chr1.1088_chr1_93951917_93952296_+_1.R fitVsDatCorrelation=0.911311214778642 cont.fitVsDatCorrelation=0.357443177781128 fstatistic=8382.5948524843,36,324 cont.fstatistic=1621.84025983679,36,324 residuals=-0.485050682513856,-0.0884252010369235,0.00467649419488717,0.0864135303035593,0.49996465787673 cont.residuals=-0.669532857588539,-0.253788830016695,-0.0241802064718725,0.203523910919774,1.10192048339373 predictedValues: Include Exclude Both chr1.1088_chr1_93951917_93952296_+_1.R.tl.Lung 112.207749617581 56.9932053734102 127.986340790819 chr1.1088_chr1_93951917_93952296_+_1.R.tl.cerebhem 85.6413116436425 62.8183619537524 83.0669394334489 chr1.1088_chr1_93951917_93952296_+_1.R.tl.cortex 88.7806139822984 52.6181089402306 100.101397454113 chr1.1088_chr1_93951917_93952296_+_1.R.tl.heart 103.943606960266 54.1569311879484 129.380130149220 chr1.1088_chr1_93951917_93952296_+_1.R.tl.kidney 85.247057765403 59.8102002236689 98.6793382769738 chr1.1088_chr1_93951917_93952296_+_1.R.tl.liver 86.6633358458118 61.8566881852724 99.7288319921846 chr1.1088_chr1_93951917_93952296_+_1.R.tl.stomach 88.590816506749 55.5881737927603 103.753670895839 chr1.1088_chr1_93951917_93952296_+_1.R.tl.testicle 99.7239404330431 57.9772331944662 122.230152236425 diffExp=55.2145442441709,22.8229496898900,36.1625050420678,49.7866757723172,25.436857541734,24.8066476605394,33.0026427139886,41.7467072385769 diffExpScore=0.996551480718885 diffExp1.5=1,0,1,1,0,0,1,1 diffExp1.5Score=0.833333333333333 diffExp1.4=1,0,1,1,1,1,1,1 diffExp1.4Score=0.875 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 66.4604089151776 72.9900537864098 86.9549068341397 cerebhem 91.5590806188415 69.9304810173257 91.361581244304 cortex 70.2397882975477 75.7266890625188 73.8842644965527 heart 82.3467639507359 71.2753436420444 92.5432529178957 kidney 78.5990530110489 72.8283732684135 67.6468845483956 liver 73.7398951377534 81.2294398393372 88.9339851860623 stomach 81.1241421221713 67.9529681268282 89.6383211943699 testicle 87.3388434913576 68.6025854032346 57.9735059588716 cont.diffExp=-6.52964487123219,21.6285996015157,-5.4869007649711,11.0714203086915,5.77067974263534,-7.48954470158381,13.1711739953432,18.7362580881230 cont.diffExpScore=1.73280672305790 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=0,1,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,1,0,0,0,0,0,1 cont.diffExp1.2Score=0.666666666666667 tran.correlation=-0.391106824098434 cont.tran.correlation=-0.618387674415053 tran.covariance=-0.00256245923456045 cont.tran.covariance=-0.00392303974675612 tran.mean=75.788583475394 cont.tran.mean=75.7464943556716 weightedLogRatios: wLogRatio Lung 2.96821197919693 cerebhem 1.33117228900120 cortex 2.20992945889722 heart 2.81508771907904 kidney 1.51261199030143 liver 1.44778895770973 stomach 1.98121107673521 testicle 2.34906481413214 cont.weightedLogRatios: wLogRatio Lung -0.397683800382061 cerebhem 1.18093927065099 cortex -0.322639573832052 heart 0.626465737287086 kidney 0.329892669735804 liver -0.420686543813388 stomach 0.763119254247862 testicle 1.05014680744505 varWeightedLogRatios=0.387228074914507 cont.varWeightedLogRatios=0.433370307336547 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.78404644626783 0.084605825561996 44.7256015898696 1.10985922523005e-140 *** df.mm.trans1 1.11544714933411 0.0716602504467052 15.5657724105177 3.50351132182561e-41 *** df.mm.trans2 0.321187209326043 0.0716602504467052 4.4820832654627 1.02676188922230e-05 *** df.mm.exp2 0.259407844494719 0.0997955873836854 2.59939192999957 0.00976619272208025 ** df.mm.exp3 -0.0683155662042962 0.0997955873836855 -0.684554978785208 0.494114160808804 df.mm.exp4 -0.138380911697631 0.0997955873836855 -1.38664359142049 0.166503794364120 df.mm.exp5 0.0334936638935746 0.0997955873836854 0.335622694065631 0.737372627574477 df.mm.exp6 0.0730357435026504 0.0997955873836854 0.731853435782174 0.464787047949844 df.mm.exp7 -0.0513814339556753 0.0997955873836854 -0.514866792237301 0.606997163842064 df.mm.exp8 -0.0548101575558951 0.0997955873836855 -0.54922425923669 0.583229877339047 df.mm.trans1:exp2 -0.529592125486488 0.0864255138598614 -6.12772897532573 2.58519175169044e-09 *** df.mm.trans2:exp2 -0.162092482712875 0.0864255138598614 -1.87551656303377 0.0616208083031295 . df.mm.trans1:exp3 -0.165868179019856 0.0864255138598615 -1.91920385094627 0.0558367103303945 . df.mm.trans2:exp3 -0.0115561536252655 0.0864255138598615 -0.133712292923172 0.893713097830905 df.mm.trans1:exp4 0.0618773626278312 0.0864255138598615 0.71596175555479 0.474530644439675 df.mm.trans2:exp4 0.0873348199768668 0.0864255138598614 1.01052126943069 0.312999666704887 df.mm.trans1:exp5 -0.308292121720144 0.0864255138598614 -3.56714247855139 0.00041535709321947 *** df.mm.trans2:exp5 0.0147504980654344 0.0864255138598615 0.170672957633232 0.864587457668142 df.mm.trans1:exp6 -0.331356894820913 0.0864255138598614 -3.83401706304236 0.000151465820684896 *** df.mm.trans2:exp6 0.00885242831001728 0.0864255138598615 0.102428413956224 0.918479994904398 df.mm.trans1:exp7 -0.184942425349611 0.0864255138598615 -2.13990541785490 0.0331090052510770 * df.mm.trans2:exp7 0.0264198542650336 0.0864255138598614 0.305695078745765 0.76003313371292 df.mm.trans1:exp8 -0.0631361299758956 0.0864255138598614 -0.73052652111818 0.465596312016154 df.mm.trans2:exp8 0.0719285031579791 0.0864255138598615 0.832260057771954 0.405875174225316 df.mm.trans1:probe2 -0.402781922873869 0.0432127569299307 -9.32090316586323 1.84284720455424e-18 *** df.mm.trans1:probe3 -0.208046406150834 0.0432127569299307 -4.81446732242009 2.2683616805265e-06 *** df.mm.trans1:probe4 -0.383559884963582 0.0432127569299307 -8.87607994059537 4.80602111222256e-17 *** df.mm.trans1:probe5 -0.279520189025537 0.0432127569299307 -6.4684646128637 3.65030558553879e-10 *** df.mm.trans1:probe6 -0.338365413994457 0.0432127569299307 -7.83022047269779 7.00301074002968e-14 *** df.mm.trans2:probe2 -0.117837298624393 0.0432127569299307 -2.72690999131265 0.00674115244035418 ** df.mm.trans2:probe3 -0.141098047801018 0.0432127569299307 -3.26519430430713 0.00121085648923463 ** df.mm.trans2:probe4 -0.00813313713331995 0.0432127569299307 -0.188211484550911 0.850828694228127 df.mm.trans2:probe5 -0.128082965736033 0.0432127569299307 -2.96400819655452 0.00326163384404312 ** df.mm.trans2:probe6 -0.165562940264732 0.0432127569299307 -3.83134407585221 0.000153048520165113 *** df.mm.trans3:probe2 -0.563847049272536 0.0432127569299307 -13.0481619163251 1.45728345844170e-31 *** df.mm.trans3:probe3 -0.542424728550346 0.0432127569299307 -12.5524212544431 1.01083588091223e-29 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05642305839483 0.191918486221244 21.1361768126838 6.21149078245039e-63 *** df.mm.trans1 0.219794564601601 0.162552953022003 1.35214132081532 0.17727315725249 df.mm.trans2 0.217546307060551 0.162552953022003 1.33831039680408 0.181733683579689 df.mm.exp2 0.228121094345145 0.226374696245975 1.00771463475437 0.314343455547243 df.mm.exp3 0.255005795576024 0.226374696245975 1.12647658861545 0.260797480385270 df.mm.exp4 0.128273555325319 0.226374696245975 0.566642639184103 0.571349310589724 df.mm.exp5 0.416624046030986 0.226374696245975 1.84041791304399 0.0666212903252326 . df.mm.exp6 0.188387430742739 0.226374696245975 0.832192969739162 0.405912979330794 df.mm.exp7 0.097473574400287 0.226374696245975 0.430585114046376 0.667056355687154 df.mm.exp8 0.61659949021872 0.226374696245975 2.72380040898534 0.00680348592167774 ** df.mm.trans1:exp2 0.092256943682881 0.196046237723000 0.470587677450019 0.638251846158992 df.mm.trans2:exp2 -0.27094265646274 0.196046237723000 -1.38203446089878 0.167913179845279 df.mm.trans1:exp3 -0.199697275949650 0.196046237723000 -1.01862335267973 0.309141817897874 df.mm.trans2:exp3 -0.218198316059339 0.196046237723001 -1.11299415175535 0.266536003873188 df.mm.trans1:exp4 0.0860591885299825 0.196046237723000 0.438973935585431 0.66097301484291 df.mm.trans2:exp4 -0.152046281548021 0.196046237723000 -0.775563373793748 0.438572537681803 df.mm.trans1:exp5 -0.248870810582758 0.196046237723000 -1.26944956186507 0.205191836584336 df.mm.trans2:exp5 -0.418841606424664 0.196046237723001 -2.13644297023673 0.0333922317275622 * df.mm.trans1:exp6 -0.084449875700412 0.196046237723000 -0.43076509236425 0.666925607493324 df.mm.trans2:exp6 -0.081432872050557 0.196046237723000 -0.415375847026536 0.67814172836028 df.mm.trans1:exp7 0.101900610093962 0.196046237723000 0.519778452662482 0.603572924274839 df.mm.trans2:exp7 -0.168980936079116 0.196046237723000 -0.861944294579498 0.389355657117362 df.mm.trans1:exp8 -0.343410599412415 0.196046237723000 -1.75168166143351 0.0807746922689278 . df.mm.trans2:exp8 -0.678592450467609 0.196046237723001 -3.4613898147151 0.000609440235593961 *** df.mm.trans1:probe2 -0.254686831212747 0.0980231188615002 -2.59823227592463 0.00979854878094279 ** df.mm.trans1:probe3 -0.154429323047893 0.0980231188615002 -1.57543776245368 0.116131323845308 df.mm.trans1:probe4 -0.0758347402048128 0.0980231188615002 -0.773641372419112 0.439706992217877 df.mm.trans1:probe5 -0.101020184075351 0.0980231188615002 -1.03057508523153 0.303508813478354 df.mm.trans1:probe6 -0.130529787047274 0.0980231188615002 -1.33162246379554 0.183920342308677 df.mm.trans2:probe2 0.0449102626035966 0.0980231188615002 0.458159902737349 0.647144695817642 df.mm.trans2:probe3 -0.0671639537341791 0.0980231188615002 -0.68518482694962 0.49371719954506 df.mm.trans2:probe4 0.077405313276897 0.0980231188615002 0.789663848446459 0.430301623137252 df.mm.trans2:probe5 0.0345773545390365 0.0980231188615002 0.352746932974984 0.724507534904931 df.mm.trans2:probe6 0.0574553754953609 0.0980231188615002 0.586141067155201 0.558188819765656 df.mm.trans3:probe2 -0.0781038255962316 0.0980231188615002 -0.796789844103888 0.42615656697613 df.mm.trans3:probe3 -0.00701894847545092 0.0980231188615002 -0.0716050311087143 0.942960415272353