chr2.13995_chr2_30230536_30238340_+_0.R fitVsDatCorrelation=0.817493912404757 cont.fitVsDatCorrelation=0.300372305429573 fstatistic=9572.9173420854,45,531 cont.fstatistic=3482.77950687634,45,531 residuals=-0.527689278233526,-0.0832199069564355,-0.00348216023592239,0.0775130027808513,0.61326298323624 cont.residuals=-0.568799703276351,-0.177503155033755,-0.0198827000619026,0.151783635424739,1.05083247432072 predictedValues: Include Exclude Both chr2.13995_chr2_30230536_30238340_+_0.R.tl.Lung 65.4256767230163 64.7914695512415 67.5159428480198 chr2.13995_chr2_30230536_30238340_+_0.R.tl.cerebhem 74.4340327189705 57.3084879741545 73.6697167762053 chr2.13995_chr2_30230536_30238340_+_0.R.tl.cortex 74.7706111697362 71.5475176640838 64.6824791551625 chr2.13995_chr2_30230536_30238340_+_0.R.tl.heart 66.2005704639199 75.218850758164 64.809946018243 chr2.13995_chr2_30230536_30238340_+_0.R.tl.kidney 66.9859021030806 65.4273519758928 69.72612560698 chr2.13995_chr2_30230536_30238340_+_0.R.tl.liver 62.1833302108276 65.9921067448734 69.2293059956171 chr2.13995_chr2_30230536_30238340_+_0.R.tl.stomach 64.1833538870257 69.514693823309 62.5244223539168 chr2.13995_chr2_30230536_30238340_+_0.R.tl.testicle 63.0840775505748 62.9609187500551 66.3425695299075 diffExp=0.634207171774719,17.1255447448161,3.22309350565249,-9.01828029424418,1.55855012718779,-3.80877653404578,-5.33133993628321,0.123158800519718 diffExpScore=7.41405426225633 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=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 62.273151948685 72.551264484127 73.2385389565242 cerebhem 65.2509137289388 67.1759110994385 63.5683658391206 cortex 63.2249113974337 57.6134124285836 55.4603011346178 heart 63.7029479520668 67.5032693754731 64.9136307896231 kidney 60.3600921235952 61.1813402051115 54.6459526217374 liver 63.2615803120512 66.5169486884861 60.9400484215974 stomach 66.1531110164732 61.5031051727355 63.0611671006876 testicle 61.0666157210636 62.8474269668601 66.0191725066613 cont.diffExp=-10.2781125354419,-1.92499737049968,5.61149896885011,-3.80032142340632,-0.821248081516323,-3.25536837643496,4.65000584373778,-1.78081124579657 cont.diffExpScore=2.54952462511121 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=-0.137338937931299 cont.tran.correlation=0.0563538682089191 tran.covariance=-0.000931032079104597 cont.tran.covariance=0.000149842680978076 tran.mean=66.8768095043078 cont.tran.mean=63.8866251638202 weightedLogRatios: wLogRatio Lung 0.0406781796032468 cerebhem 1.09270756119903 cortex 0.189136146277494 heart -0.5436153171141 kidney 0.0987039031870091 liver -0.247293193244079 stomach -0.335266837460665 testicle 0.00809722869927322 cont.weightedLogRatios: wLogRatio Lung -0.642813342966662 cerebhem -0.121903609876684 cortex 0.381087371373196 heart -0.24239652505542 kidney -0.0555034955836448 liver -0.209363492187129 stomach 0.302872693125357 testicle -0.118610196326150 varWeightedLogRatios=0.242690444422082 cont.varWeightedLogRatios=0.103193035801064 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27469728093581 0.0786332940138066 54.362434316757 3.89518191319674e-219 *** df.mm.trans1 0.0708323650858562 0.0688314807092405 1.02906932054924 0.303915612049844 df.mm.trans2 0.110525590330465 0.0639950222598064 1.72709667764087 0.084731830994173 . df.mm.exp2 -0.0809547884806377 0.0867489059071897 -0.933208178639729 0.351136629713665 df.mm.exp3 0.275571373640611 0.0867489059071897 3.17665532214824 0.00157644353824879 ** df.mm.exp4 0.201906891697886 0.0867489059071897 2.32748631912315 0.0203143377010695 * df.mm.exp5 0.00112251744726341 0.0867489059071897 0.0129398455868061 0.98968064523762 df.mm.exp6 -0.057527152625604 0.0867489059071897 -0.663145569664598 0.507525095894621 df.mm.exp7 0.127999822400776 0.0867489059071897 1.47552088481346 0.140665254359456 df.mm.exp8 -0.047574151448208 0.0867489059071897 -0.548412120599035 0.583639443327445 df.mm.trans1:exp2 0.209953262893832 0.0801504748952512 2.61948869508534 0.00905800066152582 ** df.mm.trans2:exp2 -0.0417704185497716 0.0703969688674217 -0.593355356371062 0.55319600392077 df.mm.trans1:exp3 -0.142061256520967 0.0801504748952512 -1.77243187525248 0.0768961983514167 . df.mm.trans2:exp3 -0.176383513928300 0.0703969688674217 -2.5055555198759 0.0125241090961241 * df.mm.trans1:exp4 -0.190132603367809 0.0801504748952512 -2.37219559355441 0.0180381605486655 * df.mm.trans2:exp4 -0.0526789691450386 0.0703969688674217 -0.748313030980761 0.45460266980695 df.mm.trans1:exp5 0.0224448715756272 0.0801504748952512 0.280034168293581 0.779560346003731 df.mm.trans2:exp5 0.00864392755007312 0.0703969688674217 0.122788348548816 0.902321159344998 df.mm.trans1:exp6 0.00669932155463464 0.0801504748952512 0.0835843026930283 0.93341844813088 df.mm.trans2:exp6 0.075888340760548 0.0703969688674217 1.07800580027058 0.281520632964698 df.mm.trans1:exp7 -0.147170722429426 0.0801504748952512 -1.83618029240330 0.0668898940256872 . df.mm.trans2:exp7 -0.0576356222231896 0.0703969688674217 -0.818723066496436 0.413311705410761 df.mm.trans1:exp8 0.0111277605396587 0.0801504748952512 0.138835865342053 0.889632488762526 df.mm.trans2:exp8 0.0189143960854246 0.0703969688674217 0.268681967273988 0.78827878435078 df.mm.trans1:probe2 -0.430836979145643 0.0439002230948806 -9.81400432099136 5.27121613519336e-21 *** df.mm.trans1:probe3 -0.147568809495629 0.0439002230948806 -3.36145921574684 0.000831145286355421 *** df.mm.trans1:probe4 -0.214635959021945 0.0439002230948806 -4.88917695379489 1.34364073236557e-06 *** df.mm.trans1:probe5 -0.44919679057455 0.0439002230948806 -10.2322211348154 1.50050576733176e-22 *** df.mm.trans1:probe6 -0.202729002513678 0.0439002230948806 -4.61794925450661 4.86745656909705e-06 *** df.mm.trans1:probe7 -0.055285459738187 0.0439002230948806 -1.25934348029849 0.208459577160088 df.mm.trans1:probe8 0.171005504714657 0.0439002230948806 3.89532199745470 0.000110589301295257 *** df.mm.trans1:probe9 -0.202712064934033 0.0439002230948806 -4.6175634346075 4.87616118795282e-06 *** df.mm.trans1:probe10 -0.185261121571133 0.0439002230948806 -4.22004966058446 2.8726939785286e-05 *** df.mm.trans1:probe11 -0.248723367062086 0.0439002230948806 -5.66565155089362 2.40370310746182e-08 *** df.mm.trans1:probe12 -0.325041744822747 0.0439002230948806 -7.40410234636487 5.21305588020748e-13 *** df.mm.trans1:probe13 -0.220919664976958 0.0439002230948806 -5.03231303630255 6.64425992001632e-07 *** df.mm.trans1:probe14 -0.121932207516470 0.0439002230948806 -2.77748491739873 0.00567181654900776 ** df.mm.trans2:probe2 -0.513954437916627 0.0439002230948806 -11.7073308900009 2.53425232952812e-28 *** df.mm.trans2:probe3 -0.268823928438248 0.0439002230948806 -6.12352078159704 1.78177691904303e-09 *** df.mm.trans2:probe4 -0.461677342331263 0.0439002230948806 -10.516514718694 1.26343566744923e-23 *** df.mm.trans2:probe5 -0.452548347539197 0.0439002230948806 -10.3085660079930 7.75372261834134e-23 *** df.mm.trans2:probe6 -0.443485136789275 0.0439002230948806 -10.1021157872200 4.58836430109037e-22 *** df.mm.trans3:probe2 0.0700635814339387 0.0439002230948806 1.59597324329108 0.111089708766259 df.mm.trans3:probe3 -0.513234837044826 0.0439002230948806 -11.6909391538987 2.95489135445221e-28 *** df.mm.trans3:probe4 -0.531594648473734 0.0439002230948806 -12.1091559677228 5.6550990178534e-30 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.02308646549354 0.130226287861835 30.8930441890647 1.05803569505910e-120 *** df.mm.trans1 0.0543175204370166 0.113993294230101 0.47649750631273 0.633915994757787 df.mm.trans2 0.25862495809989 0.105983531467814 2.44023721910448 0.0150035744382567 * df.mm.exp2 0.111336790482197 0.143666727103985 0.77496573303025 0.438704863594934 df.mm.exp3 0.0626842940179971 0.143666727103985 0.436317408223734 0.662783795430349 df.mm.exp4 0.071247220379798 0.143666727103985 0.495920118847211 0.620155963977778 df.mm.exp5 0.0911932301836498 0.143666727103985 0.634755395503962 0.525861350927143 df.mm.exp6 0.112742390860640 0.143666727103985 0.784749490249317 0.432950418664425 df.mm.exp7 0.0448523991319413 0.143666727103985 0.312197542437766 0.755012954779875 df.mm.exp8 -0.059371890604568 0.143666727103985 -0.413261245671693 0.679582054022237 df.mm.trans1:exp2 -0.0646271259233235 0.132738923720264 -0.486873963657559 0.626548555045507 df.mm.trans2:exp2 -0.188315481318061 0.116585932807544 -1.61525045760817 0.106850449176775 df.mm.trans1:exp3 -0.047516287902467 0.132738923720264 -0.357968006450041 0.720509506958062 df.mm.trans2:exp3 -0.293222307078268 0.116585932807544 -2.51507450356218 0.0121949269950391 * df.mm.trans1:exp4 -0.0485467652446598 0.132738923720264 -0.365731195372413 0.714711315365122 df.mm.trans2:exp4 -0.143364596800651 0.116585932807544 -1.22969035241424 0.219357644287325 df.mm.trans1:exp5 -0.12239545510385 0.132738923720264 -0.922076597229217 0.356907378274136 df.mm.trans2:exp5 -0.261644394084276 0.116585932807544 -2.24421924483967 0.0252294168959524 * df.mm.trans1:exp6 -0.0969945771285148 0.132738923720264 -0.730716917163818 0.465274364881789 df.mm.trans2:exp6 -0.199579016494588 0.116585932807544 -1.71186190038935 0.0875060161868888 . df.mm.trans1:exp7 0.0155891349961255 0.132738923720264 0.117442077720761 0.906554149428918 df.mm.trans2:exp7 -0.210058143299078 0.116585932807544 -1.80174518692435 0.0721525823106006 . df.mm.trans1:exp8 0.0398068348595593 0.132738923720264 0.299888184594963 0.764379803359797 df.mm.trans2:exp8 -0.08421152278612 0.116585932807544 -0.722312896231944 0.470420104380779 df.mm.trans1:probe2 -0.0596573770273634 0.0727041027805464 -0.820550350610009 0.412270638022027 df.mm.trans1:probe3 0.0158778507349624 0.0727041027805464 0.218390023777459 0.827209143271322 df.mm.trans1:probe4 0.179218722851062 0.0727041027805464 2.46504276920967 0.0140150215893389 * df.mm.trans1:probe5 0.126796398379337 0.0727041027805464 1.74400609498016 0.0817366927052089 . df.mm.trans1:probe6 0.163301028944271 0.0727041027805464 2.24610472722821 0.0251076066017761 * df.mm.trans1:probe7 0.0480316602893182 0.0727041027805464 0.660645801988635 0.509125987131891 df.mm.trans1:probe8 0.106718400229599 0.0727041027805464 1.46784563935440 0.142738224531425 df.mm.trans1:probe9 0.0627094292737331 0.0727041027805464 0.862529442980932 0.388785609530631 df.mm.trans1:probe10 0.0208342482687527 0.0727041027805464 0.286562208623079 0.774559327801658 df.mm.trans1:probe11 0.0324342438556693 0.0727041027805464 0.446112978707274 0.655697490407312 df.mm.trans1:probe12 0.066982927471092 0.0727041027805464 0.92130876951025 0.357307626390455 df.mm.trans1:probe13 0.0400251346635994 0.0727041027805464 0.550520990327234 0.582193601425585 df.mm.trans1:probe14 0.0627497180493286 0.0727041027805464 0.863083590189338 0.388481158318824 df.mm.trans2:probe2 0.0645999233940744 0.0727041027805464 0.88853202121297 0.374656952961969 df.mm.trans2:probe3 -0.0192586365538481 0.0727041027805464 -0.264890643269188 0.791196498499157 df.mm.trans2:probe4 -0.0458721935415603 0.0727041027805464 -0.630943671501225 0.528348826313328 df.mm.trans2:probe5 0.00488935805642133 0.0727041027805464 0.0672500982672134 0.946407916277453 df.mm.trans2:probe6 0.021461395896419 0.0727041027805464 0.295188236641873 0.767965469518817 df.mm.trans3:probe2 -0.0672698185559419 0.0727041027805464 -0.925254779073368 0.355253690957436 df.mm.trans3:probe3 -0.0816120583457413 0.0727041027805464 -1.12252342336282 0.262147254181694 df.mm.trans3:probe4 -0.0203468156283565 0.0727041027805464 -0.279857873905305 0.779695530070855