chr19.12273_chr19_41803773_41804316_-_0.R fitVsDatCorrelation=0.798638794517049 cont.fitVsDatCorrelation=0.28561145183278 fstatistic=7122.49828232182,40,416 cont.fstatistic=2802.41761659353,40,416 residuals=-0.473915309071036,-0.0934366585261187,-0.00280389995035455,0.0863111211428524,0.975720935219629 cont.residuals=-0.629320730372651,-0.177063663913981,-0.031988444663345,0.139664629317451,1.41664665519009 predictedValues: Include Exclude Both chr19.12273_chr19_41803773_41804316_-_0.R.tl.Lung 86.6324375785294 64.4472211918901 65.3747815522849 chr19.12273_chr19_41803773_41804316_-_0.R.tl.cerebhem 85.8894553002784 61.8202763695652 72.1676139445207 chr19.12273_chr19_41803773_41804316_-_0.R.tl.cortex 62.8340294102222 68.4645393217947 71.2506359573819 chr19.12273_chr19_41803773_41804316_-_0.R.tl.heart 68.8082501345039 65.885697810277 62.9927836594104 chr19.12273_chr19_41803773_41804316_-_0.R.tl.kidney 91.6532392201801 60.3283123955145 60.053302145199 chr19.12273_chr19_41803773_41804316_-_0.R.tl.liver 89.4539820240021 60.2533941919803 60.6182782322521 chr19.12273_chr19_41803773_41804316_-_0.R.tl.stomach 65.2133826457385 66.5205477704917 70.2137401140527 chr19.12273_chr19_41803773_41804316_-_0.R.tl.testicle 83.4393251377967 74.7042970773662 67.8654599329037 diffExp=22.1852163866393,24.0691789307132,-5.6305099115725,2.92255232422688,31.3249268246656,29.2005878320218,-1.30716512475325,8.73502806043041 diffExpScore=1.11444774407635 diffExp1.5=0,0,0,0,1,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,1,1,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=1,1,0,0,1,1,0,0 diffExp1.3Score=0.8 diffExp1.2=1,1,0,0,1,1,0,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 67.1571891845089 73.7052117309404 68.2796939822309 cerebhem 63.6160648170568 72.6512933949675 68.7659842398379 cortex 61.080187493965 63.7255710855178 65.1378987738003 heart 69.1483264401515 62.4320054942979 59.9640146034178 kidney 70.0407046099442 73.0112920865676 67.9833247152851 liver 61.9518481138684 65.5907496051027 61.49043712923 stomach 65.3081982671934 76.5227472818035 60.7154126080056 testicle 63.5486909156752 66.0460601588786 61.5812463162317 cont.diffExp=-6.5480225464316,-9.03522857791062,-2.64538359155276,6.7163209458537,-2.97058747662339,-3.63890149123431,-11.2145490146101,-2.49736924320339 cont.diffExpScore=1.37865467314323 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=-0.452908419120551 cont.tran.correlation=0.288123169234596 tran.covariance=-0.00513460460391779 cont.tran.covariance=0.00113211877244442 tran.mean=72.2717742237582 cont.tran.mean=67.2210087925275 weightedLogRatios: wLogRatio Lung 1.27612983863172 cerebhem 1.41023407818520 cortex -0.359016339961050 heart 0.182707144696095 kidney 1.80203140014037 liver 1.69768660514172 stomach -0.0831076454174239 testicle 0.483114282459659 cont.weightedLogRatios: wLogRatio Lung -0.395740241558483 cerebhem -0.560340655855378 cortex -0.175248826695148 heart 0.427622694129161 kidney -0.177358905087516 liver -0.237149974125124 stomach -0.674823173936703 testicle -0.160778349687086 varWeightedLogRatios=0.716235211959876 cont.varWeightedLogRatios=0.110846039191416 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.63834404586606 0.0858769088727655 54.0115393852635 4.32435714072298e-190 *** df.mm.trans1 -0.0264255854420579 0.0696553709982851 -0.379376135154151 0.704602147109134 df.mm.trans2 -0.43854017248597 0.0696553709982851 -6.29585581414486 7.77316788199269e-10 *** df.mm.exp2 -0.149083256895051 0.0941912530622303 -1.58277177602207 0.114233416309346 df.mm.exp3 -0.346775356556987 0.0941912530622303 -3.68160891041421 0.000262291624280831 *** df.mm.exp4 -0.171159478097573 0.0941912530622303 -1.81714832888454 0.0699137621032201 . df.mm.exp5 0.0751969541329894 0.0941912530622303 0.798343282292977 0.425126819241822 df.mm.exp6 0.0403024179213494 0.0941912530622303 0.42787856208604 0.668960954200013 df.mm.exp7 -0.323752772992156 0.0941912530622303 -3.43718511503673 0.000647003981293363 *** df.mm.exp8 0.0727457775821515 0.0941912530622303 0.772319883398194 0.440363532142890 df.mm.trans1:exp2 0.140470009506984 0.0759394159822916 1.84976415330532 0.0650565784088327 . df.mm.trans2:exp2 0.107468051405313 0.0759394159822916 1.41518143134487 0.157763443739540 df.mm.trans1:exp3 0.0255978395455982 0.0759394159822916 0.337082386195430 0.736224793552422 df.mm.trans2:exp3 0.407244680914896 0.0759394159822916 5.36275760943252 1.36246286366997e-07 *** df.mm.trans1:exp4 -0.0591911827233865 0.0759394159822916 -0.779452698677449 0.436156288848523 df.mm.trans2:exp4 0.193234254639919 0.0759394159822916 2.54458441825493 0.0113012016876867 * df.mm.trans1:exp5 -0.0188589504151057 0.0759394159822916 -0.248342052294732 0.803992310737911 df.mm.trans2:exp5 -0.141242047698466 0.0759394159822916 -1.85993065487102 0.0636009421081263 . df.mm.trans1:exp6 -0.00825240551622902 0.0759394159822916 -0.108670911008236 0.913515911351068 df.mm.trans2:exp6 -0.107590124737118 0.0759394159822916 -1.41678894083420 0.157293061224153 df.mm.trans1:exp7 0.0397431628487576 0.0759394159822916 0.523353548808242 0.601006903303911 df.mm.trans2:exp7 0.355417049220265 0.0759394159822916 4.68027103741678 3.88216261346404e-06 *** df.mm.trans1:exp8 -0.110300368232906 0.0759394159822916 -1.45247848967692 0.147122426902766 df.mm.trans2:exp8 0.0749452246285147 0.0759394159822916 0.986908098503039 0.324261348078311 df.mm.trans1:probe2 -0.301855739437846 0.0482586566356358 -6.25495528640444 9.88292433013227e-10 *** df.mm.trans1:probe3 -0.235668040248520 0.0482586566356358 -4.88343556738161 1.48933906087814e-06 *** df.mm.trans1:probe4 -0.525505072589936 0.0482586566356358 -10.8893431609094 1.82266454534236e-24 *** df.mm.trans1:probe5 -0.216292571464746 0.0482586566356358 -4.48194348006423 9.57487712888667e-06 *** df.mm.trans1:probe6 -0.673852488653174 0.0482586566356358 -13.9633494927329 1.30762552438091e-36 *** df.mm.trans2:probe2 -0.124503475286437 0.0482586566356358 -2.57992003852217 0.0102243761890033 * df.mm.trans2:probe3 -0.156819921136820 0.0482586566356358 -3.24957079350235 0.00124967917943631 ** df.mm.trans2:probe4 -0.181822740637950 0.0482586566356358 -3.76767099032107 0.000188558341036252 *** df.mm.trans2:probe5 0.0207899624132987 0.0482586566356358 0.430802758772749 0.66683491778396 df.mm.trans2:probe6 0.000911786279535122 0.0482586566356358 0.0188937351990405 0.984934935628781 df.mm.trans3:probe2 -0.117368056316313 0.0482586566356358 -2.43206223501971 0.0154341270404009 * df.mm.trans3:probe3 -0.126610663044404 0.0482586566356358 -2.62358448972885 0.00902091368863524 ** df.mm.trans3:probe4 -0.111210827773803 0.0482586566356358 -2.30447417161797 0.0216878167198742 * df.mm.trans3:probe5 0.168275491789655 0.0482586566356358 3.48694935833326 0.000540594649598631 *** df.mm.trans3:probe6 0.457992770310671 0.0482586566356358 9.4903754526079 1.81294257663296e-19 *** df.mm.trans3:probe7 -0.223770664070626 0.0482586566356358 -4.63690205386667 4.74245550260135e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36903322351849 0.136753557360661 31.9482235624482 5.08045555410956e-114 *** df.mm.trans1 -0.150644666597314 0.110921782098669 -1.35811617652618 0.175162927325173 df.mm.trans2 -0.0366836159274843 0.110921782098669 -0.330716070670890 0.741025467020279 df.mm.exp2 -0.0756690595657806 0.149993625732415 -0.504481835119931 0.614189925546376 df.mm.exp3 -0.193230155191915 0.149993625732415 -1.28825577919313 0.198373004228463 df.mm.exp4 -0.00690979217749338 0.149993625732415 -0.0460672388160034 0.963278763784547 df.mm.exp5 0.0369311575731118 0.149993625732415 0.246218180224513 0.80563478435777 df.mm.exp6 -0.0925866298984776 0.149993625732415 -0.617270430302486 0.537394076907061 df.mm.exp7 0.127010967684408 0.149993625732415 0.846775768398267 0.39760697437505 df.mm.exp8 -0.0616956781876353 0.149993625732415 -0.411322000427532 0.681048257933373 df.mm.trans1:exp2 0.0214991106693548 0.120928727125655 0.177783320641550 0.858979682464981 df.mm.trans2:exp2 0.0612667403442482 0.120928727125655 0.50663512136853 0.61267927534367 df.mm.trans1:exp3 0.0983817263736214 0.120928727125655 0.813551326571017 0.416367427032499 df.mm.trans2:exp3 0.0477425549565949 0.120928727125655 0.394799119211652 0.693193482332164 df.mm.trans1:exp4 0.0361276693078047 0.120928727125655 0.29875175375215 0.765278643462737 df.mm.trans2:exp4 -0.159085667553238 0.120928727125655 -1.31553247383423 0.189055576636410 df.mm.trans1:exp5 0.00510943109184347 0.120928727125655 0.0422515907782138 0.966318407248905 df.mm.trans2:exp5 -0.0463905544459633 0.120928727125655 -0.383618975810103 0.701456852087588 df.mm.trans1:exp6 0.0119080910653897 0.120928727125655 0.098471978895603 0.921604951383752 df.mm.trans2:exp6 -0.0240522083879032 0.120928727125655 -0.198895737676217 0.842441474748958 df.mm.trans1:exp7 -0.154929370355787 0.120928727125655 -1.28116266530121 0.200850250406261 df.mm.trans2:exp7 -0.0894964331387023 0.120928727125655 -0.740075871680249 0.459671415821562 df.mm.trans1:exp8 0.00646609751985111 0.120928727125655 0.053470318207619 0.95738284717243 df.mm.trans2:exp8 -0.0480254541725957 0.120928727125655 -0.397138507235697 0.691468998867507 df.mm.trans1:probe2 -0.0347296740877159 0.0768488649043912 -0.451921757477141 0.651560885320408 df.mm.trans1:probe3 -0.0542005339303248 0.0768488649043912 -0.705287371488916 0.481026133646772 df.mm.trans1:probe4 -0.0315885103767157 0.0768488649043912 -0.41104719524505 0.681249588409812 df.mm.trans1:probe5 0.028870996709355 0.0768488649043912 0.375685402058623 0.707342285682294 df.mm.trans1:probe6 -0.0559357937624062 0.0768488649043912 -0.727867533658392 0.467104151265749 df.mm.trans2:probe2 -0.05915092289657 0.0768488649043912 -0.769704574949292 0.441911987467656 df.mm.trans2:probe3 -0.0643280195307707 0.0768488649043912 -0.837071824168153 0.403032807787605 df.mm.trans2:probe4 -0.164782684244461 0.0768488649043912 -2.14424356754612 0.0325922550160102 * df.mm.trans2:probe5 -0.0584919555019399 0.0768488649043912 -0.761129726180219 0.447010814333565 df.mm.trans2:probe6 -0.0728356582220775 0.0768488649043912 -0.94777793156338 0.343792746044225 df.mm.trans3:probe2 0.119158316605441 0.0768488649043912 1.55055402254396 0.121769002485667 df.mm.trans3:probe3 0.0594420171846701 0.0768488649043912 0.773492455075592 0.439670298503098 df.mm.trans3:probe4 0.0240964464438269 0.0768488649043912 0.313556309176533 0.754015206417884 df.mm.trans3:probe5 0.0760628149104916 0.0768488649043912 0.98977148205276 0.322861163966831 df.mm.trans3:probe6 0.0692455165531288 0.0768488649043912 0.90106101943442 0.368077229778503 df.mm.trans3:probe7 -0.0546527858877134 0.0768488649043912 -0.711172324480104 0.477376015516571