chr7.21276_chr7_71298718_71301218_-_0.R fitVsDatCorrelation=0.848452084307763 cont.fitVsDatCorrelation=0.234948560991817 fstatistic=8078.04139097094,43,485 cont.fstatistic=2387.17110349778,43,485 residuals=-0.472937138048935,-0.0970915137356678,-0.000879046380337525,0.0817317335282143,1.26725530205001 cont.residuals=-0.643541710201885,-0.200350997948144,-0.0442559193340581,0.147808442032789,1.4635123654666 predictedValues: Include Exclude Both chr7.21276_chr7_71298718_71301218_-_0.R.tl.Lung 53.4311866452814 98.6853744395702 59.996858971286 chr7.21276_chr7_71298718_71301218_-_0.R.tl.cerebhem 60.2692672408897 77.1155406581837 65.382152026899 chr7.21276_chr7_71298718_71301218_-_0.R.tl.cortex 60.975596209168 84.213671432322 60.6527719910496 chr7.21276_chr7_71298718_71301218_-_0.R.tl.heart 60.1226310084096 90.3017715699297 61.7348438075191 chr7.21276_chr7_71298718_71301218_-_0.R.tl.kidney 59.290929696013 114.796561798474 64.5210939446439 chr7.21276_chr7_71298718_71301218_-_0.R.tl.liver 58.3256471184603 106.588807353143 67.4484680728703 chr7.21276_chr7_71298718_71301218_-_0.R.tl.stomach 59.5243844295969 92.30152422743 61.6844076749987 chr7.21276_chr7_71298718_71301218_-_0.R.tl.testicle 75.2655277136509 97.6059425999768 71.7251242286994 diffExp=-45.2541877942888,-16.8462734172940,-23.2380752231541,-30.1791405615201,-55.5056321024607,-48.2631602346823,-32.7771397978331,-22.3404148863259 diffExpScore=0.996368970992464 diffExp1.5=-1,0,0,-1,-1,-1,-1,0 diffExp1.5Score=0.833333333333333 diffExp1.4=-1,0,0,-1,-1,-1,-1,0 diffExp1.4Score=0.833333333333333 diffExp1.3=-1,0,-1,-1,-1,-1,-1,0 diffExp1.3Score=0.857142857142857 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 65.5313077148996 75.3949093390958 71.177929412338 cerebhem 65.6029854247375 66.6074717338758 63.5672568885044 cortex 68.60524804932 62.900403368484 67.7795045010458 heart 65.8966665275947 71.7336441406126 65.5644683057911 kidney 70.21882288182 66.1030848644501 64.3069383861212 liver 59.8341303512708 67.8320674091958 65.9199783565305 stomach 64.9309489955503 66.2826605680954 73.4253275361225 testicle 59.4551464747404 68.464876758537 67.406318666063 cont.diffExp=-9.86360162419625,-1.00448630913837,5.70484468083599,-5.83697761301796,4.11573801736985,-7.99793705792496,-1.35171157254509,-9.0097302837966 cont.diffExpScore=1.71030573035214 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=-0.0644455165840822 cont.tran.correlation=-0.231939546957700 tran.covariance=-0.00087457418671598 cont.tran.covariance=-0.000767943289201494 tran.mean=78.0508977587812 cont.tran.mean=66.5871484126425 weightedLogRatios: wLogRatio Lung -2.6291297969921 cerebhem -1.04066484967220 cortex -1.37933126153872 heart -1.74902137068599 kidney -2.91556579237982 liver -2.63333174254559 stomach -1.88881148800294 testicle -1.15688165637215 cont.weightedLogRatios: wLogRatio Lung -0.596269232532149 cerebhem -0.0636878986078833 cortex 0.363323471650799 heart -0.359053089093492 kidney 0.254977708196966 liver -0.521194259283766 stomach -0.0861992325110883 testicle -0.586373869336712 varWeightedLogRatios=0.52583336168844 cont.varWeightedLogRatios=0.141520506781888 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.807917597542 0.0824223874484786 58.332665994993 2.39627146316325e-221 *** df.mm.trans1 -0.840198231423694 0.0659834314760854 -12.7334728223131 3.03285020102836e-32 *** df.mm.trans2 -0.112878943800623 0.0659834314760854 -1.71071648253902 0.0877729205481303 . df.mm.exp2 -0.212161319919603 0.0883563099880996 -2.40120167929352 0.0167167874552755 * df.mm.exp3 -0.0373734786408285 0.0883563099880996 -0.422985960435222 0.672492989213154 df.mm.exp4 0.000655766334285336 0.0883563099880996 0.0074218393046706 0.994081335126653 df.mm.exp5 0.182586525606744 0.0883563099880996 2.06647975262136 0.039313462824141 * df.mm.exp6 0.0476174138829443 0.0883563099880996 0.538924881418857 0.590185969875043 df.mm.exp7 0.0133763809101939 0.0883563099880996 0.151391348416378 0.879729962919233 df.mm.exp8 0.153080377492787 0.0883563099880996 1.73253475064096 0.0838141006927892 . df.mm.trans1:exp2 0.332589034331899 0.0693123921197497 4.79840652097667 2.1325286559054e-06 *** df.mm.trans2:exp2 -0.0344706083551078 0.0693123921197497 -0.497322445538363 0.619187064340942 df.mm.trans1:exp3 0.169452605555497 0.0693123921197497 2.44476637399467 0.0148494729050979 * df.mm.trans2:exp3 -0.121205998225839 0.0693123921197497 -1.74869160505143 0.0809769771897439 . df.mm.trans1:exp4 0.117335965140217 0.0693123921197497 1.69285695604760 0.0911248822859017 . df.mm.trans2:exp4 -0.0894354408786717 0.0693123921197497 -1.29032396868017 0.197553022645475 df.mm.trans1:exp5 -0.078524782592084 0.0693123921197497 -1.13291116048077 0.257811305248953 df.mm.trans2:exp5 -0.0313617451499177 0.0693123921197497 -0.452469525156982 0.651133085341533 df.mm.trans1:exp6 0.0400299039396797 0.0693123921197497 0.577528818663779 0.563850280443953 df.mm.trans2:exp6 0.029424342159548 0.0693123921197497 0.424517770339136 0.671376497027481 df.mm.trans1:exp7 0.0946150749656697 0.0693123921197497 1.36505280040263 0.172869326379678 df.mm.trans2:exp7 -0.0802524791872578 0.0693123921197497 -1.15783739000967 0.247500258676631 df.mm.trans1:exp8 0.189547258135823 0.0693123921197497 2.73468065866701 0.00647265620941495 ** df.mm.trans2:exp8 -0.164078752121949 0.0693123921197497 -2.36723545536378 0.0183131592949655 * df.mm.trans1:probe2 0.0585826997895626 0.0474549508482878 1.23449079057841 0.21761786012609 df.mm.trans1:probe3 0.176835166788468 0.0474549508482878 3.72637972703428 0.000217124350408881 *** df.mm.trans1:probe4 -0.0587196764134757 0.0474549508482878 -1.2373772465006 0.216545797710359 df.mm.trans1:probe5 0.0214413067794622 0.0474549508482878 0.451824443944942 0.651597446107698 df.mm.trans1:probe6 -0.0273358337545906 0.0474549508482878 -0.57603755279365 0.564856996103184 df.mm.trans2:probe2 -0.490722330740413 0.0474549508482878 -10.3408036878858 8.76775259435873e-23 *** df.mm.trans2:probe3 -0.345248760400665 0.0474549508482878 -7.2752948686938 1.39935397920643e-12 *** df.mm.trans2:probe4 -0.224305122952548 0.0474549508482878 -4.72669592830569 2.99474457873266e-06 *** df.mm.trans2:probe5 -0.192042973014284 0.0474549508482878 -4.04684800176571 6.04021792694963e-05 *** df.mm.trans2:probe6 -0.397311216922194 0.0474549508482878 -8.37238707068493 6.09759021299193e-16 *** df.mm.trans3:probe2 0.91342304107268 0.0474549508482878 19.2482138268959 9.31521887851612e-62 *** df.mm.trans3:probe3 0.327854479938271 0.0474549508482878 6.90875185997796 1.54271170498552e-11 *** df.mm.trans3:probe4 0.0123643674257265 0.0474549508482878 0.260549578172677 0.794550462091362 df.mm.trans3:probe5 0.181056682120216 0.0474549508482878 3.81533810242580 0.000153573223000602 *** df.mm.trans3:probe6 0.0421277982810889 0.0474549508482878 0.88774295469761 0.375119060329306 df.mm.trans3:probe7 0.109989427248410 0.0474549508482878 2.31776506523088 0.0208773522673336 * df.mm.trans3:probe8 0.00666373033478131 0.0474549508482878 0.140422236577277 0.88838468837325 df.mm.trans3:probe9 0.596358985513248 0.0474549508482878 12.5668444462158 1.49418067562071e-31 *** df.mm.trans3:probe10 0.204679368620752 0.0474549508482878 4.31312992558156 1.95111131954446e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.21088086708506 0.151368622965775 27.8187168818807 1.62192101048251e-102 *** df.mm.trans1 -0.0424226844396984 0.121178498588565 -0.350084255324331 0.726427445406582 df.mm.trans2 0.112121562662511 0.121178498588565 0.92525954660649 0.355291057808196 df.mm.exp2 -0.00974552105392159 0.162266265116332 -0.0600588239763505 0.952133523492929 df.mm.exp3 -0.0864232211926738 0.162266265116332 -0.532601284257793 0.594553535529785 df.mm.exp4 0.0379288453626616 0.162266265116332 0.233744489869596 0.815281974701413 df.mm.exp5 0.0390793148814557 0.162266265116332 0.240834500340776 0.80978516176032 df.mm.exp6 -0.119915239660973 0.162266265116332 -0.739002894871607 0.460262728341991 df.mm.exp7 -0.169101198684157 0.162266265116332 -1.04212171619853 0.297874432981311 df.mm.exp8 -0.139280685014080 0.162266265116332 -0.858346526397383 0.391125135548399 df.mm.trans1:exp2 0.0108387166667566 0.127292131111692 0.0851483636270201 0.932178578288088 df.mm.trans2:exp2 -0.114177476834063 0.127292131111692 -0.89697199533786 0.370178874806783 df.mm.trans1:exp3 0.132264246349929 0.127292131111692 1.03906066458950 0.299294245940217 df.mm.trans2:exp3 -0.0947639596060483 0.127292131111692 -0.74446046883211 0.456958499583423 df.mm.trans1:exp4 -0.0323689977584987 0.127292131111692 -0.254289070941052 0.799380017740504 df.mm.trans2:exp4 -0.0877087302753327 0.127292131111692 -0.689034974191557 0.491130747635326 df.mm.trans1:exp5 0.0300090836038305 0.127292131111692 0.235749714784013 0.813726406450856 df.mm.trans2:exp5 -0.170603656791994 0.127292131111692 -1.34025297009364 0.180790623697582 df.mm.trans1:exp6 0.0289634706414118 0.127292131111692 0.227535436703452 0.8201033021805 df.mm.trans2:exp6 0.0142105360910362 0.127292131111692 0.111637192078804 0.911157266949275 df.mm.trans1:exp7 0.159897571944123 0.127292131111692 1.25614655476089 0.209667579459641 df.mm.trans2:exp7 0.0402897744400501 0.127292131111692 0.316514258094226 0.751748386884597 df.mm.trans1:exp8 0.0419748636990566 0.127292131111692 0.329752226885306 0.741729558099885 df.mm.trans2:exp8 0.042861793393434 0.127292131111692 0.336719897915961 0.736473711149407 df.mm.trans1:probe2 0.00874020965735824 0.0871509645034733 0.100288157533930 0.920157000673149 df.mm.trans1:probe3 0.088224783759385 0.0871509645034733 1.01232136972929 0.311889175434555 df.mm.trans1:probe4 0.00791287982992277 0.0871509645034733 0.0907950918845816 0.927692895549849 df.mm.trans1:probe5 0.0143857793757213 0.0871509645034733 0.165067357058887 0.86895974285103 df.mm.trans1:probe6 0.105853566253718 0.0871509645034733 1.21460005470737 0.225109689280389 df.mm.trans2:probe2 -0.0173937141045994 0.0871509645034733 -0.199581429806278 0.84189160070569 df.mm.trans2:probe3 -0.0297125994768543 0.0871509645034733 -0.340932537535716 0.733302007673176 df.mm.trans2:probe4 -0.0696597651932111 0.0871509645034733 -0.79929999157307 0.424507753136472 df.mm.trans2:probe5 0.0448864145854604 0.0871509645034733 0.515042086352028 0.606758260844201 df.mm.trans2:probe6 0.0676769056366275 0.0871509645034733 0.776547982253602 0.437804046603207 df.mm.trans3:probe2 -0.037238102146287 0.0871509645034733 -0.427282731274912 0.669363038492249 df.mm.trans3:probe3 -0.06178319338803 0.0871509645034733 -0.708921510393241 0.478713804033658 df.mm.trans3:probe4 -0.0009417658245469 0.0871509645034733 -0.0108061434536317 0.991382556245574 df.mm.trans3:probe5 -0.103589338831371 0.0871509645034733 -1.18861953417903 0.235171085628748 df.mm.trans3:probe6 -0.0456162744290703 0.0871509645034733 -0.523416748041295 0.600923333988573 df.mm.trans3:probe7 0.0864509588538068 0.0871509645034733 0.991967895551648 0.321707818328112 df.mm.trans3:probe8 0.0455158792262646 0.0871509645034733 0.522264779117282 0.601724442608976 df.mm.trans3:probe9 -0.0393120442252313 0.0871509645034733 -0.451079852635074 0.65213360763465 df.mm.trans3:probe10 0.00254432968927377 0.0871509645034733 0.0291945098229219 0.976721469041804