chr2.14345_chr2_116512080_116561658_+_2.R fitVsDatCorrelation=0.848475829447386 cont.fitVsDatCorrelation=0.232349440727603 fstatistic=12627.2698784900,55,761 cont.fstatistic=3728.84909529869,55,761 residuals=-0.376685457081059,-0.0872365664032841,-0.00263511705815294,0.0667075238731314,1.18644544811517 cont.residuals=-0.549836586448461,-0.190808266244230,-0.0217388447891827,0.151289388240797,0.890538186353514 predictedValues: Include Exclude Both chr2.14345_chr2_116512080_116561658_+_2.R.tl.Lung 55.2869301323854 68.246907093881 71.3313418030285 chr2.14345_chr2_116512080_116561658_+_2.R.tl.cerebhem 58.1596066555296 62.4733593836549 66.1443954840202 chr2.14345_chr2_116512080_116561658_+_2.R.tl.cortex 55.5108066073397 76.2051241480228 68.94732390621 chr2.14345_chr2_116512080_116561658_+_2.R.tl.heart 54.5108905153184 72.7862464536873 70.9758192310465 chr2.14345_chr2_116512080_116561658_+_2.R.tl.kidney 57.3409466417347 73.5887124224663 73.3309262260315 chr2.14345_chr2_116512080_116561658_+_2.R.tl.liver 58.0314768449225 72.9459573786341 77.1899701786612 chr2.14345_chr2_116512080_116561658_+_2.R.tl.stomach 57.5245839706881 67.8273115883146 71.9316588920082 chr2.14345_chr2_116512080_116561658_+_2.R.tl.testicle 57.6648587319899 74.3293390874792 75.2790316231405 diffExp=-12.9599769614956,-4.31375272812532,-20.6943175406831,-18.2753559383689,-16.2477657807317,-14.9144805337116,-10.3027276176264,-16.6644803554893 diffExpScore=0.991332450092264 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,-1,-1,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,0,-1,-1,-1,-1,0,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 61.7129638698335 64.2026681948342 57.5314527502339 cerebhem 59.4932347458209 62.7557486295927 60.5073840328331 cortex 63.7923412487321 57.2379919278082 55.2802494328144 heart 57.9738727839755 56.5092947440418 55.913260380107 kidney 59.4701335915219 58.8329391233163 60.8016352909185 liver 60.2843293731894 64.5424535989401 62.4486401673094 stomach 60.6254101162846 65.9470484064991 65.2906696455958 testicle 59.052270455442 65.7046429742907 60.0583257711616 cont.diffExp=-2.48970432500071,-3.26251388377175,6.55434932092388,1.46457803993372,0.637194468205529,-4.25812422575066,-5.32163829021451,-6.65237251884874 cont.diffExpScore=2.13846874650503 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=-0.308836577478481 cont.tran.correlation=-0.0434123879577722 tran.covariance=-0.000511758370725495 cont.tran.covariance=-5.39943124791852e-05 tran.mean=63.902066103503 cont.tran.mean=61.1335839865077 weightedLogRatios: wLogRatio Lung -0.86719770793535 cerebhem -0.293277491047375 cortex -1.32285390492333 heart -1.19784064235115 kidney -1.04125412120563 liver -0.955042046528793 stomach -0.681183968451122 testicle -1.06152587148269 cont.weightedLogRatios: wLogRatio Lung -0.163829862596466 cerebhem -0.219559438087784 cortex 0.444657143690788 heart 0.103556915602171 kidney 0.0439521074465098 liver -0.282094918967816 stomach -0.348902075781681 testicle -0.441055208942845 varWeightedLogRatios=0.103942344645305 cont.varWeightedLogRatios=0.0840152470344793 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15083089115510 0.0672359272026921 61.7353112219578 1.54163068755319e-298 *** df.mm.trans1 0.0241802279597286 0.0585213630172629 0.413186342782137 0.679586490265876 df.mm.trans2 0.0780695625834818 0.0523430983370168 1.49149677920902 0.136245511385246 df.mm.exp2 0.0377582792415923 0.0685332029251133 0.550948702672644 0.58183054579582 df.mm.exp3 0.148330786310355 0.0685332029251133 2.16436384087342 0.0307467713721468 * df.mm.exp4 0.0552554336599056 0.0685332029251133 0.806257861905033 0.420346059141922 df.mm.exp5 0.0841913425766162 0.0685332029251133 1.22847523511505 0.219648396154800 df.mm.exp6 0.0361020518267993 0.0685332029251133 0.526781914253274 0.598498554192299 df.mm.exp7 0.0251280129565566 0.0685332029251133 0.366654583239223 0.713978608125182 df.mm.exp8 0.0736192252327896 0.0685332029251133 1.07421252897276 0.283067921351607 df.mm.trans1:exp2 0.0128962550252400 0.0638009397743074 0.202132681287453 0.83986702204126 df.mm.trans2:exp2 -0.126150178963490 0.0500495749068848 -2.52050450374828 0.0119220851758080 * df.mm.trans1:exp3 -0.144289606657745 0.0638009397743073 -2.26155926806348 0.0240060814747962 * df.mm.trans2:exp3 -0.0380341955740595 0.0500495749068848 -0.759930441863304 0.44753151737608 df.mm.trans1:exp4 -0.0693914617754101 0.0638009397743074 -1.08762444598589 0.277105279792455 df.mm.trans2:exp4 0.00913946562276376 0.0500495749068848 0.182608256708820 0.855154040193681 df.mm.trans1:exp5 -0.0477129087756031 0.0638009397743074 -0.747840219037292 0.454787569128251 df.mm.trans2:exp5 -0.00883180816084191 0.0500495749068848 -0.176461202263418 0.859978575527898 df.mm.trans1:exp6 0.0123469799404466 0.0638009397743074 0.193523480753159 0.84660065920549 df.mm.trans2:exp6 0.0304846895427505 0.0500495749068848 0.609089879373922 0.54264664851613 df.mm.trans1:exp7 0.0145478549994556 0.0638009397743074 0.228019446906549 0.819692373638835 df.mm.trans2:exp7 -0.0312951890688176 0.0500495749068848 -0.625283813639598 0.53197225485711 df.mm.trans1:exp8 -0.0315078071542695 0.0638009397743074 -0.493845502366059 0.62155779663634 df.mm.trans2:exp8 0.0117544061338871 0.0500495749068848 0.234855264120738 0.814384249835173 df.mm.trans1:probe2 -0.357776208479007 0.0405447896296326 -8.82422160152292 7.47369749199038e-18 *** df.mm.trans1:probe3 -0.383733410374072 0.0405447896296326 -9.46443214724725 3.59980139238237e-20 *** df.mm.trans1:probe4 -0.237334707628756 0.0405447896296326 -5.85364259616966 7.1477509754246e-09 *** df.mm.trans1:probe5 -0.347298487084425 0.0405447896296326 -8.56579822603391 5.9239500668876e-17 *** df.mm.trans1:probe6 -0.346306068745872 0.0405447896296326 -8.54132113914755 7.18894096837727e-17 *** df.mm.trans1:probe7 -0.333349981691442 0.0405447896296326 -8.22177115078209 8.63444887256084e-16 *** df.mm.trans1:probe8 -0.166872901596888 0.0405447896296326 -4.11576686230792 4.28050341182556e-05 *** df.mm.trans1:probe9 -0.206854177041198 0.0405447896296326 -5.1018683024567 4.25065507698856e-07 *** df.mm.trans1:probe10 0.0523180160874609 0.0405447896296326 1.29037581808597 0.197312130119658 df.mm.trans1:probe11 -0.117695130320401 0.0405447896296326 -2.90284229849308 0.00380483951005905 ** df.mm.trans1:probe12 -0.173257048544702 0.0405447896296326 -4.27322598359408 2.17109652018709e-05 *** df.mm.trans1:probe13 0.0335499626146235 0.0405447896296326 0.827479015703243 0.408225012098609 df.mm.trans1:probe14 -0.106233565708742 0.0405447896296326 -2.62015333361355 0.00896424576506265 ** df.mm.trans1:probe15 0.0206753916412621 0.0405447896296326 0.509939546613191 0.610241672169274 df.mm.trans1:probe16 -0.298858666178895 0.0405447896296326 -7.37107453038727 4.42124263876018e-13 *** df.mm.trans1:probe17 -0.0499943273961681 0.0405447896296326 -1.23306417058406 0.217932577760140 df.mm.trans1:probe18 -0.366457662283795 0.0405447896296326 -9.03834168659652 1.29607951713600e-18 *** df.mm.trans1:probe19 -0.389872737542440 0.0405447896296326 -9.61585301351512 9.77043849718723e-21 *** df.mm.trans1:probe20 -0.354662472783678 0.0405447896296326 -8.7474241702433 1.38974304734209e-17 *** df.mm.trans1:probe21 -0.419274150001607 0.0405447896296326 -10.3410118496502 1.52762425788568e-23 *** df.mm.trans2:probe2 -0.0336701490619655 0.0405447896296326 -0.830443304048056 0.406548639243561 df.mm.trans2:probe3 -0.0671848256229814 0.0405447896296326 -1.65705202164567 0.0979211350352222 . df.mm.trans2:probe4 0.10064919015747 0.0405447896296326 2.48241983931542 0.0132639326772609 * df.mm.trans2:probe5 -0.0458540574259608 0.0405447896296326 -1.13094821418059 0.258433173768792 df.mm.trans2:probe6 -0.0289285521263948 0.0405447896296326 -0.713496170300807 0.475757530032466 df.mm.trans3:probe2 -0.211955277479094 0.0405447896296326 -5.22768225992186 2.21953395040425e-07 *** df.mm.trans3:probe3 -0.256028672102654 0.0405447896296326 -6.31471206143667 4.59870490197768e-10 *** df.mm.trans3:probe4 -0.00887893752953696 0.0405447896296326 -0.218990839776060 0.826715902181878 df.mm.trans3:probe5 -0.142558773663817 0.0405447896296326 -3.51608122686191 0.000463904369821713 *** df.mm.trans3:probe6 0.433956004809355 0.0405447896296326 10.7031263147113 5.32953113099505e-25 *** df.mm.trans3:probe7 0.285251535281731 0.0405447896296326 7.03546714355748 4.43226156629477e-12 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.30479774121151 0.123566934897610 34.837780388245 9.94399336369373e-160 *** df.mm.trans1 -0.161578628649537 0.107551211903032 -1.50234131062342 0.133423810080673 df.mm.trans2 -0.116561886190776 0.0961967283510652 -1.21170322721776 0.226002102052582 df.mm.exp2 -0.109859606020044 0.125951082650246 -0.872240267478395 0.383352426844904 df.mm.exp3 -0.0417716672305174 0.125951082650246 -0.331649925920155 0.74024492879087 df.mm.exp4 -0.161610983032775 0.125951082650246 -1.28312500085095 0.199838908356023 df.mm.exp5 -0.179647555609334 0.125951082650246 -1.42632799837217 0.154183786675805 df.mm.exp6 -0.100156054819106 0.125951082650246 -0.79519804603212 0.426746250690441 df.mm.exp7 -0.117489892524124 0.125951082650246 -0.932821616550785 0.351207936814457 df.mm.exp8 -0.0639305903803878 0.125951082650246 -0.507582698260061 0.611893096709389 df.mm.trans1:exp2 0.0732281901726196 0.117254076793374 0.624525749340576 0.532469552589251 df.mm.trans2:exp2 0.087065020963329 0.0919816654796497 0.946547559334979 0.344169828234752 df.mm.trans1:exp3 0.0749107870686226 0.117254076793374 0.638875756965198 0.523095994898468 df.mm.trans2:exp3 -0.073055230820695 0.0919816654796497 -0.794236877966272 0.427305143472766 df.mm.trans1:exp4 0.0991094026620783 0.117254076793374 0.845253362377576 0.398235063149012 df.mm.trans2:exp4 0.0339713458996992 0.0919816654796497 0.369327362388488 0.711986472850336 df.mm.trans1:exp5 0.142627765510031 0.117254076793374 1.21639920257417 0.224210031804461 df.mm.trans2:exp5 0.092304672339086 0.0919816654796497 1.00351164395374 0.315933035366633 df.mm.trans1:exp6 0.0767342268697272 0.117254076793374 0.65442694163162 0.513034508870156 df.mm.trans2:exp6 0.105434486938196 0.0919816654796497 1.14625546719985 0.252049758937317 df.mm.trans1:exp7 0.0997099863800194 0.117254076793374 0.850375433476217 0.395383858328466 df.mm.trans2:exp7 0.144297245167451 0.0919816654796497 1.56876095268546 0.117119334648511 df.mm.trans1:exp8 0.0198595618121488 0.117254076793374 0.169372036821760 0.865549028048423 df.mm.trans2:exp8 0.0870554121939942 0.0919816654796497 0.946443095371594 0.344223050178163 df.mm.trans1:probe2 -0.0186509673004655 0.0745136653726629 -0.250302641900475 0.8024208708361 df.mm.trans1:probe3 0.0255737852813363 0.0745136653726629 0.343209331515701 0.731535774268029 df.mm.trans1:probe4 -0.0762585339493936 0.074513665372663 -1.02341675943606 0.306436050104134 df.mm.trans1:probe5 -0.0886921690286845 0.0745136653726629 -1.19028058256309 0.234307305162605 df.mm.trans1:probe6 -0.0534237864854509 0.0745136653726629 -0.716966293608885 0.473614882787522 df.mm.trans1:probe7 -0.0273162849574263 0.0745136653726629 -0.366594299459169 0.714023562770558 df.mm.trans1:probe8 -0.0524708255764955 0.074513665372663 -0.704177217884461 0.481537836546668 df.mm.trans1:probe9 -0.0153334995535562 0.0745136653726629 -0.205781039986011 0.837016987732561 df.mm.trans1:probe10 0.0101670478265826 0.0745136653726629 0.136445412740528 0.891505262132202 df.mm.trans1:probe11 -0.16047271234355 0.0745136653726629 -2.15360110848101 0.0315849197258016 * df.mm.trans1:probe12 -0.00713604506744393 0.074513665372663 -0.0957682732657781 0.923729828429733 df.mm.trans1:probe13 -0.0106218785456910 0.0745136653726629 -0.142549403422421 0.88668379169581 df.mm.trans1:probe14 0.012132612677764 0.0745136653726629 0.162823994995891 0.870700241396807 df.mm.trans1:probe15 -0.0370425820795558 0.0745136653726629 -0.497124680342805 0.619244720386631 df.mm.trans1:probe16 -0.00403372138372893 0.0745136653726629 -0.0541339815127225 0.956842626445543 df.mm.trans1:probe17 -0.0202217784791106 0.0745136653726629 -0.271383488893964 0.786169698845098 df.mm.trans1:probe18 0.0266364266617799 0.0745136653726629 0.357470358337145 0.720838826749149 df.mm.trans1:probe19 -0.042856447410025 0.0745136653726629 -0.575148829354836 0.565360651151186 df.mm.trans1:probe20 -0.0407275309972497 0.0745136653726629 -0.546578010805941 0.58482889612808 df.mm.trans1:probe21 0.000446304114902250 0.0745136653726629 0.00598956060838186 0.995222620372919 df.mm.trans2:probe2 -0.126013631056051 0.0745136653726629 -1.69114793140054 0.0912179998119317 . df.mm.trans2:probe3 -0.0375471151210373 0.0745136653726629 -0.503895693940891 0.614480517120299 df.mm.trans2:probe4 -0.0670291930112605 0.0745136653726629 -0.899555708017173 0.368641289609476 df.mm.trans2:probe5 0.0158455913311144 0.0745136653726629 0.212653494521660 0.831654191563043 df.mm.trans2:probe6 -0.125739750918932 0.074513665372663 -1.68747236215094 0.0919223370942729 . df.mm.trans3:probe2 -0.00259990013579236 0.0745136653726629 -0.0348915883118829 0.972175337823708 df.mm.trans3:probe3 2.78855848815424e-06 0.0745136653726629 3.74234507752089e-05 0.99997015066216 df.mm.trans3:probe4 0.0837398030386584 0.0745136653726629 1.12381806236283 0.261444596350338 df.mm.trans3:probe5 0.0664995518387064 0.074513665372663 0.89244773433335 0.372435103340688 df.mm.trans3:probe6 -0.00519684935525178 0.0745136653726629 -0.0697435742727315 0.944416081205358 df.mm.trans3:probe7 0.0372766396767605 0.074513665372663 0.500265816885131 0.617032552501586