chr4.16851_chr4_56912579_56914779_-_1.R fitVsDatCorrelation=0.581669485337073 cont.fitVsDatCorrelation=0.291776026199797 fstatistic=9308.220088255,43,485 cont.fstatistic=6728.87789033582,43,485 residuals=-0.408983855958208,-0.0791559948607606,-0.00451115358045734,0.0669795201549921,1.4746772685638 cont.residuals=-0.417596602624992,-0.0895357487513295,-0.0166738780060127,0.0832286809523436,1.46660456885043 predictedValues: Include Exclude Both chr4.16851_chr4_56912579_56914779_-_1.R.tl.Lung 51.4955886115414 45.5489772891149 47.0322883908861 chr4.16851_chr4_56912579_56914779_-_1.R.tl.cerebhem 55.644281655481 48.7028712332393 52.64826237614 chr4.16851_chr4_56912579_56914779_-_1.R.tl.cortex 51.5576287232653 44.3441136538869 46.8436016434016 chr4.16851_chr4_56912579_56914779_-_1.R.tl.heart 53.6042339817078 44.7409775660266 48.4285965292649 chr4.16851_chr4_56912579_56914779_-_1.R.tl.kidney 55.648457238261 41.3905545037739 46.8739463766336 chr4.16851_chr4_56912579_56914779_-_1.R.tl.liver 54.2073308244892 47.3930768686669 49.1022134592164 chr4.16851_chr4_56912579_56914779_-_1.R.tl.stomach 53.2039308021425 44.6162411191805 49.2134003382063 chr4.16851_chr4_56912579_56914779_-_1.R.tl.testicle 56.4249166194463 48.0435121735122 49.2783189080456 diffExp=5.94661132242652,6.94141042224178,7.21351506937837,8.86325641568124,14.2579027344871,6.81425395582225,8.58768968296202,8.38140444593414 diffExpScore=0.985295424634898 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=0,0,0,0,1,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,0,0,1,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 45.559925874855 49.8099024517907 49.1912519628933 cerebhem 47.4024089454919 50.4364924227738 48.8156988343427 cortex 48.6420455802585 47.1696435680791 48.5461731602018 heart 46.8366456681636 49.9532389535954 49.0552906598571 kidney 50.7029294885561 47.3615959156918 49.4257103721049 liver 45.9379776867786 54.0499605484637 47.6482161251828 stomach 47.1390404920137 51.5464927939344 47.3342562138074 testicle 48.6970911308259 46.5305129961359 48.9565460763983 cont.diffExp=-4.24997657693577,-3.03408347728188,1.47240201217943,-3.1165932854318,3.34133357286431,-8.11198286168503,-4.40745230192072,2.16657813469008 cont.diffExpScore=1.76510033959102 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=0.281801392682065 cont.tran.correlation=-0.741138797979981 tran.covariance=0.000471733788959159 cont.tran.covariance=-0.00132273079253336 tran.mean=49.7854183039835 cont.tran.mean=48.610994032338 weightedLogRatios: wLogRatio Lung 0.476124387008262 cerebhem 0.526617527355285 cortex 0.58288631401312 heart 0.703299335001252 kidney 1.14583823962761 liver 0.527371281046259 stomach 0.684089699146791 testicle 0.635578229751546 cont.weightedLogRatios: wLogRatio Lung -0.344578200964105 cerebhem -0.241324084683173 cortex 0.118928030562327 heart -0.249882753620652 kidney 0.265318457852219 liver -0.635603474237451 stomach -0.348394977800249 testicle 0.175802821471153 varWeightedLogRatios=0.0448897113462552 cont.varWeightedLogRatios=0.0974088277988625 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.98396089929625 0.0708421956915764 56.237117729116 1.24430467815650e-214 *** df.mm.trans1 -0.0249585577717894 0.0567128823822592 -0.44008621539569 0.660070718756794 df.mm.trans2 -0.169908261630996 0.0567128823822592 -2.99593768635795 0.00287609733691255 ** df.mm.exp2 0.0316341040491378 0.0759424131784001 0.416553842907579 0.677189061891926 df.mm.exp3 -0.0215842307949603 0.0759424131784 -0.284218395118098 0.776364321079187 df.mm.exp4 -0.00702257777307479 0.0759424131784 -0.0924724074355881 0.926360896705663 df.mm.exp5 -0.0148049133218055 0.0759424131784 -0.194949208251081 0.845514348349551 df.mm.exp6 0.047938236299645 0.0759424131784 0.63124457458352 0.528177995805585 df.mm.exp7 -0.0333856907960912 0.0759424131784001 -0.439618513539506 0.66040924751068 df.mm.exp8 0.098083779244672 0.0759424131784001 1.29155468123272 0.197126573614387 df.mm.trans1:exp2 0.0458490669032339 0.0595741302624597 0.76961370147145 0.441903734867328 df.mm.trans2:exp2 0.0353157108981971 0.0595741302624597 0.592802794478246 0.553589434683619 df.mm.trans1:exp3 0.0227882712087075 0.0595741302624597 0.382519578688124 0.702243580354935 df.mm.trans2:exp3 -0.00522396505063615 0.0595741302624597 -0.0876884820243528 0.93016046740545 df.mm.trans1:exp4 0.0471544889536422 0.0595741302624597 0.791526267289148 0.429023880902129 df.mm.trans2:exp4 -0.0108757874347708 0.0595741302624597 -0.182558895729681 0.855220421647292 df.mm.trans1:exp5 0.0923631221036599 0.0595741302624597 1.55038976980016 0.121700253152948 df.mm.trans2:exp5 -0.0809305549249904 0.0595741302624597 -1.35848487537196 0.174941390926042 df.mm.trans1:exp6 0.0033817720916796 0.0595741302624597 0.0567657820060632 0.954755150109578 df.mm.trans2:exp6 -0.00825024685379012 0.0595741302624597 -0.138487071778352 0.889912966763592 df.mm.trans1:exp7 0.0660218256999677 0.0595741302624597 1.10822978714254 0.268311859473213 df.mm.trans2:exp7 0.0126954632249599 0.0595741302624597 0.213103626843208 0.831335680494916 df.mm.trans1:exp8 -0.00666908023826738 0.0595741302624597 -0.111945910227914 0.910912609168083 df.mm.trans2:exp8 -0.0447648463128707 0.0595741302624597 -0.751414181216827 0.452767883922377 df.mm.trans1:probe2 -0.0284775203762683 0.0407876187356254 -0.698190315077036 0.485392791709027 df.mm.trans1:probe3 -0.0379690091845474 0.0407876187356254 -0.930895461945266 0.352370766324417 df.mm.trans1:probe4 -0.0546592878026359 0.0407876187356254 -1.34009509495818 0.180841902370222 df.mm.trans1:probe5 -0.132166081406589 0.0407876187356254 -3.24034806403517 0.00127585960913747 ** df.mm.trans1:probe6 -0.0268272300612216 0.0407876187356254 -0.657729744781342 0.511023846162423 df.mm.trans2:probe2 0.0347597519677796 0.0407876187356254 0.852213319759684 0.394516324559503 df.mm.trans2:probe3 0.0123636675549341 0.0407876187356254 0.303123053960862 0.761926060975486 df.mm.trans2:probe4 0.0445939159324089 0.0407876187356254 1.09331991704284 0.274796094837388 df.mm.trans2:probe5 0.0108250141869333 0.0407876187356254 0.265399513933338 0.790814478607789 df.mm.trans2:probe6 -0.0267738168515961 0.0407876187356254 -0.656420200089074 0.511865109976943 df.mm.trans3:probe2 0.160941516106013 0.0407876187356254 3.94584241725885 9.12692740097903e-05 *** df.mm.trans3:probe3 -0.0563168117721328 0.0407876187356254 -1.38073301452491 0.167996999488821 df.mm.trans3:probe4 -0.0351834916983375 0.0407876187356254 -0.862602250118783 0.388782517088769 df.mm.trans3:probe5 -0.00574985776577753 0.0407876187356254 -0.140970665707322 0.887951646995133 df.mm.trans3:probe6 0.179651388433995 0.0407876187356254 4.40455692200244 1.30538636229361e-05 *** df.mm.trans3:probe7 0.0405159126342422 0.0407876187356254 0.993338515220898 0.321040333794082 df.mm.trans3:probe8 0.0386768998091718 0.0407876187356254 0.948250989101994 0.343473929339964 df.mm.trans3:probe9 0.0159749281150010 0.0407876187356254 0.391661210195827 0.695480579348052 df.mm.trans3:probe10 0.278892246029243 0.0407876187356254 6.83766924068181 2.42970537353697e-11 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.76507636460325 0.0833015907547969 45.198132838615 5.36029280727632e-176 *** df.mm.trans1 0.0344180556431117 0.0666872797012083 0.516111255359665 0.606011919591915 df.mm.trans2 0.150264278543145 0.0666872797012083 2.25326747794187 0.0246882258716662 * df.mm.exp2 0.0598095413570325 0.0892988107124833 0.669768621550876 0.503324037521232 df.mm.exp3 0.0241969460326575 0.0892988107124833 0.27096605027098 0.786532425790509 df.mm.exp4 0.0332787095230583 0.0892988107124832 0.372666883887247 0.709559186556937 df.mm.exp5 0.0517981174180735 0.0892988107124833 0.580053832797938 0.562147687162306 df.mm.exp6 0.121829279137700 0.0892988107124833 1.36428781263343 0.173109715147477 df.mm.exp7 0.106824931543411 0.0892988107124833 1.19626376534125 0.232178124168067 df.mm.exp8 0.00326798067934862 0.0892988107124833 0.0365960157058596 0.970822177818715 df.mm.trans1:exp2 -0.0201650039115853 0.070051750517474 -0.287858672518902 0.773577887053537 df.mm.trans2:exp2 -0.0473083809515292 0.070051750517474 -0.67533474327281 0.499784955321971 df.mm.trans1:exp3 0.0412628346570718 0.070051750517474 0.589033598050902 0.556112981647453 df.mm.trans2:exp3 -0.0786602137686799 0.070051750517474 -1.12288719678830 0.262040948432131 df.mm.trans1:exp4 -0.00564129750677977 0.070051750517474 -0.0805304287916772 0.935848615003969 df.mm.trans2:exp4 -0.030405171292093 0.070051750517474 -0.434038708062100 0.664453345425501 df.mm.trans1:exp5 0.0551570607501295 0.070051750517474 0.787375909134075 0.431446450967214 df.mm.trans2:exp5 -0.102200238543051 0.070051750517474 -1.45892483468429 0.145233112853679 df.mm.trans1:exp6 -0.113565615194016 0.070051750517474 -1.6211674134494 0.105631642715655 df.mm.trans2:exp6 -0.0401342736380609 0.070051750517474 -0.572923208079571 0.566962203154665 df.mm.trans1:exp7 -0.07275190027495 0.070051750517474 -1.03854507185802 0.29953383920799 df.mm.trans2:exp7 -0.072554567107296 0.070051750517474 -1.03572810916692 0.300845132813242 df.mm.trans1:exp8 0.0633228056530241 0.070051750517474 0.90394323032982 0.366474270135583 df.mm.trans2:exp8 -0.0713734984986914 0.070051750517474 -1.01886816491313 0.308773525039853 df.mm.trans1:probe2 0.053421093300338 0.0479611549389308 1.11384084408224 0.265899160542483 df.mm.trans1:probe3 0.101689160833022 0.0479611549389308 2.12024003513892 0.0344928783055076 * df.mm.trans1:probe4 0.0689410469571604 0.0479611549389308 1.43743508772763 0.151239244627984 df.mm.trans1:probe5 0.0399927355551885 0.0479611549389308 0.833856807787707 0.404772166125481 df.mm.trans1:probe6 0.0485014249973589 0.0479611549389308 1.0112647424591 0.312393970766681 df.mm.trans2:probe2 -0.0434654353635133 0.0479611549389308 -0.906263317029333 0.365246507596938 df.mm.trans2:probe3 -0.0874925388184162 0.0479611549389308 -1.82423752993065 0.0687313089363495 . df.mm.trans2:probe4 -0.0279838383438430 0.0479611549389308 -0.58346881720165 0.559848966133036 df.mm.trans2:probe5 -0.0165416305804859 0.0479611549389308 -0.344896418811192 0.73032175819319 df.mm.trans2:probe6 0.0614540915981936 0.0479611549389308 1.28133052001027 0.200689876089351 df.mm.trans3:probe2 -0.0490103371398572 0.0479611549389308 -1.02187566588549 0.307349184351851 df.mm.trans3:probe3 -0.109702372590385 0.0479611549389308 -2.28731715760534 0.0226070978373201 * df.mm.trans3:probe4 -0.0681157158859828 0.0479611549389308 -1.42022676419521 0.156184173014729 df.mm.trans3:probe5 -0.0555680970172216 0.0479611549389309 -1.15860631563141 0.247186854595726 df.mm.trans3:probe6 -0.0526412280343756 0.0479611549389308 -1.09758049199199 0.272932332248591 df.mm.trans3:probe7 -0.091105197683306 0.0479611549389308 -1.89956221444856 0.0580835559955186 . df.mm.trans3:probe8 -0.0844283084498947 0.0479611549389309 -1.76034769299025 0.0789791414836438 . df.mm.trans3:probe9 -0.0177073595649418 0.0479611549389308 -0.369202109237959 0.712138213244761 df.mm.trans3:probe10 -0.0121503509619796 0.0479611549389308 -0.253337330542826 0.800114900234653