chr10.2025_chr10_127981846_127982915_+_0.R fitVsDatCorrelation=0.787836250148507 cont.fitVsDatCorrelation=0.260888049316961 fstatistic=7308.34789896229,42,462 cont.fstatistic=2968.0964143022,42,462 residuals=-0.730720404790947,-0.0840777806681497,-0.00411594228749061,0.0755848806730193,1.00343247237650 cont.residuals=-0.730749694122717,-0.141841744068059,-0.0250618204741359,0.117998379568488,1.58933876288269 predictedValues: Include Exclude Both chr10.2025_chr10_127981846_127982915_+_0.R.tl.Lung 70.3851287246369 63.2955666074627 55.0095827953747 chr10.2025_chr10_127981846_127982915_+_0.R.tl.cerebhem 113.062238832630 74.0260007302587 54.2234687300427 chr10.2025_chr10_127981846_127982915_+_0.R.tl.cortex 67.26897276595 62.0980456025242 48.4813095264001 chr10.2025_chr10_127981846_127982915_+_0.R.tl.heart 64.0942850467818 61.063130723668 54.3581851094668 chr10.2025_chr10_127981846_127982915_+_0.R.tl.kidney 71.8818414845757 59.2843278380173 52.0985621318501 chr10.2025_chr10_127981846_127982915_+_0.R.tl.liver 71.1267873335138 59.4226550074236 52.9012649171695 chr10.2025_chr10_127981846_127982915_+_0.R.tl.stomach 75.2856143399749 67.2643003442323 51.4520243935023 chr10.2025_chr10_127981846_127982915_+_0.R.tl.testicle 68.6693506030034 63.6885528366622 54.1538050676203 diffExp=7.08956211717411,39.036238102371,5.1709271634258,3.03115432311380,12.5975136465584,11.7041323260902,8.02131399574257,4.98079776634121 diffExpScore=0.989204552504559 diffExp1.5=0,1,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,1,0,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,1,0,0,1,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 61.7676058937555 60.5040419738576 58.9578657331963 cerebhem 67.6738007005364 64.6752236432182 60.466516241138 cortex 59.1306396749129 67.4191465846941 60.9270552481698 heart 59.2634645612734 66.3818649669111 57.4641916546712 kidney 58.3420752135428 63.7315932042518 63.5769472893804 liver 62.2663565222733 66.0808151725417 61.8544468680939 stomach 59.5882680251894 70.5961612796745 62.194374803852 testicle 61.3241657447728 62.327369527915 59.4317982252671 cont.diffExp=1.26356391989795,2.99857705731823,-8.28850690978119,-7.11840040563762,-5.38951799070895,-3.81445865026839,-11.0078932544850,-1.00320378314218 cont.diffExpScore=1.22554910187344 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=0.874320251310979 cont.tran.correlation=-0.257474701454427 tran.covariance=0.0111863732460879 cont.tran.covariance=-0.00059047559704748 tran.mean=69.4947999263322 cont.tran.mean=63.1920370430825 weightedLogRatios: wLogRatio Lung 0.445995597605141 cerebhem 1.91270076777406 cortex 0.333432283870826 heart 0.200382943244884 kidney 0.805143445817778 liver 0.750539516939505 stomach 0.480487896574694 testicle 0.315623674046491 cont.weightedLogRatios: wLogRatio Lung 0.0850120183855829 cerebhem 0.189987081272815 cortex -0.543784857309901 heart -0.469457412349212 kidney -0.363191102578629 liver -0.247410357259819 stomach -0.707262003234033 testicle -0.0669234136847989 varWeightedLogRatios=0.301839403781115 cont.varWeightedLogRatios=0.099117891618517 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.32762969913602 0.0839403088438892 51.5560373644142 9.6783801585253e-194 *** df.mm.trans1 -0.0597660519959537 0.0674378000871465 -0.886239644809306 0.375949470402255 df.mm.trans2 -0.155649641639382 0.0674378000871465 -2.30804743687137 0.021437367026681 * df.mm.exp2 0.644951141454806 0.0905480714842755 7.12274851228397 4.08174493448270e-12 *** df.mm.exp3 0.0619453778477327 0.0905480714842755 0.684115926847653 0.494244965868315 df.mm.exp4 -0.117621627548706 0.0905480714842755 -1.29899649568055 0.194593045728342 df.mm.exp5 0.00994142801115311 0.0905480714842755 0.109791714480408 0.912622235492661 df.mm.exp6 -0.0135775647139621 0.0905480714842755 -0.149948690142119 0.88087053558165 df.mm.exp7 0.194979016669829 0.0905480714842755 2.153320479097 0.0318103411909722 * df.mm.exp8 -0.00281031939878948 0.0905480714842755 -0.0310367670202398 0.975253621057047 df.mm.trans1:exp2 -0.17099468938289 0.0715845359065135 -2.38870989687217 0.0173081082548643 * df.mm.trans2:exp2 -0.488350037644394 0.0715845359065135 -6.82200466148386 2.82511445898141e-11 *** df.mm.trans1:exp3 -0.1072282773841 0.0715845359065135 -1.49792515975986 0.134835601431055 df.mm.trans2:exp3 -0.0810461500831168 0.0715845359065135 -1.13217399619605 0.258148561132290 df.mm.trans1:exp4 0.0239948295212615 0.0715845359065135 0.335195712557218 0.737629599705662 df.mm.trans2:exp4 0.0817145975469082 0.0715845359065135 1.14151187141346 0.254248287079527 df.mm.trans1:exp5 0.0111002513059464 0.0715845359065135 0.155064933583461 0.876837848675386 df.mm.trans2:exp5 -0.0754117318745623 0.0715845359065135 -1.05346400475442 0.292679021464364 df.mm.trans1:exp6 0.0240595851712709 0.0715845359065135 0.336100316452309 0.736947810985156 df.mm.trans2:exp6 -0.0495621731001491 0.0715845359065135 -0.692358656412543 0.489059958523616 df.mm.trans1:exp7 -0.127671945880062 0.0715845359065135 -1.78351293702310 0.0751591716534936 . df.mm.trans2:exp7 -0.134164665170194 0.0715845359065135 -1.87421296333342 0.0615319215614859 . df.mm.trans1:exp8 -0.0218687159349735 0.0715845359065135 -0.305494973991774 0.760126791459347 df.mm.trans2:exp8 0.00899987263656595 0.0715845359065135 0.125723698877079 0.900005332585716 df.mm.trans1:probe2 0.0453699153721354 0.0480203665274216 0.94480568669186 0.345251930796051 df.mm.trans1:probe3 0.100608001660708 0.0480203665274216 2.09511107340793 0.0367050014519294 * df.mm.trans1:probe4 -0.120761701328496 0.0480203665274216 -2.51480174062261 0.0122484453705958 * df.mm.trans1:probe5 -0.087665396669383 0.0480203665274216 -1.82558782885013 0.0685577660343778 . df.mm.trans1:probe6 -0.145775508992737 0.0480203665274216 -3.03570171438598 0.00253532340497183 ** df.mm.trans2:probe2 0.0543784514553164 0.0480203665274216 1.13240392332832 0.258052027349136 df.mm.trans2:probe3 -0.109754579461742 0.0480203665274216 -2.28558395944496 0.0227307151306995 * df.mm.trans2:probe4 -0.101712233451285 0.0480203665274216 -2.11810614550814 0.0346994245791393 * df.mm.trans2:probe5 -0.054253346482813 0.0480203665274216 -1.12979867514822 0.259147298397333 df.mm.trans2:probe6 -0.151129821000512 0.0480203665274216 -3.14720257110513 0.00175499756750428 ** df.mm.trans3:probe2 -0.278447563431951 0.0480203665274216 -5.79853057291735 1.24146215077694e-08 *** df.mm.trans3:probe3 0.269561534543828 0.0480203665274216 5.61348348705122 3.42535280548282e-08 *** df.mm.trans3:probe4 -0.234536840211653 0.0480203665274216 -4.88411182946143 1.43434207300997e-06 *** df.mm.trans3:probe5 -0.123615749559005 0.0480203665274216 -2.5742358607033 0.0103565995413849 * df.mm.trans3:probe6 -0.232217986462793 0.0480203665274216 -4.83582286549576 1.80891233287837e-06 *** df.mm.trans3:probe7 -0.132860087075583 0.0480203665274216 -2.76674454368679 0.00588902693514014 ** df.mm.trans3:probe8 -0.0539189918985974 0.0480203665274216 -1.12283590896391 0.262090352318455 df.mm.trans3:probe9 -0.156323741120286 0.0480203665274216 -3.2553633473626 0.00121567755028091 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.20232558914107 0.131572303615286 31.9392871726906 5.11030412380097e-119 *** df.mm.trans1 -0.0613532211985942 0.105705433187228 -0.580416912817753 0.561916485940936 df.mm.trans2 -0.0540226580179506 0.105705433187228 -0.511067940304113 0.609547620926981 df.mm.exp2 0.132721364464183 0.141929646402237 0.935120799836584 0.350214555812511 df.mm.exp3 0.0317347722294223 0.141929646402237 0.223595091186827 0.823171218253181 df.mm.exp4 0.0769887312298723 0.141929646402237 0.542442915778722 0.587774976816047 df.mm.exp5 -0.0805131716879895 0.141929646402237 -0.567275222118222 0.570802735726056 df.mm.exp6 0.0482495534344231 0.141929646402237 0.339954017060545 0.73404565735393 df.mm.exp7 0.0649037470950722 0.141929646402237 0.457295207451806 0.647673832070321 df.mm.exp8 0.0144790528328622 0.141929646402237 0.102015704258346 0.918788471102986 df.mm.trans1:exp2 -0.041401301134474 0.112205237533348 -0.368978329751937 0.71231291268612 df.mm.trans2:exp2 -0.0660533507337912 0.112205237533348 -0.588683310920847 0.556361429446538 df.mm.trans1:exp3 -0.0753645949027206 0.112205237533348 -0.671667353142246 0.502131199571828 df.mm.trans2:exp3 0.0764841070139671 0.112205237533348 0.681644713699179 0.495805186077926 df.mm.trans1:exp4 -0.118374777611380 0.112205237533348 -1.05498442152666 0.291983835823530 df.mm.trans2:exp4 0.0157249976904138 0.112205237533348 0.140144952553933 0.888606541122107 df.mm.trans1:exp5 0.0234576559602957 0.112205237533348 0.209060258468987 0.834493344105575 df.mm.trans2:exp5 0.132483407693481 0.112205237533348 1.18072391811573 0.23831992270691 df.mm.trans1:exp6 -0.0402073477845514 0.112205237533348 -0.358337531014109 0.720254409617336 df.mm.trans2:exp6 0.0399187395580314 0.112205237533348 0.355765385249218 0.722178658553657 df.mm.trans1:exp7 -0.100824088173955 0.112205237533348 -0.898568466057476 0.369350622150653 df.mm.trans2:exp7 0.089361850826335 0.112205237533348 0.796414256507202 0.426200342244533 df.mm.trans1:exp8 -0.0216841171752705 0.112205237533348 -0.193254055264809 0.846844936345071 df.mm.trans2:exp8 0.015211422463227 0.112205237533348 0.135567846899358 0.89222197165326 df.mm.trans1:probe2 -0.068238533925731 0.0752695615668228 -0.906588699406069 0.365096925846127 df.mm.trans1:probe3 -0.00588510546944176 0.0752695615668228 -0.078187056586175 0.93771310904179 df.mm.trans1:probe4 -0.0688507026671767 0.0752695615668228 -0.914721717968988 0.360814893464049 df.mm.trans1:probe5 -0.0682194408249666 0.0752695615668228 -0.906335036433057 0.365230989392013 df.mm.trans1:probe6 -0.0527059778695231 0.0752695615668228 -0.70022963828123 0.484136363687532 df.mm.trans2:probe2 -0.148881045839787 0.0752695615668228 -1.97797147665877 0.0485256470177262 * df.mm.trans2:probe3 -0.183807307431166 0.0752695615668228 -2.44198722039832 0.0149806550642523 * df.mm.trans2:probe4 -0.105444176882277 0.0752695615668228 -1.40088735323197 0.161919070923714 df.mm.trans2:probe5 -0.152095372808011 0.0752695615668228 -2.02067568406100 0.0438897372145605 * df.mm.trans2:probe6 -0.0936634795296096 0.0752695615668228 -1.24437392193997 0.213992890875788 df.mm.trans3:probe2 0.0205325211079328 0.0752695615668228 0.272786511313799 0.785139213518884 df.mm.trans3:probe3 -0.0722918826026256 0.0752695615668228 -0.96043979927326 0.337336289655297 df.mm.trans3:probe4 -0.0120566401673559 0.0752695615668228 -0.16017949243204 0.872809692801125 df.mm.trans3:probe5 0.123019206860521 0.0752695615668228 1.63438187096795 0.102859865448756 df.mm.trans3:probe6 -0.0212274617016284 0.0752695615668228 -0.282019202181524 0.778055073135578 df.mm.trans3:probe7 -0.0824998175718839 0.0752695615668228 -1.09605816553936 0.273624330470398 df.mm.trans3:probe8 -0.082808103078632 0.0752695615668228 -1.10015391819596 0.271837967193896 df.mm.trans3:probe9 0.036192225870918 0.0752695615668228 0.480834817123084 0.630861525255069