chr19.12078_chr19_10813076_10816705_-_2.R fitVsDatCorrelation=0.771078009113132 cont.fitVsDatCorrelation=0.239014468225569 fstatistic=12822.2422644914,59,853 cont.fstatistic=5505.3738053265,59,853 residuals=-0.574319055066068,-0.0830731003362904,-0.00523234272993704,0.0744909593838898,0.722186128228471 cont.residuals=-0.600760898423257,-0.127957302762769,-0.027878676509167,0.100587251957253,0.930152207217972 predictedValues: Include Exclude Both chr19.12078_chr19_10813076_10816705_-_2.R.tl.Lung 46.8205762163492 60.0102190240632 52.867772281913 chr19.12078_chr19_10813076_10816705_-_2.R.tl.cerebhem 49.4402204399211 65.2781078954436 59.1647905556582 chr19.12078_chr19_10813076_10816705_-_2.R.tl.cortex 45.0159908055532 53.1686389640336 50.4201200399811 chr19.12078_chr19_10813076_10816705_-_2.R.tl.heart 48.9482818358459 58.7669273606968 53.0388009126117 chr19.12078_chr19_10813076_10816705_-_2.R.tl.kidney 46.5184138894106 62.3125729151888 52.456776533576 chr19.12078_chr19_10813076_10816705_-_2.R.tl.liver 47.7694174170714 63.496163729127 52.9918088403495 chr19.12078_chr19_10813076_10816705_-_2.R.tl.stomach 48.1444841100264 58.2081987332481 55.0939350270109 chr19.12078_chr19_10813076_10816705_-_2.R.tl.testicle 49.2781838061174 56.6207489163156 55.2860548463732 diffExp=-13.189642807714,-15.8378874555226,-8.1526481584804,-9.81864552485097,-15.7941590257782,-15.7267463120556,-10.0637146232217,-7.34256511019814 diffExpScore=0.989682851794546 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,-1,0,0,-1,-1,0,0 diffExp1.3Score=0.75 diffExp1.2=-1,-1,0,-1,-1,-1,-1,0 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 57.8432418124655 55.508677307341 54.1067466428962 cerebhem 53.410958431953 52.1445876214506 51.4976028103397 cortex 56.3858736297465 52.5781788833181 53.6350613770292 heart 53.2946875950195 51.1209187650791 52.3612793398733 kidney 53.1913716941097 51.4653370010376 52.8845195371138 liver 54.5277509312322 54.2626220795043 54.8145591527805 stomach 54.5668627884813 51.2151834970341 56.1394520740457 testicle 54.5558200378955 47.7757482222055 53.5242837660911 cont.diffExp=2.33456450512452,1.26637081050236,3.8076947464284,2.17376882994041,1.72603469307214,0.265128851727816,3.35167929144712,6.78007181569002 cont.diffExpScore=0.955957445905114 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=0.412088504037068 cont.tran.correlation=0.53694372633116 tran.covariance=0.000924119922750746 cont.tran.covariance=0.000682421662381883 tran.mean=53.7373216286507 cont.tran.mean=53.3654887686171 weightedLogRatios: wLogRatio Lung -0.985426567154574 cerebhem -1.12260506362794 cortex -0.647534691087469 heart -0.728000604842486 kidney -1.16516920784002 liver -1.14084643800512 stomach -0.753416250251239 testicle -0.550982734414587 cont.weightedLogRatios: wLogRatio Lung 0.166319054940085 cerebhem 0.0951667782859493 cortex 0.279478324757539 heart 0.164698199253169 kidney 0.130545537859847 liver 0.0194783648520334 stomach 0.251517466694154 testicle 0.521916586873976 varWeightedLogRatios=0.0600556872778219 cont.varWeightedLogRatios=0.023325393420249 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.97272409213475 0.0635552723328657 62.5081751098149 0 *** df.mm.trans1 -0.253277186367968 0.0543121835576892 -4.66335856482262 3.60954653935414e-06 *** df.mm.trans2 0.143510052259702 0.0481071234192898 2.98313517956382 0.00293440493145053 ** df.mm.exp2 0.0260506866013144 0.0614276347156511 0.424087411503035 0.671608992350774 df.mm.exp3 -0.112947495543642 0.0614276347156511 -1.8387081981339 0.0663056028699947 . df.mm.exp4 0.0202760661487422 0.0614276347156511 0.330080528781552 0.741420162067274 df.mm.exp5 0.038978223090015 0.0614276347156511 0.634538889059386 0.525899338279119 df.mm.exp6 0.0741840704399221 0.0614276347156511 1.20766607380083 0.227510469606657 df.mm.exp7 -0.0438505464171952 0.0614276347156511 -0.713856989939131 0.475510890546483 df.mm.exp8 -0.0517074335848013 0.061427634715651 -0.841761754691603 0.400157250199375 df.mm.trans1:exp2 0.0283908162316861 0.0557038398896468 0.509674311285008 0.610411486804092 df.mm.trans2:exp2 0.0580911744021676 0.0406239498381264 1.42997356568336 0.153090770313393 df.mm.trans1:exp3 0.0736425043834184 0.0557038398896468 1.32203640771102 0.186510431745709 df.mm.trans2:exp3 -0.00809863987294348 0.0406239498381264 -0.199356288721653 0.842031561366691 df.mm.trans1:exp4 0.0241654327776827 0.0557038398896468 0.433819873559096 0.66452894419948 df.mm.trans2:exp4 -0.0412116941236529 0.0406239498381264 -1.01446792564161 0.310647185691754 df.mm.trans1:exp5 -0.0454527603040976 0.0557038398896468 -0.815971760549052 0.414744082061557 df.mm.trans2:exp5 -0.0013298700026769 0.0406239498381264 -0.0327361078372736 0.973892687622958 df.mm.trans1:exp6 -0.0541212077366877 0.0557038398896468 -0.971588455013256 0.331530749731065 df.mm.trans2:exp6 -0.0177194448683096 0.0406239498381264 -0.436182225975465 0.662814892615707 df.mm.trans1:exp7 0.0717343526707391 0.0557038398896468 1.28778110831946 0.198171425498375 df.mm.trans2:exp7 0.0133618981508781 0.0406239498381264 0.32891676471936 0.742299338471946 df.mm.trans1:exp8 0.102866128504501 0.0557038398896468 1.84666135599065 0.0651424537675375 . df.mm.trans2:exp8 -0.00643192454328773 0.0406239498381264 -0.158328389260939 0.87423551323337 df.mm.trans1:probe2 0.127454618418226 0.039903467953696 3.19407372226707 0.00145429574592126 ** df.mm.trans1:probe3 0.142762681501244 0.039903467953696 3.577701107756 0.00036615986652494 *** df.mm.trans1:probe4 0.163046156279698 0.039903467953696 4.08601469097616 4.80240855988688e-05 *** df.mm.trans1:probe5 0.0988967648765967 0.039903467953696 2.47840024810266 0.0133897458043833 * df.mm.trans1:probe6 0.0992024237221723 0.039903467953696 2.48606020502496 0.0131070332130291 * df.mm.trans1:probe7 0.230185431314414 0.039903467953696 5.76855704826261 1.11795621240146e-08 *** df.mm.trans1:probe8 0.170600196087346 0.039903467953696 4.27532254302585 2.12402951685886e-05 *** df.mm.trans1:probe9 0.328010833748491 0.039903467953696 8.22010844092836 7.52990734371952e-16 *** df.mm.trans1:probe10 0.184794930675142 0.039903467953696 4.63104938371718 4.20499271662934e-06 *** df.mm.trans1:probe11 0.206745376949793 0.039903467953696 5.18113807024743 2.75392988354526e-07 *** df.mm.trans1:probe12 0.266321800753109 0.039903467953696 6.67415175698886 4.47360410599402e-11 *** df.mm.trans1:probe13 0.275231149564005 0.039903467953696 6.89742430115056 1.03128281237748e-11 *** df.mm.trans1:probe14 0.316779285083819 0.039903467953696 7.93864045730086 6.42411832919845e-15 *** df.mm.trans1:probe15 0.41039272982958 0.039903467953696 10.2846381749526 1.81479300439445e-23 *** df.mm.trans1:probe16 0.235824492623451 0.039903467953696 5.90987462285489 4.94608905588476e-09 *** df.mm.trans1:probe17 0.282977354750577 0.039903467953696 7.0915479095437 2.78441653599983e-12 *** df.mm.trans1:probe18 0.303657420186043 0.039903467953696 7.60980024438997 7.26411379762839e-14 *** df.mm.trans1:probe19 0.217143978467807 0.039903467953696 5.44173199982972 6.89849221944089e-08 *** df.mm.trans2:probe2 -0.130515983997037 0.039903467953696 -3.27079300847957 0.00111548889517484 ** df.mm.trans2:probe3 -0.247163919806814 0.039903467953696 -6.1940460937787 9.11084528589926e-10 *** df.mm.trans2:probe4 0.0503016353959623 0.039903467953696 1.26058305143634 0.207803813673645 df.mm.trans2:probe5 0.0242305940538838 0.039903467953696 0.607230280886891 0.543859700943833 df.mm.trans2:probe6 -0.109518638177688 0.039903467953696 -2.74458947540032 0.00618597542192759 ** df.mm.trans3:probe2 -0.0301819579249254 0.039903467953696 -0.756374307114074 0.449633648874101 df.mm.trans3:probe3 0.771677043514463 0.039903467953696 19.3385959438392 2.25715368283227e-69 *** df.mm.trans3:probe4 0.322662590568146 0.039903467953696 8.08607890779227 2.10610240944569e-15 *** df.mm.trans3:probe5 0.0386417109451007 0.039903467953696 0.968379765636926 0.333129227156793 df.mm.trans3:probe6 0.0456133692681725 0.039903467953696 1.14309285902424 0.253320756158929 df.mm.trans3:probe7 0.23629444567065 0.039903467953696 5.92165187108164 4.61752680477713e-09 *** df.mm.trans3:probe8 0.155257553284169 0.039903467953696 3.89082857320396 0.000107682223921617 *** df.mm.trans3:probe9 -0.099629277323069 0.039903467953696 -2.49675736050508 0.0127210759289255 * df.mm.trans3:probe10 -0.0537946908789992 0.039903467953696 -1.34812069320448 0.177977443591403 df.mm.trans3:probe11 -0.0138113743614853 0.039903467953696 -0.346119649989119 0.729338148419362 df.mm.trans3:probe12 0.0121686504742443 0.039903467953696 0.304952203361493 0.760476946366348 df.mm.trans3:probe13 -0.0220173808976658 0.039903467953696 -0.551766100210006 0.581253133174637 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10207866838066 0.0969204385051625 42.3241860194655 8.96049050901997e-212 *** df.mm.trans1 -0.00567357969674942 0.0828249247208719 -0.0685008735700025 0.945402978485401 df.mm.trans2 -0.0980314173341256 0.0733623399896665 -1.33626350179034 0.181819508547998 df.mm.exp2 -0.09281602463765 0.0936758363931535 -0.990821413625869 0.322053764018810 df.mm.exp3 -0.071000236933416 0.0936758363931535 -0.757935447039203 0.448698967518728 df.mm.exp4 -0.131454030994582 0.0936758363931535 -1.40328644030329 0.160895344549211 df.mm.exp5 -0.136623045233313 0.0936758363931535 -1.45846624373773 0.145080169666728 df.mm.exp6 -0.0947275384007244 0.0936758363931535 -1.01122703621409 0.312194530755984 df.mm.exp7 -0.175693075918493 0.0936758363931535 -1.87554317829752 0.0610594090419323 . df.mm.exp8 -0.197709998973463 0.0936758363931535 -2.11057628718342 0.0350992779433718 * df.mm.trans1:exp2 0.0130953394185448 0.0849471710269752 0.154158628948177 0.877521126298617 df.mm.trans2:exp2 0.0302970594634898 0.0619506594433532 0.489051444096298 0.624930996114443 df.mm.trans1:exp3 0.0454822722942643 0.0849471710269752 0.53541832817271 0.592500179033483 df.mm.trans2:exp3 0.0167620640645781 0.0619506594433532 0.270571196742548 0.786786304819586 df.mm.trans1:exp4 0.0495540630987027 0.0849471710269752 0.583351540723666 0.55981087209977 df.mm.trans2:exp4 0.0491084572038181 0.0619506594433532 0.79270273545227 0.428171450450270 df.mm.trans1:exp5 0.0527826180131218 0.0849471710269752 0.621358161490281 0.534530004615013 df.mm.trans2:exp5 0.0609922019387201 0.0619506594433532 0.984528695687097 0.325134796647398 df.mm.trans1:exp6 0.0357006776614862 0.0849471710269751 0.420269177064760 0.674394668929848 df.mm.trans2:exp6 0.0720238124076135 0.0619506594433532 1.16259960837820 0.245317144898717 df.mm.trans1:exp7 0.117383241576641 0.0849471710269751 1.38183814902282 0.167383178904750 df.mm.trans2:exp7 0.095189760266 0.0619506594433532 1.53654151741581 0.124776451020442 df.mm.trans1:exp8 0.139197773151903 0.0849471710269751 1.63863930333478 0.101657164329919 df.mm.trans2:exp8 0.0476887939411219 0.0619506594433532 0.769786703961205 0.441639546573044 df.mm.trans1:probe2 -0.110305566017114 0.0608519398940404 -1.81268775012244 0.070231422031952 . df.mm.trans1:probe3 -0.0521588634163668 0.0608519398940404 -0.857143806872705 0.391606158651463 df.mm.trans1:probe4 -0.00641324159336764 0.0608519398940404 -0.105390914480867 0.916090402485023 df.mm.trans1:probe5 -0.072082151544532 0.0608519398940404 -1.18454977228411 0.236525448334151 df.mm.trans1:probe6 -0.0172336542516531 0.0608519398940404 -0.283206324755817 0.777087396045748 df.mm.trans1:probe7 -0.131157505350612 0.0608519398940404 -2.15535454710224 0.0314132578394149 * df.mm.trans1:probe8 -0.0324770134251618 0.0608519398940403 -0.53370547400318 0.593684364066446 df.mm.trans1:probe9 -0.0903970988255742 0.0608519398940404 -1.48552534205121 0.137774184784443 df.mm.trans1:probe10 -0.00508149742436917 0.0608519398940404 -0.0835059232822721 0.933468860162284 df.mm.trans1:probe11 -0.116612976928700 0.0608519398940404 -1.91633951410185 0.0556558167268563 . df.mm.trans1:probe12 -0.100907570720708 0.0608519398940404 -1.65824739353282 0.0976351944162354 . df.mm.trans1:probe13 -0.0575782433151485 0.0608519398940404 -0.946202264305916 0.344313384469697 df.mm.trans1:probe14 -0.101684358971619 0.0608519398940404 -1.67101261107992 0.0950859957588487 . df.mm.trans1:probe15 -0.0799065819836762 0.0608519398940404 -1.31313121854151 0.189491864985399 df.mm.trans1:probe16 -0.0335662804536031 0.0608519398940404 -0.551605758371073 0.581362958777748 df.mm.trans1:probe17 -0.0737511677433855 0.0608519398940404 -1.21197726599688 0.225856685416932 df.mm.trans1:probe18 -0.0733950214403572 0.0608519398940404 -1.20612459632606 0.228103877559192 df.mm.trans1:probe19 -0.0826820696556073 0.0608519398940404 -1.35874172293569 0.174587623433507 df.mm.trans2:probe2 0.0316543133646961 0.0608519398940404 0.520185772545868 0.603069076850844 df.mm.trans2:probe3 0.0400065341845559 0.0608519398940404 0.657440572218702 0.511075028830937 df.mm.trans2:probe4 0.0450408282897264 0.0608519398940404 0.740170787786793 0.459400040551877 df.mm.trans2:probe5 0.0618908164434272 0.0608519398940404 1.01707220100453 0.309407468905943 df.mm.trans2:probe6 0.0587575096490232 0.0608519398940404 0.965581536945837 0.334527285849944 df.mm.trans3:probe2 0.00347747529974178 0.0608519398940404 0.0571464986292467 0.95444187968071 df.mm.trans3:probe3 0.0448207130328802 0.0608519398940404 0.736553561167075 0.461596399973508 df.mm.trans3:probe4 -0.038441548987411 0.0608519398940404 -0.631722654271139 0.527737380615489 df.mm.trans3:probe5 -0.0802390183906763 0.0608519398940404 -1.31859425567031 0.187658711732416 df.mm.trans3:probe6 -0.0116031469794999 0.0608519398940404 -0.190678341556639 0.84882297964031 df.mm.trans3:probe7 -0.0494496122807098 0.0608519398940404 -0.812621789326929 0.416661669425323 df.mm.trans3:probe8 0.0361658777703082 0.0608519398940404 0.594325798541226 0.55245181983416 df.mm.trans3:probe9 0.00508527460509486 0.0608519398940404 0.0835679949390224 0.933419521314687 df.mm.trans3:probe10 0.0995701517504062 0.0608519398940404 1.63626914645260 0.102152151888137 df.mm.trans3:probe11 -0.0796709380603531 0.0608519398940404 -1.30925880422353 0.190799259690766 df.mm.trans3:probe12 0.00950820110807143 0.0608519398940404 0.156251405043582 0.875871830187562 df.mm.trans3:probe13 -0.0356156518064185 0.0608519398940404 -0.585283753787224 0.558511732784919