chr19.12155_chr19_5907428_5908356_+_1.R fitVsDatCorrelation=0.880959711243426 cont.fitVsDatCorrelation=0.299996832694006 fstatistic=11339.7183776845,38,370 cont.fstatistic=2782.84674652228,38,370 residuals=-0.419829805719719,-0.0735807872647538,-0.00197854283516778,0.0762876051910098,0.576902223892618 cont.residuals=-0.773901864372535,-0.192312010695208,-0.0133081209505844,0.19448624043313,0.643971337107613 predictedValues: Include Exclude Both chr19.12155_chr19_5907428_5908356_+_1.R.tl.Lung 127.166606299608 82.5843188750012 84.7538072386176 chr19.12155_chr19_5907428_5908356_+_1.R.tl.cerebhem 72.6785401079695 75.0430196233985 84.3947450694452 chr19.12155_chr19_5907428_5908356_+_1.R.tl.cortex 99.1692024609323 64.3267535227036 76.3731801139713 chr19.12155_chr19_5907428_5908356_+_1.R.tl.heart 105.442637691075 70.1341907797611 77.5933003645532 chr19.12155_chr19_5907428_5908356_+_1.R.tl.kidney 125.917041231008 82.4484457414525 86.3070050940958 chr19.12155_chr19_5907428_5908356_+_1.R.tl.liver 145.283551681716 79.5913536739561 92.8925851794657 chr19.12155_chr19_5907428_5908356_+_1.R.tl.stomach 117.959051282515 72.0941649221676 86.0193949689987 chr19.12155_chr19_5907428_5908356_+_1.R.tl.testicle 139.757353854264 73.4293652301788 97.0723226661749 diffExp=44.5822874246073,-2.36447951542897,34.8424489382287,35.3084469113136,43.4685954895551,65.6921980077595,45.8648863603476,66.3279886240848 diffExpScore=1.01114045352242 diffExp1.5=1,0,1,1,1,1,1,1 diffExp1.5Score=0.875 diffExp1.4=1,0,1,1,1,1,1,1 diffExp1.4Score=0.875 diffExp1.3=1,0,1,1,1,1,1,1 diffExp1.3Score=0.875 diffExp1.2=1,0,1,1,1,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 97.8010152296471 88.794813160764 94.4393711586065 cerebhem 78.5949865459852 90.836394774637 76.2191355818235 cortex 77.282949977228 96.634393002075 94.9773272736475 heart 96.13098986654 89.1159213102388 86.1893084720377 kidney 95.5571302154495 85.30807716606 87.7637993785845 liver 91.1856961537957 96.0457055955765 93.7472721820048 stomach 92.1672687907868 91.0885465890821 90.9252812019949 testicle 88.4141706207294 90.5651263234326 87.8336792262722 cont.diffExp=9.00620206888308,-12.2414082286518,-19.3514430248470,7.01506855630119,10.2490530493896,-4.86000944178086,1.07872220170472,-2.15095570270324 cont.diffExpScore=5.38181128381415 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,-1,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.455676004377347 cont.tran.correlation=-0.600221671524527 tran.covariance=0.00787762009723198 cont.tran.covariance=-0.0021990211772813 tran.mean=95.8140998111066 cont.tran.mean=90.3451990826267 weightedLogRatios: wLogRatio Lung 1.99852316270293 cerebhem -0.137731920282564 cortex 1.89606509036912 heart 1.81626594177081 kidney 1.95799012360335 liver 2.81501319904759 stomach 2.22753316072335 testicle 2.97214455877443 cont.weightedLogRatios: wLogRatio Lung 0.438075883791686 cerebhem -0.642219391966614 cortex -0.996459759977823 heart 0.343090180969139 kidney 0.510888165530992 liver -0.235685509187197 stomach 0.0531870663965241 testicle -0.108023326139032 varWeightedLogRatios=0.893074501709686 cont.varWeightedLogRatios=0.285043849241113 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.08531845520145 0.0731758164523492 69.4945229413725 1.98649101888634e-214 *** df.mm.trans1 -0.177549987429364 0.0602631981167029 -2.94624236645273 0.00342024596778524 ** df.mm.trans2 -0.665315739486301 0.0602631981167029 -11.0401664743693 1.09531488059835e-24 *** df.mm.exp2 -0.650964673929363 0.0823781196632118 -7.90215504542609 3.17237110740892e-14 *** df.mm.exp3 -0.394395701033623 0.0823781196632119 -4.78762689226264 2.44457720579083e-06 *** df.mm.exp4 -0.262470820709239 0.0823781196632118 -3.18617154387966 0.00156419122136829 ** df.mm.exp5 -0.0296815184383421 0.0823781196632119 -0.360308277971016 0.718822202814372 df.mm.exp6 0.00458176160492227 0.0823781196632119 0.0556186718470145 0.95567561026342 df.mm.exp7 -0.225829379144184 0.0823781196632119 -2.74137574476629 0.00641521860751527 ** df.mm.exp8 -0.158791873468199 0.0823781196632119 -1.92759769362776 0.0546703611230778 . df.mm.trans1:exp2 0.0915127433358056 0.068304328464468 1.339779562922 0.1811392886403 df.mm.trans2:exp2 0.55520639962566 0.068304328464468 8.12842190395707 6.60997531987444e-15 *** df.mm.trans1:exp3 0.145725120710777 0.068304328464468 2.13346831726167 0.0335436555492756 * df.mm.trans2:exp3 0.144551500814658 0.068304328464468 2.11628609876244 0.034987772276065 * df.mm.trans1:exp4 0.0751398198407064 0.068304328464468 1.10007405870031 0.272015061830886 df.mm.trans2:exp4 0.0990614204011724 0.068304328464468 1.45029491729363 0.147823458402848 df.mm.trans1:exp5 0.0198067182708172 0.068304328464468 0.289977498001766 0.771995891437257 df.mm.trans2:exp5 0.0280348979679828 0.068304328464468 0.410441015938932 0.68172003478142 df.mm.trans1:exp6 0.128607512749508 0.068304328464468 1.88286036391398 0.0605036051944472 . df.mm.trans2:exp6 -0.0414961152168225 0.0683043284644681 -0.607518090722593 0.543879806254233 df.mm.trans1:exp7 0.150668833005792 0.068304328464468 2.20584604801686 0.0280088996119136 * df.mm.trans2:exp7 0.0899826714396944 0.068304328464468 1.31737875860244 0.188526892963332 df.mm.trans1:exp8 0.25320151826826 0.068304328464468 3.70696153465553 0.000241844496338367 *** df.mm.trans2:exp8 0.0412959819614047 0.068304328464468 0.604588067693059 0.545823301162519 df.mm.trans1:probe2 -0.271317414826955 0.0398811356934377 -6.80315166831117 4.13131225575653e-11 *** df.mm.trans1:probe3 -0.00213073982232241 0.0398811356934377 -0.0534272604145778 0.95742032001482 df.mm.trans1:probe4 -0.17745393925583 0.0398811356934377 -4.44957086026589 1.13999398629076e-05 *** df.mm.trans1:probe5 -0.106772809712295 0.0398811356934377 -2.67727605685674 0.00775286496486698 ** df.mm.trans1:probe6 -0.127299281046453 0.0398811356934377 -3.19196730065539 0.00153397380253396 ** df.mm.trans2:probe2 0.0899843172882229 0.0398811356934377 2.25631280864024 0.0246341667325804 * df.mm.trans2:probe3 0.122536467641448 0.0398811356934377 3.07254208063114 0.00227965491900291 ** df.mm.trans2:probe4 0.0626526353773284 0.0398811356934377 1.57098423322076 0.117041039504800 df.mm.trans2:probe5 -0.121312136141077 0.0398811356934377 -3.04184256620954 0.00251907323323965 ** df.mm.trans2:probe6 -0.221873154897218 0.0398811356934377 -5.56336099861184 5.08392863978758e-08 *** df.mm.trans3:probe2 0.160498877401867 0.0398811356934377 4.02443096494558 6.92857292043832e-05 *** df.mm.trans3:probe3 0.140340054020007 0.0398811356934377 3.51895831399554 0.000487218865715442 *** df.mm.trans3:probe4 0.242885256479556 0.0398811356934377 6.09022918370706 2.82627447649727e-09 *** df.mm.trans3:probe5 0.442765512775014 0.0398811356934377 11.1021289909723 6.51571412767103e-25 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.5854191004919 0.147520370292152 31.0832944047716 3.31385556411122e-105 *** df.mm.trans1 0.00564353490805716 0.121488898001629 0.0464530916066216 0.962974183017175 df.mm.trans2 -0.05944226172892 0.121488898001629 -0.489281429881133 0.62493238937804 df.mm.exp2 0.0184502454819071 0.166071952536419 0.111097901843847 0.91159895227156 df.mm.exp3 -0.156535270046614 0.166071952536419 -0.94257499629437 0.346513360352396 df.mm.exp4 0.0777984676869882 0.166071952536419 0.468462413422443 0.63972980348468 df.mm.exp5 0.0100391821724415 0.166071952536419 0.0604507986996778 0.951829239079118 df.mm.exp6 0.0158145127434599 0.166071952536419 0.0952268730626974 0.924186176832628 df.mm.exp7 0.00409391254012291 0.166071952536419 0.0246514385939139 0.980346279077649 df.mm.exp8 -0.00864868168562669 0.166071952536419 -0.0520779189594346 0.958494711440668 df.mm.trans1:exp2 -0.237077290141414 0.137699588691253 -1.72169933399716 0.085959796516207 . df.mm.trans2:exp2 0.00428154538585799 0.137699588691253 0.0310933781760088 0.975211835764427 df.mm.trans1:exp3 -0.0789263258526318 0.137699588691253 -0.573177644194701 0.566872600220508 df.mm.trans2:exp3 0.241141745177337 0.137699588691253 1.75121616171287 0.0807373010796502 . df.mm.trans1:exp4 -0.0950216861546976 0.137699588691253 -0.690065141499828 0.490585823494667 df.mm.trans2:exp4 -0.0741886967632576 0.137699588691253 -0.538772101415653 0.590368115810044 df.mm.trans1:exp5 -0.0332498490561255 0.137699588691253 -0.241466582232701 0.809327275562445 df.mm.trans2:exp5 -0.0500982788196152 0.137699588691253 -0.363823009899793 0.716197980722204 df.mm.trans1:exp6 -0.0858514260409567 0.137699588691253 -0.623469008563641 0.533360504294737 df.mm.trans2:exp6 0.0626814274382586 0.137699588691253 0.455204173331276 0.649229349856952 df.mm.trans1:exp7 -0.0634238049330522 0.137699588691253 -0.460595456644825 0.645359461045961 df.mm.trans2:exp7 0.0214099223807661 0.137699588691253 0.155482834656616 0.87652547358381 df.mm.trans1:exp8 -0.0922540180362883 0.137699588691253 -0.66996582134416 0.503297478458404 df.mm.trans2:exp8 0.0283896635263348 0.137699588691253 0.206171011810278 0.836770727933661 df.mm.trans1:probe2 -0.156969511790572 0.0803992383057124 -1.95237560825772 0.0516474284686425 . df.mm.trans1:probe3 -0.0246697534139974 0.0803992383057124 -0.306840635979566 0.759137286058918 df.mm.trans1:probe4 0.0261934141567211 0.0803992383057124 0.325791819782204 0.744766039029385 df.mm.trans1:probe5 0.0332940255287958 0.0803992383057124 0.414108718321405 0.679034439222044 df.mm.trans1:probe6 0.0327473703589812 0.0803992383057124 0.407309460252119 0.684016258861551 df.mm.trans2:probe2 -0.138021002653302 0.0803992383057124 -1.71669540112417 0.0868717488747107 . df.mm.trans2:probe3 -0.100961086936717 0.0803992383057124 -1.25574680885432 0.210000182483287 df.mm.trans2:probe4 -0.0804510480702913 0.0803992383057124 -1.00064440616193 0.317652707164517 df.mm.trans2:probe5 -0.0529636643977147 0.0803992383057124 -0.658758285698231 0.510460725641004 df.mm.trans2:probe6 -0.0637378069901366 0.0803992383057124 -0.792766303926638 0.428422020058299 df.mm.trans3:probe2 0.0753484134990062 0.0803992383057124 0.93717820077473 0.349278215601686 df.mm.trans3:probe3 0.0337976896789363 0.0803992383057124 0.420373257149813 0.674456830058627 df.mm.trans3:probe4 -0.00382777634012192 0.0803992383057124 -0.0476096095036009 0.962053060628966 df.mm.trans3:probe5 -0.0236299273871998 0.0803992383057124 -0.293907353914830 0.768993498251499