chr15.8381_chr15_78674704_78675022_-_0.R fitVsDatCorrelation=0.828470047558335 cont.fitVsDatCorrelation=0.352418809170701 fstatistic=8701.1750659853,39,393 cont.fstatistic=3109.55220064371,39,393 residuals=-0.436728463893505,-0.0837530577093113,0.00205804920794444,0.0856217400648511,0.88504335040412 cont.residuals=-0.65022884395459,-0.186519565583637,-0.0197100609908863,0.158628262869159,0.66887820317542 predictedValues: Include Exclude Both chr15.8381_chr15_78674704_78675022_-_0.R.tl.Lung 87.76576738381 61.6694274162036 78.4986877774583 chr15.8381_chr15_78674704_78675022_-_0.R.tl.cerebhem 74.972604521821 64.9454392373267 76.4454712770445 chr15.8381_chr15_78674704_78675022_-_0.R.tl.cortex 95.635270865648 57.9243341298267 80.4773734971715 chr15.8381_chr15_78674704_78675022_-_0.R.tl.heart 92.2917892599132 57.5085920582451 78.9264356778262 chr15.8381_chr15_78674704_78675022_-_0.R.tl.kidney 94.8056581784003 64.5890957311534 80.0807739861067 chr15.8381_chr15_78674704_78675022_-_0.R.tl.liver 81.3551391463698 62.8664743635653 80.3733638960717 chr15.8381_chr15_78674704_78675022_-_0.R.tl.stomach 80.246338199382 57.6318327454538 82.5518507262058 chr15.8381_chr15_78674704_78675022_-_0.R.tl.testicle 100.226707543227 61.9924399324988 88.5805938436191 diffExp=26.0963399676064,10.0271652844943,37.7109367358213,34.783197201668,30.2165624472469,18.4886647828045,22.6145054539282,38.2342676107283 diffExpScore=0.995437365882041 diffExp1.5=0,0,1,1,0,0,0,1 diffExp1.5Score=0.75 diffExp1.4=1,0,1,1,1,0,0,1 diffExp1.4Score=0.833333333333333 diffExp1.3=1,0,1,1,1,0,1,1 diffExp1.3Score=0.857142857142857 diffExp1.2=1,0,1,1,1,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 79.1717966378988 74.7400767077574 83.7133174772754 cerebhem 78.3107392403878 71.050308662583 88.9393835289737 cortex 75.3397643547247 88.33884693649 79.7860521472323 heart 77.9516747430901 76.3575376915071 85.2794502580265 kidney 80.3597199294976 80.9101426777006 80.84484096433 liver 78.2907656708954 80.8235459881503 70.1123829176644 stomach 71.2871611402826 72.7363216804514 66.0305565717615 testicle 87.2398927800775 80.5850004379801 73.029406157036 cont.diffExp=4.43171993014141,7.26043057780483,-12.9990825817652,1.59413705158296,-0.550422748202948,-2.53278031725489,-1.44916054016873,6.65489234209743 cont.diffExpScore=10.9898981062888 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=-0.21167895004318 cont.tran.correlation=0.185200840552809 tran.covariance=-0.00113610362520761 cont.tran.covariance=0.00083396878766717 tran.mean=74.7766819195528 cont.tran.mean=78.3433309549672 weightedLogRatios: wLogRatio Lung 1.51677324455132 cerebhem 0.609525066624103 cortex 2.16097134607253 heart 2.02852364853216 kidney 1.67327198446759 liver 1.10083188649847 stomach 1.39679364085277 testicle 2.0981113923271 cont.weightedLogRatios: wLogRatio Lung 0.250162323661261 cerebhem 0.419546271536166 cortex -0.700609989884178 heart 0.0897934747101963 kidney -0.0299662261004639 liver -0.139337144726584 stomach -0.0860685740203812 testicle 0.351436491559774 varWeightedLogRatios=0.289206466662294 cont.varWeightedLogRatios=0.126534895040768 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.32267983141202 0.0808067650421976 53.4940339358307 1.67613415610575e-182 *** df.mm.trans1 0.083955438920183 0.0659784473727844 1.27246763546627 0.203959341147096 df.mm.trans2 -0.191884188832874 0.0659784473727844 -2.90828590962001 0.00384040883755857 ** df.mm.exp2 -0.0792853254598911 0.089647056850466 -0.884416379582227 0.377012232984370 df.mm.exp3 -0.00167472872639785 0.089647056850466 -0.0186813575953904 0.985104780684745 df.mm.exp4 -0.0250046078620943 0.089647056850466 -0.278922797251478 0.780450785780267 df.mm.exp5 0.103460965222323 0.089647056850466 1.15409215714576 0.249163718566946 df.mm.exp6 -0.0802237262686512 0.089647056850466 -0.894884105369649 0.371396783690797 df.mm.exp7 -0.207628319012226 0.089647056850466 -2.31606397696420 0.0210686192957502 * df.mm.exp8 0.0171564066383447 0.089647056850466 0.191377243616175 0.848328881041034 df.mm.trans1:exp2 -0.0782634322647223 0.0731965154086389 -1.06922347092339 0.285625262909666 df.mm.trans2:exp2 0.131044542196801 0.073196515408639 1.79031121174566 0.0741735291264055 . df.mm.trans1:exp3 0.0875448913605465 0.073196515408639 1.19602539645233 0.232407752399778 df.mm.trans2:exp3 -0.0609760007257119 0.073196515408639 -0.833045130431377 0.405325250093786 df.mm.trans1:exp4 0.0752882568265267 0.073196515408639 1.02857706280429 0.304311086388024 df.mm.trans2:exp4 -0.0448493327173293 0.073196515408639 -0.612724970129329 0.540412595231188 df.mm.trans1:exp5 -0.0263034038063790 0.0731965154086389 -0.359353224119116 0.719523863760411 df.mm.trans2:exp5 -0.0572036697324364 0.073196515408639 -0.781508100666839 0.434974256048708 df.mm.trans1:exp6 0.00437619972877329 0.0731965154086389 0.0597869953827993 0.952355673092779 df.mm.trans2:exp6 0.0994484445716433 0.0731965154086389 1.35864998513175 0.175036822980885 df.mm.trans1:exp7 0.118057918576940 0.073196515408639 1.6128898748506 0.107571109659056 df.mm.trans2:exp7 0.139915081569557 0.0731965154086389 1.91149921261203 0.0566675045538684 . df.mm.trans1:exp8 0.115606757353805 0.073196515408639 1.57940247166686 0.115048269582342 df.mm.trans2:exp8 -0.0119322699202468 0.0731965154086389 -0.163016912125281 0.8705889101324 df.mm.trans1:probe2 0.0170994297331212 0.044823528425233 0.381483348898862 0.703050788141754 df.mm.trans1:probe3 0.254891807646585 0.044823528425233 5.68656276294163 2.53271767306320e-08 *** df.mm.trans1:probe4 0.166116090858447 0.044823528425233 3.70600210859201 0.000240761367644464 *** df.mm.trans1:probe5 0.254234328159315 0.044823528425233 5.67189458508125 2.74155764743592e-08 *** df.mm.trans1:probe6 0.124093477497232 0.044823528425233 2.7684897163824 0.00589828471079265 ** df.mm.trans2:probe2 -0.0430001641959999 0.044823528425233 -0.95932126958112 0.337986732658739 df.mm.trans2:probe3 -0.0160142734531778 0.044823528425233 -0.357273825060204 0.721078597296426 df.mm.trans2:probe4 -0.0626718734769579 0.044823528425233 -1.39819143380237 0.162844132677842 df.mm.trans2:probe5 0.00190924497529537 0.044823528425233 0.0425947051107334 0.966046248418657 df.mm.trans2:probe6 0.0116890070354653 0.044823528425233 0.26077837792183 0.794400009776422 df.mm.trans3:probe2 0.273080848843429 0.044823528425233 6.09235502954516 2.6541955779011e-09 *** df.mm.trans3:probe3 0.363433895348140 0.044823528425233 8.10810545524899 6.60741653727287e-15 *** df.mm.trans3:probe4 -0.0280917952226127 0.044823528425233 -0.626719854718057 0.531206766603541 df.mm.trans3:probe5 0.126531903423305 0.044823528425233 2.82289029598293 0.00500126131104392 ** df.mm.trans3:probe6 0.155030109239328 0.044823528425233 3.45867705390313 0.000602236249743694 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.29278337674852 0.135031945903529 31.7908725081650 1.08783592177621e-110 *** df.mm.trans1 0.0992383885662498 0.110253122146249 0.900095948617323 0.368620397554369 df.mm.trans2 0.0383646949247233 0.110253122146249 0.347969238221056 0.728049589236446 df.mm.exp2 -0.122120614513786 0.149804493778724 -0.815199941159111 0.415451621442899 df.mm.exp3 0.165600815718687 0.149804493778724 1.10544624891757 0.269642547398135 df.mm.exp4 -0.0126561899272953 0.149804493778724 -0.0844847147642293 0.9327140747366 df.mm.exp5 0.129081961706283 0.149804493778724 0.861669489681326 0.38939485730937 df.mm.exp6 0.244360061466851 0.149804493778724 1.63119313248236 0.103650635208534 df.mm.exp7 0.105200991793174 0.149804493778724 0.702255247086016 0.482935707182591 df.mm.exp8 0.308873553369379 0.149804493778724 2.0618443784845 0.0398795433252578 * df.mm.trans1:exp2 0.111185230811506 0.122314856977937 0.909008386704498 0.363902808681771 df.mm.trans2:exp2 0.0714923629635695 0.122314856977937 0.584494514648086 0.559223030111398 df.mm.trans1:exp3 -0.215212873561315 0.122314856977937 -1.75949904107016 0.0792708258336191 . df.mm.trans2:exp3 0.00156268741559008 0.122314856977937 0.0127759411587421 0.989813034123166 df.mm.trans1:exp4 -0.00287486230592271 0.122314856977937 -0.0235037866776991 0.981260347059252 df.mm.trans2:exp4 0.0340664918285758 0.122314856977937 0.278514750131463 0.780763727853284 df.mm.trans1:exp5 -0.114189039307309 0.122314856977937 -0.933566388651428 0.351100823222682 df.mm.trans2:exp5 -0.0497592228604965 0.122314856977937 -0.406812582624137 0.684367105464525 df.mm.trans1:exp6 -0.255550533006404 0.122314856977937 -2.08928448530581 0.0373247638254289 * df.mm.trans2:exp6 -0.166108178032078 0.122314856977937 -1.35803762630438 0.175230881340727 df.mm.trans1:exp7 -0.210104881089667 0.122314856977937 -1.71773802693131 0.0866321258842563 . df.mm.trans2:exp7 -0.132376571942171 0.122314856977937 -1.0822607752879 0.279800268669717 df.mm.trans1:exp8 -0.211831973402998 0.122314856977937 -1.73185808034103 0.08408350674894 . df.mm.trans2:exp8 -0.233577470344537 0.122314856977937 -1.90964103720179 0.0569072518588599 . df.mm.trans1:probe2 -0.0309629236511679 0.0749022468893621 -0.413377768185021 0.679555326644114 df.mm.trans1:probe3 -0.130099927667977 0.0749022468893621 -1.73692957248863 0.0831831252878233 . df.mm.trans1:probe4 -0.0208301373079544 0.0749022468893621 -0.278097629550720 0.781083665450128 df.mm.trans1:probe5 0.0358344439677577 0.0749022468893621 0.478416141783953 0.632620079304101 df.mm.trans1:probe6 -0.0987610509923729 0.0749022468893621 -1.31853255534847 0.188093311060967 df.mm.trans2:probe2 0.0243633192617765 0.0749022468893621 0.325268203205753 0.74515118729182 df.mm.trans2:probe3 -0.098662811707536 0.0749022468893621 -1.31722098875446 0.188532030005677 df.mm.trans2:probe4 -0.0468071473969529 0.0749022468893621 -0.624909790304309 0.532392915989893 df.mm.trans2:probe5 -0.0415550922039576 0.0749022468893621 -0.554790996661802 0.579353227611528 df.mm.trans2:probe6 -0.042917723414122 0.0749022468893621 -0.572983123957758 0.566983803197389 df.mm.trans3:probe2 0.0598205815086028 0.0749022468893621 0.79864869203408 0.424976511741607 df.mm.trans3:probe3 -0.0522778853437501 0.0749022468893621 -0.697948159298474 0.485622350049751 df.mm.trans3:probe4 -0.117018808310595 0.0749022468893621 -1.56228702302406 0.119025250102918 df.mm.trans3:probe5 0.0820220167886634 0.0749022468893621 1.09505415651707 0.274163559700943 df.mm.trans3:probe6 0.00952378082229238 0.0749022468893621 0.127149467710360 0.898887129978084