chr7.20590_chr7_131532413_131532730_-_0.R fitVsDatCorrelation=0.934862795894764 cont.fitVsDatCorrelation=0.328858534535836 fstatistic=5729.18463589387,38,370 cont.fstatistic=801.255490086818,38,370 residuals=-0.786564397330274,-0.114389798116076,-0.0168654085880380,0.104950319910447,0.797008057729702 cont.residuals=-1.17462006461287,-0.373974777189725,0.0130475760591098,0.299455250825246,1.71012787887927 predictedValues: Include Exclude Both chr7.20590_chr7_131532413_131532730_-_0.R.tl.Lung 153.181106500997 122.314603632976 83.5081181941743 chr7.20590_chr7_131532413_131532730_-_0.R.tl.cerebhem 95.7863506716013 77.155161892535 65.4990074588311 chr7.20590_chr7_131532413_131532730_-_0.R.tl.cortex 144.461379108026 122.291145867227 65.1664832954386 chr7.20590_chr7_131532413_131532730_-_0.R.tl.heart 150.949784872417 134.566197050501 70.343750202968 chr7.20590_chr7_131532413_131532730_-_0.R.tl.kidney 149.8981872247 119.331256470298 78.7981997044533 chr7.20590_chr7_131532413_131532730_-_0.R.tl.liver 151.058133537343 120.004277701323 71.9543495619687 chr7.20590_chr7_131532413_131532730_-_0.R.tl.stomach 173.632175047781 143.177281219729 73.8547231000936 chr7.20590_chr7_131532413_131532730_-_0.R.tl.testicle 135.713923806922 108.876486314391 76.7645712018429 diffExp=30.8665028680214,18.6311887790663,22.1702332407998,16.3835878219162,30.5669307544026,31.0538558360202,30.4548938280516,26.8374374925313 diffExpScore=0.995191490028786 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=1,1,0,0,1,1,1,1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 86.871788954453 116.960501811459 85.2574643822282 cerebhem 96.5487938111943 143.819403633069 96.642924938868 cortex 93.7481493685117 79.3492305947187 102.860687546814 heart 93.846787675808 92.3240063979381 101.465177295232 kidney 99.174176286704 116.042390739980 83.7253178082835 liver 106.241826539114 113.157229420292 104.425854457426 stomach 113.517310823759 117.499638566132 107.791911641973 testicle 105.850294461754 111.872827005882 80.7020216906312 cont.diffExp=-30.0887128570064,-47.2706098218749,14.3989187737930,1.52278127786991,-16.8682144532764,-6.91540288117804,-3.98232774237384,-6.02253254412759 cont.diffExpScore=1.32053050080777 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,-1,0,0,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=-1,-1,0,0,0,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=-1,-1,0,0,0,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.967139599719271 cont.tran.correlation=0.196915102915722 tran.covariance=0.0318786492715922 cont.tran.covariance=0.00352664938141893 tran.mean=131.399840682423 cont.tran.mean=105.426522255673 weightedLogRatios: wLogRatio Lung 1.10691987436324 cerebhem 0.96340120113296 cortex 0.814662369522832 heart 0.569802023420304 kidney 1.11653156794141 liver 1.12827053358206 stomach 0.975947620048064 testicle 1.05769248055611 cont.weightedLogRatios: wLogRatio Lung -1.37195987898982 cerebhem -1.90061442438647 cortex 0.74325813985088 heart 0.0741647835058702 kidney -0.734404627840179 liver -0.296210268189475 stomach -0.163751845455572 testicle -0.259513487523448 varWeightedLogRatios=0.0367479441248579 cont.varWeightedLogRatios=0.694818394247666 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.32158191389985 0.106291455806099 40.6578485648313 1.46830686316986e-138 *** df.mm.trans1 0.980134857663995 0.0875352455209961 11.1970309996892 2.93281868961063e-25 *** df.mm.trans2 0.502247418158322 0.0875352455209961 5.73765932989647 1.99918737121412e-08 *** df.mm.exp2 -0.68736982475824 0.119658251729568 -5.74444148082426 1.92700435093428e-08 *** df.mm.exp3 0.189198048762144 0.119658251729568 1.58115337661575 0.114697398119240 df.mm.exp4 0.252336022522259 0.119658251729568 2.10880586064844 0.0356329275295570 * df.mm.exp5 0.0116959384691180 0.119658251729568 0.0977445207502387 0.922188089374938 df.mm.exp6 0.115886741802598 0.119658251729568 0.968480987541971 0.333436842316222 df.mm.exp7 0.405649237357639 0.119658251729568 3.39006488473876 0.000774060255784288 *** df.mm.exp8 -0.153253482567390 0.119658251729568 -1.28075983354452 0.201080266392735 df.mm.trans1:exp2 0.217869098262524 0.0992153810142178 2.19592059250675 0.0287177723414933 * df.mm.trans2:exp2 0.226591864708619 0.0992153810142178 2.28383807422105 0.0229470731371680 * df.mm.trans1:exp3 -0.247806773531903 0.0992153810142178 -2.49766488823332 0.0129349804057248 * df.mm.trans2:exp3 -0.189389849370829 0.0992153810142178 -1.90887589640652 0.0570515129297061 . df.mm.trans1:exp4 -0.267009715518058 0.0992153810142178 -2.69121292272006 0.00744233646381009 ** df.mm.trans2:exp4 -0.156876217025024 0.0992153810142178 -1.58116831706309 0.114693982333835 df.mm.trans1:exp5 -0.0333605506542532 0.0992153810142178 -0.336243738755310 0.736877679581707 df.mm.trans2:exp5 -0.0363890886154027 0.0992153810142178 -0.366768622399263 0.714001271016673 df.mm.trans1:exp6 -0.12984291275364 0.0992153810142178 -1.30869741592822 0.191449145433005 df.mm.trans2:exp6 -0.134955795992120 0.0992153810142179 -1.36023058736004 0.174585104534567 df.mm.trans1:exp7 -0.280331036157510 0.0992153810142178 -2.82547961104274 0.00497699083852009 ** df.mm.trans2:exp7 -0.248162089964902 0.0992153810142178 -2.50124615183748 0.0128071028232237 * df.mm.trans1:exp8 0.0321817274275278 0.0992153810142178 0.324362282325118 0.745847065523468 df.mm.trans2:exp8 0.0368711253669478 0.0992153810142178 0.371627110535049 0.710383224362068 df.mm.trans1:probe2 -0.558459738989424 0.0579293020231138 -9.64036712830751 9.0679104748704e-20 *** df.mm.trans1:probe3 -0.633238965370116 0.0579293020231138 -10.9312376164562 2.72017361460885e-24 *** df.mm.trans1:probe4 -0.65652364311352 0.0579293020231138 -11.3331875266090 9.2779283534501e-26 *** df.mm.trans1:probe5 -0.599949634563968 0.0579293020231138 -10.3565831731338 3.05917561500752e-22 *** df.mm.trans1:probe6 -0.522882341278027 0.0579293020231138 -9.026215110781 9.87923653751485e-18 *** df.mm.trans2:probe2 -0.0372295213779812 0.0579293020231138 -0.642671671810004 0.520835200539789 df.mm.trans2:probe3 0.511089724279332 0.0579293020231138 8.82264599140875 4.48616398268081e-17 *** df.mm.trans2:probe4 -0.475591109196264 0.0579293020231138 -8.20985395278029 3.73158777931478e-15 *** df.mm.trans2:probe5 -0.0298501712433383 0.0579293020231138 -0.51528622304871 0.606660743746081 df.mm.trans2:probe6 -0.157980692781979 0.0579293020231138 -2.72712922933104 0.00669305914266784 ** df.mm.trans3:probe2 -1.60385317947684 0.0579293020231138 -27.6863888129863 3.44292887681950e-92 *** df.mm.trans3:probe3 -1.79906506628550 0.0579293020231138 -31.0562185880933 4.18474941552781e-105 *** df.mm.trans3:probe4 -1.76234153195275 0.0579293020231138 -30.4222814776807 1.01289211472317e-102 *** df.mm.trans3:probe5 -1.72984191582972 0.0579293020231138 -29.8612594216915 1.35894480896841e-100 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.80848374940557 0.282751633526885 17.0060334910439 2.47444850304486e-48 *** df.mm.trans1 -0.40867478070578 0.232857227088787 -1.75504443566166 0.0800793162455512 . df.mm.trans2 0.0579184869507505 0.232857227088787 0.248729608588299 0.80370794677241 df.mm.exp2 0.186989929976954 0.318309368188793 0.587447146280811 0.557261753183646 df.mm.exp3 -0.499498806364652 0.318309368188793 -1.56922433419677 0.117450442083084 df.mm.exp4 -0.33334188284525 0.318309368188793 -1.04722611446215 0.295678912585932 df.mm.exp5 0.142697851037347 0.318309368188793 0.448299250032476 0.654199642342252 df.mm.exp6 -0.0345751092032692 0.318309368188793 -0.108621085832329 0.913561916757066 df.mm.exp7 0.0375940196938911 0.318309368188793 0.118105288285432 0.906048322725989 df.mm.exp8 0.208030948653327 0.318309368188793 0.653549563548948 0.513807983897704 df.mm.trans1:exp2 -0.0813747555998233 0.263928185384329 -0.308321581801983 0.758011153790962 df.mm.trans2:exp2 0.0197321546225831 0.263928185384329 0.0747633474380518 0.94044340885383 df.mm.trans1:exp3 0.575677389424632 0.263928185384329 2.18118951026901 0.0297984382782887 * df.mm.trans2:exp3 0.111521270495292 0.263928185384329 0.422543997462401 0.672873439100576 df.mm.trans1:exp4 0.410572075484259 0.263928185384329 1.55562042336020 0.120653290104471 df.mm.trans2:exp4 0.0968097950755873 0.263928185384329 0.366803548982895 0.713975238519126 df.mm.trans1:exp5 -0.0102535318525511 0.263928185384329 -0.0388497038981267 0.969031169726852 df.mm.trans2:exp5 -0.150578575719265 0.263928185384329 -0.570528590949824 0.568665541757102 df.mm.trans1:exp6 0.235859645831598 0.263928185384329 0.893650844786215 0.372089627329261 df.mm.trans2:exp6 0.00151708515372809 0.263928185384329 0.00574809829999371 0.995416804234145 df.mm.trans1:exp7 0.229927982342343 0.263928185384329 0.871176308841455 0.384222790262264 df.mm.trans2:exp7 -0.0329950485932417 0.263928185384329 -0.125015251952703 0.9005793701291 df.mm.trans1:exp8 -0.0104385107251234 0.263928185384329 -0.0395505720994631 0.968472765899675 df.mm.trans2:exp8 -0.252504482103978 0.263928185384329 -0.956716622502004 0.339334987720358 df.mm.trans1:probe2 0.0839744915424815 0.154100860242125 0.544932010181772 0.586128580745291 df.mm.trans1:probe3 0.235389261144063 0.154100860242125 1.52750127918960 0.127490791588713 df.mm.trans1:probe4 0.286522375703325 0.154100860242125 1.85931717222822 0.0637758010667853 . df.mm.trans1:probe5 0.114768169467630 0.154100860242125 0.74476008302163 0.456889544474456 df.mm.trans1:probe6 -0.00978619676375417 0.154100860242125 -0.0635051403890801 0.949398559568955 df.mm.trans2:probe2 -0.139985141851069 0.154100860242125 -0.90839948350141 0.364258490248526 df.mm.trans2:probe3 -0.259278834431758 0.154100860242125 -1.68252684653658 0.0933103526347231 . df.mm.trans2:probe4 -0.0419194785253369 0.154100860242125 -0.272026246053867 0.78575356759601 df.mm.trans2:probe5 -0.347130114646809 0.154100860242125 -2.25261633258499 0.0248687569065316 * df.mm.trans2:probe6 -0.361911879559432 0.154100860242125 -2.34853899576415 0.0193728324961776 * df.mm.trans3:probe2 0.0594152162635474 0.154100860242125 0.385560574874102 0.700043708339977 df.mm.trans3:probe3 -0.164135942680791 0.154100860242125 -1.06512022335825 0.287516096860878 df.mm.trans3:probe4 0.125174362436321 0.154100860242125 0.812288537777436 0.417148387164588 df.mm.trans3:probe5 -0.0807125589397852 0.154100860242125 -0.523764493027285 0.600755926508052