chr13.6755_chr13_82329122_82330505_-_0.R fitVsDatCorrelation=0.735125020068141 cont.fitVsDatCorrelation=0.371329443328065 fstatistic=6129.08286823652,36,324 cont.fstatistic=3263.19769827876,36,324 residuals=-0.555301661832795,-0.0851558232306987,0.00211733618744930,0.0812074446533339,0.700579050207944 cont.residuals=-0.507384387489571,-0.137231780418694,-0.0299929352935698,0.0918189877638964,1.04499844013738 predictedValues: Include Exclude Both chr13.6755_chr13_82329122_82330505_-_0.R.tl.Lung 49.2155636350009 58.0328701615239 64.5532064865012 chr13.6755_chr13_82329122_82330505_-_0.R.tl.cerebhem 80.1205034793258 77.514779771296 83.7783134236803 chr13.6755_chr13_82329122_82330505_-_0.R.tl.cortex 50.0264989790495 64.8484510718627 85.8823795030944 chr13.6755_chr13_82329122_82330505_-_0.R.tl.heart 51.3016491649051 58.4117316417035 73.083566195161 chr13.6755_chr13_82329122_82330505_-_0.R.tl.kidney 48.5763866621843 55.8064602495704 62.7608161877097 chr13.6755_chr13_82329122_82330505_-_0.R.tl.liver 52.3075725236024 59.8682357141676 58.0503815036859 chr13.6755_chr13_82329122_82330505_-_0.R.tl.stomach 49.7276688322028 56.8511273981458 63.9739944510121 chr13.6755_chr13_82329122_82330505_-_0.R.tl.testicle 52.6455589121356 62.0122643852006 72.2474400237663 diffExp=-8.817306526523,2.60572370802984,-14.8219520928132,-7.11008247679838,-7.23007358738607,-7.56066319056519,-7.12345856594299,-9.36670547306498 diffExpScore=1.06969765818847 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,-1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 61.831642420713 66.910090818908 63.2159645286242 cerebhem 58.5255615012491 55.7323423462533 59.7862356604102 cortex 68.665437973848 57.0459923886222 52.537003113179 heart 60.7082081429764 56.6008941744737 57.690797542696 kidney 57.4700228944951 56.1113149505403 63.9147451702806 liver 62.0019150632353 59.9747210346073 54.8334777210301 stomach 57.005922185733 61.9867188575342 53.8157896300434 testicle 69.9447358457598 59.0185724429643 61.0935292382222 cont.diffExp=-5.07844839819502,2.79321915499580,11.6194455852257,4.10731396850264,1.35870794395483,2.02719402862803,-4.98079667180117,10.9261634027955 cont.diffExpScore=1.80421704354829 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=0.928568462119946 cont.tran.correlation=-0.0124245518471097 tran.covariance=0.0160737613956962 cont.tran.covariance=1.44231753841544e-05 tran.mean=57.9542076613673 cont.tran.mean=60.5958808151196 weightedLogRatios: wLogRatio Lung -0.655673570271736 cerebhem 0.144386870066460 cortex -1.04897834363438 heart -0.519515456629585 kidney -0.548419545372655 liver -0.543346709867954 stomach -0.531950383870757 testicle -0.662444779291112 cont.weightedLogRatios: wLogRatio Lung -0.328674165418473 cerebhem 0.197813132962025 cortex 0.766867746210998 heart 0.285193997318036 kidney 0.0966442474011275 liver 0.136643184869257 stomach -0.342183137078976 testicle 0.707061555122367 varWeightedLogRatios=0.108017158657524 cont.varWeightedLogRatios=0.167579423992656 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.74511052907812 0.093315472209706 40.1338646249553 9.15718469872939e-128 *** df.mm.trans1 0.117327347661022 0.0790372301751267 1.48445672249716 0.138659997489298 df.mm.trans2 0.370767760915521 0.0790372301751267 4.69105205349419 4.01388968240032e-06 *** df.mm.exp2 0.516096541504646 0.110068926096936 4.68884870422128 4.05456008372968e-06 *** df.mm.exp3 -0.158102503553869 0.110068926096936 -1.43639544020472 0.151854694959947 df.mm.exp4 -0.0760935697099931 0.110068926096936 -0.691326538817855 0.489855396133429 df.mm.exp5 -0.0240334471706622 0.110068926096936 -0.218349065652702 0.827294610980204 df.mm.exp6 0.198246252560467 0.110068926096936 1.80111008247573 0.0726152403870218 . df.mm.exp7 -0.00120881177835250 0.110068926096936 -0.0109823164558534 0.991244314470276 df.mm.exp8 0.0210874739156461 0.110068926096936 0.191584261456996 0.848187878773812 df.mm.trans1:exp2 -0.0287746541812314 0.0953224861672185 -0.30186638366475 0.762947590161965 df.mm.trans2:exp2 -0.226637493541353 0.0953224861672185 -2.37758689113266 0.0180062359826838 * df.mm.trans1:exp3 0.174445440653496 0.0953224861672185 1.83005550597449 0.0681602509959814 . df.mm.trans2:exp3 0.269145952308506 0.0953224861672186 2.82353055538605 0.00504370388959757 ** df.mm.trans1:exp4 0.117606561314103 0.0953224861672185 1.23377563933648 0.218180748934666 df.mm.trans2:exp4 0.082600746705183 0.0953224861672185 0.866539995193595 0.386835353734287 df.mm.trans1:exp5 0.0109610813350732 0.0953224861672185 0.114989461309736 0.908524704307764 df.mm.trans2:exp5 -0.0150864920017743 0.0953224861672186 -0.158267924058433 0.874344279061948 df.mm.trans1:exp6 -0.137315009364361 0.0953224861672185 -1.44053113683457 0.150682811934851 df.mm.trans2:exp6 -0.167109753610141 0.0953224861672185 -1.75309898355977 0.0805307235701934 . df.mm.trans1:exp7 0.0115603993973467 0.0953224861672185 0.12127672978511 0.90354702874795 df.mm.trans2:exp7 -0.0193647140599789 0.0953224861672185 -0.203149485904182 0.839145714144184 df.mm.trans1:exp8 0.046284502440127 0.0953224861672185 0.485557021235607 0.627609453586597 df.mm.trans2:exp8 0.0452351272914348 0.0953224861672186 0.474548337021776 0.635428618480289 df.mm.trans1:probe2 0.154052414245376 0.0476612430836093 3.23223659893073 0.00135457410648881 ** df.mm.trans1:probe3 0.0127860313467484 0.0476612430836093 0.268268943894699 0.788663028614557 df.mm.trans1:probe4 0.0529679630273532 0.0476612430836093 1.1113424577373 0.267244969443153 df.mm.trans1:probe5 0.110000824768103 0.0476612430836093 2.30797221497424 0.0216302272189913 * df.mm.trans1:probe6 -0.0258589563604689 0.0476612430836093 -0.542557320947464 0.587807624145225 df.mm.trans2:probe2 -0.102713356233539 0.0476612430836093 -2.1550708623641 0.0318926881988842 * df.mm.trans2:probe3 -0.0857346040204756 0.0476612430836093 -1.79883273019289 0.0729756815175175 . df.mm.trans2:probe4 -0.0546885053964327 0.0476612430836093 -1.14744185963626 0.252045616160372 df.mm.trans2:probe5 -0.0596447678143504 0.0476612430836093 -1.25143122494139 0.211679974127232 df.mm.trans2:probe6 -0.191037184016255 0.0476612430836093 -4.00822915342618 7.59634193861113e-05 *** df.mm.trans3:probe2 -0.123490569692580 0.0476612430836093 -2.59100606075146 0.0100023419373181 * df.mm.trans3:probe3 -0.0736569717096309 0.0476612430836093 -1.54542699569163 0.123219165422923 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27119902320496 0.127805712080602 33.4194689241375 5.16150918209889e-107 *** df.mm.trans1 -0.104507373339915 0.108250103055899 -0.965425162560345 0.335051936579272 df.mm.trans2 -0.058483329905289 0.108250103055899 -0.540261193793867 0.589388079278011 df.mm.exp2 -0.181959387339241 0.150751393575469 -1.20701628703783 0.228306339335447 df.mm.exp3 0.130377906057133 0.150751393575469 0.864853736770687 0.387758942019645 df.mm.exp4 -0.094202140173456 0.150751393575469 -0.624884042125268 0.532486984003813 df.mm.exp5 -0.260157297273327 0.150751393575469 -1.72573726254204 0.0853480995267363 . df.mm.exp6 0.0355793510163985 0.150751393575469 0.236013413690845 0.81357138378115 df.mm.exp7 0.00330023462214852 0.150751393575469 0.0218919012546068 0.982547660882338 df.mm.exp8 0.0319435034623518 0.150751393575469 0.211895244911022 0.832321991984507 df.mm.trans1:exp2 0.127007748172335 0.130554536492262 0.9728328986857 0.331362077430398 df.mm.trans2:exp2 -0.000829771828181113 0.130554536492262 -0.00635574871984853 0.994932791856316 df.mm.trans1:exp3 -0.0255471663902082 0.130554536492262 -0.195681950827671 0.844981776506135 df.mm.trans2:exp3 -0.289869869690737 0.130554536492262 -2.22029718368245 0.0270901329248468 * df.mm.trans1:exp4 0.0758658071478803 0.130554536492262 0.581104335293449 0.561574145546235 df.mm.trans2:exp4 -0.0731228667297893 0.130554536492262 -0.560094414904711 0.575802078088137 df.mm.trans1:exp5 0.187005521456162 0.130554536492262 1.43239389821774 0.152995188436925 df.mm.trans2:exp5 0.0841449918646042 0.130554536492262 0.644519862161905 0.519694427514568 df.mm.trans1:exp6 -0.032829325041352 0.130554536492262 -0.251460622690027 0.801617416712033 df.mm.trans2:exp6 -0.145005983785908 0.130554536492262 -1.11069280074004 0.267524181877593 df.mm.trans1:exp7 -0.0845603208990206 0.130554536492262 -0.647701130661457 0.517636955667433 df.mm.trans2:exp7 -0.0797298746221454 0.130554536492262 -0.610701678887053 0.541825251107546 df.mm.trans1:exp8 0.091346692149863 0.130554536492262 0.699682252368741 0.484627795696813 df.mm.trans2:exp8 -0.157441112046004 0.130554536492262 -1.20594133513955 0.228720006376473 df.mm.trans1:probe2 -0.0626431155729407 0.065277268246131 -0.959646707897485 0.337948627056254 df.mm.trans1:probe3 0.0109261224680535 0.065277268246131 0.167380203884392 0.867175320500266 df.mm.trans1:probe4 -0.149400368883612 0.065277268246131 -2.28870436673745 0.02273979807291 * df.mm.trans1:probe5 -0.112129601153476 0.065277268246131 -1.71774346822673 0.086798875315532 . df.mm.trans1:probe6 -0.0672406652559797 0.065277268246131 -1.03007780598976 0.303741812575497 df.mm.trans2:probe2 0.0262700210923631 0.065277268246131 0.4024375069329 0.687627448247423 df.mm.trans2:probe3 -0.102566925159383 0.065277268246131 -1.57125026697884 0.117100541446404 df.mm.trans2:probe4 0.0421224131621845 0.065277268246131 0.645284557609855 0.51919947729509 df.mm.trans2:probe5 0.0279103831103133 0.065277268246131 0.427566653142957 0.669250677113706 df.mm.trans2:probe6 -0.0780290207939755 0.065277268246131 -1.19534752128051 0.232825454379948 df.mm.trans3:probe2 0.0540515171808414 0.065277268246131 0.82802970518064 0.40826317248397 df.mm.trans3:probe3 0.0609941406048471 0.065277268246131 0.934385617591499 0.350800856612925