chr10.2449_chr10_68038271_68048615_+_0.R fitVsDatCorrelation=0.899292070957486 cont.fitVsDatCorrelation=0.312212959407732 fstatistic=5988.53215818307,38,370 cont.fstatistic=1261.48998028048,38,370 residuals=-0.56842775971004,-0.0843735547954144,-0.00912743453073729,0.0847976852567159,0.89223510075842 cont.residuals=-0.782702106862536,-0.272834920153147,-0.0782514945341738,0.211580084036107,1.24888880137663 predictedValues: Include Exclude Both chr10.2449_chr10_68038271_68048615_+_0.R.tl.Lung 63.3222211568114 159.506253012774 151.816086011288 chr10.2449_chr10_68038271_68048615_+_0.R.tl.cerebhem 88.7749880053391 88.0094771073777 68.6012153461973 chr10.2449_chr10_68038271_68048615_+_0.R.tl.cortex 52.4071290781247 74.9401438851311 54.3987303092049 chr10.2449_chr10_68038271_68048615_+_0.R.tl.heart 54.74061274912 76.9603981744285 54.6537901301896 chr10.2449_chr10_68038271_68048615_+_0.R.tl.kidney 52.6746615355508 82.6704468058365 53.6074179895166 chr10.2449_chr10_68038271_68048615_+_0.R.tl.liver 56.3206815259924 91.651768846624 57.8453350196368 chr10.2449_chr10_68038271_68048615_+_0.R.tl.stomach 58.9220016485279 82.8874111698847 55.7616943955269 chr10.2449_chr10_68038271_68048615_+_0.R.tl.testicle 60.3364457164532 86.3087854706674 54.9826297663456 diffExp=-96.1840318559629,0.765510897961363,-22.5330148070064,-22.2197854253084,-29.9957852702857,-35.3310873206316,-23.9654095213567,-25.9723397542142 diffExpScore=1.00207077755791 diffExp1.5=-1,0,0,0,-1,-1,0,0 diffExp1.5Score=0.75 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 66.9566324605167 73.7971927487204 83.2443860187606 cerebhem 66.5517376559122 81.1409961531569 82.6775185270701 cortex 69.0524314216226 82.3522436329317 67.9887712180309 heart 75.42338272604 70.558973144411 65.5388679782476 kidney 70.4232103138652 69.1674727679828 62.0612173124213 liver 97.1317236268658 68.1252887885833 73.0457753112266 stomach 62.7332545718676 75.8844982000074 77.8507244761217 testicle 70.6632014541744 69.6309981072312 91.4583931644664 cont.diffExp=-6.84056028820372,-14.5892584972447,-13.2998122113091,4.86440958162895,1.25573754588237,29.0064348382826,-13.1512436281398,1.03220334694318 cont.diffExpScore=6.6058064737296 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,1,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,0,0,0,0,1,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,-1,0,0,0,1,-1,0 cont.diffExp1.2Score=1.5 tran.correlation=0.170442951925946 cont.tran.correlation=-0.547525909887154 tran.covariance=0.0103360708206833 cont.tran.covariance=-0.00563520300216137 tran.mean=76.9020891180402 cont.tran.mean=73.099577360868 weightedLogRatios: wLogRatio Lung -4.25908136205142 cerebhem 0.0388141439291686 cortex -1.47989575507220 heart -1.42166128424440 kidney -1.88832274919276 liver -2.08141549742224 stomach -1.44931630590862 testicle -1.53183721297616 cont.weightedLogRatios: wLogRatio Lung -0.413682108173593 cerebhem -0.851719344914173 cortex -0.761440241252945 heart 0.285993336084070 kidney 0.0763863477080411 liver 1.56030777475016 stomach -0.805827923241541 testicle 0.0625475475890757 varWeightedLogRatios=1.42034601231689 cont.varWeightedLogRatios=0.651720006352766 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22868132690105 0.0964840595054989 43.8277716399365 1.48827735410134e-148 *** df.mm.trans1 -0.0641413506447228 0.0794584642163834 -0.807231190248673 0.420051908023848 df.mm.trans2 0.732996282231987 0.0794584642163834 9.2248986871414 2.21041913706218e-18 *** df.mm.exp2 0.537589585629198 0.108617515797889 4.9493820741539 1.1330339298858e-06 *** df.mm.exp3 0.0817419226883343 0.108617515797889 0.752566674793379 0.452188864791363 df.mm.exp4 0.147228702298954 0.108617515797889 1.35547845315218 0.176091993290963 df.mm.exp5 0.199659654082961 0.108617515797889 1.83819020916 0.066835847070574 . df.mm.exp6 0.293635665533235 0.108617515797889 2.70339146845911 0.00718020431559402 ** df.mm.exp7 0.274960994865682 0.108617515797889 2.53146090523115 0.0117721612195094 * df.mm.exp8 0.353200799053092 0.108617515797889 3.25178491202391 0.00125213971462011 ** df.mm.trans1:exp2 -0.19972095459261 0.0900608863905242 -2.21762146251343 0.0271875890510559 * df.mm.trans2:exp2 -1.13222820784589 0.0900608863905241 -12.5718083978909 1.99146928225057e-30 *** df.mm.trans1:exp3 -0.270935602185706 0.0900608863905242 -3.00836037756578 0.00280636565781063 ** df.mm.trans2:exp3 -0.837135334618511 0.0900608863905242 -9.29521536117805 1.29506308227366e-18 *** df.mm.trans1:exp4 -0.292859117619296 0.0900608863905242 -3.25179030938463 0.00125211660642112 ** df.mm.trans2:exp4 -0.876020847469027 0.0900608863905242 -9.7269845165681 4.61176696045675e-20 *** df.mm.trans1:exp5 -0.383761432616334 0.0900608863905242 -4.26113319551685 2.58361344193725e-05 *** df.mm.trans2:exp5 -0.856880595439894 0.0900608863905242 -9.51445882649065 2.40784161317280e-19 *** df.mm.trans1:exp6 -0.410810165477498 0.0900608863905242 -4.56147148825666 6.91786677545222e-06 *** df.mm.trans2:exp6 -0.847722516714338 0.0900608863905242 -9.41277118946423 5.26954228597654e-19 *** df.mm.trans1:exp7 -0.346982744217419 0.0900608863905242 -3.85275737474784 0.000137643245753596 *** df.mm.trans2:exp7 -0.929560925164516 0.0900608863905242 -10.3214720887126 4.0648015453365e-22 *** df.mm.trans1:exp8 -0.401500784004867 0.0900608863905242 -4.45810384614549 1.09778043880147e-05 *** df.mm.trans2:exp8 -0.96735252992932 0.0900608863905242 -10.7410949269882 1.31676597858222e-23 *** df.mm.trans1:probe2 -0.0684661297027411 0.0525842287239555 -1.30202783922455 0.193716839175865 df.mm.trans1:probe3 0.00845383692009362 0.0525842287239555 0.160767536678585 0.872364234366027 df.mm.trans1:probe4 0.0575796386312605 0.0525842287239554 1.0949982538211 0.274229676948421 df.mm.trans1:probe5 -0.0632113166443089 0.0525842287239555 -1.20209648744952 0.230094925779150 df.mm.trans1:probe6 -0.113696328549341 0.0525842287239555 -2.16217545276165 0.0312450637011227 * df.mm.trans2:probe2 0.209601379401880 0.0525842287239555 3.98601224146876 8.09622341892068e-05 *** df.mm.trans2:probe3 0.238413250087345 0.0525842287239555 4.53393072167916 7.83015302316697e-06 *** df.mm.trans2:probe4 0.340973181208109 0.0525842287239555 6.48432409264898 2.85560994928047e-10 *** df.mm.trans2:probe5 0.229240133348716 0.0525842287239555 4.35948456241752 1.69176831070377e-05 *** df.mm.trans2:probe6 0.196232733771782 0.0525842287239555 3.73177925270938 0.000219996408789257 *** df.mm.trans3:probe2 -0.130854473165661 0.0525842287239555 -2.48847375612545 0.0132683767427096 * df.mm.trans3:probe3 0.368225050231250 0.0525842287239554 7.00257585148339 1.19074461846526e-11 *** df.mm.trans3:probe4 0.071648533220303 0.0525842287239554 1.36254795323569 0.173853789643761 df.mm.trans3:probe5 0.316648736277927 0.0525842287239555 6.0217434763605 4.16222163760429e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10977045069642 0.209583463542061 19.6092305243903 3.24212189707862e-59 *** df.mm.trans1 0.101479889869203 0.172600326142511 0.587947266017409 0.556926361002909 df.mm.trans2 0.21146443434971 0.172600326142511 1.22516821998998 0.221291195662466 df.mm.exp2 0.0956351343373492 0.235939856582824 0.405336918155571 0.685464141211535 df.mm.exp3 0.342944096679780 0.235939856582824 1.45352337518013 0.146926128254102 df.mm.exp4 0.313337696444625 0.235939856582824 1.3280405480565 0.184983355813595 df.mm.exp5 0.279347198301269 0.235939856582824 1.18397629949905 0.237182500410166 df.mm.exp6 0.422745087229838 0.235939856582824 1.79174936084374 0.0739901659553897 . df.mm.exp7 0.0297257684710975 0.235939856582824 0.125988753666393 0.89980925807772 df.mm.exp8 -0.0983344912605497 0.235939856582824 -0.41677778686803 0.677082637955221 df.mm.trans1:exp2 -0.101700612413239 0.195630994343873 -0.51985940548088 0.603472445034011 df.mm.trans2:exp2 -0.000767491914452149 0.195630994343873 -0.00392316113827588 0.996871892645978 df.mm.trans1:exp3 -0.312123138074434 0.195630994343873 -1.59546875034431 0.111461206791187 df.mm.trans2:exp3 -0.233259087587777 0.195630994343873 -1.19234218672816 0.233891227260194 df.mm.trans1:exp4 -0.194265486884937 0.195630994343873 -0.993019984059707 0.321349173232673 df.mm.trans2:exp4 -0.35820953004172 0.195630994343873 -1.83104692200293 0.0678975551472386 . df.mm.trans1:exp5 -0.228869430623661 0.195630994343873 -1.16990373325693 0.242792745374629 df.mm.trans2:exp5 -0.344137185137392 0.195630994343873 -1.75911381676301 0.0793846948201795 . df.mm.trans1:exp6 -0.0507221875148859 0.195630994343873 -0.259274802977939 0.795567411373161 df.mm.trans2:exp6 -0.502717287404659 0.195630994343873 -2.56972208872486 0.0105688372570463 * df.mm.trans1:exp7 -0.0948792185932329 0.195630994343873 -0.484990729160523 0.627969932857431 df.mm.trans2:exp7 -0.00183403696611257 0.195630994343873 -0.0093749815680473 0.992525009186154 df.mm.trans1:exp8 0.152214306928062 0.195630994343873 0.778068462201368 0.437025905476633 df.mm.trans2:exp8 0.040223642307052 0.195630994343873 0.205609762614345 0.837208827617373 df.mm.trans1:probe2 -0.0144400415227041 0.114223891906480 -0.126418749017296 0.8994691299759 df.mm.trans1:probe3 0.0316788330124416 0.114223891906480 0.277339814671858 0.781674077268978 df.mm.trans1:probe4 -0.0983830104296683 0.114223891906480 -0.861317267233535 0.389621155274202 df.mm.trans1:probe5 -0.0273956579978932 0.114223891906480 -0.239841748872672 0.810585755919459 df.mm.trans1:probe6 0.0292825949067400 0.114223891906480 0.256361383052110 0.797814281850417 df.mm.trans2:probe2 -0.0286778656302052 0.114223891906480 -0.251067137982699 0.801901573770581 df.mm.trans2:probe3 -0.0968086606048341 0.114223891906480 -0.847534250400917 0.397245302027808 df.mm.trans2:probe4 -0.0208173934951202 0.114223891906480 -0.182250780880100 0.855485733141351 df.mm.trans2:probe5 0.0145552470079389 0.114223891906480 0.127427342607586 0.89867140111873 df.mm.trans2:probe6 -0.087307448060345 0.114223891906480 -0.764353644435676 0.445143704696306 df.mm.trans3:probe2 0.135610456778898 0.114223891906480 1.18723372593475 0.235897091715007 df.mm.trans3:probe3 -0.106608382772921 0.114223891906480 -0.933328229265782 0.351259182132544 df.mm.trans3:probe4 -0.0443434517244421 0.114223891906480 -0.388215205981143 0.69808004808857 df.mm.trans3:probe5 0.0106709600248730 0.114223891906480 0.0934214361528647 0.925619327038977