fitVsDatCorrelation=0.903485036769058 cont.fitVsDatCorrelation=0.222475656714121 fstatistic=12824.0647496288,67,1037 cont.fstatistic=2466.16619232435,67,1037 residuals=-0.598030490395298,-0.0792776230246233,-0.00171708514107234,0.0763651135150377,0.964250485535618 cont.residuals=-0.62783669107542,-0.231904332391804,-0.0636862704656413,0.142452547984054,1.57145217945398 predictedValues: Include Exclude Both Lung 55.2835698506652 44.2393147970972 69.68701073845 cerebhem 55.8331413443868 46.8146376184853 82.4903109469542 cortex 68.4032736957803 45.8857152997185 74.5131403356283 heart 61.9802041205731 48.6995525295415 70.1189173200591 kidney 58.1794387165594 45.8912512346905 70.1513304806301 liver 52.9545865397293 48.5366665854545 65.3518167437014 stomach 55.1485687750964 46.6037520852162 69.2464379233713 testicle 57.3996496638775 44.3794859313264 70.0045837842286 diffExp=11.044255053568,9.01850372590154,22.5175583960618,13.2806515910316,12.2881874818689,4.41791995427479,8.54481668988016,13.0201637325511 diffExpScore=0.98948829621186 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=1,0,1,1,1,0,0,1 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 59.8494660002063 66.7634491752917 63.180619431005 cerebhem 58.6918843347964 68.8986540464683 62.9502509289557 cortex 62.0863249014507 58.226794585809 58.889506582616 heart 64.2342715292007 61.4796066140536 62.6536991933991 kidney 58.6459539926982 61.137799989217 61.0438056020574 liver 65.4169938159792 66.1826445179221 66.6194473958402 stomach 61.9734753377995 65.4716020533772 60.114259196596 testicle 57.7210076777265 72.5970860067634 61.2957614320559 cont.diffExp=-6.91398317508543,-10.2067697116719,3.85953031564178,2.75466491514704,-2.49184599651880,-0.765650701942974,-3.49812671557770,-14.8760783290369 cont.diffExpScore=1.36901124812639 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,-1 cont.diffExp1.2Score=0.5 tran.correlation=-0.0251913700135389 cont.tran.correlation=-0.438922672373423 tran.covariance=-8.17039606844229e-05 cont.tran.covariance=-0.00141360156762836 tran.mean=52.2645505492624 cont.tran.mean=63.0860634111725 weightedLogRatios: wLogRatio Lung 0.869394204961928 cerebhem 0.693109109693826 cortex 1.60736331912944 heart 0.96608615113265 kidney 0.935957928830565 liver 0.342003428431654 stomach 0.660916820711433 testicle 1.00882475726709 cont.weightedLogRatios: wLogRatio Lung -0.453308227910282 cerebhem -0.665786882480364 cortex 0.262909721794801 heart 0.181489111120278 kidney -0.170288764176829 liver -0.0487160840915957 stomach -0.228105072814473 testicle -0.95625857396329 varWeightedLogRatios=0.132633648830321 cont.varWeightedLogRatios=0.172694299481448 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.74353507507989 0.0668748804869184 55.9781946199092 0 *** df.mm.trans1 0.231060596079671 0.057119670274682 4.04520185373141 5.61556651769243e-05 *** df.mm.trans2 0.040128723773852 0.0498404273045626 0.805144055620456 0.42092111817004 df.mm.exp2 -0.102192978683866 0.0626898583234968 -1.63013574151855 0.103376487604669 df.mm.exp3 0.182523366534386 0.0626898583234968 2.91152941505332 0.00367363895974521 ** df.mm.exp4 0.204216584940650 0.0626898583234968 3.25756973140435 0.00116015516971264 ** df.mm.exp5 0.081076044119763 0.0626898583234968 1.29328804192519 0.196199473158841 df.mm.exp6 0.113893290049085 0.0626898583234968 1.81677376684063 0.0695403344298668 . df.mm.exp7 0.0559644598565373 0.0626898583234968 0.892719514020042 0.372214507238740 df.mm.exp8 0.0361791285115201 0.0626898583234968 0.577112941057004 0.563988424654332 df.mm.trans1:exp2 0.112084847675643 0.057119670274682 1.96228106949218 0.0499968734913065 * df.mm.trans2:exp2 0.158775033548410 0.0385100738431363 4.12294804198898 4.03944378656363e-05 *** df.mm.trans1:exp3 0.0304215630069092 0.057119670274682 0.532593463173288 0.594429127623315 df.mm.trans2:exp3 -0.14598338020121 0.0385100738431363 -3.79078421910709 0.000158830840216053 *** df.mm.trans1:exp4 -0.0898772942481234 0.057119670274682 -1.57349112513980 0.115910167257102 df.mm.trans2:exp4 -0.108160611982009 0.0385100738431363 -2.80863164330928 0.00506866926385721 ** df.mm.trans1:exp5 -0.0300197934511831 0.057119670274682 -0.525559641833038 0.599306561108029 df.mm.trans2:exp5 -0.0444154189119793 0.0385100738431363 -1.15334546209642 0.249034378721286 df.mm.trans1:exp6 -0.156934356541676 0.057119670274682 -2.74746607932078 0.00610988552890136 ** df.mm.trans2:exp6 -0.0211876344501826 0.0385100738431363 -0.550184207293045 0.582311517364684 df.mm.trans1:exp7 -0.0584094209792693 0.057119670274682 -1.02257979953990 0.306745050985556 df.mm.trans2:exp7 -0.0038972738987005 0.0385100738431363 -0.101201413286698 0.919410137884128 df.mm.trans1:exp8 0.0013833163872824 0.057119670274682 0.0242178636646568 0.980683488372467 df.mm.trans2:exp8 -0.0330156631977489 0.0385100738431363 -0.85732536718138 0.391463177292742 df.mm.trans1:probe2 0.0170620709555967 0.0428397527060115 0.398276597735879 0.690508292041833 df.mm.trans1:probe3 0.633966002665936 0.0428397527060115 14.7985448706144 4.23615157802437e-45 *** df.mm.trans1:probe4 0.570156838999984 0.0428397527060115 13.3090599965106 1.99114033948938e-37 *** df.mm.trans1:probe5 -0.140507953890767 0.0428397527060115 -3.27984978940016 0.00107318299320829 ** df.mm.trans1:probe6 0.239840226745796 0.0428397527060115 5.59854367955163 2.76688527414886e-08 *** df.mm.trans1:probe7 0.221073193891159 0.0428397527060115 5.16046848842191 2.95087919951709e-07 *** df.mm.trans1:probe8 0.0350271217005231 0.0428397527060115 0.817631276746562 0.413755578977741 df.mm.trans1:probe9 -0.176680993406313 0.0428397527060115 -4.12423000241831 4.01737616813125e-05 *** df.mm.trans1:probe10 0.0361217891557815 0.0428397527060115 0.843183885856389 0.399320050466635 df.mm.trans1:probe11 -0.0957842783434759 0.0428397527060115 -2.23587374560253 0.0255721655018107 * df.mm.trans1:probe12 -0.077456171388472 0.0428397527060115 -1.80804431622228 0.0708892196099211 . df.mm.trans1:probe13 -0.139011661419231 0.0428397527060115 -3.24492212579285 0.00121238420287840 ** df.mm.trans1:probe14 -0.15986957466453 0.0428397527060115 -3.73180433046936 0.00020043304755142 *** df.mm.trans1:probe15 -0.0983925169938176 0.0428397527060115 -2.29675735219664 0.0218312086435786 * df.mm.trans1:probe16 -0.144608597408795 0.0428397527060115 -3.37557031202244 0.000763935061468557 *** df.mm.trans1:probe17 0.103362088032016 0.0428397527060115 2.41276108060986 0.0160048604625096 * df.mm.trans1:probe18 0.0626357057180265 0.0428397527060115 1.46209307387615 0.144018676523749 df.mm.trans1:probe19 0.120045067208126 0.0428397527060115 2.80218861280404 0.00517022908843385 ** df.mm.trans1:probe20 0.0897349725825975 0.0428397527060115 2.09466597994637 0.0364428848822048 * df.mm.trans1:probe21 0.123364473163955 0.0428397527060115 2.87967285923767 0.00406274502163483 ** df.mm.trans1:probe22 0.295125549966599 0.0428397527060115 6.88905820703269 9.72619975016996e-12 *** df.mm.trans2:probe2 -0.0054293875877715 0.0428397527060115 -0.126737136533695 0.89917302095825 df.mm.trans2:probe3 0.0431691458479068 0.0428397527060115 1.00768895992831 0.313838874066683 df.mm.trans2:probe4 0.0471729213875413 0.0428397527060115 1.10114831220587 0.271087635242412 df.mm.trans2:probe5 0.0430281875795834 0.0428397527060115 1.00439859853685 0.315420829927092 df.mm.trans2:probe6 0.0148608110651113 0.0428397527060115 0.346893017032424 0.728742074100135 df.mm.trans3:probe2 0.344120005236114 0.0428397527060115 8.03272622971574 2.57559438014167e-15 *** df.mm.trans3:probe3 -0.0831371250793002 0.0428397527060115 -1.94065371128144 0.0525710344367459 . df.mm.trans3:probe4 0.535087282449155 0.0428397527060115 12.4904381713219 1.87968643945695e-33 *** df.mm.trans3:probe5 0.406102645561571 0.0428397527060115 9.4795749253843 1.67740296823990e-20 *** df.mm.trans3:probe6 0.401429630604812 0.0428397527060115 9.37049364779553 4.36943456320546e-20 *** df.mm.trans3:probe7 -0.215393772974645 0.0428397527060115 -5.02789487261489 5.83957579340705e-07 *** df.mm.trans3:probe8 -0.112019505121227 0.0428397527060115 -2.6148494808073 0.00905619067965794 ** df.mm.trans3:probe9 -0.0670127142718432 0.0428397527060115 -1.56426473167852 0.118060504910485 df.mm.trans3:probe10 0.194082773613719 0.0428397527060115 4.53043636702609 6.56975614656818e-06 *** df.mm.trans3:probe11 0.304470823150278 0.0428397527060115 7.10720309801305 2.20001959249005e-12 *** df.mm.trans3:probe12 0.545995252537846 0.0428397527060115 12.7450607916611 1.14031664382769e-34 *** df.mm.trans3:probe13 0.179184883664827 0.0428397527060115 4.18267782483448 3.12454981714074e-05 *** df.mm.trans3:probe14 -0.0166371323771533 0.0428397527060115 -0.388357339299456 0.697831323172686 df.mm.trans3:probe15 0.068770027395572 0.0428397527060115 1.60528534950954 0.108735485679540 df.mm.trans3:probe16 1.30933161750442 0.0428397527060115 30.5634728213707 8.46307515319757e-147 *** df.mm.trans3:probe17 -0.0422147878085169 0.0428397527060115 -0.985411566173517 0.324651804617064 df.mm.trans3:probe18 0.37505192216546 0.0428397527060115 8.75476393944802 8.17516509349926e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11090381656148 0.152113608141960 27.0252206017292 3.37430927619328e-122 *** df.mm.trans1 -0.0327765397239411 0.129924406265826 -0.252273923475779 0.800879333789568 df.mm.trans2 0.096353447567325 0.113367039663228 0.849924703454867 0.395563130337372 df.mm.exp2 0.0156026840605539 0.142594356416992 0.109420067193449 0.912890487677146 df.mm.exp3 -0.0297822005342034 0.142594356416992 -0.208859602038601 0.834598859330383 df.mm.exp4 -0.00337098509696096 0.142594356416992 -0.0236403822820526 0.981144008783127 df.mm.exp5 -0.073933484198792 0.142594356416992 -0.518488150979737 0.604228337053384 df.mm.exp6 0.0272131194293979 0.142594356416992 0.190842892476178 0.848686038078753 df.mm.exp7 0.0650852244507197 0.142594356416992 0.45643618784175 0.648171928104015 df.mm.exp8 0.0778446064109112 0.142594356416992 0.545916460980182 0.585240778706143 df.mm.trans1:exp2 -0.0351337330941156 0.129924406265826 -0.270416730034785 0.786893479188474 df.mm.trans2:exp2 0.0158781961269138 0.0875950168350384 0.181268258179756 0.856192441737279 df.mm.trans1:exp3 0.0664754446350706 0.129924406265826 0.511647091917907 0.609006950485367 df.mm.trans2:exp3 -0.107027925359145 0.0875950168350384 -1.22184947530409 0.222042226496519 df.mm.trans1:exp4 0.0740753681398132 0.129924406265826 0.57014205620655 0.56870485198796 df.mm.trans2:exp4 -0.0790792576104013 0.0875950168350384 -0.902782606450385 0.366850931566196 df.mm.trans1:exp5 0.053619561625053 0.129924406265826 0.412698146300142 0.679913091894556 df.mm.trans2:exp5 -0.0140919459091422 0.0875950168350384 -0.160876113942424 0.872222309598804 df.mm.trans1:exp6 0.0617364398992438 0.129924406265826 0.4751719994235 0.634764446485688 df.mm.trans2:exp6 -0.0359506210182923 0.0875950168350384 -0.410418563946344 0.681583711785428 df.mm.trans1:exp7 -0.0302112577396139 0.129924406265826 -0.232529503947099 0.816172657431226 df.mm.trans2:exp7 -0.0846244950162573 0.0875950168350384 -0.96608800447661 0.334225389623635 df.mm.trans1:exp8 -0.114055924285421 0.129924406265826 -0.87786373294685 0.380221012065482 df.mm.trans2:exp8 0.00592441420942791 0.0875950168350384 0.067634146592893 0.946089910350539 df.mm.trans1:probe2 0.0918145698010243 0.0974433046993696 0.942235796336024 0.346291379589577 df.mm.trans1:probe3 0.0560071561154816 0.0974433046993696 0.574766591591633 0.565573832069194 df.mm.trans1:probe4 -0.0204336574393121 0.0974433046993696 -0.209697911029944 0.833944645115995 df.mm.trans1:probe5 0.109327094379336 0.0974433046993696 1.12195593854940 0.262140890783676 df.mm.trans1:probe6 -0.0364351118833662 0.0974433046993696 -0.373910880750352 0.708547054507972 df.mm.trans1:probe7 -0.11476695343627 0.0974433046993696 -1.17778182698490 0.239153814956749 df.mm.trans1:probe8 0.00602460899711263 0.0974433046993696 0.0618268131987072 0.950712664345345 df.mm.trans1:probe9 -0.0279868557898730 0.0974433046993696 -0.287211685566470 0.774007619119652 df.mm.trans1:probe10 -0.0697858955612386 0.0974433046993696 -0.716169220415306 0.474048105650615 df.mm.trans1:probe11 0.0199304908558928 0.0974433046993696 0.204534225490217 0.837976184594071 df.mm.trans1:probe12 0.00260628186047413 0.0974433046993696 0.0267466489207748 0.978666952010992 df.mm.trans1:probe13 -0.0528670098078027 0.0974433046993696 -0.542541224057487 0.587562290467453 df.mm.trans1:probe14 0.0922457463571501 0.0974433046993696 0.946660693022934 0.344032237859664 df.mm.trans1:probe15 0.141218924430671 0.0974433046993696 1.44924194500954 0.147572355232908 df.mm.trans1:probe16 0.0894530553536984 0.0974433046993696 0.918001043064759 0.358831777175612 df.mm.trans1:probe17 0.0104397940514615 0.0974433046993696 0.107137109970461 0.914700930079398 df.mm.trans1:probe18 0.0110702596581447 0.0974433046993696 0.113607186171471 0.909571180282624 df.mm.trans1:probe19 0.116544297214136 0.0974433046993696 1.19602160018789 0.231961527995340 df.mm.trans1:probe20 -0.0794051886069256 0.0974433046993696 -0.814886039137375 0.415324646994262 df.mm.trans1:probe21 0.0955338309850314 0.0974433046993696 0.98040425947961 0.327115274826001 df.mm.trans1:probe22 0.107673875438112 0.0974433046993696 1.10498998130559 0.269420237494391 df.mm.trans2:probe2 -0.0329294569595978 0.0974433046993696 -0.337934525734643 0.735480926441318 df.mm.trans2:probe3 -0.00700094298890352 0.0974433046993696 -0.0718463214122582 0.942738062670337 df.mm.trans2:probe4 -0.0847642233927558 0.0974433046993696 -0.869882478373131 0.384565934508228 df.mm.trans2:probe5 -0.0297749220697962 0.0974433046993696 -0.305561497135768 0.75999986462245 df.mm.trans2:probe6 0.00803351248749297 0.0974433046993696 0.0824429396383653 0.934310401273611 df.mm.trans3:probe2 -0.133857530020598 0.0974433046993696 -1.37369653496023 0.169832789165302 df.mm.trans3:probe3 0.0391532230385963 0.0974433046993696 0.401805164135095 0.68791021527658 df.mm.trans3:probe4 0.0324183887950317 0.0974433046993696 0.332689751184531 0.739435728417105 df.mm.trans3:probe5 -0.0291216326159900 0.0974433046993696 -0.29885719399435 0.765108887546151 df.mm.trans3:probe6 0.00294762801306851 0.0974433046993696 0.0302496720750849 0.975873754653548 df.mm.trans3:probe7 -0.158681219654018 0.0974433046993696 -1.62844661460917 0.103733959807144 df.mm.trans3:probe8 -0.0445121406569741 0.0974433046993696 -0.456800400954198 0.647910184200496 df.mm.trans3:probe9 0.0180501773429613 0.0974433046993696 0.18523773797132 0.853078793101452 df.mm.trans3:probe10 0.00667635869084956 0.0974433046993696 0.0685153147406828 0.94538863774709 df.mm.trans3:probe11 -0.0575350273788548 0.0974433046993696 -0.590446183617857 0.55502020249492 df.mm.trans3:probe12 -0.00943207158871864 0.0974433046993696 -0.0967954814116609 0.922907515739439 df.mm.trans3:probe13 -0.0484461117434686 0.0974433046993696 -0.497172298219294 0.619172944229531 df.mm.trans3:probe14 -0.0316077865995769 0.0974433046993696 -0.324371045266708 0.745722554568337 df.mm.trans3:probe15 -0.0524450707927744 0.0974433046993696 -0.538211126506609 0.590546794734487 df.mm.trans3:probe16 -0.0354666263368711 0.0974433046993696 -0.363971916247013 0.715953175453648 df.mm.trans3:probe17 -0.0222538935592159 0.0974433046993696 -0.228377861648610 0.819397523760217 df.mm.trans3:probe18 0.0114170877146422 0.0974433046993696 0.11716646669431 0.906750827341738