chr4.16883_chr4_143275054_143276667_-_2.R fitVsDatCorrelation=0.754574937428406 cont.fitVsDatCorrelation=0.261012592782332 fstatistic=11238.4797303369,62,922 cont.fstatistic=5185.28325605775,62,922 residuals=-0.450687238332169,-0.0850576199811535,-0.00617648050265728,0.0733893875314673,1.57303321114238 cont.residuals=-0.474759291345476,-0.14514672752784,-0.0297234161788572,0.115447398100672,1.50190630630334 predictedValues: Include Exclude Both chr4.16883_chr4_143275054_143276667_-_2.R.tl.Lung 53.1184371833511 69.5033595855528 83.0678384991573 chr4.16883_chr4_143275054_143276667_-_2.R.tl.cerebhem 59.5260643304935 55.2924047648359 64.456168647039 chr4.16883_chr4_143275054_143276667_-_2.R.tl.cortex 51.8821904961413 64.1259566921383 69.2460275087306 chr4.16883_chr4_143275054_143276667_-_2.R.tl.heart 55.6017807745954 87.3849560246311 99.433413177646 chr4.16883_chr4_143275054_143276667_-_2.R.tl.kidney 54.3932591196342 66.3632298317491 80.0401209801297 chr4.16883_chr4_143275054_143276667_-_2.R.tl.liver 52.944048992419 62.7670186055787 78.2698817097628 chr4.16883_chr4_143275054_143276667_-_2.R.tl.stomach 53.448403467567 58.2506933468222 67.469223835209 chr4.16883_chr4_143275054_143276667_-_2.R.tl.testicle 54.2696655856577 57.3373078952006 61.7648248122008 diffExp=-16.3849224022017,4.2336595656576,-12.2437661959970,-31.7831752500357,-11.9699707121150,-9.82296961315975,-4.80228987925522,-3.06764230954282 diffExpScore=1.08598832956437 diffExp1.5=0,0,0,-1,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,-1,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=-1,0,0,-1,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,0,-1,-1,-1,0,0,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 57.3283027184983 59.087154265545 51.9160346428924 cerebhem 55.5815416840287 58.4331220924129 53.3922584413424 cortex 58.608931401852 56.4229646699756 56.9100869334811 heart 56.5595190322766 57.4754246320553 59.7882553688429 kidney 57.5607282516349 54.1934738579695 53.2565513663033 liver 54.0683387601673 58.5495816994426 55.5221401254959 stomach 57.2591000132446 56.6717390938537 51.3832354069086 testicle 56.6325232648781 59.0541069553342 63.1872902042035 cont.diffExp=-1.75885154704672,-2.85158040838422,2.18596673187635,-0.915905599778654,3.36725439366543,-4.48124293927524,0.587360919390974,-2.42158369045609 cont.diffExpScore=2.54778582077595 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=-0.093912327119954 cont.tran.correlation=-0.511557715084915 tran.covariance=-0.000846339257170536 cont.tran.covariance=-0.000367591303277466 tran.mean=59.763048543523 cont.tran.mean=57.0929095245731 weightedLogRatios: wLogRatio Lung -1.10415753033367 cerebhem 0.298768762802318 cortex -0.859129057072527 heart -1.91886767438576 kidney -0.814644963984067 liver -0.690023416507898 stomach -0.346027093242042 testicle -0.221124251050075 cont.weightedLogRatios: wLogRatio Lung -0.122807536840726 cerebhem -0.202271615818508 cortex 0.154015455760908 heart -0.0649517415217175 kidney 0.242488553126983 liver -0.320894307544066 stomach 0.0416812008698361 testicle -0.169890966386097 varWeightedLogRatios=0.434189094983864 cont.varWeightedLogRatios=0.0359741191894876 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.66693722402887 0.0803940718380053 45.612035069175 1.32742422798292e-238 *** df.mm.trans1 0.365270803071379 0.0727379621450513 5.02173545009372 6.14676084384864e-07 *** df.mm.trans2 0.569466756127064 0.0661736190179357 8.60564624662157 3.23639173671106e-17 *** df.mm.exp2 0.138822819013671 0.0904251520365945 1.53522350681247 0.125072003604600 df.mm.exp3 0.077917454821275 0.0904251520365945 0.861679002648978 0.389088225592395 df.mm.exp4 0.0948085934734125 0.0904251520365945 1.04847590894892 0.294694154397769 df.mm.exp5 0.0146137775083262 0.0904251520365945 0.161611865495256 0.871646919769866 df.mm.exp6 -0.0457390156112341 0.0904251520365945 -0.50582182701472 0.613102642232977 df.mm.exp7 0.0375596422579283 0.0904251520365945 0.415367200518813 0.677969757330618 df.mm.exp8 0.125341341154317 0.0904251520365945 1.38613359592243 0.166041237396449 df.mm.trans1:exp2 -0.0249326306471554 0.0877252830728035 -0.284212598396106 0.77631127665609 df.mm.trans2:exp2 -0.367562356705788 0.0749097213814073 -4.90673773613871 1.09454154020975e-06 *** df.mm.trans1:exp3 -0.101465958143600 0.0877252830728036 -1.15663300920207 0.247721919314127 df.mm.trans2:exp3 -0.158443323028895 0.0749097213814073 -2.11512364626443 0.034687127452946 * df.mm.trans1:exp4 -0.0491174486509498 0.0877252830728035 -0.559900714258021 0.575683160670802 df.mm.trans2:exp4 0.134139455744835 0.0749097213814073 1.79068154668279 0.0736723852669038 . df.mm.trans1:exp5 0.00910237119243101 0.0877252830728035 0.103759952360335 0.917382413097996 df.mm.trans2:exp5 -0.0608457327688898 0.0749097213814073 -0.81225415936458 0.416855470813814 df.mm.trans1:exp6 0.0424506079118244 0.0877252830728035 0.4839039148679 0.628568972745413 df.mm.trans2:exp6 -0.0562063210734267 0.0749097213814073 -0.750320786633939 0.453252954445764 df.mm.trans1:exp7 -0.0313669591174983 0.0877252830728035 -0.357558938755053 0.720755242919143 df.mm.trans2:exp7 -0.214178737696129 0.0749097213814072 -2.85915811387985 0.00434344531663353 ** df.mm.trans1:exp8 -0.10389999938718 0.0877252830728036 -1.18437918633962 0.236568219289968 df.mm.trans2:exp8 -0.317764922421164 0.0749097213814073 -4.24197175695322 2.43925663559838e-05 *** df.mm.trans1:probe2 0.0233718019072978 0.0438626415364018 0.532840729345984 0.594272234603173 df.mm.trans1:probe3 0.00332051866392640 0.0438626415364018 0.0757026605698312 0.939672081710108 df.mm.trans1:probe4 -0.113414093838589 0.0438626415364018 -2.58566492728136 0.0098715490910246 ** df.mm.trans1:probe5 -0.244980691681439 0.0438626415364018 -5.58517871018161 3.07193583438607e-08 *** df.mm.trans1:probe6 -0.0977612168246644 0.0438626415364018 -2.22880367894697 0.0260673359332281 * df.mm.trans1:probe7 0.0310600605362997 0.0438626415364018 0.708121067230364 0.479049051043876 df.mm.trans1:probe8 -0.202922328393270 0.0438626415364018 -4.62631344774035 4.25435433127269e-06 *** df.mm.trans1:probe9 0.175811094673131 0.0438626415364018 4.00821948963619 6.61175527958103e-05 *** df.mm.trans1:probe10 0.0397859509546486 0.0438626415364018 0.907057795906572 0.364613232147681 df.mm.trans1:probe11 -0.0343823366071931 0.0438626415364018 -0.783863793945448 0.433321358007133 df.mm.trans1:probe12 -0.156080249720567 0.0438626415364018 -3.55838691545823 0.000392167199269238 *** df.mm.trans1:probe13 -0.121284925925362 0.0438626415364018 -2.76510765601537 0.00580401012318031 ** df.mm.trans1:probe14 -0.188479471683784 0.0438626415364018 -4.29703878019669 1.91482492724581e-05 *** df.mm.trans1:probe15 -0.178022939438962 0.0438626415364018 -4.0586461098386 5.35430110502458e-05 *** df.mm.trans1:probe16 -0.0540827247347655 0.0438626415364018 -1.23300199988826 0.217889293152974 df.mm.trans1:probe17 -0.129580446894292 0.0438626415364018 -2.95423263067164 0.00321414481834309 ** df.mm.trans1:probe18 0.0971832076022724 0.0438626415364018 2.21562596775253 0.0269607467436550 * df.mm.trans1:probe19 -0.0884701356545935 0.0438626415364018 -2.01698148026885 0.0439868781248064 * df.mm.trans1:probe20 -0.0308010565118622 0.0438626415364018 -0.70221617834622 0.482721590741416 df.mm.trans1:probe21 -0.112626694891073 0.0438626415364018 -2.56771345605356 0.0103937664785643 * df.mm.trans1:probe22 -0.108707296294501 0.0438626415364018 -2.47835726455928 0.0133769084927354 * df.mm.trans1:probe23 0.0450955195140919 0.0438626415364018 1.02810770018643 0.304168804664847 df.mm.trans1:probe24 -0.218956892519172 0.0438626415364018 -4.99187656852491 7.14827239242886e-07 *** df.mm.trans1:probe25 0.245158154124448 0.0438626415364018 5.58922457784468 3.0035141484004e-08 *** df.mm.trans1:probe26 0.00405157031535082 0.0438626415364018 0.0923695010932802 0.926424536163154 df.mm.trans1:probe27 -0.0318166697587062 0.0438626415364018 -0.725370580618166 0.468408777984651 df.mm.trans1:probe28 -0.113651255313403 0.0438626415364018 -2.59107184000953 0.00971891486313718 ** df.mm.trans1:probe29 -0.0611452953381207 0.0438626415364018 -1.39401762402695 0.163648141247327 df.mm.trans1:probe30 -0.238124066126524 0.0438626415364018 -5.42885831280598 7.25442869137218e-08 *** df.mm.trans1:probe31 -0.0126927110775356 0.0438626415364018 -0.289374069434506 0.772360192047033 df.mm.trans1:probe32 -0.215792379128267 0.0438626415364018 -4.91973058551843 1.02606797578473e-06 *** df.mm.trans2:probe2 -0.0829147277417786 0.0438626415364018 -1.89032682112789 0.0590276593683017 . df.mm.trans2:probe3 -0.0239088452311300 0.0438626415364018 -0.545084481774496 0.585827240131208 df.mm.trans2:probe4 0.168532653649546 0.0438626415364018 3.84228235569624 0.000130243652133532 *** df.mm.trans2:probe5 -0.0608977958828826 0.0438626415364018 -1.38837502142554 0.165358214732129 df.mm.trans2:probe6 0.0439287106690219 0.0438626415364018 1.00150627345517 0.316844873492316 df.mm.trans3:probe2 -0.397130654905833 0.0438626415364018 -9.05396120697045 8.03895938703313e-19 *** df.mm.trans3:probe3 -0.148920878940917 0.0438626415364018 -3.39516439786981 0.00071514240077781 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19224815972328 0.118265164526665 35.4478698482516 2.42210575192977e-174 *** df.mm.trans1 -0.181577667939440 0.107002504833350 -1.69694782586853 0.0900440332940226 . df.mm.trans2 -0.0991591073396734 0.0973459082437141 -1.01862635141705 0.30864759852937 df.mm.exp2 -0.0701120030085421 0.133021567865170 -0.527072444970784 0.598270070983562 df.mm.exp3 -0.115889542348961 0.133021567865170 -0.871208663443404 0.383867016465235 df.mm.exp4 -0.182338557309385 0.133021567865170 -1.37074431038283 0.170788267529706 df.mm.exp5 -0.107900091495724 0.133021567865170 -0.811147344204295 0.417490398332209 df.mm.exp6 -0.134839428423315 0.133021567865170 -1.01366590837350 0.311008126593680 df.mm.exp7 -0.0326300108395711 0.133021567865170 -0.245298648657072 0.806279785445672 df.mm.exp8 -0.209245940475576 0.133021567865170 -1.57302265966122 0.116056641202714 df.mm.trans1:exp2 0.0391687240322845 0.129049876421968 0.303516168463505 0.761565003045853 df.mm.trans2:exp2 0.0589813463115488 0.110197310837423 0.53523398949876 0.592617131498316 df.mm.trans1:exp3 0.137982199305463 0.129049876421968 1.06921605143028 0.285252168759705 df.mm.trans2:exp3 0.0697522481054079 0.110197310837423 0.632975955359884 0.526906302944798 df.mm.trans1:exp4 0.168837634175256 0.129049876421968 1.30831302482763 0.191093212940513 df.mm.trans2:exp4 0.154682471174983 0.110197310837423 1.40368644206927 0.160748953743539 df.mm.trans1:exp5 0.111946184464876 0.129049876421968 0.867464484032778 0.385913272715614 df.mm.trans2:exp5 0.0214470392931972 0.110197310837423 0.194623980659915 0.845730203391124 df.mm.trans1:exp6 0.0762937664582082 0.129049876421968 0.591195966811643 0.55453402939755 df.mm.trans2:exp6 0.125699829237018 0.110197310837423 1.14067964346667 0.254299464683709 df.mm.trans1:exp7 0.0314221518251009 0.129049876421968 0.243488430181495 0.80768121018296 df.mm.trans2:exp7 -0.00910787623730593 0.110197310837423 -0.0826506215813473 0.934147288191918 df.mm.trans1:exp8 0.197034935629738 0.129049876421968 1.52681227671596 0.127150654421889 df.mm.trans2:exp8 0.208686486297903 0.110197310837423 1.8937529846421 0.0585706262185261 . df.mm.trans1:probe2 0.0137833754261984 0.0645249382109839 0.213613151881361 0.830895968922255 df.mm.trans1:probe3 0.0653444828068036 0.0645249382109839 1.01270120698357 0.311468581436723 df.mm.trans1:probe4 0.0375960435934919 0.0645249382109839 0.582659118100356 0.560265263399644 df.mm.trans1:probe5 -0.0511815251298746 0.0645249382109839 -0.79320533345605 0.427862271219496 df.mm.trans1:probe6 0.0538849853091652 0.0645249382109839 0.83510324539904 0.403875917289071 df.mm.trans1:probe7 0.0387123536521059 0.0645249382109839 0.59995956176702 0.54868060424646 df.mm.trans1:probe8 0.034557779060801 0.0645249382109839 0.535572447164596 0.592383235852352 df.mm.trans1:probe9 0.0673349621904693 0.0645249382109839 1.04354942534462 0.296967433125159 df.mm.trans1:probe10 0.0850612378463615 0.0645249382109839 1.31826918715099 0.187740946856029 df.mm.trans1:probe11 0.0203577880091294 0.0645249382109839 0.315502634695494 0.752451448767999 df.mm.trans1:probe12 -0.0139120161624765 0.0645249382109839 -0.215606811074921 0.82934193208353 df.mm.trans1:probe13 0.075504376421682 0.0645249382109839 1.17015805849821 0.242239744538428 df.mm.trans1:probe14 0.0231070405618083 0.0645249382109839 0.358110231524017 0.720342792619857 df.mm.trans1:probe15 0.119629979719028 0.0645249382109839 1.85401153470092 0.064056705035864 . df.mm.trans1:probe16 0.133025075091236 0.0645249382109839 2.06160716739116 0.0395249880106284 * df.mm.trans1:probe17 0.111667701532205 0.0645249382109839 1.73061307191141 0.083855383633279 . df.mm.trans1:probe18 0.0140518028450459 0.0645249382109839 0.217773208849876 0.827654005431517 df.mm.trans1:probe19 0.000483779356829581 0.0645249382109839 0.00749755629750032 0.994019493524165 df.mm.trans1:probe20 0.153660915053591 0.0645249382109839 2.38141901897139 0.0174481803340023 * df.mm.trans1:probe21 0.0877020960979127 0.0645249382109839 1.35919690168697 0.174416558011316 df.mm.trans1:probe22 0.0454252368468813 0.0645249382109839 0.703995046044829 0.481613615832971 df.mm.trans1:probe23 0.0778675763970364 0.0645249382109839 1.20678265731034 0.227825384948499 df.mm.trans1:probe24 0.00862191650918017 0.0645249382109839 0.133621460914665 0.893731072134189 df.mm.trans1:probe25 -0.0436139592955315 0.0645249382109839 -0.675924076872765 0.499258351579759 df.mm.trans1:probe26 0.0244664416456605 0.0645249382109839 0.379178071673002 0.70464294064474 df.mm.trans1:probe27 -0.00280178712908185 0.0645249382109839 -0.043421771593497 0.965374724543113 df.mm.trans1:probe28 -0.0755515882913397 0.0645249382109839 -1.1708897425721 0.241945624117984 df.mm.trans1:probe29 0.0687491292742361 0.0645249382109839 1.06546602260106 0.28694402596074 df.mm.trans1:probe30 -0.0057362041014308 0.0645249382109839 -0.0888990250974675 0.92918146422095 df.mm.trans1:probe31 0.071387877562703 0.0645249382109839 1.10636103717416 0.268858863430514 df.mm.trans1:probe32 0.0951513503441537 0.0645249382109839 1.47464457901575 0.140649488380187 df.mm.trans2:probe2 -0.0152107679413750 0.0645249382109839 -0.235734715338103 0.813690881779316 df.mm.trans2:probe3 -0.0246405542660771 0.0645249382109839 -0.381876448846914 0.70264100516748 df.mm.trans2:probe4 0.0221582379654305 0.0645249382109839 0.343405799056753 0.731371485999745 df.mm.trans2:probe5 -0.109272392623337 0.0645249382109839 -1.69349085257607 0.090699850092915 . df.mm.trans2:probe6 0.00028590927787979 0.0645249382109839 0.00443098878986792 0.996465552534458 df.mm.trans3:probe2 0.0228982507968756 0.0645249382109839 0.354874431990973 0.72276482069874 df.mm.trans3:probe3 0.0914506812086746 0.0645249382109839 1.41729203846192 0.156735386859856