chr9.24592_chr9_123117045_123122683_-_2.R fitVsDatCorrelation=0.883515334602875 cont.fitVsDatCorrelation=0.232406583417987 fstatistic=13513.3294847326,63,945 cont.fstatistic=3122.59466598241,63,945 residuals=-0.687921968661447,-0.0857606688520542,-0.00514188433550733,0.0733075377337947,0.762263021440608 cont.residuals=-0.500016240975576,-0.200424404243122,-0.0622896553671069,0.151378097625592,1.43006895454307 predictedValues: Include Exclude Both chr9.24592_chr9_123117045_123122683_-_2.R.tl.Lung 53.1072246573046 42.6669065334442 65.7343692577723 chr9.24592_chr9_123117045_123122683_-_2.R.tl.cerebhem 59.688997136636 47.2584466060489 58.1196133790858 chr9.24592_chr9_123117045_123122683_-_2.R.tl.cortex 53.8937817953033 44.1615548468452 65.311401703773 chr9.24592_chr9_123117045_123122683_-_2.R.tl.heart 54.2734352942486 44.7514983926796 59.87221441111 chr9.24592_chr9_123117045_123122683_-_2.R.tl.kidney 54.0980645464776 43.4896641607418 62.4687147791209 chr9.24592_chr9_123117045_123122683_-_2.R.tl.liver 59.8199667708633 46.2903223647245 60.1127582260029 chr9.24592_chr9_123117045_123122683_-_2.R.tl.stomach 55.7325050596586 44.4332420303156 61.1860264423523 chr9.24592_chr9_123117045_123122683_-_2.R.tl.testicle 55.7038291382638 45.3506506692308 62.3685944866422 diffExp=10.4403181238604,12.4305505305871,9.73222694845815,9.521936901569,10.6084003857357,13.5296444061388,11.299263029343,10.3531784690330 diffExpScore=0.98875336933805 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=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 59.8322283231314 58.0030174877501 54.9375589419675 cerebhem 57.3521596325938 57.6916094700997 57.0757355249703 cortex 56.4912668320273 62.1985412681817 60.4267536311328 heart 61.6533530993419 60.2298912209448 59.5785398685494 kidney 58.5559259292075 55.8582754114322 54.5962517069131 liver 58.536542451947 52.325509708546 58.8410296911135 stomach 57.0176535396891 56.791297510691 54.8506341915897 testicle 56.6773303998518 55.1296261278732 54.3636833005 cont.diffExp=1.82921083538130,-0.339449837505867,-5.70727443615439,1.42346187839704,2.69765051777537,6.21103274340099,0.226356028998048,1.54770427197863 cont.diffExpScore=2.24804060535409 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=0.918017164980519 cont.tran.correlation=0.113126246095551 tran.covariance=0.00138884473745594 cont.tran.covariance=0.000172031771706424 tran.mean=50.2950056251742 cont.tran.mean=57.7715142758318 weightedLogRatios: wLogRatio Lung 0.845540842226774 cerebhem 0.927617705180122 cortex 0.774223450400367 heart 0.75188197746442 kidney 0.847269932581217 liver 1.01617419600251 stomach 0.885293601047745 testicle 0.805479314945215 cont.weightedLogRatios: wLogRatio Lung 0.126558033064159 cerebhem -0.0239128018378317 cortex -0.392894187777983 heart 0.0960013537680745 kidney 0.190846876367229 liver 0.450190669374247 stomach 0.0160758906758511 testicle 0.111399751352783 varWeightedLogRatios=0.00741502042619247 cont.varWeightedLogRatios=0.0558158211643689 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.95804150531946 0.0633640297166618 46.6832920593374 1.29133309232666e-247 *** df.mm.trans1 0.97587885966977 0.0540455468334402 18.0566007163786 7.66300033215276e-63 *** df.mm.trans2 0.791636301699897 0.0475296833040386 16.6556191135536 8.24545429881563e-55 *** df.mm.exp2 0.342161255584509 0.0601657388541838 5.68697837175677 1.72321582259919e-08 *** df.mm.exp3 0.0555884296184981 0.0601657388541838 0.923921665006405 0.355762951577396 df.mm.exp4 0.162832621877102 0.0601657388541838 2.70640110099436 0.00692426855272713 ** df.mm.exp5 0.0885411854019878 0.0601657388541838 1.47162134278071 0.141456081065120 df.mm.exp6 0.289935653646788 0.0601657388541838 4.81894944146649 1.68059607554045e-06 *** df.mm.exp7 0.160517936035847 0.0601657388541838 2.66792927491299 0.00776266766303655 ** df.mm.exp8 0.161296898497073 0.0601657388541838 2.68087621907192 0.00747086368442238 ** df.mm.trans1:exp2 -0.225326531309927 0.0545121357504204 -4.13351134032919 3.88993848718837e-05 *** df.mm.trans2:exp2 -0.239953450585285 0.0381938761131227 -6.28251109875809 5.07670914697664e-10 *** df.mm.trans1:exp3 -0.0408863005067119 0.0545121357504204 -0.750040334025926 0.453417102227087 df.mm.trans2:exp3 -0.0211574159886593 0.0381938761131227 -0.553947861327172 0.579745578739907 df.mm.trans1:exp4 -0.141110712314549 0.0545121357504204 -2.58861096473295 0.00978441633348682 ** df.mm.trans2:exp4 -0.115131291194807 0.0381938761131227 -3.01439138708548 0.00264371487588587 ** df.mm.trans1:exp5 -0.0700557522397723 0.0545121357504204 -1.28514047881956 0.199058078969383 df.mm.trans2:exp5 -0.0694414781534997 0.0381938761131227 -1.81813120898826 0.06936057067816 . df.mm.trans1:exp6 -0.170909132499799 0.0545121357504204 -3.13524924582470 0.00177011674348068 ** df.mm.trans2:exp6 -0.208426331716004 0.0381938761131227 -5.45706152207979 6.18530085161532e-08 *** df.mm.trans1:exp7 -0.112267362102805 0.0545121357504204 -2.05949300201357 0.0397205107777678 * df.mm.trans2:exp7 -0.119953649466315 0.0381938761131227 -3.14065137329962 0.00173812661809195 ** df.mm.trans1:exp8 -0.113560984746173 0.0545121357504204 -2.08322391304026 0.0374991349117956 * df.mm.trans2:exp8 -0.100295971429318 0.0381938761131227 -2.62596996262598 0.00877995356126113 ** df.mm.trans1:probe2 0.460644930891085 0.0402598803959222 11.4417858761881 1.75711087923848e-28 *** df.mm.trans1:probe3 -0.0937041186113799 0.0402598803959222 -2.32748129626513 0.0201500762752391 * df.mm.trans1:probe4 0.507038529672841 0.0402598803959222 12.5941389961058 9.84848761440289e-34 *** df.mm.trans1:probe5 0.224493534290603 0.0402598803959222 5.57611031336648 3.20991436164798e-08 *** df.mm.trans1:probe6 -0.0294443215592313 0.0402598803959222 -0.731356409151519 0.464742790369394 df.mm.trans1:probe7 0.359674011183341 0.0402598803959222 8.9338072454823 2.11390867144518e-18 *** df.mm.trans1:probe8 0.881654100049452 0.0402598803959222 21.8990740006955 2.66466958550046e-86 *** df.mm.trans1:probe9 -0.0666020331068228 0.0402598803959222 -1.65430280596583 0.0983980838225161 . df.mm.trans1:probe10 0.0822811513785018 0.0402598803959222 2.04375051712364 0.0412550271770609 * df.mm.trans1:probe11 -0.082756104879754 0.0402598803959222 -2.05554770818783 0.040100454085512 * df.mm.trans1:probe12 -0.0317343125056442 0.0402598803959222 -0.7882366314446 0.430755984726611 df.mm.trans1:probe13 -0.0775829130303557 0.0402598803959222 -1.92705274500055 0.0542725927847616 . df.mm.trans1:probe14 -0.094540006120744 0.0402598803959222 -2.34824359116376 0.0190674921856681 * df.mm.trans1:probe15 -0.161324932664919 0.0402598803959222 -4.00708921830923 6.63052501777558e-05 *** df.mm.trans1:probe16 -0.0212819250564634 0.0402598803959222 -0.528613717854438 0.597197565416823 df.mm.trans1:probe17 -0.215333113252278 0.0402598803959222 -5.34857806666729 1.11230813013511e-07 *** df.mm.trans1:probe18 -0.141191172513471 0.0402598803959222 -3.50699433592386 0.000474524313724211 *** df.mm.trans1:probe19 0.0613965290766936 0.0402598803959222 1.52500525269599 0.127592370141374 df.mm.trans1:probe20 -0.179553816360396 0.0402598803959222 -4.45986959212581 9.18614045031888e-06 *** df.mm.trans2:probe2 -0.0189089004828388 0.0402598803959222 -0.469671054580531 0.638698404313392 df.mm.trans2:probe3 0.0603457140488732 0.0402598803959222 1.49890445414700 0.134232390109127 df.mm.trans2:probe4 0.0203482861271027 0.0402598803959222 0.505423412265371 0.613379337049175 df.mm.trans2:probe5 0.0071840777896814 0.0402598803959222 0.17844260139454 0.85841364112506 df.mm.trans2:probe6 0.0134382045840618 0.0402598803959222 0.333786500404579 0.738614722898478 df.mm.trans3:probe2 -0.511738841347795 0.0402598803959222 -12.7108882668123 2.76311246458957e-34 *** df.mm.trans3:probe3 -0.582640770689692 0.0402598803959222 -14.4719945752423 5.05852811681112e-43 *** df.mm.trans3:probe4 -0.391135359252901 0.0402598803959222 -9.71526381614681 2.48290092835450e-21 *** df.mm.trans3:probe5 -0.494610493980548 0.0402598803959222 -12.2854437001916 2.72606207629154e-32 *** df.mm.trans3:probe6 -0.20875449227058 0.0402598803959222 -5.18517417880168 2.63991561740688e-07 *** df.mm.trans3:probe7 -0.726787312832323 0.0402598803959222 -18.0523962238581 8.10934154953385e-63 *** df.mm.trans3:probe8 -0.691023020663299 0.0402598803959222 -17.1640604459742 1.09606928424081e-57 *** df.mm.trans3:probe9 -0.408635672641125 0.0402598803959222 -10.1499475066129 4.79013225357314e-23 *** df.mm.trans3:probe10 -0.616072319301436 0.0402598803959222 -15.3023882148402 2.16859890714656e-47 *** df.mm.trans3:probe11 -0.80370996618867 0.0402598803959222 -19.9630490275891 2.9688722441882e-74 *** df.mm.trans3:probe12 -0.691364315822133 0.0402598803959222 -17.1725377478309 9.80609239648654e-58 *** df.mm.trans3:probe13 -0.393801552502214 0.0402598803959222 -9.78148838569578 1.37272525919331e-21 *** df.mm.trans3:probe14 -0.804523895966574 0.0402598803959222 -19.9832659226693 2.23343664889732e-74 *** df.mm.trans3:probe15 -0.679620857542494 0.0402598803959222 -16.8808464123339 4.44318749842804e-56 *** df.mm.trans3:probe16 -0.63453719001105 0.0402598803959222 -15.7610301811855 7.27091067746042e-50 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.1557702537229 0.131572902696292 31.5853049416688 4.84663450633467e-150 *** df.mm.trans1 -0.0550489458865304 0.112223441382774 -0.490529832343748 0.62387290095615 df.mm.trans2 -0.0851864823432112 0.0986935083597348 -0.863141697554302 0.388278566753882 df.mm.exp2 -0.0858990703763376 0.124931778160417 -0.687567820142928 0.491893692384319 df.mm.exp3 -0.0828565811938844 0.124931778160417 -0.66321461531984 0.507354854720678 df.mm.exp4 -0.0134412989635724 0.124931778160417 -0.107589111125220 0.914344472136563 df.mm.exp5 -0.0530074848138889 0.124931778160417 -0.424291446054865 0.67144984735024 df.mm.exp6 -0.193546406881137 0.124931778160417 -1.54921677839738 0.121664429710175 df.mm.exp7 -0.0677119541491237 0.124931778160417 -0.541991438416727 0.587952174385432 df.mm.exp8 -0.0944770168619073 0.124931778160417 -0.756228865490056 0.449700460798768 df.mm.trans1:exp2 0.0435651193536381 0.113192294822928 0.384877075085272 0.70041509398197 df.mm.trans2:exp2 0.0805157820096532 0.079308073806182 1.01522806122391 0.310256980340648 df.mm.trans1:exp3 0.0253981871019529 0.113192294822928 0.224380883360343 0.822509448010917 df.mm.trans2:exp3 0.152693093539977 0.079308073806182 1.92531587531855 0.0544896940959943 . df.mm.trans1:exp4 0.0434244654260052 0.113192294822928 0.383634464642106 0.701335684273814 df.mm.trans2:exp4 0.0511150251125569 0.079308073806182 0.644512250259349 0.519399729551745 df.mm.trans1:exp5 0.0314453302131543 0.113192294822928 0.277804511891431 0.781223192628116 df.mm.trans2:exp5 0.0153301366023846 0.079308073806182 0.193298561756138 0.846766689681666 df.mm.trans1:exp6 0.17165317244742 0.113192294822928 1.51647400307544 0.129733963025289 df.mm.trans2:exp6 0.0905353815266537 0.079308073806182 1.14156575972214 0.253923746209910 df.mm.trans1:exp7 0.0195284343030627 0.113192294822928 0.172524413729857 0.863062211252041 df.mm.trans2:exp7 0.046600020454001 0.079308073806182 0.587582300484123 0.556953063604367 df.mm.trans1:exp8 0.0403068801139505 0.113192294822927 0.356092083626404 0.72185108589196 df.mm.trans2:exp8 0.0436692329508193 0.079308073806182 0.550627834658308 0.582018961459484 df.mm.trans1:probe2 -0.0949099175961892 0.0835980500227581 -1.1353125769124 0.256532237478482 df.mm.trans1:probe3 -0.0836426683057095 0.0835980500227581 -1.00053372396772 0.317308408131663 df.mm.trans1:probe4 0.00540662119943289 0.0835980500227581 0.0646740109124678 0.948447233847348 df.mm.trans1:probe5 0.0518054186144108 0.0835980500227581 0.619696495316669 0.535607006450481 df.mm.trans1:probe6 -0.0719046786272149 0.0835980500227581 -0.860123873794186 0.389938915245155 df.mm.trans1:probe7 -0.0131870327603922 0.0835980500227581 -0.157743305696751 0.87469277324186 df.mm.trans1:probe8 0.0439815286759649 0.0835980500227581 0.526107112115554 0.598937229917311 df.mm.trans1:probe9 -0.0986091427455847 0.0835980500227581 -1.17956271370851 0.238471045985562 df.mm.trans1:probe10 -0.0491794883863324 0.0835980500227581 -0.588285113982254 0.556481501237084 df.mm.trans1:probe11 0.0414586517876454 0.0835980500227581 0.495928454986199 0.620060205175857 df.mm.trans1:probe12 0.0479261878910343 0.0835980500227581 0.573293131574088 0.56658262919774 df.mm.trans1:probe13 0.0244560359324137 0.0835980500227581 0.292543138575075 0.769935599640503 df.mm.trans1:probe14 -0.0614397597426245 0.0835980500227581 -0.734942498370459 0.462556854324977 df.mm.trans1:probe15 -0.0241878782593831 0.0835980500227581 -0.289335436087306 0.772388160987662 df.mm.trans1:probe16 -0.0507311461030567 0.0835980500227581 -0.606846045921478 0.54409885085871 df.mm.trans1:probe17 0.0578948698300125 0.0835980500227581 0.692538519908678 0.488769344506174 df.mm.trans1:probe18 0.00489589914690779 0.0835980500227581 0.0585647529526701 0.953311166925614 df.mm.trans1:probe19 -0.0670290693552671 0.0835980500227581 -0.801801828356278 0.422869170408963 df.mm.trans1:probe20 0.00662872098849322 0.0835980500227581 0.0792927704257296 0.93681654386014 df.mm.trans2:probe2 -0.0678920674620051 0.0835980500227581 -0.812125013005958 0.416924425738559 df.mm.trans2:probe3 -0.0826102399699114 0.0835980500227581 -0.988183814663406 0.323315577518074 df.mm.trans2:probe4 -0.0284522573456975 0.0835980500227581 -0.340345945126135 0.733671661051261 df.mm.trans2:probe5 -0.0637307178259483 0.0835980500227581 -0.762346942405938 0.446043197458555 df.mm.trans2:probe6 0.0207330788183010 0.0835980500227581 0.248009119981348 0.80418122988955 df.mm.trans3:probe2 -0.0712413373488181 0.0835980500227581 -0.852188984425162 0.394325124167926 df.mm.trans3:probe3 -0.0632338814705315 0.0835980500227581 -0.756403785175816 0.449595661424724 df.mm.trans3:probe4 0.0183164517966532 0.0835980500227581 0.219101423916788 0.826618353799374 df.mm.trans3:probe5 -0.109582256101636 0.0835980500227581 -1.31082311216356 0.190235987138346 df.mm.trans3:probe6 0.059058195486869 0.0835980500227581 0.706454223164193 0.480079849499083 df.mm.trans3:probe7 0.0243808840520293 0.0835980500227581 0.291644171668981 0.770622705054824 df.mm.trans3:probe8 -0.0576462502758515 0.0835980500227581 -0.689564532428188 0.490637364681269 df.mm.trans3:probe9 0.0104662480859504 0.0835980500227581 0.125197275332393 0.900394002077614 df.mm.trans3:probe10 0.132851069837555 0.0835980500227581 1.58916469704005 0.112357696368787 df.mm.trans3:probe11 0.0560518669100675 0.0835980500227581 0.670492516210706 0.50270769796295 df.mm.trans3:probe12 -0.0196307040996422 0.0835980500227581 -0.234822511940148 0.814397364606715 df.mm.trans3:probe13 -0.014247963885066 0.0835980500227581 -0.170434165404423 0.864705186695143 df.mm.trans3:probe14 0.0320117175609276 0.0835980500227581 0.38292421356973 0.701862072530998 df.mm.trans3:probe15 -0.109198016354574 0.0835980500227581 -1.30622683573178 0.191793269088313 df.mm.trans3:probe16 -0.0377572587060459 0.0835980500227581 -0.451652385381801 0.651623061176608