chr2.13614_chr2_32721463_32724286_+_1.R fitVsDatCorrelation=0.841081383769162 cont.fitVsDatCorrelation=0.242758279848670 fstatistic=10419.8846546642,48,600 cont.fstatistic=3230.97200969528,48,600 residuals=-0.448552298564044,-0.0905790577550194,0.00257632399129547,0.0816149781540643,0.850072351990882 cont.residuals=-0.590521399545198,-0.199533374312094,0.00413037465474469,0.176101379450851,0.80437610159366 predictedValues: Include Exclude Both chr2.13614_chr2_32721463_32724286_+_1.R.tl.Lung 45.1713482805944 78.5833706508187 78.8845906624068 chr2.13614_chr2_32721463_32724286_+_1.R.tl.cerebhem 51.0427018620922 82.0291391758709 69.3957858299246 chr2.13614_chr2_32721463_32724286_+_1.R.tl.cortex 47.5654783635295 77.5354042049623 76.7266535565279 chr2.13614_chr2_32721463_32724286_+_1.R.tl.heart 49.8613396928864 74.0239528740958 74.9103072525055 chr2.13614_chr2_32721463_32724286_+_1.R.tl.kidney 45.1579927799594 78.3860030987563 71.3008326247139 chr2.13614_chr2_32721463_32724286_+_1.R.tl.liver 46.3492933322251 81.2591810820747 66.9749735105353 chr2.13614_chr2_32721463_32724286_+_1.R.tl.stomach 46.9001667185948 70.4411781532946 69.1964344118471 chr2.13614_chr2_32721463_32724286_+_1.R.tl.testicle 45.05531066445 77.1845196033309 74.4844255805115 diffExp=-33.4120223702242,-30.9864373137787,-29.9699258414328,-24.1626131812095,-33.2280103187969,-34.9098877498496,-23.5410114346997,-32.1292089388809 diffExpScore=0.995890508637836 diffExp1.5=-1,-1,-1,0,-1,-1,-1,-1 diffExp1.5Score=0.875 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 69.8086227842905 65.841631535763 68.2876594123675 cerebhem 70.262414364815 70.8158089303594 69.1964424210366 cortex 76.7635722730455 70.5920858971384 63.9559831136634 heart 74.755449209941 67.4481622320871 66.386213520073 kidney 71.4114228718083 71.7736170515295 68.3919498325516 liver 73.276004781888 74.7483662605061 63.5150691221967 stomach 72.8354056712442 64.0318668415724 64.7036591432188 testicle 74.048662095286 74.2687289688477 66.0714395496213 cont.diffExp=3.96699124852756,-0.553394565544437,6.17148637590714,7.30728697785393,-0.362194179721172,-1.47236147861814,8.8035388296718,-0.220066873561677 cont.diffExpScore=1.17109635177549 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=0.0627462928598911 cont.tran.correlation=0.200033933708213 tran.covariance=0.000102877231175918 cont.tran.covariance=0.000363580185894749 tran.mean=62.284148783596 cont.tran.mean=71.4176138606326 weightedLogRatios: wLogRatio Lung -2.26313259240881 cerebhem -1.97823554060650 cortex -2.00650982364704 heart -1.62277922584259 kidney -2.25328781394928 liver -2.31139713996425 stomach -1.64793425934613 testicle -2.19470609428222 cont.weightedLogRatios: wLogRatio Lung 0.24668747036031 cerebhem -0.0333906628402436 cortex 0.360293768963561 heart 0.438482473712304 kidney -0.0216074259987523 liver -0.0856280382112539 stomach 0.54411431471485 testicle -0.0127787312151685 varWeightedLogRatios=0.0750535292013774 cont.varWeightedLogRatios=0.061463811106571 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.52989271684753 0.074562813635789 47.3411952248706 9.10646635775297e-205 *** df.mm.trans1 0.258322635202865 0.0590147963291474 4.37725199900893 1.41775251506050e-05 *** df.mm.trans2 0.848162705274109 0.0590147963291474 14.3720347782544 1.82611507421491e-40 *** df.mm.exp2 0.293273771672846 0.078324616659998 3.74433714685037 0.000198287255763548 *** df.mm.exp3 0.0659555628301894 0.078324616659998 0.842079612294794 0.400078906926514 df.mm.exp4 0.090705981250211 0.078324616659998 1.15807756383871 0.247293145036366 df.mm.exp5 0.0982674665146596 0.078324616659998 1.25461790564813 0.210105997680263 df.mm.exp6 0.222893639080394 0.078324616659998 2.84576737921311 0.00458179120159042 ** df.mm.exp7 0.0592127045561188 0.078324616659998 0.755990990842091 0.449951296077484 df.mm.exp8 0.0368625243016474 0.078324616659998 0.470637787627678 0.638070459679412 df.mm.trans1:exp2 -0.171074196116896 0.0598214141038066 -2.85974844760498 0.00438721822251605 ** df.mm.trans2:exp2 -0.250359339452415 0.0598214141038067 -4.18511235822631 3.27691175297484e-05 *** df.mm.trans1:exp3 -0.0143113074225023 0.0598214141038066 -0.239233853577387 0.811005949092088 df.mm.trans2:exp3 -0.079381010057317 0.0598214141038066 -1.32696645919418 0.185024533750104 df.mm.trans1:exp4 0.00807696732638435 0.0598214141038067 0.135017993930544 0.89264292849387 df.mm.trans2:exp4 -0.150477360656959 0.0598214141038066 -2.51544305515479 0.0121483812725894 * df.mm.trans1:exp5 -0.098563173326544 0.0598214141038066 -1.64762359437892 0.09995348544125 . df.mm.trans2:exp5 -0.100782194723840 0.0598214141038066 -1.68471769237945 0.0925630818457412 . df.mm.trans1:exp6 -0.197150591771975 0.0598214141038067 -3.29565248039547 0.00103984718691545 ** df.mm.trans2:exp6 -0.189409934044312 0.0598214141038066 -3.16625637962409 0.00162246775273365 ** df.mm.trans1:exp7 -0.0216544725917546 0.0598214141038067 -0.361985301019098 0.717490465076823 df.mm.trans2:exp7 -0.168594803162364 0.0598214141038067 -2.81830186878909 0.00498700360196325 ** df.mm.trans1:exp8 -0.0394346616605054 0.0598214141038067 -0.659206443901099 0.510016123823411 df.mm.trans2:exp8 -0.0548237183745191 0.0598214141038067 -0.916456409395217 0.359795759675003 df.mm.trans1:probe2 0.0553229360775328 0.043784791790842 1.26351945081315 0.206893278152760 df.mm.trans1:probe3 0.071250262019526 0.043784791790842 1.62728333527051 0.104201931502250 df.mm.trans1:probe4 0.0717962172928617 0.043784791790842 1.63975239703843 0.101580810047706 df.mm.trans1:probe5 0.127800910447573 0.043784791790842 2.91884248435102 0.00364505406262876 ** df.mm.trans1:probe6 0.141030244333779 0.043784791790842 3.22098698122111 0.00134660413357766 ** df.mm.trans2:probe2 -0.173062401012586 0.043784791790842 -3.95256877865944 8.65268699591574e-05 *** df.mm.trans2:probe3 0.0582772095930824 0.043784791790842 1.33099204562785 0.183697166164442 df.mm.trans2:probe4 -0.163834514277406 0.043784791790842 -3.74181325470351 0.000200243535701156 *** df.mm.trans2:probe5 -0.0521283346443433 0.043784791790842 -1.19055801140629 0.234297955522193 df.mm.trans2:probe6 0.0389464379740709 0.043784791790842 0.889496932179471 0.374092558956836 df.mm.trans3:probe2 -0.156674717345701 0.043784791790842 -3.57829079316237 0.000373777865606612 *** df.mm.trans3:probe3 -0.0639935100540565 0.043784791790842 -1.46154651961692 0.144389027640936 df.mm.trans3:probe4 -0.540586893459964 0.043784791790842 -12.3464534453498 2.29760063740482e-31 *** df.mm.trans3:probe5 -0.481895858221687 0.043784791790842 -11.0060100439368 8.6587748184062e-26 *** df.mm.trans3:probe6 -0.256609894843224 0.043784791790842 -5.86070834980871 7.60711868443158e-09 *** df.mm.trans3:probe7 -0.307567991328824 0.043784791790842 -7.024539314885 5.84559556474225e-12 *** df.mm.trans3:probe8 -0.310775848162722 0.043784791790842 -7.09780349412838 3.59250360541086e-12 *** df.mm.trans3:probe9 0.167856802121150 0.043784791790842 3.83367820778946 0.000139572930078966 *** df.mm.trans3:probe10 -0.359325476928417 0.043784791790842 -8.20662751223984 1.39319542011894e-15 *** df.mm.trans3:probe11 -0.583593333186102 0.043784791790842 -13.3286766778269 1.09749254169773e-35 *** df.mm.trans3:probe12 -0.268906683775882 0.043784791790842 -6.14155447079518 1.48780982867538e-09 *** df.mm.trans3:probe13 -0.25656539666004 0.043784791790842 -5.85969205667669 7.65127964847291e-09 *** df.mm.trans3:probe14 -0.225015781102629 0.043784791790842 -5.13913100643527 3.73919836218959e-07 *** df.mm.trans3:probe15 -0.382227677113083 0.043784791790842 -8.72969041257448 2.52696928668056e-17 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.28724022895767 0.133723891468604 32.0603908686299 3.72967085283046e-132 *** df.mm.trans1 -0.0248174142534638 0.105839463863428 -0.234481670140424 0.814691054159567 df.mm.trans2 -0.0826891152893365 0.105839463863428 -0.781269219164183 0.434952322682787 df.mm.exp2 0.0660890181323856 0.140470457414904 0.470483398065546 0.638180670304913 df.mm.exp3 0.230172359860856 0.140470457414904 1.63858197728367 0.101824583736380 df.mm.exp4 0.120811231481884 0.140470457414904 0.860047256235857 0.390106503755825 df.mm.exp5 0.107438867589379 0.140470457414904 0.764850272196661 0.444661423895748 df.mm.exp6 0.247802562255423 0.140470457414904 1.76409023516948 0.078225612537427 . df.mm.exp7 0.0684844960538007 0.140470457414904 0.487536648731197 0.626056161286843 df.mm.exp8 0.212395080835933 0.140470457414904 1.51202669048473 0.131053573082211 df.mm.trans1:exp2 -0.0596095463242498 0.107286084001478 -0.5556130310752 0.578682526199651 df.mm.trans2:exp2 0.00674091124451512 0.107286084001478 0.062831179898618 0.949921862017472 df.mm.trans1:exp3 -0.135199689323616 0.107286084001478 -1.26017917963856 0.208094623181793 df.mm.trans2:exp3 -0.160506655793432 0.107286084001478 -1.49606220869448 0.135163240855489 df.mm.trans1:exp4 -0.0523466604684436 0.107286084001478 -0.487916591938638 0.625787164209399 df.mm.trans2:exp4 -0.096704231947214 0.107286084001478 -0.901367897311655 0.367754410703208 df.mm.trans1:exp5 -0.0847385645709408 0.107286084001478 -0.789837427282492 0.429934746390195 df.mm.trans2:exp5 -0.0211742459715974 0.107286084001478 -0.197362464747111 0.843610740803071 df.mm.trans1:exp6 -0.199326901003617 0.107286084001478 -1.85790079728207 0.0636727365302004 . df.mm.trans2:exp6 -0.120927542644320 0.107286084001478 -1.12715030816721 0.260129498262965 df.mm.trans1:exp7 -0.0260398551689438 0.107286084001478 -0.242714191791966 0.808309758839406 df.mm.trans2:exp7 -0.096355953589396 0.107286084001478 -0.898121638851768 0.369480957464393 df.mm.trans1:exp8 -0.153430145600940 0.107286084001478 -1.43010295350910 0.153207990776398 df.mm.trans2:exp8 -0.0919574293302124 0.107286084001478 -0.857123551354017 0.391718819274518 df.mm.trans1:probe2 -0.117374667155539 0.0785253729025538 -1.49473555892813 0.135509197558142 df.mm.trans1:probe3 -0.0550362644411103 0.0785253729025538 -0.70087237292598 0.483654376751534 df.mm.trans1:probe4 -0.0210254049392538 0.0785253729025538 -0.267753009786344 0.788981460452273 df.mm.trans1:probe5 -0.0692498287672302 0.0785253729025538 -0.881878381566757 0.378195726284260 df.mm.trans1:probe6 -0.0872846511553311 0.0785253729025538 -1.11154710801115 0.266778122952464 df.mm.trans2:probe2 -0.124241372873294 0.0785253729025538 -1.58218125277127 0.114135151360205 df.mm.trans2:probe3 0.0371062510728191 0.0785253729025538 0.472538361821804 0.63671439679452 df.mm.trans2:probe4 -0.165379210915617 0.0785253729025538 -2.10606081579319 0.0356138609113975 * df.mm.trans2:probe5 -0.0678401008798923 0.0785253729025538 -0.863925867172622 0.38797384481257 df.mm.trans2:probe6 -0.0429198895827434 0.0785253729025538 -0.546573521350926 0.584874959106194 df.mm.trans3:probe2 0.00813142901714597 0.0785253729025538 0.103551612893793 0.917559790201349 df.mm.trans3:probe3 0.0646112579289924 0.0785253729025538 0.822807400216639 0.410944413476933 df.mm.trans3:probe4 0.0910958251447934 0.0785253729025538 1.16008140779973 0.246477063212109 df.mm.trans3:probe5 0.0954659345047541 0.0785253729025538 1.21573360273275 0.224564531415247 df.mm.trans3:probe6 0.0142884251393320 0.0785253729025538 0.181959341435580 0.855676090384195 df.mm.trans3:probe7 -0.00532729678956082 0.0785253729025538 -0.0678417254531441 0.94593422924785 df.mm.trans3:probe8 0.0705974541218642 0.0785253729025538 0.899040036517525 0.368991989151321 df.mm.trans3:probe9 0.0247660562585317 0.0785253729025538 0.315389221892206 0.752575812618805 df.mm.trans3:probe10 0.0777584977983654 0.0785253729025538 0.990234046960337 0.322458896404305 df.mm.trans3:probe11 0.0508008895658817 0.0785253729025538 0.646935986269344 0.517920783398778 df.mm.trans3:probe12 0.0155521994033004 0.0785253729025538 0.198053174769383 0.84307054270465 df.mm.trans3:probe13 0.121798248943734 0.0785253729025538 1.55106871119071 0.121412500098505 df.mm.trans3:probe14 -0.046436455390211 0.0785253729025538 -0.591356063317731 0.554504591296354 df.mm.trans3:probe15 0.0768280653621291 0.0785253729025538 0.978385234253762 0.328278142522913