fitVsDatCorrelation=0.910230670841555 cont.fitVsDatCorrelation=0.247606294252830 fstatistic=10943.1045224396,62,922 cont.fstatistic=1986.93236253944,62,922 residuals=-0.618424119109796,-0.0898426802689818,-0.00108168192708697,0.0877119539629783,1.09533438327568 cont.residuals=-0.780671552028004,-0.272085112829193,-0.0628425750204641,0.224741228947907,1.71645739440273 predictedValues: Include Exclude Both Lung 74.6098536417811 132.854078592435 140.621914687712 cerebhem 66.0183198522399 45.8440700393491 58.8456517433676 cortex 72.2616774169836 51.946890621856 56.8838609218928 heart 75.5887392493704 68.4238698938506 71.5209031814849 kidney 73.8008089486602 51.7857056256507 63.1683261631042 liver 77.5385172117189 50.4503347478271 61.7240414863664 stomach 78.2174146077486 69.5904599428577 70.6727722338213 testicle 71.9475340904984 53.6279706149361 60.3889673406214 diffExp=-58.244224950654,20.1742498128908,20.3147867951275,7.1648693555198,22.0151033230095,27.0881824638918,8.62695466489093,18.3195634755623 diffExpScore=2.73772712811658 diffExp1.5=-1,0,0,0,0,1,0,0 diffExp1.5Score=2 diffExp1.4=-1,1,0,0,1,1,0,0 diffExp1.4Score=1.33333333333333 diffExp1.3=-1,1,1,0,1,1,0,1 diffExp1.3Score=1.2 diffExp1.2=-1,1,1,0,1,1,0,1 diffExp1.2Score=1.2 cont.predictedValues: Include Exclude Both Lung 62.6483824942242 63.4906008033841 65.6084062276122 cerebhem 69.2304015163122 58.7885783273766 61.6396244449839 cortex 65.0660318943265 56.7197013370581 61.479449502843 heart 65.5504061521712 65.2668429790316 73.3124992359931 kidney 64.5075933572811 66.9538713785145 67.5003825612792 liver 66.3960776192782 68.1875357926396 69.6480193251589 stomach 65.3048344330674 67.566032291994 65.6462242293161 testicle 63.776119474932 70.9340298613421 68.5788982992214 cont.diffExp=-0.84221830915994,10.4418231889356,8.34633055726831,0.283563173139612,-2.44627802123343,-1.79145817336133,-2.26119785892658,-7.15791038641014 cont.diffExpScore=6.02419935685979 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=0.283111174416252 cont.tran.correlation=-0.383730770798769 tran.covariance=0.00688443860611376 cont.tran.covariance=-0.000861248950083535 tran.mean=69.6566403186102 cont.tran.mean=65.0241899820583 weightedLogRatios: wLogRatio Lung -2.65454206861719 cerebhem 1.46151331205686 cortex 1.35833172110273 heart 0.425779600879886 kidney 1.46103552935985 liver 1.77754185314306 stomach 0.502642624783737 testicle 1.21337548320238 cont.weightedLogRatios: wLogRatio Lung -0.0553419001476748 cerebhem 0.679424794249536 cortex 0.563780463987253 heart 0.0181242266988960 kidney -0.15578432775247 liver -0.112058264247873 stomach -0.142831854851295 testicle -0.447671201622749 varWeightedLogRatios=2.05483706859905 cont.varWeightedLogRatios=0.146483866645252 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.71680463938638 0.0741245502484053 63.6335009599314 0 *** df.mm.trans1 -0.0340690470047395 0.0642491546559018 -0.530264517676576 0.596056223157662 df.mm.trans2 0.093017717891407 0.0564036465817516 1.64914369067587 0.0994588441973962 . df.mm.exp2 -0.315188935011666 0.0723756251984493 -4.35490448818146 1.48040954828706e-05 *** df.mm.exp3 -0.0659449846011565 0.0723756251984493 -0.911149083967698 0.362454977630233 df.mm.exp4 0.0255901754218409 0.0723756251984493 0.353574499034368 0.723738618950715 df.mm.exp5 -0.152768280886299 0.0723756251984493 -2.11076975801478 0.035060831754438 * df.mm.exp6 -0.106358439727442 0.0723756251984493 -1.46953396859529 0.142029141009140 df.mm.exp7 0.0886102977180619 0.0723756251984493 1.22431132684655 0.221147542878531 df.mm.exp8 -0.0982476209202334 0.0723756251984493 -1.35746835555264 0.174964605557693 df.mm.trans1:exp2 0.192848627202441 0.0674906184969528 2.85741383761579 0.00436717471976286 ** df.mm.trans2:exp2 -0.748816581047336 0.0489015989736342 -15.3127218079529 2.50332995463891e-47 *** df.mm.trans1:exp3 0.0339663388442881 0.0674906184969528 0.503274967702684 0.614891170789862 df.mm.trans2:exp3 -0.873084525397646 0.0489015989736342 -17.8539054698064 1.83565815543665e-61 *** df.mm.trans1:exp4 -0.0125554398967026 0.0674906184969528 -0.18603237274036 0.85246029488401 df.mm.trans2:exp4 -0.689119809082659 0.0489015989736342 -14.0919688424545 5.49799104692199e-41 *** df.mm.trans1:exp5 0.141865388958576 0.0674906184969528 2.10200161323135 0.0358238782895949 * df.mm.trans2:exp5 -0.789368933571098 0.0489015989736342 -16.1419861546183 8.22435123038811e-52 *** df.mm.trans1:exp6 0.144860664067207 0.0674906184969528 2.14638222753504 0.0321028772361867 * df.mm.trans2:exp6 -0.861903550723551 0.0489015989736342 -17.6252631573102 3.81231816062639e-60 *** df.mm.trans1:exp7 -0.0413905666035632 0.0674906184969528 -0.613278816009547 0.539843252330462 df.mm.trans2:exp7 -0.735234182027123 0.0489015989736342 -15.0349722188743 7.40277909778356e-46 *** df.mm.trans1:exp8 0.0619121962848036 0.0674906184969528 0.91734522017455 0.359201659418634 df.mm.trans2:exp8 -0.808932979751154 0.0489015989736342 -16.5420558167699 5.07360728924283e-54 *** df.mm.trans1:probe2 -0.0686537317710137 0.0462076677134456 -1.48576492102493 0.137683146640945 df.mm.trans1:probe3 -0.1976551576319 0.0462076677134456 -4.27754023115055 2.08684013551821e-05 *** df.mm.trans1:probe4 -0.0783388761245117 0.0462076677134456 -1.69536529327397 0.090343776992105 . df.mm.trans1:probe5 -0.618095944166063 0.0462076677134456 -13.3764800248987 2.00122610780757e-37 *** df.mm.trans1:probe6 -0.498232029207729 0.0462076677134456 -10.7824535161023 1.30263391493909e-25 *** df.mm.trans1:probe7 -0.532028874182899 0.0462076677134456 -11.5138655662573 9.33090470364192e-29 *** df.mm.trans1:probe8 -0.444568755043358 0.0462076677134456 -9.62110353200096 6.03416018211849e-21 *** df.mm.trans1:probe9 -0.129619576054915 0.0462076677134456 -2.80515296419511 0.00513505994059064 ** df.mm.trans1:probe10 -0.589137912936492 0.0462076677134456 -12.7497868230442 2.08232584890336e-34 *** df.mm.trans1:probe11 -0.757642338301767 0.0462076677134456 -16.3964635263620 3.25755936213472e-53 *** df.mm.trans1:probe12 -0.88920956706656 0.0462076677134456 -19.2437664800774 1.22644866503589e-69 *** df.mm.trans1:probe13 -0.831302556993755 0.0462076677134456 -17.9905759829523 2.96774688642534e-62 *** df.mm.trans1:probe14 -0.884144312207122 0.0462076677134456 -19.1341471222936 5.53636885735079e-69 *** df.mm.trans1:probe15 -0.745704325945351 0.0462076677134456 -16.138107869235 8.63730871345006e-52 *** df.mm.trans1:probe16 -0.881826698699165 0.0462076677134456 -19.0839906521092 1.10203651583261e-68 *** df.mm.trans1:probe17 -0.881436949784973 0.0462076677134456 -19.0755559283182 1.23720145006817e-68 *** df.mm.trans1:probe18 -0.611678593945988 0.0462076677134456 -13.2375993901982 9.5136867287019e-37 *** df.mm.trans1:probe19 0.0707287371037993 0.0462076677134456 1.53067100340186 0.12619373608852 df.mm.trans1:probe20 -0.31125038020238 0.0462076677134456 -6.73590327329617 2.86447645164139e-11 *** df.mm.trans1:probe21 -0.8815463682382 0.0462076677134456 -19.0779239000995 1.19766598215390e-68 *** df.mm.trans1:probe22 -0.934161243074324 0.0462076677134456 -20.2165850236691 1.63375898708599e-75 *** df.mm.trans1:probe23 -0.557357094245498 0.0462076677134456 -12.0620044643222 3.26708252174447e-31 *** df.mm.trans1:probe24 -0.134466693273786 0.0462076677134456 -2.91005151152995 0.00370060164739361 ** df.mm.trans1:probe25 -0.57887602203214 0.0462076677134456 -12.5277048307655 2.31174090653072e-33 *** df.mm.trans2:probe2 0.109756810550749 0.0462076677134456 2.37529431763143 0.0177385986072293 * df.mm.trans2:probe3 0.564530008407369 0.0462076677134456 12.2172365830769 6.3653641474347e-32 *** df.mm.trans2:probe4 0.410032914709011 0.0462076677134456 8.87369856561056 3.6184505393629e-18 *** df.mm.trans2:probe5 0.0208918391406571 0.0462076677134456 0.452129271492705 0.651282205808863 df.mm.trans2:probe6 0.16565267023542 0.0462076677134456 3.58496064468578 0.000354815460156735 *** df.mm.trans3:probe2 0.378844537087096 0.0462076677134456 8.19873747873362 8.0948285993181e-16 *** df.mm.trans3:probe3 0.350758621860924 0.0462076677134456 7.59091811419991 7.7702660490389e-14 *** df.mm.trans3:probe4 -0.00135958822308982 0.0462076677134456 -0.0294234331739320 0.976533251458563 df.mm.trans3:probe5 0.341176720439593 0.0462076677134456 7.38355206662631 3.44359272763546e-13 *** df.mm.trans3:probe6 -0.0731679107723428 0.0462076677134456 -1.58345820927578 0.113659992611286 df.mm.trans3:probe7 0.462360476459181 0.0462076677134456 10.0061418231815 1.90431240151689e-22 *** df.mm.trans3:probe8 -0.0548231605959117 0.0462076677134456 -1.18645158495977 0.23574964174724 df.mm.trans3:probe9 -0.0344625865396217 0.0462076677134456 -0.745819649529589 0.455966467572572 df.mm.trans3:probe10 0.333888114748811 0.0462076677134456 7.22581621776284 1.04370092743643e-12 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05671814817952 0.173426927462179 23.3915125381219 2.26389901525743e-95 *** df.mm.trans1 0.0645252521232667 0.150321768518996 0.429247558480613 0.667843358087105 df.mm.trans2 0.0801604868714541 0.131965874889693 0.607433451552972 0.543712862934754 df.mm.exp2 0.0853568590792334 0.169335021383032 0.504070914463453 0.614331971201688 df.mm.exp3 -0.00990462588531003 0.169335021383032 -0.0584913020615267 0.953369964817598 df.mm.exp4 -0.0381534744197855 0.169335021383032 -0.225313547712514 0.821785290615316 df.mm.exp5 0.0539276657678675 0.169335021383032 0.318467292397148 0.750202625101958 df.mm.exp6 0.0697196014436235 0.169335021383032 0.411725825373826 0.68063608806808 df.mm.exp7 0.103165450638583 0.169335021383032 0.609238713858396 0.542516311762916 df.mm.exp8 0.0844183156376703 0.169335021383032 0.498528390336443 0.618230568194908 df.mm.trans1:exp2 0.0145453724585096 0.157905721643154 0.0921142838090455 0.926627249712172 df.mm.trans2:exp2 -0.162301145274358 0.114413565135493 -1.41854809857690 0.156368728897467 df.mm.trans1:exp3 0.0477693925462841 0.157905721643154 0.302518439795593 0.762325111841324 df.mm.trans2:exp3 -0.102865633569788 0.114413565135493 -0.89906851034639 0.368850908077205 df.mm.trans1:exp4 0.0834350177971051 0.157905721643154 0.528385019421001 0.597359288250973 df.mm.trans2:exp4 0.0657457411611998 0.114413565135493 0.574632396808377 0.56568011373079 df.mm.trans1:exp5 -0.0246825853057630 0.157905721643154 -0.156312165568911 0.875821185901705 df.mm.trans2:exp5 -0.000815646479113575 0.114413565135493 -0.00712893159257518 0.994313525874988 df.mm.trans1:exp6 -0.0116194815847569 0.157905721643154 -0.0735849307032419 0.941356622751709 df.mm.trans2:exp6 0.00165031096606787 0.114413565135493 0.0144240848024752 0.988494764989897 df.mm.trans1:exp7 -0.0616372457794228 0.157905721643154 -0.390342067013346 0.696373784376402 df.mm.trans2:exp7 -0.0409519508615855 0.114413565135493 -0.357929156504201 0.720478255115091 df.mm.trans1:exp8 -0.0665773611363745 0.157905721643154 -0.421627287748512 0.673395366009716 df.mm.trans2:exp8 0.0264400964888753 0.114413565135493 0.231092322466867 0.81729435048541 df.mm.trans1:probe2 0.0128232756905495 0.108110657128859 0.118612503439557 0.905608195407386 df.mm.trans1:probe3 -0.0216718131602821 0.108110657128859 -0.200459545208860 0.841165401921325 df.mm.trans1:probe4 -0.108396815790362 0.108110657128859 -1.00264690520901 0.316294319371444 df.mm.trans1:probe5 0.0497004380349294 0.108110657128859 0.459718212383915 0.645826874184215 df.mm.trans1:probe6 0.0351700425601603 0.108110657128859 0.325315223255377 0.745016317389513 df.mm.trans1:probe7 0.107576987046137 0.108110657128859 0.99506366812584 0.319966371502708 df.mm.trans1:probe8 0.0918834334152707 0.108110657128859 0.849901719732896 0.395600366329725 df.mm.trans1:probe9 0.0204278371778645 0.108110657128859 0.188953038676994 0.850171207262565 df.mm.trans1:probe10 0.0575781576960315 0.108110657128859 0.532585401154329 0.594448936408273 df.mm.trans1:probe11 -0.0755123853754159 0.108110657128859 -0.698473095815257 0.485057508046688 df.mm.trans1:probe12 -0.124866671499036 0.108110657128859 -1.15498947851372 0.248393980432758 df.mm.trans1:probe13 0.0829247592563709 0.108110657128859 0.767035937609106 0.443256510282272 df.mm.trans1:probe14 0.0722065185854427 0.108110657128859 0.667894549002495 0.504368054032947 df.mm.trans1:probe15 0.228712740955673 0.108110657128859 2.11554297263281 0.0346513162638647 * df.mm.trans1:probe16 -0.00251854857911674 0.108110657128859 -0.0232960250728551 0.981419183074069 df.mm.trans1:probe17 -0.0606205938917751 0.108110657128859 -0.56072727242348 0.575119705161463 df.mm.trans1:probe18 0.0906695473729034 0.108110657128859 0.838673538584016 0.401869908171389 df.mm.trans1:probe19 0.0414674148162809 0.108110657128859 0.383564543196285 0.701389650640691 df.mm.trans1:probe20 -0.0112967770246880 0.108110657128859 -0.104492723702744 0.916801068213147 df.mm.trans1:probe21 0.0508279761847056 0.108110657128859 0.470147694358409 0.638360685316672 df.mm.trans1:probe22 -0.00152381360730372 0.108110657128859 -0.0140949435307517 0.988757283808886 df.mm.trans1:probe23 0.116163906857233 0.108110657128859 1.07449080361037 0.282883875773828 df.mm.trans1:probe24 -0.144447945188269 0.108110657128859 -1.33611198955251 0.181842339439937 df.mm.trans1:probe25 0.0630285186650046 0.108110657128859 0.583000051418426 0.560035826890561 df.mm.trans2:probe2 0.206842830724276 0.108110657128859 1.91325107272020 0.0560253867880606 . df.mm.trans2:probe3 -0.089587655538109 0.108110657128859 -0.82866627506785 0.40750773572277 df.mm.trans2:probe4 0.0320591236045304 0.108110657128859 0.296539901393057 0.76688458432684 df.mm.trans2:probe5 0.0805294181611574 0.108110657128859 0.744879554891365 0.456534357429437 df.mm.trans2:probe6 -0.00563186019253765 0.108110657128859 -0.0520934784979147 0.958465493266361 df.mm.trans3:probe2 -0.0983828053496436 0.108110657128859 -0.910019492642427 0.363050062275971 df.mm.trans3:probe3 -0.00744728579376029 0.108110657128859 -0.0688857693731686 0.945095482374646 df.mm.trans3:probe4 -0.111116475189627 0.108110657128859 -1.02780316150687 0.304311987050016 df.mm.trans3:probe5 -0.00368727377060626 0.108110657128859 -0.0341064782004917 0.972799624725316 df.mm.trans3:probe6 0.0339378202114691 0.108110657128859 0.313917435272066 0.753654757899016 df.mm.trans3:probe7 -0.152279071487330 0.108110657128859 -1.40854819988584 0.159305926790946 df.mm.trans3:probe8 0.056429101247876 0.108110657128859 0.521956879612869 0.60182572195534 df.mm.trans3:probe9 0.0500879734363776 0.108110657128859 0.463302830327605 0.643256658548848 df.mm.trans3:probe10 0.0554574594922375 0.108110657128859 0.512969405283854 0.608095601211718