chr8.22802_chr8_105116608_105117087_+_1.R fitVsDatCorrelation=0.959219427339123 cont.fitVsDatCorrelation=0.282911887219008 fstatistic=7244.93404702528,44,508 cont.fstatistic=618.675760839908,44,508 residuals=-0.753995775085629,-0.101317192769111,-0.00156134275932258,0.101305529325782,0.760993167242676 cont.residuals=-1.28437315085637,-0.419491039692706,-0.166728870307781,0.282030866517371,2.21080956395024 predictedValues: Include Exclude Both chr8.22802_chr8_105116608_105117087_+_1.R.tl.Lung 73.0137544065046 390.128944994278 62.894956802665 chr8.22802_chr8_105116608_105117087_+_1.R.tl.cerebhem 60.373972098226 183.554582157259 65.4602361876146 chr8.22802_chr8_105116608_105117087_+_1.R.tl.cortex 54.5282000536917 190.533096841293 60.7802282619955 chr8.22802_chr8_105116608_105117087_+_1.R.tl.heart 65.4038894147707 307.682539447030 59.6858251904465 chr8.22802_chr8_105116608_105117087_+_1.R.tl.kidney 86.0009029995075 399.755765283589 62.9482037158715 chr8.22802_chr8_105116608_105117087_+_1.R.tl.liver 72.4643032390336 346.657127420122 65.7768206214172 chr8.22802_chr8_105116608_105117087_+_1.R.tl.stomach 57.1818061559331 272.145474926358 61.9622889480424 chr8.22802_chr8_105116608_105117087_+_1.R.tl.testicle 66.1931882088363 308.413238046806 64.8174191563832 diffExp=-317.115190587774,-123.180610059033,-136.004896787601,-242.278650032259,-313.754862284082,-274.192824181088,-214.963668770425,-242.220049837970 diffExpScore=0.999463723798108 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 85.9747897001093 83.5071794926773 82.0535917530044 cerebhem 74.4101995869619 94.100816976873 88.5234133714953 cortex 95.7787054649788 97.7094425156066 104.854967998600 heart 101.763259432676 146.308208540441 87.2088896734379 kidney 85.351212770734 99.8648842890178 86.6212545843118 liver 80.9496920347289 92.1297939444028 89.3189611460285 stomach 77.6709376221971 84.4846143147928 93.9961804530298 testicle 98.4277686416086 75.011211405661 90.89808988243 cont.diffExp=2.46761020743200,-19.6906173899112,-1.93073705062781,-44.5449491077644,-14.5136715182838,-11.1801019096739,-6.81367669259573,23.4165572359476 cont.diffExpScore=1.68801490133889 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,-1,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,0,0,-1,0,0,0,1 cont.diffExp1.3Score=2 cont.diffExp1.2=0,-1,0,-1,0,0,0,1 cont.diffExp1.2Score=1.5 tran.correlation=0.873047914932938 cont.tran.correlation=0.442523840806109 tran.covariance=0.0373415815812454 cont.tran.covariance=0.00840242469585813 tran.mean=183.376924105827 cont.tran.mean=92.0901697958417 weightedLogRatios: wLogRatio Lung -8.59459631815706 cerebhem -5.17785274876065 cortex -5.7854632532139 heart -7.67248134961322 kidney -8.02451278147296 liver -7.92907065980395 stomach -7.52949395212157 testicle -7.63585217862494 cont.weightedLogRatios: wLogRatio Lung 0.129284706545178 cerebhem -1.03933846701197 cortex -0.0912475532679316 heart -1.74423660690264 kidney -0.710668187283727 liver -0.576800121256348 stomach -0.369528093108593 testicle 1.20994547294253 varWeightedLogRatios=1.3854063292667 cont.varWeightedLogRatios=0.758830743923412 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.87534977961967 0.0942599656022193 62.3313380403073 3.96725614669976e-240 *** df.mm.trans1 -1.60458826526251 0.0752343246392862 -21.3278749155491 1.48915571336015e-72 *** df.mm.trans2 0.223540768389042 0.0752343246392862 2.97126038494818 0.00310630146510208 ** df.mm.exp2 -0.984031887057288 0.100512059781875 -9.790187259049 7.54869800323426e-21 *** df.mm.exp3 -0.974379796947422 0.100512059781875 -9.6941580847309 1.67390241012528e-20 *** df.mm.exp4 -0.295103465125157 0.100512059781875 -2.93600057312099 0.00347543391231396 ** df.mm.exp5 0.187240174913285 0.100512059781875 1.8628627780549 0.0630585222248018 . df.mm.exp6 -0.170496418679041 0.100512059781875 -1.69627822819512 0.0904458373455799 . df.mm.exp7 -0.589612596135713 0.100512059781875 -5.86608808351208 8.04981827062352e-09 *** df.mm.exp8 -0.363215425304438 0.100512059781875 -3.61365020369361 0.000331971326209331 *** df.mm.trans1:exp2 0.793942133669225 0.0783183660149666 10.1373684624307 4.06276192433603e-22 *** df.mm.trans2:exp2 0.230066648101880 0.0783183660149666 2.93758232976815 0.00345805222541278 ** df.mm.trans1:exp3 0.682449957044896 0.0783183660149666 8.71379207419215 4.16826886488962e-17 *** df.mm.trans2:exp3 0.257728400454713 0.0783183660149666 3.29077856917317 0.00106852741799749 ** df.mm.trans1:exp4 0.185037353066148 0.0783183660149666 2.36263040818277 0.0185216949739025 * df.mm.trans2:exp4 0.0576946877958602 0.0783183660149666 0.736668686178089 0.461663913040122 df.mm.trans1:exp5 -0.0235302186483147 0.0783183660149666 -0.300443176301943 0.763962046326044 df.mm.trans2:exp5 -0.162863713749052 0.0783183660149666 -2.07950857552275 0.0380715877083862 * df.mm.trans1:exp6 0.162942650241731 0.0783183660149666 2.08051646800947 0.037978733849516 * df.mm.trans2:exp6 0.0523552920417142 0.0783183660149666 0.668493160744813 0.504122480173115 df.mm.trans1:exp7 0.345200529800478 0.0783183660149667 4.40765745463217 1.27573353959227e-05 *** df.mm.trans2:exp7 0.229472041078217 0.0783183660149666 2.92999015115260 0.00354221122476012 ** df.mm.trans1:exp8 0.265145145877952 0.0783183660149666 3.38547852016323 0.000765549345258345 *** df.mm.trans2:exp8 0.128178678328463 0.0783183660149666 1.63663626873890 0.102325812792143 df.mm.trans1:probe2 0.0656192533522983 0.0545589660803406 1.20272171682416 0.229644289614253 df.mm.trans1:probe3 0.217437917697736 0.0545589660803406 3.98537460144588 7.72424034634806e-05 *** df.mm.trans1:probe4 -0.0204855965172920 0.0545589660803406 -0.375476259706353 0.707463026111137 df.mm.trans1:probe5 0.154140370190233 0.0545589660803406 2.82520695064591 0.00491091233775123 ** df.mm.trans1:probe6 -0.078644410023977 0.0545589660803406 -1.441457118307 0.150071719533010 df.mm.trans2:probe2 -0.311168297782485 0.0545589660803406 -5.70333934342296 1.99621723674205e-08 *** df.mm.trans2:probe3 -0.659343169333926 0.0545589660803406 -12.0849645200940 9.9141229694406e-30 *** df.mm.trans2:probe4 -0.341510490958340 0.0545589660803406 -6.25947512376702 8.2225488347716e-10 *** df.mm.trans2:probe5 -0.554108510312657 0.0545589660803406 -10.1561402299433 3.46247729471074e-22 *** df.mm.trans2:probe6 -0.384894532392878 0.0545589660803406 -7.0546522422383 5.69371468545198e-12 *** df.mm.trans3:probe2 -0.718615217284896 0.0545589660803406 -13.1713496224745 2.72273022534012e-34 *** df.mm.trans3:probe3 -0.221575694655325 0.0545589660803406 -4.06121505911685 5.65311907127044e-05 *** df.mm.trans3:probe4 -0.656090304007496 0.0545589660803406 -12.0253434246055 1.74023207157674e-29 *** df.mm.trans3:probe5 -0.0364332811649334 0.0545589660803406 -0.667778071734052 0.504578519826379 df.mm.trans3:probe6 -0.431968710974004 0.0545589660803406 -7.91746512090993 1.53532445291849e-14 *** df.mm.trans3:probe7 -0.280977089774950 0.0545589660803406 -5.14997093898735 3.73289167055532e-07 *** df.mm.trans3:probe8 -0.568243989592463 0.0545589660803406 -10.4152265047636 3.73843301346381e-23 *** df.mm.trans3:probe9 0.0417151905601943 0.0545589660803406 0.764589096112393 0.444871278545083 df.mm.trans3:probe10 -0.276283012721221 0.0545589660803406 -5.06393417196326 5.75484475784723e-07 *** df.mm.trans3:probe11 -0.732727193379944 0.0545589660803406 -13.4300051122847 2.09251261317866e-35 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.37997954855882 0.319848084285758 13.6939371024829 1.49026362173979e-36 *** df.mm.trans1 0.0923628667877976 0.255289236047015 0.3617969492877 0.717654211409163 df.mm.trans2 0.0391889047098847 0.255289236047015 0.153507861579669 0.878058749935535 df.mm.exp2 -0.100921451282106 0.341063033106937 -0.295902638180267 0.767425354046655 df.mm.exp3 0.0198463147497843 0.341063033106937 0.0581895802925136 0.953620502012978 df.mm.exp4 0.668444143580609 0.341063033106937 1.95988447499388 0.0505555876108319 . df.mm.exp5 0.117433425696534 0.341063033106937 0.344315901453072 0.730751211896837 df.mm.exp6 -0.046801662548218 0.341063033106937 -0.137222911911253 0.890908969627488 df.mm.exp7 -0.225817683680600 0.341063033106937 -0.66209955861677 0.508207655605872 df.mm.exp8 -0.0743925625193572 0.341063033106937 -0.218119688439035 0.827423447627966 df.mm.trans1:exp2 -0.0435396353090928 0.26575417436486 -0.163834247996858 0.869926790796438 df.mm.trans2:exp2 0.2203555697341 0.26575417436486 0.829170680990202 0.40739726425862 df.mm.trans1:exp3 0.0881399541033194 0.26575417436486 0.331659716405094 0.740283004050423 df.mm.trans2:exp3 0.137219277581506 0.26575417436486 0.516339123964668 0.605842256031805 df.mm.trans1:exp4 -0.499849124170066 0.26575417436486 -1.88087026427593 0.0605613149892444 . df.mm.trans2:exp4 -0.107661339647146 0.26575417436486 -0.40511626921553 0.685562590793196 df.mm.trans1:exp5 -0.124712877431976 0.26575417436486 -0.469279091212899 0.63907150175434 df.mm.trans2:exp5 0.0614520794370937 0.26575417436486 0.231236553796234 0.817224138552882 df.mm.trans1:exp6 -0.0134245719808543 0.26575417436486 -0.0505149994837837 0.959731852362478 df.mm.trans2:exp6 0.145067438983378 0.26575417436486 0.545870782011542 0.585394387550452 df.mm.trans1:exp7 0.124244727653281 0.26575417436486 0.467517501654377 0.64033015498506 df.mm.trans2:exp7 0.237454512241631 0.26575417436486 0.893511881080083 0.372006204582769 df.mm.trans1:exp8 0.209661418151827 0.26575417436486 0.78892991484671 0.430520999875137 df.mm.trans2:exp8 -0.0329024598161542 0.26575417436486 -0.123807875811507 0.901516366282315 df.mm.trans1:probe2 -0.108466439236012 0.185132475594684 -0.585885533521847 0.55821263960822 df.mm.trans1:probe3 0.0244257395268457 0.185132475594684 0.131936546780273 0.895086719312497 df.mm.trans1:probe4 -0.0268077011816653 0.185132475594684 -0.144802801861496 0.884924000607228 df.mm.trans1:probe5 -0.136262033529828 0.185132475594684 -0.7360244770245 0.462055515819778 df.mm.trans1:probe6 -0.0637907521077935 0.185132475594684 -0.344568136427088 0.730561662885852 df.mm.trans2:probe2 0.0151886578220699 0.185132475594684 0.082042104029997 0.934645546604755 df.mm.trans2:probe3 0.304455776405061 0.185132475594684 1.64452927789728 0.100685555956523 df.mm.trans2:probe4 -0.128240543440046 0.185132475594684 -0.692696097905628 0.488816819250702 df.mm.trans2:probe5 -0.055520836930953 0.185132475594684 -0.299897879897131 0.764377724011559 df.mm.trans2:probe6 -0.0378923875744190 0.185132475594684 -0.204677150525324 0.837906352477954 df.mm.trans3:probe2 -0.23910945514207 0.185132475594684 -1.29155867642347 0.197097352755685 df.mm.trans3:probe3 0.0194743301766004 0.185132475594684 0.105191323748277 0.916265515877972 df.mm.trans3:probe4 0.282553911009903 0.185132475594684 1.52622553175655 0.127576041093563 df.mm.trans3:probe5 -0.145669755216837 0.185132475594684 -0.786840638029147 0.431742074981077 df.mm.trans3:probe6 -0.231419052475347 0.185132475594684 -1.25001867841923 0.211868260951648 df.mm.trans3:probe7 -0.137834042158418 0.185132475594684 -0.744515740502399 0.456908785291211 df.mm.trans3:probe8 -0.127855111627945 0.185132475594684 -0.690614173538423 0.490123457451351 df.mm.trans3:probe9 -0.125769354503238 0.185132475594684 -0.679347878319247 0.497226878048809 df.mm.trans3:probe10 -0.162011709884377 0.185132475594684 -0.875112318160072 0.381926354185177 df.mm.trans3:probe11 -0.278105771530841 0.185132475594684 -1.50219873978084 0.133667122927169