chr12.5921_chr12_46538251_46579507_+_2.R fitVsDatCorrelation=0.765869027484219 cont.fitVsDatCorrelation=0.269858138798804 fstatistic=10004.3912324516,60,876 cont.fstatistic=4453.04749874408,60,876 residuals=-0.474759069752923,-0.0888670077991879,-0.0074088924771706,0.0776038545233438,0.888715856827324 cont.residuals=-0.607247642303444,-0.162450617690329,-0.0246041995756591,0.127274337053556,0.936667178590382 predictedValues: Include Exclude Both chr12.5921_chr12_46538251_46579507_+_2.R.tl.Lung 56.453988769614 76.2139623046331 47.1516425933518 chr12.5921_chr12_46538251_46579507_+_2.R.tl.cerebhem 69.1610093857424 76.0609362752037 43.8425091924828 chr12.5921_chr12_46538251_46579507_+_2.R.tl.cortex 54.5829322275293 67.2123715067596 46.9758847348122 chr12.5921_chr12_46538251_46579507_+_2.R.tl.heart 52.8478084981043 72.0116043479093 47.4046009768647 chr12.5921_chr12_46538251_46579507_+_2.R.tl.kidney 56.5969888393546 86.6394740202097 50.0837871037198 chr12.5921_chr12_46538251_46579507_+_2.R.tl.liver 57.735014506931 75.385726667823 46.9737570720363 chr12.5921_chr12_46538251_46579507_+_2.R.tl.stomach 57.0291353954225 61.3521588071415 48.7578877285405 chr12.5921_chr12_46538251_46579507_+_2.R.tl.testicle 57.848044957111 75.6865016608234 47.9250519587203 diffExp=-19.7599735350191,-6.89992688946131,-12.6294392792303,-19.163795849805,-30.0424851808552,-17.6507121608920,-4.323023411719,-17.8384567037124 diffExpScore=0.9922665152498 diffExp1.5=0,0,0,0,-1,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,-1,0,0,0 diffExp1.4Score=0.5 diffExp1.3=-1,0,0,-1,-1,-1,0,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,0,-1,-1,-1,-1,0,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 58.9726225632161 59.8404149755766 65.3900559653165 cerebhem 60.859854490797 58.2028492359494 58.769761465155 cortex 57.7540941858435 60.4934239596483 60.0996265772219 heart 57.6813969542243 59.4459930816809 54.045696129679 kidney 59.5631096677221 57.6197035097518 46.2190534506954 liver 58.8366284160932 57.5320068491133 51.9078687268778 stomach 59.8218052489775 63.0686255384976 57.8498269795968 testicle 57.5684757993992 65.699590185322 64.9440588260226 cont.diffExp=-0.867792412360522,2.65700525484757,-2.73932977380479,-1.76459612745663,1.94340615797027,1.30462156697990,-3.24682028952004,-8.13111438592286 cont.diffExpScore=1.91265620603597 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=0.183825444054200 cont.tran.correlation=-0.406895339580928 tran.covariance=0.00160066094026268 cont.tran.covariance=-0.000380384571174002 tran.mean=65.8011036356445 cont.tran.mean=59.5600371663633 weightedLogRatios: wLogRatio Lung -1.25554229890068 cerebhem -0.407396603784842 cortex -0.854146668326907 heart -1.27542995033435 kidney -1.80916167134153 liver -1.11749730481481 stomach -0.298124781541509 testicle -1.12678286002638 cont.weightedLogRatios: wLogRatio Lung -0.0596644059211115 cerebhem 0.182408108413622 cortex -0.189039471737003 heart -0.122643427194998 kidney 0.135024304634153 liver 0.0911176970289138 stomach -0.217638727186429 testicle -0.544196730016812 varWeightedLogRatios=0.241181641239169 cont.varWeightedLogRatios=0.056003432214917 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.70080108594757 0.0852822991453114 55.1204779075895 5.26420346194323e-287 *** df.mm.trans1 -0.653653766622186 0.0770770682961204 -8.48052191231418 9.45469801070555e-17 *** df.mm.trans2 -0.342317802203015 0.07023336859642 -4.87400517793851 1.29800337560202e-06 *** df.mm.exp2 0.273766411037827 0.0960070660601526 2.85152356250841 0.00445331022032218 ** df.mm.exp3 -0.155657592071701 0.0960070660601525 -1.62131391427143 0.105310186086650 df.mm.exp4 -0.128077548969665 0.0960070660601526 -1.33404294314565 0.182536488934607 df.mm.exp5 0.0704121893800267 0.0960070660601526 0.733406323821108 0.463506864949991 df.mm.exp6 0.0152909161869820 0.0960070660601526 0.159268654011379 0.87349392846631 df.mm.exp7 -0.240276120957022 0.0960070660601526 -2.50269204983807 0.0125064266721898 * df.mm.exp8 0.00117933539017703 0.0960070660601526 0.0122838394982107 0.990201957521293 df.mm.trans1:exp2 -0.0707551037165046 0.0929584419923015 -0.761147693529161 0.446773698040361 df.mm.trans2:exp2 -0.275776277172951 0.0796048538360976 -3.46431484869956 0.000557371326063418 *** df.mm.trans1:exp3 0.121952881065861 0.0929584419923015 1.31190754117803 0.189895018923709 df.mm.trans2:exp3 0.0299702442076154 0.0796048538360976 0.376487648219575 0.706645565302769 df.mm.trans1:exp4 0.0620678456530943 0.0929584419923015 0.667694556006377 0.504504446249346 df.mm.trans2:exp4 0.0713601482458587 0.0796048538360977 0.896429612103623 0.370269631572766 df.mm.trans1:exp5 -0.0678823546270248 0.0929584419923015 -0.730244108788383 0.465436224220765 df.mm.trans2:exp5 0.0577986641768709 0.0796048538360977 0.726069597412683 0.467990047827522 df.mm.trans1:exp6 0.00714696216152911 0.0929584419923015 0.0768834116445389 0.938733849940593 df.mm.trans2:exp6 -0.0262176388324891 0.0796048538360977 -0.329347239132803 0.74197197077113 df.mm.trans1:exp7 0.2504124571656 0.0929584419923016 2.69381082340363 0.00719911708210807 ** df.mm.trans2:exp7 0.0233618016234392 0.0796048538360977 0.29347207485036 0.769230852504277 df.mm.trans1:exp8 0.023214374318658 0.0929584419923016 0.249728521919295 0.80285584736135 df.mm.trans2:exp8 -0.00812418269386922 0.0796048538360977 -0.102056373479392 0.918735284707214 df.mm.trans1:probe2 -0.0135620784814127 0.0464792209961508 -0.291787990219025 0.770517811891432 df.mm.trans1:probe3 0.0527313677759541 0.0464792209961508 1.13451487881695 0.256888983633882 df.mm.trans1:probe4 -0.0253168942168923 0.0464792209961508 -0.544692739557511 0.586103489500255 df.mm.trans1:probe5 -0.147566984677691 0.0464792209961508 -3.17490227923381 0.00155124004636924 ** df.mm.trans1:probe6 0.00726904342142195 0.0464792209961508 0.156393400440682 0.875758973031307 df.mm.trans1:probe7 -0.102765809455963 0.0464792209961508 -2.21100541819479 0.0272930844871218 * df.mm.trans1:probe8 0.0341397340239132 0.0464792209961508 0.734516054534144 0.462830845539964 df.mm.trans1:probe9 -0.0182344270959482 0.0464792209961508 -0.392313526456443 0.694922001495844 df.mm.trans1:probe10 0.299432923515407 0.0464792209961508 6.44229651654887 1.93922194511650e-10 *** df.mm.trans1:probe11 -0.29522943539159 0.0464792209961508 -6.35185850933344 3.41402847686172e-10 *** df.mm.trans1:probe12 -0.221014804187016 0.0464792209961508 -4.75513142109932 2.31793787129870e-06 *** df.mm.trans1:probe13 -0.246179060919960 0.0464792209961508 -5.29654016663376 1.49349079663199e-07 *** df.mm.trans1:probe14 0.008732261744775 0.0464792209961508 0.187874528824357 0.851018519364526 df.mm.trans1:probe15 -0.137185727671927 0.0464792209961508 -2.95154963297014 0.00324616814183329 ** df.mm.trans1:probe16 -0.170311965799685 0.0464792209961508 -3.66426033288703 0.000262961476062283 *** df.mm.trans1:probe17 0.166941669789818 0.0464792209961508 3.59174844612055 0.000346751276964408 *** df.mm.trans1:probe18 -0.0943375967618887 0.0464792209961508 -2.02967250181971 0.0426916630613817 * df.mm.trans1:probe19 0.183825115790872 0.0464792209961508 3.95499562710175 8.27174689694593e-05 *** df.mm.trans1:probe20 0.163047359020381 0.0464792209961508 3.50796238675953 0.000474525191447625 *** df.mm.trans1:probe21 -0.00707773248635653 0.0464792209961508 -0.15227734748271 0.879003255207529 df.mm.trans1:probe22 0.0796067641017999 0.0464792209961508 1.71273877650386 0.0871144715125792 . df.mm.trans1:probe23 0.0424654869015647 0.0464792209961508 0.91364454892825 0.361155041177984 df.mm.trans1:probe24 -0.0379537913420044 0.0464792209961508 -0.816575461648713 0.414393084248826 df.mm.trans1:probe25 -0.0251625909083124 0.0464792209961508 -0.541372905333252 0.588388228941642 df.mm.trans1:probe26 -0.140852126495928 0.0464792209961508 -3.03043216898134 0.00251382054410766 ** df.mm.trans1:probe27 -0.0912338371718192 0.0464792209961508 -1.96289514360352 0.0499744507410356 * df.mm.trans1:probe28 0.0269790700872668 0.0464792209961508 0.580454437683048 0.561757485184247 df.mm.trans1:probe29 0.000345430241731309 0.0464792209961508 0.00743192838279102 0.994071925776653 df.mm.trans1:probe30 0.255663393564505 0.0464792209961508 5.50059549375147 4.96902632547883e-08 *** df.mm.trans2:probe2 -0.184130656343432 0.0464792209961508 -3.96156932919941 8.0511533138476e-05 *** df.mm.trans2:probe3 0.129861775692869 0.0464792209961508 2.79397487543139 0.00532008824291774 ** df.mm.trans2:probe4 0.105148905906031 0.0464792209961508 2.26227771577193 0.0239244956113844 * df.mm.trans2:probe5 -0.149958291881799 0.0464792209961508 -3.22635123110643 0.00130031199714913 ** df.mm.trans2:probe6 -0.12536918280784 0.0464792209961508 -2.69731678201368 0.00712438063777434 ** df.mm.trans3:probe2 0.170803569420573 0.0464792209961508 3.67483718013946 0.000252463274133140 *** df.mm.trans3:probe3 0.274814417462832 0.0464792209961508 5.91262959173931 4.82156285489612e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00153794527673 0.127711924268386 31.3325319323161 3.72641805879157e-145 *** df.mm.trans1 0.0822043306464875 0.115424429309661 0.71219178763232 0.476535654140247 df.mm.trans2 0.124871754480936 0.105175854089210 1.18726636985538 0.235444412507724 df.mm.exp2 0.110496218421871 0.143772474156827 0.768549189056466 0.442368119197257 df.mm.exp3 0.0743408951518543 0.143772474156827 0.51707321299043 0.605235577121888 df.mm.exp4 0.161788636630580 0.143772474156827 1.1253102346566 0.260765813403049 df.mm.exp5 0.31912449292156 0.143772474156827 2.21964944815138 0.0266979565374826 * df.mm.exp6 0.189251252272522 0.143772474156827 1.31632465381437 0.188409409928441 df.mm.exp7 0.189358812963662 0.143772474156827 1.31707278513625 0.188158643452633 df.mm.exp8 0.076157161831581 0.143772474156827 0.529706136575966 0.596449911160402 df.mm.trans1:exp2 -0.0789957767007847 0.139207099513107 -0.567469453620408 0.5705406691408 df.mm.trans2:exp2 -0.138243177333094 0.119209837989816 -1.15966248813209 0.246502229016641 df.mm.trans1:exp3 -0.0952199651307562 0.139207099513107 -0.684016587255958 0.494145592978049 df.mm.trans2:exp3 -0.0634874992990817 0.119209837989816 -0.532569294360632 0.594466821174007 df.mm.trans1:exp4 -0.183927236768047 0.139207099513107 -1.32124896942292 0.186763356801028 df.mm.trans2:exp4 -0.168401683963234 0.119209837989816 -1.41264921421687 0.158114063432016 df.mm.trans1:exp5 -0.309161387655875 0.139207099513107 -2.22087371073173 0.0266145822359119 * df.mm.trans2:exp5 -0.356941177286348 0.119209837989816 -2.99422583995828 0.00282878762426819 ** df.mm.trans1:exp6 -0.191559970924046 0.139207099513107 -1.37607903328242 0.169148844198924 df.mm.trans2:exp6 -0.228591086876586 0.119209837989816 -1.91755219813413 0.0554927425662175 . df.mm.trans1:exp7 -0.175061894061032 0.139207099513107 -1.25756441067540 0.208884473241779 df.mm.trans2:exp7 -0.136816653555231 0.119209837989816 -1.14769599441045 0.251407419107376 df.mm.trans1:exp8 -0.100255350960546 0.139207099513107 -0.720188491184724 0.471601064181767 df.mm.trans2:exp8 0.017254257578738 0.119209837989816 0.144738537269147 0.884950604125386 df.mm.trans1:probe2 0.0188322797608728 0.0696035497565536 0.270564932776286 0.786789403272717 df.mm.trans1:probe3 0.00561050679993257 0.0696035497565536 0.0806066187652205 0.93577321369305 df.mm.trans1:probe4 0.0403401882188155 0.0696035497565536 0.579570846025956 0.562353074430161 df.mm.trans1:probe5 0.135124579722326 0.0696035497565536 1.94134609793523 0.0525366949459 . df.mm.trans1:probe6 -0.0322501665669419 0.0696035497565536 -0.463340830744129 0.643235175812097 df.mm.trans1:probe7 -0.0618561867810736 0.0696035497565536 -0.888692990478542 0.374412057283197 df.mm.trans1:probe8 -0.00305301336803291 0.0696035497565536 -0.0438628974917397 0.965023686375817 df.mm.trans1:probe9 0.00617054733425213 0.0696035497565536 0.0886527677946646 0.929378133289755 df.mm.trans1:probe10 -0.046698419383921 0.0696035497565536 -0.670920083059758 0.50244829714598 df.mm.trans1:probe11 -0.0490894345703378 0.0696035497565536 -0.705271997506358 0.48082846881785 df.mm.trans1:probe12 0.123448471469810 0.0696035497565536 1.77359447760329 0.0764776131970423 . df.mm.trans1:probe13 -0.0444907642923679 0.0696035497565536 -0.639202518377 0.522858297609934 df.mm.trans1:probe14 -0.0797971992573274 0.0696035497565536 -1.14645301189993 0.251920814083078 df.mm.trans1:probe15 0.0882979658362355 0.0696035497565536 1.26858423377928 0.204926391983115 df.mm.trans1:probe16 0.043398377954626 0.0696035497565536 0.623508112824946 0.533112980911769 df.mm.trans1:probe17 -0.040985378175191 0.0696035497565536 -0.588840343898294 0.556120182034838 df.mm.trans1:probe18 -0.0229622684595083 0.0696035497565536 -0.329900824596181 0.741553772878022 df.mm.trans1:probe19 0.0125011632091645 0.0696035497565536 0.179605253652850 0.857504015016988 df.mm.trans1:probe20 0.0186285330832778 0.0696035497565536 0.267637687279359 0.789041219747997 df.mm.trans1:probe21 -0.0459674928898844 0.0696035497565536 -0.6604188012057 0.509158728903186 df.mm.trans1:probe22 -0.0369623600815775 0.0696035497565536 -0.531041307675508 0.59552476490758 df.mm.trans1:probe23 -0.0481136635796469 0.0696035497565536 -0.691253014363922 0.489589694041369 df.mm.trans1:probe24 0.0281446270511008 0.0696035497565536 0.404356202370998 0.686049505665088 df.mm.trans1:probe25 -0.00605964613447442 0.0696035497565536 -0.087059441015131 0.930644160178598 df.mm.trans1:probe26 -0.0546255125543912 0.0696035497565536 -0.784809291271065 0.432777575475211 df.mm.trans1:probe27 -0.0483334932200747 0.0696035497565536 -0.694411325128196 0.48760841230026 df.mm.trans1:probe28 0.0101930067036751 0.0696035497565536 0.146443776780442 0.88360476412388 df.mm.trans1:probe29 -0.0683055445594224 0.0696035497565536 -0.981351451159155 0.326690406415525 df.mm.trans1:probe30 -0.0612155173287121 0.0696035497565536 -0.879488439064104 0.379377689864784 df.mm.trans2:probe2 0.0165267971814103 0.0696035497565536 0.237441872421947 0.812369528370771 df.mm.trans2:probe3 -0.020088598279006 0.0696035497565536 -0.288614565625865 0.772944627040113 df.mm.trans2:probe4 -0.0779816651869161 0.0696035497565536 -1.12036908260665 0.262863576722147 df.mm.trans2:probe5 -0.0890766466093356 0.0696035497565536 -1.27977160534041 0.200964296940329 df.mm.trans2:probe6 -0.141935769375029 0.0696035497565536 -2.03920302730917 0.0417294475583302 * df.mm.trans3:probe2 -0.0731558153007383 0.0696035497565536 -1.05103569511338 0.293532046163778 df.mm.trans3:probe3 -0.0115756835843232 0.0696035497565536 -0.166308810754774 0.867952309608447