chr5.18150_chr5_142432711_142439495_-_2.R fitVsDatCorrelation=0.924534244863023 cont.fitVsDatCorrelation=0.211957307932497 fstatistic=11280.9508994266,66,1014 cont.fstatistic=1702.44694697422,66,1014 residuals=-0.827256560593898,-0.0911591333484832,-0.00079547736780141,0.0922707658803144,1.1268394207478 cont.residuals=-0.786428974271566,-0.31719370764503,-0.0822907923667707,0.273147617298619,1.15193714103856 predictedValues: Include Exclude Both chr5.18150_chr5_142432711_142439495_-_2.R.tl.Lung 99.1145608543098 65.0699439316528 88.7459879499538 chr5.18150_chr5_142432711_142439495_-_2.R.tl.cerebhem 80.2246524107651 53.9059400144505 84.3050904790613 chr5.18150_chr5_142432711_142439495_-_2.R.tl.cortex 86.607890479987 56.2719050877581 84.6731225180305 chr5.18150_chr5_142432711_142439495_-_2.R.tl.heart 90.5023751726303 63.6998913385582 86.6431210416891 chr5.18150_chr5_142432711_142439495_-_2.R.tl.kidney 93.8362005256824 66.3426909976269 83.4339118973358 chr5.18150_chr5_142432711_142439495_-_2.R.tl.liver 88.4844581197081 61.5344413918636 84.7904465248934 chr5.18150_chr5_142432711_142439495_-_2.R.tl.stomach 90.0095431240545 63.2234321132605 84.7952385099932 chr5.18150_chr5_142432711_142439495_-_2.R.tl.testicle 90.5573366545018 56.1143090228599 85.2406944303653 diffExp=34.0446169226570,26.3187123963146,30.3359853922288,26.8024838340721,27.4935095280555,26.9500167278445,26.786111010794,34.4430276316419 diffExpScore=0.995729679550474 diffExp1.5=1,0,1,0,0,0,0,1 diffExp1.5Score=0.75 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 84.0692573150188 74.1760136335367 80.3247761007157 cerebhem 91.2261045862056 71.8936640995372 86.8705462688113 cortex 77.2114362223208 74.5485695251308 78.5673612142517 heart 82.6433151054625 91.0728350068332 81.3893158835638 kidney 91.505884195696 74.2180599245366 82.576445608539 liver 96.0923588891865 84.1096596802114 82.752442119203 stomach 89.8195909262048 73.4597187998765 81.1308003235829 testicle 84.8142960842666 93.9598643923942 81.4141628626727 cont.diffExp=9.89324368148215,19.3324404866684,2.66286669719001,-8.42951990137071,17.2878242711594,11.9826992089751,16.3598721263284,-9.1455683081276 cont.diffExpScore=1.56035468368302 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=0,1,0,0,1,0,1,0 cont.diffExp1.2Score=0.75 tran.correlation=0.787236081698603 cont.tran.correlation=-0.132429275226491 tran.covariance=0.00378581702910013 cont.tran.covariance=-0.000906546365107567 tran.mean=75.3437232024794 cont.tran.mean=83.4262892741511 weightedLogRatios: wLogRatio Lung 1.84563359945611 cerebhem 1.66432670549775 cortex 1.83076705808598 heart 1.52058927669453 kidney 1.51452699611514 liver 1.56232871803370 stomach 1.52716389916014 testicle 2.04200381832354 cont.weightedLogRatios: wLogRatio Lung 0.547004441531826 cerebhem 1.04650708464498 cortex 0.151933720000891 heart -0.433481093194236 kidney 0.923792415969846 liver 0.599176648250944 stomach 0.884141086476255 testicle -0.459962768968285 varWeightedLogRatios=0.0386984677287977 cont.varWeightedLogRatios=0.3547615634973 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.55334561719741 0.076236874160361 59.726289506829 0 *** df.mm.trans1 0.0736487154863795 0.0651723721026995 1.13006037850399 0.258718019442762 df.mm.trans2 -0.377100409808009 0.056923848228154 -6.62464716539487 5.63958100250545e-11 *** df.mm.exp2 -0.348331610137357 0.0717327592814549 -4.85596279338179 1.38678965968513e-06 *** df.mm.exp3 -0.233172803262548 0.0717327592814549 -3.25057624435801 0.00118962058048539 ** df.mm.exp4 -0.0881995652115229 0.0717327592814549 -1.22955768180418 0.219148003403886 df.mm.exp5 0.0263685747268272 0.0717327592814549 0.367594596819647 0.713252254515409 df.mm.exp6 -0.123719806903838 0.0717327592814549 -1.72473230004166 0.0848804578307815 . df.mm.exp7 -0.0796095906033652 0.0717327592814549 -1.10980800684112 0.267344887621538 df.mm.exp8 -0.198065799045880 0.0717327592814549 -2.76116241770008 0.00586357709128924 ** df.mm.trans1:exp2 0.136886102980076 0.0654396556799504 2.09179130846215 0.0367054700449733 * df.mm.trans2:exp2 0.160109534485477 0.0445014001935741 3.59785386053082 0.000336346639286134 *** df.mm.trans1:exp3 0.0982873673038687 0.0654396556799504 1.50195422458469 0.133420264297017 df.mm.trans2:exp3 0.0879054404791752 0.0445014001935741 1.97534100268307 0.0485003142800119 * df.mm.trans1:exp4 -0.00270070088959220 0.0654396556799504 -0.04127009626702 0.967088696908108 df.mm.trans2:exp4 0.0669196701125662 0.0445014001935741 1.50376549550073 0.132953139804249 df.mm.trans1:exp5 -0.0810942215220965 0.0654396556799504 -1.23922139686536 0.215550267977206 df.mm.trans2:exp5 -0.00699773014845013 0.0445014001935741 -0.157247415092809 0.875081165959715 df.mm.trans1:exp6 0.0102703675650786 0.0654396556799504 0.156944095416830 0.875320150251224 df.mm.trans2:exp6 0.0678540957495765 0.0445014001935741 1.52476316373017 0.127629996464375 df.mm.trans1:exp7 -0.0167510714399787 0.0654396556799504 -0.25597737741629 0.79802022422025 df.mm.trans2:exp7 0.050821832482454 0.0445014001935741 1.14202771736141 0.253712098944451 df.mm.trans1:exp8 0.107772641870378 0.0654396556799504 1.64690111447817 0.0998882191111272 . df.mm.trans2:exp8 0.0499938900031128 0.0445014001935741 1.12342285378993 0.261523850571968 df.mm.trans1:probe2 0.301352927767181 0.0487227934462597 6.18505029067283 8.99056755168891e-10 *** df.mm.trans1:probe3 0.0165536729360335 0.0487227934462597 0.339752131705910 0.734113558559631 df.mm.trans1:probe4 -0.0310386702233485 0.0487227934462597 -0.637046195998255 0.524238548643151 df.mm.trans1:probe5 0.596923095869686 0.0487227934462597 12.251413633089 2.78926223340655e-32 *** df.mm.trans1:probe6 -0.366551417761203 0.0487227934462597 -7.52320201356069 1.17599279960981e-13 *** df.mm.trans1:probe7 -0.0862119596865889 0.0487227934462597 -1.76943794862006 0.0771213385798406 . df.mm.trans1:probe8 -0.240412319607416 0.0487227934462597 -4.93428850446733 9.3980348207069e-07 *** df.mm.trans1:probe9 -0.70974766460048 0.0487227934462597 -14.5670560819407 8.6799883316735e-44 *** df.mm.trans1:probe10 -0.292303305511239 0.0487227934462597 -5.99931335697417 2.75420607461462e-09 *** df.mm.trans1:probe11 -0.68101213233645 0.0487227934462597 -13.9772801222408 9.93662920356378e-41 *** df.mm.trans1:probe12 -0.53876702625792 0.0487227934462597 -11.0578024811358 6.46465887444445e-27 *** df.mm.trans1:probe13 -0.511454712436968 0.0487227934462597 -10.4972370478120 1.52907306765218e-24 *** df.mm.trans1:probe14 -0.619927386302907 0.0487227934462597 -12.7235600107099 1.63030930257699e-34 *** df.mm.trans1:probe15 -0.459351245588057 0.0487227934462597 -9.42785117800588 2.75080735916377e-20 *** df.mm.trans1:probe16 -0.547174670324835 0.0487227934462597 -11.2303632780899 1.14981216530984e-27 *** df.mm.trans1:probe17 0.510498792826614 0.0487227934462597 10.477617491076 1.84400776947555e-24 *** df.mm.trans1:probe18 0.461120391528865 0.0487227934462597 9.46416161539407 2.00009497747960e-20 *** df.mm.trans1:probe19 0.225200609645742 0.0487227934462597 4.62207918957139 4.28871908660558e-06 *** df.mm.trans1:probe20 0.633092303312151 0.0487227934462597 12.9937603846635 8.07567974354719e-36 *** df.mm.trans1:probe21 0.59188251405549 0.0487227934462597 12.1479593469599 8.44547277570978e-32 *** df.mm.trans1:probe22 0.549327325125339 0.0487227934462597 11.2745449566892 7.36536409276373e-28 *** df.mm.trans2:probe2 -0.0724573895190666 0.0487227934462597 -1.48713537123001 0.13728993589706 df.mm.trans2:probe3 -0.07012644796627 0.0487227934462597 -1.43929448633971 0.150375720994874 df.mm.trans2:probe4 0.00151883594956961 0.0487227934462597 0.0311730063516341 0.975137701743214 df.mm.trans2:probe5 0.0368828525140968 0.0487227934462597 0.756993799109196 0.449229432114564 df.mm.trans2:probe6 0.0861856714890601 0.0487227934462597 1.76889840243091 0.0772114081067072 . df.mm.trans3:probe2 0.149589733483349 0.0487227934462597 3.07022079200659 0.00219594265255437 ** df.mm.trans3:probe3 0.18449649290554 0.0487227934462597 3.78665671353668 0.000161657175457782 *** df.mm.trans3:probe4 -0.159748798831119 0.0487227934462597 -3.27872824055787 0.00107820635397111 ** df.mm.trans3:probe5 0.27451454462303 0.0487227934462597 5.6342119407791 2.27759306247869e-08 *** df.mm.trans3:probe6 0.809949102500987 0.0487227934462597 16.6236179252395 4.62725233060605e-55 *** df.mm.trans3:probe7 -0.0314647778420389 0.0487227934462597 -0.645791745843636 0.518560344832257 df.mm.trans3:probe8 1.01272324772647 0.0487227934462597 20.7854101970464 3.24338509034324e-80 *** df.mm.trans3:probe9 0.336950183791386 0.0487227934462597 6.91565815418681 8.23185905944245e-12 *** df.mm.trans3:probe10 0.131254433905269 0.0487227934462597 2.69390206557102 0.00717871353960017 ** df.mm.trans3:probe11 0.20798036327071 0.0487227934462597 4.26864612145254 2.15103081691465e-05 *** df.mm.trans3:probe12 0.504622059980585 0.0487227934462597 10.3570018114247 5.79625691701174e-24 *** df.mm.trans3:probe13 0.0594719510477387 0.0487227934462597 1.22061866410298 0.222514202555680 df.mm.trans3:probe14 -0.118774401872837 0.0487227934462597 -2.43775845906377 0.0149495764082747 * df.mm.trans3:probe15 0.4351859321616 0.0487227934462597 8.93187564546358 1.93254160055547e-18 *** df.mm.trans3:probe16 0.286581185806744 0.0487227934462597 5.88187099992199 5.50390505033426e-09 *** df.mm.trans3:probe17 -0.0732981822339388 0.0487227934462597 -1.50439203193030 0.132791852347575 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.48172881921468 0.195499581640246 22.9244931452685 4.85841422777088e-94 *** df.mm.trans1 0.00182894439063163 0.167126100340626 0.0109434994707828 0.991270677648266 df.mm.trans2 -0.183211911901159 0.145973830072682 -1.25510108085768 0.209731244744209 df.mm.exp2 -0.0278934710614533 0.183949362875590 -0.151636682103207 0.87950366262018 df.mm.exp3 -0.0579615898316312 0.183949362875590 -0.315095355186592 0.752754085212467 df.mm.exp4 0.174945835289122 0.183949362875590 0.951054314917325 0.341803433362636 df.mm.exp5 0.05768265147581 0.183949362875590 0.313578968549201 0.75390532802681 df.mm.exp6 0.229574029819810 0.183949362875590 1.24802840429014 0.212308764611053 df.mm.exp7 0.0464739897425677 0.183949362875590 0.252645559713080 0.80059332489019 df.mm.exp8 0.231778917295563 0.183949362875590 1.26001478707117 0.207953932998737 df.mm.trans1:exp2 0.109593610700244 0.167811514428066 0.65307563115535 0.513855564428787 df.mm.trans2:exp2 -0.00335922061936238 0.114118072337919 -0.0294363596452565 0.97652236698401 df.mm.trans1:exp3 -0.0271317774961845 0.167811514428066 -0.161680070575937 0.871590008208963 df.mm.trans2:exp3 0.0629716112141591 0.114118072337919 0.551811031540138 0.581199414832295 df.mm.trans1:exp4 -0.192052847241873 0.167811514428066 -1.14445571805025 0.252704765926973 df.mm.trans2:exp4 0.0302729042640809 0.114118072337919 0.265277038455739 0.790849970332965 df.mm.trans1:exp5 0.0270796759055839 0.167811514428066 0.161369593724702 0.871834458897998 df.mm.trans2:exp5 -0.0571159672260017 0.114118072337919 -0.500498878537605 0.61683245491991 df.mm.trans1:exp6 -0.095905180066126 0.167811514428066 -0.57150536060049 0.567783779949721 df.mm.trans2:exp6 -0.10389344149831 0.114118072337919 -0.910403053345202 0.362826345135921 df.mm.trans1:exp7 0.0196881725514312 0.167811514428066 0.117323132554595 0.906627228269892 df.mm.trans2:exp7 -0.0561776088107294 0.114118072337919 -0.492276180799654 0.622630678060282 df.mm.trans1:exp8 -0.222955753789426 0.167811514428066 -1.32860819800895 0.184276180251837 df.mm.trans2:exp8 0.00464796772874927 0.114118072337919 0.0407294623325394 0.967519591361104 df.mm.trans1:probe2 -0.0667835003457478 0.124943288139698 -0.534510507447808 0.59310555558554 df.mm.trans1:probe3 -0.141358965096768 0.124943288139698 -1.13138502436974 0.258160571972463 df.mm.trans1:probe4 -0.166677921886512 0.124943288139698 -1.33402861704865 0.1824940093495 df.mm.trans1:probe5 -0.148059698074535 0.124943288139698 -1.18501521993714 0.236289081430949 df.mm.trans1:probe6 -0.0812684799022771 0.124943288139698 -0.650442941852237 0.515553490892768 df.mm.trans1:probe7 -0.188821522456705 0.124943288139698 -1.51125782959694 0.131034336265101 df.mm.trans1:probe8 -0.0300653524302349 0.124943288139698 -0.240631992945623 0.80988899393018 df.mm.trans1:probe9 -0.113057529344961 0.124943288139698 -0.904870769997284 0.365748792218306 df.mm.trans1:probe10 -0.129344336123932 0.124943288139698 -1.03522436498800 0.300810960899147 df.mm.trans1:probe11 0.0127412857769670 0.124943288139698 0.101976552455711 0.918795447993327 df.mm.trans1:probe12 -0.0872335696326798 0.124943288139698 -0.698185320168175 0.485221381912181 df.mm.trans1:probe13 -0.0887945856157008 0.124943288139698 -0.710679116403757 0.477446527468182 df.mm.trans1:probe14 -0.00208280857466244 0.124943288139698 -0.0166700317053740 0.986703134404668 df.mm.trans1:probe15 -0.109037945877106 0.124943288139698 -0.87269950631675 0.383033490632857 df.mm.trans1:probe16 -0.081271092639531 0.124943288139698 -0.650463853237657 0.515539992824333 df.mm.trans1:probe17 -0.238514751606202 0.124943288139698 -1.90898410917056 0.0565462028992908 . df.mm.trans1:probe18 -0.099741606703931 0.124943288139698 -0.79829503600394 0.42488621753812 df.mm.trans1:probe19 -0.143396890316961 0.124943288139698 -1.14769582625863 0.251364856714469 df.mm.trans1:probe20 -0.101231300420578 0.124943288139698 -0.810217995122649 0.418004998070377 df.mm.trans1:probe21 -0.0280145743863853 0.124943288139698 -0.224218321796225 0.822632596950234 df.mm.trans1:probe22 0.00725945335246364 0.124943288139698 0.0581019873940479 0.953678838839832 df.mm.trans2:probe2 0.145443462243891 0.124943288139698 1.16407583319939 0.244667099668558 df.mm.trans2:probe3 -0.0544108019768527 0.124943288139698 -0.435483992673671 0.663303835995781 df.mm.trans2:probe4 -0.0208364150964017 0.124943288139698 -0.166766982097547 0.86758664776525 df.mm.trans2:probe5 0.101347068426545 0.124943288139698 0.811144559547926 0.417473007331025 df.mm.trans2:probe6 0.0107069430852048 0.124943288139698 0.0856944238031695 0.931726252000778 df.mm.trans3:probe2 0.00299719102994021 0.124943288139698 0.0239884116591286 0.980866571910458 df.mm.trans3:probe3 0.202926369817233 0.124943288139698 1.62414782609485 0.104655048973359 df.mm.trans3:probe4 0.164850187520771 0.124943288139698 1.31940010524178 0.187333220260384 df.mm.trans3:probe5 0.117172376551618 0.124943288139698 0.937804489510543 0.348568206040328 df.mm.trans3:probe6 0.0544170620441162 0.124943288139698 0.435534095943376 0.663267488341914 df.mm.trans3:probe7 0.208535062618536 0.124943288139698 1.66903773482713 0.095418588728048 . df.mm.trans3:probe8 0.105828044074455 0.124943288139698 0.847008636079189 0.397190168073247 df.mm.trans3:probe9 0.0310529396810093 0.124943288139698 0.248536277085082 0.803769831408985 df.mm.trans3:probe10 0.172544090164358 0.124943288139698 1.38097926453991 0.167589608286245 df.mm.trans3:probe11 0.00955240071720938 0.124943288139698 0.0764538924774327 0.939073076713625 df.mm.trans3:probe12 0.107927756050640 0.124943288139698 0.863813956376483 0.387894379084852 df.mm.trans3:probe13 0.0247486847953468 0.124943288139698 0.198079345948343 0.84302268692767 df.mm.trans3:probe14 0.0218054255611618 0.124943288139698 0.174522584492744 0.861489621437338 df.mm.trans3:probe15 0.074824312445135 0.124943288139698 0.59886620209222 0.549395870949312 df.mm.trans3:probe16 0.0817082954895765 0.124943288139698 0.653963063611861 0.513283881148461 df.mm.trans3:probe17 0.0765583213429555 0.124943288139698 0.612744569819198 0.540182640387926