chr17.10852_chr17_55452991_55454163_+_1.R fitVsDatCorrelation=0.829622928103273 cont.fitVsDatCorrelation=0.254363069252489 fstatistic=12135.0343811543,60,876 cont.fstatistic=4034.74970053429,60,876 residuals=-0.509038514316144,-0.078043129908896,0.000859928320019662,0.0829750886027808,0.735385429451266 cont.residuals=-0.503402166667575,-0.138957877397546,-0.0390259807446602,0.0933255887441189,1.27250912383849 predictedValues: Include Exclude Both chr17.10852_chr17_55452991_55454163_+_1.R.tl.Lung 54.0296337963815 61.3840014935908 66.0249881761444 chr17.10852_chr17_55452991_55454163_+_1.R.tl.cerebhem 56.5090567748534 67.1455666138068 62.8580907466973 chr17.10852_chr17_55452991_55454163_+_1.R.tl.cortex 57.6122371205378 58.0011833980446 65.4942920990427 chr17.10852_chr17_55452991_55454163_+_1.R.tl.heart 55.0977865020072 59.6952834199158 63.3640690495707 chr17.10852_chr17_55452991_55454163_+_1.R.tl.kidney 53.9150593369151 62.7306181543276 64.6792778625873 chr17.10852_chr17_55452991_55454163_+_1.R.tl.liver 50.5035399524848 58.4212272465656 61.706633026156 chr17.10852_chr17_55452991_55454163_+_1.R.tl.stomach 52.2359444084085 59.7228225059538 60.9865577759143 chr17.10852_chr17_55452991_55454163_+_1.R.tl.testicle 53.697873880951 58.8079171450082 62.7114641212949 diffExp=-7.35436769720931,-10.6365098389534,-0.388946277506840,-4.59749691790851,-8.81555881741244,-7.91768729408085,-7.48687809754529,-5.11004326405719 diffExpScore=0.981240909416694 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 59.6284288599526 55.4717103246151 58.3520341017477 cerebhem 61.6868620537563 55.0210641896215 59.5597277494658 cortex 60.8703048883899 57.7313861987218 59.8634380617364 heart 59.0420765550959 49.6000760222131 57.3783880005028 kidney 57.762695916408 57.7019383251686 60.302065416726 liver 58.2388633343517 56.0197950257584 59.2342592506943 stomach 64.6520198149541 58.5598723009785 61.0782840439133 testicle 58.0361975122152 56.718484817545 62.7025467686333 cont.diffExp=4.15671853533749,6.66579786413486,3.13891868966810,9.44200053288278,0.060757591239394,2.21906830859333,6.09214751397559,1.31771269467016 cont.diffExpScore=0.970668570396552 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=0.321337661861069 cont.tran.correlation=0.279606901279988 tran.covariance=0.000649261179531173 cont.tran.covariance=0.00052885959778285 tran.mean=57.4693594843595 cont.tran.mean=57.9213610087341 weightedLogRatios: wLogRatio Lung -0.517273466254635 cerebhem -0.710652323500006 cortex -0.0272978617734472 heart -0.324515436686913 kidney -0.61531965084195 liver -0.581795119430588 stomach -0.538819755978169 testicle -0.366232608658209 cont.weightedLogRatios: wLogRatio Lung 0.292794836954425 cerebhem 0.464840405985637 cortex 0.216133795065916 heart 0.695484766884444 kidney 0.004268349244542 liver 0.157144264818199 stomach 0.407708922514934 testicle 0.0930057420774465 varWeightedLogRatios=0.0465223875700279 cont.varWeightedLogRatios=0.0503414643801295 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.09308257834659 0.0669627635649055 61.1247559157151 0 *** df.mm.trans1 -0.155007599500132 0.0565592865002998 -2.74062155114546 0.00625711449423906 ** df.mm.trans2 0.0420721972875141 0.0504689157958886 0.83362593834325 0.404719046600305 df.mm.exp2 0.183735532903599 0.0641809910412564 2.8627718257808 0.00429968040300991 ** df.mm.exp3 0.0155867834262374 0.0641809910412564 0.242856695936933 0.808173264443026 df.mm.exp4 0.032816958850283 0.0641809910412564 0.51131897962105 0.609256567806787 df.mm.exp5 0.0401699891326727 0.0641809910412564 0.625886083729229 0.531552696371855 df.mm.exp6 -0.0493173238302717 0.0641809910412564 -0.76841013250434 0.44245065944188 df.mm.exp7 0.0181829762803002 0.0641809910412564 0.2833078141254 0.77700782522483 df.mm.exp8 0.00245699743042256 0.0641809910412564 0.0382823230143512 0.969471306922545 df.mm.trans1:exp2 -0.138867280747267 0.0567912462699817 -2.44522333753870 0.01467222234684 * df.mm.trans2:exp2 -0.0940218734536346 0.0416572009301013 -2.25703771147271 0.0242513402225953 * df.mm.trans1:exp3 0.0486155416706887 0.0567912462699817 0.856039352254636 0.3922101026493 df.mm.trans2:exp3 -0.0722726088806898 0.0416572009301013 -1.73493675203861 0.0831037241396898 . df.mm.trans1:exp4 -0.0132400859256587 0.0567912462699817 -0.233136034076735 0.815710237894022 df.mm.trans2:exp4 -0.0607131854715777 0.0416572009301013 -1.45744755086765 0.145351254850926 df.mm.trans1:exp5 -0.0422928262850605 0.0567912462699817 -0.74470678252073 0.45664873146008 df.mm.trans2:exp5 -0.0184695720740958 0.0416572009301013 -0.443370453648261 0.657607280777661 df.mm.trans1:exp6 -0.0181719143261126 0.0567912462699817 -0.31997738242483 0.749061776867608 df.mm.trans2:exp6 -0.000152611336366113 0.0416572009301013 -0.00366350433919425 0.997077787082187 df.mm.trans1:exp7 -0.0519447982117136 0.0567912462699817 -0.914662058387865 0.360620760799831 df.mm.trans2:exp7 -0.0456179816100208 0.0416572009301013 -1.09508033644808 0.273782433259254 df.mm.trans1:exp8 -0.00861625928285887 0.0567912462699817 -0.151718087711930 0.879444223348682 df.mm.trans2:exp8 -0.0453297454737277 0.0416572009301013 -1.08816109728037 0.276823282269051 df.mm.trans1:probe2 0.0800706216092837 0.0422836601570897 1.89365398623984 0.0586002228760841 . df.mm.trans1:probe3 0.0065049719648761 0.0422836601570897 0.153841269670346 0.877770325445235 df.mm.trans1:probe4 -0.0379129919014301 0.0422836601570897 -0.896634580842295 0.370160274160382 df.mm.trans1:probe5 0.155677223658323 0.0422836601570897 3.68173481387279 0.000245830429615911 *** df.mm.trans1:probe6 -0.0271222578075992 0.0422836601570897 -0.64143590471677 0.521407299379268 df.mm.trans1:probe7 0.0439463082318021 0.0422836601570897 1.03932129026994 0.298942111566253 df.mm.trans1:probe8 -0.00608785030873785 0.0422836601570897 -0.143976426972515 0.88555219837575 df.mm.trans1:probe9 0.0278651455775970 0.0422836601570897 0.65900505003763 0.510065709304275 df.mm.trans1:probe10 0.0718911154150685 0.0422836601570897 1.70021032114966 0.0894463307753704 . df.mm.trans1:probe11 0.157113287571165 0.0422836601570897 3.71569743459926 0.000215492590168092 *** df.mm.trans1:probe12 0.182874794929229 0.0422836601570897 4.32495186674531 1.70104252910054e-05 *** df.mm.trans1:probe13 0.0930866779576215 0.0422836601570897 2.20148108304228 0.0279620647914703 * df.mm.trans1:probe14 0.111414865298645 0.0422836601570897 2.63493900208078 0.00856387596358246 ** df.mm.trans1:probe15 0.143911797207338 0.0422836601570897 3.40348486088209 0.000695501374604788 *** df.mm.trans1:probe16 0.180293188277209 0.0422836601570897 4.2638973922171 2.22713921355581e-05 *** df.mm.trans2:probe2 0.007542727713798 0.0422836601570897 0.178383982980086 0.858462671492119 df.mm.trans2:probe3 -0.0410527869590128 0.0422836601570897 -0.97089009812528 0.331871003565181 df.mm.trans2:probe4 -0.0718438746340899 0.0422836601570897 -1.69909308624608 0.0896566986402121 . df.mm.trans2:probe5 -0.207034983860653 0.0422836601570897 -4.89633544237867 1.16241960853301e-06 *** df.mm.trans2:probe6 -0.101738420076720 0.0422836601570897 -2.40609303212511 0.0163303832269193 * df.mm.trans3:probe2 0.0800706216092837 0.0422836601570897 1.89365398623984 0.0586002228760841 . df.mm.trans3:probe3 0.00650497196487616 0.0422836601570897 0.153841269670347 0.877770325445235 df.mm.trans3:probe4 -0.0379129919014301 0.0422836601570897 -0.896634580842294 0.370160274160382 df.mm.trans3:probe5 0.155677223658323 0.0422836601570897 3.68173481387279 0.000245830429615911 *** df.mm.trans3:probe6 -0.0271222578075992 0.0422836601570897 -0.64143590471677 0.521407299379268 df.mm.trans3:probe7 0.0439463082318021 0.0422836601570897 1.03932129026994 0.298942111566253 df.mm.trans3:probe8 -0.0060878503087378 0.0422836601570897 -0.143976426972514 0.88555219837575 df.mm.trans3:probe9 0.0278651455775971 0.0422836601570897 0.659005050037632 0.510065709304275 df.mm.trans3:probe10 0.0718911154150685 0.0422836601570897 1.70021032114966 0.0894463307753704 . df.mm.trans3:probe11 0.300791220806353 0.0422836601570897 7.11365145989899 2.34901119719723e-12 *** df.mm.trans3:probe12 0.316053641890875 0.0422836601570897 7.47460462780876 1.87688721213908e-13 *** df.mm.trans3:probe13 0.326119563504895 0.0422836601570897 7.71266163556596 3.34590380039287e-14 *** df.mm.trans3:probe14 0.312853345481814 0.0422836601570897 7.39891826581523 3.21647162507234e-13 *** df.mm.trans3:probe15 0.508144645322654 0.0422836601570897 12.0175179592974 6.66291358942028e-31 *** df.mm.trans3:probe16 0.324587237321988 0.0422836601570897 7.6764224316462 4.36314244271072e-14 *** df.mm.trans3:probe17 1.16468180968345 0.0422836601570897 27.5444889434003 8.46715290892412e-121 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.12930301470819 0.115990476152521 35.6003626476917 2.18354593430052e-172 *** df.mm.trans1 0.0112139580849378 0.0979699496072599 0.114463242350252 0.908896812545477 df.mm.trans2 -0.131714666020489 0.0874204298392278 -1.50668060386710 0.13225305484324 df.mm.exp2 0.00529603288452161 0.111171990439140 0.0476381943293611 0.96201545017095 df.mm.exp3 0.0349690929824652 0.111171990439140 0.314549490787509 0.753178635196685 df.mm.exp4 -0.104936401719908 0.111171990439140 -0.943910433782823 0.345475675703366 df.mm.exp5 -0.0252438245193713 0.111171990439140 -0.227070005849998 0.820422286437768 df.mm.exp6 -0.0287534834907337 0.111171990439140 -0.258639639149707 0.795974053678536 df.mm.exp7 0.089401246581775 0.111171990439140 0.804170602942628 0.421516628532234 df.mm.exp8 -0.076746388797907 0.111171990439140 -0.690339252672833 0.490163726568727 df.mm.trans1:exp2 0.0286424880350646 0.0983717419273685 0.29116581117586 0.770993434796719 df.mm.trans2:exp2 -0.0134531026245440 0.0721570961804811 -0.186441851691132 0.852141441407715 df.mm.trans1:exp3 -0.0143560962844583 0.0983717419273685 -0.145937197036299 0.884004541209022 df.mm.trans2:exp3 0.00495872064072839 0.0721570961804811 0.0687211778634428 0.94522724340505 df.mm.trans1:exp4 0.0950542987570667 0.0983717419273685 0.966276462068231 0.334172543672322 df.mm.trans2:exp4 -0.00694439876267578 0.0721570961804811 -0.096239997592285 0.923351976864378 df.mm.trans1:exp5 -0.0065454621724241 0.0983717419273684 -0.0665380326116097 0.946964661553083 df.mm.trans2:exp5 0.0646614237081546 0.0721570961804811 0.896120092560569 0.370434808293845 df.mm.trans1:exp6 0.00517391562460437 0.0983717419273685 0.0525955474939588 0.958066155824162 df.mm.trans2:exp6 0.0385854274502532 0.0721570961804811 0.534741965693053 0.592963995664317 df.mm.trans1:exp7 -0.00851435418812704 0.0983717419273685 -0.0865528455764614 0.931046728185404 df.mm.trans2:exp7 -0.0352247245019866 0.0721570961804812 -0.488167157030289 0.625553611389917 df.mm.trans1:exp8 0.049680845076004 0.0983717419273685 0.505031669691132 0.613663619494875 df.mm.trans2:exp8 0.0989733903748548 0.0721570961804812 1.37163765747031 0.170527465445671 df.mm.trans1:probe2 -0.0392678515161968 0.0732422261864745 -0.536136782847383 0.592000128013694 df.mm.trans1:probe3 -0.141690761037375 0.0732422261864745 -1.93455016886886 0.0533672223623196 . df.mm.trans1:probe4 -0.107416045339338 0.0732422261864746 -1.46658629771652 0.142847511410491 df.mm.trans1:probe5 -0.057366598804043 0.0732422261864746 -0.783244881961776 0.433695022307606 df.mm.trans1:probe6 -0.107871154050901 0.0732422261864746 -1.47280004537630 0.141164157328531 df.mm.trans1:probe7 -0.0169555438320574 0.0732422261864745 -0.231499569509106 0.816980782412342 df.mm.trans1:probe8 -0.0066024917073271 0.0732422261864746 -0.0901459724956634 0.928191823922093 df.mm.trans1:probe9 -0.112135764376461 0.0732422261864745 -1.53102616093295 0.126123952910002 df.mm.trans1:probe10 -0.0638632992480768 0.0732422261864745 -0.871946451838874 0.383476505690019 df.mm.trans1:probe11 -0.128598333727474 0.0732422261864745 -1.75579498908270 0.0794728816591691 . df.mm.trans1:probe12 0.00326628879152189 0.0732422261864745 0.0445957060781566 0.964439728682306 df.mm.trans1:probe13 -0.145610285808967 0.0732422261864746 -1.98806471881730 0.0471154106165415 * df.mm.trans1:probe14 -0.0913813439275673 0.0732422261864745 -1.24765929007825 0.212489272952806 df.mm.trans1:probe15 -0.0839513795921459 0.0732422261864745 -1.14621556393447 0.252018971594851 df.mm.trans1:probe16 -0.105399353341037 0.0732422261864746 -1.43905174417679 0.150493067706586 df.mm.trans2:probe2 -0.00353064008310306 0.0732422261864746 -0.0482049804727953 0.961563868719678 df.mm.trans2:probe3 0.00930079284167785 0.0732422261864746 0.126986757857387 0.898980021265257 df.mm.trans2:probe4 0.124443517966003 0.0732422261864746 1.69906793451595 0.0896614391317449 . df.mm.trans2:probe5 0.128085861935445 0.0732422261864745 1.74879804457798 0.0806761775794031 . df.mm.trans2:probe6 0.16225128664145 0.0732422261864746 2.21526973017394 0.0269980752708639 * df.mm.trans3:probe2 -0.000835911208808269 0.0732422261864745 -0.0114129683426066 0.990896565061347 df.mm.trans3:probe3 0.0511897805143742 0.0732422261864745 0.698910767458721 0.484793311022516 df.mm.trans3:probe4 0.0279969760347309 0.0732422261864746 0.382251844222358 0.702367286310329 df.mm.trans3:probe5 0.0277917163404222 0.0732422261864745 0.379449366676330 0.704446160458464 df.mm.trans3:probe6 0.0368134360667908 0.0732422261864746 0.502625848278613 0.615353683292222 df.mm.trans3:probe7 -0.00460059906196188 0.0732422261864745 -0.0628134793479484 0.949929361233606 df.mm.trans3:probe8 0.0766801898044796 0.0732422261864745 1.04693963847101 0.295416198587454 df.mm.trans3:probe9 -0.00661144886602076 0.0732422261864746 -0.0902682675044314 0.928094671005158 df.mm.trans3:probe10 0.112685253353477 0.0732422261864745 1.53852851313640 0.124280555512550 df.mm.trans3:probe11 0.119570054531600 0.0732422261864745 1.63252894890407 0.102927478787731 df.mm.trans3:probe12 0.0519965576651069 0.0732422261864745 0.709925959005175 0.477938981003478 df.mm.trans3:probe13 0.106677882961286 0.0732422261864745 1.45650792603825 0.145610580781568 df.mm.trans3:probe14 0.0789948841225005 0.0732422261864745 1.07854291486689 0.281088429824616 df.mm.trans3:probe15 0.0848820815570523 0.0732422261864745 1.15892274138340 0.246803496921058 df.mm.trans3:probe16 0.187911011374695 0.0732422261864745 2.56561032014885 0.0104648068288772 * df.mm.trans3:probe17 0.0296206338443963 0.0732422261864746 0.404420173807692 0.686002488660377