chr4.17063_chr4_41132845_41137806_+_1.R fitVsDatCorrelation=0.826585134370519 cont.fitVsDatCorrelation=0.252027918310381 fstatistic=13820.5705766553,60,876 cont.fstatistic=4665.0283087103,60,876 residuals=-0.784008548225069,-0.0802466687624232,-0.00306865926798636,0.0691111675982949,0.603023954861755 cont.residuals=-0.502583974399262,-0.159650006792728,-0.0310651021415525,0.124469368379623,0.97095567426422 predictedValues: Include Exclude Both chr4.17063_chr4_41132845_41137806_+_1.R.tl.Lung 48.6540001833592 44.2439175483852 60.5733632739807 chr4.17063_chr4_41132845_41137806_+_1.R.tl.cerebhem 56.8800488938673 51.3343738749613 61.6298513009985 chr4.17063_chr4_41132845_41137806_+_1.R.tl.cortex 50.3230643807297 42.254703537703 62.2361542326687 chr4.17063_chr4_41132845_41137806_+_1.R.tl.heart 48.9833891751076 46.37002733873 60.8980510744098 chr4.17063_chr4_41132845_41137806_+_1.R.tl.kidney 48.4534237273129 39.9087984767446 58.9075905682476 chr4.17063_chr4_41132845_41137806_+_1.R.tl.liver 49.0043757745551 45.5870584323491 57.3298155551956 chr4.17063_chr4_41132845_41137806_+_1.R.tl.stomach 48.9399205218575 43.2440912361122 59.8188864763113 chr4.17063_chr4_41132845_41137806_+_1.R.tl.testicle 50.2750313188249 44.961862717575 62.2853280739077 diffExp=4.410082634974,5.545675018906,8.06836084302674,2.61336183637762,8.54462525056831,3.41731734220602,5.69582928574534,5.31316860124985 diffExpScore=0.977582707888476 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=0,0,0,0,1,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 55.2327956637947 63.4754658625113 54.2057137836229 cerebhem 55.1676122601957 63.2343232549425 53.9340876826422 cortex 55.6744549915581 66.4655424189607 55.5954177086811 heart 53.5942896610528 59.077453686446 55.212945619407 kidney 54.3656875184754 61.9347384774103 57.8927640239499 liver 54.5491192555074 61.7827011734796 53.6559994557306 stomach 54.7939929599891 62.7125107435646 52.3289210460143 testicle 54.3420892321728 55.9497298358977 54.6684156033949 cont.diffExp=-8.24267019871657,-8.06671099474682,-10.7910874274026,-5.48316402539324,-7.56905095893494,-7.2335819179722,-7.9185177835755,-1.60764060372484 cont.diffExpScore=0.98273254800134 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=0.790144338284175 cont.tran.correlation=0.788899598364253 tran.covariance=0.00299215012648058 cont.tran.covariance=0.00047519923460092 tran.mean=47.4636304461359 cont.tran.mean=58.2720316872474 weightedLogRatios: wLogRatio Lung 0.364598336347932 cerebhem 0.409274768207659 cortex 0.66947460969224 heart 0.211858910938481 kidney 0.734041795681207 liver 0.278716896461347 stomach 0.473739224822959 testicle 0.431324134337473 cont.weightedLogRatios: wLogRatio Lung -0.567667715219916 cerebhem -0.556613011405839 cortex -0.727800607998643 heart -0.392563745019971 kidney -0.529331193964511 liver -0.505728339958915 stomach -0.549514722522988 testicle -0.116906161596616 varWeightedLogRatios=0.0320959031038051 cont.varWeightedLogRatios=0.0315660772099304 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.75522725662600 0.0615188478055854 61.0418983868721 0 *** df.mm.trans1 0.0533674233300009 0.05196114904714 1.02706395660314 0.304673860115202 df.mm.trans2 0.0320288475796940 0.0463659112090076 0.690784387592747 0.489884043901347 df.mm.exp2 0.287562188800711 0.0589632268693878 4.87697509225036 1.27912946545518e-06 *** df.mm.exp3 -0.0393535942117681 0.0589632268693878 -0.667426060295888 0.504675802062869 df.mm.exp4 0.0483366721307908 0.0589632268693878 0.819776574268291 0.412566474877110 df.mm.exp5 -0.0793668286069867 0.0589632268693878 -1.34603943543992 0.178638026432892 df.mm.exp6 0.0921159479882642 0.0589632268693878 1.562260969745 0.118587690332173 df.mm.exp7 -0.00446408712927961 0.0589632268693878 -0.0757096815472501 0.939667359666446 df.mm.exp8 0.0210006123400251 0.0589632268693878 0.356164569936862 0.721803082507989 df.mm.trans1:exp2 -0.131351572668382 0.0521742510311142 -2.51755550051021 0.0119946115110163 * df.mm.trans2:exp2 -0.138919510441433 0.0382705681127038 -3.62993070895437 0.000299930275567942 *** df.mm.trans1:exp3 0.0730830736588793 0.0521742510311142 1.40074983760277 0.16164301527985 df.mm.trans2:exp3 -0.00664863731745683 0.0382705681127038 -0.173727165425847 0.862120023790093 df.mm.trans1:exp4 -0.0415894568779285 0.0521742510311142 -0.797126092967326 0.425593800815044 df.mm.trans2:exp4 -0.00140128962189167 0.0382705681127038 -0.03661533368841 0.97080005916301 df.mm.trans1:exp5 0.0752358006544026 0.0521742510311142 1.44201017106188 0.149656911173281 df.mm.trans2:exp5 -0.0237542640772951 0.0382705681127038 -0.620692747683826 0.534963252504514 df.mm.trans1:exp6 -0.0849403813023604 0.0521742510311142 -1.62801342853405 0.103881614065519 df.mm.trans2:exp6 -0.0622099835289466 0.0382705681127038 -1.62553070405809 0.104409208705836 df.mm.trans1:exp7 0.0103234921919059 0.0521742510311142 0.197865651885439 0.843196083614927 df.mm.trans2:exp7 -0.0183932131165050 0.0382705681127038 -0.480609879172383 0.630913794717632 df.mm.trans1:exp8 0.0117739173096556 0.0521742510311142 0.225665286553596 0.821514395800913 df.mm.trans2:exp8 -0.00490388253402195 0.0382705681127038 -0.128137176317331 0.898069826141116 df.mm.trans1:probe2 0.0713276023645354 0.0388460976725635 1.83615875565574 0.0666729312353532 . df.mm.trans1:probe3 0.103199397491525 0.0388460976725635 2.65662199486291 0.00803636134338593 ** df.mm.trans1:probe4 0.175141503857209 0.0388460976725635 4.50859968827471 7.41232473786572e-06 *** df.mm.trans1:probe5 0.0180296793827245 0.0388460976725635 0.464131031505351 0.642669186475265 df.mm.trans1:probe6 -0.0090285351786731 0.0388460976725635 -0.232418073361583 0.81626760016497 df.mm.trans1:probe7 0.178454153781393 0.0388460976725635 4.59387594825085 4.98868440774615e-06 *** df.mm.trans1:probe8 0.0556041523085007 0.0388460976725635 1.43139608969714 0.152673386726552 df.mm.trans1:probe9 0.0196095766640249 0.0388460976725635 0.504801713400286 0.6138250726643 df.mm.trans1:probe10 0.111990566341354 0.0388460976725635 2.88292963904201 0.00403635633220751 ** df.mm.trans1:probe11 0.239465119128144 0.0388460976725635 6.16445752535073 1.07809667314353e-09 *** df.mm.trans1:probe12 0.190381764327386 0.0388460976725635 4.90092379245214 1.13630301733555e-06 *** df.mm.trans1:probe13 0.0654095362038432 0.0388460976725635 1.68381227775270 0.0925742174733234 . df.mm.trans1:probe14 0.399182446948554 0.0388460976725635 10.2759986424709 1.82350795485813e-23 *** df.mm.trans1:probe15 0.234739766454356 0.0388460976725635 6.04281460735114 2.23790633490075e-09 *** df.mm.trans1:probe16 0.202255693009228 0.0388460976725635 5.20658972528092 2.39797109400335e-07 *** df.mm.trans2:probe2 -0.0396065736024999 0.0388460976725635 -1.01957663640622 0.308210784263363 df.mm.trans2:probe3 0.0121662594492924 0.0388460976725635 0.313191290199665 0.754209886732959 df.mm.trans2:probe4 -0.00562541847813706 0.0388460976725635 -0.144812962309731 0.884891858078525 df.mm.trans2:probe5 0.0104444947786947 0.0388460976725635 0.268868571219998 0.788094132161138 df.mm.trans2:probe6 0.0792426669836901 0.0388460976725635 2.03991318900632 0.0416584907447118 * df.mm.trans3:probe2 0.103199397491525 0.0388460976725635 2.65662199486290 0.00803636134338593 ** df.mm.trans3:probe3 0.0180296793827244 0.0388460976725635 0.464131031505347 0.642669186475265 df.mm.trans3:probe4 -0.00902853517867329 0.0388460976725635 -0.232418073361588 0.81626760016497 df.mm.trans3:probe5 0.178454153781393 0.0388460976725635 4.59387594825084 4.98868440774615e-06 *** df.mm.trans3:probe6 0.0556041523085005 0.0388460976725635 1.43139608969714 0.152673386726562 df.mm.trans3:probe7 0.0714915701181741 0.0388460976725635 1.84037971383333 0.0660506133418002 . df.mm.trans3:probe8 0.628383056022335 0.0388460976725635 16.1762208734329 1.07075672818795e-51 *** df.mm.trans3:probe9 0.707689592707583 0.0388460976725635 18.2177782353519 3.97184255713308e-63 *** df.mm.trans3:probe10 0.265698455654521 0.0388460976725635 6.8397721154416 1.48779931342399e-11 *** df.mm.trans3:probe11 0.491531242196175 0.0388460976725635 12.6532977994167 8.00148864707442e-34 *** df.mm.trans3:probe12 0.365383644518918 0.0388460976725635 9.40592920294754 4.39117767038965e-20 *** df.mm.trans3:probe13 0.311888157080941 0.0388460976725635 8.0288156537593 3.15832627701172e-15 *** df.mm.trans3:probe14 0.3421102363569 0.0388460976725635 8.80681089875671 6.79800292992257e-18 *** df.mm.trans3:probe15 0.30321606496066 0.0388460976725635 7.80557335556559 1.6859810190639e-14 *** df.mm.trans3:probe16 0.259878823061643 0.0388460976725635 6.68995957463165 3.97732913604186e-11 *** df.mm.trans3:probe17 0.381409532109587 0.0388460976725635 9.818477400858 1.17025434723667e-21 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15679742962017 0.105777858937827 39.2974245400769 1.62524325513297e-195 *** df.mm.trans1 -0.141683875548011 0.089343986277597 -1.58582442368075 0.11314003352983 df.mm.trans2 0.0364702198831249 0.07972332041864 0.457459871109405 0.647453971403812 df.mm.exp2 3.65424980453469e-05 0.101383626592287 0.000360437866286851 0.999712494208092 df.mm.exp3 0.028680250874383 0.101383626592287 0.282888389756663 0.777329226459912 df.mm.exp4 -0.120329609158002 0.101383626592287 -1.18687418474292 0.235599014143562 df.mm.exp5 -0.106202011736552 0.101383626592287 -1.04752626539630 0.295145858358047 df.mm.exp6 -0.0292923579735327 0.101383626592287 -0.288925923821323 0.77270642302276 df.mm.exp7 0.0151682848241995 0.101383626592287 0.149612766223075 0.881104573335334 df.mm.exp8 -0.150957519470492 0.101383626592287 -1.48897336329825 0.136854292771248 df.mm.trans1:exp2 -0.00121739703289979 0.0897104019762692 -0.0135702996094235 0.989175889659765 df.mm.trans2:exp2 -0.00384276615881372 0.0658038779934457 -0.0583972597967018 0.953445515574961 df.mm.trans1:exp3 -0.0207157280894046 0.0897104019762692 -0.230917793623135 0.817432587163847 df.mm.trans2:exp3 0.0173499365124268 0.0658038779934457 0.263661307531980 0.792102921091892 df.mm.trans1:exp4 0.0902152341798272 0.0897104019762692 1.00562735415779 0.314872553111858 df.mm.trans2:exp4 0.0485254996046131 0.0658038779934457 0.737426137855376 0.461060716032431 df.mm.trans1:exp5 0.090378321244842 0.0897104019762692 1.00744528230683 0.313999031297427 df.mm.trans2:exp5 0.0816297703298655 0.0658038779934457 1.24050090692217 0.215122318383284 df.mm.trans1:exp6 0.0168370232802126 0.0897104019762692 0.187681951137243 0.851169442820635 df.mm.trans2:exp6 0.00226230009576276 0.0658038779934457 0.0343794342331631 0.972582414812957 df.mm.trans1:exp7 -0.0231446156022795 0.0897104019762692 -0.25799255261839 0.796473251700137 df.mm.trans2:exp7 -0.0272607905700808 0.0658038779934457 -0.414273313387337 0.678775372860607 df.mm.trans1:exp8 0.134699668932857 0.0897104019762692 1.50149443058441 0.133588079329949 df.mm.trans2:exp8 0.0247576586756849 0.0658038779934458 0.376234037120894 0.706834014108892 df.mm.trans1:probe2 -0.00729661337245988 0.066793465522617 -0.109241425270698 0.913036022794093 df.mm.trans1:probe3 -0.00346295591135669 0.066793465522617 -0.0518457289835351 0.958663437831168 df.mm.trans1:probe4 0.053437595100846 0.066793465522617 0.80004225986375 0.423903211836695 df.mm.trans1:probe5 0.00537810841604889 0.066793465522617 0.0805184814707332 0.935843288978487 df.mm.trans1:probe6 0.0471565259362015 0.066793465522617 0.706005079497392 0.480372691417582 df.mm.trans1:probe7 -0.0565568995190527 0.066793465522617 -0.846743002126487 0.397369640306486 df.mm.trans1:probe8 0.0111301510612391 0.066793465522617 0.166635328383588 0.867695448598312 df.mm.trans1:probe9 0.0885080351457294 0.066793465522617 1.32510020932751 0.185483435372657 df.mm.trans1:probe10 -0.0285851453094633 0.066793465522617 -0.427963200977857 0.668783103488603 df.mm.trans1:probe11 -0.0836554868942539 0.066793465522617 -1.25245016469354 0.210740134600187 df.mm.trans1:probe12 -0.0108391379275155 0.066793465522617 -0.162278418146237 0.871124032687889 df.mm.trans1:probe13 -0.0461006011411045 0.066793465522617 -0.690196275644573 0.490253578699215 df.mm.trans1:probe14 -0.0459323187817509 0.066793465522617 -0.68767683219248 0.491838346327294 df.mm.trans1:probe15 -0.0246417967648842 0.066793465522617 -0.368925261956054 0.712272580635567 df.mm.trans1:probe16 0.00543091255283525 0.066793465522617 0.0813090398939742 0.935214757852453 df.mm.trans2:probe2 -0.226500178556589 0.066793465522617 -3.39105295382367 0.000727395858198804 *** df.mm.trans2:probe3 -0.171728829004763 0.066793465522617 -2.57104235663014 0.0103033150820937 * df.mm.trans2:probe4 -0.181454862310899 0.066793465522617 -2.71665590175819 0.00672452940468426 ** df.mm.trans2:probe5 -0.250139215011586 0.066793465522617 -3.74496536531536 0.000192209289983491 *** df.mm.trans2:probe6 -0.150303115663522 0.066793465522617 -2.25026676617980 0.0246794140432632 * df.mm.trans3:probe2 -0.126998650340786 0.066793465522617 -1.90136339456414 0.0575823243396079 . df.mm.trans3:probe3 -0.0295559436957541 0.066793465522617 -0.442497532722661 0.658238448708329 df.mm.trans3:probe4 0.0104264145270411 0.066793465522617 0.156099319678369 0.875990699453536 df.mm.trans3:probe5 -0.0545898140115084 0.066793465522617 -0.817292733419015 0.413983380774163 df.mm.trans3:probe6 0.0189675313461677 0.066793465522617 0.283972858688475 0.776498285763008 df.mm.trans3:probe7 -0.0897285472236835 0.066793465522617 -1.34337313570443 0.179499077442120 df.mm.trans3:probe8 -0.122545480287823 0.066793465522617 -1.83469265038101 0.0668902153503071 . df.mm.trans3:probe9 -0.123559787546875 0.066793465522617 -1.84987837627674 0.0646676998426446 . df.mm.trans3:probe10 -0.0796137208723292 0.066793465522617 -1.19193876600655 0.233608057973182 df.mm.trans3:probe11 -0.0649689127559714 0.066793465522617 -0.972683663703184 0.330979049313142 df.mm.trans3:probe12 -0.00758841226174071 0.066793465522617 -0.113610099466559 0.909572912356438 df.mm.trans3:probe13 -0.0325452615953104 0.066793465522617 -0.487252178647479 0.626201535586699 df.mm.trans3:probe14 -0.0566438199412008 0.066793465522617 -0.848044333349055 0.396644934604108 df.mm.trans3:probe15 -0.0561383416290645 0.066793465522617 -0.840476552456399 0.400870583578731 df.mm.trans3:probe16 -0.0700595640371737 0.066793465522617 -1.04889847366058 0.294514140558787 df.mm.trans3:probe17 -0.114414351641495 0.066793465522617 -1.71295725931084 0.0870742466351283 .