chr18.11211_chr18_44233357_44241311_+_2.R fitVsDatCorrelation=0.880329955731673 cont.fitVsDatCorrelation=0.226517112255008 fstatistic=10257.2157408079,59,853 cont.fstatistic=2421.87395763569,59,853 residuals=-0.491501013809616,-0.089982476024261,-0.00746485680695021,0.0843297311615045,0.842193806952461 cont.residuals=-0.552864527639143,-0.209433364938112,-0.0705899850418651,0.120992253245538,1.52865572053761 predictedValues: Include Exclude Both chr18.11211_chr18_44233357_44241311_+_2.R.tl.Lung 57.3550260233999 60.5935962258955 50.2043180581781 chr18.11211_chr18_44233357_44241311_+_2.R.tl.cerebhem 54.9145339625383 74.9751267398345 52.632008736953 chr18.11211_chr18_44233357_44241311_+_2.R.tl.cortex 57.5547713188299 54.6667352544408 45.9439345650172 chr18.11211_chr18_44233357_44241311_+_2.R.tl.heart 61.8373947415715 57.5030323269485 49.541954724464 chr18.11211_chr18_44233357_44241311_+_2.R.tl.kidney 58.847049793765 54.781682939191 49.1527141410498 chr18.11211_chr18_44233357_44241311_+_2.R.tl.liver 59.8892238528166 54.5077026482115 51.080772973113 chr18.11211_chr18_44233357_44241311_+_2.R.tl.stomach 66.9071864906415 57.2508275657093 47.7731166290939 chr18.11211_chr18_44233357_44241311_+_2.R.tl.testicle 59.4238944717654 56.6070498016751 50.547277483559 diffExp=-3.23857020249562,-20.0605927772962,2.88803606438902,4.33436241462301,4.06536685457394,5.38152120460509,9.6563589249323,2.81684467009028 diffExpScore=7.66318077994903 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,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 63.7434381154385 65.2202103699161 62.5277127482796 cerebhem 59.3003234660741 65.3412298236314 51.8224392720131 cortex 60.2858133370454 56.9801756856907 54.3950680782306 heart 60.9749700429792 63.2951715571657 59.5772268544957 kidney 59.0066032464175 59.6203484637386 54.809532726009 liver 66.7861191038603 51.7252844806934 53.5661639419056 stomach 61.5967716790082 55.0151619870305 60.8900989697058 testicle 59.7357595150327 56.8206980617951 61.7432988106315 cont.diffExp=-1.47677225447767,-6.04090635755738,3.30563765135471,-2.32020151418651,-0.613745217321068,15.0608346231668,6.58160969197776,2.91506145323758 cont.diffExpScore=2.08102170634260 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=0,0,0,0,0,1,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.486719416952345 cont.tran.correlation=-0.391582970882495 tran.covariance=-0.00311811788841849 cont.tran.covariance=-0.00147361997800832 tran.mean=59.2259271348271 cont.tran.mean=60.3405049334698 weightedLogRatios: wLogRatio Lung -0.223929347678427 cerebhem -1.29579086840893 cortex 0.207316390783923 heart 0.297089949891774 kidney 0.289145763761831 liver 0.380894682707574 stomach 0.642998506814167 testicle 0.197185433054565 cont.weightedLogRatios: wLogRatio Lung -0.0954217965186554 cerebhem -0.400753521367071 cortex 0.229571880601499 heart -0.154205048947142 kidney -0.0422473223979377 liver 1.04103344224589 stomach 0.459246940513944 testicle 0.203368409834046 varWeightedLogRatios=0.358694143811474 cont.varWeightedLogRatios=0.198801315321980 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.04209847447102 0.0784981592360934 51.4929077803453 5.78700442195151e-264 *** df.mm.trans1 -0.258188021694857 0.0694986949950433 -3.71500532079446 0.000216428539967763 *** df.mm.trans2 0.0631002304880978 0.06221191697529 1.01427883203086 0.310737327885354 df.mm.exp2 0.122261345941424 0.0826548518403868 1.47917930066006 0.13946158823119 df.mm.exp3 -0.0107780584380399 0.0826548518403867 -0.130398375873364 0.896281996093789 df.mm.exp4 0.0361774241109732 0.0826548518403868 0.437692685976073 0.66171987402239 df.mm.exp5 -0.053983132025416 0.0826548518403868 -0.65311510242208 0.513858068523547 df.mm.exp6 -0.0799182043398645 0.0826548518403868 -0.966890661109563 0.333872746702138 df.mm.exp7 0.146936760693473 0.0826548518403868 1.77771488813772 0.0758068988100182 . df.mm.exp8 -0.0394277965707677 0.0826548518403868 -0.47701732799553 0.633471966752032 df.mm.trans1:exp2 -0.165743774719229 0.0787721501537369 -2.10409103211926 0.0356626521363094 * df.mm.trans2:exp2 0.0907058544686616 0.0633351917550259 1.43215567767605 0.152465602859399 df.mm.trans1:exp3 0.0142546200756840 0.0787721501537369 0.180960149594289 0.856441842018734 df.mm.trans2:exp3 -0.0921557624037451 0.0633351917550258 -1.45504828911223 0.146023784587538 df.mm.trans1:exp4 0.0390703728052914 0.0787721501537369 0.495992209544097 0.620027677202393 df.mm.trans2:exp4 -0.0885289562288185 0.0633351917550258 -1.39778460877232 0.162541142600791 df.mm.trans1:exp5 0.0796643560698636 0.0787721501537369 1.01132641313441 0.312147008301016 df.mm.trans2:exp5 -0.0468501974815405 0.0633351917550258 -0.739718254311952 0.459674495010122 df.mm.trans1:exp6 0.123154313528533 0.0787721501537369 1.56342455154742 0.118323667207505 df.mm.trans2:exp6 -0.0259289856342907 0.0633351917550259 -0.409393023306559 0.682354036481725 df.mm.trans1:exp7 0.00710914459804933 0.0787721501537369 0.0902494674091624 0.928110162196809 df.mm.trans2:exp7 -0.203683877718861 0.0633351917550258 -3.21596685941507 0.00134901379222039 ** df.mm.trans1:exp8 0.0748637284292256 0.0787721501537369 0.950383203747983 0.342186768630571 df.mm.trans2:exp8 -0.0286278858279793 0.0633351917550259 -0.452005986477614 0.651379583637306 df.mm.trans1:probe2 0.379605813684316 0.0459930267930128 8.25355146537093 5.81281066937643e-16 *** df.mm.trans1:probe3 0.567236549020973 0.0459930267930128 12.333098918968 2.81898813797526e-32 *** df.mm.trans1:probe4 0.181458764381220 0.0459930267930128 3.9453538293502 8.62333902196266e-05 *** df.mm.trans1:probe5 0.0157826337228921 0.0459930267930128 0.343152752131759 0.731568099431144 df.mm.trans1:probe6 0.428681394608038 0.0459930267930128 9.32057367168457 9.64536916070866e-20 *** df.mm.trans1:probe7 0.158174121671343 0.0459930267930128 3.43908919896031 0.000611968946275817 *** df.mm.trans1:probe8 0.200476211588638 0.0459930267930128 4.35883927558978 1.46698631958423e-05 *** df.mm.trans1:probe9 0.262760900873248 0.0459930267930128 5.71305954826104 1.53272319834252e-08 *** df.mm.trans1:probe10 0.284145219101522 0.0459930267930128 6.17800651347193 1.00421159479184e-09 *** df.mm.trans1:probe11 0.58231968521212 0.0459930267930128 12.6610428974982 8.63367584115305e-34 *** df.mm.trans1:probe12 0.879777375209657 0.0459930267930128 19.1284948296404 3.81018696756744e-68 *** df.mm.trans1:probe13 0.919660758027468 0.0459930267930128 19.9956563451741 3.03797581513174e-73 *** df.mm.trans1:probe14 0.783727205346293 0.0459930267930128 17.0401310805958 2.92556732286844e-56 *** df.mm.trans1:probe15 0.846330856745988 0.0459930267930128 18.4012863635789 6.12704798626105e-64 *** df.mm.trans1:probe16 0.933549636644276 0.0459930267930128 20.2976342662905 4.87449029256135e-75 *** df.mm.trans1:probe17 0.0659222017149868 0.0459930267930128 1.43330861897094 0.152136076052170 df.mm.trans1:probe18 0.105917207616701 0.0459930267930128 2.30289709119104 0.0215244688720772 * df.mm.trans1:probe19 0.00759027468620604 0.0459930267930128 0.165030988727168 0.868958691946029 df.mm.trans1:probe20 0.0999448820939352 0.0459930267930128 2.17304424307031 0.0300515574174655 * df.mm.trans1:probe21 -0.0515692265342032 0.0459930267930128 -1.12124011246934 0.262501219092862 df.mm.trans1:probe22 0.0418839514767852 0.0459930267930128 0.910658732361318 0.362732474354493 df.mm.trans1:probe23 -0.00386475421816557 0.0459930267930128 -0.0840291341458897 0.933052984004325 df.mm.trans1:probe24 0.179517310100996 0.0459930267930128 3.90314190255181 0.000102437767909618 *** df.mm.trans1:probe25 0.00928555101884495 0.0459930267930128 0.201890409618694 0.840050536904436 df.mm.trans1:probe26 0.442872663234297 0.0459930267930128 9.62912628532588 6.65409152801598e-21 *** df.mm.trans1:probe27 0.170013602796057 0.0459930267930128 3.69650824593882 0.000232545026422453 *** df.mm.trans2:probe2 0.0963995909291472 0.0459930267930128 2.09596101085028 0.0363798066347835 * df.mm.trans2:probe3 -0.0398688213606472 0.0459930267930128 -0.866844914123025 0.3862707588632 df.mm.trans2:probe4 0.0154156909373322 0.0459930267930128 0.335174525623396 0.737575846580542 df.mm.trans2:probe5 0.0166232128123626 0.0459930267930128 0.361428981118675 0.717868289436369 df.mm.trans2:probe6 -0.0996740667020122 0.0459930267930128 -2.16715605934321 0.0304990588238679 * df.mm.trans3:probe2 0.207539141729582 0.0459930267930128 4.51240451435371 7.30828486126387e-06 *** df.mm.trans3:probe3 -0.00833032157714745 0.0459930267930128 -0.181121403786649 0.856315310588154 df.mm.trans3:probe4 0.0536884904728427 0.0459930267930128 1.16731805267922 0.243408153833066 df.mm.trans3:probe5 0.0925544735214698 0.0459930267930128 2.01235882861118 0.0444960253163053 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27162412735724 0.16118026867264 26.5021529157082 2.19654959876934e-113 *** df.mm.trans1 -0.0584167169426321 0.142701668939879 -0.409362534976681 0.682376398736216 df.mm.trans2 -0.0934589154219003 0.127739727788504 -0.731635467210625 0.464592040477863 df.mm.exp2 0.117388961913214 0.169715205507713 0.691682053838594 0.489325233697956 df.mm.exp3 -0.0514990497393355 0.169715205507713 -0.303443934709757 0.76162556257489 df.mm.exp4 -0.0260266816478486 0.169715205507713 -0.153355037163513 0.878154570979077 df.mm.exp5 -0.0352436243040492 0.169715205507713 -0.207663327505722 0.835541421299536 df.mm.exp6 -0.0305013950902798 0.169715205507713 -0.179721050916051 0.857414249469537 df.mm.exp7 -0.177878082590143 0.169715205507713 -1.04809749991469 0.294890563502789 df.mm.exp8 -0.190179739236716 0.169715205507713 -1.12058161593584 0.262781384598925 df.mm.trans1:exp2 -0.189640448402734 0.161742854217950 -1.17248115423506 0.241331271858805 df.mm.trans2:exp2 -0.115535129905926 0.130046147869598 -0.88841639524592 0.374567258467497 df.mm.trans1:exp3 -0.00427038946794608 0.161742854217950 -0.0264023377638168 0.97894260487581 df.mm.trans2:exp3 -0.0835669338076968 0.130046147869598 -0.642594457249841 0.520659956164652 df.mm.trans1:exp4 -0.0183761127685320 0.161742854217950 -0.113613135228652 0.909571208271176 df.mm.trans2:exp4 -0.0039336667281489 0.130046147869598 -0.0302482372034067 0.975876154029638 df.mm.trans1:exp5 -0.0419732658401269 0.161742854217950 -0.259506152794655 0.795307352369292 df.mm.trans2:exp5 -0.0545288386164034 0.130046147869598 -0.419303758778626 0.675099721574669 df.mm.trans1:exp6 0.0771304089753231 0.161742854217950 0.476870581691289 0.63357642264571 df.mm.trans2:exp6 -0.201321277323827 0.130046147869598 -1.54807566869031 0.121974991590250 df.mm.trans1:exp7 0.143621296729782 0.161742854217950 0.88796069182909 0.374812204618767 df.mm.trans2:exp7 0.00771750642440041 0.130046147869598 0.0593443677558142 0.952691722808281 df.mm.trans1:exp8 0.125244319947532 0.161742854217950 0.774342214703127 0.438943016171691 df.mm.trans2:exp8 0.052311005182753 0.130046147869598 0.402249555559363 0.68760114982884 df.mm.trans1:probe2 -0.0611132761401075 0.0944374809257588 -0.647129461110374 0.517722263410126 df.mm.trans1:probe3 -0.0671580545246659 0.0944374809257588 -0.711137716363502 0.477193308423212 df.mm.trans1:probe4 -0.0676767400211802 0.0944374809257588 -0.71663008540416 0.473798535170208 df.mm.trans1:probe5 -0.150360405260594 0.0944374809257588 -1.59216874260760 0.111717279151161 df.mm.trans1:probe6 -0.0585912569286631 0.0944374809257588 -0.620423759235214 0.535144552836651 df.mm.trans1:probe7 -0.0562161420972326 0.0944374809257588 -0.595273630195902 0.551818466513024 df.mm.trans1:probe8 -0.084700157534861 0.0944374809257588 -0.896891326458054 0.370029962839253 df.mm.trans1:probe9 -0.0463888578663063 0.0944374809257587 -0.491212359876208 0.623402615128664 df.mm.trans1:probe10 -0.0351442283849422 0.0944374809257588 -0.372142797969914 0.709878965572018 df.mm.trans1:probe11 -0.0373538273627844 0.0944374809257588 -0.395540276981231 0.692543104815113 df.mm.trans1:probe12 -0.108013550739012 0.0944374809257588 -1.14375722096957 0.253045206751755 df.mm.trans1:probe13 -0.0118618927959419 0.0944374809257588 -0.125605773043301 0.900073589987643 df.mm.trans1:probe14 -0.0117412345694142 0.0944374809257587 -0.124328121147624 0.901084774049427 df.mm.trans1:probe15 -0.0320461507705943 0.0944374809257588 -0.339337204428263 0.734439244420161 df.mm.trans1:probe16 -0.120310753153857 0.0944374809257587 -1.27397249454841 0.203020159711178 df.mm.trans1:probe17 -0.0295173017938053 0.0944374809257588 -0.312559182058341 0.754691987760516 df.mm.trans1:probe18 -0.161171846199406 0.0944374809257588 -1.70665126408984 0.0882508530041604 . df.mm.trans1:probe19 -0.157776552790296 0.0944374809257588 -1.67069844773106 0.095148086387617 . df.mm.trans1:probe20 -0.00909591845975627 0.0944374809257587 -0.096316826439991 0.923291571418792 df.mm.trans1:probe21 0.0122527598349194 0.0944374809257588 0.129744670387299 0.896799027353475 df.mm.trans1:probe22 -0.148157768662952 0.0944374809257588 -1.56884498835187 0.117054921393750 df.mm.trans1:probe23 -0.0831934362167121 0.0944374809257587 -0.880936630257153 0.378600260546925 df.mm.trans1:probe24 -0.0629389975400424 0.0944374809257588 -0.666462054293055 0.505296021101217 df.mm.trans1:probe25 -0.127245275032152 0.0944374809257588 -1.34740225792538 0.178208500204278 df.mm.trans1:probe26 -0.055973109137613 0.0944374809257588 -0.592700150289013 0.553538930320747 df.mm.trans1:probe27 -0.095423248137627 0.0944374809257588 -1.01043830481505 0.312571874749772 df.mm.trans2:probe2 -0.00388021066172535 0.0944374809257587 -0.0410876129232603 0.967235664605833 df.mm.trans2:probe3 0.0280896353221382 0.0944374809257587 0.297441598894623 0.766201810560517 df.mm.trans2:probe4 0.0404191756879555 0.0944374809257588 0.427999299554917 0.66875966777196 df.mm.trans2:probe5 -0.062980983725023 0.0944374809257588 -0.66690664667914 0.505012116906453 df.mm.trans2:probe6 -0.00600159302317052 0.0944374809257588 -0.0635509647688361 0.949342666114322 df.mm.trans3:probe2 -0.0390421767410571 0.0944374809257588 -0.413418235623521 0.679404126782064 df.mm.trans3:probe3 -0.0129738929043246 0.0944374809257588 -0.137380760023913 0.890762258219909 df.mm.trans3:probe4 0.117056729733108 0.0944374809257587 1.23951558836191 0.215495529099873 df.mm.trans3:probe5 0.0142661771503918 0.0944374809257588 0.151064778629652 0.879960333059285