chr17.10945_chr17_68410709_68414994_+_2.R fitVsDatCorrelation=0.89485976008676 cont.fitVsDatCorrelation=0.212023828577280 fstatistic=5874.21050841961,58,830 cont.fstatistic=1214.05628954624,58,830 residuals=-0.630289960173249,-0.113100070144056,-0.0129952175139969,0.102222514009758,1.23880586809218 cont.residuals=-0.912422787237397,-0.356055304211879,-0.120144255729334,0.251212334406779,1.62994696235397 predictedValues: Include Exclude Both chr17.10945_chr17_68410709_68414994_+_2.R.tl.Lung 117.229947542490 61.1608674698452 60.0087750786513 chr17.10945_chr17_68410709_68414994_+_2.R.tl.cerebhem 181.492463285103 65.424973601729 86.847105237164 chr17.10945_chr17_68410709_68414994_+_2.R.tl.cortex 197.828861627080 65.8996485186666 118.901106313517 chr17.10945_chr17_68410709_68414994_+_2.R.tl.heart 116.603460575271 59.1452071069609 65.7484959210333 chr17.10945_chr17_68410709_68414994_+_2.R.tl.kidney 122.124898436936 70.2756494764146 67.6851933021128 chr17.10945_chr17_68410709_68414994_+_2.R.tl.liver 128.895093127858 65.6056839497811 62.0358850015534 chr17.10945_chr17_68410709_68414994_+_2.R.tl.stomach 143.805598843111 56.8479742143809 64.1101143148882 chr17.10945_chr17_68410709_68414994_+_2.R.tl.testicle 116.165203738985 59.3426689250633 57.6909476894223 diffExp=56.0690800726443,116.067489683374,131.929213108413,57.4582534683097,51.849248960521,63.2894091780768,86.9576246287297,56.8225348139214 diffExpScore=0.998390841581488 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 82.1853607033939 87.8142330289809 75.828800947849 cerebhem 83.9592209063988 83.4859477611877 70.1000966123093 cortex 69.9945699465302 79.9067100341657 74.6798421390104 heart 74.7066702034105 78.0199459920472 75.2940923801577 kidney 71.7063470095328 94.1739304145812 83.9225853585174 liver 84.4983596109386 94.3625148592165 76.1512383154438 stomach 67.3932264247824 90.363697333818 74.1836531068204 testicle 74.3466915616096 76.6309230130203 69.0074914039026 cont.diffExp=-5.62887232558698,0.473273145211166,-9.91214008763544,-3.3132757886367,-22.4675834050483,-9.86415524827792,-22.9704709090356,-2.28423145141075 cont.diffExpScore=0.999305502451208 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,-1,0,-1,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,0,0,0,-1,0,-1,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.301325613278068 cont.tran.correlation=0.159165431986337 tran.covariance=0.00455908612072948 cont.tran.covariance=0.00105997068406419 tran.mean=101.740512527480 cont.tran.mean=80.8467718002259 weightedLogRatios: wLogRatio Lung 2.88803019634433 cerebhem 4.78633488747221 cortex 5.20808219098524 heart 2.99980668410557 kidney 2.50266454472178 liver 3.05341959475142 stomach 4.18047156944662 testicle 2.96828978057641 cont.weightedLogRatios: wLogRatio Lung -0.294273169854604 cerebhem 0.0250281892904076 cortex -0.571440087521246 heart -0.188129474793756 kidney -1.20169745578007 liver -0.495962892614756 stomach -1.27795656620605 testicle -0.130846860064289 varWeightedLogRatios=1.01338375291758 cont.varWeightedLogRatios=0.235991941171538 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.42887958266836 0.102592387680357 52.9169824917506 3.50240199427474e-268 *** df.mm.trans1 -0.407448028901876 0.085260373406243 -4.77886751633721 2.08455314707469e-06 *** df.mm.trans2 -1.32692929204888 0.0779089303164361 -17.0317996494036 5.16244490988428e-56 *** df.mm.exp2 0.134814880297088 0.099784766672097 1.35105672732697 0.177045428841550 df.mm.exp3 -0.0859108099053208 0.0997847666720969 -0.860961174440911 0.389508066239080 df.mm.exp4 -0.130216416231815 0.099784766672097 -1.30497289891672 0.192263746784934 df.mm.exp5 0.0594480605222769 0.099784766672097 0.595762885507658 0.55149605817522 df.mm.exp6 0.131794040413057 0.099784766672097 1.32078316970110 0.186937713279009 df.mm.exp7 0.0650866927032444 0.099784766672097 0.652270831249483 0.514407067270583 df.mm.exp8 8.75320309695562e-05 0.099784766672097 0.000877208354429445 0.999300299858086 df.mm.trans1:exp2 0.302261878337168 0.0849424342998605 3.55843202315270 0.000394247323547359 *** df.mm.trans2:exp2 -0.0674183999444957 0.0668837396514832 -1.00799387557871 0.313751165441233 df.mm.trans1:exp3 0.609175762021525 0.0849424342998604 7.17163061128006 1.64130218068371e-12 *** df.mm.trans2:exp3 0.160536353269178 0.0668837396514832 2.40022992293341 0.0166040867882232 * df.mm.trans1:exp4 0.124857999132490 0.0849424342998604 1.46991312600862 0.141964231815524 df.mm.trans2:exp4 0.0967044093305418 0.0668837396514832 1.44585828834404 0.148594431810939 df.mm.trans1:exp5 -0.0185411514002263 0.0849424342998605 -0.218279020998775 0.827265374716681 df.mm.trans2:exp5 0.0794697335800493 0.0668837396514832 1.18817718617633 0.235103482693476 df.mm.trans1:exp6 -0.0369325680790528 0.0849424342998604 -0.434795263209375 0.66382406418086 df.mm.trans2:exp6 -0.0616392672150805 0.0668837396514832 -0.92158822960961 0.35701119397263 df.mm.trans1:exp7 0.139238317148488 0.0849424342998604 1.63920799181425 0.101548900184084 df.mm.trans2:exp7 -0.138213671588015 0.0668837396514832 -2.06647643071719 0.0390931981927927 * df.mm.trans1:exp8 -0.0092115535379339 0.0849424342998604 -0.108444661538845 0.913669185280736 df.mm.trans2:exp8 -0.0302665059246954 0.0668837396514832 -0.452524127424807 0.65100969510545 df.mm.trans1:probe2 -0.447084314548347 0.0641301315492809 -6.97151719086658 6.39706047156094e-12 *** df.mm.trans1:probe3 -0.287264086611576 0.064130131549281 -4.47939337830342 8.53392966735764e-06 *** df.mm.trans1:probe4 -0.207650170732001 0.0641301315492809 -3.23795017592365 0.00125172745954885 ** df.mm.trans1:probe5 -0.436593732656856 0.0641301315492809 -6.80793446246645 1.89813004966396e-11 *** df.mm.trans1:probe6 -0.245910083218453 0.064130131549281 -3.83454824241992 0.000135314976170670 *** df.mm.trans1:probe7 -1.01504864161235 0.0641301315492809 -15.8279519640832 1.6484076126142e-49 *** df.mm.trans1:probe8 -1.12751191005647 0.0641301315492809 -17.5816247810132 4.63663903589927e-59 *** df.mm.trans1:probe9 -0.985278288594003 0.0641301315492809 -15.3637340948983 4.55531956820343e-47 *** df.mm.trans1:probe10 -1.03343548494558 0.0641301315492809 -16.1146634191985 4.89761588917486e-51 *** df.mm.trans1:probe11 -1.15185582228776 0.064130131549281 -17.9612265632515 3.44619480105763e-61 *** df.mm.trans1:probe12 -1.03848717363406 0.0641301315492809 -16.1934358864684 1.85328411752275e-51 *** df.mm.trans2:probe2 0.0445768510448835 0.064130131549281 0.695099947060428 0.487187213785568 df.mm.trans2:probe3 0.239706756512179 0.064130131549281 3.73781794487628 0.000198348394895135 *** df.mm.trans2:probe4 0.23860553751137 0.064130131549281 3.72064631316113 0.000212095029547674 *** df.mm.trans2:probe5 -0.0621262179983062 0.064130131549281 -0.96875238670866 0.332950949648664 df.mm.trans2:probe6 -0.171831078194777 0.064130131549281 -2.67941253266779 0.00752119170608345 ** df.mm.trans3:probe2 0.082169455625704 0.0641301315492809 1.2812924851489 0.200448803802726 df.mm.trans3:probe3 1.13882803041212 0.0641301315492809 17.7580803734510 4.77659530491234e-60 *** df.mm.trans3:probe4 0.317859362325205 0.0641301315492809 4.9564745096608 8.70332675754377e-07 *** df.mm.trans3:probe5 0.280291375385936 0.0641301315492809 4.37066584169636 1.39580399083811e-05 *** df.mm.trans3:probe6 -0.0526789087095859 0.0641301315492809 -0.821437714798771 0.411632870185681 df.mm.trans3:probe7 0.759608686066396 0.0641301315492809 11.8448016200103 5.13677762870021e-30 *** df.mm.trans3:probe8 0.69590076258026 0.0641301315492809 10.8513852344977 9.56147540680519e-26 *** df.mm.trans3:probe9 0.153038679530006 0.0641301315492809 2.38637713400608 0.0172384578884897 * df.mm.trans3:probe10 0.860678436399915 0.0641301315492809 13.4208119585491 2.61368080155566e-37 *** df.mm.trans3:probe11 1.15300806575780 0.064130131549281 17.9791938345202 2.72949576950086e-61 *** df.mm.trans3:probe12 0.056866964889674 0.0641301315492809 0.886743306396846 0.375473974371709 df.mm.trans3:probe13 0.619508553280697 0.0641301315492809 9.6601790502275 5.39320699407861e-21 *** df.mm.trans3:probe14 0.343902208356017 0.064130131549281 5.36256826624072 1.06438461160573e-07 *** df.mm.trans3:probe15 0.267023329386729 0.0641301315492809 4.16377329869555 3.45753675446608e-05 *** df.mm.trans3:probe16 -0.0879490502222277 0.0641301315492809 -1.37141540329826 0.170616131906751 df.mm.trans3:probe17 0.453520890776177 0.0641301315492809 7.07188461679154 3.24670878091885e-12 *** df.mm.trans3:probe18 0.169977801804987 0.0641301315492809 2.65051384892868 0.00819000176601685 ** df.mm.trans3:probe19 0.388227637666050 0.064130131549281 6.05374772025401 2.14169394765400e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.66163950277585 0.224623008904794 20.7531700581558 2.18207392992731e-77 *** df.mm.trans1 -0.271339616602844 0.186675074514553 -1.45353961855090 0.146451975345223 df.mm.trans2 -0.147366180400406 0.170579306553974 -0.86391593082117 0.387883660430368 df.mm.exp2 0.0493627634819696 0.218475805462131 0.225941556217426 0.821302500969548 df.mm.exp3 -0.239655315589884 0.218475805462131 -1.09694213088242 0.272984837509383 df.mm.exp4 -0.206590391312039 0.218475805462131 -0.945598488011284 0.344628607356266 df.mm.exp5 -0.167894716750973 0.218475805462131 -0.768481967125988 0.442419495309219 df.mm.exp6 0.0954320792360339 0.218475805462131 0.4368084559028 0.662363877915611 df.mm.exp7 -0.147879301318287 0.218475805462131 -0.676868090750301 0.498678244284275 df.mm.exp8 -0.142197813298214 0.218475805462131 -0.650862977698746 0.515315079444792 df.mm.trans1:exp2 -0.0280087404840506 0.185978956212417 -0.150601665126350 0.880326539297445 df.mm.trans2:exp2 -0.0999080309674055 0.146439976561707 -0.682245608836917 0.495274006436839 df.mm.trans1:exp3 0.0790957898690373 0.185978956212417 0.425294299311464 0.670732389690421 df.mm.trans2:exp3 0.145291550343861 0.146439976561707 0.992157700070637 0.321409752477354 df.mm.trans1:exp4 0.111182580113853 0.185978956212417 0.597823443996885 0.550120819864054 df.mm.trans2:exp4 0.0883313082634851 0.146439976561707 0.603191220986464 0.546546274590714 df.mm.trans1:exp5 0.0314967896206332 0.185978956212417 0.169356739397219 0.865557314697171 df.mm.trans2:exp5 0.237814517945768 0.146439976561707 1.62397265780466 0.104761325603041 df.mm.trans1:exp6 -0.0676771505185405 0.185978956212417 -0.363896818741378 0.716027734855388 df.mm.trans2:exp6 -0.0235117681954625 0.146439976561707 -0.160555667567695 0.87248244154544 df.mm.trans1:exp7 -0.0505533765312598 0.185978956212417 -0.271823100638978 0.7858256399721 df.mm.trans2:exp7 0.176498315001914 0.146439976561707 1.20526047016636 0.228446274092330 df.mm.trans1:exp8 0.0419597944827889 0.185978956212417 0.225615818785779 0.82155577730771 df.mm.trans2:exp8 0.00597490837426386 0.146439976561707 0.0408010743688295 0.967464295107417 df.mm.trans1:probe2 0.0435526246642360 0.140411032784822 0.310179505131765 0.756502320794795 df.mm.trans1:probe3 0.158295711742713 0.140411032784822 1.12737374409388 0.259910297685209 df.mm.trans1:probe4 -0.0170895479392612 0.140411032784822 -0.121710862745742 0.903157470105574 df.mm.trans1:probe5 0.156323389708184 0.140411032784822 1.11332697016585 0.265890344273561 df.mm.trans1:probe6 0.0215670533041211 0.140411032784822 0.153599420760421 0.877962929795697 df.mm.trans1:probe7 0.102502576324526 0.140411032784822 0.730017964340526 0.465585186641522 df.mm.trans1:probe8 0.000526539728499202 0.140411032784822 0.00374998828835707 0.99700885034952 df.mm.trans1:probe9 -0.0533550694375543 0.140411032784822 -0.379992001905722 0.704048565046646 df.mm.trans1:probe10 0.107480521585058 0.140411032784822 0.765470629005134 0.444208958070813 df.mm.trans1:probe11 0.0929110306929242 0.140411032784822 0.661707480175788 0.508342367515575 df.mm.trans1:probe12 -0.0337183321005487 0.140411032784822 -0.240140190067697 0.810280874415507 df.mm.trans2:probe2 -0.256283598497581 0.140411032784822 -1.82523832646636 0.0683243269470692 . df.mm.trans2:probe3 -0.176730941076122 0.140411032784822 -1.25866847904296 0.208504011165327 df.mm.trans2:probe4 -0.114042002125034 0.140411032784822 -0.812201148750199 0.416909099495530 df.mm.trans2:probe5 -0.265833305692655 0.140411032784822 -1.89325083948383 0.0586721189984511 . df.mm.trans2:probe6 -0.16335333962782 0.140411032784822 -1.16339390422515 0.245004066170268 df.mm.trans3:probe2 0.239334680517147 0.140411032784822 1.70452902290040 0.0886565410720497 . df.mm.trans3:probe3 0.0634624729407017 0.140411032784822 0.451976398734687 0.65140407386179 df.mm.trans3:probe4 0.0276905217063573 0.140411032784822 0.197210441068350 0.843711142760781 df.mm.trans3:probe5 0.161059431918165 0.140411032784822 1.14705681401109 0.251688683391833 df.mm.trans3:probe6 0.149916292602714 0.140411032784822 1.06769596113191 0.285968122411923 df.mm.trans3:probe7 0.107611551578438 0.140411032784822 0.766403817735258 0.443653976277632 df.mm.trans3:probe8 -0.0401743990633658 0.140411032784822 -0.286119959853387 0.774857641020981 df.mm.trans3:probe9 0.130996570984529 0.140411032784822 0.93295069758001 0.351116734486308 df.mm.trans3:probe10 0.00673273852441234 0.140411032784822 0.0479502101144017 0.961767458694858 df.mm.trans3:probe11 -0.0161901606371327 0.140411032784822 -0.115305473622888 0.90823088909822 df.mm.trans3:probe12 0.104901713502805 0.140411032784822 0.74710449330264 0.455212074886324 df.mm.trans3:probe13 0.111544349547542 0.140411032784822 0.794412998289689 0.427182118747558 df.mm.trans3:probe14 0.102242050131937 0.140411032784822 0.728162510481795 0.466719486249942 df.mm.trans3:probe15 0.154300796630156 0.140411032784822 1.09892216850666 0.272120673382312 df.mm.trans3:probe16 0.0780597414888669 0.140411032784822 0.555937378571188 0.578403491882459 df.mm.trans3:probe17 0.12787292872399 0.140411032784822 0.910704281478748 0.362715608870434 df.mm.trans3:probe18 0.0378366526285646 0.140411032784822 0.269470652541591 0.787634499196733 df.mm.trans3:probe19 0.0781470807000297 0.140411032784822 0.556559403845347 0.577978519369495