fitVsDatCorrelation=0.871215620150477 cont.fitVsDatCorrelation=0.225310627851270 fstatistic=8827.29341556894,69,1083 cont.fstatistic=2229.28351303359,69,1083 residuals=-0.743223413723377,-0.103898519741306,-0.0106428027089204,0.0922059697973101,1.01929173471867 cont.residuals=-0.641761626771471,-0.241631604897968,-0.080357463615953,0.155755422962721,1.87201416540112 predictedValues: Include Exclude Both Lung 61.6970850119727 47.2796753535799 58.8791481242578 cerebhem 64.219629494332 47.2116665570103 68.5466074257406 cortex 60.7484565711883 50.4096914377531 57.649630442177 heart 66.8904981111446 51.3238536339551 60.941625261591 kidney 112.355551895164 53.9675407720275 103.521267983288 liver 64.3969782187475 49.1663106422412 58.3254867881928 stomach 62.1522411505274 50.6904553808733 61.3815935889911 testicle 62.2024993437403 46.7576519331489 57.6354688712592 diffExp=14.4174096583928,17.0079629373217,10.3387651334353,15.5666444771895,58.3880111231364,15.2306675765064,11.4617857696541,15.4448474105914 diffExpScore=0.993704994411752 diffExp1.5=0,0,0,0,1,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,1,0,0,0 diffExp1.4Score=0.5 diffExp1.3=1,1,0,1,1,1,0,1 diffExp1.3Score=0.857142857142857 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 64.8991681513969 57.9912661801018 68.9496889877346 cerebhem 62.5734327826593 65.8983889844299 63.940338068234 cortex 63.685370300115 57.5617377566366 64.3837720021534 heart 67.5579486244343 64.6915727272893 62.7808702670504 kidney 59.5306959577713 56.9093995493092 63.5839221632969 liver 60.6116576569501 70.0304203671203 67.2955917024353 stomach 63.3128747325471 65.1166801738379 62.8287414158062 testicle 58.833636153007 63.2096578553403 59.4574130578599 cont.diffExp=6.90790197129511,-3.3249562017706,6.12363254347833,2.86637589714496,2.62129640846205,-9.41876271017015,-1.80380544129085,-4.37602170233323 cont.diffExpScore=26.6621852739375 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=0.72696392334769 cont.tran.correlation=-0.0375583911571476 tran.covariance=0.00731981378287981 cont.tran.covariance=-0.000101009225741744 tran.mean=59.4668615942129 cont.tran.mean=62.6508692470591 weightedLogRatios: wLogRatio Lung 1.06173920790862 cerebhem 1.23327901610749 cortex 0.748744466152416 heart 1.07830888873889 kidney 3.19347774731341 liver 1.08756515153735 stomach 0.821035896346333 testicle 1.13815457405051 cont.weightedLogRatios: wLogRatio Lung 0.463287907932001 cerebhem -0.215491844849861 cortex 0.414841257875249 heart 0.181713276772053 kidney 0.183007177523959 liver -0.603294128306749 stomach -0.116922852953191 testicle -0.294907246026820 varWeightedLogRatios=0.614294141633517 cont.varWeightedLogRatios=0.137751909591158 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.97158660580438 0.0814717596142027 48.7480155652858 2.24066584121499e-275 *** df.mm.trans1 0.173401405500913 0.0694834324678691 2.49557915235547 0.0127230896908685 * df.mm.trans2 -0.149010277635563 0.0605234076495223 -2.46202722917467 0.0139703287207369 * df.mm.exp2 -0.113394145632863 0.07587989775638 -1.49438980528053 0.135365060121035 df.mm.exp3 0.0697111008118197 0.07587989775638 0.918703146327821 0.358455424030082 df.mm.exp4 0.128465924430275 0.07587989775638 1.69301657262016 0.0907399071000466 . df.mm.exp5 0.167443917590282 0.07587989775638 2.20669666856798 0.0275444909674483 * df.mm.exp6 0.0914060062963128 0.07587989775638 1.20461425224611 0.228615430701886 df.mm.exp7 0.03538431666112 0.07587989775638 0.466320036101325 0.641080218831378 df.mm.exp8 0.0184047586164494 0.07587989775638 0.242551178383763 0.808399031378412 df.mm.trans1:exp2 0.153466379701807 0.0689877060482422 2.2245467851111 0.0263171691449548 * df.mm.trans2:exp2 0.111954673889997 0.0457903496952361 2.44494035610400 0.0146461138059581 * df.mm.trans1:exp3 -0.0852061104062425 0.0689877060482422 -1.23509122548094 0.217064431070471 df.mm.trans2:exp3 -0.00560816039854579 0.0457903496952361 -0.122474723077497 0.902545785838294 df.mm.trans1:exp4 -0.0476456838637835 0.0689877060482422 -0.690640211032173 0.489939659635964 df.mm.trans2:exp4 -0.0463908038168892 0.0457903496952361 -1.01311311500457 0.311232418485009 df.mm.trans1:exp5 0.431987810630693 0.0689877060482423 6.26180859425314 5.47706958225762e-10 *** df.mm.trans2:exp5 -0.0351416551816539 0.045790349695236 -0.767446752766555 0.442983219124821 df.mm.trans1:exp6 -0.0485759816127707 0.0689877060482423 -0.704125189766451 0.481506183114796 df.mm.trans2:exp6 -0.0522778669957432 0.0457903496952361 -1.14167870181568 0.253839960757727 df.mm.trans1:exp7 -0.0280341242649339 0.0689877060482423 -0.406364059203968 0.684555407060184 df.mm.trans2:exp7 0.0342728128157527 0.045790349695236 0.748472397434397 0.454337779139230 df.mm.trans1:exp8 -0.0102462625601127 0.0689877060482423 -0.148523021666318 0.881957667835741 df.mm.trans2:exp8 -0.0295073451712953 0.045790349695236 -0.644400957138033 0.519451922074849 df.mm.trans1:probe2 1.00738105256335 0.0523999243198882 19.2248570134098 4.27156729073841e-71 *** df.mm.trans1:probe3 -0.14434304359136 0.0523999243198882 -2.75464221494257 0.00597407391929577 ** df.mm.trans1:probe4 -0.244115608367631 0.0523999243198882 -4.65870154463139 3.5772677300683e-06 *** df.mm.trans1:probe5 -0.158476676940643 0.0523999243198882 -3.02436843177832 0.00255024176036680 ** df.mm.trans1:probe6 0.0824070933708122 0.0523999243198882 1.57265672499330 0.116090404453667 df.mm.trans1:probe7 -0.338563844871126 0.0523999243198882 -6.46115140938526 1.56724385976920e-10 *** df.mm.trans1:probe8 0.124244662940963 0.0523999243198882 2.37108477833824 0.0179101774508555 * df.mm.trans1:probe9 0.199704854022675 0.0523999243198882 3.81116684069098 0.000146101240531259 *** df.mm.trans1:probe10 0.297954916904024 0.0523999243198882 5.68617074874164 1.67011048963688e-08 *** df.mm.trans1:probe11 -0.286887248586815 0.0523999243198882 -5.47495539946663 5.43748470081528e-08 *** df.mm.trans1:probe12 -0.361578209628744 0.0523999243198882 -6.90035747802615 8.81110127808194e-12 *** df.mm.trans1:probe13 -0.280632268828013 0.0523999243198882 -5.35558538433805 1.04109845989240e-07 *** df.mm.trans1:probe14 -0.3179463776499 0.0523999243198882 -6.06768772620584 1.79151864717547e-09 *** df.mm.trans1:probe15 -0.300531072536934 0.0523999243198882 -5.73533409518434 1.26167614039886e-08 *** df.mm.trans1:probe16 -0.32177924732404 0.0523999243198882 -6.14083419967669 1.15072939926842e-09 *** df.mm.trans1:probe17 0.269730887439876 0.0523999243198881 5.14754345432329 3.13259575814502e-07 *** df.mm.trans1:probe18 0.0197606670578586 0.0523999243198881 0.377112511407932 0.706163874635394 df.mm.trans1:probe19 0.000820939037975634 0.0523999243198882 0.0156667981610815 0.987503100565522 df.mm.trans1:probe20 -0.0318989769304044 0.0523999243198882 -0.608759980943279 0.542811205670436 df.mm.trans1:probe21 -0.0565792362827099 0.0523999243198882 -1.07975797707852 0.280490371817469 df.mm.trans1:probe22 -0.114228955837795 0.0523999243198882 -2.17994505374582 0.0294763210774236 * df.mm.trans2:probe2 0.131572814183762 0.0523999243198882 2.51093519487821 0.0121859329247044 * df.mm.trans2:probe3 0.09950979188781 0.0523999243198882 1.89904457266633 0.0578241592735099 . df.mm.trans2:probe4 0.504057328160639 0.0523999243198882 9.61942855267305 4.47712385025578e-21 *** df.mm.trans2:probe5 0.0623225377243199 0.0523999243198882 1.18936312472241 0.234557496864112 df.mm.trans2:probe6 0.0736461675200713 0.0523999243198882 1.40546324209326 0.160170368353860 df.mm.trans3:probe2 -0.0388155355493748 0.0523999243198882 -0.74075556507326 0.459002232757874 df.mm.trans3:probe3 -0.0822137764857611 0.0523999243198882 -1.56896746613348 0.116947628746928 df.mm.trans3:probe4 0.116703007787044 0.0523999243198882 2.22715985379295 0.0261415329318335 * df.mm.trans3:probe5 -0.235813201888914 0.0523999243198882 -4.50025844406444 7.5205413450999e-06 *** df.mm.trans3:probe6 -0.0902019763545069 0.0523999243198882 -1.7214142486895 0.0854612551875194 . df.mm.trans3:probe7 0.557954379696595 0.0523999243198882 10.6479997240154 3.00356428750949e-25 *** df.mm.trans3:probe8 0.184621908614325 0.0523999243198882 3.52332395534114 0.000443946159829284 *** df.mm.trans3:probe9 0.128739056873796 0.0523999243198882 2.45685577879612 0.0141718904208037 * df.mm.trans3:probe10 -0.00177474048054002 0.0523999243198882 -0.0338691420564977 0.972987741138878 df.mm.trans3:probe11 -0.00931093478717998 0.0523999243198882 -0.177689851808547 0.858999783719409 df.mm.trans3:probe12 0.202825480617602 0.0523999243198882 3.87072086935477 0.000115001492597976 *** df.mm.trans3:probe13 -0.146011216670954 0.0523999243198882 -2.78647762503611 0.00542149515612149 ** df.mm.trans3:probe14 -0.0395003129927352 0.0523999243198882 -0.753823855767346 0.451118853399657 df.mm.trans3:probe15 0.175275380566060 0.0523999243198882 3.34495484184383 0.000851173533667805 *** df.mm.trans3:probe16 0.377422014944565 0.0523999243198882 7.20272061158905 1.10306215626084e-12 *** df.mm.trans3:probe17 0.136711741219447 0.0523999243198882 2.60900646315549 0.00920567975792777 ** df.mm.trans3:probe18 -0.126050978128144 0.0523999243198882 -2.40555649200244 0.0163147076111058 * df.mm.trans3:probe19 0.474083036630494 0.0523999243198882 9.04739926218858 6.62215478136844e-19 *** df.mm.trans3:probe20 0.00554211934602416 0.0523999243198882 0.105765789129597 0.91578776693802 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.92597223181859 0.161696449134595 24.2798914436930 2.55233188280933e-104 *** df.mm.trans1 0.224174118107210 0.137903297497692 1.62558925112694 0.104328002149371 df.mm.trans2 0.118816945627914 0.120120396966943 0.989148792611904 0.322811286351396 df.mm.exp2 0.166754226725841 0.150598318804003 1.10727814261236 0.268419560260221 df.mm.exp3 0.0422011998781606 0.150598318804003 0.280223578943691 0.779359493452672 df.mm.exp4 0.243216126937072 0.150598318804003 1.61499895130707 0.106602195153926 df.mm.exp5 -0.0241581713716473 0.150598318804003 -0.160414615272619 0.872584392513285 df.mm.exp6 0.144572108352297 0.150598318804003 0.95998487566419 0.337277095421266 df.mm.exp7 0.184106677589859 0.150598318804003 1.22250154617905 0.221784000002283 df.mm.exp8 0.136160400249964 0.150598318804003 0.904129616660404 0.366127744461251 df.mm.trans1:exp2 -0.203248241382612 0.136919432632426 -1.48443677770893 0.137984066385056 df.mm.trans2:exp2 -0.0389326479889195 0.0908797966978035 -0.428397173008426 0.66844716792234 df.mm.trans1:exp3 -0.061081135533542 0.136919432632426 -0.446110054352333 0.655606925460975 df.mm.trans2:exp3 -0.0496355440690917 0.0908797966978035 -0.546166979599893 0.585063651379705 df.mm.trans1:exp4 -0.203065205455597 0.136919432632426 -1.48309996288655 0.138338790600787 df.mm.trans2:exp4 -0.133877601529845 0.0908797966978035 -1.47312831228066 0.141007008758721 df.mm.trans1:exp5 -0.0621845568947081 0.136919432632426 -0.45416896417946 0.649798223754818 df.mm.trans2:exp5 0.00532627694313347 0.0908797966978035 0.0586079319790358 0.9532752046147 df.mm.trans1:exp6 -0.212919669439163 0.136919432632426 -1.55507268285845 0.12022092160537 df.mm.trans2:exp6 0.0440651998631621 0.0908797966978035 0.484873442330523 0.627864177193531 df.mm.trans1:exp7 -0.208852783059839 0.136919432632426 -1.52536991312640 0.127458819969750 df.mm.trans2:exp7 -0.0682183534325388 0.0908797966978035 -0.750643772447916 0.453030127098645 df.mm.trans1:exp8 -0.234281471719385 0.136919432632426 -1.71108999807454 0.0873508500321497 . df.mm.trans2:exp8 -0.049995712711224 0.0908797966978035 -0.550130111728478 0.582343572919043 df.mm.trans1:probe2 -0.031971765225825 0.103997774659213 -0.307427397659154 0.758577183748229 df.mm.trans1:probe3 0.101506582608952 0.103997774659213 0.976045717724018 0.329259772015876 df.mm.trans1:probe4 -0.0267413947464025 0.103997774659213 -0.257134297671566 0.797123945244792 df.mm.trans1:probe5 -0.0366585063905238 0.103997774659213 -0.352493180845926 0.724537016685356 df.mm.trans1:probe6 0.0871505470425735 0.103997774659213 0.8380039604516 0.402213381264334 df.mm.trans1:probe7 0.0479185217764312 0.103997774659213 0.460764876300997 0.645059812206757 df.mm.trans1:probe8 0.058993093918748 0.103997774659213 0.567253425489733 0.570659571873779 df.mm.trans1:probe9 0.0307770852307862 0.103997774659213 0.295939844209538 0.767332756101444 df.mm.trans1:probe10 0.181959781107570 0.103997774659213 1.74965071804495 0.0804618239201924 . df.mm.trans1:probe11 0.0661033427847022 0.103997774659213 0.635622665978326 0.525156675492682 df.mm.trans1:probe12 -0.0285308722789595 0.103997774659213 -0.274341180592098 0.783874759666409 df.mm.trans1:probe13 0.00449116682267003 0.103997774659213 0.0431852204279081 0.965561846994205 df.mm.trans1:probe14 -0.074707212408333 0.103997774659213 -0.718353951833475 0.472694012789201 df.mm.trans1:probe15 0.0526760011913175 0.103997774659213 0.506510849524711 0.612601172870816 df.mm.trans1:probe16 0.0387438984901783 0.103997774659213 0.372545457026719 0.70955966594101 df.mm.trans1:probe17 0.134434407640276 0.103997774659213 1.29266619483734 0.196402245336517 df.mm.trans1:probe18 0.0701895495549294 0.103997774659213 0.674913956427734 0.499874487969432 df.mm.trans1:probe19 0.0470913881497536 0.103997774659213 0.452811498169705 0.650775179044833 df.mm.trans1:probe20 0.128070612545686 0.103997774659213 1.23147454804063 0.218412768543533 df.mm.trans1:probe21 0.0387841845569357 0.103997774659213 0.372932831342078 0.7092714118557 df.mm.trans1:probe22 0.0626347527419855 0.103997774659213 0.602270124983264 0.547120415583613 df.mm.trans2:probe2 0.041442933485559 0.103997774659213 0.398498271923241 0.690341481924361 df.mm.trans2:probe3 0.0564929589685475 0.103997774659213 0.543213151951255 0.587094835867922 df.mm.trans2:probe4 0.0503367416544098 0.103997774659213 0.484017488060265 0.628471314763852 df.mm.trans2:probe5 0.211073410482763 0.103997774659213 2.02959545215677 0.0426417965845933 * df.mm.trans2:probe6 0.0437381605544425 0.103997774659213 0.420568235212404 0.674153828900127 df.mm.trans3:probe2 -0.0482293755439138 0.103997774659213 -0.463753918792543 0.64291725379745 df.mm.trans3:probe3 -0.0491391967722538 0.103997774659213 -0.472502387029689 0.636663437532572 df.mm.trans3:probe4 -0.0946731044918262 0.103997774659213 -0.910337791381185 0.362846953937821 df.mm.trans3:probe5 0.0208172395998263 0.103997774659213 0.200170048523073 0.841385171210436 df.mm.trans3:probe6 -0.081245604788698 0.103997774659213 -0.781224454609045 0.434841229892108 df.mm.trans3:probe7 -0.0588273277351766 0.103997774659213 -0.565659485772133 0.571742453523669 df.mm.trans3:probe8 -0.0790767574945083 0.103997774659213 -0.760369707463764 0.447199126227965 df.mm.trans3:probe9 -0.0251448131207953 0.103997774659213 -0.241782222775358 0.808994686824574 df.mm.trans3:probe10 0.0199947426160546 0.103997774659213 0.192261254450633 0.847573610309381 df.mm.trans3:probe11 -0.220122606214400 0.103997774659213 -2.11660881144537 0.0345206574798843 * df.mm.trans3:probe12 0.0327653532534799 0.103997774659213 0.315058215051693 0.752778146077188 df.mm.trans3:probe13 -0.0705751719683938 0.103997774659213 -0.67862194359119 0.497522416882754 df.mm.trans3:probe14 -0.0142727159133875 0.103997774659213 -0.137240589619897 0.890866137774645 df.mm.trans3:probe15 -0.0112367103333433 0.103997774659213 -0.108047603616178 0.913977944428837 df.mm.trans3:probe16 -0.0363468608429186 0.103997774659213 -0.349496524921062 0.72678452545547 df.mm.trans3:probe17 -0.100612028387983 0.103997774659213 -0.967444050775852 0.333538088310633 df.mm.trans3:probe18 0.117857910429051 0.103997774659213 1.13327338796677 0.257350276715943 df.mm.trans3:probe19 0.0530770819708977 0.103997774659213 0.51036747800445 0.609898039531756 df.mm.trans3:probe20 -0.0667340946082959 0.103997774659213 -0.641687717136013 0.521211751172269