chr7.21617_chr7_91826953_91833332_+_2.R fitVsDatCorrelation=0.89927455333802 cont.fitVsDatCorrelation=0.293455307714151 fstatistic=12909.6535985825,49,623 cont.fstatistic=2692.35246736063,49,623 residuals=-0.542987347809922,-0.0824352191911881,0.00401002333343743,0.0812929791581474,0.425508463556914 cont.residuals=-0.711212465457721,-0.225446909586915,-0.0232483320777325,0.225619638764835,0.830279001173355 predictedValues: Include Exclude Both chr7.21617_chr7_91826953_91833332_+_2.R.tl.Lung 80.2695674137338 81.5276502845035 121.297339221925 chr7.21617_chr7_91826953_91833332_+_2.R.tl.cerebhem 75.9853700552754 79.2424294706919 120.526156483393 chr7.21617_chr7_91826953_91833332_+_2.R.tl.cortex 79.2723999847798 75.0820715517017 107.887146047634 chr7.21617_chr7_91826953_91833332_+_2.R.tl.heart 82.8580075703783 80.704427278109 129.340197641740 chr7.21617_chr7_91826953_91833332_+_2.R.tl.kidney 66.7625804178788 80.4088682259485 91.9462301352385 chr7.21617_chr7_91826953_91833332_+_2.R.tl.liver 66.053803475307 73.4589410677985 77.9054226353879 chr7.21617_chr7_91826953_91833332_+_2.R.tl.stomach 75.6511403591682 85.7277427913774 113.6951266097 chr7.21617_chr7_91826953_91833332_+_2.R.tl.testicle 74.6309737981986 75.0295523296953 105.111534142009 diffExp=-1.25808287076968,-3.25705941541656,4.19032843307808,2.15358029226938,-13.6462878080697,-7.40513759249149,-10.0766024322093,-0.398578531496639 diffExpScore=1.38073745511769 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,-1,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 80.1747366501084 91.4589338996513 84.3674477336591 cerebhem 79.8594416012968 74.4209375374901 94.9618710571572 cortex 82.0786934604374 79.2956486425175 86.232007925188 heart 80.5932870963905 86.3985472164803 84.2852586118246 kidney 76.5915513410266 83.7062090099653 77.2105204512215 liver 80.6784093112931 97.4160229524772 75.6641681175629 stomach 77.5507374433509 70.7364167439924 85.2557896205595 testicle 74.152556647379 78.9781571605255 89.5809318796915 cont.diffExp=-11.2841972495429,5.43850406380668,2.78304481791996,-5.80526012008973,-7.11465766893878,-16.7376136411841,6.8143206993585,-4.82560051314651 cont.diffExpScore=1.91618033074482 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,-1,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.310714049318372 cont.tran.correlation=0.346709067472956 tran.covariance=0.00137420987189289 cont.tran.covariance=0.00120847142335423 tran.mean=77.0415953796591 cont.tran.mean=80.8806429196489 weightedLogRatios: wLogRatio Lung -0.068321060936365 cerebhem -0.182638167192715 cortex 0.236009181449422 heart 0.115978116467643 kidney -0.798630360079192 liver -0.45091205251445 stomach -0.548774820732297 testicle -0.0229847869777384 cont.weightedLogRatios: wLogRatio Lung -0.585989610414071 cerebhem 0.306456468083069 cortex 0.151448827368016 heart -0.307726971159329 kidney -0.389317144906596 liver -0.845460145650462 stomach 0.395933977524193 testicle -0.273474714295294 varWeightedLogRatios=0.125724593150446 cont.varWeightedLogRatios=0.19332383016517 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.85508823081547 0.0766266258044502 50.3100350608362 1.36606442135117e-221 *** df.mm.trans1 0.303391314605172 0.0685697540215031 4.42456472149557 1.14067717570416e-05 *** df.mm.trans2 0.5911878127218 0.0633988260206717 9.32490157671119 1.90767699357677e-19 *** df.mm.exp2 -0.0769020049321466 0.0869418920494898 -0.884521869944719 0.376755794182658 df.mm.exp3 0.0222981754812213 0.0869418920494898 0.256472167278445 0.797670938042769 df.mm.exp4 -0.0426122115558148 0.0869418920494898 -0.4901228918685 0.624219494375953 df.mm.exp5 0.0789753576862748 0.0869418920494898 0.908369438766295 0.364034312560956 df.mm.exp6 0.143612765838102 0.0869418920494898 1.65182471249134 0.0990741843163221 . df.mm.exp7 0.0557005565330158 0.0869418920494898 0.640664186389105 0.521976461634268 df.mm.exp8 -0.0126722427179890 0.0869418920494898 -0.145755313339346 0.884161699801962 df.mm.trans1:exp2 0.0220522644264378 0.0826982349295597 0.266659432879328 0.789819627715425 df.mm.trans2:exp2 0.0484716559396228 0.0726582863339774 0.667118072628639 0.504943723433125 df.mm.trans1:exp3 -0.0347987160004853 0.0826982349295597 -0.420791520280039 0.674052438229843 df.mm.trans2:exp3 -0.104658602888069 0.0726582863339774 -1.44042212070624 0.150250354294657 df.mm.trans1:exp4 0.0743500391917469 0.0826982349295597 0.899052310549027 0.368972147102594 df.mm.trans2:exp4 0.0324634162668173 0.0726582863339775 0.446795787580202 0.655177785942381 df.mm.trans1:exp5 -0.263223170473435 0.0826982349295597 -3.1829357748402 0.00153049498561589 ** df.mm.trans2:exp5 -0.0927931162695337 0.0726582863339774 -1.27711677430714 0.202036785645691 df.mm.trans1:exp6 -0.338533714727789 0.0826982349295597 -4.09360266293645 4.80651960522268e-05 *** df.mm.trans2:exp6 -0.247828370639464 0.0726582863339774 -3.41087552630003 0.000689424301974619 *** df.mm.trans1:exp7 -0.114958605254942 0.0826982349295597 -1.39009744709619 0.164995740255413 df.mm.trans2:exp7 -0.00546629346574003 0.0726582863339774 -0.075232898290691 0.940053560418628 df.mm.trans1:exp8 -0.060162700864997 0.0826982349295597 -0.72749679501917 0.467194978426985 df.mm.trans2:exp8 -0.0703879202961535 0.0726582863339775 -0.968752827070705 0.333044458495344 df.mm.trans1:probe2 -0.127709847959129 0.0413491174647799 -3.08857493918484 0.00210068087062229 ** df.mm.trans1:probe3 -0.104096356525017 0.0413491174647798 -2.51749887077235 0.0120688555651835 * df.mm.trans1:probe4 -0.0728784352865047 0.0413491174647799 -1.76251489160756 0.078472640198678 . df.mm.trans1:probe5 -0.0747430308250846 0.0413491174647799 -1.80760885377418 0.0711495893300246 . df.mm.trans1:probe6 -0.0477035769788139 0.0413491174647799 -1.15367823798045 0.249074503957512 df.mm.trans1:probe7 -0.114796847578109 0.0413491174647799 -2.77628289590194 0.0056636627981486 ** df.mm.trans1:probe8 0.458854185215781 0.0413491174647799 11.0970732472494 3.09642728348375e-26 *** df.mm.trans1:probe9 0.711020183902662 0.0413491174647799 17.1955346932928 1.60565538242116e-54 *** df.mm.trans1:probe10 0.619573944191058 0.0413491174647799 14.9839702073157 1.43047220969757e-43 *** df.mm.trans1:probe11 0.723857780432785 0.0413491174647798 17.5060031462425 4.19942353789729e-56 *** df.mm.trans1:probe12 0.644671806020453 0.0413491174647799 15.59094475401 1.63212160868407e-46 *** df.mm.trans1:probe13 0.523561651702784 0.0413491174647798 12.6619788717073 7.3503298573757e-33 *** df.mm.trans1:probe14 0.27687887897479 0.0413491174647798 6.69612547863032 4.78425072071498e-11 *** df.mm.trans1:probe15 0.39404269467049 0.0413491174647799 9.52965187240394 3.4579913308605e-20 *** df.mm.trans1:probe16 0.437050511263733 0.0413491174647799 10.5697663713380 3.94274423208222e-24 *** df.mm.trans1:probe17 0.269429093882323 0.0413491174647799 6.51595754399875 1.49090170036074e-10 *** df.mm.trans1:probe18 0.366923624200791 0.0413491174647799 8.87379578326739 7.45552218282808e-18 *** df.mm.trans1:probe19 0.108106127198628 0.0413491174647799 2.61447241989409 0.00915267388254637 ** df.mm.trans2:probe2 0.0936468140317396 0.0413491174647798 2.26478386416604 0.0238686581687669 * df.mm.trans2:probe3 -0.106898650392512 0.0413491174647799 -2.58527042284677 0.00995630657140305 ** df.mm.trans2:probe4 -0.104586738192676 0.0413491174647799 -2.52935841452385 0.0116726405735412 * df.mm.trans2:probe5 -0.163353949480897 0.0413491174647798 -3.95060304781687 8.68671583036688e-05 *** df.mm.trans2:probe6 -0.126811797772209 0.0413491174647799 -3.06685621235384 0.00225698954948629 ** df.mm.trans3:probe2 0.179969872143272 0.0413491174647798 4.3524477226525 1.5733086409774e-05 *** df.mm.trans3:probe3 -0.0342373059899407 0.0413491174647799 -0.828005725130728 0.407984325712671 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.41518462081300 0.16747953991617 26.3625313457571 1.87924757102715e-103 *** df.mm.trans1 -0.0156250085330408 0.149869979724715 -0.104257093793842 0.916998880502145 df.mm.trans2 0.0803690533493885 0.138568103471819 0.579996776572276 0.562126486848482 df.mm.exp2 -0.328386909325660 0.190025176327733 -1.72812316594985 0.0844616568214982 . df.mm.exp3 -0.141096597669747 0.190025176327733 -0.74251528348223 0.458055057004843 df.mm.exp4 -0.050737646823089 0.190025176327733 -0.267004866426661 0.789553772238106 df.mm.exp5 -0.0456526774393957 0.190025176327733 -0.240245415484627 0.810219038080416 df.mm.exp6 0.178240107154339 0.190025176327733 0.937981538019631 0.348617381989706 df.mm.exp7 -0.300679985585045 0.190025176327733 -1.58231657191818 0.114084819328629 df.mm.exp8 -0.284763499242047 0.190025176327733 -1.49855668993524 0.13449518654488 df.mm.trans1:exp2 0.324446557830679 0.180749996394564 1.79500173887935 0.07313802038923 . df.mm.trans2:exp2 0.122234168392278 0.158806109998454 0.769706961485726 0.441765476988355 df.mm.trans1:exp3 0.164566600200421 0.180749996394564 0.910465302810762 0.362929291744646 df.mm.trans2:exp3 -0.00161020899069817 0.158806109998454 -0.0101394649784813 0.991913262011012 df.mm.trans1:exp4 0.0559445453364331 0.180749996394564 0.309513396693576 0.757034514817684 df.mm.trans2:exp4 -0.00618155382865777 0.158806109998454 -0.0389251637025676 0.96896252395312 df.mm.trans1:exp5 -6.90085915763416e-05 0.180749996394564 -0.000381790279130633 0.99969549763606 df.mm.trans2:exp5 -0.0429242277928045 0.158806109998454 -0.270293301644518 0.787024132785075 df.mm.trans1:exp6 -0.171977571170770 0.180749996394564 -0.951466526147836 0.341736578303381 df.mm.trans2:exp6 -0.115139465032966 0.158806109998454 -0.725031707118112 0.468704802696104 df.mm.trans1:exp7 0.267403923540013 0.180749996394564 1.47941316112831 0.139535378942972 df.mm.trans2:exp7 0.0437504524955275 0.158806109998454 0.275496027803675 0.783026526395394 df.mm.trans1:exp8 0.206679585744386 0.180749996394564 1.14345554559912 0.253288686138961 df.mm.trans2:exp8 0.138044760142972 0.158806109998454 0.86926605118856 0.385036390232003 df.mm.trans1:probe2 0.035509436658109 0.090374998197282 0.39291217002953 0.694518757207353 df.mm.trans1:probe3 -0.0453624885390457 0.090374998197282 -0.501936259406863 0.61588969892317 df.mm.trans1:probe4 0.153690409883847 0.090374998197282 1.70058548215240 0.0895199069057799 . df.mm.trans1:probe5 -0.0892103183995494 0.090374998197282 -0.987112809726532 0.323970545164643 df.mm.trans1:probe6 0.0916051302505438 0.090374998197282 1.01361141994799 0.311161692897348 df.mm.trans1:probe7 0.0126824234608870 0.090374998197282 0.140331106100851 0.88844374602617 df.mm.trans1:probe8 -0.00878195156806265 0.090374998197282 -0.0971723567716404 0.922620772251975 df.mm.trans1:probe9 0.0231187661930985 0.090374998197282 0.255809312910103 0.798182519944119 df.mm.trans1:probe10 -0.150748087369710 0.090374998197282 -1.66802866253604 0.0958125305088883 . df.mm.trans1:probe11 -0.0611793638741892 0.090374998197282 -0.676950097864889 0.498688889018668 df.mm.trans1:probe12 -0.00280125384868171 0.090374998197282 -0.0309958938263741 0.975282741335062 df.mm.trans1:probe13 0.0420429016338826 0.090374998197282 0.465205006611519 0.641947215935503 df.mm.trans1:probe14 -0.155885101963121 0.090374998197282 -1.72486976567164 0.0850469054233696 . df.mm.trans1:probe15 -0.0502323446090017 0.090374998197282 -0.555821251573894 0.578532604024737 df.mm.trans1:probe16 0.0381491851460584 0.090374998197282 0.422121005886844 0.673082325657405 df.mm.trans1:probe17 0.0344722281347502 0.090374998197282 0.381435450316689 0.703010211233037 df.mm.trans1:probe18 -0.0951860428084246 0.090374998197282 -1.05323424295556 0.292641961617263 df.mm.trans1:probe19 -0.109608859061059 0.090374998197282 -1.21282280771713 0.225657251125656 df.mm.trans2:probe2 -0.0151329793152066 0.090374998197282 -0.167446524116907 0.86707305847062 df.mm.trans2:probe3 0.0610445161378292 0.090374998197282 0.675458006699746 0.499635447023731 df.mm.trans2:probe4 0.0373558806798176 0.090374998197282 0.413343086306596 0.679497500795752 df.mm.trans2:probe5 0.0587478586860506 0.090374998197282 0.650045475606078 0.515902467344753 df.mm.trans2:probe6 0.0410122116168395 0.090374998197282 0.453800414217579 0.650130522475336 df.mm.trans3:probe2 -0.078355049008874 0.090374998197282 -0.866999176451773 0.386276259021127 df.mm.trans3:probe3 -0.0558860373448877 0.090374998197282 -0.618379402043169 0.536551279257534