chr13.6633_chr13_48240651_48242683_-_1.R fitVsDatCorrelation=0.916665835168595 cont.fitVsDatCorrelation=0.280847891241475 fstatistic=10465.2453578248,60,876 cont.fstatistic=1802.61388779112,60,876 residuals=-0.987923706050422,-0.0946409610758189,-0.0065366321627165,0.0825022833874405,0.729693266149911 cont.residuals=-0.752817320425499,-0.282300137880245,-0.0921186051063684,0.187511911314229,1.41109659274569 predictedValues: Include Exclude Both chr13.6633_chr13_48240651_48242683_-_1.R.tl.Lung 53.2588581806243 92.3386984395805 66.7963506884212 chr13.6633_chr13_48240651_48242683_-_1.R.tl.cerebhem 65.990865541844 81.9418371956191 72.7566849969092 chr13.6633_chr13_48240651_48242683_-_1.R.tl.cortex 59.0172238200572 93.7050059032856 74.9700039957466 chr13.6633_chr13_48240651_48242683_-_1.R.tl.heart 55.1399632455778 102.808321124656 67.6849799755124 chr13.6633_chr13_48240651_48242683_-_1.R.tl.kidney 59.1120074488027 95.9921736922152 71.9716494153567 chr13.6633_chr13_48240651_48242683_-_1.R.tl.liver 56.7123346228921 108.013104835573 66.9999171485424 chr13.6633_chr13_48240651_48242683_-_1.R.tl.stomach 53.0683948468702 108.922803174740 66.6916111728735 chr13.6633_chr13_48240651_48242683_-_1.R.tl.testicle 58.0283824539375 90.2107731592497 68.2448741957763 diffExp=-39.0798402589562,-15.9509716537751,-34.6877820832284,-47.6683578790778,-36.8801662434125,-51.3007702126805,-55.8544083278693,-32.1823907053122 diffExpScore=0.996821407816973 diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.875 diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.875 diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 70.0646541232223 70.9720218650286 73.2174663945532 cerebhem 82.9359319717256 63.074788464827 74.7493299680302 cortex 82.7077905080403 63.9017480175985 74.9816318713159 heart 71.583717313194 76.1510000332264 68.5307241679268 kidney 79.2546946430304 90.2424548432537 75.8150419993483 liver 72.2415580653056 80.5637952957731 67.0198352724138 stomach 73.1139756812778 62.6591946324082 68.919791672517 testicle 76.5028714809632 62.5420650132194 80.7886403868993 cont.diffExp=-0.907367741806311,19.8611435068986,18.8060424904418,-4.56728272003247,-10.9877602002233,-8.32223723046748,10.4547810488696,13.9608064677438 cont.diffExpScore=2.23591888969332 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=0,1,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,1,1,0,0,0,0,1 cont.diffExp1.2Score=0.75 tran.correlation=-0.715830563586953 cont.tran.correlation=-0.214869484604257 tran.covariance=-0.00494481436731758 cont.tran.covariance=-0.00225835171766163 tran.mean=77.1412967303452 cont.tran.mean=73.6570163720059 weightedLogRatios: wLogRatio Lung -2.33894416172219 cerebhem -0.930437161602517 cortex -1.99214230858840 heart -2.69217734017606 kidney -2.09537390236355 liver -2.8090559202813 stomach -3.11431900130226 testicle -1.88908681892985 cont.weightedLogRatios: wLogRatio Lung -0.0547613045516004 cerebhem 1.17196535993839 cortex 1.10573211386857 heart -0.266068367556009 kidney -0.576146270516097 liver -0.47261089589666 stomach 0.650392029829104 testicle 0.853624636590707 varWeightedLogRatios=0.460361490899880 cont.varWeightedLogRatios=0.521376160125843 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.4909152373866 0.0757581701732866 59.2796160086 0 *** df.mm.trans1 -0.662998293190726 0.0637434034650883 -10.4010494757131 5.70040518021873e-24 *** df.mm.trans2 0.0361664758699760 0.057125079915848 0.633110289267926 0.526826863077888 df.mm.exp2 0.00942621824075744 0.0726786621430729 0.129697189832709 0.896835779875561 df.mm.exp3 0.00191384165340582 0.0726786621430729 0.0263329235427902 0.978997792225863 df.mm.exp4 0.128897725175873 0.0726786621430729 1.77352914012272 0.0764884370592911 . df.mm.exp5 0.0684494416480926 0.0726786621430729 0.941809323806006 0.346549917237734 df.mm.exp6 0.216573892354528 0.0726786621430729 2.97988276019979 0.00296324753914319 ** df.mm.exp7 0.163162763892536 0.0726786621430729 2.24498854383614 0.0250176480067583 * df.mm.exp8 0.0409997573102592 0.0726786621430729 0.564123720790958 0.572814332528275 df.mm.trans1:exp2 0.204925971761612 0.0637434034650883 3.21485770482664 0.00135287140457490 ** df.mm.trans2:exp2 -0.128879846817965 0.0472733732340116 -2.72626719866143 0.00653342441215023 ** df.mm.trans1:exp3 0.100751347238182 0.0637434034650883 1.58057684029003 0.114335804921483 df.mm.trans2:exp3 0.0127744492838727 0.0472733732340116 0.270225042343326 0.787050776567987 df.mm.trans1:exp4 -0.0941871277105745 0.0637434034650883 -1.47759803509958 0.139874825590451 df.mm.trans2:exp4 -0.0214947523132745 0.0472733732340116 -0.454690470402263 0.649444589729126 df.mm.trans1:exp5 0.0358204922853108 0.0637434034650883 0.56194822268832 0.574295045615462 df.mm.trans2:exp5 -0.0296460992179282 0.0472733732340116 -0.62712045258913 0.530743691375452 df.mm.trans1:exp6 -0.153746304994288 0.0637434034650883 -2.41195632232744 0.0160718654977154 * df.mm.trans2:exp6 -0.059784653196405 0.0472733732340116 -1.26465807507453 0.206330271341554 df.mm.trans1:exp7 -0.166745355035384 0.0637434034650883 -2.61588409107632 0.00905282975322303 ** df.mm.trans2:exp7 0.00201331799519076 0.0472733732340116 0.0425888371710747 0.966038999226246 df.mm.trans1:exp8 0.0447683446316728 0.0637434034650883 0.70232121597009 0.482665435481837 df.mm.trans2:exp8 -0.0643142226932988 0.0472733732340116 -1.36047458206403 0.174029741346034 df.mm.trans1:probe2 0.186876910464773 0.0478075525988162 3.90894116737113 9.9854269064406e-05 *** df.mm.trans1:probe3 1.09124703794952 0.0478075525988162 22.8258293643866 7.28058623515323e-91 *** df.mm.trans1:probe4 0.135084028498790 0.0478075525988162 2.82557924753787 0.00482683190301008 ** df.mm.trans1:probe5 0.0472934663993419 0.0478075525988162 0.989246757645421 0.322815585457999 df.mm.trans1:probe6 0.124849776426516 0.0478075525988162 2.61150737989477 0.00916860416981117 ** df.mm.trans1:probe7 0.144201467426256 0.0478075525988162 3.01629051452065 0.00263280621322213 ** df.mm.trans1:probe8 1.46344945598739 0.0478075525988162 30.6112606990811 1.60996408242619e-140 *** df.mm.trans1:probe9 0.193833872528346 0.0478075525988162 4.05446131398798 5.47230272219351e-05 *** df.mm.trans1:probe10 0.0674298735548873 0.0478075525988162 1.41044395476034 0.158763618306563 df.mm.trans1:probe11 0.161183172682695 0.0478075525988162 3.37150019025834 0.000780324330659899 *** df.mm.trans1:probe12 0.151250241725852 0.0478075525988162 3.16373111577348 0.00161131849778115 ** df.mm.trans1:probe13 0.138574602253012 0.0478075525988162 2.89859226670481 0.00384197212407026 ** df.mm.trans1:probe14 0.332727105486914 0.0478075525988162 6.95971844195917 6.68000456584238e-12 *** df.mm.trans1:probe15 0.179414909677945 0.0478075525988162 3.75285702624292 0.000186349184328814 *** df.mm.trans2:probe2 0.163833115883925 0.0478075525988162 3.42692957447025 0.000638867243385339 *** df.mm.trans2:probe3 -0.220970606332258 0.0478075525988162 -4.62208572328653 4.36996395935695e-06 *** df.mm.trans2:probe4 0.238015039372716 0.0478075525988162 4.97860748844544 7.71222985468142e-07 *** df.mm.trans2:probe5 -0.199194288158630 0.0478075525988162 -4.16658618419974 3.39842615428365e-05 *** df.mm.trans2:probe6 -0.0205246587241155 0.0478075525988162 -0.429318331694388 0.667797144544061 df.mm.trans3:probe2 0.135084028498790 0.0478075525988162 2.82557924753787 0.00482683190301008 ** df.mm.trans3:probe3 0.124849776426516 0.0478075525988162 2.61150737989477 0.00916860416981117 ** df.mm.trans3:probe4 1.23496820572661 0.0478075525988162 25.8320733564844 7.72709550384052e-110 *** df.mm.trans3:probe5 0.112083642661164 0.0478075525988162 2.34447564387430 0.0192763667533644 * df.mm.trans3:probe6 0.140789468855519 0.0478075525988162 2.94492106795287 0.00331582062196508 ** df.mm.trans3:probe7 0.125993192138225 0.0478075525988162 2.63542443169000 0.00855173472367345 ** df.mm.trans3:probe8 0.408456787665556 0.0478075525988162 8.54377112949451 5.71208592717165e-17 *** df.mm.trans3:probe9 0.0589210455040009 0.0478075525988162 1.23246312143282 0.218106808266078 df.mm.trans3:probe10 0.300307220236782 0.0478075525988162 6.28158531261476 5.2729682542444e-10 *** df.mm.trans3:probe11 0.509647719809135 0.0478075525988162 10.6604018006509 4.94041605377721e-25 *** df.mm.trans3:probe12 0.997690704084821 0.0478075525988162 20.8688930901166 7.97434133954297e-79 *** df.mm.trans3:probe13 0.665863036529281 0.0478075525988162 13.9279883686363 5.59556442554106e-40 *** df.mm.trans3:probe14 0.126442094336995 0.0478075525988162 2.64481420745486 0.00831989735559157 ** df.mm.trans3:probe15 0.408743455710827 0.0478075525988162 8.54976742149624 5.4447416956368e-17 *** df.mm.trans3:probe16 0.098100972834271 0.0478075525988162 2.05199738329002 0.0404666779189130 * df.mm.trans3:probe17 0.48155982974131 0.0478075525988162 10.0728818683187 1.17802647534655e-22 *** df.mm.trans3:probe18 0.146669333528917 0.0478075525988162 3.06791135617656 0.00222191363009696 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.34918936907531 0.181929820020447 23.9058630882309 1.25531395886355e-97 *** df.mm.trans1 -0.0354968609494675 0.153076901057247 -0.231889074735009 0.81667832829469 df.mm.trans2 -0.0841653907468716 0.137183296322652 -0.613525064661781 0.539688484913741 df.mm.exp2 0.0299791152042509 0.174534520735805 0.171766107231191 0.863661082798549 df.mm.exp3 0.0371471582066489 0.174534520735805 0.212835592924788 0.831504718242524 df.mm.exp4 0.158033492450249 0.174534520735805 0.905456936450215 0.365472327633842 df.mm.exp5 0.328599741263135 0.174534520735805 1.88272062098557 0.060069420498516 . df.mm.exp6 0.245806028291141 0.174534520735805 1.40835192519433 0.159381689713828 df.mm.exp7 -0.0214836049621908 0.174534520735805 -0.123090864040076 0.902063380184725 df.mm.exp8 -0.136938902768215 0.174534520735805 -0.784594945406249 0.432903211906818 df.mm.trans1:exp2 0.138670844468901 0.153076901057247 0.905890069051254 0.365243133620933 df.mm.trans2:exp2 -0.147943715532333 0.113524868203005 -1.30318332779546 0.192854602288589 df.mm.trans1:exp3 0.128748195321113 0.153076901057247 0.841068733635809 0.400538951623355 df.mm.trans2:exp3 -0.142086182820273 0.113524868203005 -1.25158641511211 0.211054714397498 df.mm.trans1:exp4 -0.136584302269617 0.153076901057247 -0.89225938940675 0.372498942759984 df.mm.trans2:exp4 -0.087601021923548 0.113524868203005 -0.771646101071883 0.440532163172206 df.mm.trans1:exp5 -0.205351537959487 0.153076901057247 -1.34149265200169 0.180108214119467 df.mm.trans2:exp5 -0.088385491530823 0.113524868203005 -0.778556213540565 0.436451426023145 df.mm.trans1:exp6 -0.21520899760917 0.153076901057247 -1.40588812631298 0.160111933261899 df.mm.trans2:exp6 -0.119042410714230 0.113524868203005 -1.04860206048739 0.294650522126013 df.mm.trans1:exp7 0.064084693137566 0.153076901057247 0.418643784234957 0.675579104193927 df.mm.trans2:exp7 -0.103091703715141 0.113524868203005 -0.90809798193989 0.364076204627058 df.mm.trans1:exp8 0.224848732498046 0.153076901057247 1.46886127786163 0.142229419169277 df.mm.trans2:exp8 0.0104925321911608 0.113524868203005 0.0924249669455514 0.926381535640178 df.mm.trans1:probe2 -0.223277369351954 0.114807675792935 -1.94479478667134 0.0521193893887331 . df.mm.trans1:probe3 -0.258527946023811 0.114807675792935 -2.25183502965505 0.0245796858296940 * df.mm.trans1:probe4 -0.151775248816827 0.114807675792935 -1.32199565724652 0.186514693130659 df.mm.trans1:probe5 0.00649701583350272 0.114807675792935 0.0565904308107465 0.954884362197385 df.mm.trans1:probe6 0.0396373299817006 0.114807675792935 0.345249825048194 0.729989446731638 df.mm.trans1:probe7 -0.127257678730812 0.114807675792935 -1.10844225224393 0.267975095741279 df.mm.trans1:probe8 -0.266616211536886 0.114807675792935 -2.32228559367188 0.0204459556457222 * df.mm.trans1:probe9 -0.188124426910407 0.114807675792935 -1.63860495921635 0.101654642074536 df.mm.trans1:probe10 -0.0369027069807249 0.114807675792935 -0.321430659804331 0.74796072801217 df.mm.trans1:probe11 -0.0612084541508851 0.114807675792935 -0.533139040818833 0.594072561846047 df.mm.trans1:probe12 -0.297448841102174 0.114807675792935 -2.59084454978992 0.00973325601855089 ** df.mm.trans1:probe13 -0.0199961622531531 0.114807675792935 -0.174170952552143 0.861771355118147 df.mm.trans1:probe14 -0.130187448763841 0.114807675792935 -1.13396119087581 0.257121047973980 df.mm.trans1:probe15 -0.213033712056667 0.114807675792935 -1.85557028818256 0.0638505416116291 . df.mm.trans2:probe2 -0.0340293637289313 0.114807675792935 -0.296403210794947 0.766992429999231 df.mm.trans2:probe3 -0.0479546564998154 0.114807675792935 -0.417695560585212 0.676272076204102 df.mm.trans2:probe4 -0.0199955169473947 0.114807675792935 -0.17416533179766 0.861775770988164 df.mm.trans2:probe5 0.0528570558575033 0.114807675792935 0.460396532657235 0.645345883574943 df.mm.trans2:probe6 -0.0165952111814855 0.114807675792935 -0.14454792388112 0.885101064211237 df.mm.trans3:probe2 0.0155889449099452 0.114807675792935 0.135783124275254 0.892023927697543 df.mm.trans3:probe3 -0.0427185071532899 0.114807675792935 -0.372087553016369 0.709917654846617 df.mm.trans3:probe4 0.0589096271153677 0.114807675792935 0.513115753877083 0.607999725169292 df.mm.trans3:probe5 0.0776479607897133 0.114807675792935 0.676330744032808 0.499009203142553 df.mm.trans3:probe6 0.106072184394271 0.114807675792935 0.92391195677178 0.355786575852998 df.mm.trans3:probe7 0.111818603413388 0.114807675792935 0.97396452494223 0.330343018606183 df.mm.trans3:probe8 0.172342178946698 0.114807675792935 1.50113812300783 0.133680182083971 df.mm.trans3:probe9 0.0169151672767811 0.114807675792935 0.147334811544212 0.882901659113453 df.mm.trans3:probe10 0.0772623200691877 0.114807675792935 0.672971728898479 0.501142765503636 df.mm.trans3:probe11 0.0296004193064623 0.114807675792935 0.257826134899282 0.796601648946926 df.mm.trans3:probe12 0.0833239474199087 0.114807675792935 0.725769830670469 0.468173733633011 df.mm.trans3:probe13 0.128095826606338 0.114807675792935 1.11574270380118 0.264838255974587 df.mm.trans3:probe14 0.148161796618639 0.114807675792935 1.29052169722398 0.197210096071155 df.mm.trans3:probe15 0.128746460260315 0.114807675792935 1.12140986542153 0.26242074557657 df.mm.trans3:probe16 -0.111169399880473 0.114807675792935 -0.968309819989529 0.333156923214942 df.mm.trans3:probe17 0.0448829837810485 0.114807675792935 0.390940618482675 0.69593618878236 df.mm.trans3:probe18 0.104843453429601 0.114807675792935 0.913209440967125 0.361383662164308