chr9.24262_chr9_44583137_44584158_+_2.R fitVsDatCorrelation=0.920406549676656 cont.fitVsDatCorrelation=0.259902509469897 fstatistic=8358.11038838495,49,623 cont.fstatistic=1359.4713955552,49,623 residuals=-1.02651879176427,-0.0939472021070035,0.00248641489032452,0.0894747430461499,0.91385567747777 cont.residuals=-0.711489223345474,-0.292110171474002,-0.138592286757671,0.190769960108110,1.35022632008701 predictedValues: Include Exclude Both chr9.24262_chr9_44583137_44584158_+_2.R.tl.Lung 86.332678828588 55.4533652989883 68.5891017291987 chr9.24262_chr9_44583137_44584158_+_2.R.tl.cerebhem 78.901771550727 59.2016957542668 57.9912499291192 chr9.24262_chr9_44583137_44584158_+_2.R.tl.cortex 81.3922032175785 56.9818290717143 64.8664568022827 chr9.24262_chr9_44583137_44584158_+_2.R.tl.heart 91.5569669887087 57.4605457000762 69.4681502193484 chr9.24262_chr9_44583137_44584158_+_2.R.tl.kidney 75.6832460397735 54.656325626646 64.8277415178181 chr9.24262_chr9_44583137_44584158_+_2.R.tl.liver 84.7500995243645 61.5761948889209 69.1104132165526 chr9.24262_chr9_44583137_44584158_+_2.R.tl.stomach 98.3288828657237 61.8713363448276 74.0644973995746 chr9.24262_chr9_44583137_44584158_+_2.R.tl.testicle 88.2798507738468 55.1813970568462 64.7263805307342 diffExp=30.8793135295998,19.7000757964602,24.4103741458642,34.0964212886325,21.0269204131275,23.1739046354436,36.4575465208961,33.0984537170006 diffExpScore=0.99553258330564 diffExp1.5=1,0,0,1,0,0,1,1 diffExp1.5Score=0.8 diffExp1.4=1,0,1,1,0,0,1,1 diffExp1.4Score=0.833333333333333 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 74.7016486189366 84.5235793470854 72.783365433062 cerebhem 72.1634248967312 88.0523486463707 74.6815108495581 cortex 75.6161337401176 76.6682305475036 84.9842318956446 heart 75.93386581914 66.51259227294 73.2601155907484 kidney 75.5528307523323 104.446402050321 90.3125644922576 liver 75.2743739179233 83.4937682256877 64.2734710359084 stomach 75.7227123466046 87.4697322001959 72.1312983670938 testicle 82.1940561226574 71.1750489514792 70.3404205058352 cont.diffExp=-9.82193072814889,-15.8889237496395,-1.05209680738601,9.4212735461999,-28.8935712979887,-8.21939430776445,-11.7470198535913,11.0190071711782 cont.diffExpScore=1.70983759499534 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,-1,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,-1,0,0,-1,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.44080687834239 cont.tran.correlation=-0.443413448262372 tran.covariance=0.00175101794360083 cont.tran.covariance=-0.0023717514632699 tran.mean=71.7255243457248 cont.tran.mean=79.3437967785017 weightedLogRatios: wLogRatio Lung 1.87551974727787 cerebhem 1.21352448788767 cortex 1.50498724750842 heart 1.99577026955256 kidney 1.35528654809548 liver 1.36716429357859 stomach 2.01828249013915 testicle 1.99493340145376 cont.weightedLogRatios: wLogRatio Lung -0.540469560970883 cerebhem -0.871300045874977 cortex -0.0598664820432306 heart 0.564809015545211 kidney -1.45299857731359 liver -0.453179212852017 stomach -0.634427079641675 testicle 0.624286895290538 varWeightedLogRatios=0.114493016244722 cont.varWeightedLogRatios=0.498057707497761 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.06018751387691 0.0943222006509006 43.0459370737566 7.93439795559714e-189 *** df.mm.trans1 0.459394027190353 0.0844047356842312 5.44275180138001 7.5520394574066e-08 *** df.mm.trans2 -0.0840116936022088 0.0780396725834434 -1.07652544944214 0.282109016987221 df.mm.exp2 0.143244791491607 0.107019596658067 1.33849122931458 0.181224608917272 df.mm.exp3 0.0240643285635217 0.107019596658067 0.224859084831056 0.822162631440217 df.mm.exp4 0.0815746025087037 0.107019596658067 0.762239861259602 0.44620533723867 df.mm.exp5 -0.0897287895482083 0.107019596658067 -0.838433262226693 0.40210897868929 df.mm.exp6 0.0786599133051009 0.107019596658067 0.735004763253062 0.462613173473294 df.mm.exp7 0.162821576802402 0.107019596658067 1.52141833726607 0.128662257294152 df.mm.exp8 0.0753519837475642 0.107019596658067 0.704095194717631 0.481636579116063 df.mm.trans1:exp2 -0.233249302644080 0.101795941379533 -2.29134186965705 0.0222765245583152 * df.mm.trans2:exp2 -0.0778370081630201 0.0894374428026295 -0.870295546517255 0.384474113175999 df.mm.trans1:exp3 -0.0829930355770425 0.101795941379533 -0.815288256607535 0.415218896685045 df.mm.trans2:exp3 0.00312569758284848 0.0894374428026294 0.0349484229971363 0.972132062511731 df.mm.trans1:exp4 -0.0228214257821156 0.101795941379533 -0.22418797324177 0.82268454288494 df.mm.trans2:exp4 -0.0460184546087892 0.0894374428026295 -0.514532316295567 0.607062349950005 df.mm.trans1:exp5 -0.041922586848579 0.101795941379533 -0.411829649399046 0.680605938684124 df.mm.trans2:exp5 0.0752513427951625 0.0894374428026295 0.841385223426246 0.400454994638754 df.mm.trans1:exp6 -0.0971611845830912 0.101795941379533 -0.954470121955434 0.340215916706924 df.mm.trans2:exp6 0.0260730332873352 0.0894374428026294 0.29152257119955 0.770748721557978 df.mm.trans1:exp7 -0.0327119610141967 0.101795941379533 -0.321348381584628 0.748054173662283 df.mm.trans2:exp7 -0.0533069709040866 0.0894374428026294 -0.596025213083567 0.55137486225127 df.mm.trans1:exp8 -0.0530482846003829 0.101795941379533 -0.521123768604871 0.602465733450689 df.mm.trans2:exp8 -0.0802684997537129 0.0894374428026295 -0.897482052688485 0.369808436214776 df.mm.trans1:probe2 -0.235622603629658 0.0508979706897666 -4.62931233674963 4.46677335991114e-06 *** df.mm.trans1:probe3 -0.232650464691369 0.0508979706897666 -4.57091828099436 5.85768464111214e-06 *** df.mm.trans1:probe4 -0.409101808509026 0.0508979706897666 -8.03768407590519 4.60939546907333e-15 *** df.mm.trans1:probe5 -0.196954230917523 0.0508979706897666 -3.86958906707694 0.000120505510155399 *** df.mm.trans1:probe6 -0.411553408957964 0.0508979706897666 -8.0858510345425 3.22595413570937e-15 *** df.mm.trans1:probe7 -0.418840127800399 0.0508979706897666 -8.22901428336533 1.10597383114721e-15 *** df.mm.trans1:probe8 0.520498003091297 0.0508979706897666 10.2263016783879 8.49926369058378e-23 *** df.mm.trans1:probe9 0.74164476144323 0.0508979706897666 14.5712049300296 1.33594775036739e-41 *** df.mm.trans1:probe10 0.557138335553877 0.0508979706897666 10.946179739655 1.25938252521264e-25 *** df.mm.trans1:probe11 0.553115619293242 0.0508979706897666 10.8671448349993 2.61272205422190e-25 *** df.mm.trans1:probe12 0.322651356641719 0.0508979706897666 6.33917919062714 4.43516202256919e-10 *** df.mm.trans1:probe13 0.449599787401102 0.0508979706897666 8.83335389030543 1.02876319200515e-17 *** df.mm.trans1:probe14 -0.451980881728373 0.0508979706897666 -8.88013560468427 7.08782672904244e-18 *** df.mm.trans1:probe15 -0.548087498933183 0.0508979706897666 -10.7683566064723 6.4727540306145e-25 *** df.mm.trans1:probe16 -0.405305204359369 0.0508979706897666 -7.96309162952263 7.98408427212195e-15 *** df.mm.trans1:probe17 -0.211699163995119 0.0508979706897666 -4.15928496020927 3.64053425668640e-05 *** df.mm.trans1:probe18 -0.536010972441347 0.0508979706897666 -10.5310872943922 5.59093016677126e-24 *** df.mm.trans1:probe19 -0.437055179874768 0.0508979706897666 -8.58688812052464 7.14600330465065e-17 *** df.mm.trans2:probe2 -0.0533225573323524 0.0508979706897666 -1.04763621436627 0.295212507110947 df.mm.trans2:probe3 0.0353549714815134 0.0508979706897666 0.694624382905343 0.487549687175614 df.mm.trans2:probe4 0.280558772087371 0.0508979706897666 5.51217992162071 5.19468962624255e-08 *** df.mm.trans2:probe5 -0.0241188632115940 0.0508979706897666 -0.47386689262335 0.635760848941219 df.mm.trans2:probe6 0.115826918342637 0.0508979706897666 2.27566869116699 0.0232045330521555 * df.mm.trans3:probe2 -0.431961145987906 0.0508979706897666 -8.48680487913352 1.55127517325282e-16 *** df.mm.trans3:probe3 -0.190347430837706 0.0508979706897666 -3.73978428330497 0.00020115829729551 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.53671852041687 0.232965497463431 19.4737786059028 2.49095195103135e-66 *** df.mm.trans1 -0.247185466196847 0.208470445995246 -1.18570987372706 0.236188929785442 df.mm.trans2 0.0185740005963412 0.192749437776308 0.0963634488931531 0.923262910026436 df.mm.exp2 -0.0194129741191557 0.264326673908496 -0.0734431142801577 0.94147708327782 df.mm.exp3 -0.240353914431808 0.264326673908496 -0.909306317360212 0.363540093440679 df.mm.exp4 -0.229807543507676 0.264326673908496 -0.869407313721314 0.384959207391280 df.mm.exp5 0.00718433456045531 0.264326673908496 0.0271797562244602 0.97832506576629 df.mm.exp6 0.119719518663674 0.264326673908496 0.452922578313524 0.650762182120038 df.mm.exp7 0.0568376450901791 0.264326673908496 0.215028034249222 0.82981583144152 df.mm.exp8 -0.0421665652174833 0.264326673908496 -0.159524442213805 0.873307406063524 df.mm.trans1:exp2 -0.0151558504754544 0.251424817907011 -0.0602798506592129 0.951952080982397 df.mm.trans2:exp2 0.0603139417484394 0.220900680969987 0.273036468170209 0.784915663882936 df.mm.trans1:exp3 0.252521422349076 0.251424817907011 1.00436156005280 0.315594300509906 df.mm.trans2:exp3 0.142810791959743 0.220900680969987 0.646493217371051 0.518198075229878 df.mm.trans1:exp4 0.246168156611089 0.251424817907011 0.979092512267958 0.327914313719217 df.mm.trans2:exp4 -0.00983171023336606 0.220900680969987 -0.044507378565763 0.964514232084757 df.mm.trans1:exp5 0.00414566029058177 0.251424817907011 0.0164886677659444 0.986849821644581 df.mm.trans2:exp5 0.204459165261911 0.220900680969987 0.925570552178107 0.355027586421044 df.mm.trans1:exp6 -0.112081923355370 0.251424817907011 -0.445787032037638 0.655905965833913 df.mm.trans2:exp6 -0.131978062584470 0.220900680969987 -0.597454303920416 0.550421192884245 df.mm.trans1:exp7 -0.0432616604088536 0.251424817907011 -0.172065990815807 0.863441527274336 df.mm.trans2:exp7 -0.0225753702467946 0.220900680969987 -0.102196924643532 0.918633252882282 df.mm.trans1:exp8 0.137747392961269 0.251424817907011 0.547867128265017 0.583979374342576 df.mm.trans2:exp8 -0.129721654821192 0.220900680969987 -0.587239723533566 0.557255472477335 df.mm.trans1:probe2 -0.036616401442146 0.125712408953505 -0.291271177976460 0.770940876950504 df.mm.trans1:probe3 0.113741626239630 0.125712408953505 0.904776443204564 0.365933579400904 df.mm.trans1:probe4 0.0969443924244485 0.125712408953505 0.771160088582053 0.440904419138832 df.mm.trans1:probe5 -0.0644560169276868 0.125712408953505 -0.512725970842908 0.608324758909452 df.mm.trans1:probe6 0.0565039407638088 0.125712408953505 0.449469875203066 0.653249061475092 df.mm.trans1:probe7 0.178282844872738 0.125712408953505 1.41818016500404 0.156638171481217 df.mm.trans1:probe8 -0.110535058079587 0.125712408953505 -0.879269270231454 0.379594303644114 df.mm.trans1:probe9 -0.0804690566144215 0.125712408953505 -0.640104324499763 0.522340110927687 df.mm.trans1:probe10 0.109669315704729 0.125712408953505 0.872382580348855 0.383335788682907 df.mm.trans1:probe11 -0.101211508629521 0.125712408953505 -0.805103565129789 0.42106706896565 df.mm.trans1:probe12 -0.0552526318813678 0.125712408953505 -0.439516133222798 0.660440007320855 df.mm.trans1:probe13 0.0550969145676043 0.125712408953505 0.438277454280443 0.661337094590304 df.mm.trans1:probe14 0.0707696424377017 0.125712408953505 0.562948741709943 0.573672317027244 df.mm.trans1:probe15 -0.0207558263393081 0.125712408953505 -0.165105628888113 0.868914400786066 df.mm.trans1:probe16 0.00869322346648668 0.125712408953505 0.069151673560737 0.944891079025761 df.mm.trans1:probe17 -0.0295725552357315 0.125712408953505 -0.235239746671857 0.814099959342365 df.mm.trans1:probe18 0.149941129398264 0.125712408953505 1.19273133532681 0.233428670969283 df.mm.trans1:probe19 0.186546391165169 0.125712408953505 1.48391390092734 0.138337494082795 df.mm.trans2:probe2 -0.185969337749472 0.125712408953505 -1.47932363477541 0.139559287564414 df.mm.trans2:probe3 -0.211958562354409 0.125712408953505 -1.68605918953316 0.092285090222005 . df.mm.trans2:probe4 -0.201655352351686 0.125712408953505 -1.60410061369731 0.109198672321725 df.mm.trans2:probe5 -0.225443615532235 0.125712408953505 -1.79332825938937 0.0734053602994105 . df.mm.trans2:probe6 -0.239330952447876 0.125712408953505 -1.90379736129623 0.0573970697969938 . df.mm.trans3:probe2 -0.0299071310560791 0.125712408953505 -0.237901184974828 0.812035956289987 df.mm.trans3:probe3 -0.0319517365090049 0.125712408953505 -0.254165334790635 0.799451694197388