chr5.19031_chr5_123255809_123257045_-_1.R fitVsDatCorrelation=0.842721752955028 cont.fitVsDatCorrelation=0.269835933565713 fstatistic=6233.95894236546,36,324 cont.fstatistic=1942.42036396453,36,324 residuals=-0.917636347848502,-0.0934677120966856,-0.00671760558173112,0.0810850585213106,0.967404879164156 cont.residuals=-0.500251925672508,-0.186773088614694,-0.0455615665202301,0.0903923424823906,1.34055894169357 predictedValues: Include Exclude Both chr5.19031_chr5_123255809_123257045_-_1.R.tl.Lung 85.243164424301 83.8139645728568 102.439458249573 chr5.19031_chr5_123255809_123257045_-_1.R.tl.cerebhem 105.549124606347 92.1868857594081 76.0481835104208 chr5.19031_chr5_123255809_123257045_-_1.R.tl.cortex 70.4998065693613 70.508003238275 86.7683743917626 chr5.19031_chr5_123255809_123257045_-_1.R.tl.heart 72.985698499943 68.0385653643915 93.781831333427 chr5.19031_chr5_123255809_123257045_-_1.R.tl.kidney 86.0339527033694 82.439567265263 101.532508550984 chr5.19031_chr5_123255809_123257045_-_1.R.tl.liver 84.1637406850844 77.7265772965686 101.609415937230 chr5.19031_chr5_123255809_123257045_-_1.R.tl.stomach 76.5059842728361 72.3334385697976 89.5788894045823 chr5.19031_chr5_123255809_123257045_-_1.R.tl.testicle 74.7814347885068 73.8711041910252 96.888265900045 diffExp=1.42919985144422,13.3622388469385,-0.008196668913655,4.94713313555157,3.59438543810646,6.43716338851576,4.17254570303854,0.91033059748159 diffExpScore=0.972559293003294 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 83.3387935751644 88.4433030052228 85.25663479459 cerebhem 85.093410447917 86.5245291361565 79.0719536565971 cortex 94.2610702294736 81.9330030881333 79.4478629178358 heart 83.8668770362244 75.5943556052787 91.343375705943 kidney 73.6454565674343 80.2495300974249 96.7666391917457 liver 87.5037446166985 86.2765015114305 101.459117157236 stomach 82.5813330570331 91.5165292264508 72.9047755900455 testicle 80.2494892366275 89.493840992207 84.1004055682655 cont.diffExp=-5.10450943005839,-1.43111868823951,12.3280671413403,8.27252143094576,-6.60407352999061,1.22724310526796,-8.9351961694177,-9.24435175557957 cont.diffExpScore=5.06576725653658 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=0.955036580548669 cont.tran.correlation=-0.0148516754235365 tran.covariance=0.0126209483626434 cont.tran.covariance=5.29275722943743e-05 tran.mean=79.7925633004584 cont.tran.mean=84.4107354643048 weightedLogRatios: wLogRatio Lung 0.0750230140988337 cerebhem 0.621498616960259 cortex -0.000494757027578563 heart 0.298665335530201 kidney 0.189202201297843 liver 0.349536061080561 stomach 0.241677630923634 testicle 0.0527694851780696 cont.weightedLogRatios: wLogRatio Lung -0.264698428026319 cerebhem -0.074253486025717 cortex 0.627382704116719 heart 0.454579418780834 kidney -0.372901571558399 liver 0.063059693203014 stomach -0.45873155164862 testicle -0.484052975719735 varWeightedLogRatios=0.0403733968825936 cont.varWeightedLogRatios=0.175877255233441 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.4847632693539 0.100337787807683 34.7303179140558 2.99884695975829e-111 *** df.mm.trans1 0.922077250794974 0.0849850581305206 10.8498749201164 1.33164936433266e-23 *** df.mm.trans2 0.88539029457915 0.0849850581305206 10.4181877856618 4.12020918758447e-22 *** df.mm.exp2 0.60679163855129 0.118351997684958 5.12700799665852 5.0737904676559e-07 *** df.mm.exp3 -0.196741607023124 0.118351997684958 -1.66234293355006 0.0974111259624574 . df.mm.exp4 -0.27546854100023 0.118351997684958 -2.32753604830145 0.0205523455963586 * df.mm.exp5 0.00159290296875798 0.118351997684958 0.0134590289975345 0.989269856377705 df.mm.exp6 -0.0800103557323987 0.118351997684958 -0.676037221994164 0.499499274766553 df.mm.exp7 -0.121299792258553 0.118351997684958 -1.02490701155245 0.306171650858105 df.mm.exp8 -0.201502613949689 0.118351997684958 -1.70257044993926 0.0896075791890606 . df.mm.trans1:exp2 -0.393123088173566 0.102495836583811 -3.83550299481785 0.000150592694293292 *** df.mm.trans2:exp2 -0.511573390273866 0.102495836583811 -4.99116263962149 9.8172181153454e-07 *** df.mm.trans1:exp3 0.00684364289790426 0.102495836583811 0.0667699598930365 0.946806020915667 df.mm.trans2:exp3 0.023868196222872 0.102495836583811 0.232869909826581 0.816009463634038 df.mm.trans1:exp4 0.120224121753203 0.102495836583811 1.17296590535066 0.24167110118631 df.mm.trans2:exp4 0.0669435878404399 0.102495836583811 0.653134703532082 0.514132633681217 df.mm.trans1:exp5 0.00764118400589319 0.102495836583811 0.0745511648138508 0.940617837437975 df.mm.trans2:exp5 -0.0181270313330575 0.102495836583811 -0.176856269846972 0.859731789978283 df.mm.trans1:exp6 0.0672666207951311 0.102495836583811 0.656286372570142 0.512105694983774 df.mm.trans2:exp6 0.00460796948334996 0.102495836583811 0.0449576259576362 0.964168779789042 df.mm.trans1:exp7 0.0131608255775978 0.102495836583811 0.128403513901135 0.897909314769729 df.mm.trans2:exp7 -0.0260133232738861 0.102495836583811 -0.253798828722327 0.799811958195978 df.mm.trans1:exp8 0.0705643398666062 0.102495836583811 0.688460548433164 0.491655447054693 df.mm.trans2:exp8 0.0752247178528136 0.102495836583811 0.733929497626984 0.463522469384921 df.mm.trans1:probe2 0.207535713491272 0.0512479182919053 4.04964182757941 6.42359231693826e-05 *** df.mm.trans1:probe3 -0.0933577493189382 0.0512479182919053 -1.82168861547073 0.0694241743936226 . df.mm.trans1:probe4 0.239916117180676 0.0512479182919053 4.68148024694636 4.19347238019275e-06 *** df.mm.trans1:probe5 0.0144297233772603 0.0512479182919053 0.281567014977456 0.778455280526065 df.mm.trans1:probe6 -0.0205171138860431 0.0512479182919053 -0.400350191185889 0.689162445723474 df.mm.trans2:probe2 0.160587193702748 0.0512479182919053 3.13353593775365 0.00188495804170459 ** df.mm.trans2:probe3 0.102371288103436 0.0512479182919053 1.99756968703265 0.0465991364191864 * df.mm.trans2:probe4 0.180029062102982 0.0512479182919053 3.51290487698536 0.000506203574068743 *** df.mm.trans2:probe5 0.111347756853590 0.0512479182919053 2.17272741146986 0.0305252442305146 * df.mm.trans2:probe6 -0.0283206585886939 0.0512479182919053 -0.552620663094665 0.580904228707261 df.mm.trans3:probe2 -0.858224791884975 0.0512479182919053 -16.7465298199348 8.7077446945854e-46 *** df.mm.trans3:probe3 -1.13065173374420 0.0512479182919053 -22.0623933894070 1.67847388880324e-66 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.53202974634995 0.179466893211281 25.2527341687167 1.5513183642292e-78 *** df.mm.trans1 -0.0771390248614983 0.152006583813649 -0.507471603704128 0.612169205737769 df.mm.trans2 -0.0177296202949785 0.152006583813649 -0.116637186693927 0.907219831212492 df.mm.exp2 0.0742093913718487 0.211687598400906 0.350560882793458 0.726145613517717 df.mm.exp3 0.117259247060689 0.211687598400906 0.553925916994991 0.580011632525744 df.mm.exp4 -0.219623313048785 0.211687598400906 -1.03748785808817 0.300282208920452 df.mm.exp5 -0.347508852446858 0.211687598400906 -1.64161176692423 0.101640746207217 df.mm.exp6 -0.150026982046674 0.211687598400906 -0.708718806297495 0.479008597910957 df.mm.exp7 0.181539208525960 0.211687598400906 0.857580745859991 0.391757912977004 df.mm.exp8 -0.0123110969161471 0.211687598400906 -0.0581569114541687 0.953659493149577 df.mm.trans1:exp2 -0.053373941784645 0.183326837881303 -0.291140906598752 0.771129844578712 df.mm.trans2:exp2 -0.0961431465101155 0.183326837881303 -0.524435743403617 0.600334103912108 df.mm.trans1:exp3 0.00589487843607083 0.183326837881303 0.0321550216225708 0.974368224001903 df.mm.trans2:exp3 -0.193719071967473 0.183326837881303 -1.05668692160009 0.291441735257646 df.mm.trans1:exp4 0.225939907635291 0.183326837881303 1.23244316133123 0.218677153250796 df.mm.trans2:exp4 0.062643229436099 0.183326837881303 0.341702448807076 0.73279634478549 df.mm.trans1:exp5 0.223857153990950 0.183326837881303 1.22108228439465 0.222942686169973 df.mm.trans2:exp5 0.250288056250997 0.183326837881303 1.36525595021199 0.173119790101402 df.mm.trans1:exp6 0.198794420164369 0.183326837881303 1.08437161989932 0.27900628988335 df.mm.trans2:exp6 0.125222551957861 0.183326837881303 0.683056302094391 0.495059389766242 df.mm.trans1:exp7 -0.190669695534260 0.183326837881303 -1.04005336991473 0.299090597093472 df.mm.trans2:exp7 -0.147381307980665 0.183326837881303 -0.803926526437383 0.42202884232664 df.mm.trans1:exp8 -0.0254626556961763 0.183326837881303 -0.138892133800194 0.88962167524278 df.mm.trans2:exp8 0.0241192013944753 0.183326837881303 0.131563941609529 0.895410865455856 df.mm.trans1:probe2 0.00869192116637969 0.0916634189406515 0.0948243177794554 0.92451298770206 df.mm.trans1:probe3 -0.00583147145040312 0.0916634189406515 -0.0636183061661574 0.949313385550735 df.mm.trans1:probe4 -0.125191784970501 0.0916634189406515 -1.36577695243462 0.172956312332032 df.mm.trans1:probe5 -0.0967307962928695 0.0916634189406515 -1.05528243884836 0.292082427341845 df.mm.trans1:probe6 -0.0687270123024811 0.0916634189406515 -0.749775789478016 0.453933819594374 df.mm.trans2:probe2 -0.0791995051799696 0.0916634189406515 -0.864025214150567 0.388213230185652 df.mm.trans2:probe3 -0.0252882681308833 0.0916634189406515 -0.275881790392920 0.78281473777969 df.mm.trans2:probe4 -0.0862992507747893 0.0916634189406515 -0.94147972846905 0.347160494132185 df.mm.trans2:probe5 -0.0673361343813276 0.0916634189406515 -0.734602038190667 0.463113222309626 df.mm.trans2:probe6 -0.0293226516824158 0.0916634189406515 -0.319894806688381 0.749254257237614 df.mm.trans3:probe2 0.039877606092518 0.0916634189406515 0.435043843589744 0.663820238809415 df.mm.trans3:probe3 -0.0143630877597028 0.0916634189406515 -0.156693781725536 0.875583780968499