chr16.9586_chr16_35614229_35617472_+_0.R fitVsDatCorrelation=0.793153397249167 cont.fitVsDatCorrelation=0.288557413426658 fstatistic=12260.8194784643,45,531 cont.fstatistic=4953.65982516979,45,531 residuals=-0.345700190969672,-0.0750460529443472,-0.00679093159227836,0.0737853612763866,0.632081789627048 cont.residuals=-0.426504191677035,-0.143134968863514,-0.0224399501740123,0.118758078598306,1.05047466704382 predictedValues: Include Exclude Both chr16.9586_chr16_35614229_35617472_+_0.R.tl.Lung 61.7851810971215 47.9352523494646 56.0478964973958 chr16.9586_chr16_35614229_35617472_+_0.R.tl.cerebhem 56.6545723820094 51.2624000160924 63.9233957808014 chr16.9586_chr16_35614229_35617472_+_0.R.tl.cortex 66.3545849959344 46.3698568958319 54.609858532943 chr16.9586_chr16_35614229_35617472_+_0.R.tl.heart 67.7268202683731 47.5343889253156 56.9223529414499 chr16.9586_chr16_35614229_35617472_+_0.R.tl.kidney 61.9463410949609 46.0036796056885 55.1430965686773 chr16.9586_chr16_35614229_35617472_+_0.R.tl.liver 66.9529074166676 49.6629702753881 54.9650324249393 chr16.9586_chr16_35614229_35617472_+_0.R.tl.stomach 63.726334895565 46.3248769212636 56.5789065645251 chr16.9586_chr16_35614229_35617472_+_0.R.tl.testicle 61.1343592245199 50.7511638019605 55.3367359498869 diffExp=13.8499287476569,5.39217236591699,19.9847281001026,20.1924313430575,15.9426614892723,17.2899371412795,17.4014579743014,10.3831954225593 diffExpScore=0.991765244416855 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,1,1,0,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,1,1,1,1,1,0 diffExp1.3Score=0.833333333333333 diffExp1.2=1,0,1,1,1,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 55.1574272115656 57.8038544551116 54.7528520148208 cerebhem 53.8219533195033 58.4231131949878 51.3469767928646 cortex 56.1247510766264 56.2887358692733 52.1649100759485 heart 50.7208377041577 57.981651738048 54.1876076773488 kidney 55.6878146055966 55.4261800965659 58.7709332781263 liver 50.5344657295354 59.2509071165609 55.0699539364747 stomach 56.5642375628709 53.5634064868792 56.683073446196 testicle 56.7793704238645 61.0412525359576 55.2639475627587 cont.diffExp=-2.64642724354596,-4.60115987548453,-0.163984792646893,-7.26081403389027,0.261634509030692,-8.71644138702544,3.00083107599175,-4.26188211209306 cont.diffExpScore=1.21761769741084 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=-0.493268887316494 cont.tran.correlation=-0.333306409480603 tran.covariance=-0.00126305062880340 cont.tran.covariance=-0.000661377764723534 tran.mean=55.7578556353848 cont.tran.mean=55.948122445444 weightedLogRatios: wLogRatio Lung 1.01442640710692 cerebhem 0.398756941355363 cortex 1.43912633073415 heart 1.42973408068609 kidney 1.18349232812729 liver 1.21123761446322 stomach 1.27412470835521 testicle 0.748281600647283 cont.weightedLogRatios: wLogRatio Lung -0.189031930225051 cerebhem -0.330310507678413 cortex -0.0117548359463884 heart -0.534253612746986 kidney 0.0189192041044544 liver -0.636854981487522 stomach 0.218485992106211 testicle -0.294961801185171 varWeightedLogRatios=0.127842191104411 cont.varWeightedLogRatios=0.0834269289235548 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.01599726004692 0.0641277845291436 62.6249181931711 2.3176812451442e-247 *** df.mm.trans1 0.154984098031543 0.0510532411529531 3.03573474536549 0.00251721070132591 ** df.mm.trans2 -0.134463095171279 0.051053241152953 -2.63378175674359 0.00869022483741986 ** df.mm.exp2 -0.151063306978467 0.0680709882039374 -2.21920249675073 0.0268948039257765 * df.mm.exp3 0.064139929381689 0.0680709882039374 0.942250598588768 0.346492832502650 df.mm.exp4 0.0679394625820662 0.0680709882039374 0.998067816769794 0.318701068276933 df.mm.exp5 -0.0222497522848911 0.0680709882039374 -0.326861014831045 0.743901810285069 df.mm.exp6 0.135243760666312 0.0680709882039374 1.98680472011260 0.0474575077916145 * df.mm.exp7 -0.0126673458860503 0.0680709882039374 -0.186090230511999 0.852445070233825 df.mm.exp8 0.05926349228434 0.0680709882039374 0.870613073910275 0.384358866605879 df.mm.trans1:exp2 0.0643724563913081 0.0527275607347462 1.22085026301792 0.222684542181112 df.mm.trans2:exp2 0.218169656340440 0.0527275607347462 4.13767777800256 4.07881337337230e-05 *** df.mm.trans1:exp3 0.00720938455070984 0.0527275607347462 0.136728960153831 0.891296861118589 df.mm.trans2:exp3 -0.0973415080444862 0.0527275607347462 -1.84612196521241 0.0654308673538037 . df.mm.trans1:exp4 0.0238792547381846 0.0527275607347462 0.452879943722653 0.650820177337199 df.mm.trans2:exp4 -0.0763372270361077 0.0527275607347462 -1.44776708750351 0.148272346055466 df.mm.trans1:exp5 0.0248547487501448 0.0527275607347462 0.471380591170911 0.637562582678622 df.mm.trans2:exp5 -0.018880053921649 0.0527275607347462 -0.358068032326166 0.720434695926537 df.mm.trans1:exp6 -0.0549178104228667 0.0527275607347462 -1.04153899132825 0.298099440414868 df.mm.trans2:exp6 -0.0998353610685423 0.0527275607347462 -1.89341891939168 0.058845551473614 . df.mm.trans1:exp7 0.0436016960444875 0.0527275607347462 0.826924201250884 0.408651457604612 df.mm.trans2:exp7 -0.0215047297240162 0.0527275607347462 -0.407846094610728 0.683551057795721 df.mm.trans1:exp8 -0.0698529877002409 0.0527275607347462 -1.32479080630425 0.1858103745043 df.mm.trans2:exp8 -0.00218013349154624 0.0527275607347462 -0.0413471334756737 0.96703469981071 df.mm.trans1:probe2 -0.100946630252594 0.0372840157509646 -2.70750422719642 0.0069975585438326 ** df.mm.trans1:probe3 -0.178497995262028 0.0372840157509646 -4.78752064837355 2.19260444650985e-06 *** df.mm.trans1:probe4 -0.244091256364059 0.0372840157509646 -6.54680702836426 1.38826201119623e-10 *** df.mm.trans1:probe5 -0.126102824984307 0.0372840157509646 -3.38222217871059 0.000772023555843015 *** df.mm.trans1:probe6 -0.20208188107577 0.0372840157509646 -5.42006747410362 9.05355314961324e-08 *** df.mm.trans2:probe2 0.0248214571397843 0.0372840157509646 0.665739905958014 0.505866448409131 df.mm.trans2:probe3 -0.0774606516489816 0.0372840157509646 -2.07758338496511 0.0382277275727266 * df.mm.trans2:probe4 -0.0972303311054713 0.0372840157509646 -2.60782882817433 0.00936827745081222 ** df.mm.trans2:probe5 -0.0261821716829048 0.0372840157509646 -0.702235828291308 0.482839806509241 df.mm.trans2:probe6 -0.0342418338229476 0.0372840157509646 -0.91840519679165 0.358823742560310 df.mm.trans3:probe2 -0.164752463992279 0.0372840157509646 -4.41884975837176 1.20345324601150e-05 *** df.mm.trans3:probe3 0.293583724923244 0.0372840157509646 7.87425171376955 1.94364358959543e-14 *** df.mm.trans3:probe4 0.172720448704465 0.0372840157509646 4.63256023326823 4.54858021414184e-06 *** df.mm.trans3:probe5 -0.186828860411464 0.0372840157509646 -5.01096399216681 7.38813060128391e-07 *** df.mm.trans3:probe6 -0.237671236450564 0.0372840157509646 -6.37461474209402 3.98691036292323e-10 *** df.mm.trans3:probe7 -0.083730935005733 0.0372840157509646 -2.24575956530559 0.0251298672970642 * df.mm.trans3:probe8 0.0128182997041166 0.0372840157509646 0.343801477548325 0.731131681215105 df.mm.trans3:probe9 -0.217757697017361 0.0372840157509646 -5.84051080956126 9.07609159077917e-09 *** df.mm.trans3:probe10 0.0242099169875111 0.0372840157509646 0.649337698739835 0.516400887221643 df.mm.trans3:probe11 0.267099171119628 0.0372840157509646 7.16390565071356 2.63280756085974e-12 *** df.mm.trans3:probe12 -0.00343008614174912 0.0372840157509646 -0.091998838447556 0.926733659089228 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.07373087274428 0.100817316738095 40.4070551027168 4.13279448585384e-164 *** df.mm.trans1 -0.0555634906995924 0.0802624138915092 -0.692272858559894 0.489068600555591 df.mm.trans2 0.0129819981902861 0.0802624138915091 0.161744427570219 0.871568641106233 df.mm.exp2 0.0503695533670763 0.107016551855346 0.470670681252755 0.63806920086329 df.mm.exp3 0.0392438944238415 0.107016551855346 0.366708642200391 0.713982438083938 df.mm.exp4 -0.0704062144160158 0.107016551855346 -0.657900233145093 0.510887345447873 df.mm.exp5 -0.103251409076877 0.107016551855346 -0.96481719217082 0.335075568993613 df.mm.exp6 -0.0685849939000226 0.107016551855346 -0.640882113196182 0.521875763748654 df.mm.exp7 -0.085649987570885 0.107016551855346 -0.800343368254456 0.423869802256333 df.mm.exp8 0.074184806417522 0.107016551855346 0.69320871520695 0.488481666824148 df.mm.trans1:exp2 -0.0748795247174056 0.0828946646208591 -0.903309339146096 0.366771336805894 df.mm.trans2:exp2 -0.0397134274734532 0.0828946646208591 -0.479083034536579 0.632076786289019 df.mm.trans1:exp3 -0.021858393084018 0.0828946646208591 -0.263688781226068 0.79212203828676 df.mm.trans2:exp3 -0.0658049121949759 0.0828946646208591 -0.793837727626407 0.427644506813664 df.mm.trans1:exp4 -0.0134483688946670 0.0828946646208591 -0.162234432772925 0.871182960660791 df.mm.trans2:exp4 0.0734773660399166 0.0828946646208591 0.886394394331468 0.375806294609764 df.mm.trans1:exp5 0.112821354222735 0.0828946646208591 1.36102069703465 0.174084463863469 df.mm.trans2:exp5 0.0612479965969327 0.0828946646208591 0.738865364581264 0.460315216726782 df.mm.trans1:exp6 -0.0189508224674988 0.0828946646208591 -0.228613295610464 0.819257504736498 df.mm.trans2:exp6 0.0933106242527073 0.0828946646208591 1.12565295582639 0.260820949606911 df.mm.trans1:exp7 0.110835518350347 0.0828946646208591 1.33706456087714 0.181774066225184 df.mm.trans2:exp7 0.0094606481652302 0.0828946646208591 0.114128553490155 0.90917902651984 df.mm.trans1:exp8 -0.0452031529777117 0.0828946646208591 -0.545308352286126 0.585770434112212 df.mm.trans2:exp8 -0.0196903592606912 0.0828946646208591 -0.237534699618489 0.812333623207489 df.mm.trans1:probe2 -0.0428333498452117 0.0586153794775941 -0.730752751700343 0.465252490965767 df.mm.trans1:probe3 -0.00145032204721690 0.0586153794775941 -0.0247430292210476 0.980269228634289 df.mm.trans1:probe4 -0.0151652786759396 0.0586153794775941 -0.258725249432814 0.795947494837672 df.mm.trans1:probe5 -0.0562611348634592 0.0586153794775941 -0.959835718285594 0.337574741111156 df.mm.trans1:probe6 -0.0278574194288004 0.0586153794775941 -0.475257853435018 0.634798624356516 df.mm.trans2:probe2 -0.143865665235133 0.0586153794775941 -2.45440132806998 0.0144318171063251 * df.mm.trans2:probe3 -0.0779864941889057 0.0586153794775941 -1.33047836393717 0.183931795739728 df.mm.trans2:probe4 -0.118088668675005 0.0586153794775941 -2.01463625634541 0.0444463833898374 * df.mm.trans2:probe5 -0.161466858551135 0.0586153794775941 -2.75468417999164 0.00607644195855598 ** df.mm.trans2:probe6 -0.0324257186408027 0.0586153794775941 -0.553194723463277 0.580362904509107 df.mm.trans3:probe2 0.00221145675314717 0.0586153794775941 0.0377282681244521 0.969918516753319 df.mm.trans3:probe3 -0.0338675671695778 0.0586153794775941 -0.577793191333407 0.563648677704957 df.mm.trans3:probe4 0.0603211525886615 0.0586153794775941 1.02910111861887 0.303900685129007 df.mm.trans3:probe5 -0.0141368431350281 0.0586153794775941 -0.241179759664134 0.809508895951824 df.mm.trans3:probe6 -0.0814403721472459 0.0586153794775941 -1.38940279621284 0.165292614361391 df.mm.trans3:probe7 -0.0354651737811471 0.0586153794775941 -0.605048949562867 0.545404948140494 df.mm.trans3:probe8 -0.0756588466829902 0.0586153794775941 -1.29076783870197 0.197345895274880 df.mm.trans3:probe9 -0.0525351870530932 0.0586153794775941 -0.89626967395434 0.370514885484199 df.mm.trans3:probe10 -0.0218430189521453 0.0586153794775941 -0.372649962293512 0.7095576754089 df.mm.trans3:probe11 -0.0117431679227821 0.0586153794775941 -0.200342777398054 0.841289215012445 df.mm.trans3:probe12 -0.0756814880999766 0.0586153794775941 -1.29115410962930 0.197212044595764