fitVsDatCorrelation=0.837898303881995 cont.fitVsDatCorrelation=0.245873015661685 fstatistic=10295.0987517407,62,922 cont.fstatistic=3254.37927840915,62,922 residuals=-0.589303700191424,-0.0983899427164497,-0.00953901337482949,0.0853738176325733,0.733902180216703 cont.residuals=-0.619043280144126,-0.206133657802878,-0.0351666068539711,0.173749296936046,1.2760011736194 predictedValues: Include Exclude Both Lung 81.6311490369001 45.2006626420612 82.4167149857774 cerebhem 71.4503263356034 54.1128004056329 85.3086501708187 cortex 84.0076699941147 47.1693554038 84.1663384787577 heart 64.4802921268106 46.9985733715148 60.9581496773475 kidney 62.7699580341255 45.7163613888296 60.2642652867684 liver 64.6899489803023 50.3706743937975 58.6126248756909 stomach 72.0392299364825 47.8020910198669 72.5846267808918 testicle 64.53104882191 48.8491306690537 64.7355801594587 diffExp=36.4304863948389,17.3375259299706,36.8383145903147,17.4817187552958,17.0535966452959,14.3192745865048,24.2371389166156,15.6819181528562 diffExpScore=0.99445614733176 diffExp1.5=1,0,1,0,0,0,1,0 diffExp1.5Score=0.75 diffExp1.4=1,0,1,0,0,0,1,0 diffExp1.4Score=0.75 diffExp1.3=1,1,1,1,1,0,1,1 diffExp1.3Score=0.875 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 66.3877935353324 63.4823178185978 67.3147533041171 cerebhem 65.8675514343852 63.2151897715069 68.8068871463148 cortex 63.7624456216093 65.6282361231341 63.912837445225 heart 63.2652419042692 58.2012435698556 60.339819214236 kidney 64.3916434665032 66.0463618801899 67.6068156019628 liver 62.3736892920642 60.1186620626784 69.9152168623419 stomach 70.6906728127475 62.9896656765282 63.4842486739811 testicle 66.3040094593587 61.6940873765712 65.7469771844107 cont.diffExp=2.90547571673459,2.65236166287823,-1.86579050152486,5.06399833441361,-1.65471841368674,2.25502722938581,7.70100713621929,4.60992208278751 cont.diffExpScore=1.26650824293941 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=-0.19596253804053 cont.tran.correlation=0.226525606241297 tran.covariance=-0.00120142648508778 cont.tran.covariance=0.000409776179930747 tran.mean=59.4887045350504 cont.tran.mean=64.0261757378332 weightedLogRatios: wLogRatio Lung 2.42744410729963 cerebhem 1.14786812902059 cortex 2.39080024073208 heart 1.26757486822912 kidney 1.26204729802081 liver 1.01192239967493 stomach 1.67015343654797 testicle 1.12141881030900 cont.weightedLogRatios: wLogRatio Lung 0.186755404952703 cerebhem 0.171273085046449 cortex -0.120257682395496 heart 0.342529347745765 kidney -0.106000379216920 liver 0.151517721336639 stomach 0.484514508162433 testicle 0.299650725980100 varWeightedLogRatios=0.326802972568528 cont.varWeightedLogRatios=0.0437474955322465 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95381362409999 0.0751011553158809 52.6465086651459 4.2194244556415e-280 *** df.mm.trans1 0.52109440666324 0.0644990394381087 8.0791033665434 2.03473223355629e-15 *** df.mm.trans2 -0.140038421753425 0.0566343132433053 -2.47267802386532 0.0135898993132468 * df.mm.exp2 0.0122629814257442 0.0720599264761706 0.170177545626548 0.864907843841063 df.mm.exp3 0.0503230788051223 0.0720599264761706 0.6983504045312 0.48513417872652 df.mm.exp4 0.104754921922643 0.0720599264761706 1.45371952269871 0.146364373370957 df.mm.exp5 0.0616591258969494 0.0720599264761706 0.855664568535736 0.392405648981444 df.mm.exp6 0.216530478534988 0.0720599264761706 3.00486677025118 0.00272886209747104 ** df.mm.exp7 0.0579926711458243 0.0720599264761706 0.804783934452137 0.421151846127177 df.mm.exp8 0.0840376955637266 0.0720599264761706 1.16621955743345 0.243827260263476 df.mm.trans1:exp2 -0.145471427255746 0.0661517164390966 -2.19905748613003 0.0281215173322054 * df.mm.trans2:exp2 0.167696035875275 0.0468880988319216 3.57651600412313 0.000366307758704977 *** df.mm.trans1:exp3 -0.0216258922002144 0.0661517164390966 -0.326913546077440 0.743807476030949 df.mm.trans2:exp3 -0.00769039394042093 0.0468880988319216 -0.164015904504648 0.869754570340956 df.mm.trans1:exp4 -0.340606210755584 0.0661517164390966 -5.14886429393208 3.20398631956502e-07 *** df.mm.trans2:exp4 -0.065749421424146 0.0468880988319216 -1.40226247303897 0.161173471997078 df.mm.trans1:exp5 -0.324393459657219 0.0661517164390966 -4.90377993375086 1.11073398783475e-06 *** df.mm.trans2:exp5 -0.0503146217619552 0.0468880988319216 -1.07307873459141 0.28351656366132 df.mm.trans1:exp6 -0.449135554682925 0.0661517164390966 -6.78947696083483 2.01369446659276e-11 *** df.mm.trans2:exp6 -0.108233077021537 0.0468880988319216 -2.30832726678720 0.0212014333548406 * df.mm.trans1:exp7 -0.182992757806982 0.0661517164390966 -2.76625864992418 0.00578373360632349 ** df.mm.trans2:exp7 -0.00203503437302591 0.0468880988319216 -0.0434019383110593 0.96539053000559 df.mm.trans1:exp8 -0.319102128026603 0.0661517164390966 -4.82379211309488 1.64741089680996e-06 *** df.mm.trans2:exp8 -0.00641286011180822 0.0468880988319216 -0.136769463287394 0.891242879870234 df.mm.trans1:probe2 -0.113039636271071 0.0473878084928951 -2.38541599339879 0.0172609130127357 * df.mm.trans1:probe3 0.45140081104138 0.0473878084928951 9.5256739106021 1.39751177587248e-20 *** df.mm.trans1:probe4 -0.159243724094838 0.0473878084928951 -3.36043655867128 0.000810138716250534 *** df.mm.trans1:probe5 -0.202148498678559 0.0473878084928951 -4.26583345184378 2.19709364608717e-05 *** df.mm.trans1:probe6 0.0969963261534907 0.0473878084928951 2.04686245763050 0.0409546543782867 * df.mm.trans1:probe7 -0.234417694353861 0.0473878084928951 -4.94679331687193 8.96455507997648e-07 *** df.mm.trans1:probe8 -0.227772722478567 0.0473878084928951 -4.80656797017143 1.79201398691590e-06 *** df.mm.trans1:probe9 0.083626521361633 0.0473878084928951 1.76472649867679 0.0779407861323706 . df.mm.trans1:probe10 0.147467157516105 0.0473878084928951 3.11192186779886 0.00191598182427033 ** df.mm.trans1:probe11 -0.214298448164198 0.0473878084928951 -4.52222744582771 6.91686698446125e-06 *** df.mm.trans1:probe12 -0.310459990243433 0.0473878084928951 -6.5514738941764 9.45978125431383e-11 *** df.mm.trans1:probe13 -0.191576227586522 0.0473878084928951 -4.04273237525312 5.72429522342297e-05 *** df.mm.trans1:probe14 -0.307138346068941 0.0473878084928951 -6.48137898411129 1.47838397998769e-10 *** df.mm.trans1:probe15 -0.253598627434377 0.0473878084928951 -5.35155845985997 1.10077429079985e-07 *** df.mm.trans1:probe16 -0.0211129981262044 0.0473878084928951 -0.445536495518038 0.656036683474904 df.mm.trans1:probe17 -0.192087397220891 0.0473878084928951 -4.05351931920824 5.47094678533471e-05 *** df.mm.trans1:probe18 -0.179159270355158 0.0473878084928951 -3.7807038572383 0.000166447217011752 *** df.mm.trans1:probe19 -0.27184011044937 0.0473878084928951 -5.73649888219936 1.31010029534787e-08 *** df.mm.trans1:probe20 -0.126345544063517 0.0473878084928951 -2.6662035675792 0.00780560571226147 ** df.mm.trans1:probe21 -0.190524657619470 0.0473878084928951 -4.02054164728963 6.280902582009e-05 *** df.mm.trans1:probe22 -0.12912588624721 0.0473878084928951 -2.72487566641893 0.00655436217809852 ** df.mm.trans2:probe2 0.0386145047042332 0.0473878084928951 0.814861584283281 0.415361971002629 df.mm.trans2:probe3 -0.0532188728190373 0.0473878084928951 -1.1230498837484 0.261708653504951 df.mm.trans2:probe4 -0.0132519933538475 0.0473878084928951 -0.279649846137839 0.779808882565474 df.mm.trans2:probe5 -0.097909100829194 0.0473878084928951 -2.06612426155714 0.0390955860192515 * df.mm.trans2:probe6 0.075159809826219 0.0473878084928951 1.58605793803459 0.113069050366797 df.mm.trans3:probe2 0.120387098392812 0.0473878084928951 2.54046562231005 0.0112335200627483 * df.mm.trans3:probe3 -0.0848373902276833 0.0473878084928951 -1.79027882752592 0.0737371209159416 . df.mm.trans3:probe4 0.35107189601613 0.0473878084928951 7.4084855827162 2.88453951881052e-13 *** df.mm.trans3:probe5 -0.0145188511940793 0.0473878084928951 -0.306383680862897 0.759381704627954 df.mm.trans3:probe6 0.190797791119326 0.0473878084928951 4.02630543988825 6.13159004287639e-05 *** df.mm.trans3:probe7 0.0413410817712553 0.0473878084928951 0.872399106142535 0.383217815997073 df.mm.trans3:probe8 0.361991879018834 0.0473878084928951 7.63892424088587 5.47746112088062e-14 *** df.mm.trans3:probe9 0.419465672205875 0.0473878084928951 8.851763471374 4.33798706926214e-18 *** df.mm.trans3:probe10 0.113777339368183 0.0473878084928951 2.40098335387765 0.0165482734662103 * df.mm.trans3:probe11 -0.105598052420592 0.0473878084928951 -2.22838016314733 0.0260956453874808 * df.mm.trans3:probe12 -0.192908880992104 0.0473878084928951 -4.07085465918996 5.08595945769791e-05 *** df.mm.trans3:probe13 -0.201027014717281 0.0473878084928951 -4.24216736562994 2.43717186011221e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.16819870489301 0.133367874656479 31.2533937848916 8.47349113957596e-147 *** df.mm.trans1 0.0357942304043448 0.114540179456147 0.312503704589087 0.754728412655653 df.mm.trans2 -0.0199082917188124 0.100573659061830 -0.197947374138724 0.84312987192595 df.mm.exp2 -0.034008483005301 0.127967129155426 -0.265759521447067 0.790483832108262 df.mm.exp3 0.0447549970482398 0.127967129155426 0.349738228431158 0.726615030844834 df.mm.exp4 -0.0256446238485452 0.127967129155426 -0.200400087255203 0.841211885518366 df.mm.exp5 0.00473682729559182 0.127967129155426 0.0370159690762351 0.97048028563875 df.mm.exp6 -0.154714654829533 0.127967129155426 -1.20901872106250 0.226965598621410 df.mm.exp7 0.113597306005704 0.127967129155426 0.887706919389678 0.374929931467034 df.mm.exp8 -0.00627041324669282 0.127967129155426 -0.0490001869079748 0.960929759251044 df.mm.trans1:exp2 0.0261412049850683 0.117475074641026 0.222525544801306 0.823954073737015 df.mm.trans2:exp2 0.0297916921939267 0.0832659106453717 0.357789784114763 0.720582525795172 df.mm.trans1:exp3 -0.0851038139106506 0.117475074641026 -0.724441454246412 0.46897854516877 df.mm.trans2:exp3 -0.0115103725716921 0.0832659106453717 -0.138236314026693 0.890083840561775 df.mm.trans1:exp4 -0.0225325061348596 0.117475074641026 -0.191806697750253 0.847935857563788 df.mm.trans2:exp4 -0.0612100620346343 0.0832659106453717 -0.735115505976113 0.462456090162868 df.mm.trans1:exp5 -0.0352661697256764 0.117475074641026 -0.300201296602201 0.764091287284472 df.mm.trans2:exp5 0.0348587132845975 0.0832659106453717 0.418643272071571 0.67557435942601 df.mm.trans1:exp6 0.092344988042803 0.117475074641026 0.786081543893341 0.432021690614871 df.mm.trans2:exp6 0.100273557448260 0.0832659106453717 1.2042570203228 0.228799307457084 df.mm.trans1:exp7 -0.0507968753596457 0.117475074641026 -0.432405559348382 0.665547792442555 df.mm.trans2:exp7 -0.121388037522143 0.0832659106453717 -1.45783594488184 0.145226289228569 df.mm.trans1:exp8 0.00500757582618222 0.117475074641026 0.042626709040059 0.966008333337657 df.mm.trans2:exp8 -0.0223028965864577 0.0832659106453717 -0.267851470230661 0.788873559353488 df.mm.trans1:probe2 0.0279355319686958 0.0841533166399804 0.331959964076139 0.739994937341877 df.mm.trans1:probe3 -0.00302577281455612 0.0841533166399804 -0.0359554790633006 0.97132564133609 df.mm.trans1:probe4 0.00234468374652027 0.0841533166399804 0.0278620479873794 0.977778207367774 df.mm.trans1:probe5 -0.0102757721007930 0.0841533166399804 -0.122107749415916 0.902840293306222 df.mm.trans1:probe6 -0.103937432624802 0.0841533166399804 -1.23509609335376 0.217109383513043 df.mm.trans1:probe7 -0.0300185841468416 0.0841533166399804 -0.35671302505242 0.721388272411356 df.mm.trans1:probe8 0.0713972626592853 0.0841533166399804 0.848418880086838 0.396424938852851 df.mm.trans1:probe9 -0.00315445364623499 0.0841533166399804 -0.0374846027724633 0.970106730964828 df.mm.trans1:probe10 -0.148804637624323 0.0841533166399804 -1.76825636309654 0.0773486998713803 . df.mm.trans1:probe11 0.00448767932189358 0.0841533166399804 0.053327420725347 0.95748258468103 df.mm.trans1:probe12 -0.0737895557333588 0.0841533166399804 -0.876846673186285 0.380798328271432 df.mm.trans1:probe13 -0.096484296986747 0.0841533166399804 -1.14652993891519 0.251873404170555 df.mm.trans1:probe14 -0.0194836680828167 0.0841533166399804 -0.231525849018769 0.816957678085733 df.mm.trans1:probe15 -0.0574074018162352 0.0841533166399804 -0.682176343231153 0.495298788525255 df.mm.trans1:probe16 0.00178452448625152 0.0841533166399804 0.0212056346380971 0.98308620766822 df.mm.trans1:probe17 -0.0444867503510996 0.0841533166399804 -0.528639299404207 0.597182918861899 df.mm.trans1:probe18 0.01860628354816 0.0841533166399804 0.221099824594677 0.825063660935988 df.mm.trans1:probe19 0.0471495264607998 0.0841533166399804 0.560281262145758 0.575423712969427 df.mm.trans1:probe20 0.0625636634377872 0.0841533166399804 0.743448576191515 0.457399543135182 df.mm.trans1:probe21 0.0982184211178137 0.0841533166399804 1.16713666245629 0.243456944941947 df.mm.trans1:probe22 -0.0404097312886318 0.0841533166399804 -0.48019178449627 0.631204930442915 df.mm.trans2:probe2 0.0999511989308254 0.0841533166399804 1.18772738760173 0.235246711308276 df.mm.trans2:probe3 0.0588952182005544 0.0841533166399804 0.69985617385131 0.484193667355681 df.mm.trans2:probe4 -0.0943200470356087 0.0841533166399804 -1.12081199887965 0.262659741468031 df.mm.trans2:probe5 0.0134419306568076 0.0841533166399804 0.159731442485078 0.873127616294805 df.mm.trans2:probe6 -0.0310194071953425 0.0841533166399804 -0.368605878340456 0.712506132894247 df.mm.trans3:probe2 -0.125892235069462 0.0841533166399804 -1.49598661224544 0.134999348186246 df.mm.trans3:probe3 -0.016117836092697 0.0841533166399804 -0.191529421967423 0.84815300189045 df.mm.trans3:probe4 0.0620000075059448 0.0841533166399804 0.736750611638867 0.461461443757036 df.mm.trans3:probe5 0.0649977687423389 0.0841533166399804 0.772373227075629 0.440091341670283 df.mm.trans3:probe6 0.0794194890342224 0.0841533166399804 0.94374758126278 0.345545882929013 df.mm.trans3:probe7 0.0454301597013145 0.0841533166399804 0.539849901527601 0.589430907146379 df.mm.trans3:probe8 0.00310790247741444 0.0841533166399804 0.0369314318378025 0.970547672182947 df.mm.trans3:probe9 -0.0115677085569580 0.0841533166399804 -0.137459924561812 0.890697279357281 df.mm.trans3:probe10 0.080000217841907 0.0841533166399804 0.950648424044403 0.342032003674843 df.mm.trans3:probe11 0.0587574816615769 0.0841533166399804 0.69821944051177 0.485216026352088 df.mm.trans3:probe12 0.0628619321142427 0.0841533166399804 0.746992924630348 0.455258277345772 df.mm.trans3:probe13 0.0258350392623183 0.0841533166399804 0.306999656030721 0.758912956474827