chr15.8529_chr15_27812563_27815389_-_2.R fitVsDatCorrelation=0.893325021051294 cont.fitVsDatCorrelation=0.245104725229132 fstatistic=10281.9383509900,52,692 cont.fstatistic=2198.93048122141,52,692 residuals=-0.710691475054317,-0.0838911452822703,-0.0060151023168109,0.0843541687178502,0.690416553443976 cont.residuals=-0.60197246453978,-0.240353175830651,-0.0661433050453699,0.154141493984353,1.12509570048641 predictedValues: Include Exclude Both chr15.8529_chr15_27812563_27815389_-_2.R.tl.Lung 71.9369940237227 45.5756919567523 84.1326045049103 chr15.8529_chr15_27812563_27815389_-_2.R.tl.cerebhem 74.6081223633527 53.4555122132406 123.133257873528 chr15.8529_chr15_27812563_27815389_-_2.R.tl.cortex 84.937971046728 46.7353783725449 115.681206180156 chr15.8529_chr15_27812563_27815389_-_2.R.tl.heart 74.1245188764848 47.8609023774879 105.100317967305 chr15.8529_chr15_27812563_27815389_-_2.R.tl.kidney 69.0146024826055 46.881848353012 83.0181137443121 chr15.8529_chr15_27812563_27815389_-_2.R.tl.liver 64.0731827720327 51.5273451137909 79.0433545925946 chr15.8529_chr15_27812563_27815389_-_2.R.tl.stomach 67.8600417209075 47.2978634935954 78.7549370091433 chr15.8529_chr15_27812563_27815389_-_2.R.tl.testicle 66.5450786571244 47.8906592867784 92.5219720768198 diffExp=26.3613020669704,21.1526101501122,38.2025926741831,26.2636164989969,22.1327541295934,12.5458376582418,20.5621782273122,18.6544193703460 diffExpScore=0.994648838330495 diffExp1.5=1,0,1,1,0,0,0,0 diffExp1.5Score=0.75 diffExp1.4=1,0,1,1,1,0,1,0 diffExp1.4Score=0.833333333333333 diffExp1.3=1,1,1,1,1,0,1,1 diffExp1.3Score=0.875 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 65.92741616289 57.3956625418989 73.6200213704685 cerebhem 70.1921872358541 62.0074506785034 60.2077147958876 cortex 65.4082566309759 55.2964149197952 82.1895849345514 heart 64.4709061184646 67.1611918594001 62.4585553828487 kidney 67.0359652370169 58.7543445633974 70.8808434579996 liver 64.3689526116326 65.2900694761614 65.3055068840028 stomach 65.228504442032 68.4444055194164 72.5100821593162 testicle 63.1462550574614 68.2003811849596 61.1329977476674 cont.diffExp=8.53175362099118,8.18473655735074,10.1118417111807,-2.69028574093556,8.28162067361949,-0.921116864528827,-3.21590107738446,-5.05412612749821 cont.diffExpScore=1.93950670674167 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=-0.182320321778895 cont.tran.correlation=-0.425541716811241 tran.covariance=-0.000908736123383637 cont.tran.covariance=-0.00112598549203593 tran.mean=60.020357069385 cont.tran.mean=64.2705227649912 weightedLogRatios: wLogRatio Lung 1.84738205563881 cerebhem 1.38212464974564 cortex 2.47523646170023 heart 1.78785755438497 kidney 1.56259442986475 liver 0.882781757084884 stomach 1.45726954594611 testicle 1.32682369282591 cont.weightedLogRatios: wLogRatio Lung 0.570870527183442 cerebhem 0.519393005190432 cortex 0.68799788006093 heart -0.171156831486226 kidney 0.545824986231859 liver -0.0592742514321996 stomach -0.20222037589358 testicle -0.322150106104043 varWeightedLogRatios=0.217148029078262 cont.varWeightedLogRatios=0.176654037023924 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.12118329860203 0.083470390918628 49.3729962594714 5.87398339852016e-229 *** df.mm.trans1 0.438841488402250 0.0749686176731365 5.8536692021667 7.42550314824738e-09 *** df.mm.trans2 -0.257462123115872 0.0689438256223996 -3.73437535256562 0.000203667250949793 *** df.mm.exp2 -0.184939002544181 0.0944359671149661 -1.95835345572346 0.0505898992908034 . df.mm.exp3 -0.127186479778932 0.0944359671149662 -1.34680126295626 0.178485152379816 df.mm.exp4 -0.143640976823585 0.0944359671149662 -1.52104098906211 0.128706332928812 df.mm.exp5 0.000118885352753584 0.0944359671149662 0.00125889908670977 0.998995906947801 df.mm.exp6 0.0693710795021257 0.0944359671149662 0.734583248537853 0.462842187273335 df.mm.exp7 0.0448005544692021 0.0944359671149662 0.474401394276631 0.635363360675364 df.mm.exp8 -0.123417055759391 0.0944359671149662 -1.30688613173351 0.191685508276109 df.mm.trans1:exp2 0.221397729788785 0.0904155548630547 2.44866859606313 0.014585640106965 * df.mm.trans2:exp2 0.344414259839179 0.0786966392624718 4.376479898849 1.39227771313692e-05 *** df.mm.trans1:exp3 0.293317064575371 0.0904155548630547 3.24409959126651 0.00123487513360212 ** df.mm.trans2:exp3 0.152313421245204 0.0786966392624718 1.93545013704083 0.0533421441829292 . df.mm.trans1:exp4 0.173596690511759 0.0904155548630547 1.91998700638062 0.0552703243957086 . df.mm.trans2:exp4 0.192565410385864 0.0786966392624718 2.44693308622257 0.0146554394735975 * df.mm.trans1:exp5 -0.0415914258668695 0.0904155548630548 -0.460002993178162 0.645658566806883 df.mm.trans2:exp5 0.0281371832005469 0.0786966392624718 0.357539832250051 0.720796730196934 df.mm.trans1:exp6 -0.185135821509225 0.0904155548630547 -2.04761030101110 0.0409752079025887 * df.mm.trans2:exp6 0.0533670570709919 0.0786966392624718 0.678136418163934 0.497911890202796 df.mm.trans1:exp7 -0.103143833208321 0.0904155548630547 -1.14077531642144 0.254358077620143 df.mm.trans2:exp7 -0.00770993252363569 0.0786966392624718 -0.0979702894036078 0.921984255092862 df.mm.trans1:exp8 0.0455059954077 0.0904155548630547 0.503298303888355 0.614914719022213 df.mm.trans2:exp8 0.172963033445468 0.0786966392624718 2.19784523286433 0.0282906606547244 * df.mm.trans1:probe2 -0.392914500463934 0.0452077774315274 -8.69130319576207 2.58012648715547e-17 *** df.mm.trans1:probe3 0.262899441589185 0.0452077774315274 5.81535869546736 9.24315769484022e-09 *** df.mm.trans1:probe4 -0.494538091422879 0.0452077774315274 -10.9392259367742 8.43154318322389e-26 *** df.mm.trans1:probe5 -0.34624877200604 0.0452077774315274 -7.65905319124515 6.32849628631704e-14 *** df.mm.trans1:probe6 -0.519781712515167 0.0452077774315274 -11.4976170483594 4.03851791222883e-28 *** df.mm.trans1:probe7 -0.245575833117955 0.0452077774315274 -5.43215895738093 7.7173669127003e-08 *** df.mm.trans1:probe8 -0.127714444865396 0.0452077774315274 -2.8250547167207 0.00486342581163339 ** df.mm.trans1:probe9 0.277300920188626 0.0452077774315274 6.13392066461642 1.44185787588029e-09 *** df.mm.trans1:probe10 -0.126297722428493 0.0452077774315274 -2.7937166922171 0.00535472470345522 ** df.mm.trans1:probe11 -0.295917224281785 0.0452077774315274 -6.54571494318622 1.15342967545424e-10 *** df.mm.trans1:probe12 -0.411134185268757 0.0452077774315274 -9.09432422090356 9.94565733369583e-19 *** df.mm.trans1:probe13 -0.523172588285515 0.0452077774315274 -11.5726235176663 1.94418781842843e-28 *** df.mm.trans1:probe14 -0.284429903654419 0.0452077774315274 -6.29161440385389 5.57167795983325e-10 *** df.mm.trans1:probe15 -0.508852891064293 0.0452077774315274 -11.2558705597729 4.16943153397376e-27 *** df.mm.trans1:probe16 -0.522516769553962 0.0452077774315274 -11.5581167498309 2.24000223562694e-28 *** df.mm.trans1:probe17 -0.449704405722484 0.0452077774315274 -9.9475008786622 7.00546799762675e-22 *** df.mm.trans1:probe18 -0.555809222305678 0.0452077774315274 -12.2945487233368 1.46126245145094e-31 *** df.mm.trans1:probe19 -0.355861311906219 0.0452077774315274 -7.87168341653632 1.35246654012876e-14 *** df.mm.trans1:probe20 -0.552661934563747 0.0452077774315274 -12.2249304425731 2.95971932860052e-31 *** df.mm.trans1:probe21 -0.437607924005369 0.0452077774315274 -9.67992564262154 7.18902803774171e-21 *** df.mm.trans1:probe22 -0.495314276863946 0.0452077774315274 -10.9563952267762 7.17391517418491e-26 *** df.mm.trans2:probe2 -0.114259857123614 0.0452077774315274 -2.52743805635379 0.0117113627842185 * df.mm.trans2:probe3 -0.0800327258547594 0.0452077774315274 -1.77033091210862 0.0771122637287269 . df.mm.trans2:probe4 -0.0305257062970586 0.0452077774315274 -0.675231299377491 0.499754376512491 df.mm.trans2:probe5 -0.0927177205717575 0.0452077774315274 -2.05092410729967 0.0406503869771613 * df.mm.trans2:probe6 -0.0815840401562009 0.0452077774315274 -1.80464612045504 0.071564697802907 . df.mm.trans3:probe2 -0.108969301554692 0.0452077774315274 -2.41041050336391 0.0161943553884738 * df.mm.trans3:probe3 0.498463847438525 0.0452077774315274 11.0260640039982 3.71813897642709e-26 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.86157522780728 0.180067307817992 21.4451766653305 1.28215214384612e-78 *** df.mm.trans1 0.269562384936030 0.161726775287276 1.66677647815092 0.0960113579707793 . df.mm.trans2 0.162580057992827 0.148729734386904 1.09312410637333 0.274719698203392 df.mm.exp2 0.341084966590192 0.203722903084972 1.67425930725092 0.0945315074552167 . df.mm.exp3 -0.155278113144963 0.203722903084972 -0.762202534882382 0.446198840550523 df.mm.exp4 0.299200311305902 0.203722903084972 1.46866310451656 0.142378603481891 df.mm.exp5 0.0779880657166134 0.203722903084972 0.382814423590287 0.701974921813173 df.mm.exp6 0.224788900567231 0.203722903084972 1.10340515063970 0.270234818052533 df.mm.exp7 0.180586660606861 0.203722903084972 0.88643278626134 0.375692303824795 df.mm.exp8 0.31524581851338 0.203722903084972 1.54742453469697 0.122218092501704 df.mm.trans1:exp2 -0.278402336432810 0.195049829884369 -1.42733955009268 0.153933121747939 df.mm.trans2:exp2 -0.263799151529441 0.169769085904143 -1.55387036529365 0.120672543597972 df.mm.trans1:exp3 0.147372230075231 0.195049829884370 0.755561951336213 0.450168971688143 df.mm.trans2:exp3 0.118017454913435 0.169769085904143 0.695164577725717 0.487185525609699 df.mm.trans1:exp4 -0.321540638941013 0.195049829884370 -1.64850509806458 0.0997029584384615 . df.mm.trans2:exp4 -0.142073467685781 0.169769085904143 -0.836863006766734 0.402958478075754 df.mm.trans1:exp5 -0.0613131775102927 0.195049829884370 -0.31434622397077 0.753352841227522 df.mm.trans2:exp5 -0.054591700409398 0.169769085904143 -0.321564436296853 0.747879793678369 df.mm.trans1:exp6 -0.248711867753758 0.195049829884370 -1.27511963430679 0.202694833137606 df.mm.trans2:exp6 -0.0959176862509914 0.169769085904143 -0.564989118838451 0.572264274140886 df.mm.trans1:exp7 -0.19124448426221 0.195049829884370 -0.980490392509362 0.327186769525276 df.mm.trans2:exp7 -0.00453357811770908 0.169769085904143 -0.0267043796199084 0.97870321891642 df.mm.trans1:exp8 -0.358346655555967 0.195049829884370 -1.83720568107342 0.0666081773929369 . df.mm.trans2:exp8 -0.142764399455463 0.169769085904143 -0.840932839422085 0.400676038718889 df.mm.trans1:probe2 0.145354719421387 0.0975249149421848 1.49043677205519 0.136565081555886 df.mm.trans1:probe3 0.0429083795060106 0.0975249149421848 0.439973513757461 0.660093670728225 df.mm.trans1:probe4 0.134149530922634 0.0975249149421848 1.37554112200110 0.169408828041035 df.mm.trans1:probe5 -0.088963265712999 0.0975249149421848 -0.912210646538259 0.361975516073104 df.mm.trans1:probe6 0.0243025838480427 0.0975249149421848 0.249193591837018 0.803284981052154 df.mm.trans1:probe7 0.0750937840578574 0.0975249149421848 0.769995893894138 0.441565098402129 df.mm.trans1:probe8 0.0744918094395675 0.0975249149421848 0.763823372557958 0.445232850796896 df.mm.trans1:probe9 0.104510104492710 0.0975249149421848 1.07162466693425 0.284262240697626 df.mm.trans1:probe10 0.0160788071876825 0.0975249149421848 0.164868712751141 0.869095466824043 df.mm.trans1:probe11 0.131094226841409 0.0975249149421848 1.34421267549041 0.179320122850589 df.mm.trans1:probe12 0.0193828168814937 0.0975249149421848 0.198747334391261 0.842518772059235 df.mm.trans1:probe13 -0.00543188926151958 0.0975249149421848 -0.0556974519253848 0.955598906314128 df.mm.trans1:probe14 0.0955442004214408 0.0975249149421848 0.979690169205292 0.327581454998437 df.mm.trans1:probe15 -0.0178712855525680 0.0975249149421848 -0.183248409528606 0.854656788808291 df.mm.trans1:probe16 0.0816042102632867 0.0975249149421848 0.836752437176323 0.403020596350165 df.mm.trans1:probe17 0.108498918838547 0.0975249149421848 1.11252513168423 0.266298758508203 df.mm.trans1:probe18 0.0628061647418118 0.0975249149421848 0.64400122552319 0.519788024962039 df.mm.trans1:probe19 0.135124629047793 0.0975249149421848 1.38553957343001 0.166333934606175 df.mm.trans1:probe20 0.18983915398134 0.0975249149421848 1.94657082340324 0.0519905262483401 . df.mm.trans1:probe21 0.131165608958288 0.0975249149421848 1.34494461272842 0.179083735807246 df.mm.trans1:probe22 -0.024263983299134 0.0975249149421848 -0.248797789913668 0.803591021852925 df.mm.trans2:probe2 0.0726592192467038 0.0975249149421848 0.745032377518893 0.456505101483791 df.mm.trans2:probe3 0.056074990620884 0.0975249149421848 0.574981179467079 0.565490913889949 df.mm.trans2:probe4 0.0243431810091212 0.0975249149421848 0.249609866602318 0.802963142911675 df.mm.trans2:probe5 0.0945654154905024 0.0975249149421848 0.969653914044048 0.332557812712583 df.mm.trans2:probe6 -0.0153217635847248 0.0975249149421848 -0.157106146606822 0.875207019955151 df.mm.trans3:probe2 0.0307411446949388 0.0975249149421848 0.315213242822749 0.752694780718878 df.mm.trans3:probe3 -0.0151431022086148 0.0975249149421848 -0.155274190370656 0.876650441720773