chr2.13670_chr2_133854264_133865693_+_2.R fitVsDatCorrelation=0.857639537129723 cont.fitVsDatCorrelation=0.217626235868201 fstatistic=9328.8372932861,61,899 cont.fstatistic=2579.05722750694,61,899 residuals=-0.580837833840538,-0.0959158501292713,-0.0022557147396315,0.0878019683941824,1.46422328578405 cont.residuals=-0.591520644423415,-0.214567397243069,-0.0436785990420675,0.149414956596278,1.55891336151032 predictedValues: Include Exclude Both chr2.13670_chr2_133854264_133865693_+_2.R.tl.Lung 78.3647576742513 54.2320163392336 62.7522682615433 chr2.13670_chr2_133854264_133865693_+_2.R.tl.cerebhem 89.8284118311857 68.1191888155475 65.4202786746834 chr2.13670_chr2_133854264_133865693_+_2.R.tl.cortex 89.9603176690556 58.1427306743129 65.7139575530833 chr2.13670_chr2_133854264_133865693_+_2.R.tl.heart 89.14738479782 59.585016867577 63.7332967571345 chr2.13670_chr2_133854264_133865693_+_2.R.tl.kidney 93.4653656359071 58.2057999093313 68.807158278507 chr2.13670_chr2_133854264_133865693_+_2.R.tl.liver 94.9033048556802 61.8246963075526 69.976167397376 chr2.13670_chr2_133854264_133865693_+_2.R.tl.stomach 84.5323339145649 63.260754425189 63.3179785666368 chr2.13670_chr2_133854264_133865693_+_2.R.tl.testicle 83.5596195849426 63.8703148343218 65.541394702686 diffExp=24.1327413350176,21.7092230156382,31.8175869947426,29.562367930243,35.2595657265758,33.0786085481276,21.2715794893759,19.6893047506209 diffExpScore=0.995402742254295 diffExp1.5=0,0,1,0,1,1,0,0 diffExp1.5Score=0.75 diffExp1.4=1,0,1,1,1,1,0,0 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 63.8813161865431 64.5651540144692 65.4923819846983 cerebhem 67.6630919688903 65.3778254653242 69.35329462535 cortex 66.872543923248 73.7504699293203 65.791421599018 heart 59.417692256956 66.9500658492306 67.1346535921276 kidney 66.0916084852786 62.3527108309815 63.4622812241955 liver 69.3819425922278 65.7877593582192 66.000232359797 stomach 65.5114562031841 70.2084027863454 67.0289031309278 testicle 66.3878778575571 73.3809913617253 63.4259118426694 cont.diffExp=-0.683837827926176,2.28526650356611,-6.87792600607233,-7.53237359227462,3.73889765429713,3.59418323400857,-4.69694658316131,-6.99311350416815 cont.diffExpScore=2.00389988145548 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=0.248199853892565 cont.tran.correlation=0.0832576693139587 tran.covariance=0.00126141754825937 cont.tran.covariance=0.000231064843622342 tran.mean=74.4376258835296 cont.tran.mean=66.7238068168438 weightedLogRatios: wLogRatio Lung 1.53768451974253 cerebhem 1.20604457024597 cortex 1.86857745751935 heart 1.72792033984828 kidney 2.03687874093854 liver 1.85932318468129 stomach 1.24417547408614 testicle 1.15307187116621 cont.weightedLogRatios: wLogRatio Lung -0.0443204064786182 cerebhem 0.144211788894420 cortex -0.416240418785115 heart -0.494639391794021 kidney 0.242368165456005 liver 0.224103073519528 stomach -0.291986978802274 testicle -0.425197725132882 varWeightedLogRatios=0.118370143598019 cont.varWeightedLogRatios=0.0963997436191975 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.0035690087464 0.0790628110800685 63.2859993262722 0 *** df.mm.trans1 -0.150833074915982 0.0680082801546932 -2.21786339211775 0.0268134414119034 * df.mm.trans2 -0.991767555139794 0.0598216764699027 -16.5787322198964 4.59222399265184e-54 *** df.mm.exp2 0.322876957754425 0.0763574625067157 4.22849250295645 2.59329755213392e-05 *** df.mm.exp3 0.161507149611970 0.0763574625067157 2.11514558380939 0.0346921082412574 * df.mm.exp4 0.207536995306966 0.0763574625067157 2.71796610958245 0.0066948518290054 ** df.mm.exp5 0.154817143673588 0.0763574625067157 2.02753127973539 0.0429026717583756 * df.mm.exp6 0.213555689269987 0.0763574625067157 2.79678871271036 0.00527148405077903 ** df.mm.exp7 0.220778920935400 0.0763574625067157 2.89138629922364 0.00392787850124372 ** df.mm.exp8 0.184282134430310 0.0763574625067157 2.41341354702694 0.0160029387630198 * df.mm.trans1:exp2 -0.186349949215894 0.0702386497182227 -2.65309697671406 0.00811637706756275 ** df.mm.trans2:exp2 -0.0948894516121642 0.0504131279625823 -1.88223693801728 0.0601266397974258 . df.mm.trans1:exp3 -0.0235127981326035 0.0702386497182227 -0.334755839227122 0.737887362271367 df.mm.trans2:exp3 -0.0918777297743356 0.0504131279625823 -1.82249611336415 0.0687118119660045 . df.mm.trans1:exp4 -0.078620293112605 0.0702386497182227 -1.11933093002225 0.263297956013502 df.mm.trans2:exp4 -0.113404289092568 0.0504131279625823 -2.24949916174094 0.0247220117366295 * df.mm.trans1:exp5 0.0213994961607843 0.0702386497182227 0.304668387655983 0.760689242774353 df.mm.trans2:exp5 -0.084103580514513 0.0504131279625824 -1.66828728772665 0.0956069300644384 . df.mm.trans1:exp6 -0.0220714664795727 0.0702386497182227 -0.314235347178756 0.753415204416096 df.mm.trans2:exp6 -0.0825242294173496 0.0504131279625823 -1.63695911665312 0.101988988470029 df.mm.trans1:exp7 -0.145019116718678 0.0702386497182227 -2.06466265084043 0.0392412397534856 * df.mm.trans2:exp7 -0.0667852187561503 0.0504131279625823 -1.32475847969044 0.185587906431554 df.mm.trans1:exp8 -0.120096057181052 0.0702386497182227 -1.70982867214621 0.0876426253006411 . df.mm.trans2:exp8 -0.0206988789826595 0.0504131279625824 -0.410585095969895 0.681474631724091 df.mm.trans1:probe2 -0.604552924341954 0.0496662255171418 -12.1723146473713 1.16101325312972e-31 *** df.mm.trans1:probe3 -0.557621724732457 0.0496662255171418 -11.2273827722221 1.82371975778296e-27 *** df.mm.trans1:probe4 -1.09606059909346 0.0496662255171418 -22.0685302271493 1.38278384254379e-86 *** df.mm.trans1:probe5 -1.14137126753819 0.0496662255171419 -22.9808336682291 2.66166083959465e-92 *** df.mm.trans1:probe6 -0.91756285330535 0.0496662255171418 -18.4745839602541 7.53296462056476e-65 *** df.mm.trans1:probe7 -0.822725628591829 0.0496662255171418 -16.5650926766696 5.46372437466582e-54 *** df.mm.trans1:probe8 -1.17563355405763 0.0496662255171418 -23.6706844906479 1.15837257798025e-96 *** df.mm.trans1:probe9 -0.869663292779402 0.0496662255171418 -17.5101547122651 2.71695165983849e-59 *** df.mm.trans1:probe10 -1.02969126683927 0.0496662255171419 -20.7322230775094 2.42786208704552e-78 *** df.mm.trans1:probe11 -0.806026764313849 0.0496662255171418 -16.2288709464273 3.86653024022644e-52 *** df.mm.trans1:probe12 -0.681082256223993 0.0496662255171418 -13.713187364901 5.29910793513382e-39 *** df.mm.trans1:probe13 -0.761124922727128 0.0496662255171418 -15.3247989917098 2.86632798059942e-47 *** df.mm.trans1:probe14 -0.721761979839545 0.0496662255171418 -14.5322494778758 3.91580788796882e-43 *** df.mm.trans1:probe15 -0.597955958285855 0.0496662255171418 -12.0394886476621 4.67004944794397e-31 *** df.mm.trans1:probe16 -0.787972503149984 0.0496662255171419 -15.8653591036030 3.66561813325067e-50 *** df.mm.trans1:probe17 -0.728949110941414 0.0496662255171419 -14.6769581008290 7.03960698482185e-44 *** df.mm.trans1:probe18 -0.715690747131914 0.0496662255171418 -14.4100088073917 1.65525462412867e-42 *** df.mm.trans1:probe19 -0.706553124853583 0.0496662255171418 -14.2260282011912 1.42871903749362e-41 *** df.mm.trans1:probe20 -0.744118959975185 0.0496662255171418 -14.9823940157957 1.81951604450194e-45 *** df.mm.trans1:probe21 -0.588035290499672 0.0496662255171418 -11.8397418844868 3.71087578954235e-30 *** df.mm.trans1:probe22 -0.65214058875458 0.0496662255171419 -13.1304640520649 3.70870001645941e-36 *** df.mm.trans2:probe2 -0.0207727084276794 0.0496662255171418 -0.418246166512288 0.675867031832811 df.mm.trans2:probe3 0.214751541951681 0.0496662255171418 4.32389495508495 1.70436092052641e-05 *** df.mm.trans2:probe4 -0.209990705977974 0.0496662255171418 -4.22803834580701 2.59843372457463e-05 *** df.mm.trans2:probe5 -0.159447232477739 0.0496662255171418 -3.2103754778528 0.00137264767650836 ** df.mm.trans2:probe6 -0.158081114560194 0.0496662255171418 -3.18286950365564 0.00150833372768701 ** df.mm.trans3:probe2 0.536427237123396 0.0496662255171418 10.8006443319969 1.18574606142973e-25 *** df.mm.trans3:probe3 -0.0341326851580769 0.0496662255171418 -0.687241375858053 0.492107880996623 df.mm.trans3:probe4 0.233695303030248 0.0496662255171418 4.70531635124135 2.93339882801284e-06 *** df.mm.trans3:probe5 0.0646572289141306 0.0496662255171418 1.30183496412094 0.193306278248711 df.mm.trans3:probe6 0.385851781388375 0.0496662255171418 7.76889681812445 2.15663725736118e-14 *** df.mm.trans3:probe7 0.151800888210794 0.0496662255171418 3.05642087012232 0.00230616623304365 ** df.mm.trans3:probe8 -0.0127142790989071 0.0496662255171418 -0.255994470417707 0.798013670162401 df.mm.trans3:probe9 0.479038474653781 0.0496662255171418 9.64515562972357 5.14784534053541e-21 *** df.mm.trans3:probe10 1.27414641600783 0.0496662255171418 25.6541825504349 2.373298200862e-109 *** df.mm.trans3:probe11 0.176873464288414 0.0496662255171418 3.56124232205582 0.000388474941948315 *** df.mm.trans3:probe12 0.0830723257873014 0.0496662255171419 1.67261201998589 0.09475163397924 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19520625382188 0.150058627838661 27.9571145907881 2.87971066876096e-124 *** df.mm.trans1 -0.00854834606648089 0.129077489938291 -0.0662264665246254 0.94721225025097 df.mm.trans2 -0.0235751803518416 0.113539584078170 -0.207638424460058 0.835558325494084 df.mm.exp2 0.0127423982961306 0.144923964787894 0.0879247149688455 0.929956094036612 df.mm.exp3 0.174218435691451 0.144923964787894 1.20213683048508 0.229627067181779 df.mm.exp4 -0.060929221077266 0.144923964787894 -0.4204219858768 0.674277680468556 df.mm.exp5 0.0306352522719334 0.144923964787894 0.211388449914202 0.832632078214688 df.mm.exp6 0.0936342202782851 0.144923964787894 0.646092041542783 0.518384636172697 df.mm.exp7 0.0858011659146622 0.144923964787894 0.592042634496218 0.553970899071555 df.mm.exp8 0.198538973552918 0.144923964787894 1.36995267720899 0.171043713018962 df.mm.trans1:exp2 0.0447715354542659 0.133310658373668 0.335843630212761 0.73706715026506 df.mm.trans2:exp2 -0.000234111203198747 0.0956824669370636 -0.00244675132961128 0.998048319652123 df.mm.trans1:exp3 -0.128456884712925 0.133310658373668 -0.96359050566581 0.335510384380162 df.mm.trans2:exp3 -0.0412059222819251 0.0956824669370636 -0.43065279983875 0.666824070239427 df.mm.trans1:exp4 -0.0115056746406468 0.133310658373668 -0.0863072374033027 0.931241403514337 df.mm.trans2:exp4 0.0972014230592102 0.0956824669370636 1.01587496822323 0.309962247142422 df.mm.trans1:exp5 0.0033796075335522 0.133310658373668 0.0253513678109608 0.979780327503118 df.mm.trans2:exp5 -0.0655029571620582 0.0956824669370637 -0.684586834546643 0.493781123119358 df.mm.trans1:exp6 -0.0110345069990897 0.133310658373668 -0.0827728790308733 0.934050560259691 df.mm.trans2:exp6 -0.074875280956046 0.0956824669370636 -0.782539198171972 0.43410382921046 df.mm.trans1:exp7 -0.060603061883823 0.133310658373668 -0.454600274450325 0.649506583020179 df.mm.trans2:exp7 -0.00200801794800889 0.0956824669370636 -0.020986268564017 0.983261266196704 df.mm.trans1:exp8 -0.160051423305134 0.133310658373668 -1.20058984973663 0.230226587971955 df.mm.trans2:exp8 -0.0705488984584042 0.0956824669370636 -0.737323155608108 0.461118242588641 df.mm.trans1:probe2 -0.000398547621630244 0.094264870540464 -0.00422795490350951 0.996627528089387 df.mm.trans1:probe3 -0.0859318158807786 0.094264870540464 -0.911599574561466 0.362223918096822 df.mm.trans1:probe4 -0.071541500913181 0.094264870540464 -0.758941273700378 0.448086622024925 df.mm.trans1:probe5 -0.0415629687787521 0.094264870540464 -0.440916839332112 0.659379211503403 df.mm.trans1:probe6 -0.0320766894183375 0.094264870540464 -0.340282538282046 0.73372325960547 df.mm.trans1:probe7 -0.0530915177210278 0.094264870540464 -0.563216364873039 0.573428000458325 df.mm.trans1:probe8 -0.0492554831919675 0.094264870540464 -0.522522153900633 0.601435561580807 df.mm.trans1:probe9 0.077591910497403 0.094264870540464 0.823126473865957 0.410654363264567 df.mm.trans1:probe10 -0.0727736397082607 0.094264870540464 -0.77201230204864 0.440310030596172 df.mm.trans1:probe11 -0.0699530584522362 0.094264870540464 -0.742090431474239 0.458226379090307 df.mm.trans1:probe12 -0.114900738733291 0.094264870540464 -1.21891366396105 0.223196700993597 df.mm.trans1:probe13 -0.0177541932316272 0.094264870540464 -0.188343686569919 0.850649746695244 df.mm.trans1:probe14 -0.126234219727453 0.094264870540464 -1.33914382954853 0.180862332597003 df.mm.trans1:probe15 -0.0126589997016511 0.094264870540464 -0.134291805940763 0.89320189011065 df.mm.trans1:probe16 -0.115244870829265 0.094264870540464 -1.22256435688622 0.22181469687931 df.mm.trans1:probe17 -0.0304674732124016 0.094264870540464 -0.323211319738918 0.746610372560253 df.mm.trans1:probe18 -0.0144197526591066 0.094264870540464 -0.152970587838624 0.878455796395499 df.mm.trans1:probe19 -0.0201380374920639 0.094264870540464 -0.21363247386437 0.830882112627737 df.mm.trans1:probe20 0.00134463502264633 0.094264870540464 0.0142644339820012 0.98862217920537 df.mm.trans1:probe21 -0.0492829429436053 0.094264870540464 -0.522813458089354 0.60123289207151 df.mm.trans1:probe22 -0.108703434803156 0.094264870540464 -1.15317014896333 0.24914706046464 df.mm.trans2:probe2 -0.0440131456083975 0.094264870540464 -0.466909309438923 0.640677897702202 df.mm.trans2:probe3 0.0113886105081989 0.094264870540464 0.1208150018443 0.903864563671011 df.mm.trans2:probe4 -0.0851764252755862 0.094264870540464 -0.90358608447909 0.366456976475488 df.mm.trans2:probe5 -0.00996194929205597 0.094264870540464 -0.105680400714917 0.915859506247472 df.mm.trans2:probe6 0.0565509540428097 0.094264870540464 0.599915469236599 0.548713748620544 df.mm.trans3:probe2 0.0135897560141161 0.094264870540464 0.144165646610448 0.885401977883101 df.mm.trans3:probe3 0.0753803782461023 0.094264870540464 0.799665642289771 0.424115781898432 df.mm.trans3:probe4 -0.0910635529998242 0.094264870540464 -0.966039124413101 0.334284399918844 df.mm.trans3:probe5 0.125809748630124 0.094264870540464 1.33464086789489 0.182331829445992 df.mm.trans3:probe6 -0.092274570088568 0.094264870540464 -0.978886085129226 0.327899610284134 df.mm.trans3:probe7 0.0278728056284749 0.094264870540464 0.295686033075388 0.767538145935312 df.mm.trans3:probe8 0.0969506844587789 0.094264870540464 1.02849220396650 0.303994988685545 df.mm.trans3:probe9 0.0920471388793239 0.094264870540464 0.976473402568477 0.329092601155784 df.mm.trans3:probe10 0.0249061798642636 0.094264870540464 0.264214863092317 0.791674915398167 df.mm.trans3:probe11 0.0176085855322324 0.094264870540464 0.186799021006174 0.851860360077186 df.mm.trans3:probe12 -0.0646139194069429 0.094264870540464 -0.685450677823896 0.493236280570425