chr11.4942_chr11_21711000_21716738_+_2.R fitVsDatCorrelation=0.82112411379947 cont.fitVsDatCorrelation=0.282179122277268 fstatistic=11667.2080105620,54,738 cont.fstatistic=4120.63270330045,54,738 residuals=-0.77030249430962,-0.082290841584285,-0.00506381522556492,0.0723183834946897,0.98922580334384 cont.residuals=-0.471607605405602,-0.142198875881725,-0.0462148570227192,0.0734315681141521,1.01491378214736 predictedValues: Include Exclude Both chr11.4942_chr11_21711000_21716738_+_2.R.tl.Lung 46.4973128252066 45.3009369389872 73.0214517428458 chr11.4942_chr11_21711000_21716738_+_2.R.tl.cerebhem 48.9116014820937 59.9971895031383 69.091893944434 chr11.4942_chr11_21711000_21716738_+_2.R.tl.cortex 47.6123895707312 44.3715065484107 69.1829988850188 chr11.4942_chr11_21711000_21716738_+_2.R.tl.heart 48.2105181510882 46.0375530573936 67.4600320272322 chr11.4942_chr11_21711000_21716738_+_2.R.tl.kidney 47.0696104003789 45.5690832910478 69.9935853272956 chr11.4942_chr11_21711000_21716738_+_2.R.tl.liver 51.2946443112115 50.824444637912 61.6664991395598 chr11.4942_chr11_21711000_21716738_+_2.R.tl.stomach 48.0209747390414 45.9451908957472 73.994606679747 chr11.4942_chr11_21711000_21716738_+_2.R.tl.testicle 48.5435433626017 51.0095802601313 70.4530364029255 diffExp=1.1963758862194,-11.0855880210446,3.24088302232059,2.17296509369457,1.50052710933108,0.470199673299526,2.07578384329413,-2.46603689752958 diffExpScore=6.21541500316683 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 52.3356488399389 47.3858940455229 50.2391516312962 cerebhem 48.9142405425391 52.2097738007569 47.2334906364496 cortex 48.9710803476199 56.0009362082762 51.5890514267521 heart 52.6842064211261 51.1737511350658 52.3101954374512 kidney 51.2251852445434 49.8129599809169 53.6320092617249 liver 48.0731128805112 53.7668263246454 50.2247062845603 stomach 52.6905766493555 56.7259640209318 58.1739677277657 testicle 49.870810934632 51.4825911323568 47.8384767412765 cont.diffExp=4.94975479441599,-3.2955332582178,-7.02985586065638,1.51045528606034,1.41222526362650,-5.69371344413417,-4.03538737157631,-1.61178019772481 cont.diffExpScore=1.99669023612187 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=0.522342632843012 cont.tran.correlation=-0.298483729018887 tran.covariance=0.00166007663761425 cont.tran.covariance=-0.000698500539923314 tran.mean=48.4510049984451 cont.tran.mean=51.4577224067962 weightedLogRatios: wLogRatio Lung 0.0997410166539691 cerebhem -0.815530045952137 cortex 0.269845840826778 heart 0.177677258804010 kidney 0.124260557972757 liver 0.0362184952426273 stomach 0.170106345155789 testicle -0.193612466892008 cont.weightedLogRatios: wLogRatio Lung 0.388272307579946 cerebhem -0.255762512540821 cortex -0.53096024052256 heart 0.114894874571762 kidney 0.109650981192783 liver -0.439752511781315 stomach -0.295280054412821 testicle -0.124856556119981 varWeightedLogRatios=0.122920457514776 cont.varWeightedLogRatios=0.097983336632587 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.78337886760811 0.0691171134640671 54.7386700339421 4.80937879663816e-262 *** df.mm.trans1 0.0444525115704 0.0608860227747017 0.730093863001841 0.465564494352666 df.mm.trans2 0.0399934756905103 0.0549393176307904 0.727957270224563 0.466870637663954 df.mm.exp2 0.386905942133577 0.0731628180631617 5.28828648726405 1.62835628556122e-07 *** df.mm.exp3 0.0569663999065955 0.0731628180631617 0.778625009460628 0.436450216043008 df.mm.exp4 0.131530366010862 0.0731628180631617 1.79777610394000 0.0726211316789223 . df.mm.exp5 0.060484487254218 0.0731628180631617 0.826710737167089 0.408668236919971 df.mm.exp6 0.382253980089971 0.0731628180631617 5.22470279589244 2.27197230310157e-07 *** df.mm.exp7 0.0331258851682711 0.0731628180631617 0.452769399063789 0.650847872372988 df.mm.exp8 0.197559342242186 0.0731628180631617 2.70026971995027 0.00708709271573409 ** df.mm.trans1:exp2 -0.336285846887548 0.0690102588057907 -4.87298341879758 1.34612048844231e-06 *** df.mm.trans2:exp2 -0.105935937877890 0.0564629390229196 -1.87620304063322 0.0610218562695632 . df.mm.trans1:exp3 -0.0332679096563598 0.0690102588057907 -0.482071944549326 0.629897771806968 df.mm.trans2:exp3 -0.0776965961346114 0.0564629390229196 -1.37606361764258 0.169219353313283 df.mm.trans1:exp4 -0.0953476720765894 0.0690102588057907 -1.38164489927386 0.167498868849143 df.mm.trans2:exp4 -0.115400647013214 0.0564629390229196 -2.0438299707773 0.041324860782857 * df.mm.trans1:exp5 -0.0482514311198213 0.0690102588057906 -0.699192148454492 0.484652253772162 df.mm.trans2:exp5 -0.0545827139367501 0.0564629390229196 -0.966699836765383 0.334010785576475 df.mm.trans1:exp6 -0.284062154973077 0.0690102588057906 -4.11623083130999 4.28571704768171e-05 *** df.mm.trans2:exp6 -0.267204262837342 0.0564629390229196 -4.73238317843988 2.65991494657669e-06 *** df.mm.trans1:exp7 -0.00088251819381345 0.0690102588057906 -0.0127882174199207 0.989800213037639 df.mm.trans2:exp7 -0.0190044166036689 0.0564629390229196 -0.336582135690007 0.73652760935675 df.mm.trans1:exp8 -0.154492668008755 0.0690102588057906 -2.23869132911862 0.0254733368177635 * df.mm.trans2:exp8 -0.0788735941762128 0.0564629390229196 -1.39690911491866 0.162860797219964 df.mm.trans1:probe2 -0.0241816498936747 0.0402933102124661 -0.600140563437584 0.548596790628769 df.mm.trans1:probe3 -0.112731355687682 0.0402933102124661 -2.79776854006908 0.00527979667586322 ** df.mm.trans1:probe4 -0.0651818471832504 0.0402933102124661 -1.61768409791966 0.106157993186366 df.mm.trans1:probe5 0.0659205513280668 0.0402933102124661 1.63601726888331 0.102262342622891 df.mm.trans1:probe6 -0.0389998576151113 0.0402933102124661 -0.967899073306847 0.333411859014209 df.mm.trans1:probe7 -0.108973629442715 0.0402933102124661 -2.7045092316342 0.0069981806126566 ** df.mm.trans1:probe8 0.186943277884341 0.0402933102124661 4.63956118022053 4.1306472627655e-06 *** df.mm.trans1:probe9 0.0267190479628402 0.0402933102124661 0.663113748211577 0.507464767235732 df.mm.trans1:probe10 0.0391991074933226 0.0402933102124661 0.97284405988553 0.330949558325728 df.mm.trans1:probe11 0.050989048575357 0.0402933102124661 1.26544700116452 0.206110510315684 df.mm.trans1:probe12 0.0478553744329319 0.0402933102124661 1.18767542752361 0.235343412205439 df.mm.trans1:probe13 -0.0688354798554574 0.0402933102124661 -1.70836001044563 0.0879900590970212 . df.mm.trans1:probe14 -0.0243975555106799 0.0402933102124661 -0.60549891240089 0.545033701887157 df.mm.trans1:probe15 0.158324508293782 0.0402933102124661 3.92930011108393 9.31953780587156e-05 *** df.mm.trans1:probe16 0.0615252498133398 0.0402933102124661 1.52693460747995 0.127205717179346 df.mm.trans1:probe17 -0.033468227899762 0.0402933102124661 -0.830615000934014 0.406459783480813 df.mm.trans1:probe18 0.0143208337357929 0.0402933102124661 0.355414674552161 0.722380513738434 df.mm.trans1:probe19 -0.029901302379442 0.0402933102124661 -0.74209098785314 0.458268316753214 df.mm.trans1:probe20 -0.00950979616219703 0.0402933102124661 -0.236014269169051 0.813487081113352 df.mm.trans1:probe21 0.0147870107620201 0.0402933102124661 0.3669842632449 0.713735956632161 df.mm.trans1:probe22 0.161801553421024 0.0402933102124661 4.01559347117044 6.53626085387984e-05 *** df.mm.trans2:probe2 -0.0456207629793589 0.0402933102124661 -1.13221680568812 0.257911028520121 df.mm.trans2:probe3 -0.0389729801702408 0.0402933102124661 -0.967232028461718 0.333744910936428 df.mm.trans2:probe4 -0.00210405281111255 0.0402933102124661 -0.0522184154148148 0.958368790284845 df.mm.trans2:probe5 -0.0122343284570361 0.0402933102124661 -0.303631754068469 0.761494036631361 df.mm.trans2:probe6 -0.0115587840626364 0.0402933102124661 -0.286866082773719 0.774295380695564 df.mm.trans3:probe2 0.357153731906599 0.0402933102124661 8.8638468773931 5.73391357432236e-18 *** df.mm.trans3:probe3 0.904797456395853 0.0402933102124661 22.4552773555925 1.63578314733929e-85 *** df.mm.trans3:probe4 0.585213944255025 0.0402933102124661 14.5238487771096 3.18861474214005e-42 *** df.mm.trans3:probe5 0.26747688045335 0.0402933102124661 6.63824538224702 6.15069892145802e-11 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.90122168430911 0.116177576849533 33.5798162614611 9.05989077575329e-151 *** df.mm.trans1 0.0555137629944226 0.102342100754074 0.542433295636767 0.587683803310512 df.mm.trans2 -0.0460521975005166 0.0923464027390314 -0.498689674254653 0.61814661889181 df.mm.exp2 0.0910273824963016 0.122977920981616 0.740192888038086 0.459418369234323 df.mm.exp3 0.0740811513793286 0.122977920981616 0.60239391581846 0.547096997697058 df.mm.exp4 0.0431434403658026 0.122977920981616 0.350822651915316 0.72582152056731 df.mm.exp5 -0.0368472921729449 0.122977920981616 -0.299625265078707 0.764547376007141 df.mm.exp6 0.0416649244889209 0.122977920981616 0.338800039522131 0.734856743852638 df.mm.exp7 0.0400229729518539 0.122977920981616 0.325448443366001 0.744933942968859 df.mm.exp8 0.0836416104237825 0.122977920981616 0.680135180007526 0.496632166241347 df.mm.trans1:exp2 -0.158636571822817 0.115997966986629 -1.36758062183196 0.171859706108086 df.mm.trans2:exp2 0.00591774199307401 0.0949074275345144 0.062352780459906 0.950298780363391 df.mm.trans1:exp3 -0.140528985541405 0.115997966986629 -1.21147800424471 0.226100023334854 df.mm.trans2:exp3 0.0929626667861635 0.0949074275345144 0.979508866704414 0.327649590606952 df.mm.trans1:exp4 -0.0365054791882613 0.115997966986629 -0.314707922359272 0.753072381241813 df.mm.trans2:exp4 0.0337586966397572 0.0949074275345144 0.355701313550833 0.722165908015788 df.mm.trans1:exp5 0.0154008415325372 0.115997966986629 0.132768202172995 0.894412893977054 df.mm.trans2:exp5 0.0867978924926597 0.0949074275345144 0.9145532098749 0.360724935361134 df.mm.trans1:exp6 -0.126619648490589 0.115997966986629 -1.09156782467734 0.275379334471052 df.mm.trans2:exp6 0.0846671509951134 0.0949074275345144 0.892102474954586 0.372628783109178 df.mm.trans1:exp7 -0.0332641056513956 0.115997966986629 -0.286764557306681 0.774373091524341 df.mm.trans2:exp7 0.139884461774407 0.0949074275345144 1.47390426027021 0.140933779049644 df.mm.trans1:exp8 -0.131883491077347 0.115997966986629 -1.13694657331839 0.255929640251489 df.mm.trans2:exp8 -0.000722486600991528 0.0949074275345144 -0.00761254013263383 0.993928187699592 df.mm.trans1:probe2 0.00303546441358996 0.0677282211179803 0.0448183100557489 0.964264254296946 df.mm.trans1:probe3 0.0846757013276666 0.0677282211179803 1.25022774745795 0.211612640995439 df.mm.trans1:probe4 -0.0932187331025881 0.0677282211179803 -1.37636470534497 0.169126202454254 df.mm.trans1:probe5 0.0377321594785604 0.0677282211179803 0.557111331963559 0.577620280442308 df.mm.trans1:probe6 0.00575342681723668 0.0677282211179803 0.084948736616224 0.932325184500117 df.mm.trans1:probe7 0.0842079670986197 0.0677282211179803 1.24332170118468 0.214144135995274 df.mm.trans1:probe8 -0.0304159708256932 0.0677282211179803 -0.449088582626578 0.653499601194367 df.mm.trans1:probe9 0.125718429646368 0.0677282211179803 1.85621927715140 0.0638206852242062 . df.mm.trans1:probe10 -0.0127936268980694 0.0677282211179803 -0.188896543964788 0.850225836154878 df.mm.trans1:probe11 0.00783927400222985 0.0677282211179803 0.115746049029903 0.907885296260842 df.mm.trans1:probe12 -0.0618983579344013 0.0677282211179803 -0.913922688543325 0.361055953072808 df.mm.trans1:probe13 -0.039201590881863 0.0677282211179802 -0.578807330751758 0.562895787236856 df.mm.trans1:probe14 0.0216396425131264 0.0677282211179803 0.319507026110001 0.749432485864953 df.mm.trans1:probe15 0.0134501910002405 0.0677282211179803 0.198590643283112 0.842637667636128 df.mm.trans1:probe16 0.0248156835800151 0.0677282211179803 0.366400935538924 0.714170937615809 df.mm.trans1:probe17 -0.0493228091038501 0.0677282211179803 -0.728246044111087 0.466693985260094 df.mm.trans1:probe18 0.00417341563089341 0.0677282211179802 0.0616200390620545 0.950882102134044 df.mm.trans1:probe19 -0.00731566590890963 0.0677282211179803 -0.108015031077902 0.914013127661897 df.mm.trans1:probe20 -0.0299518091633727 0.0677282211179803 -0.442235284922036 0.658448523392598 df.mm.trans1:probe21 0.0128325986654437 0.0677282211179803 0.189471957975830 0.849775028606133 df.mm.trans1:probe22 -0.0763129191857363 0.0677282211179802 -1.12675215628064 0.260213515248713 df.mm.trans2:probe2 0.0490223415968519 0.0677282211179803 0.723809673244726 0.469411954592179 df.mm.trans2:probe3 -0.0438365798967223 0.0677282211179803 -0.647242451863019 0.517676308882766 df.mm.trans2:probe4 0.0366494200834268 0.0677282211179803 0.54112479965456 0.588584863068691 df.mm.trans2:probe5 -0.0277201777564653 0.0677282211179803 -0.409285483937009 0.682448922222667 df.mm.trans2:probe6 0.0205911358616605 0.0677282211179803 0.304025936629746 0.761193830780724 df.mm.trans3:probe2 -0.070132972755542 0.0677282211179803 -1.03550590282554 0.300771892320974 df.mm.trans3:probe3 0.0154251369418400 0.0677282211179803 0.227750510602809 0.81990328434125 df.mm.trans3:probe4 0.000582865968826107 0.0677282211179803 0.0086059541975977 0.993135852534334 df.mm.trans3:probe5 0.0846818993179168 0.0677282211179802 1.25031926012650 0.211579242059623