chr15.8576_chr15_82787767_82793984_+_2.R fitVsDatCorrelation=0.829128965712082 cont.fitVsDatCorrelation=0.253514812832407 fstatistic=12689.9955332529,53,715 cont.fstatistic=4229.62663213454,53,715 residuals=-0.438422356589423,-0.0785383648114152,0.00292860164906122,0.0782219870505436,0.608421281922478 cont.residuals=-0.531741512127773,-0.163020750778005,-0.0325689851946262,0.111666411692309,1.06138169276134 predictedValues: Include Exclude Both chr15.8576_chr15_82787767_82793984_+_2.R.tl.Lung 59.2715769224278 69.4174282833781 63.5063961949609 chr15.8576_chr15_82787767_82793984_+_2.R.tl.cerebhem 61.6124939481501 68.1382954368223 66.103691074295 chr15.8576_chr15_82787767_82793984_+_2.R.tl.cortex 60.7796102847049 71.3414638684527 61.1768296251248 chr15.8576_chr15_82787767_82793984_+_2.R.tl.heart 69.2144082145586 70.572543082914 71.6733935815449 chr15.8576_chr15_82787767_82793984_+_2.R.tl.kidney 61.6005961113532 75.0448672335342 63.069827853987 chr15.8576_chr15_82787767_82793984_+_2.R.tl.liver 63.0162968570463 80.392055638187 63.000767638766 chr15.8576_chr15_82787767_82793984_+_2.R.tl.stomach 62.5268836905002 69.7275093791271 63.357055567805 chr15.8576_chr15_82787767_82793984_+_2.R.tl.testicle 62.1213306898854 70.8896888321823 63.7081796992654 diffExp=-10.1458513609503,-6.52580148867217,-10.5618535837477,-1.35813486835545,-13.4442711221810,-17.3757587811406,-7.20062568862688,-8.7683581422969 diffExpScore=0.986907679705954 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=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,-1,-1,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 63.1069115609072 58.7308588555724 59.8320162135297 cerebhem 62.1291453062412 66.3465304783804 68.2125658503861 cortex 65.7387378911965 71.5434569529937 57.6467277929604 heart 64.8888701291302 69.128888864762 62.7128495174669 kidney 65.19249029316 60.4471602856023 68.6860263205773 liver 62.9823373970784 65.032014563946 61.1085583004784 stomach 62.8190493704474 64.3425524665733 63.4365996286392 testicle 65.4485955055135 57.6708060609732 62.108690579368 cont.diffExp=4.37605270533486,-4.21738517213921,-5.8047190617972,-4.24001873563174,4.74533000755765,-2.04967716686753,-1.52350309612591,7.77778944454025 cont.diffExpScore=17.9401466337620 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=0.064647626223039 cont.tran.correlation=0.063061319281712 tran.covariance=0.000187903303531235 cont.tran.covariance=6.41006526592716e-05 tran.mean=67.2291905295765 cont.tran.mean=64.0967753739048 weightedLogRatios: wLogRatio Lung -0.657492793375916 cerebhem -0.41993460741292 cortex -0.67091370429826 heart -0.0825267096165355 kidney -0.83296706009835 liver -1.03865886897338 stomach -0.456713395529063 testicle -0.553903375540419 cont.weightedLogRatios: wLogRatio Lung 0.295285955569681 cerebhem -0.273348100317390 cortex -0.357759066231758 heart -0.266119442190075 kidney 0.312845703760012 liver -0.133189180099714 stomach -0.0994994206996716 testicle 0.520985459165799 varWeightedLogRatios=0.0824205440019611 cont.varWeightedLogRatios=0.108237397472320 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.30095183476065 0.0715409778984995 60.1187174274124 8.13259785580308e-282 *** df.mm.trans1 -0.175812860813179 0.0635297729268721 -2.76740892833906 0.00579648782251369 ** df.mm.trans2 0.0219973692133090 0.0577885899168515 0.380652465217782 0.703574207965068 df.mm.exp2 -0.0199477328070985 0.0779076067597985 -0.256043455019746 0.797990929147877 df.mm.exp3 0.0898363410847377 0.0779076067597986 1.153113859109 0.249248993507968 df.mm.exp4 0.0506033969695668 0.0779076067597986 0.649530887601066 0.516203890450914 df.mm.exp5 0.123388003973727 0.0779076067597985 1.58377351205451 0.113687451275877 df.mm.exp6 0.216034606111261 0.0779076067597985 2.77295908700327 0.00569959590220826 ** df.mm.exp7 0.0602780340038118 0.0779076067597985 0.773711791579718 0.439356977817956 df.mm.exp8 0.0647742288143438 0.0779076067597985 0.831423676176488 0.40601189682538 df.mm.trans1:exp2 0.0586825254247076 0.0739769276733384 0.793254427702558 0.42789275492673 df.mm.trans2:exp2 0.00134916513621435 0.0622941478779898 0.0216579756232776 0.982726829010224 df.mm.trans1:exp3 -0.0647118467063602 0.0739769276733385 -0.87475715390763 0.38199979057352 df.mm.trans2:exp3 -0.0624966057498386 0.0622941478779898 -1.00325003035992 0.31607963899994 df.mm.trans1:exp4 0.104475773984660 0.0739769276733385 1.41227511428963 0.158304053842332 df.mm.trans2:exp4 -0.034100200276757 0.0622941478779898 -0.54740615994212 0.584270546301782 df.mm.trans1:exp5 -0.0848463375382172 0.0739769276733384 -1.14692972804812 0.251794317088386 df.mm.trans2:exp5 -0.0454398035130296 0.0622941478779898 -0.729439362458711 0.465971830649874 df.mm.trans1:exp6 -0.154771114090622 0.0739769276733385 -2.09215385064447 0.0367768610324064 * df.mm.trans2:exp6 -0.0692572092666952 0.0622941478779898 -1.11177713518681 0.266607671793598 df.mm.trans1:exp7 -0.00681131188623119 0.0739769276733385 -0.092073462638352 0.926665462975636 df.mm.trans2:exp7 -0.0558210754646385 0.0622941478779898 -0.896088595255696 0.370507033389799 df.mm.trans1:exp8 -0.0178146906732427 0.0739769276733384 -0.240814146160643 0.809768199663416 df.mm.trans2:exp8 -0.043787202390888 0.0622941478779898 -0.702910367706613 0.48234040348747 df.mm.trans1:probe2 -0.288525808951285 0.0405188320216156 -7.12078296821005 2.61998256510589e-12 *** df.mm.trans1:probe3 0.138202872472085 0.0405188320216156 3.41083060830474 0.000684055503947587 *** df.mm.trans1:probe4 -0.175968315371811 0.0405188320216156 -4.34287728920561 1.60955971497961e-05 *** df.mm.trans1:probe5 -0.157046814599935 0.0405188320216156 -3.87589687965722 0.000116003819623629 *** df.mm.trans1:probe6 -0.198222499322470 0.0405188320216156 -4.8921079269196 1.23334087120086e-06 *** df.mm.trans1:probe7 0.202969844645933 0.0405188320216156 5.00927185012773 6.89112653300764e-07 *** df.mm.trans1:probe8 0.600788837948639 0.0405188320216156 14.8273977302242 1.44835960879469e-43 *** df.mm.trans1:probe9 -0.00703511919809313 0.0405188320216156 -0.173625912867876 0.862208609011906 df.mm.trans1:probe10 0.406307800214741 0.0405188320216156 10.0276286344579 3.12712979822992e-22 *** df.mm.trans1:probe11 -0.137263148723146 0.0405188320216156 -3.38763833690765 0.00074358288069588 *** df.mm.trans1:probe12 -0.0437258289342860 0.0405188320216156 -1.07914830592747 0.280885522386179 df.mm.trans1:probe13 0.0864655501080178 0.0405188320216156 2.13395958851654 0.0331857277213140 * df.mm.trans1:probe14 0.0242821462467651 0.0405188320216157 0.599280508229142 0.54917567952334 df.mm.trans1:probe15 -0.148698418364261 0.0405188320216156 -3.66985944424396 0.000260736398644344 *** df.mm.trans1:probe16 0.0106554004814016 0.0405188320216156 0.262974028365804 0.79264635551214 df.mm.trans1:probe17 -0.240616216117094 0.0405188320216156 -5.93837986220167 4.49107892255825e-09 *** df.mm.trans1:probe18 -0.211327933043078 0.0405188320216156 -5.21554848694407 2.40260774724503e-07 *** df.mm.trans1:probe19 -0.198646457835201 0.0405188320216156 -4.90257117305921 1.17143505050960e-06 *** df.mm.trans1:probe20 -0.221639057825951 0.0405188320216156 -5.47002583163583 6.22640636127159e-08 *** df.mm.trans1:probe21 -0.239614633730873 0.0405188320216156 -5.91366092692517 5.18353373429495e-09 *** df.mm.trans1:probe22 -0.319578611807406 0.0405188320216156 -7.88716248377841 1.15811432437617e-14 *** df.mm.trans2:probe2 -0.273606274733236 0.0405188320216156 -6.75257062166242 3.00821842561301e-11 *** df.mm.trans2:probe3 0.0126612430954523 0.0405188320216156 0.312477987734145 0.754768400116717 df.mm.trans2:probe4 -0.13298309186671 0.0405188320216156 -3.28200703800562 0.00108079340826458 ** df.mm.trans2:probe5 -0.186686728085094 0.0405188320216156 -4.60740645202955 4.82739573764222e-06 *** df.mm.trans2:probe6 -0.247497548270334 0.0405188320216156 -6.10821032892314 1.65383704589455e-09 *** df.mm.trans3:probe2 0.043309197799943 0.0405188320216156 1.06886589862311 0.285490819567117 df.mm.trans3:probe3 -0.0228770506699012 0.0405188320216156 -0.564602914953149 0.57252096105854 df.mm.trans3:probe4 -0.00441500655047116 0.0405188320216156 -0.108961841449820 0.913263327883741 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.13400669698086 0.123786651979365 33.3962235093811 4.40403159343173e-148 *** df.mm.trans1 0.0728151783260496 0.109924942636153 0.662408153962519 0.507923133204705 df.mm.trans2 -0.0587268098878544 0.099991030015898 -0.587320781459269 0.557173702857026 df.mm.exp2 -0.0247768065902146 0.134802767418178 -0.183800429803888 0.85422213074618 df.mm.exp3 0.275405254606192 0.134802767418178 2.04302374410345 0.0414162389087181 * df.mm.exp4 0.143827785609895 0.134802767418178 1.06694979906251 0.286354626899663 df.mm.exp5 -0.076686578992745 0.134802767418178 -0.568879856560007 0.569616318739283 df.mm.exp6 0.0788273975962922 0.134802767418178 0.584760974170198 0.558892921580727 df.mm.exp7 0.0281838935192344 0.134802767418178 0.209075036507254 0.834449212682465 df.mm.exp8 -0.0191244906796221 0.134802767418178 -0.141870163691041 0.887222526081164 df.mm.trans1:exp2 0.00916171703016425 0.128001551969201 0.0715750464679422 0.94296011464717 df.mm.trans2:exp2 0.146702981569600 0.107786952740068 1.36104582085532 0.173928012692643 df.mm.trans1:exp3 -0.234547182047670 0.128001551969201 -1.83237764260941 0.0673109219149501 . df.mm.trans2:exp3 -0.0780654932072015 0.107786952740068 -0.724257354185147 0.469144664395096 df.mm.trans1:exp4 -0.115981966225979 0.128001551969201 -0.90609812491872 0.365189156910966 df.mm.trans2:exp4 0.0191796378674917 0.107786952740068 0.177940255104382 0.858820296542446 df.mm.trans1:exp5 0.109200564736737 0.128001551969201 0.853119068142335 0.393878973460355 df.mm.trans2:exp5 0.105490885474651 0.107786952740068 0.978698096503808 0.328060195766630 df.mm.trans1:exp6 -0.0808033662913413 0.128001551969201 -0.631268645170677 0.528066633269611 df.mm.trans2:exp6 0.0230869895743402 0.107786952740068 0.214190947860037 0.830459223058533 df.mm.trans1:exp7 -0.032755829119373 0.128001551969201 -0.255901812247203 0.79810025816997 df.mm.trans2:exp7 0.063072005815861 0.107786952740068 0.585154364350211 0.558628544902297 df.mm.trans1:exp8 0.0555592267582358 0.128001551969201 0.434051196282404 0.664382205968174 df.mm.trans2:exp8 0.000910282113221758 0.107786952740068 0.00844519758728992 0.993264143044928 df.mm.trans1:probe2 -0.0174492127907184 0.070109337409201 -0.248885718158683 0.803520656511156 df.mm.trans1:probe3 -0.100188369440041 0.070109337409201 -1.42903032808997 0.153432243883471 df.mm.trans1:probe4 -0.100942295742209 0.070109337409201 -1.43978390714275 0.150366220341228 df.mm.trans1:probe5 -0.119280048145534 0.070109337409201 -1.70134325260190 0.089313316329159 . df.mm.trans1:probe6 -0.0885553177369347 0.070109337409201 -1.26310304745959 0.206963883121018 df.mm.trans1:probe7 -0.0507650257973842 0.070109337409201 -0.724083662367088 0.469251219217935 df.mm.trans1:probe8 -0.094151787741784 0.070109337409201 -1.34292793543685 0.179721447319204 df.mm.trans1:probe9 -0.0318667317268266 0.070109337409201 -0.454529067088922 0.64958603329923 df.mm.trans1:probe10 -0.1441274596537 0.070109337409201 -2.05575269970794 0.0401689415735741 * df.mm.trans1:probe11 -0.178109935041384 0.070109337409201 -2.54045953967338 0.0112810867720271 * df.mm.trans1:probe12 -0.064416520459039 0.070109337409201 -0.918800873599258 0.35850958067335 df.mm.trans1:probe13 -0.0667260023366815 0.070109337409201 -0.951742019001373 0.341549445533846 df.mm.trans1:probe14 -0.0179376918704209 0.070109337409201 -0.255853107920926 0.798137852225301 df.mm.trans1:probe15 0.0167728090121923 0.070109337409201 0.239237876608303 0.810989693471502 df.mm.trans1:probe16 -0.0673222379946545 0.070109337409201 -0.960246387748906 0.337255829017103 df.mm.trans1:probe17 -0.00365663973226964 0.070109337409201 -0.0521562443377157 0.958418765185127 df.mm.trans1:probe18 -0.121347602745004 0.070109337409201 -1.73083368391781 0.0839128555404592 . df.mm.trans1:probe19 -0.0956409944804747 0.070109337409201 -1.36416913944366 0.172943577853376 df.mm.trans1:probe20 -0.114316967255744 0.070109337409201 -1.63055266930452 0.103425136495291 df.mm.trans1:probe21 -0.0598478207590918 0.070109337409201 -0.853635520897641 0.393592858780839 df.mm.trans1:probe22 -0.0919051834852626 0.070109337409201 -1.31088364091715 0.190317946762333 df.mm.trans2:probe2 0.0260589573353853 0.070109337409201 0.371690252659061 0.71023360447492 df.mm.trans2:probe3 0.0471519975299403 0.070109337409201 0.672549467337459 0.501451243920366 df.mm.trans2:probe4 -0.0558718858638645 0.070109337409201 -0.79692503065265 0.425759116586027 df.mm.trans2:probe5 -0.030237190468776 0.070109337409201 -0.431286210740992 0.666390276643726 df.mm.trans2:probe6 -0.0102478172220571 0.070109337409201 -0.146169078196312 0.883829115072761 df.mm.trans3:probe2 -0.114983385747269 0.070109337409201 -1.64005808635953 0.101432929629072 df.mm.trans3:probe3 -0.0332083556405508 0.070109337409201 -0.473665233016345 0.635883241240877 df.mm.trans3:probe4 -0.078024896794078 0.070109337409201 -1.11290306936831 0.266124077034253