chr10.2021_chr10_24809101_24812035_+_2.R fitVsDatCorrelation=0.718718127682267 cont.fitVsDatCorrelation=0.259050246786804 fstatistic=10206.7719983049,42,462 cont.fstatistic=5284.05882368128,42,462 residuals=-0.39300104281507,-0.0809842198679776,-0.00356889938043384,0.0664037756612457,0.631189366278981 cont.residuals=-0.365963297558667,-0.105947257759049,-0.0246865546868621,0.065289273181974,1.15441044601188 predictedValues: Include Exclude Both chr10.2021_chr10_24809101_24812035_+_2.R.tl.Lung 44.0486274211258 48.2337861022814 62.7842429245963 chr10.2021_chr10_24809101_24812035_+_2.R.tl.cerebhem 52.5336913474681 53.97051815538 75.2386946162242 chr10.2021_chr10_24809101_24812035_+_2.R.tl.cortex 44.6469649516089 52.8414984626798 59.5766394228065 chr10.2021_chr10_24809101_24812035_+_2.R.tl.heart 46.1139561303229 48.5784999147738 54.6277238676165 chr10.2021_chr10_24809101_24812035_+_2.R.tl.kidney 44.3770224709013 47.6502840956234 61.2248485216241 chr10.2021_chr10_24809101_24812035_+_2.R.tl.liver 45.4850312614517 48.7714600858387 57.7973443511203 chr10.2021_chr10_24809101_24812035_+_2.R.tl.stomach 45.8291742540834 49.1661691370932 56.8008715787537 chr10.2021_chr10_24809101_24812035_+_2.R.tl.testicle 46.7601913082551 52.3501257152555 56.6542884313716 diffExp=-4.18515868115561,-1.43682680791192,-8.19453351107084,-2.46454378445090,-3.27326162472212,-3.28642882438704,-3.33699488300985,-5.58993440700041 diffExpScore=0.969482126199298 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 47.6693949281831 48.0862277586979 49.2579951045725 cerebhem 50.7297604697597 49.8404726933941 50.3120220613011 cortex 49.9013102934435 53.6617612313671 48.6284039198257 heart 49.132232148741 49.3954205149782 51.0868005956345 kidney 50.1125616372077 51.4734165916085 53.1067845622585 liver 46.9488935240314 51.039137774439 56.3960863673763 stomach 47.7768965067124 50.4130485351237 53.6767654155327 testicle 49.5497021434286 49.8778013483096 47.8067380610862 cont.diffExp=-0.416832830514821,0.889287776365535,-3.76045093792364,-0.263188366237188,-1.36085495440082,-4.09024425040761,-2.63615202841138,-0.328099204881028 cont.diffExpScore=1.06004499775426 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=0.676281598786287 cont.tran.correlation=0.288131625842961 tran.covariance=0.0018187842278117 cont.tran.covariance=0.000259017180778650 tran.mean=48.2098125508839 cont.tran.mean=49.7255023812141 weightedLogRatios: wLogRatio Lung -0.347693461692068 cerebhem -0.107256905377041 cortex -0.654333305832149 heart -0.200823987640635 kidney -0.272448111048138 liver -0.268741734785322 stomach -0.271304114719926 testicle -0.440564857118957 cont.weightedLogRatios: wLogRatio Lung -0.0336813113395144 cerebhem 0.0692854491578388 cortex -0.286717508678773 heart -0.0208204815448407 kidney -0.105237103183092 liver -0.325012802547619 stomach -0.209105954320322 testicle -0.0257806080687476 varWeightedLogRatios=0.0276861065755392 cont.varWeightedLogRatios=0.0199872625461411 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.45418594589405 0.0727620402374045 47.472362438215 8.28041746210738e-180 *** df.mm.trans1 0.296277897736978 0.0641161406647103 4.62095651212598 4.96179701700433e-06 *** df.mm.trans2 0.387304330131529 0.0606207647855837 6.3889713615694 4.0817788730246e-10 *** df.mm.exp2 0.107577195262716 0.0835226791282804 1.28799981496631 0.198390759921977 df.mm.exp3 0.157169836140272 0.0835226791282804 1.88176238813986 0.0604966949526358 . df.mm.exp4 0.192105376452075 0.0835226791282804 2.30003848603833 0.0218909182783916 * df.mm.exp5 0.0204074959083684 0.0835226791282804 0.244334785729575 0.807080017039676 df.mm.exp6 0.125935975731188 0.0835226791282804 1.50780574863704 0.132287737015665 df.mm.exp7 0.158925187119235 0.0835226791282804 1.90277884734931 0.0576908111498328 . df.mm.exp8 0.244369090388534 0.0835226791282804 2.92578127209275 0.00360516380934512 ** df.mm.trans1:exp2 0.0685833161287017 0.0773269751238153 0.886926147297067 0.375580119648573 df.mm.trans2:exp2 0.00480101014535542 0.0705895476315908 0.0680130459315599 0.94580467387551 df.mm.trans1:exp3 -0.143677697667106 0.0773269751238153 -1.85805402884375 0.0637972398968852 . df.mm.trans2:exp3 -0.0659327303771244 0.0705895476315908 -0.934029648712717 0.350776503353535 df.mm.trans1:exp4 -0.146283928579996 0.0773269751238153 -1.89175806173418 0.0591483431627695 . df.mm.trans2:exp4 -0.184984063993400 0.0705895476315908 -2.62055885325739 0.00906779434836494 ** df.mm.trans1:exp5 -0.0129798646732412 0.0773269751238153 -0.167856878566088 0.866769327049155 df.mm.trans2:exp5 -0.0325786356733983 0.0705895476315908 -0.461522091676054 0.6446412948182 df.mm.trans1:exp6 -0.0938468794968517 0.0773269751238153 -1.21363701795635 0.225506438905693 df.mm.trans2:exp6 -0.114850400241133 0.0705895476315908 -1.62701708814654 0.104415265200732 df.mm.trans1:exp7 -0.11929849867634 0.0773269751238153 -1.54277984474784 0.123568852486699 df.mm.trans2:exp7 -0.139779151284405 0.0705895476315908 -1.98016782900943 0.0482775309622417 * df.mm.trans1:exp8 -0.184631054843891 0.0773269751238153 -2.38766684650811 0.0173566864482021 * df.mm.trans2:exp8 -0.162474483617499 0.0705895476315908 -2.30167905970242 0.0217973353879512 * df.mm.trans1:probe2 0.0200569967531831 0.0386634875619077 0.518758084641691 0.60417783134288 df.mm.trans1:probe3 0.112986581348301 0.0386634875619077 2.92230702590883 0.00364487745268065 ** df.mm.trans1:probe4 -0.0093848690881778 0.0386634875619077 -0.24273208859265 0.808320671207444 df.mm.trans1:probe5 0.084605827168898 0.0386634875619077 2.18826165211888 0.0291506393544343 * df.mm.trans1:probe6 0.184073876355226 0.0386634875619077 4.76092271967158 2.58262243018808e-06 *** df.mm.trans1:probe7 0.0318250811773627 0.0386634875619077 0.82313012054085 0.41085867131294 df.mm.trans1:probe8 -0.0149573843298931 0.0386634875619077 -0.386860712085102 0.699037455584728 df.mm.trans1:probe9 0.0243834705610899 0.0386634875619077 0.630658848921667 0.528575445798228 df.mm.trans1:probe10 0.0317829213854229 0.0386634875619077 0.822039691440983 0.411478341176094 df.mm.trans1:probe11 -0.00850455226471409 0.0386634875619077 -0.219963402191710 0.82599681587531 df.mm.trans1:probe12 0.0655872814314686 0.0386634875619077 1.69636226753861 0.0904909101980197 . df.mm.trans2:probe2 -0.0613002920241667 0.0386634875619077 -1.58548273551405 0.113540891648645 df.mm.trans2:probe3 0.0777240571708997 0.0386634875619077 2.01027020768493 0.0449833075546582 * df.mm.trans2:probe4 0.0582361559926731 0.0386634875619077 1.50623132223718 0.132691202764172 df.mm.trans2:probe5 0.0551596152336594 0.0386634875619077 1.42665907066294 0.154353614767636 df.mm.trans2:probe6 0.181305567218301 0.0386634875619077 4.68932263102224 3.61416364070833e-06 *** df.mm.trans3:probe2 -0.167879833023007 0.0386634875619077 -4.34207681741588 1.73583653521545e-05 *** df.mm.trans3:probe3 0.173687711177108 0.0386634875619077 4.49229291328157 8.91399386179433e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81492590029535 0.101075890747653 37.7431835829152 3.04160051220286e-143 *** df.mm.trans1 0.0485622174429966 0.0890656172894938 0.545240901268924 0.585850965373611 df.mm.trans2 0.0738622670471687 0.0842100878220976 0.877118988442456 0.380877875195199 df.mm.exp2 0.0768823372718892 0.116023810808174 0.662642751831358 0.507889818638388 df.mm.exp3 0.168326428781469 0.116023810808174 1.45079210559433 0.147516362447297 df.mm.exp4 0.0206331424156837 0.116023810808174 0.177835413885837 0.858930179766217 df.mm.exp5 0.0428188144910211 0.116023810808174 0.36905195746255 0.712258070178907 df.mm.exp6 -0.0909610769716867 0.116023810808174 -0.783986289866619 0.433449908707371 df.mm.exp7 -0.0364017025580684 0.116023810808174 -0.313743379953729 0.753857557714397 df.mm.exp8 0.105172001810972 0.116023810808174 0.90646912110874 0.365160120390256 df.mm.trans1:exp2 -0.0146591835823396 0.107417176098415 -0.136469642144651 0.891509468316151 df.mm.trans2:exp2 -0.0410507895501264 0.0980580173543446 -0.418637768310004 0.6756754264107 df.mm.trans1:exp3 -0.122568744192555 0.107417176098415 -1.14105349483641 0.254438777931321 df.mm.trans2:exp3 -0.0586215731994023 0.0980580173543446 -0.597825397464096 0.550249363801554 df.mm.trans1:exp4 0.0095925600413817 0.107417176098415 0.0893019197655414 0.928880667294908 df.mm.trans2:exp4 0.0062287644253001 0.0980580173543446 0.0635212152290587 0.949378939381836 df.mm.trans1:exp5 0.00716331724409898 0.107417176098415 0.0666868884873308 0.946859820948613 df.mm.trans2:exp5 0.0252508662934715 0.0980580173543446 0.257509451799585 0.796900174563861 df.mm.trans1:exp6 0.075731139025513 0.107417176098415 0.705018897128039 0.481153749774531 df.mm.trans2:exp6 0.15055801180166 0.0980580173543446 1.53539726647338 0.125370795817759 df.mm.trans1:exp7 0.0386543123215411 0.107417176098415 0.359852248267318 0.719122064146788 df.mm.trans2:exp7 0.0836559327032823 0.0980580173543446 0.853126903443106 0.394031010393841 df.mm.trans1:exp8 -0.0664853284950186 0.107417176098415 -0.618945041285623 0.536257628444768 df.mm.trans2:exp8 -0.0685917717234575 0.0980580173543446 -0.69950192318893 0.484590442593398 df.mm.trans1:probe2 -0.0505471323637888 0.0537085880492076 -0.941136868418096 0.347126550016112 df.mm.trans1:probe3 0.00883025354379184 0.0537085880492076 0.16441045770374 0.869479936898204 df.mm.trans1:probe4 -0.0141648643168897 0.0537085880492076 -0.263735555734809 0.792101288955023 df.mm.trans1:probe5 0.0187124369160179 0.0537085880492076 0.348406792948525 0.72769340710944 df.mm.trans1:probe6 0.0619444327110449 0.0537085880492076 1.15334316095392 0.249365894022997 df.mm.trans1:probe7 -5.03539190135329e-05 0.0537085880492076 -0.000937539429772364 0.999252356541057 df.mm.trans1:probe8 -0.00228370733258509 0.0537085880492076 -0.0425203382835678 0.966102268786496 df.mm.trans1:probe9 0.0019460130299511 0.0537085880492076 0.0362328093259159 0.97111237482918 df.mm.trans1:probe10 -0.00502907651346066 0.0537085880492076 -0.0936363567936852 0.92543862095401 df.mm.trans1:probe11 0.0479653250031566 0.0537085880492076 0.893066206827313 0.372286663044922 df.mm.trans1:probe12 -0.05530144969163 0.0537085880492076 -1.02965748496242 0.303709562394288 df.mm.trans2:probe2 -0.00871802027081133 0.0537085880492076 -0.162320786813906 0.871124214712016 df.mm.trans2:probe3 -0.0112750710534856 0.0537085880492076 -0.209930505772287 0.83381445736612 df.mm.trans2:probe4 -0.0608981215248566 0.0537085880492076 -1.13386189689183 0.257440488779715 df.mm.trans2:probe5 0.000628781097234486 0.0537085880492076 0.0117072728975560 0.99066421473924 df.mm.trans2:probe6 -0.0618687759622196 0.0537085880492076 -1.15193450823052 0.249943727630787 df.mm.trans3:probe2 -0.0859615708592353 0.0537085880492076 -1.60051816630289 0.110167227853094 df.mm.trans3:probe3 -0.0348745087227307 0.0537085880492076 -0.649328347466122 0.516448840674528