chr1.814_chr1_106582860_106585164_+_2.R fitVsDatCorrelation=0.72873336719832 cont.fitVsDatCorrelation=0.233135437819188 fstatistic=17218.0485541343,58,830 cont.fstatistic=8531.23286964168,58,830 residuals=-0.326687213779431,-0.0680949141642312,-0.00625422956088833,0.0588291884767025,0.56402635896036 cont.residuals=-0.38616976435942,-0.103985682392421,-0.0198435240408761,0.070953774660963,0.881360079957624 predictedValues: Include Exclude Both chr1.814_chr1_106582860_106585164_+_2.R.tl.Lung 46.2373109837294 44.8823375106357 49.5721576669596 chr1.814_chr1_106582860_106585164_+_2.R.tl.cerebhem 51.8594204219667 43.2190470548467 53.3781680582464 chr1.814_chr1_106582860_106585164_+_2.R.tl.cortex 46.7027654170436 43.830542686833 51.4066151984125 chr1.814_chr1_106582860_106585164_+_2.R.tl.heart 47.8530809902521 45.8208947868771 50.7100672478423 chr1.814_chr1_106582860_106585164_+_2.R.tl.kidney 45.4249726755966 43.8735194224712 48.1395001826676 chr1.814_chr1_106582860_106585164_+_2.R.tl.liver 50.4714330872467 45.3053023153966 52.3707406882636 chr1.814_chr1_106582860_106585164_+_2.R.tl.stomach 48.1188996383324 46.1489056761274 51.4867160197122 chr1.814_chr1_106582860_106585164_+_2.R.tl.testicle 47.6009608950433 44.7763051973408 53.3857932692886 diffExp=1.35497347309373,8.64037336712003,2.87222273021056,2.032186203375,1.55145325312542,5.16613077185009,1.96999396220498,2.82465569770248 diffExpScore=0.963519612412398 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 46.6585814782669 45.4707033792084 47.8437073924373 cerebhem 47.2140682477007 48.2254085254342 46.6234777665679 cortex 47.6533366283034 46.2306540809961 46.3091912249683 heart 45.6202685232682 48.424410639522 50.3283336509278 kidney 48.6238125731728 44.4215005534047 47.9604028398612 liver 47.5533979106591 47.1964000354422 48.6548091712371 stomach 47.1137860614463 45.5673107322129 45.9837368517534 testicle 47.2921245688197 47.5934932973734 48.9973062664686 cont.diffExp=1.18787809905853,-1.01134027773353,1.42268254730733,-2.80414211625378,4.20231201976816,0.356997875216919,1.54647532923337,-0.301368728553690 cont.diffExpScore=2.29184909899411 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=-0.0848299288648598 cont.tran.correlation=-0.592688451382523 tran.covariance=-7.51420673184694e-05 cont.tran.covariance=-0.000333826747060172 tran.mean=46.3828561724837 cont.tran.mean=46.9287035772019 weightedLogRatios: wLogRatio Lung 0.113584962624719 cerebhem 0.70303323408506 cortex 0.241961368795999 heart 0.166917321555624 kidney 0.132008445766078 liver 0.417616783911322 stomach 0.161052799373187 testicle 0.234434467781708 cont.weightedLogRatios: wLogRatio Lung 0.0987694798357223 cerebhem -0.0819214203003063 cortex 0.116655389244424 heart -0.229670574752617 kidney 0.346999007846112 liver 0.0290730924908313 stomach 0.128022686861392 testicle -0.024516729684303 varWeightedLogRatios=0.0396211946906596 cont.varWeightedLogRatios=0.0289560748190896 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.91682251140528 0.0536821390474543 72.9632347165388 0 *** df.mm.trans1 -0.0498225600920509 0.0462909941755292 -1.07629056103506 0.282110068722083 df.mm.trans2 -0.102401590249716 0.0410004109420173 -2.49757472905654 0.0126972115005945 * df.mm.exp2 0.00301400797397503 0.0527898529875301 0.0570944566692956 0.954483693712034 df.mm.exp3 -0.0500346628667937 0.0527898529875301 -0.947808338822476 0.343502900778911 df.mm.exp4 0.032349179670491 0.0527898529875301 0.61279162262741 0.540181990896305 df.mm.exp5 -0.0111321749815467 0.0527898529875301 -0.210877173387400 0.833034877656728 df.mm.exp6 0.0420814438927705 0.0527898529875301 0.797150238374615 0.425591755146530 df.mm.exp7 0.0298223652468954 0.0527898529875301 0.564926090132131 0.572276687388731 df.mm.exp8 -0.0474146952348876 0.0527898529875301 -0.898178201899668 0.369351022491327 df.mm.trans1:exp2 0.111735527046853 0.0486264425497515 2.29783470038000 0.0218191049171657 * df.mm.trans2:exp2 -0.0407770492746149 0.0361792310109318 -1.12708446628658 0.260032501854004 df.mm.trans1:exp3 0.060050973220373 0.0486264425497515 1.23494481750197 0.217200517747045 df.mm.trans2:exp3 0.0263212154221808 0.0361792310109318 0.727522799316207 0.467110918405469 df.mm.trans1:exp4 0.00199925545955271 0.0486264425497514 0.0411145737734608 0.967214444013125 df.mm.trans2:exp4 -0.0116533179605349 0.0361792310109318 -0.322099658696829 0.747458304629595 df.mm.trans1:exp5 -0.006592881471351 0.0486264425497515 -0.135582229043500 0.892184429265 df.mm.trans2:exp5 -0.0116012325870450 0.0361792310109318 -0.320660010256702 0.748548770280822 df.mm.trans1:exp6 0.0455389816826885 0.0486264425497515 0.936506544481348 0.349284849365999 df.mm.trans2:exp6 -0.0327017127769220 0.0361792310109318 -0.903880814023963 0.366320866780714 df.mm.trans1:exp7 0.0100655893572192 0.0486264425497515 0.206998267391671 0.836061982869414 df.mm.trans2:exp7 -0.0019934603342295 0.0361792310109318 -0.0550995772582111 0.95607239288292 df.mm.trans1:exp8 0.0764805739320833 0.0486264425497515 1.57281861312049 0.116141916597455 df.mm.trans2:exp8 0.0450494494643853 0.0361792310109318 1.24517432254912 0.213419103802353 df.mm.trans1:probe2 0.199580502102934 0.0332922493446599 5.99480377660175 3.03652463273491e-09 *** df.mm.trans1:probe3 -0.0272595132768705 0.0332922493446599 -0.818794578722059 0.41313864771365 df.mm.trans1:probe4 0.00706247940488794 0.0332922493446599 0.212135843744687 0.832053139428015 df.mm.trans1:probe5 0.0236166516020035 0.0332922493446599 0.709373865295515 0.478291718305069 df.mm.trans1:probe6 0.0165912183683641 0.0332922493446599 0.49835077818271 0.618368851025892 df.mm.trans1:probe7 -0.0598560325217093 0.0332922493446599 -1.79789691895091 0.0725566641084423 . df.mm.trans1:probe8 -0.0530243980004341 0.0332922493446599 -1.59269496787364 0.111609373790128 df.mm.trans1:probe9 -0.0421138271549674 0.0332922493446599 -1.26497391987491 0.206235714206402 df.mm.trans1:probe10 -0.122791372874634 0.0332922493446599 -3.68828707256845 0.000240468982634233 *** df.mm.trans1:probe11 -0.144887420961111 0.0332922493446599 -4.35198653780213 1.51730573149676e-05 *** df.mm.trans1:probe12 -0.150579939099049 0.0332922493446599 -4.52297282590195 6.98589047136799e-06 *** df.mm.trans1:probe13 -0.160066472695329 0.0332922493446599 -4.80792003682997 1.81047011461327e-06 *** df.mm.trans1:probe14 -0.160159946489141 0.0332922493446599 -4.81072771115812 1.78590208320462e-06 *** df.mm.trans1:probe15 -0.122955517533191 0.0332922493446599 -3.69321748916053 0.000235926755045492 *** df.mm.trans1:probe16 0.0235924750925562 0.0332922493446599 0.708647674968242 0.478742122226021 df.mm.trans1:probe17 -0.0684542830731616 0.0332922493446599 -2.05616275321276 0.0400790392724677 * df.mm.trans1:probe18 -0.0279088643247557 0.0332922493446599 -0.838299149926087 0.402104057542812 df.mm.trans1:probe19 -0.0634855182455762 0.0332922493446599 -1.90691585865343 0.0568764942287121 . df.mm.trans1:probe20 -0.0694213163805397 0.0332922493446599 -2.08520955318614 0.037355309125653 * df.mm.trans1:probe21 -0.0270782492307123 0.0332922493446599 -0.813349946721329 0.416250700982976 df.mm.trans2:probe2 -0.0463078308487517 0.0332922493446599 -1.39094929781846 0.164613564506407 df.mm.trans2:probe3 -0.0269732581826678 0.0332922493446599 -0.810196328383391 0.418059573755742 df.mm.trans2:probe4 -0.0143243734273906 0.0332922493446599 -0.430261508590085 0.667117115633766 df.mm.trans2:probe5 -0.0460982382751224 0.0332922493446599 -1.38465376123697 0.166530465891719 df.mm.trans2:probe6 -0.0323215428998408 0.0332922493446599 -0.97084286991937 0.331909359084218 df.mm.trans3:probe2 0.0773671174503876 0.0332922493446599 2.32387774852460 0.0203726761407055 * df.mm.trans3:probe3 0.0298065237903467 0.0332922493446599 0.895299187560834 0.370886736896027 df.mm.trans3:probe4 0.166425909721205 0.0332922493446599 4.99893858171826 7.03399170344407e-07 *** df.mm.trans3:probe5 0.0881564741182893 0.0332922493446599 2.64795788369973 0.00825164802563631 ** df.mm.trans3:probe6 0.160555543163679 0.0332922493446599 4.82261025686547 1.68543363450941e-06 *** df.mm.trans3:probe7 -0.00137208221689651 0.0332922493446599 -0.0412132626633891 0.967135792130805 df.mm.trans3:probe8 0.0792976611356056 0.0332922493446599 2.38186553016206 0.0174496095934274 * df.mm.trans3:probe9 0.248032469845896 0.0332922493446599 7.45015655980844 2.34092178820628e-13 *** df.mm.trans3:probe10 0.540040159980332 0.0332922493446599 16.2211977445422 1.31507214482569e-51 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.70994359756467 0.0762311270719508 48.6670437662956 2.36079150040424e-245 *** df.mm.trans1 0.112545538935999 0.0657353585735896 1.71210047953121 0.0872516887849368 . df.mm.trans2 0.0916547101519566 0.0582224850198353 1.57421501539708 0.115818824344649 df.mm.exp2 0.0964882058845577 0.0749640394851749 1.28712655490823 0.19840900374848 df.mm.exp3 0.0702697822756497 0.0749640394851749 0.937379879182557 0.348835860348992 df.mm.exp4 -0.0101975696062160 0.0749640394851749 -0.136032818885550 0.891828321972281 df.mm.exp5 0.0154757514644485 0.0749640394851749 0.206442336495341 0.836496032809104 df.mm.exp6 0.0394347098457349 0.0749640394851749 0.526048357539932 0.598995133054598 df.mm.exp7 0.0514829587597698 0.0749640394851749 0.686768737561844 0.492420296649693 df.mm.exp8 0.0352890166545919 0.0749640394851749 0.470745932275578 0.63794586479308 df.mm.trans1:exp2 -0.0846531665014314 0.0690518035764226 -1.22593708081415 0.220570066801613 df.mm.trans2:exp2 -0.037670412691397 0.0513761859251135 -0.733227117059756 0.463626954028011 df.mm.trans1:exp3 -0.0491739958418448 0.0690518035764226 -0.712131954488658 0.476583188625677 df.mm.trans2:exp3 -0.0536949327659355 0.0513761859251135 -1.04513271662871 0.296265914648683 df.mm.trans1:exp4 -0.0123071923621621 0.0690518035764226 -0.17823129483564 0.858584824748426 df.mm.trans2:exp4 0.0731333714419208 0.0513761859251135 1.42348775264324 0.154970594638721 df.mm.trans1:exp5 0.0257807651931532 0.0690518035764226 0.373353972783933 0.70898034661558 df.mm.trans2:exp5 -0.0388203893379319 0.0513761859251135 -0.75561057402192 0.450097090572231 df.mm.trans1:exp6 -0.0204383285608392 0.0690518035764226 -0.295985441397186 0.767315236937924 df.mm.trans2:exp6 -0.00218532739888017 0.0513761859251135 -0.0425358044691275 0.966081799907518 df.mm.trans1:exp7 -0.0417741677636037 0.0690518035764226 -0.604968525077994 0.545365265668496 df.mm.trans2:exp7 -0.0493606060215099 0.0513761859251135 -0.960768206761367 0.336948526879617 df.mm.trans1:exp8 -0.0218020995152655 0.0690518035764226 -0.315735410026419 0.752282720972041 df.mm.trans2:exp8 0.0103388030054074 0.0513761859251135 0.201237262347137 0.840562361152184 df.mm.trans1:probe2 0.0220607664509411 0.0472765380690282 0.466632442898638 0.640885325109748 df.mm.trans1:probe3 0.0201785545883277 0.0472765380690282 0.42681963215803 0.669621392119934 df.mm.trans1:probe4 0.0695619361824468 0.0472765380690282 1.47138388350010 0.141566355101076 df.mm.trans1:probe5 0.0576188685051819 0.0472765380690282 1.21876243182301 0.223280653833073 df.mm.trans1:probe6 0.000400113501477343 0.0472765380690282 0.0084632572057865 0.993249412067123 df.mm.trans1:probe7 0.0578294674476426 0.0472765380690282 1.22321705035183 0.221594900296306 df.mm.trans1:probe8 0.0374251514537799 0.0472765380690282 0.791622081107878 0.428807235333724 df.mm.trans1:probe9 0.0266421338919058 0.0472765380690282 0.563538173057549 0.573220687806088 df.mm.trans1:probe10 0.0296027576004432 0.0472765380690282 0.626161703236822 0.531381039475118 df.mm.trans1:probe11 0.0574981508712393 0.0472765380690282 1.21620899540670 0.224251080704862 df.mm.trans1:probe12 0.0267249175653330 0.0472765380690282 0.565289224991732 0.572029821123148 df.mm.trans1:probe13 0.0479401426202262 0.0472765380690282 1.01403665704602 0.310860765087824 df.mm.trans1:probe14 0.0129240440280342 0.0472765380690282 0.273371201782243 0.784635895257811 df.mm.trans1:probe15 0.0263885747790959 0.0472765380690282 0.558174854947418 0.576875518531304 df.mm.trans1:probe16 0.047777767833385 0.0472765380690282 1.01060208265726 0.312501434468248 df.mm.trans1:probe17 0.105352620300812 0.0472765380690282 2.22843348104269 0.0261187702169363 * df.mm.trans1:probe18 -0.0327379629497610 0.0472765380690282 -0.692478008900747 0.488830880255867 df.mm.trans1:probe19 -0.00728394962651447 0.0472765380690282 -0.154071129655882 0.877591105005798 df.mm.trans1:probe20 0.0473071492114078 0.0472765380690282 1.00064749120028 0.317288893251284 df.mm.trans1:probe21 -0.0218116240669572 0.0472765380690282 -0.461362548059465 0.644659396582877 df.mm.trans2:probe2 0.0903402428987406 0.0472765380690282 1.91088955724371 0.0563630176552008 . df.mm.trans2:probe3 0.0275342678707385 0.0472765380690282 0.582408716783277 0.560449584084999 df.mm.trans2:probe4 0.00736212471875656 0.0472765380690282 0.155724700230951 0.876287891288866 df.mm.trans2:probe5 0.0582184858246398 0.0472765380690282 1.23144562192002 0.218505038930101 df.mm.trans2:probe6 0.064063744027053 0.0472765380690282 1.35508534769432 0.175759041480323 df.mm.trans3:probe2 -0.0222978480004062 0.0472765380690282 -0.471647225265294 0.63730256824751 df.mm.trans3:probe3 -0.0814787023085944 0.0472765380690282 -1.723449001059 0.0851797758736681 . df.mm.trans3:probe4 -0.0581409182600934 0.0472765380690282 -1.22980490185644 0.219118647502641 df.mm.trans3:probe5 -0.0495845646785115 0.0472765380690282 -1.04881970431323 0.294566405654369 df.mm.trans3:probe6 -0.0683325335735873 0.0472765380690282 -1.44537938615165 0.148728795851872 df.mm.trans3:probe7 -0.0638042953250353 0.0472765380690282 -1.34959745216274 0.177513122619097 df.mm.trans3:probe8 -0.0361840588891428 0.0472765380690282 -0.765370316166353 0.444268639291647 df.mm.trans3:probe9 -0.0132716684429892 0.0472765380690282 -0.28072420242809 0.77899190297991 df.mm.trans3:probe10 -0.0689478540373044 0.0472765380690282 -1.45839473137043 0.145110061923910