chr17.10620_chr17_12286992_12288875_-_0.R fitVsDatCorrelation=0.855680630499864 cont.fitVsDatCorrelation=0.270941098722746 fstatistic=13451.9009859775,47,577 cont.fstatistic=3879.24681948307,47,577 residuals=-0.476196163291067,-0.0791011684518186,-0.00360109403391166,0.0687195131954257,0.740559384253857 cont.residuals=-0.429905831648266,-0.160007517063454,-0.0486257712301095,0.0978941609250397,0.882021122114198 predictedValues: Include Exclude Both chr17.10620_chr17_12286992_12288875_-_0.R.tl.Lung 45.6882533523825 53.8302516952393 52.8702072443812 chr17.10620_chr17_12286992_12288875_-_0.R.tl.cerebhem 46.1325903726008 55.6036504107231 54.6247930418242 chr17.10620_chr17_12286992_12288875_-_0.R.tl.cortex 45.8296836259461 52.2057637243445 60.0609862700114 chr17.10620_chr17_12286992_12288875_-_0.R.tl.heart 44.9310597382807 48.0761883665923 55.9597744248246 chr17.10620_chr17_12286992_12288875_-_0.R.tl.kidney 44.5421420506449 57.2736528954625 55.784882343095 chr17.10620_chr17_12286992_12288875_-_0.R.tl.liver 51.979998550887 57.1681377945451 56.5222271053911 chr17.10620_chr17_12286992_12288875_-_0.R.tl.stomach 47.459466305016 50.6173167097219 54.9468400445576 chr17.10620_chr17_12286992_12288875_-_0.R.tl.testicle 45.1767425087713 51.6462821154535 52.8103309988934 diffExp=-8.14199834285684,-9.47106003812227,-6.3760800983984,-3.14512862831162,-12.7315108448176,-5.18813924365813,-3.15785040470593,-6.4695396066822 diffExpScore=0.982040651519324 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=0,-1,0,0,-1,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 47.162128597373 54.8795083552834 51.7672893152149 cerebhem 55.8500585898713 55.298355435417 49.6726345409902 cortex 51.4755749360017 49.6139984893646 55.5527344983455 heart 56.712664040195 52.9266681441156 52.0667248742835 kidney 53.1572594937745 57.3851091442039 54.1333899422106 liver 51.5685683104259 56.218220488537 54.0914989645111 stomach 53.114880584402 54.482631412701 52.0623980681061 testicle 60.8963786645182 55.2179466438858 53.3879887253165 cont.diffExp=-7.71737975791039,0.551703154454344,1.86157644663704,3.78599589607944,-4.22784965042937,-4.64965217811118,-1.36775082829896,5.67843202063244 cont.diffExpScore=4.21180751618318 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=0.377178614395931 cont.tran.correlation=0.0782637821517634 tran.covariance=0.00112509552276364 cont.tran.covariance=0.000271673642651935 tran.mean=49.8850737635382 cont.tran.mean=54.1224969581294 weightedLogRatios: wLogRatio Lung -0.640207567472677 cerebhem -0.73289051302409 cortex -0.506723073465096 heart -0.259735083128068 kidney -0.986044984274567 liver -0.380401271013427 stomach -0.250719309314091 testicle -0.518948365804453 cont.weightedLogRatios: wLogRatio Lung -0.595490701211954 cerebhem 0.0398854050879673 cortex 0.144489942898531 heart 0.276599337253456 kidney -0.307002275899904 liver -0.344112282223136 stomach -0.101322278963270 testicle 0.397438493727059 varWeightedLogRatios=0.0621732591289647 cont.varWeightedLogRatios=0.114931248561822 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.99980217603089 0.061675959536966 64.8518840413592 3.63703664728303e-267 *** df.mm.trans1 -0.181914965888299 0.0488982405952024 -3.72027630593621 0.000218460423683327 *** df.mm.trans2 -0.0334196671224927 0.0488982405952024 -0.683453365922774 0.494594757552255 df.mm.exp2 0.0094437942174737 0.0649856286739721 0.145321271951565 0.884507969473888 df.mm.exp3 -0.155072428373649 0.0649856286739721 -2.38625726238083 0.0173422152888247 * df.mm.exp4 -0.186553644163097 0.0649856286739721 -2.87069076609264 0.00424601600070833 ** df.mm.exp5 -0.0170632901909986 0.0649856286739721 -0.262570210355336 0.792975583701945 df.mm.exp6 0.122384907272848 0.064985628673972 1.88326111126575 0.060167714840565 . df.mm.exp7 -0.0620332669779196 0.0649856286739721 -0.95456900615882 0.340195430983322 df.mm.exp8 -0.0515430603819546 0.0649856286739721 -0.79314552207446 0.428019059425465 df.mm.trans1:exp2 0.000234628909064107 0.0498417019711724 0.00470748188333962 0.996245613817036 df.mm.trans2:exp2 0.0229694514481800 0.0498417019711724 0.460848055739854 0.645081269632769 df.mm.trans1:exp3 0.158163197043089 0.0498417019711724 3.17331051685529 0.00158731417163075 ** df.mm.trans2:exp3 0.124429725037191 0.0498417019711724 2.49649831599169 0.0128204472524489 * df.mm.trans1:exp4 0.169841726777499 0.0498417019711724 3.40762293542329 0.000700976988466961 *** df.mm.trans2:exp4 0.073505046041496 0.0498417019711724 1.47476998446020 0.140819801883461 df.mm.trans1:exp5 -0.00834218301516471 0.0498417019711724 -0.167373558390717 0.86713477130309 df.mm.trans2:exp5 0.079068390009597 0.0498417019711724 1.58639024917987 0.113198650049521 df.mm.trans1:exp6 0.00663286744313023 0.0498417019711724 0.133078670687582 0.89417756103062 df.mm.trans2:exp6 -0.0622238039555282 0.0498417019711723 -1.24842855469738 0.212380476460213 df.mm.trans1:exp7 0.100068046149749 0.0498417019711724 2.00771727674201 0.0451390573302349 * df.mm.trans2:exp7 0.000491403884922083 0.0498417019711724 0.00985929182768084 0.992136958535364 df.mm.trans1:exp8 0.0402842419105763 0.0498417019711724 0.808243705920718 0.419283401841261 df.mm.trans2:exp8 0.0101256627761099 0.0498417019711724 0.203156440804658 0.839084378948556 df.mm.trans1:probe2 -0.021682185402232 0.0361137440467776 -0.600385974219328 0.548484765554082 df.mm.trans1:probe3 0.0215244823245032 0.0361137440467776 0.596019130462432 0.551396176064326 df.mm.trans1:probe4 -0.0196221386351373 0.0361137440467776 -0.543342684428428 0.58710372328725 df.mm.trans1:probe5 -0.000577741530707511 0.0361137440467776 -0.0159978298001772 0.987241653061223 df.mm.trans1:probe6 0.099437912773536 0.0361137440467776 2.75346451602292 0.00608258501102623 ** df.mm.trans2:probe2 0.0447948850062241 0.0361137440467776 1.24038329972661 0.215337796061996 df.mm.trans2:probe3 0.099863142908793 0.0361137440467776 2.76523926124751 0.0058701027513709 ** df.mm.trans2:probe4 0.0152440110267002 0.0361137440467776 0.422111066826937 0.67310118281084 df.mm.trans2:probe5 0.050637576206018 0.0361137440467776 1.40216910604528 0.161402501668805 df.mm.trans2:probe6 0.178522372780733 0.0361137440467776 4.94333604816757 1.00808950960374e-06 *** df.mm.trans3:probe2 -0.034543076103058 0.0361137440467776 -0.956507751129734 0.339216349443948 df.mm.trans3:probe3 0.313465669355987 0.0361137440467776 8.6799548933492 4.05020897082501e-17 *** df.mm.trans3:probe4 -0.0179501585784242 0.0361137440467776 -0.497045073896898 0.61934659733323 df.mm.trans3:probe5 0.00786166111876096 0.0361137440467776 0.217691666324540 0.827746317487032 df.mm.trans3:probe6 -0.0425242282528384 0.0361137440467776 -1.17750815860459 0.239478201131355 df.mm.trans3:probe7 -0.00940456766328142 0.0361137440467776 -0.260415194035263 0.794636429595953 df.mm.trans3:probe8 0.685629903787048 0.0361137440467776 18.985290002027 8.3346786932833e-63 *** df.mm.trans3:probe9 0.0236326769125584 0.0361137440467776 0.654395647317746 0.51311768421752 df.mm.trans3:probe10 0.0856883870283627 0.0361137440467776 2.37273617815344 0.0179836651339140 * df.mm.trans3:probe11 0.540644543639693 0.0361137440467776 14.9706035170268 4.76975578348091e-43 *** df.mm.trans3:probe12 0.302595465437396 0.0361137440467776 8.37895580822217 4.08142340518308e-16 *** df.mm.trans3:probe13 0.535871781639604 0.0361137440467776 14.8384443591752 1.99022156885330e-42 *** df.mm.trans3:probe14 0.176246201158571 0.0361137440467776 4.8803081987368 1.37250804132290e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.86433022344081 0.114721814228329 33.6843541869876 2.34482918693994e-138 *** df.mm.trans1 -0.0134530863771446 0.0909543185994975 -0.147910364062900 0.882465180843093 df.mm.trans2 0.143809330212021 0.0909543185994974 1.58111601984797 0.114399490765716 df.mm.exp2 0.217986809350975 0.120878041885642 1.80336152001208 0.0718529832039628 . df.mm.exp3 -0.0839250629106857 0.120878041885642 -0.694295354238809 0.487776446909888 df.mm.exp4 0.142406040592995 0.120878041885642 1.17809685176502 0.239243643128847 df.mm.exp5 0.119615577042348 0.120878041885642 0.989555879433512 0.322806023340322 df.mm.exp6 0.0695034542940029 0.120878041885642 0.574988254357703 0.565523369589281 df.mm.exp7 0.105923359391834 0.120878041885642 0.876282885952471 0.381240925963672 df.mm.exp8 0.2309031739427 0.120878041885642 1.91021603544131 0.0566008475180766 . df.mm.trans1:exp2 -0.0489074450817492 0.0927092260467138 -0.527535900872541 0.598024277775419 df.mm.trans2:exp2 -0.210383665063364 0.0927092260467137 -2.26928509744388 0.0236192936203522 * df.mm.trans1:exp3 0.171441274686865 0.0927092260467137 1.84923639207689 0.064934944404232 . df.mm.trans2:exp3 -0.0169419404057403 0.0927092260467137 -0.182742766045784 0.855064047582693 df.mm.trans1:exp4 0.0420002865772511 0.0927092260467137 0.453032436664806 0.650695689849005 df.mm.trans2:exp4 -0.178638729885865 0.0927092260467137 -1.92687111632076 0.0544864231426984 . df.mm.trans1:exp5 4.78931348890373e-05 0.0927092260467137 0.000516595132235332 0.999587995289073 df.mm.trans2:exp5 -0.0749707547842772 0.0927092260467137 -0.80866552317567 0.419040861753175 df.mm.trans1:exp6 0.0198176810176669 0.0927092260467137 0.213761691934321 0.830808412355596 df.mm.trans2:exp6 -0.0454025667940697 0.0927092260467137 -0.489730836186601 0.624510514516405 df.mm.trans1:exp7 0.0129425562882841 0.0927092260467137 0.139603757254566 0.889021786128466 df.mm.trans2:exp7 -0.113181423026940 0.0927092260467137 -1.22082157141417 0.222652223939335 df.mm.trans1:exp8 0.0246793250651965 0.0927092260467137 0.266201392434893 0.790179206508012 df.mm.trans2:exp8 -0.22475517797632 0.0927092260467137 -2.42430217099506 0.0156440315202050 * df.mm.trans1:probe2 0.104814927158293 0.0671742161245284 1.56034462633678 0.119226746123412 df.mm.trans1:probe3 -0.0211280064657304 0.0671742161245284 -0.314525537991587 0.753235619420607 df.mm.trans1:probe4 -0.0584578857211312 0.0671742161245284 -0.870242916013509 0.384529556946624 df.mm.trans1:probe5 -0.00588119015122186 0.0671742161245284 -0.0875513030225651 0.93026368356559 df.mm.trans1:probe6 0.0349336199686260 0.0671742161245284 0.520045070624504 0.603231622074399 df.mm.trans2:probe2 0.0227560429602335 0.0671742161245284 0.338761570630733 0.734912594832264 df.mm.trans2:probe3 0.0196729163501978 0.0671742161245284 0.292864100025044 0.769731319255013 df.mm.trans2:probe4 -0.0979203697090363 0.0671742161245284 -1.45770766461212 0.145465175434913 df.mm.trans2:probe5 -0.0241122526229695 0.0671742161245284 -0.358951008498111 0.719763047615885 df.mm.trans2:probe6 0.0196130866997954 0.0671742161245284 0.291973436108824 0.770411880084234 df.mm.trans3:probe2 -0.0137992073230876 0.0671742161245284 -0.205424165985152 0.837313192381121 df.mm.trans3:probe3 -0.0567884737868184 0.0671742161245284 -0.845390941094769 0.398243020482644 df.mm.trans3:probe4 -0.102457734105992 0.0671742161245284 -1.52525388485450 0.127743696434008 df.mm.trans3:probe5 -0.0126576271058516 0.0671742161245284 -0.188429844605656 0.850605958214155 df.mm.trans3:probe6 -0.0649582842014731 0.0671742161245284 -0.967012165516791 0.333943088589931 df.mm.trans3:probe7 -0.122024800883857 0.0671742161245284 -1.81654223783789 0.0698059625738387 . df.mm.trans3:probe8 -0.00630131316344477 0.0671742161245284 -0.0938055332385765 0.925296199744161 df.mm.trans3:probe9 -0.0753041514181233 0.0671742161245284 -1.12102761688389 0.262742450099079 df.mm.trans3:probe10 -0.0270163148404338 0.0671742161245284 -0.402182807616998 0.687698433326108 df.mm.trans3:probe11 -0.0289042524662786 0.0671742161245284 -0.430287901130034 0.667146892175726 df.mm.trans3:probe12 -0.0741041509083083 0.0671742161245284 -1.10316361222486 0.270415999164612 df.mm.trans3:probe13 -0.0533566339015641 0.0671742161245284 -0.794302293050223 0.427346029021686 df.mm.trans3:probe14 -0.0333190262040768 0.0671742161245284 -0.496009155392443 0.620076829296144