fitVsDatCorrelation=0.883797277189283 cont.fitVsDatCorrelation=0.297344775590334 fstatistic=7382.2396798452,66,1014 cont.fstatistic=1761.04886801116,66,1014 residuals=-0.678293270316228,-0.100639432151187,-0.00985163160426413,0.0838426406162743,1.12496009499397 cont.residuals=-0.774521174020507,-0.267285521787489,-0.0783200283290126,0.171629302659192,1.97282909929756 predictedValues: Include Exclude Both Lung 72.8120642089267 56.776968841906 52.2559360632942 cerebhem 82.8432416370458 50.3516993430765 67.4387614270197 cortex 93.5675130000406 55.5163559517093 54.4198118623033 heart 59.3739328643584 59.6084026327127 50.1038355354912 kidney 55.0263740603011 56.6412087573097 48.8753839975501 liver 56.672785843163 60.2628442352791 52.1416099313298 stomach 73.0178446891422 63.0164260502687 49.785497920403 testicle 79.4101479890754 58.3273581585838 56.4874848937595 diffExp=16.0350953670208,32.4915422939692,38.0511570483314,-0.23446976835433,-1.61483469700862,-3.59005839211604,10.0014186388734,21.0827898304915 diffExpScore=1.08725044467195 diffExp1.5=0,1,1,0,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,1,1,0,0,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,1,1,0,0,0,0,1 diffExp1.3Score=0.75 diffExp1.2=1,1,1,0,0,0,0,1 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 66.9273818810508 92.9832000456254 75.0857861656355 cerebhem 63.8570316646901 75.3000368135794 64.6123809813148 cortex 65.8612524811683 68.1420759726589 60.3668380339678 heart 62.9012015779617 91.677828699536 66.1557951781516 kidney 66.5976277744413 78.8023044391514 73.1020521457793 liver 64.7177232315173 62.955541958769 65.6765490240319 stomach 65.0795063131663 68.8321931399385 73.2570284448512 testicle 62.0369695131973 65.2100491496578 62.2393108921537 cont.diffExp=-26.0558181645746,-11.4430051488892,-2.28082349149058,-28.7766271215743,-12.2046766647101,1.76218127274826,-3.75268682677212,-3.17307963646048 cont.diffExpScore=1.02904085161680 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,-1,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=-1,0,0,-1,0,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=-1,0,0,-1,0,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=-0.450675247200863 cont.tran.correlation=0.230074525464185 tran.covariance=-0.00565104337734594 cont.tran.covariance=0.000946459806707855 tran.mean=64.5765730164312 cont.tran.mean=70.1176202910068 weightedLogRatios: wLogRatio Lung 1.03567588935043 cerebhem 2.07531703373397 cortex 2.23297298692253 heart -0.0161032962208027 kidney -0.116341043717985 liver -0.249863387714492 stomach 0.621206985953323 testicle 1.30220935841654 cont.weightedLogRatios: wLogRatio Lung -1.43624937347987 cerebhem -0.698741478364379 cortex -0.143142924418913 heart -1.63114822269041 kidney -0.720681757258634 liver 0.114738182994841 stomach -0.235663923151989 testicle -0.207148135027997 varWeightedLogRatios=0.942582270494905 cont.varWeightedLogRatios=0.398630581959246 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.44286812178938 0.0938480209706655 47.3410954843481 4.62838766404088e-259 *** df.mm.trans1 -0.271164744508021 0.0825486248597536 -3.28490928793445 0.00105507546072385 ** df.mm.trans2 -0.333015934270825 0.0725536201554931 -4.58992857361387 4.98950140390733e-06 *** df.mm.exp2 -0.246096342437753 0.0944296890894602 -2.60613314319618 0.00929134968246253 ** df.mm.exp3 0.187773712132610 0.0944296890894602 1.98850291622498 0.0470245673351769 * df.mm.exp4 -0.113304656201231 0.0944296890894602 -1.19988382143130 0.230464732904977 df.mm.exp5 -0.215583416858511 0.0944296890894602 -2.28300462425830 0.0226363399617839 * df.mm.exp6 -0.1888123558503 0.0944296890894602 -1.99950203872242 0.0458205059113027 * df.mm.exp7 0.155516616498942 0.0944296890894602 1.64690382864239 0.0998876610143194 . df.mm.exp8 0.0358193789106036 0.0944296890894602 0.379323274872475 0.704527237990643 df.mm.trans1:exp2 0.375164850872727 0.0899958506020348 4.16869053809738 3.32516232101149e-05 *** df.mm.trans2:exp2 0.125997946276906 0.0674968879515261 1.86672230529196 0.0622288156492189 . df.mm.trans1:exp3 0.0630278691420203 0.0899958506020347 0.700341945994067 0.483874435725602 df.mm.trans2:exp3 -0.210226798326836 0.0674968879515261 -3.11461468383274 0.00189355772612744 ** df.mm.trans1:exp4 -0.0907217130449398 0.0899958506020347 -1.00806551011018 0.313663500933673 df.mm.trans2:exp4 0.161970438742445 0.0674968879515261 2.39967269096564 0.0165893042479759 * df.mm.trans1:exp5 -0.0644856431055483 0.0899958506020347 -0.716540181287984 0.473822861491316 df.mm.trans2:exp5 0.213189441707296 0.067496887951526 3.15850772053966 0.00163268189424738 ** df.mm.trans1:exp6 -0.0617751747586321 0.0899958506020348 -0.686422477762941 0.492603663229176 df.mm.trans2:exp6 0.248397322466064 0.0674968879515261 3.68013000309666 0.000245387266321671 *** df.mm.trans1:exp7 -0.152694416055233 0.0899958506020347 -1.69668284741765 0.090063543611329 . df.mm.trans2:exp7 -0.0512519584983311 0.0674968879515261 -0.759323282210262 0.447835673287188 df.mm.trans1:exp8 0.0509251309736711 0.0899958506020347 0.565860877284933 0.571613554581496 df.mm.trans2:exp8 -0.00887889583196461 0.067496887951526 -0.131545262328852 0.895370078411496 df.mm.trans1:probe2 1.33863092833383 0.0571913649408146 23.4061720631976 3.24921824139588e-97 *** df.mm.trans1:probe3 -0.0463555462765728 0.0571913649408146 -0.810534008491397 0.417823512790035 df.mm.trans1:probe4 0.200872261091100 0.0571913649408146 3.51228303956333 0.000463849779173628 *** df.mm.trans1:probe5 -0.125212648667455 0.0571913649408146 -2.18936283120771 0.0287978160965751 * df.mm.trans1:probe6 0.503239534485191 0.0571913649408146 8.79922231277355 5.83459324273251e-18 *** df.mm.trans1:probe7 0.139771281424887 0.0571913649408146 2.44392281194078 0.014698076000776 * df.mm.trans1:probe8 0.278950973903435 0.0571913649408146 4.87750159822399 1.24675980355974e-06 *** df.mm.trans1:probe9 -0.125136114504294 0.0571913649408146 -2.18802461934233 0.0288954422335801 * df.mm.trans1:probe10 0.0877388935558909 0.0571913649408146 1.53412833644885 0.125310018923200 df.mm.trans1:probe11 0.232976667306947 0.0571913649408146 4.0736336254266 4.98958435475778e-05 *** df.mm.trans1:probe12 -0.0603802474651574 0.0571913649408146 -1.05575811186956 0.291330213128299 df.mm.trans1:probe13 -0.0367767083040074 0.0571913649408146 -0.64304652183186 0.520339296716645 df.mm.trans1:probe14 0.227967480845391 0.0571913649408146 3.98604721326911 7.1997537049997e-05 *** df.mm.trans1:probe15 0.146823082192949 0.0571913649408146 2.56722465611532 0.0103940663461107 * df.mm.trans1:probe16 0.283755437124299 0.0571913649408146 4.96150839235866 8.19890164911729e-07 *** df.mm.trans1:probe17 -0.0621940497129484 0.0571913649408146 -1.08747272909662 0.277086363097154 df.mm.trans1:probe18 -0.0980749667669098 0.0571913649408146 -1.71485620020442 0.0866771331105447 . df.mm.trans1:probe19 0.0158369618485276 0.0571913649408146 0.276911765699537 0.781904294841534 df.mm.trans1:probe20 0.0203030010557312 0.0571913649408146 0.355001162793405 0.722662543647441 df.mm.trans1:probe21 -0.190703872750087 0.0571913649408146 -3.33448717210089 0.000885514629784786 *** df.mm.trans1:probe22 -0.168783898883631 0.0571913649408146 -2.95121298570684 0.00323812077257224 ** df.mm.trans1:probe23 1.20820323658223 0.0571913649408146 21.125623384449 2.21603311207045e-82 *** df.mm.trans1:probe24 -0.0301993630892797 0.0571913649408146 -0.528040607538078 0.597586684956112 df.mm.trans1:probe25 0.183661685737241 0.0571913649408146 3.21135342594648 0.00136266828106249 ** df.mm.trans1:probe26 0.0313408822787680 0.0571913649408146 0.548000249883905 0.583812342618872 df.mm.trans1:probe27 -0.18315088629358 0.0571913649408146 -3.20242201743421 0.0014051941536174 ** df.mm.trans1:probe28 0.46650567158926 0.0571913649408146 8.15692494963236 1.00945145774377e-15 *** df.mm.trans1:probe29 -0.0480015239243781 0.0571913649408146 -0.839314186224672 0.401490852579037 df.mm.trans1:probe30 0.0148640962464345 0.0571913649408146 0.259901057822572 0.79499285633362 df.mm.trans1:probe31 0.263611008001538 0.0571913649408146 4.60927988472281 4.555575932024e-06 *** df.mm.trans1:probe32 0.0608697155794558 0.0571913649408146 1.06431653873706 0.287438705633344 df.mm.trans2:probe2 -0.246769884430188 0.0571913649408146 -4.31481019355915 1.75370236313343e-05 *** df.mm.trans2:probe3 -0.301398886353210 0.0571913649408146 -5.27000687367956 1.66636960588381e-07 *** df.mm.trans2:probe4 -0.0654365710248243 0.0571913649408146 -1.14416872359214 0.252823689006150 df.mm.trans2:probe5 -0.188336901041135 0.0571913649408146 -3.29310029994979 0.0010251309190989 ** df.mm.trans2:probe6 -0.117436245170732 0.0571913649408146 -2.05339119449697 0.0402907253222024 * df.mm.trans3:probe2 0.405247890825350 0.0571913649408146 7.08582303018518 2.58410280131578e-12 *** df.mm.trans3:probe3 -0.0622326602784783 0.0571913649408146 -1.08814784090012 0.276788409840728 df.mm.trans3:probe4 0.0178219431202636 0.0571913649408146 0.311619475050244 0.755393789488954 df.mm.trans3:probe5 0.0295636702478067 0.0571913649408146 0.516925418345953 0.605320949944779 df.mm.trans3:probe6 0.237932137131284 0.0571913649408146 4.16028079374416 3.44785673531681e-05 *** df.mm.trans3:probe7 0.193929370691647 0.0571913649408146 3.3908855102923 0.000723544126440285 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.56725431065409 0.191513206492426 23.8482473052568 3.81103977421945e-100 *** df.mm.trans1 -0.420748993410188 0.168454823819602 -2.49769631922661 0.0126578645594794 * df.mm.trans2 0.0430565627949674 0.148058278639197 0.290808208704708 0.77125747513101 df.mm.exp2 -0.107674456317861 0.192700201438005 -0.55876670348215 0.576444319755045 df.mm.exp3 -0.108690478663824 0.192700201438005 -0.564039258146763 0.572852141578725 df.mm.exp4 0.0504376065602862 0.192700201438005 0.261741327636924 0.793574032961337 df.mm.exp5 -0.143640976815143 0.192700201438005 -0.745411658852649 0.456195643251031 df.mm.exp6 -0.289673769849391 0.192700201438005 -1.50323542833754 0.133089712026868 df.mm.exp7 -0.304088675061673 0.192700201438005 -1.57804025523815 0.114868254807453 df.mm.exp8 -0.24303846891735 0.192700201438005 -1.26122581659853 0.207517582645131 df.mm.trans1:exp2 0.0607129815293319 0.183652182982062 0.330586767570642 0.741024936584348 df.mm.trans2:exp2 -0.103263752148602 0.137739137236546 -0.749705234259327 0.453606152482704 df.mm.trans1:exp3 0.0926325936819655 0.183652182982062 0.504391465311433 0.614095896138593 df.mm.trans2:exp3 -0.202133475446805 0.137739137236546 -1.46750937679878 0.142547632291660 df.mm.trans1:exp4 -0.112480519124521 0.183652182982062 -0.612464917640034 0.540367522080784 df.mm.trans2:exp4 -0.064575869507945 0.137739137236546 -0.468827312291391 0.639293850160115 df.mm.trans1:exp5 0.138701755648299 0.183652182982062 0.755241529918797 0.450279458650613 df.mm.trans2:exp5 -0.0218356147613052 0.137739137236546 -0.158528760956342 0.874071725123024 df.mm.trans1:exp6 0.256100684157765 0.183652182982062 1.39448755794414 0.163475856416850 df.mm.trans2:exp6 -0.100316268229154 0.137739137236546 -0.7283062043352 0.466594318164457 df.mm.trans1:exp7 0.276090192461674 0.183652182982062 1.50333193964072 0.133064837718631 df.mm.trans2:exp7 0.00334140194015862 0.137739137236546 0.0242589144029577 0.980650857992636 df.mm.trans1:exp8 0.167160779651178 0.183652182982062 0.91020306394891 0.362931734464191 df.mm.trans2:exp8 -0.111766778128664 0.137739137236546 -0.811438058717628 0.417304576961118 df.mm.trans1:probe2 0.093062842876299 0.116708925454246 0.79739268024349 0.425409687477617 df.mm.trans1:probe3 0.172834210415682 0.116708925454246 1.48089968049135 0.138943955905039 df.mm.trans1:probe4 0.329673305762497 0.116708925454246 2.82474801716635 0.00482455999141471 ** df.mm.trans1:probe5 0.161971362413280 0.116708925454246 1.38782326872488 0.165495738864157 df.mm.trans1:probe6 0.049667716438519 0.116708925454246 0.425569134881551 0.670512140036887 df.mm.trans1:probe7 0.176860627213301 0.116708925454246 1.51539932807141 0.129982957758752 df.mm.trans1:probe8 0.0446542175356482 0.116708925454246 0.382611847053242 0.702087768271884 df.mm.trans1:probe9 -0.0054021167360939 0.116708925454246 -0.0462870917118649 0.963090541679034 df.mm.trans1:probe10 -0.0179067349902756 0.116708925454246 -0.153430724518971 0.878089144714952 df.mm.trans1:probe11 -0.0218231742646132 0.116708925454246 -0.186988048940341 0.851707408622161 df.mm.trans1:probe12 0.0427345894636611 0.116708925454246 0.366163849914071 0.714319196461499 df.mm.trans1:probe13 0.0582029941603707 0.116708925454246 0.498702168097574 0.618097378123537 df.mm.trans1:probe14 0.0821023981442119 0.116708925454246 0.703480027981226 0.481918143221846 df.mm.trans1:probe15 0.165288876234036 0.116708925454246 1.41624880522814 0.15700977809946 df.mm.trans1:probe16 0.174653801626892 0.116708925454246 1.49649052929857 0.134837051496625 df.mm.trans1:probe17 0.130584298197745 0.116708925454246 1.11888870272341 0.263452605825517 df.mm.trans1:probe18 -0.0663496374894192 0.116708925454246 -0.568505255542178 0.569817815884613 df.mm.trans1:probe19 -0.105961551215468 0.116708925454246 -0.907913004965576 0.364139905092624 df.mm.trans1:probe20 0.0491713231867750 0.116708925454246 0.421315876188508 0.673613729351937 df.mm.trans1:probe21 0.0614917173611318 0.116708925454246 0.526881017212678 0.598391456604425 df.mm.trans1:probe22 0.105617662587383 0.116708925454246 0.904966455447224 0.365698121352156 df.mm.trans1:probe23 0.0619486200496517 0.116708925454246 0.530795907926834 0.59567644727724 df.mm.trans1:probe24 0.103422978796872 0.116708925454246 0.886161691527332 0.375740511281738 df.mm.trans1:probe25 -0.0498570171956862 0.116708925454246 -0.427191125285717 0.669330817562248 df.mm.trans1:probe26 0.0954125918741898 0.116708925454246 0.817526093251494 0.413819891459733 df.mm.trans1:probe27 0.0416446194180515 0.116708925454246 0.356824632357511 0.721297334661496 df.mm.trans1:probe28 0.125201282861918 0.116708925454246 1.07276527801638 0.283631679254870 df.mm.trans1:probe29 -0.0390102005032662 0.116708925454246 -0.334252074992838 0.73825846972984 df.mm.trans1:probe30 0.0428450267314985 0.116708925454246 0.367110112313519 0.713613483338911 df.mm.trans1:probe31 -0.00468547791796975 0.116708925454246 -0.0401466974332363 0.967984075543757 df.mm.trans1:probe32 0.168960458431733 0.116708925454246 1.44770811464605 0.148007799198688 df.mm.trans2:probe2 -0.384103504640863 0.116708925454246 -3.29112364924861 0.00103228413303001 ** df.mm.trans2:probe3 -0.314200619391698 0.116708925454246 -2.69217301220784 0.00721576877509323 ** df.mm.trans2:probe4 -0.0180862926370404 0.116708925454246 -0.154969232787006 0.876876413654862 df.mm.trans2:probe5 -0.224199009878593 0.116708925454246 -1.92100997422418 0.0550105836429213 . df.mm.trans2:probe6 -0.0720071101530941 0.116708925454246 -0.616980319824153 0.537386212140674 df.mm.trans3:probe2 0.31503656420451 0.116708925454246 2.69933565901963 0.00706337734907441 ** df.mm.trans3:probe3 0.146128460169399 0.116708925454246 1.25207613385735 0.210830846016863 df.mm.trans3:probe4 0.277260463103523 0.116708925454246 2.37565774874878 0.0177027479668144 * df.mm.trans3:probe5 0.106188037387375 0.116708925454246 0.909853612087314 0.36311593277159 df.mm.trans3:probe6 -0.0247826601365451 0.116708925454246 -0.212345885630322 0.831879876942575 df.mm.trans3:probe7 0.083654902936223 0.116708925454246 0.716782393554112 0.473673437671771