chr17.10864_chr17_42731140_42746781_+_2.R fitVsDatCorrelation=0.741766086086812 cont.fitVsDatCorrelation=0.245060472457291 fstatistic=9353.50059225653,55,761 cont.fstatistic=4468.62587771731,55,761 residuals=-0.411432855519919,-0.0885911818153592,-0.00465020583491677,0.0756005244167353,1.72718820646732 cont.residuals=-0.527893195884603,-0.161198655926324,-0.035966590805324,0.122601654413662,1.60682915534981 predictedValues: Include Exclude Both chr17.10864_chr17_42731140_42746781_+_2.R.tl.Lung 67.2526898015632 51.8506849873736 51.5620867630997 chr17.10864_chr17_42731140_42746781_+_2.R.tl.cerebhem 71.1637969134014 76.1966765806121 53.9916153464299 chr17.10864_chr17_42731140_42746781_+_2.R.tl.cortex 61.3528721679444 49.8867392607746 48.1426476177172 chr17.10864_chr17_42731140_42746781_+_2.R.tl.heart 63.1013468312342 50.1904162430868 48.361429679596 chr17.10864_chr17_42731140_42746781_+_2.R.tl.kidney 63.6875064941178 47.9721308242099 47.6674031329528 chr17.10864_chr17_42731140_42746781_+_2.R.tl.liver 64.5432560386583 50.7028757967301 50.3932580897828 chr17.10864_chr17_42731140_42746781_+_2.R.tl.stomach 64.9271403846973 53.7952171413436 54.0445256849146 chr17.10864_chr17_42731140_42746781_+_2.R.tl.testicle 62.2642001181634 55.6011231592341 52.7057532509906 diffExp=15.4020048141896,-5.03287966721072,11.4661329071698,12.9109305881474,15.7153756699079,13.8403802419281,11.1319232433538,6.6630769589293 diffExpScore=1.1090985879324 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=0,0,0,0,1,0,0,0 diffExp1.3Score=0.5 diffExp1.2=1,0,1,1,1,1,1,0 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 56.5722885473063 59.7264908883441 54.7410175519945 cerebhem 54.8342568244745 60.471123500739 53.481644620469 cortex 56.6985341792581 55.6318568728928 55.9601826020023 heart 55.159408055494 56.0416629130425 55.7282074385452 kidney 58.7575988385819 56.0201164987164 54.9684392682946 liver 55.0935395496652 56.4113766436777 55.8635793456459 stomach 57.5881597787874 56.2934710963604 58.980382643953 testicle 55.6897593966359 57.7178573070112 53.8409032217603 cont.diffExp=-3.15420234103783,-5.63686667626459,1.06667730636531,-0.882254857548403,2.73748233986541,-1.31783709401245,1.29468868242706,-2.02809791037527 cont.diffExpScore=2.03108445571675 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=0.802932157507079 cont.tran.correlation=-0.384869058971985 tran.covariance=0.00544893100533315 cont.tran.covariance=-0.000297548344016283 tran.mean=59.6555420464465 cont.tran.mean=56.7942188056867 weightedLogRatios: wLogRatio Lung 1.06074971609933 cerebhem -0.293776589678773 cortex 0.830277676418113 heart 0.922604415350298 kidney 1.13695936775812 liver 0.976673339813506 stomach 0.767225403138597 testicle 0.461198428205248 cont.weightedLogRatios: wLogRatio Lung -0.220424592204347 cerebhem -0.396612952517654 cortex 0.0765059981275316 heart -0.063760478760561 kidney 0.193203308726115 liver -0.0950464674939285 stomach 0.0919075266161668 testicle -0.144429213180682 varWeightedLogRatios=0.214958516722273 cont.varWeightedLogRatios=0.0360661935018858 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22084241562348 0.0750591031607127 56.2335844405978 3.11738214187748e-273 *** df.mm.trans1 0.116143472700326 0.0643816974080019 1.80398276802641 0.0716293228039409 . df.mm.trans2 -0.254598254752176 0.0575433575461905 -4.42445949643811 1.10795847650660e-05 *** df.mm.exp2 0.395434792462607 0.0743415806587898 5.31916040738437 1.37194975205143e-07 *** df.mm.exp3 -0.0618097322960695 0.0743415806587898 -0.831428814780809 0.40599222345089 df.mm.exp4 -0.0321748883751093 0.0743415806587898 -0.432798012767369 0.665284144730191 df.mm.exp5 -0.0536776548925938 0.0743415806587898 -0.722040807000883 0.470491191035774 df.mm.exp6 -0.0405776424590514 0.0743415806587898 -0.545827006897972 0.585344818150491 df.mm.exp7 -0.0453964804844278 0.0743415806587898 -0.610647232438962 0.541615473459177 df.mm.exp8 -0.0291731044907274 0.0743415806587898 -0.392419749919295 0.694857978327013 df.mm.trans1:exp2 -0.338907588408296 0.0676298482247902 -5.01121320399573 6.7310653334135e-07 *** df.mm.trans2:exp2 -0.0104850906796763 0.0516531497880793 -0.202990344687480 0.839196839697969 df.mm.trans1:exp3 -0.0300052962707922 0.0676298482247902 -0.443669430856323 0.657407713942415 df.mm.trans2:exp3 0.0231968079288112 0.0516531497880793 0.449087965089879 0.653496062433495 df.mm.trans1:exp4 -0.0315400126432974 0.0676298482247902 -0.466362315918021 0.641089646881405 df.mm.trans2:exp4 -0.000369160176290011 0.0516531497880793 -0.0071469054221202 0.994299516116921 df.mm.trans1:exp5 -0.000790946887339152 0.0676298482247901 -0.0116952338072707 0.99067183145491 df.mm.trans2:exp5 -0.0240702561773409 0.0516531497880793 -0.46599783897198 0.641350394833961 df.mm.trans1:exp6 -0.000543736988315371 0.0676298482247902 -0.00803989662239195 0.993587266840113 df.mm.trans2:exp6 0.0181921277456938 0.0516531497880793 0.352197839247594 0.724787407629736 df.mm.trans1:exp7 0.0102051897927129 0.0676298482247902 0.150897718397838 0.88009638919572 df.mm.trans2:exp7 0.0822128974508732 0.0516531497880793 1.59163376847633 0.111882297345790 df.mm.trans1:exp8 -0.0478972865619258 0.0676298482247902 -0.708227030211916 0.479021148631456 df.mm.trans2:exp8 0.0990083607180108 0.0516531497880793 1.91679231807196 0.0556385311016364 . df.mm.trans1:probe2 -0.413105283133025 0.0463029917917103 -8.9217838231996 3.37765603588082e-18 *** df.mm.trans1:probe3 -0.0786555578282769 0.0463029917917103 -1.69871437642953 0.0897817540815854 . df.mm.trans1:probe4 -0.34353624620866 0.0463029917917103 -7.41930991746765 3.15112848755365e-13 *** df.mm.trans1:probe5 -0.128125827669961 0.0463029917917103 -2.76711768963705 0.0057927273614394 ** df.mm.trans1:probe6 -0.319828505491836 0.0463029917917103 -6.90729676670904 1.04373143710602e-11 *** df.mm.trans1:probe7 -0.368374743183694 0.0463029917917103 -7.95574387160107 6.44922977982143e-15 *** df.mm.trans1:probe8 -0.282421418164077 0.0463029917917103 -6.09942051767462 1.69303465798105e-09 *** df.mm.trans1:probe9 -0.360887499903808 0.0463029917917104 -7.7940428024009 2.13223597119905e-14 *** df.mm.trans1:probe10 -0.371595720561332 0.0463029917917103 -8.02530692256172 3.83190886594981e-15 *** df.mm.trans1:probe11 -0.346733342736122 0.0463029917917103 -7.48835721665394 1.93437799783455e-13 *** df.mm.trans1:probe12 -0.294975008515232 0.0463029917917104 -6.3705388593927 3.25924495607785e-10 *** df.mm.trans1:probe13 -0.0227342129625902 0.0463029917917103 -0.490987991982417 0.623576496195946 df.mm.trans1:probe14 -0.0980445753976099 0.0463029917917103 -2.11745659629629 0.0345448738270746 * df.mm.trans1:probe15 -0.00102170560070785 0.0463029917917103 -0.0220656497814202 0.98240137154442 df.mm.trans1:probe16 0.0699603166477656 0.0463029917917104 1.51092432563484 0.131222839979606 df.mm.trans1:probe17 -0.133404723951576 0.0463029917917103 -2.88112536122254 0.00407403345346854 ** df.mm.trans1:probe18 -0.105324405927788 0.0463029917917103 -2.27467819793546 0.0232027028188427 * df.mm.trans2:probe2 -0.136332243021092 0.0463029917917103 -2.94435062931505 0.00333490591996003 ** df.mm.trans2:probe3 0.0093999453473616 0.0463029917917103 0.203009459726628 0.839181904442686 df.mm.trans2:probe4 -0.120950383428343 0.0463029917917104 -2.61215050579079 0.00917488371399356 ** df.mm.trans2:probe5 -0.0313049639859532 0.0463029917917104 -0.676089444215088 0.499189269845603 df.mm.trans2:probe6 -0.00682860035884786 0.0463029917917104 -0.147476439310135 0.882795038217756 df.mm.trans3:probe2 -0.240676018215823 0.0463029917917103 -5.19785026631715 2.59246688153846e-07 *** df.mm.trans3:probe3 -0.0406257118075812 0.0463029917917103 -0.877388484751312 0.38055256527133 df.mm.trans3:probe4 -0.154816974812573 0.0463029917917103 -3.34356310082515 0.000867563967780233 *** df.mm.trans3:probe5 0.0402094174817225 0.0463029917917104 0.86839782756589 0.38545035623631 df.mm.trans3:probe6 -0.26968890659034 0.0463029917917103 -5.82443803639104 8.45317318335422e-09 *** df.mm.trans3:probe7 -0.264783926246257 0.0463029917917104 -5.71850578116773 1.54391694406706e-08 *** df.mm.trans3:probe8 0.030724142103703 0.0463029917917104 0.663545505696751 0.50718220279802 df.mm.trans3:probe9 -0.269741387379531 0.0463029917917104 -5.82557145751914 8.39843429225468e-09 *** df.mm.trans3:probe10 -0.226610472535728 0.0463029917917103 -4.89407841193316 1.20617276775513e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.24108301065435 0.108505842234453 39.0862180627148 3.88642659352363e-184 *** df.mm.trans1 -0.199382787504948 0.093070527192169 -2.14227633086540 0.0324879622326276 * df.mm.trans2 -0.139841331315881 0.083184986399035 -1.68108858785007 0.0931560387746688 . df.mm.exp2 0.00446094606938377 0.107468587856038 0.0415093020051545 0.96690076518633 df.mm.exp3 -0.0908176790454986 0.107468587856038 -0.845062551367617 0.398341517474449 df.mm.exp4 -0.106845346657741 0.107468587856038 -0.994200712871261 0.320441172548706 df.mm.exp5 -0.0303094798966354 0.107468587856038 -0.282031061366854 0.7779963733844 df.mm.exp6 -0.103890950977166 0.107468587856038 -0.966709929382675 0.333996174113276 df.mm.exp7 -0.115990968645259 0.107468587856038 -1.07930113309609 0.280795465342879 df.mm.exp8 -0.0333521998898371 0.107468587856038 -0.310343706521153 0.756384570910903 df.mm.trans1:exp2 -0.0356650865675815 0.0977660714398201 -0.364800242480185 0.715361876362639 df.mm.trans2:exp2 0.00792935206517997 0.074670070461906 0.106191838525521 0.915458131924663 df.mm.trans1:exp3 0.0930467735499377 0.0977660714398201 0.951728674167016 0.341536776452517 df.mm.trans2:exp3 0.0197980261087918 0.074670070461906 0.265140048567278 0.790973378509185 df.mm.trans1:exp4 0.081553404439101 0.0977660714398201 0.834168778984856 0.404447645932261 df.mm.trans2:exp4 0.043165085915491 0.074670070461906 0.578077476671357 0.563382827486068 df.mm.trans1:exp5 0.0682107028128431 0.09776607143982 0.697692991119421 0.485582318100398 df.mm.trans2:exp5 -0.033755326133127 0.074670070461906 -0.452059652874545 0.651354829721723 df.mm.trans1:exp6 0.0774041470319918 0.0977660714398201 0.791728110703906 0.428765934025973 df.mm.trans2:exp6 0.0467861472290501 0.074670070461906 0.62657162286888 0.531127979935864 df.mm.trans1:exp7 0.133788692143403 0.09776607143982 1.36845727943314 0.171572839434795 df.mm.trans2:exp7 0.0567938752983493 0.074670070461906 0.760597585445209 0.447133054313159 df.mm.trans1:exp8 0.0176292133994863 0.0977660714398201 0.180320362062804 0.85694907788673 df.mm.trans2:exp8 -0.00085684453776609 0.074670070461906 -0.0114750733790083 0.990847424565445 df.mm.trans1:probe2 -0.0341715315446251 0.0669358533578138 -0.510511629125829 0.609841117794544 df.mm.trans1:probe3 0.0276382000417899 0.0669358533578138 0.412905769558902 0.679791962070943 df.mm.trans1:probe4 0.0049252431958761 0.0669358533578138 0.0735815403674263 0.941362695758082 df.mm.trans1:probe5 0.0785430357049572 0.0669358533578138 1.17340755013753 0.240999534557359 df.mm.trans1:probe6 -0.0341780400488000 0.0669358533578138 -0.510608864073146 0.609773048548345 df.mm.trans1:probe7 0.0287054414235262 0.0669358533578138 0.428850010622524 0.668153748564262 df.mm.trans1:probe8 -0.052970959710231 0.0669358533578138 -0.791369005592088 0.428975269500569 df.mm.trans1:probe9 -0.0905751770403647 0.0669358533578138 -1.35316385011459 0.176405120056258 df.mm.trans1:probe10 -0.0444418474437172 0.0669358533578138 -0.663946826914237 0.506925443990909 df.mm.trans1:probe11 -0.0306472593477037 0.0669358533578138 -0.457860142364586 0.64718362386477 df.mm.trans1:probe12 -0.0191474596105174 0.0669358533578138 -0.286056853688894 0.774912427528642 df.mm.trans1:probe13 -0.0598260206402063 0.0669358533578138 -0.893781398743047 0.371721430968147 df.mm.trans1:probe14 -0.0267654033276283 0.0669358533578138 -0.399866468939307 0.689367079132404 df.mm.trans1:probe15 0.00897916339668217 0.0669358533578138 0.134145797001839 0.893322754239163 df.mm.trans1:probe16 -0.0312151494120956 0.0669358533578138 -0.466344236253046 0.64110258008507 df.mm.trans1:probe17 0.0402628092739838 0.0669358533578138 0.601513348291147 0.54767726463651 df.mm.trans1:probe18 0.0618180898736554 0.0669358533578138 0.923542268792768 0.356017411981298 df.mm.trans2:probe2 -0.101517038588706 0.0669358533578138 -1.51663172270374 0.129774958755249 df.mm.trans2:probe3 -0.0511581828850634 0.0669358533578138 -0.764286706133275 0.444933316499061 df.mm.trans2:probe4 2.44500770068971e-05 0.0669358533578138 0.000365276242557127 0.999708647497197 df.mm.trans2:probe5 -0.0486413954561764 0.0669358533578138 -0.726686715954123 0.467641356057792 df.mm.trans2:probe6 0.0178357846546515 0.0669358533578138 0.2664608540853 0.789956485266436 df.mm.trans3:probe2 0.134127962962121 0.0669358533578138 2.00382838544127 0.045442454806009 * df.mm.trans3:probe3 0.216662961430679 0.0669358533578138 3.23687456814035 0.00126079494589111 ** df.mm.trans3:probe4 0.118249383337929 0.0669358533578138 1.76660754148919 0.077694804510226 . df.mm.trans3:probe5 0.0861240015430587 0.0669358533578138 1.28666472783535 0.198602435023412 df.mm.trans3:probe6 0.0134996664764281 0.0669358533578138 0.201680650939998 0.840220287595902 df.mm.trans3:probe7 0.179097662554034 0.0669358533578138 2.67566115272551 0.0076185326331723 ** df.mm.trans3:probe8 0.0831030781518495 0.0669358533578138 1.24153310943258 0.21479140241947 df.mm.trans3:probe9 0.0556520449921499 0.0669358533578138 0.831423552556432 0.405995193273178 df.mm.trans3:probe10 0.121027242811946 0.0669358533578138 1.80810786358365 0.070984372632994 .