fitVsDatCorrelation=0.844897593409374 cont.fitVsDatCorrelation=0.241691793837723 fstatistic=14315.1522203969,73,1175 cont.fstatistic=4339.1757651951,73,1175 residuals=-0.879925227726704,-0.0772196155600175,-0.00350641905477450,0.0688367586210369,1.07819233846282 cont.residuals=-0.520341819873963,-0.172120422897015,-0.0363091945008885,0.129755710856205,1.71005233984105 predictedValues: Include Exclude Both Lung 51.9789799038432 55.7971386129382 56.8712090045309 cerebhem 56.5405070942918 63.0918307605764 55.7729754115647 cortex 50.0678169655549 49.2868277893816 53.644254401752 heart 51.986568066142 48.162816436286 56.0225654154076 kidney 53.1999449758757 57.6175559967525 57.7153807674628 liver 55.8445422276056 56.7873681149071 58.1593920406573 stomach 52.6502713309073 52.9300391411897 58.4984177817663 testicle 53.2230423194076 53.9703208199604 56.6776963031097 diffExp=-3.81815870909497,-6.55132366628462,0.780989176173307,3.82375162985596,-4.41761102087677,-0.942825887301424,-0.279767810282401,-0.74727850055276 diffExpScore=1.62418957584437 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 54.3043756454241 62.9698478989893 57.5825951223766 cerebhem 55.2897271432168 55.4961871709411 56.263424353652 cortex 57.0763322585747 55.3290234549424 54.0488724848113 heart 55.1856993083245 57.3772709689129 56.1177292413168 kidney 58.7587966359101 57.4125630491598 57.0896597117527 liver 56.7577368886455 59.7270243626885 55.5058439685502 stomach 58.8909762155969 58.3655271080143 53.1405014138833 testicle 59.6156297871768 61.2296050737618 57.4067715770481 cont.diffExp=-8.66547225356516,-0.20646002772434,1.74730880363227,-2.19157166058838,1.34623358675034,-2.96928747404306,0.525449107582531,-1.61397528658498 cont.diffExpScore=1.47882181707864 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=0.820843082384823 cont.tran.correlation=-0.0119786181381065 tran.covariance=0.00280273678984803 cont.tran.covariance=-1.48009223672426e-05 tran.mean=53.9459731597263 cont.tran.mean=57.7366451856425 weightedLogRatios: wLogRatio Lung -0.282560291324519 cerebhem -0.448378215134216 cortex 0.0614008502395268 heart 0.298929436125773 kidney -0.320191982828241 liver -0.0674864882073717 stomach -0.0210200479922242 testicle -0.0555129479097329 cont.weightedLogRatios: wLogRatio Lung -0.602365616371901 cerebhem -0.0149626344107418 cortex 0.125264510605859 heart -0.156952786259370 kidney 0.0941444846465467 liver -0.207248657043029 stomach 0.0364879756741378 testicle -0.109557468111362 varWeightedLogRatios=0.0567853696089146 cont.varWeightedLogRatios=0.0544785942888018 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.65436056814997 0.0620880057556549 58.8577539844261 0 *** df.mm.trans1 0.281096509389386 0.0528306015993553 5.32071376966519 1.23692356789223e-07 *** df.mm.trans2 0.347854468953201 0.0458944086516325 7.57945203289656 7.01459869324983e-14 *** df.mm.exp2 0.2264864039556 0.0572463181792214 3.95634883009485 8.06605276354877e-05 *** df.mm.exp3 -0.103111855744156 0.0572463181792214 -1.80119628691828 0.0719282493984651 . df.mm.exp4 -0.131954677302084 0.0572463181792214 -2.30503343269994 0.0213383081054743 * df.mm.exp5 0.0405881997228979 0.0572463181792214 0.70900978462629 0.478459053589941 df.mm.exp6 0.0669255546758727 0.0572463181792214 1.16908050691310 0.242608308963006 df.mm.exp7 -0.0681300627861873 0.0572463181792214 -1.19012130304856 0.234239119201559 df.mm.exp8 -0.00622783296860921 0.0572463181792215 -0.108790105052899 0.91338754708668 df.mm.trans1:exp2 -0.142368487198736 0.0518670447205931 -2.74487370479025 0.0061456750030095 ** df.mm.trans2:exp2 -0.103617696491247 0.0335408509528047 -3.08929837937169 0.00205340586358755 ** df.mm.trans1:exp3 0.0656508772471256 0.0518670447205931 1.26575318876920 0.205852372379062 df.mm.trans2:exp3 -0.0209538724413899 0.0335408509528046 -0.624726917956678 0.532271592319151 df.mm.trans1:exp4 0.132100651857315 0.0518670447205931 2.54690917072564 0.0109948121410458 * df.mm.trans2:exp4 -0.0151806313218064 0.0335408509528047 -0.452601257587861 0.650919444334152 df.mm.trans1:exp5 -0.0173702419028838 0.0518670447205931 -0.334899395106412 0.737760753153137 df.mm.trans2:exp5 -0.008483475584703 0.0335408509528046 -0.25292964679519 0.800366821105676 df.mm.trans1:exp6 0.00480683984861123 0.0518670447205931 0.0926761853216353 0.926176615325212 df.mm.trans2:exp6 -0.0493342347592063 0.0335408509528046 -1.47087009893173 0.141593988831401 df.mm.trans1:exp7 0.0809620505974138 0.0518670447205931 1.56095360808689 0.118803976262362 df.mm.trans2:exp7 0.0153784994110977 0.0335408509528046 0.458500573904245 0.646677639953587 df.mm.trans1:exp8 0.0298798576097415 0.0518670447205931 0.576085600610249 0.564667646602982 df.mm.trans2:exp8 -0.0270604747400892 0.0335408509528046 -0.806791538418808 0.419949864590099 df.mm.trans1:probe2 -0.176489412206578 0.0401760400839705 -4.3929021336524 1.21937463826894e-05 *** df.mm.trans1:probe3 -0.141309603480391 0.0401760400839705 -3.51726061565663 0.000452662486270333 *** df.mm.trans1:probe4 -0.149213336889288 0.0401760400839705 -3.71398815257607 0.000213594866996358 *** df.mm.trans1:probe5 0.0712264031672516 0.0401760400839705 1.77285772859605 0.0765113169765778 . df.mm.trans1:probe6 0.0243915803361264 0.0401760400839705 0.607117582647429 0.543890179354492 df.mm.trans1:probe7 -0.129939133967357 0.0401760400839705 -3.23424443264634 0.00125360325021274 ** df.mm.trans1:probe8 -0.159443098695330 0.0401760400839705 -3.96861159940312 7.667313554472e-05 *** df.mm.trans1:probe9 -0.0292099327210021 0.0401760400839705 -0.727048575717057 0.467341024087867 df.mm.trans1:probe10 0.0884182578724857 0.0401760400839705 2.20077084968269 0.0279462251065434 * df.mm.trans1:probe11 0.00538654247344118 0.0401760400839705 0.134073504063192 0.893367411172853 df.mm.trans1:probe12 -0.136951458159424 0.0401760400839705 -3.40878438674361 0.000674650358191906 *** df.mm.trans1:probe13 -0.168817254329047 0.0401760400839705 -4.20193861754936 2.84675553369508e-05 *** df.mm.trans1:probe14 -0.071912476178226 0.0401760400839705 -1.78993439940632 0.0737218801210573 . df.mm.trans1:probe15 -0.112716860606002 0.0401760400839705 -2.8055741773061 0.00510556048752633 ** df.mm.trans1:probe16 -0.0416155062772508 0.0401760400839705 -1.03582897145343 0.300495065060307 df.mm.trans1:probe17 0.312795381089288 0.0401760400839705 7.78561999728012 1.51460357594533e-14 *** df.mm.trans1:probe18 0.350768320028126 0.0401760400839705 8.73078380285857 8.53686918207058e-18 *** df.mm.trans1:probe19 0.230399988722096 0.0401760400839705 5.7347610227525 1.24081606518824e-08 *** df.mm.trans1:probe20 0.387340235790872 0.0401760400839705 9.64107550125165 3.19438646885570e-21 *** df.mm.trans1:probe21 0.31262108968081 0.0401760400839705 7.78128180446385 1.56483012330159e-14 *** df.mm.trans1:probe22 0.24185730253301 0.0401760400839705 6.01993880002889 2.32910460834021e-09 *** df.mm.trans2:probe2 -0.0323042555761476 0.0401760400839705 -0.804067685830401 0.421520528098115 df.mm.trans2:probe3 -0.0591482527201851 0.0401760400839705 -1.47222704369474 0.141227365424950 df.mm.trans2:probe4 0.123240953178463 0.0401760400839705 3.06752365143210 0.00220777525803727 ** df.mm.trans2:probe5 0.429099998660201 0.0401760400839705 10.6804950852138 1.77365113624276e-25 *** df.mm.trans2:probe6 0.124338105394826 0.0401760400839705 3.09483227154672 0.00201578677977440 ** df.mm.trans3:probe2 -0.290696365484239 0.0401760400839705 -7.23556539859739 8.34658225567278e-13 *** df.mm.trans3:probe3 -0.508542663120142 0.0401760400839705 -12.6578593125966 1.61909228646265e-34 *** df.mm.trans3:probe4 -0.560792826604798 0.0401760400839705 -13.958389762473 4.3900427018774e-41 *** df.mm.trans3:probe5 -0.441441210940503 0.0401760400839705 -10.9876734993758 8.39136833503839e-27 *** df.mm.trans3:probe6 -0.395666525187752 0.0401760400839705 -9.84832064983966 4.83658215073126e-22 *** df.mm.trans3:probe7 -0.37816808884043 0.0401760400839705 -9.41277657155943 2.45853390219704e-20 *** df.mm.trans3:probe8 0.200321971695466 0.0401760400839705 4.98610543191366 7.08659171625953e-07 *** df.mm.trans3:probe9 -0.225963292582385 0.0401760400839705 -5.6243296280596 2.32584483194923e-08 *** df.mm.trans3:probe10 0.014197974390065 0.0401760400839705 0.353394071700206 0.723856422462763 df.mm.trans3:probe11 -0.440532704940005 0.0401760400839705 -10.9650603697941 1.05293399037588e-26 *** df.mm.trans3:probe12 -0.232875094692785 0.0401760400839705 -5.79636754160094 8.69807399833972e-09 *** df.mm.trans3:probe13 -0.470680722231689 0.0401760400839705 -11.7154582992235 4.63551427175054e-30 *** df.mm.trans3:probe14 -0.395439433092401 0.0401760400839705 -9.84266822379478 5.09437601695812e-22 *** df.mm.trans3:probe15 -0.316331123737793 0.0401760400839705 -7.87362624779945 7.78740656366261e-15 *** df.mm.trans3:probe16 -0.380759783664052 0.0401760400839705 -9.47728504024388 1.38690157679412e-20 *** df.mm.trans3:probe17 -0.407152769196766 0.0401760400839705 -10.1342185129692 3.38785118527854e-23 *** df.mm.trans3:probe18 0.00225603669463281 0.0401760400839705 0.056153784442607 0.955228844496807 df.mm.trans3:probe19 -0.305261770340158 0.0401760400839705 -7.59810498252545 6.1152744356243e-14 *** df.mm.trans3:probe20 0.44257857803503 0.0401760400839705 11.015983086188 6.31256492499415e-27 *** df.mm.trans3:probe21 -0.0709179125770548 0.0401760400839705 -1.76517925681157 0.0777933646178345 . df.mm.trans3:probe22 -0.150564747001410 0.0401760400839705 -3.74762536792376 0.000187190465191846 *** df.mm.trans3:probe23 -0.288142776462447 0.0401760400839705 -7.17200540073661 1.30461976618943e-12 *** df.mm.trans3:probe24 -0.219960581519189 0.0401760400839705 -5.47491940617984 5.34837456732253e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15241211012149 0.112626890439197 36.8687450566099 2.40990541929634e-198 *** df.mm.trans1 -0.135098670424281 0.0958340714241015 -1.40971439923928 0.158888654464412 df.mm.trans2 -0.000461250261974979 0.0832519014271668 -0.0055404171444481 0.995580349783525 df.mm.exp2 -0.085183683193053 0.103844127817414 -0.820303323677855 0.412209587399157 df.mm.exp3 -0.0162420011003971 0.103844127817414 -0.156407506536671 0.875738688738374 df.mm.exp4 -0.05114015338896 0.103844127817414 -0.492470344388447 0.622478885482297 df.mm.exp5 -0.00495944928428475 0.103844127817414 -0.0477585915402438 0.961916754029113 df.mm.exp6 0.0280477958332396 0.103844127817414 0.270095155332761 0.787134478685723 df.mm.exp7 0.0854334645468349 0.103844127817414 0.822708672531293 0.410840609410432 df.mm.exp8 0.0683458724350337 0.103844127817414 0.658158278869691 0.510565356938065 df.mm.trans1:exp2 0.103166002037335 0.094086190916498 1.09650524728857 0.273082403640161 df.mm.trans2:exp2 -0.0411580050829288 0.0608426973861197 -0.676465818432277 0.498878075930993 df.mm.trans1:exp3 0.0660267290058724 0.094086190916498 0.701768541830666 0.482962494139524 df.mm.trans2:exp3 -0.113116398658975 0.0608426973861196 -1.85916146914257 0.0632540903355119 . df.mm.trans1:exp4 0.0672391961241524 0.094086190916498 0.714655311998204 0.47496401263316 df.mm.trans2:exp4 -0.0418676049018812 0.0608426973861197 -0.688128677730726 0.491507576255633 df.mm.trans1:exp5 0.0837955145911475 0.094086190916498 0.890625008568117 0.373312700509066 df.mm.trans2:exp5 -0.0874334099319001 0.0608426973861196 -1.43704033003387 0.150972755132589 df.mm.trans1:exp6 0.0161393775934335 0.094086190916498 0.171538218693084 0.863830118244893 df.mm.trans2:exp6 -0.0809192155125569 0.0608426973861196 -1.32997416270071 0.183784885307938 df.mm.trans1:exp7 -0.00435039727646911 0.094086190916498 -0.0462384249387895 0.963128073638843 df.mm.trans2:exp7 -0.161364045294032 0.0608426973861196 -2.65215140397186 0.00810577149395668 ** df.mm.trans1:exp8 0.0249671055254844 0.094086190916498 0.265364186628012 0.790775470062243 df.mm.trans2:exp8 -0.096371063893134 0.0608426973861196 -1.58393805720914 0.113476907721279 df.mm.trans1:probe2 -0.0408291404341232 0.0728788501055376 -0.560233049437492 0.575427280732607 df.mm.trans1:probe3 -0.122734997626615 0.0728788501055376 -1.68409624258451 0.092428664860755 . df.mm.trans1:probe4 -0.130859513841649 0.0728788501055376 -1.79557599567157 0.0728188430985925 . df.mm.trans1:probe5 0.0428985730854245 0.0728788501055376 0.588628566769399 0.556223598038908 df.mm.trans1:probe6 0.00874590857342778 0.0728788501055376 0.120006127439753 0.90449880951872 df.mm.trans1:probe7 -0.0981874079367402 0.0728788501055376 -1.34726889618254 0.178153392553899 df.mm.trans1:probe8 -0.0773057072953408 0.0728788501055376 -1.06074268712243 0.289024837733411 df.mm.trans1:probe9 -0.0928829774650884 0.0728788501055376 -1.27448467327054 0.202743688680985 df.mm.trans1:probe10 -0.0667352864728049 0.0728788501055376 -0.915701693648623 0.360011262895398 df.mm.trans1:probe11 -0.0190392857507019 0.0728788501055376 -0.261245693683842 0.793948824800328 df.mm.trans1:probe12 -0.057723280245475 0.0728788501055376 -0.792044333326946 0.428494696768036 df.mm.trans1:probe13 0.072453597721006 0.0728788501055376 0.994164941078025 0.320347257314228 df.mm.trans1:probe14 -0.0584248142246098 0.0728788501055376 -0.801670363075205 0.422905753967415 df.mm.trans1:probe15 -0.113838848105092 0.0728788501055376 -1.56202859869824 0.118550527264990 df.mm.trans1:probe16 -0.0100629360835533 0.0728788501055376 -0.138077591358548 0.890202753768143 df.mm.trans1:probe17 -0.0756986812191672 0.0728788501055376 -1.03869203629786 0.299161677921494 df.mm.trans1:probe18 -0.0558725525597492 0.0728788501055376 -0.766649754748309 0.443443783871588 df.mm.trans1:probe19 -0.0404862716102375 0.0728788501055376 -0.555528408469787 0.578639035482387 df.mm.trans1:probe20 0.00268797525190410 0.0728788501055376 0.0368827890123346 0.970584727696505 df.mm.trans1:probe21 0.0052159831785653 0.0728788501055376 0.0715706020472594 0.94295581594015 df.mm.trans1:probe22 -0.115917464842161 0.0728788501055376 -1.59055013456302 0.111979849164180 df.mm.trans2:probe2 -0.0380501903522194 0.0728788501055376 -0.522101958210345 0.601697752296558 df.mm.trans2:probe3 -0.0137662956673905 0.0728788501055376 -0.188892876979470 0.850209396746995 df.mm.trans2:probe4 -0.0655909123141913 0.0728788501055376 -0.899999275773527 0.368305047655925 df.mm.trans2:probe5 -0.0587616199020167 0.0728788501055376 -0.806291809172655 0.420237766793637 df.mm.trans2:probe6 -0.102676566176778 0.0728788501055376 -1.4088664410606 0.159139232747427 df.mm.trans3:probe2 0.0595428122316089 0.0728788501055376 0.817010863170639 0.414087839471753 df.mm.trans3:probe3 0.0872887423247105 0.0728788501055376 1.19772392399586 0.231266063971986 df.mm.trans3:probe4 0.084470557231232 0.0728788501055376 1.15905447340220 0.246669474312955 df.mm.trans3:probe5 -0.0075032109790696 0.0728788501055376 -0.102954574176239 0.918016591706101 df.mm.trans3:probe6 0.0408967705530853 0.0728788501055376 0.561161029487454 0.574794766469821 df.mm.trans3:probe7 0.0337121578069936 0.0728788501055376 0.46257806974416 0.643752479129427 df.mm.trans3:probe8 -0.0968601408640587 0.0728788501055376 -1.32905693110955 0.184087198218277 df.mm.trans3:probe9 0.0212812633747373 0.0728788501055376 0.292008769950670 0.770331466425278 df.mm.trans3:probe10 0.0310580877527658 0.0728788501055376 0.42616050757922 0.670068947258841 df.mm.trans3:probe11 0.0696458071577148 0.0728788501055376 0.955638118011728 0.339451662345679 df.mm.trans3:probe12 0.0392561860067106 0.0728788501055376 0.53864990940256 0.59023047171668 df.mm.trans3:probe13 -0.0380108452273914 0.0728788501055376 -0.521562087935621 0.602073557274504 df.mm.trans3:probe14 0.0751836786408315 0.0728788501055376 1.03162547888662 0.302459891632445 df.mm.trans3:probe15 0.0112563036135347 0.0728788501055376 0.154452266977788 0.877279669493324 df.mm.trans3:probe16 0.105388420936441 0.0728788501055376 1.44607689039859 0.148422312879451 df.mm.trans3:probe17 0.0866041802358778 0.0728788501055376 1.18833077237723 0.234943245498766 df.mm.trans3:probe18 -0.00408937011029935 0.0728788501055376 -0.0561118912328807 0.955262210677995 df.mm.trans3:probe19 -0.0421870453739767 0.0728788501055376 -0.578865409002538 0.562790936196035 df.mm.trans3:probe20 0.0760643484464792 0.0728788501055376 1.04370950332406 0.296834490373121 df.mm.trans3:probe21 0.0503047114994497 0.0728788501055376 0.690251169256955 0.490172563343299 df.mm.trans3:probe22 0.0705544399274894 0.0728788501055376 0.968105833521218 0.333190739545021 df.mm.trans3:probe23 -0.0285121707305182 0.0728788501055376 -0.391226956644199 0.695700415128506 df.mm.trans3:probe24 0.147490265714358 0.0728788501055376 2.02377322777148 0.0432194544074159 *