fitVsDatCorrelation=0.725481626719077 cont.fitVsDatCorrelation=0.245323050789953 fstatistic=12629.1694261580,58,830 cont.fstatistic=6358.12241929179,58,830 residuals=-0.388832133984411,-0.0801090486627331,-0.0129981045841481,0.0706910645956189,0.98095108044973 cont.residuals=-0.462066090479974,-0.128555840682791,-0.0226509366670317,0.0898243200916156,1.05395488478274 predictedValues: Include Exclude Both Lung 49.6026786173314 49.7661717108488 55.1048621094897 cerebhem 54.4206482818533 52.6077027259322 52.3242594144278 cortex 50.515021910868 63.9785961075329 50.5934029060749 heart 52.1247357439426 55.933588845757 55.0869240946078 kidney 49.145684149839 50.6634873328113 53.7156689178556 liver 50.678558338112 54.5766448922848 53.257558560452 stomach 54.9875177270991 65.1900290763307 52.8895387593208 testicle 52.9919391658386 50.8135094615451 51.0844754103832 diffExp=-0.163493093517381,1.81294555592109,-13.4635741966649,-3.80885310181445,-1.51780318297228,-3.89808655417288,-10.2025113492316,2.17842970429356 diffExpScore=1.23227099798394 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,-1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 53.3592207327779 52.6574854281888 55.9778906673051 cerebhem 52.6005509432417 58.6157302176075 52.5191161811027 cortex 51.1460924180732 56.6382032762802 55.2612125461681 heart 54.1725319437558 56.7818076546452 54.8417375840739 kidney 51.311460320349 52.1005782019553 54.4919956320301 liver 53.5053491002809 53.8217702816502 52.6271304277615 stomach 52.8040101447625 51.2209111182474 53.4822952473098 testicle 52.6728193319446 53.756859754493 51.2325360370021 cont.diffExp=0.701735304589178,-6.01517927436581,-5.49211085820698,-2.60927571088943,-0.789117881606266,-0.316421181369286,1.58309902651515,-1.08404042254846 cont.diffExpScore=1.23764028736986 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=0.363879142079357 cont.tran.correlation=0.0460417014182308 tran.covariance=0.00161421965664196 cont.tran.covariance=4.38352887062928e-05 tran.mean=53.6247821304954 cont.tran.mean=53.5728363042658 weightedLogRatios: wLogRatio Lung -0.0128521954235273 cerebhem 0.134840095461576 cortex -0.954659254132165 heart -0.281319184805801 kidney -0.118928225226765 liver -0.293637702627343 stomach -0.696494833109665 testicle 0.165775713587787 cont.weightedLogRatios: wLogRatio Lung 0.052562091635505 cerebhem -0.434932067954232 cortex -0.406530423414552 heart -0.188906313084775 kidney -0.060216598742754 liver -0.0234837893325289 stomach 0.120276736755521 testicle -0.080963037392496 varWeightedLogRatios=0.156109659099895 cont.varWeightedLogRatios=0.0410671960977664 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.64928051122459 0.0648882760107091 56.2394431718654 2.3799806740244e-285 *** df.mm.trans1 0.181655862986002 0.05617691594277 3.23363894114555 0.00127053369712911 ** df.mm.trans2 0.271184616410029 0.0497694238831318 5.44881968187581 6.68667829708686e-08 *** df.mm.exp2 0.200003726732028 0.0643253791250913 3.10925064806983 0.00193964879392810 ** df.mm.exp3 0.354855803654044 0.0643253791250913 5.51657539342238 4.62002968190286e-08 *** df.mm.exp4 0.166749955086173 0.0643253791250913 2.59228872575940 0.00970163919698621 ** df.mm.exp5 0.0341474148982771 0.0643253791250913 0.530854467750152 0.595661643552593 df.mm.exp6 0.147826858246351 0.0643253791250913 2.29811095180454 0.0218033039355133 * df.mm.exp7 0.414064820598058 0.0643253791250913 6.4370366133846 2.05874427636629e-10 *** df.mm.exp8 0.162679068116604 0.0643253791250913 2.52900286526485 0.0116228960813614 * df.mm.trans1:exp2 -0.107304917536047 0.0596312224186696 -1.79947539533336 0.0723066077213495 . df.mm.trans2:exp2 -0.144476648351850 0.0447685691897767 -3.22718931979721 0.00129915623176048 ** df.mm.trans1:exp3 -0.336629884679085 0.0596312224186696 -5.64519510124433 2.26511287527095e-08 *** df.mm.trans2:exp3 -0.103642682349053 0.0447685691897767 -2.31507694404315 0.0208518111527711 * df.mm.trans1:exp4 -0.117155181295011 0.0596312224186696 -1.96466174167061 0.0497866399505228 * df.mm.trans2:exp4 -0.0499203518702238 0.0447685691897767 -1.11507588412326 0.265140660267454 df.mm.trans1:exp5 -0.043403218584281 0.0596312224186696 -0.727860621061024 0.466904186309897 df.mm.trans2:exp5 -0.0162774049980293 0.0447685691897767 -0.363590020691267 0.716256768311489 df.mm.trans1:exp6 -0.126368786219564 0.0596312224186697 -2.11917148590937 0.0343723525957269 * df.mm.trans2:exp6 -0.0555562865305926 0.0447685691897767 -1.24096631936317 0.214968821022744 df.mm.trans1:exp7 -0.311003448193489 0.0596312224186696 -5.21544646544288 2.31720046107184e-07 *** df.mm.trans2:exp7 -0.144093761936059 0.0447685691897767 -3.21863674769761 0.00133803115714168 ** df.mm.trans1:exp8 -0.0965840939964934 0.0596312224186696 -1.61968998922038 0.105678749262481 df.mm.trans2:exp8 -0.141852284902790 0.0447685691897767 -3.1685686514007 0.00158801830736927 ** df.mm.trans1:probe2 0.0214851191081030 0.0400018400728664 0.537103269973735 0.59134023194431 df.mm.trans1:probe3 0.0313384379243082 0.0400018400728664 0.78342490913475 0.433601121047384 df.mm.trans1:probe4 0.0682271430503939 0.0400018400728664 1.70560011554751 0.0884567022610008 . df.mm.trans1:probe5 0.316056688609598 0.0400018400728664 7.9010537523743 8.76993106437536e-15 *** df.mm.trans1:probe6 0.0624530015520136 0.0400018400728664 1.56125321830822 0.118845201848611 df.mm.trans1:probe7 0.310306322933487 0.0400018400728664 7.75730122334974 2.54300600763190e-14 *** df.mm.trans1:probe8 0.129724939070529 0.0400018400728664 3.24297429403811 0.00123013654012029 ** df.mm.trans1:probe9 -0.0329107365748789 0.0400018400728664 -0.822730567267143 0.410897530121296 df.mm.trans1:probe10 0.54095895133603 0.0400018400728664 13.5233516845883 8.36146404695382e-38 *** df.mm.trans1:probe11 0.104658818184932 0.0400018400728664 2.61635009775271 0.00904909412056773 ** df.mm.trans1:probe12 -0.0747708265726821 0.0400018400728664 -1.86918467841683 0.0619490896759736 . df.mm.trans1:probe13 0.356224771122632 0.0400018400728664 8.9052096222009 3.31173781949394e-18 *** df.mm.trans1:probe14 0.257806719322542 0.0400018400728664 6.44487150723385 1.95996830858234e-10 *** df.mm.trans1:probe15 -0.00145323600498867 0.0400018400728664 -0.0363292289140071 0.971028579334557 df.mm.trans1:probe16 0.156746130642611 0.0400018400728664 3.91847300916871 9.64483917432582e-05 *** df.mm.trans1:probe17 -0.0214246573903340 0.0400018400728664 -0.535591796560041 0.592384179900609 df.mm.trans1:probe18 0.0295800909340158 0.0400018400728664 0.739468256463537 0.459831790523052 df.mm.trans1:probe19 -0.011341966270627 0.0400018400728664 -0.283536113587943 0.776836609787165 df.mm.trans1:probe20 0.0142386851498766 0.0400018400728664 0.355950754363794 0.721967896617506 df.mm.trans1:probe21 0.0544035013131764 0.0400018400728664 1.36002496920332 0.174191307032219 df.mm.trans1:probe22 -0.0459455616834671 0.0400018400728664 -1.14858620502891 0.251057551723729 df.mm.trans2:probe2 0.0114648800372222 0.0400018400728664 0.286608816402896 0.774483389955104 df.mm.trans2:probe3 -0.0577214276902501 0.0400018400728664 -1.44296931303925 0.149406393367575 df.mm.trans2:probe4 -0.0546807096816583 0.0400018400728664 -1.36695485962779 0.172009569492111 df.mm.trans2:probe5 -0.081575580318883 0.0400018400728664 -2.03929569665513 0.0417368014552791 * df.mm.trans2:probe6 -0.0144320223844913 0.0400018400728664 -0.360783962892762 0.718352754534937 df.mm.trans3:probe2 -0.240724704434197 0.0400018400728664 -6.01784077921661 2.6501641190541e-09 *** df.mm.trans3:probe3 -0.029299548456001 0.0400018400728664 -0.732455017134952 0.464097670908353 df.mm.trans3:probe4 -0.0841689638405848 0.0400018400728664 -2.10412730232571 0.0356675825987001 * df.mm.trans3:probe5 -0.108629703647727 0.0400018400728664 -2.71561766783351 0.00675272331534092 ** df.mm.trans3:probe6 0.125458336899230 0.0400018400728664 3.13631414631673 0.00177117056488279 ** df.mm.trans3:probe7 -0.178109512082324 0.0400018400728664 -4.45253297743014 9.64631542883393e-06 *** df.mm.trans3:probe8 -0.218323138151057 0.0400018400728664 -5.45782738377446 6.3674280107457e-08 *** df.mm.trans3:probe9 -0.102150097516818 0.0400018400728664 -2.55363496606015 0.0108381802968329 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.90637336705473 0.0914001511205292 42.7392440730585 6.95833681720875e-212 *** df.mm.trans1 0.064609727021212 0.0791295272786573 0.816505914330648 0.414445121569308 df.mm.trans2 0.0520816757377192 0.0701040795620641 0.742919328847483 0.457740736723694 df.mm.exp2 0.156654071018912 0.0906072673582568 1.72893494734268 0.0841925867939402 . df.mm.exp3 0.043400164034504 0.0906072673582568 0.478992086395254 0.632070422312568 df.mm.exp4 0.111040041002010 0.0906072673582568 1.22550921399012 0.220731049297321 df.mm.exp5 -0.0228620459638506 0.0906072673582568 -0.252320223646688 0.80085602668425 df.mm.exp6 0.0863294989826898 0.0906072673582568 0.95278780057837 0.340974978627529 df.mm.exp7 0.00748594118016516 0.0906072673582568 0.0826196551162515 0.934173892292786 df.mm.exp8 0.0962976466785644 0.0906072673582568 1.06280268113382 0.288180564084575 df.mm.trans1:exp2 -0.17097427466628 0.083995184887772 -2.03552471364546 0.0421152796970123 * df.mm.trans2:exp2 -0.049459378559481 0.0630599892763393 -0.784322660486696 0.433074587439924 df.mm.trans1:exp3 -0.0857608666609506 0.083995184887772 -1.02102122610406 0.307541884566186 df.mm.trans2:exp3 0.0294751611815124 0.0630599892763392 0.467414624070858 0.640325949446017 df.mm.trans1:exp4 -0.0959128492661317 0.083995184887772 -1.14188509013086 0.253831100685096 df.mm.trans2:exp4 -0.035632456197532 0.0630599892763392 -0.565056489962038 0.572188033169023 df.mm.trans1:exp5 -0.0162706262938647 0.083995184887772 -0.193709036007293 0.846451111346847 df.mm.trans2:exp5 0.0122296907385822 0.0630599892763393 0.193937405935635 0.846272347021368 df.mm.trans1:exp6 -0.0835946644381882 0.083995184887772 -0.99523162607334 0.319913673854795 df.mm.trans2:exp6 -0.0644598634384528 0.0630599892763393 -1.02219908658689 0.306984520747938 df.mm.trans1:exp7 -0.0179456011828661 0.083995184887772 -0.213650356348922 0.830872197215266 df.mm.trans2:exp7 -0.0351464740865072 0.0630599892763393 -0.557349826567359 0.57743870882174 df.mm.trans1:exp8 -0.109244883926808 0.083995184887772 -1.30060888695909 0.193753354560213 df.mm.trans2:exp8 -0.0756347663237656 0.0630599892763393 -1.19940975556341 0.230710936409329 df.mm.trans1:probe2 0.0426146060842985 0.0563456829575164 0.756306496744909 0.449680058310673 df.mm.trans1:probe3 -0.00358063451646573 0.0563456829575164 -0.063547628292401 0.949345734582413 df.mm.trans1:probe4 -0.0451840155792145 0.0563456829575164 -0.801907319382079 0.422836097025981 df.mm.trans1:probe5 -0.0434559113855850 0.0563456829575164 -0.771237637111434 0.440785587290605 df.mm.trans1:probe6 -0.0204595570083004 0.0563456829575164 -0.363107800534188 0.716616811038387 df.mm.trans1:probe7 0.0304061699723649 0.0563456829575164 0.539636195292735 0.589592687058664 df.mm.trans1:probe8 -0.0162703701496358 0.0563456829575164 -0.288759835636448 0.772837271392317 df.mm.trans1:probe9 0.0927751676102356 0.0563456829575164 1.64653550619284 0.100032104546699 df.mm.trans1:probe10 -0.0408618615480205 0.0563456829575164 -0.7251995078102 0.468534047570672 df.mm.trans1:probe11 0.0492063003844726 0.0563456829575164 0.873293175300994 0.382755881413942 df.mm.trans1:probe12 0.00276786383623700 0.0563456829575164 0.0491229086410031 0.960833164965853 df.mm.trans1:probe13 0.0359069536841373 0.0563456829575164 0.637261841536475 0.524130037526975 df.mm.trans1:probe14 -0.0194678587619682 0.0563456829575164 -0.345507548052023 0.729800383738031 df.mm.trans1:probe15 -0.0450023348334123 0.0563456829575164 -0.798682924250705 0.424702763745364 df.mm.trans1:probe16 0.0341954862734903 0.0563456829575164 0.60688742204568 0.544091595052078 df.mm.trans1:probe17 -0.066354307666743 0.0563456829575164 -1.17762895369949 0.239282117677529 df.mm.trans1:probe18 0.0409146644935807 0.0563456829575164 0.726136632764388 0.467959723773095 df.mm.trans1:probe19 0.0240744567021045 0.0563456829575164 0.427263553096983 0.66929819195865 df.mm.trans1:probe20 0.0384318475143216 0.0563456829575164 0.682072618470141 0.495383324077121 df.mm.trans1:probe21 0.0895710818852715 0.0563456829575164 1.58967071093639 0.112289855754915 df.mm.trans1:probe22 0.0077470604657429 0.0563456829575164 0.137491641934379 0.890675544505259 df.mm.trans2:probe2 0.00785305338850878 0.0563456829575164 0.139372760721169 0.889189406478925 df.mm.trans2:probe3 0.0148580992931775 0.0563456829575164 0.263695433497189 0.79208006783506 df.mm.trans2:probe4 0.0256582636935594 0.0563456829575164 0.455372307988622 0.64896051095277 df.mm.trans2:probe5 0.0182660116777129 0.0563456829575164 0.324177660451558 0.745885209487285 df.mm.trans2:probe6 0.0136649577984943 0.0563456829575164 0.242520049118890 0.808437137749596 df.mm.trans3:probe2 -0.0236925698035012 0.0563456829575164 -0.420485981532338 0.674239308485159 df.mm.trans3:probe3 -0.0557888396820835 0.0563456829575164 -0.99011737463804 0.322405301374662 df.mm.trans3:probe4 -0.0141004309911310 0.0563456829575164 -0.250248648184146 0.802456993834923 df.mm.trans3:probe5 -0.00639964949406142 0.0563456829575164 -0.11357834634619 0.909599519456184 df.mm.trans3:probe6 0.0714297531351732 0.0563456829575164 1.26770587178844 0.205258525912243 df.mm.trans3:probe7 -0.0081350899302089 0.0563456829575164 -0.144378229230846 0.885236855832904 df.mm.trans3:probe8 0.0328356827071364 0.0563456829575164 0.582754187785671 0.56021707115282 df.mm.trans3:probe9 0.0208794451202015 0.0563456829575164 0.370559801998393 0.71105997457305