chr4.16690_chr4_151572432_151575647_+_2.R fitVsDatCorrelation=0.870436518728673 cont.fitVsDatCorrelation=0.26477475127349 fstatistic=6178.58053289025,45,531 cont.fstatistic=1601.47843409040,45,531 residuals=-0.677997157890916,-0.0882818022442577,-0.00335322412585663,0.0822458035641433,2.6823083552717 cont.residuals=-0.641334321675948,-0.238839842719102,-0.0734425250495823,0.155938348674241,2.63455892583656 predictedValues: Include Exclude Both chr4.16690_chr4_151572432_151575647_+_2.R.tl.Lung 56.8722778301205 44.5046561373442 64.0172615577466 chr4.16690_chr4_151572432_151575647_+_2.R.tl.cerebhem 49.1370276875886 52.4273013780821 62.7641435615211 chr4.16690_chr4_151572432_151575647_+_2.R.tl.cortex 55.2579249702396 46.2971018313839 64.5087844032671 chr4.16690_chr4_151572432_151575647_+_2.R.tl.heart 65.1658281788646 46.3814642920639 64.9308134733682 chr4.16690_chr4_151572432_151575647_+_2.R.tl.kidney 59.4440090347842 45.2212011395525 64.2510356953988 chr4.16690_chr4_151572432_151575647_+_2.R.tl.liver 61.2943928279501 44.612815526489 64.5183221106868 chr4.16690_chr4_151572432_151575647_+_2.R.tl.stomach 63.5498782004569 46.5346176750567 69.3476456054379 chr4.16690_chr4_151572432_151575647_+_2.R.tl.testicle 56.6229097684454 47.8814487836582 65.553248918294 diffExp=12.3676216927763,-3.29027369049351,8.96082313885564,18.7843638868007,14.2228078952316,16.6815773014610,17.0152605254001,8.74146098478723 diffExpScore=1.05906363555132 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,1,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,1,1,1,1,0 diffExp1.3Score=0.8 diffExp1.2=1,0,0,1,1,1,1,0 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 64.608650060733 62.6855596979525 68.9172618478285 cerebhem 58.0295520341782 52.5283215477628 63.1735039617362 cortex 65.362742356443 56.2818329505156 51.461952710131 heart 59.0670963667129 53.3242271364388 62.6705974245615 kidney 63.3170625086172 62.6095618898482 59.4170223175181 liver 57.438374202705 57.0478805898112 55.0590716576028 stomach 59.4337530435667 51.2462127570243 55.6407361158819 testicle 63.4692203088483 60.6488320608441 60.3061516677799 cont.diffExp=1.92309036278044,5.50123048641544,9.08090940592744,5.74286923027407,0.707500618768968,0.390493612893728,8.18754028654238,2.82038824800419 cont.diffExpScore=0.971714675267125 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=-0.667515746172176 cont.tran.correlation=0.68028105437097 tran.covariance=-0.00326304462534536 cont.tran.covariance=0.00279449883793999 tran.mean=52.57530345388 cont.tran.mean=59.1936799695001 weightedLogRatios: wLogRatio Lung 0.960798494164595 cerebhem -0.254528224902774 cortex 0.694202352305647 heart 1.36249349948407 kidney 1.07973704666537 liver 1.25696299906957 stomach 1.24527116576368 testicle 0.662788949578105 cont.weightedLogRatios: wLogRatio Lung 0.125499235160652 cerebhem 0.399510447820045 cortex 0.614052640366946 heart 0.411949117246129 kidney 0.046548979433355 liver 0.0276094562356501 stomach 0.594475989568694 testicle 0.187629132304893 varWeightedLogRatios=0.274781841289593 cont.varWeightedLogRatios=0.0556121778180415 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.7961465068287 0.092695386888841 40.9529172296458 1.87118362431169e-166 *** df.mm.trans1 0.00323904838373709 0.079328751428234 0.0408306991528455 0.967446212645843 df.mm.trans2 0.0284216350168624 0.0737964047152072 0.385135768152206 0.700291049090969 df.mm.exp2 0.0374073339754320 0.098395206286943 0.380174353884108 0.703968013723066 df.mm.exp3 0.00304065439551750 0.098395206286943 0.0309024647669345 0.975358935628953 df.mm.exp4 0.163263760938277 0.098395206286943 1.65926539614300 0.0976527856160227 . df.mm.exp5 0.056553959131278 0.0983952062869429 0.57476335753953 0.565694779285272 df.mm.exp6 0.0695112109988195 0.098395206286943 0.706449161721445 0.480218713795609 df.mm.exp7 0.0756403150913836 0.098395206286943 0.768739839528352 0.442389537795894 df.mm.exp8 0.045029964496575 0.098395206286943 0.457643885264668 0.64739550479854 df.mm.trans1:exp2 -0.183602469127972 0.0880073479670876 -2.08621749625536 0.0374355372704004 * df.mm.trans2:exp2 0.126426324335668 0.0762165990591946 1.65877677430185 0.0977512919226233 . df.mm.trans1:exp3 -0.0318368997067037 0.0880073479670876 -0.361752744993632 0.717680713303728 df.mm.trans2:exp3 0.0364448933045351 0.0762165990591946 0.478175276178745 0.632722258481687 df.mm.trans1:exp4 -0.0271365508823617 0.0880073479670876 -0.308344149769291 0.757941398050965 df.mm.trans2:exp4 -0.121957673980674 0.0762165990591946 -1.60014584074991 0.110160997735972 df.mm.trans1:exp5 -0.0123271280449319 0.0880073479670876 -0.140069304776027 0.888658345904525 df.mm.trans2:exp5 -0.040581746501959 0.0762165990591946 -0.532452864637015 0.594635105746707 df.mm.trans1:exp6 0.00536914296557407 0.0880073479670876 0.061007894108819 0.951375877007232 df.mm.trans2:exp6 -0.0670838656033148 0.0762165990591946 -0.88017395726636 0.379163249866524 df.mm.trans1:exp7 0.0353767521502058 0.0880073479670876 0.401974982400740 0.687864183012658 df.mm.trans2:exp7 -0.0310376293437594 0.0762165990591946 -0.407229261432324 0.68400372154532 df.mm.trans1:exp8 -0.0494243087543634 0.0880073479670876 -0.561592979404933 0.574630306209043 df.mm.trans2:exp8 0.028104358376403 0.0762165990591946 0.368743275393007 0.712466061978671 df.mm.trans1:probe2 0.0353045574902134 0.0538932740337328 0.655082811783074 0.512698113138921 df.mm.trans1:probe3 0.0796567640806985 0.0538932740337328 1.47804648184558 0.139988228331321 df.mm.trans1:probe4 0.195193313093842 0.0538932740337328 3.62184923060467 0.000320569826557494 *** df.mm.trans1:probe5 0.146207327692896 0.0538932740337328 2.71290490908720 0.00688609229705138 ** df.mm.trans1:probe6 0.317422858334458 0.0538932740337328 5.88984180355748 6.86504374882422e-09 *** df.mm.trans1:probe7 0.950965892606273 0.0538932740337328 17.6453538898202 3.52433854854984e-55 *** df.mm.trans1:probe8 0.35656649799391 0.0538932740337328 6.61615951873194 9.01884306165906e-11 *** df.mm.trans1:probe9 0.505233117915522 0.0538932740337328 9.37469706515303 1.98795603242181e-19 *** df.mm.trans1:probe10 0.453444885089089 0.0538932740337328 8.4137565070786 3.69588119001274e-16 *** df.mm.trans1:probe11 0.294885604311474 0.0538932740337328 5.47165874774837 6.88013598182502e-08 *** df.mm.trans1:probe12 1.01072343593402 0.0538932740337328 18.7541665273740 1.36313716450561e-60 *** df.mm.trans2:probe2 -0.0103517468857704 0.0538932740337328 -0.192078641933891 0.847754036164924 df.mm.trans2:probe3 -0.0672505071642487 0.0538932740337328 -1.24784601362603 0.212637443583101 df.mm.trans2:probe4 -0.0975413066672124 0.0538932740337328 -1.80989758770565 0.0708768937521099 . df.mm.trans2:probe5 -0.105496949434983 0.0538932740337328 -1.95751605977678 0.0508102247540976 . df.mm.trans2:probe6 -0.0670514000126646 0.0538932740337328 -1.24415154237421 0.213992701747013 df.mm.trans3:probe2 0.672967143939057 0.0538932740337328 12.4870339760363 1.48406001665182e-31 *** df.mm.trans3:probe3 0.239312071057590 0.0538932740337328 4.44048121678042 1.09251584155502e-05 *** df.mm.trans3:probe4 0.233982038721704 0.0538932740337328 4.34158144809036 1.69449843528741e-05 *** df.mm.trans3:probe5 0.07758709559107 0.0538932740337328 1.43964338745697 0.150557573030466 df.mm.trans3:probe6 0.764225062317376 0.0538932740337328 14.1803420931346 6.1606572453196e-39 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05930387529886 0.181577541082028 22.3557596997366 1.63135356998094e-78 *** df.mm.trans1 0.167189986572139 0.155394136697650 1.07590923393358 0.282456447038488 df.mm.trans2 0.0181723578752768 0.144557028765092 0.125710648804267 0.900008553041282 df.mm.exp2 -0.197152826840106 0.192742705020122 -1.02288087541120 0.306829942580139 df.mm.exp3 0.195908719697296 0.192742705020122 1.01642611935348 0.309889403542129 df.mm.exp4 -0.156400337467992 0.192742705020122 -0.811446209866483 0.417473045056971 df.mm.exp5 0.126919363178953 0.192742705020122 0.658491138046977 0.510507994145981 df.mm.exp6 0.0126241711096821 0.192742705020122 0.0654975300277339 0.947802523907527 df.mm.exp7 -0.0709843165551267 0.192742705020122 -0.368285360256389 0.712807238884391 df.mm.exp8 0.0826485952537438 0.192742705020122 0.428802715231766 0.66824070335927 df.mm.trans1:exp2 0.0897569217636165 0.172394316236873 0.520648961769068 0.602828359627736 df.mm.trans2:exp2 0.0203741957688198 0.149297857329180 0.13646676605611 0.891504017920225 df.mm.trans1:exp3 -0.184304616001264 0.172394316236873 -1.06908754316485 0.285515970545007 df.mm.trans2:exp3 -0.303668032830628 0.149297857329180 -2.03397448739725 0.0424507025635365 * df.mm.trans1:exp4 0.0667260580824513 0.172394316236873 0.387054860850333 0.698870667522619 df.mm.trans2:exp4 -0.00534000500230257 0.149297857329180 -0.0357674590769820 0.971481221874314 df.mm.trans1:exp5 -0.147112824145137 0.172394316236873 -0.853350779517586 0.39384957638478 df.mm.trans2:exp5 -0.128132464159441 0.149297857329180 -0.858233778110608 0.391150606941335 df.mm.trans1:exp6 -0.130259854734175 0.172394316236873 -0.755592513590736 0.450228579950062 df.mm.trans2:exp6 -0.106864358805110 0.149297857329180 -0.715779587978209 0.474442090498538 df.mm.trans1:exp7 -0.0125016890962997 0.172394316236873 -0.0725179888130543 0.94221699655359 df.mm.trans2:exp7 -0.130505078847124 0.149297857329180 -0.87412559819515 0.382445020537855 df.mm.trans1:exp8 -0.100441830108722 0.172394316236873 -0.582628431732705 0.560390779740195 df.mm.trans2:exp8 -0.115679330350129 0.149297857329180 -0.774822441658169 0.438789468485779 df.mm.trans1:probe2 -0.0960987791794856 0.105569527334085 -0.910288997272589 0.363083133668037 df.mm.trans1:probe3 -0.146969439111407 0.105569527334085 -1.39215778286387 0.164457381728902 df.mm.trans1:probe4 -0.0496790921741321 0.105569527334085 -0.470581742939112 0.638132682515429 df.mm.trans1:probe5 -0.217903785928257 0.105569527334085 -2.06407844603376 0.0394954700564638 * df.mm.trans1:probe6 0.0154929386936695 0.105569527334085 0.146755783462406 0.883380486260486 df.mm.trans1:probe7 -0.106816151566852 0.105569527334085 -1.01180856127944 0.312090414435624 df.mm.trans1:probe8 -0.08057140582574 0.105569527334085 -0.763207033889276 0.445678869743619 df.mm.trans1:probe9 -0.091179646936161 0.105569527334085 -0.86369285947084 0.388146590867517 df.mm.trans1:probe10 -0.202838419203414 0.105569527334085 -1.92137280828692 0.055219699327198 . df.mm.trans1:probe11 -0.0796458415777796 0.105569527334085 -0.754439690970032 0.450919689414107 df.mm.trans1:probe12 0.00958957561332527 0.105569527334085 0.0908365875597615 0.92765669225926 df.mm.trans2:probe2 0.224421920844298 0.105569527334085 2.12582102536172 0.0339789862809329 * df.mm.trans2:probe3 0.208003348624787 0.105569527334085 1.97029724275017 0.0493234371293958 * df.mm.trans2:probe4 0.0667392931173728 0.105569527334085 0.632183308978638 0.52753919800792 df.mm.trans2:probe5 0.113491857209534 0.105569527334085 1.07504371834855 0.282843390819454 df.mm.trans2:probe6 0.115202141309998 0.105569527334085 1.09124426545388 0.275660313302764 df.mm.trans3:probe2 -0.0291629829373824 0.105569527334085 -0.276244326121621 0.782467896338197 df.mm.trans3:probe3 -0.0844040609092531 0.105569527334085 -0.799511592413861 0.424351322983446 df.mm.trans3:probe4 0.082459770782817 0.105569527334085 0.781094439514396 0.435095042369636 df.mm.trans3:probe5 -0.0487245050011577 0.105569527334085 -0.461539482382678 0.644600610748808 df.mm.trans3:probe6 0.00927454824283928 0.105569527334085 0.0878525127188365 0.930027020683466