0.00257086791854981 cont.tran.covariance=-0.000663169338105248 tran.mean=67.718088579942 cont.tran.mean=67.6309767129322 weightedLogRatios: wLogRatio Lung -0.811427479030781 cerebhem 0.0412199013882804 cortex 0.208406524740259 heart -0.907990271238476 kidney -0.962675096595019 liver -0.418993552302431 stomach -1.87408845217628 testicle -2.30924839134453 cont.weightedLogRatios: wLogRatio Lung -0.406955158361303 cerebhem -0.0166471428960386 cortex -0.468552407168349 heart -0.0820402126496422 kidney -0.0141789192860303 liver -0.434936695314102 stomach 0.112398220883572 testicle -0.69365736857324 varWeightedLogRatios=0.75558315184489 cont.varWeightedLogRatios=0.0818627152602313 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.82628679959163 0.0736290197994741 51.9671022378456 1.88140954408998e-296 *** df.mm.trans1 0.211291258972075 0.062794728494121 3.36479293786351 0.00079286286263472 *** df.mm.trans2 0.415164549460391 0.054697225164405 7.5902305503162 6.86775502535485e-14 *** df.mm.exp2 0.215427508535239 0.068575448974501 3.14146697917127 0.00172662802245144 ** df.mm.exp3 -0.0852292659368209 0.068575448974501 -1.24285392529493 0.21419068032905 df.mm.exp4 -0.0379068158339586 0.068575448974501 -0.5527753212094 0.580531341739349 df.mm.exp5 -0.00680909019127161 0.068575448974501 -0.099293410296205 0.920923685980376 df.mm.exp6 -0.0333390465205827 0.068575448974501 -0.486165924090113 0.626947883836651 df.mm.exp7 0.104216759555433 0.068575448974501 1.51973863990572 0.128868551089187 df.mm.exp8 0.182295356370729 0.068575448974501 2.65831808754928 0.00796889975974019 ** df.mm.trans1:exp2 0.043152446552453 0.0623467223317567 0.692136570112411 0.488999931091813 df.mm.trans2:exp2 -0.162753817650692 0.0413824198753115 -3.93292171267612 8.92560073415851e-05 *** df.mm.trans1:exp3 0.298261686062431 0.0623467223317567 4.78391926483855 1.95680702009155e-06 *** df.mm.trans2:exp3 0.0526254986927051 0.0413824198753114 1.27168732160347 0.20375725565775 df.mm.trans1:exp4 0.0226869535376908 0.0623467223317567 0.363883660426766 0.716015911301412 df.mm.trans2:exp4 0.0462303098508740 0.0413824198753114 1.11714853771649 0.264178592075417 df.mm.trans1:exp5 0.0173780323284238 0.0623467223317567 0.278732091736155 0.78050364446495 df.mm.trans2:exp5 0.0523958858929617 0.0413824198753114 1.26613876256717 0.205735682002289 df.mm.trans1:exp6 0.0292352074210788 0.0623467223317567 0.468913301737238 0.639225982460994 df.mm.trans2:exp6 -0.0644357362255463 0.0413824198753115 -1.55707994891783 0.119743664685605 df.mm.trans1:exp7 -0.096641018928932 0.0623467223317567 -1.55005773061637 0.121419818001883 df.mm.trans2:exp7 0.146687541635278 0.0413824198753114 3.54468255064009 0.000409862748597978 *** df.mm.trans1:exp8 -0.156835843903198 0.0623467223317567 -2.51554272682773 0.0120287340919620 * df.mm.trans2:exp8 0.180453824358002 0.0413824198753114 4.36063973304905 1.42043688552435e-05 *** df.mm.trans1:probe2 -0.0828504427117084 0.0473557350855033 -1.74953345275113 0.0804820835071673 . df.mm.trans1:probe3 -0.240177658889355 0.0473557350855033 -5.07177554008403 4.63396466901207e-07 *** df.mm.trans1:probe4 -0.126623842141811 0.0473557350855033 -2.6738861072093 0.00761062354561344 ** df.mm.trans1:probe5 0.0257741450462399 0.0473557350855033 0.544266602549898 0.586370060852838 df.mm.trans1:probe6 -0.101526133689547 0.0473557350855033 -2.14390365826306 0.0322627488584244 * df.mm.trans1:probe7 0.186442504469202 0.0473557350855033 3.9370628316205 8.77516690039996e-05 *** df.mm.trans1:probe8 0.00192131009474143 0.0473557350855033 0.0405718566351552 0.967644698787295 df.mm.trans1:probe9 -0.233392402838382 0.0473557350855033 -4.9284928724468 9.58074914588426e-07 *** df.mm.trans1:probe10 0.320310637435094 0.0473557350855033 6.76392493658384 2.193294188285e-11 *** df.mm.trans1:probe11 -0.232518429743668 0.0473557350855033 -4.91003738668281 1.05062327935927e-06 *** df.mm.trans1:probe12 -0.216834545837956 0.0473557350855033 -4.57884447251109 5.21708671852765e-06 *** df.mm.trans1:probe13 -0.19299647922486 0.0473557350855033 -4.0754615861499 4.92773264699629e-05 *** df.mm.trans1:probe14 -0.0868193603588867 0.0473557350855033 -1.83334415994451 0.0670257462243673 . df.mm.trans1:probe15 -0.156590624884124 0.0473557350855033 -3.30668766098534 0.00097505888458155 *** df.mm.trans1:probe16 -0.0663723458010773 0.0473557350855033 -1.40156932800723 0.161330403517302 df.mm.trans1:probe17 0.204269729242383 0.0473557350855033 4.31351617440977 1.75348385543827e-05 *** df.mm.trans1:probe18 0.104082031946327 0.0473557350855033 2.19787596493648 0.0281690178506347 * df.mm.trans1:probe19 -0.0212885496720997 0.0473557350855033 -0.449545332443094 0.653128267733082 df.mm.trans1:probe20 0.23797567995133 0.0473557350855033 5.02527686502284 5.87762581002137e-07 *** df.mm.trans1:probe21 0.399786181097509 0.0473557350855033 8.44219143416682 9.88706060500149e-17 *** df.mm.trans1:probe22 0.191734436272853 0.0473557350855033 4.0488113198257 5.51432727285393e-05 *** df.mm.trans2:probe2 0.0556145440657412 0.0473557350855033 1.17439934076255 0.240493276159251 df.mm.trans2:probe3 -0.0753172009489374 0.0473557350855033 -1.59045574549626 0.112023957825171 df.mm.trans2:probe4 0.0830124366495223 0.0473557350855033 1.75295424090955 0.0798927862360382 . df.mm.trans2:probe5 -0.159947568922611 0.0473557350855033 -3.37757546438286 0.000757281251894696 *** df.mm.trans2:probe6 -0.153411272621171 0.0473557350855033 -3.23955002164318 0.00123359019224097 ** df.mm.trans3:probe2 -0.399833520972744 0.0473557350855033 -8.44319109925807 9.8080180437175e-17 *** df.mm.trans3:probe3 -0.326268581672527 0.0473557350855033 -6.88973745383598 9.46475976256343e-12 *** df.mm.trans3:probe4 -0.520201080554571 0.0473557350855033 -10.9849647485214 1.08917886687553e-26 *** df.mm.trans3:probe5 -0.221208987385707 0.0473557350855033 -4.671218533221 3.37006235173350e-06 *** df.mm.trans3:probe6 -0.170175301811981 0.0473557350855033 -3.59355211158101 0.000340866407855576 *** df.mm.trans3:probe7 -0.581872353414057 0.0473557350855033 -12.2872626169450 1.40108968870393e-32 *** df.mm.trans3:probe8 -0.590457448025288 0.0473557350855033 -12.4685520551879 1.93736519459282e-33 *** df.mm.trans3:probe9 -0.583931069551769 0.0473557350855033 -12.3307360449046 8.73503856844645e-33 *** df.mm.trans3:probe10 -0.457489077238625 0.0473557350855033 -9.66069001806443 3.09193643639363e-21 *** df.mm.trans3:probe11 -0.393294685142784 0.0473557350855033 -8.30511202988761 2.94949536802654e-16 *** df.mm.trans3:probe12 -0.225289861590966 0.0473557350855033 -4.75739340090894 2.22619806048679e-06 *** df.mm.trans3:probe13 -0.320003365774819 0.0473557350855033 -6.75743635268328 2.28960286083474e-11 *** df.mm.trans3:probe14 -0.269389262869737 0.0473557350855033 -5.68863016028238 1.64692713176225e-08 *** df.mm.trans3:probe15 -0.288191098477063 0.0473557350855033 -6.0856641324798 1.60754745826691e-09 *** df.mm.trans3:probe16 -0.316492334705652 0.0473557350855033 -6.68329472943896 3.73160966397716e-11 *** df.mm.trans3:probe17 -0.53209719711869 0.0473557350855033 -11.2361722642033 8.7163697319279e-28 *** df.mm.trans3:probe18 0.187440391757721 0.0473557350855033 3.95813498448049 8.04612335080091e-05 *** df.mm.trans3:probe19 -0.433561450587399 0.0473557350855033 -9.15541591329077 2.62850845873131e-19 *** df.mm.trans3:probe20 -0.158693883755868 0.0473557350855033 -3.35110168745851 0.000832693352764301 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.17410818010125 0.122383447260586 34.1068034406115 9.14704034940965e-174 *** df.mm.trans1 -0.0106504338778282 0.104375086940353 -0.102040000061648 0.918743814317376 df.mm.trans2 0.0967453210650674 0.0909157148830626 1.06412099590817 0.287511088223143 df.mm.exp2 0.25119778657557 0.113983587800008 2.20380663062049 0.0277477821910569 * df.mm.exp3 0.178123216419690 0.113983587800008 1.56270933261216 0.118413122383311 df.mm.exp4 0.133656674959699 0.113983587800008 1.17259578803757 0.241215796335489 df.mm.exp5 0.184331001738184 0.113983587800008 1.61717143051862 0.106132490325460 df.mm.exp6 0.17375253873196 0.113983587800008 1.52436453427682 0.12770962139278 df.mm.exp7 0.120374112225654 0.113983587800008 1.05606530333875 0.291173900236890 df.mm.exp8 0.213537012263301 0.113983587800008 1.87340139387406 0.0612821318226199 . df.mm.trans1:exp2 -0.156576667287971 0.103630427583303 -1.51091403306338 0.131102063741230 df.mm.trans2:exp2 -0.24964718706539 0.068784335498675 -3.62941918760267 0.000297323715424363 *** df.mm.trans1:exp3 -0.179799725845121 0.103630427583303 -1.73500901268202 0.0830236928667634 . df.mm.trans2:exp3 -0.165267288025948 0.068784335498675 -2.40268786240047 0.0164425362030345 * df.mm.trans1:exp4 -0.0922827752988407 0.103630427583303 -0.890498837560618 0.373395869037144 df.mm.trans2:exp4 -0.169734960992264 0.068784335498675 -2.46763975782733 0.0137544470093357 * df.mm.trans1:exp5 -0.134862658968726 0.103630427583303 -1.30138089858133 0.193404881574218 df.mm.trans2:exp5 -0.228478636670717 0.0687843354986749 -3.32166669946414 0.00092469832070982 *** df.mm.trans1:exp6 -0.170990974454031 0.103630427583303 -1.65000742003676 0.0992313867518962 . df.mm.trans2:exp6 -0.164470453697135 0.068784335498675 -2.39110333050035 0.0169677591678910 * df.mm.trans1:exp7 -0.0411340180179244 0.103630427583303 -0.396929926636258 0.691497340389795 df.mm.trans2:exp7 -0.164805439165254 0.0687843354986749 -2.39597341415661 0.0167451877952863 * df.mm.trans1:exp8 -0.191478270549597 0.103630427583303 -1.84770317960599 0.0649178407024152 . df.mm.trans2:exp8 -0.125290996284232 0.0687843354986749 -1.82150478558663 0.0688059214474333 . df.mm.trans1:probe2 0.0620221294263588 0.0787129602309927 0.787953206744447 0.430896500737629 df.mm.trans1:probe3 0.0415506020949305 0.0787129602309927 0.527874977297198 0.59769424737727 df.mm.trans1:probe4 -0.0157933207535069 0.0787129602309927 -0.200644477188502 0.841014250113405 df.mm.trans1:probe5 -0.062797689209993 0.0787129602309927 -0.79780621927705 0.425157842192816 df.mm.trans1:probe6 -0.110999563423378 0.0787129602309927 -1.41018153932512 0.158773215856130 df.mm.trans1:probe7 -0.0521273348410627 0.0787129602309927 -0.662245895569024 0.507954539673372 df.mm.trans1:probe8 -0.0096508181545617 0.0787129602309927 -0.122607739897473 0.902440472683813 df.mm.trans1:probe9 -0.075889912634777 0.0787129602309927 -0.96413490754342 0.335193521475773 df.mm.trans1:probe10 0.0133734720084765 0.0787129602309927 0.16990177944306 0.865119113065805 df.mm.trans1:probe11 -0.0359732814448688 0.0787129602309927 -0.457018530865831 0.64774937966644 df.mm.trans1:probe12 0.00474769086359408 0.0787129602309927 0.0603165075949553 0.95191468291565 df.mm.trans1:probe13 -0.067134275270712 0.0787129602309927 -0.852899891881824 0.393903404476896 df.mm.trans1:probe14 -0.0620780654645808 0.0787129602309927 -0.788663839886154 0.430481109321276 df.mm.trans1:probe15 -0.117364630203399 0.0787129602309927 -1.49104581836306 0.136240661618626 df.mm.trans1:probe16 0.0203059562574081 0.0787129602309927 0.257974750254822 0.796475406633739 df.mm.trans1:probe17 -0.136329479393272 0.0787129602309927 -1.73198262386774 0.083561389915951 . df.mm.trans1:probe18 0.103543173909743 0.0787129602309927 1.31545267267147 0.188636230435489 df.mm.trans1:probe19 -0.0294432225323883 0.0787129602309927 -0.374058127733777 0.70843428957301 df.mm.trans1:probe20 -0.056110714709131 0.0787129602309927 -0.712852299601836 0.476090717908096 df.mm.trans1:probe21 -0.0589905836326064 0.0787129602309927 -0.749439272255692 0.453755241903147 df.mm.trans1:probe22 -0.0372443295594918 0.0787129602309927 -0.473166419484083 0.636189805385088 df.mm.trans2:probe2 -0.1344754844952 0.0787129602309927 -1.70842875303591 0.087843357920168 . df.mm.trans2:probe3 -0.105569138453393 0.0787129602309927 -1.34119131263247 0.180139541156004 df.mm.trans2:probe4 -0.185516041106869 0.0787129602309927 -2.35686779613483 0.0186070675495688 * df.mm.trans2:probe5 -0.121752615158355 0.0787129602309927 -1.54679248247121 0.122205442272522 df.mm.trans2:probe6 -0.145580848094508 0.0787129602309927 -1.84951560285985 0.0646557122219979 . df.mm.trans3:probe2 -0.0107776610122040 0.0787129602309927 -0.136923588956326 0.891116642385622 df.mm.trans3:probe3 0.0874991110478505 0.0787129602309927 1.11162267040998 0.26654713132032 df.mm.trans3:probe4 0.101495639310563 0.0787129602309927 1.28943999835239 0.197520472642343 df.mm.trans3:probe5 -0.0222768956708211 0.0787129602309927 -0.283014329602735 0.777219937430357 df.mm.trans3:probe6 0.0150379445801189 0.0787129602309927 0.191047885074939 0.848523885618155 df.mm.trans3:probe7 0.131676943944405 0.0787129602309927 1.67287500759701 0.0946407151114059 . df.mm.trans3:probe8 0.016244728909834 0.0787129602309927 0.206379341625088 0.836533397556833 df.mm.trans3:probe9 0.0493366185905112 0.0787129602309927 0.626791553077497 0.530927981587071 df.mm.trans3:probe10 0.0145362506705013 0.0787129602309927 0.184674170909631 0.853519181547184 df.mm.trans3:probe11 -0.0237204442552369 0.0787129602309927 -0.301353731147023 0.763202622685616 df.mm.trans3:probe12 0.00318746012685327 0.0787129602309927 0.0404947306961812 0.967706171613613 df.mm.trans3:probe13 0.123500668451118 0.0787129602309927 1.56900043002691 0.116939947385028 df.mm.trans3:probe14 0.0115086351134147 0.0787129602309927 0.146210167673039 0.883782688795693 df.mm.trans3:probe15 0.101333320701580 0.0787129602309927 1.28737783973827 0.198237674539977 df.mm.trans3:probe16 -0.0285255472720786 0.0787129602309927 -0.36239962502194 0.71712412555395 df.mm.trans3:probe17 0.080793660510864 0.0787129602309927 1.02643402400018 0.304916245060032 df.mm.trans3:probe18 -0.0122559291351924 0.0787129602309927 -0.155704080995377 0.876295296364642 df.mm.trans3:probe19 0.00928670586113011 0.0787129602309927 0.117981915987877 0.906103864228932 df.mm.trans3:probe20 0.0370903028067726 0.0787129602309927 0.471209603830508 0.637585963075943