chr15.8578_chr15_31673696_31675929_+_0.R fitVsDatCorrelation=0.852625289681949 cont.fitVsDatCorrelation=0.247314353250825 fstatistic=7501.3199476356,61,899 cont.fstatistic=2171.06571460991,61,899 residuals=-0.544931305388882,-0.105439764742361,-0.00551830358352478,0.0919930563539896,1.26959820538494 cont.residuals=-0.679769878480399,-0.227450196369005,-0.0779064860584157,0.167132893615645,1.58019824449586 predictedValues: Include Exclude Both chr15.8578_chr15_31673696_31675929_+_0.R.tl.Lung 45.8792867747305 51.5681527827558 60.5363502596949 chr15.8578_chr15_31673696_31675929_+_0.R.tl.cerebhem 52.4617683191451 47.944397894562 63.8781019202471 chr15.8578_chr15_31673696_31675929_+_0.R.tl.cortex 48.4633171825826 52.5203343031115 60.6701439889314 chr15.8578_chr15_31673696_31675929_+_0.R.tl.heart 53.8645572403196 54.0127772347691 72.3709853558762 chr15.8578_chr15_31673696_31675929_+_0.R.tl.kidney 81.8723002322682 59.5963503940812 115.074484997326 chr15.8578_chr15_31673696_31675929_+_0.R.tl.liver 87.5365014524646 55.7808537582188 122.146254819362 chr15.8578_chr15_31673696_31675929_+_0.R.tl.stomach 50.6686656692648 61.7949699852143 64.8393111055838 chr15.8578_chr15_31673696_31675929_+_0.R.tl.testicle 49.5868529106094 57.5345702740912 63.0223486830247 diffExp=-5.68886600802534,4.51737042458313,-4.05701712052890,-0.148219994449498,22.2759498381870,31.7556476942457,-11.1263043159495,-7.9477173634818 diffExpScore=2.86182733147897 diffExp1.5=0,0,0,0,0,1,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,1,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,1,1,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,0,0,1,1,-1,0 diffExp1.2Score=1.5 cont.predictedValues: Include Exclude Both Lung 66.2958233901655 56.662999345799 63.2361903472822 cerebhem 66.9499610795931 60.6181626863428 66.2859800838786 cortex 75.3668883801547 64.2105311310436 66.8094470820171 heart 70.087720946989 74.8913951698737 71.9278607249773 kidney 69.8855167250772 73.2896187131913 64.6710255799078 liver 71.5833272412304 63.393409873354 69.3481877851302 stomach 66.5779391047514 67.065871742032 60.1186719349437 testicle 71.7776979633388 59.4123722799555 65.5256423026436 cont.diffExp=9.63282404436647,6.33179839325035,11.1563572491111,-4.80367422288462,-3.40410198811406,8.18991736787636,-0.487932637280622,12.3653256833833 cont.diffExpScore=1.40998516781892 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=0,0,0,0,0,0,0,1 cont.diffExp1.2Score=0.5 tran.correlation=0.329753432578757 cont.tran.correlation=0.142575932901806 tran.covariance=0.00700302482008896 cont.tran.covariance=0.000731909639000607 tran.mean=56.9428535255118 cont.tran.mean=67.3793272358058 weightedLogRatios: wLogRatio Lung -0.454056516494055 cerebhem 0.352522948721695 cortex -0.315222236554422 heart -0.0109583534931077 kidney 1.34850674802287 liver 1.91368944479738 stomach -0.798933604824094 testicle -0.591378790973749 cont.weightedLogRatios: wLogRatio Lung 0.646175509223725 cerebhem 0.412730583702776 cortex 0.67961466376739 heart -0.283918932481522 kidney -0.203113898250118 liver 0.511538075780804 stomach -0.0306832600709503 testicle 0.790136269423939 varWeightedLogRatios=0.948274738578492 cont.varWeightedLogRatios=0.180456582148489 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.44431141306435 0.0876606802727444 39.2914063905030 2.41270454293441e-197 *** df.mm.trans1 0.318460403281073 0.0739733644817838 4.30506852719258 1.85274355018950e-05 *** df.mm.trans2 0.493055519419583 0.0659534618072307 7.47580954674811 1.82070312838170e-13 *** df.mm.exp2 0.00747601524621283 0.083732163168296 0.0892848693182158 0.928875404912133 df.mm.exp3 0.0708818452679683 0.0837321631682959 0.846530682904974 0.397482042464408 df.mm.exp4 0.0282138470755657 0.083732163168296 0.336953519507884 0.736230584868851 df.mm.exp5 0.0815014228662513 0.0837321631682959 0.973358620897433 0.330636918068293 df.mm.exp6 0.0225933978460967 0.083732163168296 0.269829382058189 0.787353441139655 df.mm.exp7 0.211543563288983 0.0837321631682959 2.52643136501556 0.011693430701769 * df.mm.exp8 0.146948301970163 0.083732163168296 1.75498036130760 0.0796032773508687 . df.mm.trans1:exp2 0.126594919886269 0.0739733644817838 1.71135814590999 0.0873599508315444 . df.mm.trans2:exp2 -0.0803383407845543 0.0539168912241424 -1.49004030018298 0.13656438411552 df.mm.trans1:exp3 -0.0160884269397412 0.0739733644817838 -0.217489457894038 0.827876272816346 df.mm.trans2:exp3 -0.0525857184123486 0.0539168912241424 -0.975310653459972 0.329668546622812 df.mm.trans1:exp4 0.132245102744135 0.0739733644817838 1.78773946096099 0.0741547895226312 . df.mm.trans2:exp4 0.0181024991461999 0.0539168912241424 0.335748199408317 0.737139094647997 df.mm.trans1:exp5 0.497645549593303 0.0739733644817838 6.72736130199742 3.07179299715143e-11 *** df.mm.trans2:exp5 0.0631886265193016 0.0539168912241424 1.17196346237054 0.241522211946531 df.mm.trans1:exp6 0.623448721297569 0.0739733644817838 8.42801629566403 1.38504063236896e-16 *** df.mm.trans2:exp6 0.0559330022363951 0.0539168912241424 1.03739293877073 0.299831710834129 df.mm.trans1:exp7 -0.112249624580282 0.0739733644817838 -1.51743300263062 0.129508949835777 df.mm.trans2:exp7 -0.0306258817750192 0.0539168912241424 -0.568020171038828 0.57016310741469 df.mm.trans1:exp8 -0.0692363123504249 0.0739733644817838 -0.935962732470747 0.3495437242999 df.mm.trans2:exp8 -0.03746660048256 0.0539168912241424 -0.694895414626272 0.487300401972329 df.mm.trans1:probe2 0.333343386203815 0.0554800233613378 6.008349780835 2.72001665545339e-09 *** df.mm.trans1:probe3 0.257054523573351 0.0554800233613378 4.63328073781028 4.13070661393665e-06 *** df.mm.trans1:probe4 0.207587156064877 0.0554800233613378 3.74165588779361 0.000194407127619003 *** df.mm.trans1:probe5 0.369543250400025 0.0554800233613378 6.66083444113232 4.73755156950658e-11 *** df.mm.trans1:probe6 0.0926471385791011 0.0554800233613378 1.66991888189549 0.0952835275104547 . df.mm.trans1:probe7 0.0857919577302484 0.0554800233613378 1.54635763527875 0.122370149615582 df.mm.trans1:probe8 0.174995444408544 0.0554800233613378 3.15420639369256 0.00166281206385834 ** df.mm.trans1:probe9 0.259977548999317 0.0554800233613378 4.68596682640345 3.21735131950591e-06 *** df.mm.trans1:probe10 0.129424047315625 0.0554800233613378 2.33280448482681 0.0198782323248143 * df.mm.trans1:probe11 -0.0352597395842601 0.0554800233613378 -0.635539378824981 0.525238429042103 df.mm.trans1:probe12 0.109409946645690 0.0554800233613378 1.97206021225171 0.0489089081541573 * df.mm.trans1:probe13 -0.0360119456591589 0.0554800233613378 -0.649097521546006 0.516441129638862 df.mm.trans1:probe14 0.0246015395220647 0.0554800233613378 0.44343059053592 0.657561006792326 df.mm.trans1:probe15 -0.100392662391964 0.0554800233613378 -1.80952812038511 0.0707028719232253 . df.mm.trans1:probe16 0.0245463185246584 0.0554800233613378 0.442435259350737 0.658280691254666 df.mm.trans2:probe2 -0.141812858244232 0.0554800233613378 -2.55610667862581 0.0107484739450927 * df.mm.trans2:probe3 0.00361236586368837 0.0554800233613378 0.0651111092755182 0.948100010712575 df.mm.trans2:probe4 0.208482074597091 0.0554800233613378 3.7577863520219 0.000182478045250697 *** df.mm.trans2:probe5 0.150228030989233 0.0554800233613378 2.7077860081422 0.00690195228567626 ** df.mm.trans2:probe6 -0.087613085328083 0.0554800233613378 -1.57918256013457 0.114645956146063 df.mm.trans3:probe2 0.186135941699885 0.0554800233613378 3.35500835116081 0.000826845011753158 *** df.mm.trans3:probe3 0.0206436205531329 0.0554800233613378 0.372091057328551 0.709912744748694 df.mm.trans3:probe4 0.105562004935866 0.0554800233613378 1.90270296478333 0.0573985817528866 . df.mm.trans3:probe5 -0.190074396613709 0.0554800233613378 -3.42599705439499 0.000640296383411304 *** df.mm.trans3:probe6 -0.150896781064609 0.0554800233613378 -2.71983989772009 0.00665734768030173 ** df.mm.trans3:probe7 -0.182605180371543 0.0554800233613378 -3.29136812330173 0.00103584543792916 ** df.mm.trans3:probe8 -0.273971605841513 0.0554800233613378 -4.9382027843996 9.39823224285812e-07 *** df.mm.trans3:probe9 -0.310254929526096 0.0554800233613378 -5.59219176072486 2.97473066779812e-08 *** df.mm.trans3:probe10 -0.307693001054162 0.0554800233613378 -5.54601426625539 3.84316399125914e-08 *** df.mm.trans3:probe11 -0.115538217325485 0.0554800233613378 -2.08251926234767 0.0375772844163021 * df.mm.trans3:probe12 -0.392434329146408 0.0554800233613378 -7.07343482158449 3.03818759455144e-12 *** df.mm.trans3:probe13 -0.399289509995261 0.0554800233613378 -7.19699606820123 1.29837230108031e-12 *** df.mm.trans3:probe14 -0.355657420409884 0.0554800233613378 -6.41054921865317 2.33836498357880e-10 *** df.mm.trans3:probe15 -0.0518581396123001 0.0554800233613378 -0.93471733554529 0.350184984949189 df.mm.trans3:probe16 0.0065939917855194 0.0554800233613378 0.118853442843258 0.905418019348145 df.mm.trans3:probe17 -0.115552799167443 0.0554800233613378 -2.08278209284186 0.0375532489291406 * df.mm.trans3:probe18 -0.219609016333602 0.0554800233613378 -3.95834397731419 8.14261468864967e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11057759830931 0.162552756108111 25.2876524319 5.41937738147443e-107 *** df.mm.trans1 0.144656522256472 0.137171811098099 1.05456449906475 0.291907878076859 df.mm.trans2 -0.0695322926196056 0.122300180175190 -0.568537940990792 0.569811737181904 df.mm.exp2 0.0301900811125836 0.155267947448640 0.194438592179947 0.845876402917497 df.mm.exp3 0.198319212928985 0.155267947448640 1.27727078375004 0.201836431335594 df.mm.exp4 0.205751372090983 0.155267947448640 1.32513745091556 0.185462252976487 df.mm.exp5 0.287592566223608 0.155267947448640 1.85223396682524 0.0643198733023914 . df.mm.exp6 0.0967105238239392 0.155267947448640 0.622862125848151 0.533533093282108 df.mm.exp7 0.223356541223888 0.155267947448640 1.43852317811936 0.150633698709606 df.mm.exp8 0.0912631960277173 0.155267947448640 0.58777872398877 0.556828434802462 df.mm.trans1:exp2 -0.0203714898950607 0.137171811098099 -0.148510759841845 0.881972999425727 df.mm.trans2:exp2 0.0372830524926007 0.099980278980228 0.372904065410477 0.709307748317947 df.mm.trans1:exp3 -0.0700780801038413 0.137171811098099 -0.510878142840328 0.609561816986653 df.mm.trans2:exp3 -0.0732734082372763 0.099980278980228 -0.732878613509037 0.463823510892902 df.mm.trans1:exp4 -0.150130657785221 0.137171811098099 -1.09447164532845 0.274041323729736 df.mm.trans2:exp4 0.073166198724331 0.099980278980228 0.731806306909788 0.464477517114052 df.mm.trans1:exp5 -0.234861037964142 0.137171811098099 -1.71216692470568 0.0872107719774831 . df.mm.trans2:exp5 -0.0302950235999378 0.0999802789802281 -0.303009992659941 0.761952347328375 df.mm.trans1:exp6 -0.0199752363167971 0.137171811098099 -0.14562202071176 0.884252426616485 df.mm.trans2:exp6 0.0155279581405137 0.099980278980228 0.155310210162391 0.87661163035349 df.mm.trans1:exp7 -0.219110162808644 0.137171811098099 -1.59734103570263 0.110541117798825 df.mm.trans2:exp7 -0.0548026733504721 0.099980278980228 -0.548134831283175 0.583735401519788 df.mm.trans1:exp8 -0.0118162811318837 0.137171811098099 -0.0861421966896188 0.931372561328149 df.mm.trans2:exp8 -0.0438821326674307 0.099980278980228 -0.438907883784848 0.660833747189072 df.mm.trans1:probe2 -0.111038596200258 0.102878858323575 -1.07931403992664 0.280737240177692 df.mm.trans1:probe3 -0.0859264367349037 0.102878858323575 -0.835219578979462 0.403815993790997 df.mm.trans1:probe4 -0.00439544964093224 0.102878858323575 -0.0427245180648066 0.965930621904295 df.mm.trans1:probe5 -0.203786412322853 0.102878858323575 -1.98083858669877 0.0479137779412552 * df.mm.trans1:probe6 -0.233630646460590 0.102878858323575 -2.27092961826787 0.0233870082002654 * df.mm.trans1:probe7 -0.0934803275638245 0.102878858323575 -0.908644682562575 0.363781230723281 df.mm.trans1:probe8 -0.0303119294241024 0.102878858323575 -0.294637109295725 0.768339132318249 df.mm.trans1:probe9 -0.150884037430211 0.102878858323575 -1.46661850538475 0.14282956701808 df.mm.trans1:probe10 -0.151363504305297 0.102878858323575 -1.47127900495580 0.141565615298984 df.mm.trans1:probe11 -0.155106154029379 0.102878858323575 -1.50765819680404 0.131993344616660 df.mm.trans1:probe12 -0.0845098829507268 0.102878858323575 -0.82145043527725 0.411607530938591 df.mm.trans1:probe13 -0.108082891980738 0.102878858323575 -1.05058409222229 0.293731972388193 df.mm.trans1:probe14 -0.113906893166254 0.102878858323575 -1.10719437426098 0.268506056194025 df.mm.trans1:probe15 -0.178233039570345 0.102878858323575 -1.73245545756123 0.083535465099314 . df.mm.trans1:probe16 -0.128560427671215 0.102878858323575 -1.24962922184524 0.211760251120109 df.mm.trans2:probe2 -0.11635216045338 0.102878858323575 -1.13096278817004 0.258372367650889 df.mm.trans2:probe3 -0.0132227794725961 0.102878858323575 -0.128527665334386 0.897760152472487 df.mm.trans2:probe4 -0.0719030600805785 0.102878858323575 -0.698909972877314 0.484789062222088 df.mm.trans2:probe5 0.0227134828840484 0.102878858323575 0.220778916622596 0.825314710888062 df.mm.trans2:probe6 0.0845914732290335 0.102878858323575 0.822243506658836 0.411156345310021 df.mm.trans3:probe2 -0.0259866560708626 0.102878858323575 -0.252594716682502 0.800639154626043 df.mm.trans3:probe3 -0.116657715805438 0.102878858323575 -1.13393283815928 0.257125001068473 df.mm.trans3:probe4 0.00430363692266313 0.102878858323575 0.0418320828282068 0.96664184755739 df.mm.trans3:probe5 -0.11467327876401 0.102878858323575 -1.11464377261400 0.265300995502257 df.mm.trans3:probe6 -0.134392561669284 0.102878858323575 -1.30631855620513 0.19177836047395 df.mm.trans3:probe7 -0.109243945987893 0.102878858323575 -1.06186973463779 0.288579994976993 df.mm.trans3:probe8 0.0278025471323232 0.102878858323575 0.270245486637095 0.787033426101056 df.mm.trans3:probe9 0.0269132480758012 0.102878858323575 0.261601348560398 0.793688725953278 df.mm.trans3:probe10 -0.0269460777184574 0.102878858323575 -0.261920458270509 0.793442765852041 df.mm.trans3:probe11 -0.0584402527773213 0.102878858323575 -0.568049196206232 0.570143407551789 df.mm.trans3:probe12 0.0517270725564878 0.102878858323575 0.502795942717364 0.615230893704875 df.mm.trans3:probe13 -0.165016334317294 0.102878858323575 -1.60398683467389 0.109068185885494 df.mm.trans3:probe14 -0.0741581422137623 0.102878858323575 -0.720829754744363 0.471201661401519 df.mm.trans3:probe15 -0.0699611741261529 0.102878858323575 -0.680034511134552 0.496657683613138 df.mm.trans3:probe16 -0.0223276570776940 0.102878858323575 -0.217028624165609 0.828235279669169 df.mm.trans3:probe17 0.00135976150657876 0.102878858323575 0.0132171131050272 0.989457509024678 df.mm.trans3:probe18 0.106846475402938 0.102878858323575 1.03856591280285 0.299285911541759