fitVsDatCorrelation=0.88553693578308 cont.fitVsDatCorrelation=0.248699939464879 fstatistic=13390.1527354215,53,715 cont.fstatistic=3070.0648625768,53,715 residuals=-0.442701771224228,-0.0771312133209626,-0.00901828757843274,0.074135838649077,0.762399600020074 cont.residuals=-0.548460564248366,-0.21673592019524,-0.0300501395180136,0.189069568740975,0.918167444386056 predictedValues: Include Exclude Both Lung 68.1364153770439 61.324706810548 51.3209087594442 cerebhem 69.1245673728786 67.1186881541769 46.9988447120609 cortex 70.7962350117854 66.1986799279594 56.7385366377335 heart 73.1908057111597 72.7741953587007 57.5198478641231 kidney 72.5957465179047 63.4232393272539 55.9929326509054 liver 72.2390898442631 65.944826140771 60.0698871818797 stomach 71.5882465104732 83.4579482938583 54.608379950792 testicle 74.645371679744 68.8430774646888 53.5203398615893 diffExp=6.8117085664959,2.00587921870164,4.59755508382597,0.416610352459031,9.17250719065088,6.29426370349213,-11.8697017833851,5.80229421505528 diffExpScore=1.93843812448287 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 63.6302786490127 56.923298277665 64.9641189162147 cerebhem 66.5368929718888 61.7577548001273 58.273597868447 cortex 62.4808156172019 66.5665287334915 58.3020797535174 heart 63.789547676603 62.9535719251453 74.3442017425862 kidney 63.9854107596664 57.9196017316815 61.2184017767444 liver 59.7947483548422 64.5733770243267 69.7898750446908 stomach 66.4848365364093 57.8368116655431 61.0914666445665 testicle 65.1427794898241 57.4469145514646 63.5998260995915 cont.diffExp=6.70698037134774,4.77913817176156,-4.08571311628954,0.835975751457681,6.06580902798497,-4.77862866948444,8.64802487086627,7.69586493835947 cont.diffExpScore=1.62263753104502 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=0.302193824556149 cont.tran.correlation=-0.564940556246834 tran.covariance=0.00095316531243922 cont.tran.covariance=-0.00118673166665733 tran.mean=70.0876149689506 cont.tran.mean=62.3639480478058 weightedLogRatios: wLogRatio Lung 0.439100496672853 cerebhem 0.124304162084886 cortex 0.283771691683822 heart 0.0244901597254832 kidney 0.56966519099253 liver 0.386020108697502 stomach -0.666979611658896 testicle 0.345708707238154 cont.weightedLogRatios: wLogRatio Lung 0.456387422258661 cerebhem 0.310110398336615 cortex -0.263917722741337 heart 0.054732883429302 kidney 0.409238836931076 liver -0.317483237664447 stomach 0.575132798832071 testicle 0.517177760728288 varWeightedLogRatios=0.149108316696539 cont.varWeightedLogRatios=0.123511233127345 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.15892990779462 0.0697542875833716 73.9586065104395 0 *** df.mm.trans1 -0.436414086051962 0.0619431573487099 -7.04539621051544 4.35657405444207e-12 *** df.mm.trans2 -0.945615535174651 0.0563453567243192 -16.7824926515465 1.52268605235437e-53 *** df.mm.exp2 0.192653331536161 0.0759619139476313 2.53618322030403 0.0114185170011829 * df.mm.exp3 0.0144163391424715 0.0759619139476313 0.189783779703211 0.849532431132075 df.mm.exp4 0.128704806521952 0.0759619139476313 1.6943333814717 0.0906374264117233 . df.mm.exp5 0.00991483124293698 0.0759619139476313 0.130523715473682 0.896188805716841 df.mm.exp6 -0.0263053019666858 0.0759619139476313 -0.346295934365488 0.729222202722448 df.mm.exp7 0.295490081399335 0.0759619139476313 3.88997677971948 0.000109606848266609 *** df.mm.exp8 0.164920064719078 0.0759619139476313 2.1710888542484 0.0302526988739239 * df.mm.trans1:exp2 -0.178254934447 0.0721294010655194 -2.47132142806898 0.0136934198787750 * df.mm.trans2:exp2 -0.102373623571775 0.0607383912477036 -1.68548460814963 0.0923314366073745 . df.mm.trans1:exp3 0.0238776782300212 0.0721294010655194 0.331039463482189 0.740711668910326 df.mm.trans2:exp3 0.0620613737156685 0.0607383912477036 1.02178165145286 0.307229842281125 df.mm.trans1:exp4 -0.0571468025960125 0.0721294010655194 -0.792281673656249 0.428459238671375 df.mm.trans2:exp4 0.0424738172658741 0.0607383912477036 0.699291113797485 0.484597538514801 df.mm.trans1:exp5 0.0534796968502763 0.0721294010655194 0.741441022111046 0.458669531839432 df.mm.trans2:exp5 0.0237327047191909 0.0607383912477036 0.390736472133483 0.696108429620037 df.mm.trans1:exp6 0.0847748078274461 0.0721294010655194 1.17531556584589 0.240259718751376 df.mm.trans2:exp6 0.0989409175925538 0.0607383912477036 1.62896836020981 0.103760196584718 df.mm.trans1:exp7 -0.246070979902832 0.0721294010655194 -3.41152118647584 0.000682353015121824 *** df.mm.trans2:exp7 0.0126700011337846 0.0607383912477036 0.208599550852668 0.834820271044614 df.mm.trans1:exp8 -0.0736833470408719 0.0721294010655194 -1.02154386356184 0.307342346626509 df.mm.trans2:exp8 -0.0492731989953402 0.0607383912477036 -0.811236484588371 0.41749983669215 df.mm.trans1:probe2 -0.471457909937628 0.0395069000229221 -11.9335586863076 4.58149325607137e-30 *** df.mm.trans1:probe3 -0.719922944388766 0.0395069000229221 -18.2227141074360 3.13476543420659e-61 *** df.mm.trans1:probe4 -0.689976577545596 0.0395069000229221 -17.4647106491592 3.73907313844131e-57 *** df.mm.trans1:probe5 -0.838128195095918 0.0395069000229221 -21.2147294424425 7.4899501576033e-78 *** df.mm.trans1:probe6 -0.671349655513423 0.0395069000229221 -16.9932253637694 1.18553331169165e-54 *** df.mm.trans1:probe7 -0.648548081385194 0.0395069000229222 -16.4160711422284 1.24783396886405e-51 *** df.mm.trans1:probe8 -0.590311587518915 0.0395069000229221 -14.9419870244543 3.91600516610659e-44 *** df.mm.trans1:probe9 -0.942085455182584 0.0395069000229221 -23.8460991532107 6.2670266056304e-93 *** df.mm.trans1:probe10 -0.5473760626562 0.0395069000229221 -13.8552015556424 7.67557715364656e-39 *** df.mm.trans1:probe11 -0.347054202977748 0.0395069000229221 -8.7846478153534 1.15267241296512e-17 *** df.mm.trans1:probe12 -0.328944789108354 0.0395069000229221 -8.32626171421948 4.2378132808276e-16 *** df.mm.trans1:probe13 -0.243780695349893 0.0395069000229221 -6.17058527012876 1.13900743498759e-09 *** df.mm.trans1:probe14 -0.222578932843871 0.0395069000229221 -5.63392553489971 2.53401336296481e-08 *** df.mm.trans1:probe15 -0.288298780706652 0.0395069000229222 -7.29742856410854 7.81946295296549e-13 *** df.mm.trans1:probe16 -0.319932427916274 0.0395069000229221 -8.09814052053305 2.40599271386434e-15 *** df.mm.trans1:probe17 -0.79611700181565 0.0395069000229222 -20.151340686152 7.30958790008443e-72 *** df.mm.trans1:probe18 -0.864187185569748 0.0395069000229221 -21.8743354975546 1.33153158707432e-81 *** df.mm.trans1:probe19 -0.90001084938314 0.0395069000229222 -22.7811052970734 8.58611405415602e-87 *** df.mm.trans1:probe20 -0.864430330469487 0.0395069000229221 -21.8804899895446 1.22814900816134e-81 *** df.mm.trans1:probe21 -0.875418578079473 0.0395069000229222 -22.1586248875905 3.1698385049872e-83 *** df.mm.trans1:probe22 -0.856194214912207 0.0395069000229221 -21.6720171518251 1.89291863860808e-80 *** df.mm.trans2:probe2 -0.237975571201923 0.0395069000229222 -6.02364576982371 2.72792761085194e-09 *** df.mm.trans2:probe3 -0.0452937613892204 0.0395069000229221 -1.14647723215288 0.251981270301504 df.mm.trans2:probe4 -0.146392295833661 0.0395069000229221 -3.70548678202348 0.00022727682934654 *** df.mm.trans2:probe5 -0.207619283322777 0.0395069000229222 -5.25526637631187 1.95317899955420e-07 *** df.mm.trans2:probe6 -0.334034722244663 0.0395069000229222 -8.45509827525961 1.56260961540068e-16 *** df.mm.trans3:probe2 0.524309411532752 0.0395069000229222 13.2713376961631 4.31541835555387e-36 *** df.mm.trans3:probe3 0.079126656220717 0.0395069000229221 2.00285661934516 0.0455697358615967 * df.mm.trans3:probe4 0.0413557751589808 0.0395069000229221 1.04679879046409 0.295546125761041 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.01621544134619 0.14543082195162 27.6159853011231 8.1477366656421e-115 *** df.mm.trans1 0.124849173017362 0.129145384457326 0.966733527039955 0.33400413407686 df.mm.trans2 -0.00133772352155482 0.117474521287038 -0.0113873502687975 0.990917581888498 df.mm.exp2 0.234867949730259 0.15837311174913 1.48300394641674 0.138513991238925 df.mm.exp3 0.246464620092813 0.15837311174913 1.55622767887028 0.120096458547450 df.mm.exp4 -0.0316778883695563 0.15837311174913 -0.200020622312047 0.841521327513051 df.mm.exp5 0.082304092389455 0.15837311174913 0.519684758861267 0.603444154092042 df.mm.exp6 -0.00772795318539306 0.15837311174913 -0.048795866293481 0.961095603359824 df.mm.exp7 0.121268105300156 0.15837311174913 0.765711451652538 0.444100701831051 df.mm.exp8 0.0538729755042132 0.15837311174913 0.340164911260634 0.733832250249242 df.mm.trans1:exp2 -0.1902008108139 0.150382699720055 -1.26477853614790 0.206362807244979 df.mm.trans2:exp2 -0.153353115690229 0.126633565751997 -1.21099895418375 0.226295923680090 df.mm.trans1:exp3 -0.264694497037290 0.150382699720055 -1.76013928151331 0.0788117391043376 . df.mm.trans2:exp3 -0.0899674578378854 0.126633565751997 -0.710455062239028 0.477653649440815 df.mm.trans1:exp4 0.0341777993352167 0.150382699720055 0.227272148982831 0.820277096051458 df.mm.trans2:exp4 0.132370672117399 0.126633565751997 1.04530478417260 0.296235384703731 df.mm.trans1:exp5 -0.0767384283393463 0.150382699720055 -0.510287609427139 0.610007473357913 df.mm.trans2:exp5 -0.0649529379059669 0.126633565751997 -0.512920389789643 0.60816543849419 df.mm.trans1:exp6 -0.0544436463108082 0.150382699720055 -0.362033973403574 0.717433614050287 df.mm.trans2:exp6 0.133825441271753 0.126633565751997 1.05679280589666 0.290963082872156 df.mm.trans1:exp7 -0.0773836420914328 0.150382699720055 -0.514578088008037 0.607006899630352 df.mm.trans2:exp7 -0.105347369707943 0.126633565751997 -0.83190715733503 0.405739101613745 df.mm.trans1:exp8 -0.0303809435312578 0.150382699720055 -0.202024192861369 0.839955276756162 df.mm.trans2:exp8 -0.0447163967184896 0.126633565751997 -0.353116462076598 0.724105211351222 df.mm.trans1:probe2 -0.0205111386812678 0.0823679968952001 -0.249018301457117 0.803418138391054 df.mm.trans1:probe3 0.0326555351545148 0.0823679968952001 0.396459017888509 0.691884727232029 df.mm.trans1:probe4 -0.0330671764941739 0.0823679968952001 -0.401456606213777 0.688203936954959 df.mm.trans1:probe5 -0.061950810262949 0.0823679968952001 -0.75212233632161 0.452225011591993 df.mm.trans1:probe6 0.000808852929072189 0.0823679968952001 0.00981999028216411 0.992167646533283 df.mm.trans1:probe7 -0.0983932142835326 0.0823679968952001 -1.19455635674523 0.232656522560201 df.mm.trans1:probe8 0.0446236000163202 0.0823679968952001 0.54175895612827 0.588153347688666 df.mm.trans1:probe9 0.0192608376371044 0.0823679968952001 0.233838849591191 0.815177023792123 df.mm.trans1:probe10 0.0604617101643858 0.0823679968952001 0.734043711677407 0.463162733910018 df.mm.trans1:probe11 0.0257994692242584 0.0823679968952001 0.313222006079425 0.754203345712045 df.mm.trans1:probe12 0.107115840479270 0.0823679968952001 1.30045460029285 0.193864209811337 df.mm.trans1:probe13 0.0611121483633936 0.0823679968952001 0.741940446131632 0.458367061533636 df.mm.trans1:probe14 0.00258069563630937 0.0823679968952001 0.0313312904718672 0.975014080040546 df.mm.trans1:probe15 0.097150290051816 0.0823679968952001 1.17946646408585 0.238604783616734 df.mm.trans1:probe16 -0.0121057362841242 0.0823679968952001 -0.146971357085772 0.883196059010063 df.mm.trans1:probe17 -0.0127194445470937 0.0823679968952001 -0.154422166697548 0.877320454349644 df.mm.trans1:probe18 -0.0434917942602566 0.0823679968952001 -0.528018112612267 0.597650592792241 df.mm.trans1:probe19 0.0634101159075137 0.0823679968952001 0.76983923729738 0.44164952145351 df.mm.trans1:probe20 0.0805126257642713 0.0823679968952001 0.977474611489102 0.328664841269249 df.mm.trans1:probe21 -0.0135075682792612 0.0823679968952001 -0.163990491312389 0.869784974064409 df.mm.trans1:probe22 0.0129005358360428 0.0823679968952001 0.156620730408883 0.875587976062375 df.mm.trans2:probe2 0.176887743389511 0.0823679968952001 2.14752998806771 0.0320866178358609 * df.mm.trans2:probe3 0.0601336196010431 0.0823679968952001 0.730060483048451 0.465592334819316 df.mm.trans2:probe4 -0.0194227139680122 0.0823679968952001 -0.23580413146048 0.813652188402439 df.mm.trans2:probe5 0.00573371736043033 0.0823679968952001 0.0696109845638902 0.9445227573101 df.mm.trans2:probe6 0.0449376292786454 0.0823679968952001 0.545571471597412 0.585530696461587 df.mm.trans3:probe2 0.0353099089876100 0.0823679968952001 0.428684808646447 0.668281735172219 df.mm.trans3:probe3 0.0208719169843317 0.0823679968952001 0.253398380087934 0.80003322143503 df.mm.trans3:probe4 0.0802510138789371 0.0823679968952001 0.97429847639786 0.330237862447561