chr9.24985_chr9_58248542_58262683_+_2.R fitVsDatCorrelation=0.711829066401472 cont.fitVsDatCorrelation=0.249366290249018 fstatistic=9893.49835419905,56,784 cont.fstatistic=5197.42786585193,56,784 residuals=-0.571067990214964,-0.0872440473907831,-0.00520573179659425,0.0718562550802817,0.967037397268445 cont.residuals=-0.498469571290312,-0.15055925719952,-0.00756600402984718,0.128863659728375,1.14698972492858 predictedValues: Include Exclude Both chr9.24985_chr9_58248542_58262683_+_2.R.tl.Lung 59.8888737367773 53.2930634921318 63.3316637442166 chr9.24985_chr9_58248542_58262683_+_2.R.tl.cerebhem 64.60404625202 72.3560586399224 63.4931066732215 chr9.24985_chr9_58248542_58262683_+_2.R.tl.cortex 59.7228800275909 50.4888528503801 62.2243601039915 chr9.24985_chr9_58248542_58262683_+_2.R.tl.heart 56.149317113258 51.7709963424785 60.1072838648014 chr9.24985_chr9_58248542_58262683_+_2.R.tl.kidney 56.8873677405079 56.6763700600628 64.1618816636634 chr9.24985_chr9_58248542_58262683_+_2.R.tl.liver 56.9967428706672 56.2451827934636 64.2952138616319 chr9.24985_chr9_58248542_58262683_+_2.R.tl.stomach 57.2644976313661 49.5449150219752 60.2558911251732 chr9.24985_chr9_58248542_58262683_+_2.R.tl.testicle 61.9785360882745 56.1996342019624 67.4489354627815 diffExp=6.59581024464551,-7.75201238790238,9.23402717721082,4.37832077077945,0.210997680445097,0.751560077203585,7.71958260939086,5.77890188631206 diffExpScore=1.51953745289918 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 61.6554126228338 71.1122351342545 64.1524406538209 cerebhem 62.1043093299612 64.998435219834 62.2006884531368 cortex 60.6285960466973 59.134572913689 60.3747424680224 heart 64.2547229993145 60.2239086549475 60.5529121980638 kidney 62.8496352961948 61.2857868071272 62.7236607944214 liver 62.0574191699739 64.8477219673879 59.8383035376294 stomach 60.5592221701308 58.3964702298251 57.7681784848268 testicle 58.8151383695418 62.5257959074694 56.468609691466 cont.diffExp=-9.45682251142068,-2.89412588987278,1.49402313300827,4.03081434436696,1.56384848906762,-2.79030279741396,2.16275194030572,-3.71065753792762 cont.diffExpScore=2.65114135913193 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=0.720591156244035 cont.tran.correlation=0.056359034852381 tran.covariance=0.00407774123763812 cont.tran.covariance=0.000109808087391252 tran.mean=57.5042084289274 cont.tran.mean=62.2155864274489 weightedLogRatios: wLogRatio Lung 0.470722833034384 cerebhem -0.478779054033647 cortex 0.672813561592107 heart 0.323716658232338 kidney 0.0150094751122415 liver 0.0535774978650333 stomach 0.575625395624034 testicle 0.399131253974661 cont.weightedLogRatios: wLogRatio Lung -0.598321510567841 cerebhem -0.189095825561478 cortex 0.102106431588663 heart 0.267595321156812 kidney 0.104017647577429 liver -0.182526337682192 stomach 0.148572360962699 testicle -0.251142950662952 varWeightedLogRatios=0.140855823196920 cont.varWeightedLogRatios=0.0800344989388596 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81625947892486 0.076796712148185 49.693006017772 1.77919026631277e-244 *** df.mm.trans1 0.0802789349604986 0.0669352190350561 1.19935268932868 0.230753178897518 df.mm.trans2 0.150883561005139 0.0597324597868722 2.52598941251537 0.0117331733047692 * df.mm.exp2 0.379033389703998 0.0781486063158591 4.85016185921512 1.48809487056588e-06 *** df.mm.exp3 -0.0391902925166106 0.0781486063158592 -0.501484215319365 0.616171167574558 df.mm.exp4 -0.0411979355848571 0.0781486063158591 -0.527174284060093 0.598221701310888 df.mm.exp5 -0.00289011351714954 0.0781486063158591 -0.0369822784230898 0.970508550074108 df.mm.exp6 -0.0106821734006947 0.0781486063158591 -0.136690517007044 0.891310518020153 df.mm.exp7 -0.0679513703490985 0.0781486063158591 -0.869514807141336 0.384831733658038 df.mm.exp8 0.0244160798823706 0.0781486063158591 0.312431417953717 0.754795756519372 df.mm.trans1:exp2 -0.303247085965804 0.072973862163394 -4.15555757877815 3.60202439134818e-05 *** df.mm.trans2:exp2 -0.0732403812709045 0.0569192199424565 -1.28674253345264 0.198563844675742 df.mm.trans1:exp3 0.0364147489679989 0.072973862163394 0.499010849754169 0.617911725965287 df.mm.trans2:exp3 -0.0148633130459858 0.0569192199424565 -0.261129949795730 0.794060874732149 df.mm.trans1:exp4 -0.0232782857034523 0.072973862163394 -0.318994842993652 0.749815392709866 df.mm.trans2:exp4 0.0122218300281550 0.0569192199424565 0.214722373927663 0.830039616014177 df.mm.trans1:exp5 -0.0485273186424642 0.072973862163394 -0.664995893102216 0.506248682721069 df.mm.trans2:exp5 0.0644413017519045 0.0569192199424565 1.13215363487154 0.257915996128013 df.mm.trans1:exp6 -0.0388144435863568 0.072973862163394 -0.531895153081638 0.594949297719978 df.mm.trans2:exp6 0.0645963897484863 0.0569192199424565 1.13487833835023 0.256773143646705 df.mm.trans1:exp7 0.0231414738918220 0.072973862163394 0.31712003730879 0.751236950266434 df.mm.trans2:exp7 -0.00497517915221535 0.0569192199424565 -0.087407718469176 0.930369754221084 df.mm.trans1:exp8 0.00988131253075907 0.072973862163394 0.135408929140054 0.892323322377067 df.mm.trans2:exp8 0.0286879862920372 0.0569192199424565 0.504012288310343 0.614394342306824 df.mm.trans1:probe2 0.354645980067941 0.0463740800737875 7.64750437105493 6.00411429682847e-14 *** df.mm.trans1:probe3 0.248480413215684 0.0463740800737875 5.35817449791603 1.10602498614887e-07 *** df.mm.trans1:probe4 0.405935675953515 0.0463740800737875 8.75350358018134 1.25731812221269e-17 *** df.mm.trans1:probe5 0.107631783152405 0.0463740800737875 2.32094702431074 0.0205453458568181 * df.mm.trans1:probe6 0.355262267238821 0.0463740800737875 7.66079384590596 5.45338841576162e-14 *** df.mm.trans1:probe7 0.0187772994159248 0.0463740800737875 0.404909367173376 0.685654585249253 df.mm.trans1:probe8 -0.00168671796104618 0.0463740800737874 -0.0363719982878881 0.970995000200716 df.mm.trans1:probe9 0.166631596896536 0.0463740800737875 3.59320544216516 0.000347024791095291 *** df.mm.trans1:probe10 0.378170117226572 0.0463740800737875 8.15477345587993 1.38445847874292e-15 *** df.mm.trans1:probe11 0.357346581141994 0.0463740800737874 7.70573951167134 3.93468880447126e-14 *** df.mm.trans1:probe12 0.406501493786117 0.0463740800737875 8.76570474582608 1.13928017239624e-17 *** df.mm.trans1:probe13 0.448028517266525 0.0463740800737875 9.66118393192168 6.12402725743747e-21 *** df.mm.trans1:probe14 0.469613925613454 0.0463740800737875 10.1266467144197 9.80231675677742e-23 *** df.mm.trans1:probe15 0.461975512019523 0.0463740800737875 9.96193371996722 4.30615361834069e-22 *** df.mm.trans1:probe16 0.482539334467791 0.0463740800737875 10.4053672590379 7.68694644333878e-24 *** df.mm.trans1:probe17 0.0657324951794613 0.0463740800737875 1.41744041229221 0.156751489175812 df.mm.trans1:probe18 0.241510847867125 0.0463740800737875 5.20788439323968 2.4429056158234e-07 *** df.mm.trans1:probe19 0.0513428948812771 0.0463740800737875 1.10714638003781 0.268570216499446 df.mm.trans1:probe20 0.375461068002553 0.0463740800737875 8.09635614130012 2.15877521543883e-15 *** df.mm.trans1:probe21 0.117119623124110 0.0463740800737875 2.52554062393814 0.0117480463842379 * df.mm.trans1:probe22 0.171596765270119 0.0463740800737875 3.70027319134062 0.000230418680207754 *** df.mm.trans2:probe2 0.0620998338732982 0.0463740800737875 1.33910653913757 0.180924097996119 df.mm.trans2:probe3 -0.0357455911568100 0.0463740800737875 -0.770809708784173 0.441051956701838 df.mm.trans2:probe4 0.0458806758994277 0.0463740800737874 0.989360345831666 0.322792107069567 df.mm.trans2:probe5 0.0746057664793298 0.0463740800737875 1.60878159438682 0.108066684348149 df.mm.trans2:probe6 -0.0342198402369629 0.0463740800737875 -0.737908766761831 0.460790696371896 df.mm.trans3:probe2 0.177026034253692 0.0463740800737875 3.81734869936006 0.000145529058459024 *** df.mm.trans3:probe3 0.245088348213837 0.0463740800737875 5.28502878814779 1.63050252444608e-07 *** df.mm.trans3:probe4 0.121439437930936 0.0463740800737875 2.61869211718507 0.0089972067049519 ** df.mm.trans3:probe5 -0.0239396405526952 0.0463740800737875 -0.516228904478622 0.605840108667959 df.mm.trans3:probe6 0.103978253273384 0.0463740800737875 2.24216314604927 0.0252296744799403 * df.mm.trans3:probe7 0.0831516911276556 0.0463740800737875 1.79306394855380 0.0733480577029532 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19779831021219 0.105887883155846 39.6438023417075 1.77048526337772e-189 *** df.mm.trans1 -0.111446665523386 0.0922907824298375 -1.20756008985092 0.227580682184949 df.mm.trans2 0.0879479173810924 0.0823595639136108 1.06785312114260 0.285915466484096 df.mm.exp2 -0.0517457829004017 0.107751884981726 -0.480230883285036 0.631197189209007 df.mm.exp3 -0.140546730071458 0.107751884981726 -1.30435518687487 0.192495289837948 df.mm.exp4 -0.0671511974337227 0.107751884981726 -0.62320206690687 0.533332989793616 df.mm.exp5 -0.107003946969650 0.107751884981726 -0.993058701365618 0.320987737561774 df.mm.exp6 -0.0161024195882443 0.107751884981726 -0.149439794867395 0.88124504534068 df.mm.exp7 -0.110119157829473 0.107751884981726 -1.02196966529309 0.307110496045652 df.mm.exp8 -0.0482647107667705 0.107751884981726 -0.447924514498805 0.65433142140754 df.mm.trans1:exp2 0.0590001410820193 0.100616908902016 0.58638395599566 0.55778630976207 df.mm.trans2:exp2 -0.0381504265027778 0.0784806476995928 -0.486112533739649 0.627023221225867 df.mm.trans1:exp3 0.123752372056823 0.100616908902016 1.22993613506192 0.219089963135886 df.mm.trans2:exp3 -0.0438969319451725 0.0784806476995929 -0.559334475846843 0.576093215216625 df.mm.trans1:exp4 0.108445406476975 0.100616908902016 1.07780498984105 0.281452301419798 df.mm.trans2:exp4 -0.0990387808275316 0.0784806476995928 -1.26195162413326 0.207341486412513 df.mm.trans1:exp5 0.126188057476347 0.100616908902016 1.25414365093677 0.210163594276647 df.mm.trans2:exp5 -0.0417075052932975 0.0784806476995928 -0.531436812970057 0.595266649903689 df.mm.trans1:exp6 0.0226014699502458 0.100616908902016 0.224628943553173 0.8223264766983 df.mm.trans2:exp6 -0.076115203453864 0.0784806476995928 -0.969859521868586 0.332415624686134 df.mm.trans1:exp7 0.092179901174609 0.100616908902016 0.916147218002657 0.359871393966171 df.mm.trans2:exp7 -0.0868848009011393 0.0784806476995929 -1.10708567586898 0.268596442378521 df.mm.trans1:exp8 0.00110296605242232 0.100616908902016 0.0109620347559714 0.99125652564514 df.mm.trans2:exp8 -0.0804154885908547 0.0784806476995928 -1.02465373245476 0.30584264728307 df.mm.trans1:probe2 0.0600746110800832 0.0639409296955054 0.93953296216001 0.347746543904391 df.mm.trans1:probe3 0.0809478148317027 0.0639409296955054 1.26597807096622 0.205896982640167 df.mm.trans1:probe4 0.0444883985556035 0.0639409296955054 0.695773407854135 0.486776936044597 df.mm.trans1:probe5 -0.000339381427834039 0.0639409296955055 -0.00530773370750496 0.995766411340502 df.mm.trans1:probe6 0.0521094222069518 0.0639409296955054 0.814961910236577 0.415341603285034 df.mm.trans1:probe7 0.066911035244089 0.0639409296955054 1.04645077202861 0.295675475191693 df.mm.trans1:probe8 0.0248440494757271 0.0639409296955054 0.388546891545643 0.697716849639711 df.mm.trans1:probe9 0.0334088537759013 0.0639409296955055 0.522495589835781 0.601472923103012 df.mm.trans1:probe10 0.0871200106255143 0.0639409296955054 1.36250772455750 0.173428971172535 df.mm.trans1:probe11 0.0238347990449368 0.0639409296955054 0.372762785252592 0.709425754274711 df.mm.trans1:probe12 0.156688804853854 0.0639409296955054 2.45052434489185 0.0144823250019923 * df.mm.trans1:probe13 0.0179033769313329 0.0639409296955054 0.279998695930619 0.77955236411384 df.mm.trans1:probe14 0.145586735034939 0.0639409296955055 2.27689424799172 0.0230611968302536 * df.mm.trans1:probe15 -0.0328229909933863 0.0639409296955054 -0.513333027056276 0.607863007100802 df.mm.trans1:probe16 0.0340309556692088 0.0639409296955054 0.532224911825155 0.594721022461527 df.mm.trans1:probe17 0.0827370508528888 0.0639409296955055 1.29396071103271 0.196060038386283 df.mm.trans1:probe18 0.0916999980665958 0.0639409296955054 1.43413613945375 0.151932138440330 df.mm.trans1:probe19 -0.0261744311156115 0.0639409296955054 -0.40935330844042 0.68239220901865 df.mm.trans1:probe20 0.0215476917125867 0.0639409296955054 0.336993719284337 0.736211826170354 df.mm.trans1:probe21 0.0405553565973353 0.0639409296955054 0.634262854645137 0.52609431301214 df.mm.trans1:probe22 0.0159197736746904 0.0639409296955054 0.248976262161065 0.803444352545659 df.mm.trans2:probe2 -0.0542153184118367 0.0639409296955054 -0.847896936594709 0.396754149941721 df.mm.trans2:probe3 -0.0387441466196183 0.0639409296955054 -0.605936554318535 0.544732267637543 df.mm.trans2:probe4 -0.0817921500490585 0.0639409296955054 -1.27918299653388 0.201211087368777 df.mm.trans2:probe5 -0.060349437995001 0.0639409296955054 -0.943831099772751 0.345546726589522 df.mm.trans2:probe6 -0.0442276345527911 0.0639409296955054 -0.691695206238141 0.489333537624785 df.mm.trans3:probe2 -0.0375852547219468 0.0639409296955054 -0.587812140063217 0.556827660968054 df.mm.trans3:probe3 -0.0199785541961579 0.0639409296955055 -0.31245329542905 0.754779138659295 df.mm.trans3:probe4 0.0144708060289959 0.0639409296955054 0.226315227162127 0.821015245831081 df.mm.trans3:probe5 -0.0325733461682155 0.0639409296955055 -0.509428723093859 0.610595101998406 df.mm.trans3:probe6 0.00602798069522217 0.0639409296955054 0.0942742109620263 0.924915423289863 df.mm.trans3:probe7 -0.0216236114430621 0.0639409296955055 -0.338181060957925 0.735317285151293