chr6.20288_chr6_121205812_121211748_+_2.R fitVsDatCorrelation=0.825042454254057 cont.fitVsDatCorrelation=0.228803631302058 fstatistic=8579.92913347923,62,922 cont.fstatistic=2881.09842570788,62,922 residuals=-0.779450435489497,-0.0912140952792168,-0.0117026694467654,0.0820942528199651,1.25139862407905 cont.residuals=-0.52014096339776,-0.220567257985102,-0.0612287209918423,0.165554180998305,1.46664863514975 predictedValues: Include Exclude Both chr6.20288_chr6_121205812_121211748_+_2.R.tl.Lung 64.8592534681768 46.7969295063437 74.8581746127722 chr6.20288_chr6_121205812_121211748_+_2.R.tl.cerebhem 62.5187510564064 58.0990203714879 67.1154708885195 chr6.20288_chr6_121205812_121211748_+_2.R.tl.cortex 70.5099891088627 45.2388276341334 76.1925974977189 chr6.20288_chr6_121205812_121211748_+_2.R.tl.heart 67.7879427461208 46.8594972904197 73.1440521718638 chr6.20288_chr6_121205812_121211748_+_2.R.tl.kidney 59.2449432413408 45.2889824559651 70.9706131503451 chr6.20288_chr6_121205812_121211748_+_2.R.tl.liver 58.7572917179711 46.9753848206251 67.8150528733365 chr6.20288_chr6_121205812_121211748_+_2.R.tl.stomach 64.4574880867921 49.1266040955274 65.4265732080658 chr6.20288_chr6_121205812_121211748_+_2.R.tl.testicle 63.9506390170163 48.3845165842569 69.8295959549174 diffExp=18.0623239618331,4.41973068491856,25.2711614747294,20.9284454557011,13.9559607853757,11.7819068973460,15.3308839912647,15.5661224327594 diffExpScore=0.992083380100748 diffExp1.5=0,0,1,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,1,1,0,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=1,0,1,1,1,0,1,1 diffExp1.3Score=0.857142857142857 diffExp1.2=1,0,1,1,1,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 61.2511153649695 61.9182704286506 73.0924569743912 cerebhem 61.6163021508203 54.3808164372763 59.8963453730105 cortex 62.3340475540517 60.6977388682497 57.1908193877774 heart 57.7225711392241 62.8172667770534 57.4785218565879 kidney 63.4685844295407 61.5369034805054 58.7364750583142 liver 58.6814241549201 65.4025328234959 69.0472848564616 stomach 64.4913299689713 60.1595265049437 55.7816872051506 testicle 59.8008291533525 60.8947606835522 68.071891999159 cont.diffExp=-0.667155063681172,7.23548571354405,1.63630868580203,-5.09469563782928,1.93168094903530,-6.72110866857583,4.33180346402757,-1.09393153019969 cont.diffExpScore=11.2227585100139 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=-0.169404135158744 cont.tran.correlation=-0.429752954606749 tran.covariance=-0.000769923585090699 cont.tran.covariance=-0.000840746275333225 tran.mean=56.1785038250904 cont.tran.mean=61.0733762449736 weightedLogRatios: wLogRatio Lung 1.30855168220904 cerebhem 0.300515257393344 cortex 1.79021944219179 heart 1.48865265077526 kidney 1.06032983392004 liver 0.88655447948334 stomach 1.09462411322073 testicle 1.12092752565657 cont.weightedLogRatios: wLogRatio Lung -0.04463725639373 cerebhem 0.506963765360806 cortex 0.109576428086492 heart -0.34661068450479 kidney 0.127807388575601 liver -0.447451307454527 stomach 0.287285718146070 testicle -0.074324724931625 varWeightedLogRatios=0.192841005544516 cont.varWeightedLogRatios=0.0991424046290558 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.84603803477541 0.0939068512187019 40.9558832487981 9.99931490995955e-210 *** df.mm.trans1 0.419571322757859 0.0849638889154738 4.93823114870917 9.3565197371383e-07 *** df.mm.trans2 -0.0340860140290831 0.0772961992551134 -0.440979173071414 0.659331456379352 df.mm.exp2 0.288758884545296 0.105623973317828 2.73383849778503 0.00638001508065389 ** df.mm.exp3 0.0320039488574156 0.105623973317828 0.302998910683981 0.761959041577122 df.mm.exp4 0.0686653698249312 0.105623973317828 0.650092660482614 0.515794311257195 df.mm.exp5 -0.0699635365038281 0.105623973317828 -0.66238311536818 0.507891254958913 df.mm.exp6 0.00381294319881461 0.105623973317828 0.0360992214082052 0.97121105709982 df.mm.exp7 0.177036271282532 0.105623973317828 1.67609933352745 0.0940577620363708 . df.mm.exp8 0.0887915822797976 0.105623973317828 0.8406385358428 0.400768411691602 df.mm.trans1:exp2 -0.325511946813924 0.102470305549841 -3.17664659110045 0.00153942101655930 ** df.mm.trans2:exp2 -0.0724276738379556 0.0875006813285048 -0.82773839858503 0.408032858569054 df.mm.trans1:exp3 0.0515308494937994 0.102470305549841 0.50288568202556 0.615164748628236 df.mm.trans2:exp3 -0.0658658042425837 0.0875006813285048 -0.752746187144565 0.451794593691527 df.mm.trans1:exp4 -0.0245006172135603 0.102470305549841 -0.239099679483666 0.81108142728893 df.mm.trans2:exp4 -0.0673292566392381 0.0875006813285048 -0.769471227160656 0.441810698833202 df.mm.trans1:exp5 -0.0205756241782437 0.102470305549841 -0.200795967844907 0.840902400727603 df.mm.trans2:exp5 0.0372097345867254 0.0875006813285048 0.425250798299826 0.670753087678154 df.mm.trans1:exp6 -0.102617274445066 0.102470305549841 -1.00143425838770 0.316879654464791 df.mm.trans2:exp6 -6.79783493951637e-06 0.0875006813285048 -7.76889372323306e-05 0.999938029833398 df.mm.trans1:exp7 -0.183249955129317 0.102470305549841 -1.78832252081248 0.074052253279425 . df.mm.trans2:exp7 -0.128453140214583 0.0875006813285048 -1.46802445723057 0.142438631679311 df.mm.trans1:exp8 -0.102899652774805 0.102470305549841 -1.00418996725598 0.315550523787715 df.mm.trans2:exp8 -0.0554293169542075 0.0875006813285048 -0.63347297544014 0.526581925597479 df.mm.trans1:probe2 0.0245511523741349 0.0512351527749205 0.479185696624927 0.631920158248543 df.mm.trans1:probe3 -0.292216146542279 0.0512351527749205 -5.7034307641475 1.58097962192308e-08 *** df.mm.trans1:probe4 -0.365160671892648 0.0512351527749205 -7.12715103040336 2.06662064818421e-12 *** df.mm.trans1:probe5 -0.160588258190540 0.0512351527749205 -3.13433745178853 0.00177690665679630 ** df.mm.trans1:probe6 -0.167003679089807 0.0512351527749205 -3.25955267125807 0.00115673081054007 ** df.mm.trans1:probe7 0.552823413539157 0.0512351527749205 10.7899241750629 1.21195335926827e-25 *** df.mm.trans1:probe8 -0.173721398024308 0.0512351527749205 -3.39066809827772 0.000726829071831916 *** df.mm.trans1:probe9 -0.366912104985862 0.0512351527749205 -7.16133523789284 1.63254822964875e-12 *** df.mm.trans1:probe10 -0.293641701606057 0.0512351527749205 -5.73125453330929 1.34982281620010e-08 *** df.mm.trans1:probe11 0.0848468148412235 0.0512351527749205 1.65602736101835 0.0980565452519741 . df.mm.trans1:probe12 0.303238734223354 0.0512351527749205 5.9185679713985 4.57613015509177e-09 *** df.mm.trans1:probe13 -0.000703691720826048 0.0512351527749205 -0.0137345490881507 0.98904473115011 df.mm.trans1:probe14 0.319887127618313 0.0512351527749205 6.24350880778279 6.52003273458951e-10 *** df.mm.trans1:probe15 0.101276409383622 0.0512351527749205 1.97669771433173 0.0483734317098617 * df.mm.trans1:probe16 0.0801588870689321 0.0512351527749205 1.56452909238068 0.118036506143911 df.mm.trans1:probe17 -0.359673354208604 0.0512351527749205 -7.02005038979143 4.29870307997614e-12 *** df.mm.trans1:probe18 -0.188760068372098 0.0512351527749205 -3.68419060252115 0.000242793061446354 *** df.mm.trans1:probe19 -0.275365877894625 0.0512351527749205 -5.37454975696717 9.72912775196828e-08 *** df.mm.trans1:probe20 -0.244496454264907 0.0512351527749205 -4.77204499299526 2.11949656878895e-06 *** df.mm.trans1:probe21 -0.287702694108501 0.0512351527749205 -5.6153378789051 2.59621640978428e-08 *** df.mm.trans1:probe22 -0.264885111611745 0.0512351527749205 -5.16998773821175 2.87126110981786e-07 *** df.mm.trans1:probe23 0.0111023723383599 0.0512351527749205 0.216694432182789 0.828494424081567 df.mm.trans1:probe24 0.0195101586257813 0.0512351527749205 0.380796339409598 0.703442095134278 df.mm.trans1:probe25 -0.24151936199143 0.0512351527749205 -4.71393855411031 2.80462316668871e-06 *** df.mm.trans1:probe26 -0.31557881832642 0.0512351527749205 -6.15941987550576 1.08901914543230e-09 *** df.mm.trans1:probe27 -0.169592805717316 0.0512351527749205 -3.31008685506119 0.000968842014439438 *** df.mm.trans1:probe28 0.550550665943362 0.0512351527749205 10.7455650295798 1.85914476691222e-25 *** df.mm.trans1:probe29 -0.185124132142133 0.0512351527749205 -3.61322494646197 0.000318760462276361 *** df.mm.trans1:probe30 -0.343317537672832 0.0512351527749205 -6.7008200245064 3.60332364065922e-11 *** df.mm.trans1:probe31 -0.282419628437995 0.0512351527749205 -5.51222379834962 4.59981056223516e-08 *** df.mm.trans1:probe32 -0.338204070710038 0.0512351527749205 -6.6010161460002 6.88236233521473e-11 *** df.mm.trans2:probe2 -0.00709969652382754 0.0512351527749205 -0.138570808113269 0.889819571268672 df.mm.trans2:probe3 0.0661152961831264 0.0512351527749205 1.29042839929795 0.1972254721774 df.mm.trans2:probe4 0.00287775227426028 0.0512351527749205 0.0561675357327896 0.95522051214803 df.mm.trans2:probe5 0.0860633100012086 0.0512351527749205 1.67977073044537 0.0933407063323238 . df.mm.trans2:probe6 0.156833478740244 0.0512351527749205 3.06105223164307 0.00226950920713206 ** df.mm.trans3:probe2 0.092466450665028 0.0512351527749205 1.80474626612785 0.0714405738420626 . df.mm.trans3:probe3 0.159749473319805 0.0512351527749205 3.11796617493452 0.00187752638813297 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92653256226847 0.16177752420799 24.2711871225097 5.14423509961832e-101 *** df.mm.trans1 0.142740976351538 0.146371083871361 0.975199285105974 0.329717202858768 df.mm.trans2 0.171533438400196 0.133161612639498 1.28815981573144 0.198013518301443 df.mm.exp2 0.075250260188526 0.181963133452034 0.413546737522863 0.67930225591007 df.mm.exp3 0.242948665183187 0.181963133452034 1.33515322897662 0.182155750925942 df.mm.exp4 0.195394735043269 0.181963133452034 1.07381496095623 0.283186572359732 df.mm.exp5 0.248049004610439 0.181963133452034 1.36318274973994 0.173157712396106 df.mm.exp6 0.0688204688937878 0.181963133452034 0.378211056207872 0.705360872731067 df.mm.exp7 0.293012793290620 0.181963133452034 1.61028658790304 0.107677589203892 df.mm.exp8 0.0305301016988825 0.181963133452034 0.167781797992231 0.866791736658691 df.mm.trans1:exp2 -0.0693058386552726 0.176530169221435 -0.392600533727111 0.694705289692482 df.mm.trans2:exp2 -0.205054104076569 0.150741329393213 -1.36030446926522 0.174066071534677 df.mm.trans1:exp3 -0.225422938275315 0.176530169221435 -1.27696551399410 0.201935918740915 df.mm.trans2:exp3 -0.262857515284021 0.150741329393213 -1.74376540489668 0.0815332925532463 . df.mm.trans1:exp4 -0.254728516397449 0.176530169221435 -1.44297440783578 0.149367321745898 df.mm.trans2:exp4 -0.180980047218905 0.150741329393213 -1.20060004742835 0.230214745499177 df.mm.trans1:exp5 -0.212486013825151 0.176530169221435 -1.20368101816417 0.229021837447249 df.mm.trans2:exp5 -0.254227249703876 0.150741329393213 -1.68651325238559 0.0920352805329937 . df.mm.trans1:exp6 -0.111679305360788 0.176530169221435 -0.632635803009402 0.527128360324614 df.mm.trans2:exp6 -0.0140747795300639 0.150741329393213 -0.0933704086776982 0.92562958328744 df.mm.trans1:exp7 -0.241464056909018 0.176530169221435 -1.36783450655470 0.171697170349088 df.mm.trans2:exp7 -0.321828280813548 0.150741329393213 -2.1349704298683 0.0330265043624616 * df.mm.trans1:exp8 -0.0544926346970966 0.176530169221435 -0.308687375860057 0.757629081248556 df.mm.trans2:exp8 -0.0471982586902377 0.150741329393213 -0.313107618728236 0.754269713132059 df.mm.trans1:probe2 0.109648518385571 0.0882650846107175 1.24226378832766 0.214455125842977 df.mm.trans1:probe3 0.0538830935056195 0.0882650846107175 0.610468949792145 0.541701649439119 df.mm.trans1:probe4 0.0916554464437604 0.0882650846107175 1.03841113219339 0.299350931746834 df.mm.trans1:probe5 -0.0191683540213538 0.0882650846107174 -0.217168024093486 0.828125449043595 df.mm.trans1:probe6 0.0112400741855933 0.0882650846107175 0.127344512670739 0.898695498409521 df.mm.trans1:probe7 0.130193139292649 0.0882650846107175 1.47502423939036 0.140547409177587 df.mm.trans1:probe8 0.089299049355978 0.0882650846107174 1.01171431206145 0.311940095019015 df.mm.trans1:probe9 0.0563652699526329 0.0882650846107175 0.638590788206063 0.523247765663397 df.mm.trans1:probe10 0.147512201513170 0.0882650846107175 1.67124069685941 0.0950134899712623 . df.mm.trans1:probe11 -0.0269704271215868 0.0882650846107175 -0.305561675270994 0.760007376857807 df.mm.trans1:probe12 0.0520709481634914 0.0882650846107175 0.589938234276261 0.555376604146483 df.mm.trans1:probe13 0.0771732623745836 0.0882650846107175 0.874335109006546 0.382163468519397 df.mm.trans1:probe14 0.0134947920494742 0.0882650846107174 0.152889357201563 0.878518981219654 df.mm.trans1:probe15 0.0625985374105392 0.0882650846107174 0.709210642992329 0.478373064205369 df.mm.trans1:probe16 0.0145935374595728 0.0882650846107174 0.165337602336596 0.868714507796863 df.mm.trans1:probe17 0.068131399345872 0.0882650846107175 0.771895247666247 0.44037426689412 df.mm.trans1:probe18 -0.0264933947428092 0.0882650846107175 -0.300157133023269 0.764124961670722 df.mm.trans1:probe19 -0.0291394137991213 0.0882650846107175 -0.330135227622985 0.741372782107246 df.mm.trans1:probe20 0.101394144148967 0.0882650846107175 1.14874578771611 0.250958748006296 df.mm.trans1:probe21 0.0917041458729672 0.0882650846107175 1.03896287277599 0.299094385034094 df.mm.trans1:probe22 0.146164359348389 0.0882650846107175 1.65597030799925 0.0980681023821902 . df.mm.trans1:probe23 0.0260852549252599 0.0882650846107175 0.295533109613000 0.767653204796769 df.mm.trans1:probe24 0.0881283141911996 0.0882650846107175 0.998450458410355 0.318322939420965 df.mm.trans1:probe25 0.0296219480850311 0.0882650846107175 0.335602103772688 0.737247286228344 df.mm.trans1:probe26 0.064639355325187 0.0882650846107175 0.732332106294024 0.464152004144051 df.mm.trans1:probe27 0.100114728816773 0.0882650846107175 1.13425064121694 0.256984237776723 df.mm.trans1:probe28 -0.0212039202970847 0.0882650846107175 -0.240229988909001 0.810205367553805 df.mm.trans1:probe29 0.0110968601272402 0.0882650846107174 0.125721967822061 0.899979420154796 df.mm.trans1:probe30 0.116653971818689 0.0882650846107175 1.32163213045314 0.186618514977959 df.mm.trans1:probe31 0.00892127739690495 0.0882650846107174 0.101073685435766 0.919513941769833 df.mm.trans1:probe32 -0.0396098957360305 0.0882650846107175 -0.448760638600474 0.653709785925955 df.mm.trans2:probe2 0.0251074389280526 0.0882650846107175 0.284454935253118 0.77612563814194 df.mm.trans2:probe3 0.125960204946402 0.0882650846107174 1.42706717499830 0.153899093050569 df.mm.trans2:probe4 -0.0268801238154595 0.0882650846107175 -0.304538583223605 0.760786326417398 df.mm.trans2:probe5 0.0246567550115613 0.0882650846107174 0.279348908124962 0.780039726330985 df.mm.trans2:probe6 0.100899387749131 0.0882650846107174 1.14314044102643 0.253277018701094 df.mm.trans3:probe2 0.069976578090239 0.0882650846107174 0.79280021538372 0.428098182457677 df.mm.trans3:probe3 0.0827780118491884 0.0882650846107175 0.93783416414623 0.348575238705858