chr9.25173_chr9_119776888_119780976_+_2.R fitVsDatCorrelation=0.845407941837574 cont.fitVsDatCorrelation=0.231195666607299 fstatistic=9245.9327037026,70,1106 cont.fstatistic=2775.6439156019,70,1106 residuals=-0.525440293273481,-0.104125359485224,-0.00192412628490701,0.0950151097032294,0.832540464850315 cont.residuals=-0.632051040566952,-0.230615947253830,-0.0463242618543909,0.186939876964316,1.69112066450904 predictedValues: Include Exclude Both chr9.25173_chr9_119776888_119780976_+_2.R.tl.Lung 57.2488680751309 84.5740402657042 66.7161284139285 chr9.25173_chr9_119776888_119780976_+_2.R.tl.cerebhem 56.4627063521959 94.936125424494 71.0745701028247 chr9.25173_chr9_119776888_119780976_+_2.R.tl.cortex 57.0059564841465 75.9476479612792 70.7443448698565 chr9.25173_chr9_119776888_119780976_+_2.R.tl.heart 66.5243111058377 78.3289817208787 89.247537612799 chr9.25173_chr9_119776888_119780976_+_2.R.tl.kidney 59.3397410206527 95.5163691685774 66.8016459659292 chr9.25173_chr9_119776888_119780976_+_2.R.tl.liver 57.1050618038534 82.2661141527913 64.0799586829523 chr9.25173_chr9_119776888_119780976_+_2.R.tl.stomach 56.9713318996017 78.5170907634522 77.1191138240158 chr9.25173_chr9_119776888_119780976_+_2.R.tl.testicle 53.5031755561723 74.8241056654166 68.9434082222783 diffExp=-27.3251721905733,-38.4734190722982,-18.9416914771326,-11.8046706150410,-36.1766281479247,-25.1610523489378,-21.5457588638505,-21.3209301092443 diffExpScore=0.995043353871044 diffExp1.5=0,-1,0,0,-1,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=-1,-1,0,0,-1,-1,0,0 diffExp1.4Score=0.8 diffExp1.3=-1,-1,-1,0,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,-1,-1,0,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 71.7546233408058 75.7811829245105 67.9069908861454 cerebhem 69.814524042887 79.9392255390647 65.346973806283 cortex 68.2639045468585 71.3461966569253 65.8177087411667 heart 67.242898958145 79.177714550046 69.2371932226867 kidney 67.8927682951308 71.8145541520141 63.4602623696625 liver 67.3368420177803 84.1133163784624 69.0654000320434 stomach 70.3090758856919 76.6780821274826 67.1669623428212 testicle 65.9836370153939 73.8489808375632 66.5009456084664 cont.diffExp=-4.02655958370471,-10.1247014961777,-3.08229211006686,-11.9348155919009,-3.92178585688337,-16.7764743606821,-6.36900624179071,-7.86534382216931 cont.diffExpScore=0.984639247913207 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=0.0299676363850504 cont.tran.correlation=0.0166143803659794 tran.covariance=0.000345147593539226 cont.tran.covariance=4.59099520511557e-05 tran.mean=70.5669767137615 cont.tran.mean=72.5810954542976 weightedLogRatios: wLogRatio Lung -1.65551308800531 cerebhem -2.23094946289713 cortex -1.20108738983607 heart -0.699015172040348 kidney -2.05701275571560 liver -1.54329173428287 stomach -1.34816670162922 testicle -1.39104780030471 cont.weightedLogRatios: wLogRatio Lung -0.234800313821711 cerebhem -0.584161358532326 cortex -0.187491914868454 heart -0.700915541095573 kidney -0.238445790533774 liver -0.961224137503634 stomach -0.372550106355929 testicle -0.478132926318426 varWeightedLogRatios=0.233495863914887 cont.varWeightedLogRatios=0.0722807137548753 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.53698853499681 0.0805096094627897 56.3533789974939 0 *** df.mm.trans1 -0.50702536676977 0.0684696452884773 -7.40511163207525 2.59181904271062e-13 *** df.mm.trans2 -0.102176035397577 0.0597245909967518 -1.71078669091487 0.0874009140389367 . df.mm.exp2 0.0384665396427513 0.0747714416610077 0.51445496821029 0.60703671811069 df.mm.exp3 -0.170461088253184 0.0747714416610077 -2.27976195812842 0.0228115552515162 * df.mm.exp4 -0.217517206180425 0.0747714416610077 -2.90909472050286 0.00369728302128003 ** df.mm.exp5 0.156260659280809 0.0747714416610077 2.08984414115285 0.0368597873323959 * df.mm.exp6 0.0101318815071921 0.0747714416610077 0.135504696473917 0.892237508490757 df.mm.exp7 -0.224075157858109 0.0747714416610077 -2.99680135731502 0.00278914524677164 ** df.mm.exp8 -0.222993396233464 0.0747714416610077 -2.98233377984675 0.00292328660323628 ** df.mm.trans1:exp2 -0.0522940546172352 0.0675706612758339 -0.773916573108005 0.439145519646879 df.mm.trans2:exp2 0.0771103949950631 0.0447588952971749 1.72279486531318 0.0852051587807988 . df.mm.trans1:exp3 0.166208979724433 0.0675706612758339 2.45978027425161 0.0140543545776472 * df.mm.trans2:exp3 0.0628779816916652 0.0447588952971749 1.4048153171384 0.160356990535784 df.mm.trans1:exp4 0.36767679694741 0.0675706612758339 5.44136745157039 6.50802045635143e-08 *** df.mm.trans2:exp4 0.140807510741938 0.0447588952971749 3.14591121624097 0.00169989647532944 ** df.mm.trans1:exp5 -0.120389279383197 0.0675706612758339 -1.7816797573099 0.075075793782693 . df.mm.trans2:exp5 -0.0345903884477166 0.0447588952971749 -0.772815955757066 0.439796430010949 df.mm.trans1:exp6 -0.0126469913462134 0.0675706612758339 -0.187166902135032 0.851564102633516 df.mm.trans2:exp6 -0.0377999611716486 0.0447588952971749 -0.844523997312206 0.398559296917004 df.mm.trans1:exp7 0.219215479416073 0.0675706612758339 3.24424055169746 0.00121289251511859 ** df.mm.trans2:exp7 0.149764108809480 0.0447588952971749 3.34601888217141 0.000847356477152917 *** df.mm.trans1:exp8 0.155326533938597 0.0675706612758339 2.29872745072791 0.0217061951703386 * df.mm.trans2:exp8 0.100506130678849 0.0447588952971749 2.24550069905753 0.0249328886797802 * df.mm.trans1:probe2 -0.162513011972747 0.0518705825577611 -3.13304774998003 0.00177549350540326 ** df.mm.trans1:probe3 -0.0775149358058705 0.0518705825577611 -1.49439107840273 0.135358671017923 df.mm.trans1:probe4 -0.133945402357012 0.0518705825577611 -2.58229994251280 0.00994206398695863 ** df.mm.trans1:probe5 0.00692195579920061 0.0518705825577611 0.133446656233185 0.893864425257775 df.mm.trans1:probe6 -0.139422877965615 0.0518705825577611 -2.68789882609009 0.00729827697706813 ** df.mm.trans1:probe7 0.0322590138823651 0.0518705825577611 0.62191346793614 0.534126944362744 df.mm.trans1:probe8 -0.174164678964795 0.0518705825577612 -3.35767732646634 0.000812762870285971 *** df.mm.trans1:probe9 -0.139813742317758 0.0518705825577612 -2.69543420226806 0.00713631270202957 ** df.mm.trans1:probe10 -0.0453310225083605 0.0518705825577612 -0.873925455876296 0.382348565562066 df.mm.trans1:probe11 -0.156379034406534 0.0518705825577611 -3.01479232920503 0.00263019504454232 ** df.mm.trans1:probe12 -0.114536468203078 0.0518705825577611 -2.20811995075503 0.0274404988219268 * df.mm.trans1:probe13 -0.0657783597037473 0.0518705825577611 -1.26812456039990 0.205020348259517 df.mm.trans1:probe14 -0.137946909545591 0.0518705825577611 -2.65944399972717 0.00794008019111756 ** df.mm.trans1:probe15 -0.0973605504389623 0.0518705825577612 -1.87698972400291 0.0607825763308594 . df.mm.trans1:probe16 0.381664979866017 0.0518705825577611 7.35802377852628 3.63092341407113e-13 *** df.mm.trans1:probe17 0.264304232984653 0.0518705825577611 5.09545526484753 4.08866799951241e-07 *** df.mm.trans1:probe18 0.340261177079901 0.0518705825577611 6.55981021036344 8.25651973366893e-11 *** df.mm.trans1:probe19 0.427714980233750 0.0518705825577611 8.24581022889926 4.61197383063057e-16 *** df.mm.trans1:probe20 0.442668577339929 0.0518705825577611 8.53409689098806 4.59927192840275e-17 *** df.mm.trans1:probe21 0.299034280136116 0.0518705825577611 5.76500716573065 1.05837575790007e-08 *** df.mm.trans2:probe2 -0.120799365027546 0.0518705825577611 -2.32886077369632 0.0200458929460929 * df.mm.trans2:probe3 -0.0673980661091443 0.0518705825577611 -1.29935047546637 0.194094458651739 df.mm.trans2:probe4 0.0672058830039505 0.0518705825577611 1.29564542540297 0.195368009310895 df.mm.trans2:probe5 0.0786253350687139 0.0518705825577611 1.51579818833073 0.129856128482030 df.mm.trans2:probe6 0.121182496702036 0.0518705825577611 2.33624707351400 0.0196562671719096 * df.mm.trans3:probe2 0.170157896589839 0.0518705825577611 3.28043157025194 0.00106882237029157 ** df.mm.trans3:probe3 0.154239629534153 0.0518705825577611 2.9735472772529 0.00300760531124640 ** df.mm.trans3:probe4 0.489677565216614 0.0518705825577611 9.4403714219197 2.12246484841469e-20 *** df.mm.trans3:probe5 -0.109789501160636 0.0518705825577611 -2.11660435928936 0.0345163001479583 * df.mm.trans3:probe6 0.0821053725522902 0.0518705825577611 1.58288896140429 0.113732667164769 df.mm.trans3:probe7 0.058215456538433 0.0518705825577611 1.12232124005175 0.261969463404583 df.mm.trans3:probe8 0.194974138428188 0.0518705825577611 3.75885769570974 0.00017961263360373 *** df.mm.trans3:probe9 0.153366470992629 0.0518705825577611 2.95671387191084 0.00317537901254898 ** df.mm.trans3:probe10 0.394468212986667 0.0518705825577611 7.60485411065901 6.0743706561594e-14 *** df.mm.trans3:probe11 0.368846248148172 0.0518705825577611 7.11089465281094 2.06549756689967e-12 *** df.mm.trans3:probe12 1.01077521350446 0.0518705825577611 19.4864827742951 6.16623155736817e-73 *** df.mm.trans3:probe13 0.63486148462709 0.0518705825577611 12.2393359264884 2.14843939155227e-32 *** df.mm.trans3:probe14 0.293756573161494 0.0518705825577611 5.66325957173081 1.89232403304802e-08 *** df.mm.trans3:probe15 -0.00467974217275899 0.0518705825577611 -0.0902195800008184 0.928129060575576 df.mm.trans3:probe16 0.167405921958918 0.0518705825577611 3.22737693899044 0.00128598252944941 ** df.mm.trans3:probe17 0.195197528414145 0.0518705825577611 3.76316437542957 0.000176584929968595 *** df.mm.trans3:probe18 0.235321989260647 0.0518705825577611 4.53671383001341 6.33769275817508e-06 *** df.mm.trans3:probe19 0.0853257825691998 0.0518705825577611 1.64497444142225 0.10025923868275 df.mm.trans3:probe20 0.455699413145105 0.0518705825577611 8.78531511840368 5.83997272429527e-18 *** df.mm.trans3:probe21 0.0922604709939469 0.0518705825577611 1.77866656676179 0.075569067919551 . df.mm.trans3:probe22 0.876324990411752 0.0518705825577611 16.8944505189605 3.85653504211103e-57 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.39003521757837 0.146649095313201 29.9356447320898 1.05647810718411e-144 *** df.mm.trans1 -0.0814682656272008 0.124718174699528 -0.653218873860803 0.513750949393488 df.mm.trans2 -0.0463173526600107 0.108788966883173 -0.425754136536201 0.670369844463754 df.mm.exp2 0.0644342856577434 0.136196962673363 0.473096348060817 0.636237795663313 df.mm.exp3 -0.078927068922372 0.136196962673363 -0.579506821394104 0.562365262149213 df.mm.exp4 -0.0404952110622259 0.136196962673363 -0.297328297690047 0.766271713959933 df.mm.exp5 -0.0413605543082195 0.136196962673363 -0.303681914018988 0.761427356635827 df.mm.exp6 0.0238552115680764 0.136196962673363 0.175152302223417 0.860992021332214 df.mm.exp7 0.00237198735158231 0.136196962673363 0.0174158608607960 0.986107997072366 df.mm.exp8 -0.0887504885123606 0.136196962673363 -0.651633390130793 0.514773081474877 df.mm.trans1:exp2 -0.0918445060610041 0.123080665922192 -0.746213918919363 0.455696777319757 df.mm.trans2:exp2 -0.0110176359008109 0.0815287957095694 -0.135137969412171 0.892527379589807 df.mm.trans1:exp3 0.0290559224922544 0.123080665922192 0.236072191148386 0.81342036126701 df.mm.trans2:exp3 0.0186210905337991 0.0815287957095694 0.22839894017487 0.819378333913673 df.mm.trans1:exp4 -0.0244456571008913 0.123080665922193 -0.198614923942198 0.8426004811055 df.mm.trans2:exp4 0.0843400728918118 0.0815287957095695 1.03448201531465 0.301137016008331 df.mm.trans1:exp5 -0.0139622113949898 0.123080665922192 -0.113439517818471 0.909702698465139 df.mm.trans2:exp5 -0.0124023015638503 0.0815287957095694 -0.152121731419058 0.879118668972936 df.mm.trans1:exp6 -0.087399984387086 0.123080665922192 -0.710103278465664 0.477789784009313 df.mm.trans2:exp6 0.0804596672400458 0.0815287957095694 0.986886492554947 0.323914130946155 df.mm.trans1:exp7 -0.0227233840226833 0.123080665922192 -0.184621880718847 0.853559469042757 df.mm.trans2:exp7 0.00939390368278523 0.0815287957095694 0.115221911485718 0.908290146366068 df.mm.trans1:exp8 0.00490498642193958 0.123080665922192 0.0398518027603162 0.968218468679534 df.mm.trans2:exp8 0.0629226815036925 0.0815287957095695 0.771784753546984 0.440406790703256 df.mm.trans1:probe2 -0.0467752105432017 0.0944828084028917 -0.495065836143903 0.620651983428535 df.mm.trans1:probe3 -0.195963543144948 0.0944828084028917 -2.07406560471112 0.0383041665891819 * df.mm.trans1:probe4 -0.077029506697011 0.0944828084028917 -0.815275371245775 0.415090229547926 df.mm.trans1:probe5 -0.0307550398867619 0.0944828084028917 -0.325509374738490 0.744857158012531 df.mm.trans1:probe6 -0.0720145118329726 0.0944828084028917 -0.762196986417781 0.446104953958798 df.mm.trans1:probe7 -0.0362721561921767 0.0944828084028917 -0.383902180780928 0.701124739431958 df.mm.trans1:probe8 -0.116259604095199 0.0944828084028917 -1.23048421252940 0.218777470349106 df.mm.trans1:probe9 -0.111205247428534 0.0944828084028917 -1.17698922490041 0.239453040225892 df.mm.trans1:probe10 -0.0314495838750929 0.0944828084028917 -0.332860383880485 0.739302757345052 df.mm.trans1:probe11 -0.126914540415135 0.0944828084028917 -1.34325537693533 0.179464849880681 df.mm.trans1:probe12 -0.0808659157596127 0.0944828084028917 -0.855879679346383 0.392249716619401 df.mm.trans1:probe13 0.0239940010118184 0.0944828084028917 0.253950971794823 0.799580637514136 df.mm.trans1:probe14 -0.120340330313478 0.0944828084028917 -1.27367435777655 0.203046447699427 df.mm.trans1:probe15 -0.143331394496774 0.0944828084028917 -1.51701031033691 0.12954984083656 df.mm.trans1:probe16 -0.102664591844765 0.0944828084028916 -1.08659547255396 0.277452382378071 df.mm.trans1:probe17 -0.0524080525307162 0.0944828084028917 -0.554683475402624 0.579223329784763 df.mm.trans1:probe18 -0.102353099759054 0.0944828084028917 -1.08329866024517 0.278911985794708 df.mm.trans1:probe19 -0.116180647241616 0.0944828084028917 -1.22964853824201 0.21909025763603 df.mm.trans1:probe20 0.0318654728897137 0.0944828084028917 0.337262126606500 0.735983299390315 df.mm.trans1:probe21 -0.0116069888596020 0.0944828084028917 -0.122847627582234 0.902250077274324 df.mm.trans2:probe2 -0.0517869000090908 0.0944828084028917 -0.548109236849335 0.58372750416414 df.mm.trans2:probe3 -0.132916501128346 0.0944828084028917 -1.40677974517403 0.159773636092356 df.mm.trans2:probe4 -0.152187940834776 0.0944828084028917 -1.61074743021841 0.107520116857775 df.mm.trans2:probe5 -0.0363290655378942 0.0944828084028917 -0.384504505655468 0.700678475012303 df.mm.trans2:probe6 -0.0710793775591309 0.0944828084028917 -0.752299585084682 0.452030991310894 df.mm.trans3:probe2 -0.171382570124508 0.0944828084028917 -1.81390215872609 0.0699636833201742 . df.mm.trans3:probe3 -0.00102319446114048 0.0944828084028917 -0.0108294247221928 0.991361491119939 df.mm.trans3:probe4 0.0483539421356931 0.0944828084028917 0.511775030326186 0.608910631857898 df.mm.trans3:probe5 -0.110989800357186 0.0944828084028917 -1.17470894687958 0.240364030199018 df.mm.trans3:probe6 0.0437981911389896 0.0944828084028917 0.463557253211888 0.643056196620556 df.mm.trans3:probe7 0.0351745464670503 0.0944828084028917 0.372285149665108 0.709751875514598 df.mm.trans3:probe8 -0.0394998638733274 0.0944828084028917 -0.418064032399343 0.675981456136531 df.mm.trans3:probe9 -0.0089665004195921 0.0944828084028917 -0.0949008668471976 0.924410758298995 df.mm.trans3:probe10 -0.0333499063019494 0.0944828084028917 -0.352973274881283 0.724175735481491 df.mm.trans3:probe11 -0.0900069518546923 0.0944828084028917 -0.952627820617763 0.340986816747512 df.mm.trans3:probe12 0.0291188118892886 0.0944828084028917 0.308191642283967 0.757994544632073 df.mm.trans3:probe13 -0.125176737384739 0.0944828084028917 -1.32486258083019 0.185490390901558 df.mm.trans3:probe14 -0.0268389864936191 0.0944828084028917 -0.284062116138344 0.776415948396804 df.mm.trans3:probe15 -0.0812996757548028 0.0944828084028917 -0.860470567387523 0.389716187347204 df.mm.trans3:probe16 -0.094974572460954 0.0944828084028917 -1.00520479933202 0.315018110548457 df.mm.trans3:probe17 -0.138588650351141 0.0944828084028917 -1.46681340969644 0.142711094688579 df.mm.trans3:probe18 -0.09078497849289 0.0944828084028916 -0.9608624047855 0.336831368596137 df.mm.trans3:probe19 -0.107455555274597 0.0944828084028917 -1.13730272301377 0.255658086191865 df.mm.trans3:probe20 -0.0261264395619670 0.0944828084028917 -0.276520564995901 0.782199931520274 df.mm.trans3:probe21 0.113531953318981 0.0944828084028917 1.20161493120379 0.229769988749240 df.mm.trans3:probe22 -0.0939465140687204 0.0944828084028916 -0.994323895074283 0.320282691849580