chr2.12927_chr2_25643619_25644397_-_1.R fitVsDatCorrelation=0.913197045961775 cont.fitVsDatCorrelation=0.2524010918024 fstatistic=9947.93553347692,43,485 cont.fstatistic=1755.19473454671,43,485 residuals=-0.643484439125871,-0.0832397196487667,-0.00930963590429737,0.0701808691042288,0.586892346359335 cont.residuals=-0.77273629256475,-0.266366595960954,-0.0625542670035966,0.191235029619850,1.04678909581156 predictedValues: Include Exclude Both chr2.12927_chr2_25643619_25644397_-_1.R.tl.Lung 83.3702455749935 48.5346217689589 73.0438139272896 chr2.12927_chr2_25643619_25644397_-_1.R.tl.cerebhem 69.3563029828197 51.412067379811 66.778155345069 chr2.12927_chr2_25643619_25644397_-_1.R.tl.cortex 67.7912824597181 46.1420795890242 67.2439249175262 chr2.12927_chr2_25643619_25644397_-_1.R.tl.heart 69.6979043396253 48.8015927474143 66.1011261599087 chr2.12927_chr2_25643619_25644397_-_1.R.tl.kidney 93.7717368083713 48.5180370400762 81.0362448257264 chr2.12927_chr2_25643619_25644397_-_1.R.tl.liver 83.8697122093909 48.5134280778629 71.4604311434713 chr2.12927_chr2_25643619_25644397_-_1.R.tl.stomach 67.1408169693764 46.1658606478996 70.6583314370745 chr2.12927_chr2_25643619_25644397_-_1.R.tl.testicle 82.8799432531846 50.8660037059098 68.0323453986147 diffExp=34.8356238060346,17.9442356030087,21.649202870694,20.8963115922111,45.2536997682951,35.3562841315281,20.9749563214768,32.0139395472748 diffExpScore=0.995650741563074 diffExp1.5=1,0,0,0,1,1,0,1 diffExp1.5Score=0.8 diffExp1.4=1,0,1,1,1,1,1,1 diffExp1.4Score=0.875 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 61.5369689810937 61.120258819061 66.5054831523315 cerebhem 59.5855688191959 70.780607815609 73.6896206095528 cortex 60.5166826276189 59.8847734933414 58.3707675953363 heart 67.0167771804002 64.029084715475 68.5732354008296 kidney 63.4251435350166 66.1398623686567 64.4705712084537 liver 63.0897620095052 61.1180240026647 67.505928368761 stomach 64.0737518830716 66.7920233225979 65.1055609487166 testicle 66.078407186792 63.3821965468809 65.6353182302198 cont.diffExp=0.416710162032643,-11.1950389964130,0.631909134277507,2.98769246492516,-2.71471883364009,1.97173800684045,-2.71827143952632,2.69621063991116 cont.diffExpScore=2.83874336846569 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=0.261543291483017 cont.tran.correlation=-0.107690899970814 tran.covariance=0.00144428431272684 cont.tran.covariance=-0.000204309697668121 tran.mean=62.9269772221523 cont.tran.mean=63.6606183316863 weightedLogRatios: wLogRatio Lung 2.24671486709285 cerebhem 1.22435077149100 cortex 1.54809671568232 heart 1.44914017740471 kidney 2.77500854609424 liver 2.27484889765798 stomach 1.50551621362443 testicle 2.03739499711226 cont.weightedLogRatios: wLogRatio Lung 0.0279687708420233 cerebhem -0.718558255785409 cortex 0.0430124250389721 heart 0.190728866883768 kidney -0.174804145731518 liver 0.131092658550657 stomach -0.173707894967797 testicle 0.173718610520269 varWeightedLogRatios=0.283151060433701 cont.varWeightedLogRatios=0.0903016418749886 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.34109398428540 0.0736667154039017 45.3541869753085 1.38615964112655e-176 *** df.mm.trans1 1.10144872000704 0.0589740581217702 18.6768344435915 4.77157218233532e-59 *** df.mm.trans2 0.535855237287572 0.0589740581217702 9.08628733300214 2.61081018904334e-18 *** df.mm.exp2 -0.0367554835581005 0.0789702815403269 -0.465434374060462 0.641829342895645 df.mm.exp3 -0.174677166510519 0.0789702815403268 -2.21193546614518 0.0274364722310962 * df.mm.exp4 -0.0737620031939259 0.0789702815403268 -0.934047615826958 0.350744118758912 df.mm.exp5 0.0133931819207771 0.0789702815403268 0.169597748159701 0.86539720892106 df.mm.exp6 0.0274518705670143 0.0789702815403268 0.347622802294250 0.728274288220055 df.mm.exp7 -0.233332710454417 0.0789702815403269 -2.95469011763956 0.0032820355163803 ** df.mm.exp8 0.112095210597683 0.0789702815403269 1.41946069345645 0.156407135764604 df.mm.trans1:exp2 -0.147278965274709 0.0619493856258527 -2.37740800472518 0.0178215725259914 * df.mm.trans2:exp2 0.094351008210979 0.0619493856258527 1.52303380021907 0.128401981512734 df.mm.trans1:exp3 -0.0321807021222326 0.0619493856258527 -0.519467655685724 0.60367164173338 df.mm.trans2:exp3 0.124125095344582 0.0619493856258527 2.00365337106398 0.0456629348551207 * df.mm.trans1:exp4 -0.105359224376992 0.0619493856258527 -1.70073073869233 0.0896345806569337 . df.mm.trans2:exp4 0.0792475596076564 0.0619493856258527 1.27923075922459 0.201427480443245 df.mm.trans1:exp5 0.104178837321787 0.0619493856258527 1.68167668281621 0.093275282706366 . df.mm.trans2:exp5 -0.0137349495600705 0.0619493856258527 -0.221712441879950 0.824631064668204 df.mm.trans1:exp6 -0.0214787989877847 0.0619493856258527 -0.346715286532579 0.728955602817442 df.mm.trans2:exp6 -0.0278886375389722 0.0619493856258527 -0.450184247304846 0.652778749549484 df.mm.trans1:exp7 0.0168333920570372 0.0619493856258527 0.271728151731859 0.785946680446936 df.mm.trans2:exp7 0.183295894191592 0.0619493856258527 2.95880083942429 0.00323934925692825 ** df.mm.trans1:exp8 -0.117993594795265 0.0619493856258527 -1.90467740080417 0.0574133176638867 . df.mm.trans2:exp8 -0.0651778079843366 0.0619493856258527 -1.05211387208878 0.293271214103401 df.mm.trans1:probe2 0.262914013778006 0.0424138449135823 6.19877811864712 1.21773473348526e-09 *** df.mm.trans1:probe3 0.153205979225069 0.0424138449135823 3.61216908151628 0.000335303341440168 *** df.mm.trans1:probe4 -0.312521633747997 0.0424138449135823 -7.36838724206109 7.49186348113418e-13 *** df.mm.trans1:probe5 -0.36648892497784 0.0424138449135823 -8.64078523709786 8.14298337308213e-17 *** df.mm.trans1:probe6 -0.0451290546036057 0.0424138449135823 -1.06401706083369 0.287850426008312 df.mm.trans2:probe2 0.0607836266182534 0.0424138449135823 1.43310814527896 0.152471215110193 df.mm.trans2:probe3 -0.00254713075206590 0.0424138449135823 -0.0600542289258532 0.952137181307188 df.mm.trans2:probe4 0.0730733966821969 0.0424138449135823 1.72286659771316 0.0855500761949261 . df.mm.trans2:probe5 -0.0406127019035619 0.0424138449135823 -0.957534078466827 0.33877481328264 df.mm.trans2:probe6 -0.00544642894128467 0.0424138449135823 -0.128411582406210 0.897876494971003 df.mm.trans3:probe2 -0.716109514615216 0.0424138449135823 -16.8838622406028 1.20959634247527e-50 *** df.mm.trans3:probe3 -0.715629992037989 0.0424138449135823 -16.8725564375519 1.36467547876211e-50 *** df.mm.trans3:probe4 -1.06517006332568 0.0424138449135823 -25.1137350432612 8.79373034228291e-90 *** df.mm.trans3:probe5 -0.436569569399317 0.0424138449135823 -10.2930910953445 1.31766964699719e-22 *** df.mm.trans3:probe6 -0.337834291147797 0.0424138449135823 -7.96518900458402 1.18411026907959e-14 *** df.mm.trans3:probe7 -0.736185421983731 0.0424138449135823 -17.3571960637782 7.61481198974326e-53 *** df.mm.trans3:probe8 -1.08268981172923 0.0424138449135823 -25.5268017774667 9.6578247159094e-92 *** df.mm.trans3:probe9 -0.739265689507968 0.0424138449135823 -17.4298201687259 3.48902360626366e-53 *** df.mm.trans3:probe10 -1.04393054314591 0.0424138449135823 -24.6129664799997 2.11447172502206e-87 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00595445521816 0.174916287416814 22.9021237208873 3.13352547758486e-79 *** df.mm.trans1 0.120805518021524 0.140029635419541 0.862713936657622 0.388721153389436 df.mm.trans2 0.0999201743954398 0.140029635419541 0.713564482947271 0.475839746473434 df.mm.exp2 0.0119394191905643 0.187509221601089 0.0636737707544032 0.949256224179902 df.mm.exp3 0.0933290397002077 0.187509221601089 0.497730399088094 0.618899669599263 df.mm.exp4 0.101181020807690 0.187509221601089 0.53960557216191 0.589716717084427 df.mm.exp5 0.140226038214399 0.187509221601089 0.747835423863681 0.454921877449993 df.mm.exp6 0.00995280133856665 0.187509221601089 0.0530789966145796 0.957690821526166 df.mm.exp7 0.150411390300779 0.187509221601089 0.802154630137432 0.42285637966897 df.mm.exp8 0.120713998695596 0.187509221601089 0.643776330917769 0.520024785670709 df.mm.trans1:exp2 -0.0441641241995441 0.147094335372699 -0.300243541586041 0.764120042364595 df.mm.trans2:exp2 0.134802263627572 0.147094335372699 0.916434091673328 0.359894711209359 df.mm.trans1:exp3 -0.110048082618300 0.147094335372699 -0.748146299036375 0.454734537619113 df.mm.trans2:exp3 -0.113750145122746 0.147094335372699 -0.773314246497207 0.439713160051519 df.mm.trans1:exp4 -0.0158761429404079 0.147094335372699 -0.107931708588110 0.914094499126007 df.mm.trans2:exp4 -0.0546869714536033 0.147094335372699 -0.371781627858344 0.71021781640296 df.mm.trans1:exp5 -0.110003785519767 0.147094335372699 -0.747845151487617 0.454916014721096 df.mm.trans2:exp5 -0.0612977910266051 0.147094335372699 -0.416724348162642 0.67706441288578 df.mm.trans1:exp6 0.0149675889440183 0.147094335372699 0.101755032959592 0.918993172728968 df.mm.trans2:exp6 -0.00998936625660946 0.147094335372699 -0.0679112912900348 0.945884237888424 df.mm.trans1:exp7 -0.110014713095445 0.147094335372699 -0.747919441062743 0.454871242642643 df.mm.trans2:exp7 -0.0616711076809905 0.147094335372699 -0.419262288549261 0.675210085050032 df.mm.trans1:exp8 -0.049510089944516 0.147094335372699 -0.33658733233384 0.736573593463126 df.mm.trans2:exp8 -0.0843743677051133 0.147094335372699 -0.573607185425124 0.566499531128174 df.mm.trans1:probe2 -0.145043797978959 0.100708606956072 -1.44023239287011 0.150446851783581 df.mm.trans1:probe3 -0.0318954613333702 0.100708606956072 -0.316710381539511 0.751599644234586 df.mm.trans1:probe4 -0.0440514152750296 0.100708606956072 -0.437414602450458 0.662005404782219 df.mm.trans1:probe5 0.0780956592338965 0.100708606956072 0.775461617376568 0.438444874038629 df.mm.trans1:probe6 0.0289452971989025 0.100708606956072 0.287416319952953 0.77391632845928 df.mm.trans2:probe2 -0.0473930154539859 0.100708606956072 -0.470595482217903 0.638141197945491 df.mm.trans2:probe3 -0.0184120410947634 0.100708606956072 -0.182824900981845 0.855011807317264 df.mm.trans2:probe4 0.0200365771342011 0.100708606956072 0.198955955601102 0.842380574613983 df.mm.trans2:probe5 0.0218595758222042 0.100708606956072 0.217057672456329 0.828254676876517 df.mm.trans2:probe6 0.135408901044364 0.100708606956072 1.34456135515238 0.179395415166715 df.mm.trans3:probe2 0.0219725693641249 0.100708606956072 0.218179657412093 0.827380903361881 df.mm.trans3:probe3 -0.0435191922189781 0.100708606956072 -0.432129820224410 0.665839117810123 df.mm.trans3:probe4 0.0300539910591376 0.100708606956072 0.298425248521676 0.765506431297233 df.mm.trans3:probe5 0.0141201271908327 0.100708606956072 0.140207750038601 0.888554056721147 df.mm.trans3:probe6 -0.0993175286268364 0.100708606956072 -0.986187095906885 0.324533029649111 df.mm.trans3:probe7 -0.153898487997322 0.100708606956072 -1.52815625842637 0.127125638902837 df.mm.trans3:probe8 0.0261972126706479 0.100708606956072 0.260128835682087 0.794874791084268 df.mm.trans3:probe9 -0.117091015111802 0.100708606956072 -1.16267138083715 0.245534619838029 df.mm.trans3:probe10 0.0275248241312283 0.100708606956072 0.273311536751118 0.78473009172654