chr3.15439_chr3_97194941_97195680_-_1.R fitVsDatCorrelation=0.748081407306465 cont.fitVsDatCorrelation=0.264041080556896 fstatistic=15953.6474883356,53,715 cont.fstatistic=7544.98465874676,53,715 residuals=-0.417341337932168,-0.0735903346859028,-0.00158722698627676,0.0705981174445808,0.457857215433223 cont.residuals=-0.416252713214474,-0.105577894898528,-0.0253251502378878,0.0922630125192947,0.787202126726866 predictedValues: Include Exclude Both chr3.15439_chr3_97194941_97195680_-_1.R.tl.Lung 47.9391561552504 45.167770907223 50.4365760318809 chr3.15439_chr3_97194941_97195680_-_1.R.tl.cerebhem 51.6951704340607 46.0031708117086 60.6203036035165 chr3.15439_chr3_97194941_97195680_-_1.R.tl.cortex 49.2109341477246 44.2110964488382 56.2221409956001 chr3.15439_chr3_97194941_97195680_-_1.R.tl.heart 49.3939301378183 45.7237630699549 53.9616952679395 chr3.15439_chr3_97194941_97195680_-_1.R.tl.kidney 48.1407671902997 43.6719741447705 50.7590620261383 chr3.15439_chr3_97194941_97195680_-_1.R.tl.liver 50.1757291420468 48.1716353704753 54.1166737175619 chr3.15439_chr3_97194941_97195680_-_1.R.tl.stomach 47.6455969011361 45.6869240098773 53.5071305942883 chr3.15439_chr3_97194941_97195680_-_1.R.tl.testicle 51.1576176218933 45.6295315322933 54.9122220271454 diffExp=2.77138524802743,5.69199962235214,4.99983769888647,3.67016706786333,4.46879304552912,2.00409377157156,1.95867289125882,5.52808608960002 diffExpScore=0.968840591535114 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 49.3793766905765 52.9868021599645 51.6708127533041 cerebhem 49.4463364473557 51.1561561356129 51.2639083795333 cortex 49.2906863745123 52.9012034674242 51.2152652600499 heart 50.0673396325424 51.5594069125088 49.9281552907172 kidney 49.8161799793221 48.9252430899923 49.8949858294602 liver 51.4184567195696 49.037629244166 50.0636921673429 stomach 48.031829499965 50.9287953921568 51.5089637167157 testicle 52.9423776165554 50.0581956326169 51.1337350814301 cont.diffExp=-3.60742546938803,-1.70981968825721,-3.61051709291186,-1.49206727996646,0.890936889329822,2.38082747540356,-2.89696589219172,2.88418198393844 cont.diffExpScore=2.38611713005727 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=0.436171023621162 cont.tran.correlation=-0.467974111689277 tran.covariance=0.000389715638498893 cont.tran.covariance=-0.000423841930626309 tran.mean=47.4765480015857 cont.tran.mean=50.4966259371776 weightedLogRatios: wLogRatio Lung 0.228676918109093 cerebhem 0.4534385986428 cortex 0.411690471401638 heart 0.298122708810489 kidney 0.372682916216209 liver 0.158770465885480 stomach 0.161313455743703 testicle 0.443443152161642 cont.weightedLogRatios: wLogRatio Lung -0.277441914183398 cerebhem -0.133188006520490 cortex -0.278033176201404 heart -0.115350316983972 kidney 0.070368427485574 liver 0.18566845817951 stomach -0.228468998942634 testicle 0.220777343406147 varWeightedLogRatios=0.0148706734109141 cont.varWeightedLogRatios=0.0410129318581405 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.85519092594713 0.0553422484577033 69.6609016327472 0 *** df.mm.trans1 0.0487022699245358 0.0459532889866985 1.05982120101638 0.289583831946743 df.mm.trans2 -0.0426050230306002 0.0424675285180939 -1.00323763866898 0.316085612222588 df.mm.exp2 -0.0901549926125144 0.0549109413466133 -1.64184023077351 0.101062847785038 df.mm.exp3 -0.103818800612872 0.0549109413466133 -1.89067603043880 0.059071686189993 . df.mm.exp4 -0.0254285600628649 0.0549109413466133 -0.463087309000089 0.643442701692825 df.mm.exp5 -0.0358540221896735 0.0549109413466133 -0.652948598410521 0.513999305056117 df.mm.exp6 0.0395596569879338 0.0549109413466133 0.720433050641439 0.471493862834002 df.mm.exp7 -0.0538123755206972 0.0549109413466133 -0.979993680695042 0.327420707744288 df.mm.exp8 -0.00986920997521425 0.0549109413466133 -0.179731210814927 0.857414512556015 df.mm.trans1:exp2 0.165586727811763 0.0465813812958541 3.55478354667213 0.000403184736177249 *** df.mm.trans2:exp2 0.108481517932122 0.0382797129035542 2.83391670688444 0.00472776797764001 ** df.mm.trans1:exp3 0.130002011444904 0.0465813812958541 2.79085780258893 0.00539701973343927 ** df.mm.trans2:exp3 0.0824108095840242 0.0382797129035542 2.15285861186207 0.0316636640707772 * df.mm.trans1:exp4 0.055323478253569 0.0465813812958541 1.18767363084815 0.235356406699743 df.mm.trans2:exp4 0.0376629029861630 0.0382797129035542 0.98388676741267 0.325504000037819 df.mm.trans1:exp5 0.0400507643172177 0.0465813812958541 0.859801989615587 0.3901863885252 df.mm.trans2:exp5 0.00217679515357873 0.0382797129035542 0.0568655036432267 0.954668221104371 df.mm.trans1:exp6 0.0060391428181468 0.0465813812958541 0.129647139053051 0.896882069115651 df.mm.trans2:exp6 0.0248269135734413 0.0382797129035542 0.64856582482718 0.516827290073919 df.mm.trans1:exp7 0.0476699695886761 0.0465813812958541 1.02336960095511 0.306479237971134 df.mm.trans2:exp7 0.065240706297081 0.0382797129035542 1.70431545454521 0.0887566241689002 . df.mm.trans1:exp8 0.0748479916488042 0.0465813812958541 1.60682207282389 0.108534901432948 df.mm.trans2:exp8 0.0200405387183905 0.0382797129035542 0.523528971308712 0.600768413775456 df.mm.trans1:probe2 -0.0730394103786783 0.0340940800535006 -2.14229010620215 0.032507250491612 * df.mm.trans1:probe3 0.0644904974022038 0.0340940800535006 1.89154531522789 0.0589554827382004 . df.mm.trans1:probe4 -0.0517511849043006 0.0340940800535006 -1.51789357047007 0.129483242080016 df.mm.trans1:probe5 -0.090697206387364 0.0340940800535006 -2.66020394875126 0.00798437670240887 ** df.mm.trans1:probe6 -0.0772558516322647 0.0340940800535006 -2.26596087974905 0.0237519346276656 * df.mm.trans1:probe7 -0.0742445970916527 0.0340940800535006 -2.17763896181236 0.0297591315998315 * df.mm.trans1:probe8 -0.0529850253762949 0.0340940800535006 -1.55408285817217 0.120607151589358 df.mm.trans1:probe9 -0.154178610401942 0.0340940800535006 -4.52215194426727 7.16447930328035e-06 *** df.mm.trans1:probe10 -0.126882059959705 0.0340940800535006 -3.72152760129034 0.000213569735062604 *** df.mm.trans1:probe11 -0.0766285028302571 0.0340940800535006 -2.24756035974607 0.0249083077560619 * df.mm.trans2:probe2 -0.0299979418113100 0.0340940800535006 -0.879857786578697 0.379231975638013 df.mm.trans2:probe3 -0.0385392620963029 0.0340940800535006 -1.13037987931708 0.258695226505571 df.mm.trans2:probe4 -0.0526508768105078 0.0340940800535006 -1.54428207852764 0.122962473290622 df.mm.trans2:probe5 0.118275509365322 0.0340940800535006 3.46909226410343 0.000553525959340791 *** df.mm.trans2:probe6 -0.0433316007471571 0.0340940800535006 -1.27094207203013 0.204162592439441 df.mm.trans3:probe2 -0.0730394103786782 0.0340940800535006 -2.14229010620214 0.032507250491612 * df.mm.trans3:probe3 0.0644904974022039 0.0340940800535006 1.89154531522789 0.0589554827382004 . df.mm.trans3:probe4 -0.0517511849043005 0.0340940800535006 -1.51789357047007 0.129483242080016 df.mm.trans3:probe5 -0.090697206387364 0.0340940800535006 -2.66020394875126 0.00798437670240887 ** df.mm.trans3:probe6 0.228920391446133 0.0340940800535006 6.71437361227842 3.85094296689756e-11 *** df.mm.trans3:probe7 -0.0738539317164234 0.0340940800535006 -2.16618050994576 0.0306271600904588 * df.mm.trans3:probe8 0.00326425530044165 0.0340940800535006 0.0957425833258844 0.923751846802046 df.mm.trans3:probe9 -0.0225277628239935 0.0340940800535006 -0.660752916302267 0.508983611580209 df.mm.trans3:probe10 0.364820820405337 0.0340940800535006 10.7004154337896 6.897582087476e-25 *** df.mm.trans3:probe11 0.0362548879774312 0.0340940800535006 1.06337780402169 0.287969656391892 df.mm.trans3:probe12 0.080360077099621 0.0340940800535006 2.35700969122849 0.0186914296011819 * df.mm.trans3:probe13 0.205109976472749 0.0340940800535006 6.01599973223765 2.85335845926905e-09 *** df.mm.trans3:probe14 0.156904935134169 0.0340940800535006 4.60211669849877 4.94803440575359e-06 *** df.mm.trans3:probe15 0.0631704642345994 0.0340940800535006 1.85282794360405 0.0643188837847349 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.86422672307836 0.0804365144320845 48.0407032845893 4.96007475306556e-226 *** df.mm.trans1 0.0405077168979989 0.0667902460740337 0.606491505557534 0.544380931369163 df.mm.trans2 0.116532320800801 0.0617239101362376 1.88796076825966 0.0594358820894611 . df.mm.exp2 -0.0258988111281194 0.0798096363844322 -0.324507318932871 0.745648896350513 df.mm.exp3 0.00544094114262722 0.0798096363844322 0.0681739873668756 0.945666193773234 df.mm.exp4 0.0208358332410115 0.0798096363844322 0.261069141333361 0.794114365224944 df.mm.exp5 -0.0359698569388814 0.0798096363844322 -0.450695662433788 0.652345546589746 df.mm.exp6 -0.0053935587696134 0.0798096363844322 -0.0675802949863517 0.946138635033549 df.mm.exp7 -0.0641461071079527 0.0798096363844322 -0.803738871819559 0.421814923690602 df.mm.exp8 0.0232632355252950 0.0798096363844322 0.291484043521251 0.770765687864154 df.mm.trans1:exp2 0.0272539193261732 0.0677031391620096 0.402550305104112 0.687399395571353 df.mm.trans2:exp2 -0.00926121585163728 0.0556371807295841 -0.166457317394460 0.867844130338035 df.mm.trans1:exp3 -0.00723865642109588 0.0677031391620096 -0.106917589209183 0.914884350676796 df.mm.trans2:exp3 -0.00705771937315825 0.0556371807295841 -0.126852570180743 0.899092751196438 df.mm.trans1:exp4 -0.00699980240105934 0.0677031391620096 -0.103389628423421 0.91768273486868 df.mm.trans2:exp4 -0.0481440247895579 0.0556371807295841 -0.865321070518552 0.387152827393752 df.mm.trans1:exp5 0.0447768262570622 0.0677031391620096 0.66137001638748 0.508588111763526 df.mm.trans2:exp5 -0.0437795278588337 0.0556371807295841 -0.786875382338609 0.431615552354945 df.mm.trans1:exp6 0.0458578857968634 0.0677031391620096 0.677337659146472 0.498410838146479 df.mm.trans2:exp6 -0.0720613607720035 0.0556371807295841 -1.29520151501289 0.195668758391441 df.mm.trans1:exp7 0.0364771517078299 0.0677031391620096 0.538780803361897 0.590205813488263 df.mm.trans2:exp7 0.0245317288064761 0.0556371807295841 0.440923290590672 0.659401818210122 df.mm.trans1:exp8 0.0464080106708458 0.0677031391620096 0.685463203704546 0.493273845797637 df.mm.trans2:exp8 -0.0801198609588188 0.0556371807295841 -1.44004171146322 0.150293294884909 df.mm.trans1:probe2 -0.0439185407110862 0.0495536238352874 -0.886283127487673 0.375762966370117 df.mm.trans1:probe3 0.00223616677066925 0.0495536238352874 0.0451262006206066 0.964019317539722 df.mm.trans1:probe4 -0.00350932358522843 0.0495536238352874 -0.0708187073642315 0.943561845805981 df.mm.trans1:probe5 0.0289687992515485 0.0495536238352874 0.584594970245539 0.559004502282396 df.mm.trans1:probe6 -0.0356218726949921 0.0495536238352874 -0.718855049095835 0.47246509093523 df.mm.trans1:probe7 -0.0550940497509951 0.0495536238352874 -1.11180667500976 0.266594976547138 df.mm.trans1:probe8 -0.00327151937860645 0.0495536238352874 -0.0660197807022295 0.9473805320917 df.mm.trans1:probe9 -0.0265842448374248 0.0495536238352874 -0.536474283410409 0.591797675078187 df.mm.trans1:probe10 -0.00533038332128892 0.0495536238352874 -0.107567982091617 0.914368572341635 df.mm.trans1:probe11 0.0328918124270174 0.0495536238352874 0.663761999250495 0.507056615604807 df.mm.trans2:probe2 -0.0112682709234045 0.0495536238352874 -0.227395497065147 0.820181225989336 df.mm.trans2:probe3 -0.0764137768369318 0.0495536238352874 -1.54204215398909 0.123505793030038 df.mm.trans2:probe4 -0.0627336546600709 0.0495536238352874 -1.26597511553530 0.205934316702374 df.mm.trans2:probe5 -0.00430157143670129 0.0495536238352874 -0.0868063948461044 0.930849708213623 df.mm.trans2:probe6 -0.0703224470564257 0.0495536238352874 -1.41911815148317 0.156300351919970 df.mm.trans3:probe2 -0.155398741847342 0.0495536238352874 -3.13597129372164 0.00178303577505879 ** df.mm.trans3:probe3 -0.089665451442904 0.0495536238352874 -1.80946305240855 0.0707989158935874 . df.mm.trans3:probe4 -0.116613911824282 0.0495536238352874 -2.35328726334724 0.0188781232500402 * df.mm.trans3:probe5 -0.0890473227630084 0.0495536238352874 -1.79698911746586 0.0727592592575497 . df.mm.trans3:probe6 -0.113977869219321 0.0495536238352874 -2.30009150487511 0.0217301640753969 * df.mm.trans3:probe7 -0.125722583792645 0.0495536238352874 -2.53710171047303 0.011388874066656 * df.mm.trans3:probe8 -0.0897817553230301 0.0495536238352874 -1.81181008318298 0.0704349636510729 . df.mm.trans3:probe9 -0.0146873540378034 0.0495536238352874 -0.296393137394414 0.767015935869443 df.mm.trans3:probe10 -0.0077327041586425 0.0495536238352874 -0.156047198169511 0.876039864846812 df.mm.trans3:probe11 -0.115619346122577 0.0495536238352874 -2.3332167695119 0.0199131551995177 * df.mm.trans3:probe12 -0.0449010368588669 0.0495536238352874 -0.906110055807718 0.365182846889127 df.mm.trans3:probe13 -0.0301910358899467 0.0495536238352874 -0.609259899746172 0.542545720869217 df.mm.trans3:probe14 -0.0838450044227675 0.0495536238352874 -1.69200550703339 0.0910806277463727 . df.mm.trans3:probe15 -0.068421215909092 0.0495536238352874 -1.38075100494202 0.167786972941526