fitVsDatCorrelation=0.870177537700085
cont.fitVsDatCorrelation=0.270691008448738

fstatistic=10376.7465214708,54,738
cont.fstatistic=2708.49536288859,54,738

residuals=-0.72172323942952,-0.0755980618897474,-0.00061797159492952,0.077178640911768,0.860235838568508
cont.residuals=-0.65983069148175,-0.21708696665582,-0.0324854150141402,0.168713696938706,1.22921952310763

predictedValues:
Include	Exclude	Both
Lung	65.9536223006981	49.0730839894565	68.1297835661863
cerebhem	64.6984014049802	59.6146229582957	66.8315492071953
cortex	77.6490255750914	47.6433068832047	78.1709934711571
heart	66.774470394112	47.1365355196621	66.1298274812834
kidney	70.8210921478122	48.2945391683962	70.699845581771
liver	61.5450561370214	50.4378989218493	59.4346819645139
stomach	64.1580911909537	49.5253044826756	63.7932380155469
testicle	100.240949288449	48.481958765529	94.7009314515402


diffExp=16.8805383112416,5.0837784466845,30.0057186918867,19.6379348744499,22.5265529794160,11.1071572151721,14.6327867082781,51.7589905229198
diffExpScore=0.994207380116038
diffExp1.5=0,0,1,0,0,0,0,1
diffExp1.5Score=0.666666666666667
diffExp1.4=0,0,1,1,1,0,0,1
diffExp1.4Score=0.8
diffExp1.3=1,0,1,1,1,0,0,1
diffExp1.3Score=0.833333333333333
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	68.943263350681	67.6432659905425	73.3485360943805
cerebhem	67.249260668174	70.2171925288578	63.305233462559
cortex	63.3827203685798	64.4712518664748	68.2410465502731
heart	69.807964603392	63.8677628272705	70.2249965825766
kidney	69.5230156709737	69.2643274170548	58.365737844983
liver	69.2051534128929	70.5875844773808	60.8232049842809
stomach	69.917980825422	67.013594138287	62.4122107768093
testicle	67.6697148851817	74.147162166714	58.9949976318852
cont.diffExp=1.29999736013851,-2.96793186068379,-1.08853149789500,5.94020177612159,0.258688253918834,-1.38243106448796,2.904386687135,-6.47744728153231
cont.diffExpScore=8.88142266430963

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=-0.294758006079177
cont.tran.correlation=0.140508035103630

tran.covariance=-0.00379701309252476
cont.tran.covariance=0.000258167192795498

tran.mean=60.7529974455117
cont.tran.mean=68.3069509498675

weightedLogRatios:
wLogRatio
Lung	1.19472451097870
cerebhem	0.337884345946335
cortex	2.00656634597580
heart	1.40255735722985
kidney	1.55766891979688
liver	0.800138208109113
stomach	1.04372812199357
testicle	3.08305721613707

cont.weightedLogRatios:
wLogRatio
Lung	0.0804039797338085
cerebhem	-0.182681687690667
cortex	-0.0707979972106105
heart	0.373634112627242
kidney	0.0158052764261681
liver	-0.0840004647006884
stomach	0.179302639196982
testicle	-0.389451194953298

varWeightedLogRatios=0.696579855061611
cont.varWeightedLogRatios=0.0535462057407907

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07585029368931	0.078488102834363	51.9295300370651	1.23331774233527e-248	***
df.mm.trans1	0.0281601934967168	0.0691410300171257	0.407286288470706	0.683915838448337	   
df.mm.trans2	-0.187862062726467	0.0623880627494229	-3.01118602577870	0.00269110198146623	** 
df.mm.exp2	0.194614094946382	0.0830823293970257	2.34242463299721	0.0194237595604787	*  
df.mm.exp3	-0.00380552610748173	0.0830823293970257	-0.0458042779385284	0.963478640209866	   
df.mm.exp4	0.00190130473049434	0.0830823293970257	0.0228845862205978	0.981748521544875	   
df.mm.exp5	0.0181839413208081	0.0830823293970257	0.218866532182944	0.8268145304364	   
df.mm.exp6	0.0947864229747563	0.0830823293970257	1.14087344038947	0.254292673045035	   
df.mm.exp7	0.0473387239520583	0.0830823293970257	0.569780894392605	0.568999711208762	   
df.mm.exp8	0.077196657757727	0.0830823293970257	0.92915856257264	0.353110722921750	   
df.mm.trans1:exp2	-0.213829404159058	0.0783667606806358	-2.72857270482911	0.00651226965815631	** 
df.mm.trans2:exp2	-2.38962228957579e-05	0.064118258738702	-0.00037268982916615	0.999702737302012	   
df.mm.trans1:exp3	0.167052724078350	0.0783667606806358	2.13167831140975	0.0333630123234523	*  
df.mm.trans2:exp3	-0.0257630147126046	0.064118258738702	-0.40180465314249	0.687944128603555	   
df.mm.trans1:exp4	0.0104677204252319	0.0783667606806358	0.133573473425685	0.893776275904598	   
df.mm.trans2:exp4	-0.042163600257576	0.064118258738702	-0.657591161815596	0.511005918310148	   
df.mm.trans1:exp5	0.0530211240955232	0.0783667606806358	0.676576696995267	0.498886558859115	   
df.mm.trans2:exp5	-0.0341761446531355	0.064118258738702	-0.533017354579323	0.594182031606816	   
df.mm.trans1:exp6	-0.163968698891957	0.0783667606806358	-2.09232457061956	0.0367505644616193	*  
df.mm.trans2:exp6	-0.0673542646479348	0.064118258738702	-1.05046933545748	0.293846237812715	   
df.mm.trans1:exp7	-0.074940314209527	0.0783667606806358	-0.956276788253729	0.339245553770934	   
df.mm.trans2:exp7	-0.0381656802295416	0.064118258738702	-0.595238875482821	0.551866279369515	   
df.mm.trans1:exp8	0.341428320332630	0.0783667606806358	4.35680022202317	1.50670413231163e-05	***
df.mm.trans2:exp8	-0.0893156101367582	0.064118258738702	-1.39298246542754	0.164044543171818	   
df.mm.trans1:probe2	0.512204903581815	0.0457563303354253	11.1941866803348	5.63324001347398e-27	***
df.mm.trans1:probe3	-0.113357950760203	0.0457563303354253	-2.47742661898826	0.0134561858984604	*  
df.mm.trans1:probe4	0.260181714035579	0.0457563303354253	5.68624520647238	1.87125828802661e-08	***
df.mm.trans1:probe5	-0.060809003986936	0.0457563303354253	-1.32897466954112	0.184267049139653	   
df.mm.trans1:probe6	0.490780277837479	0.0457563303354253	10.7259536383211	4.82032408741748e-25	***
df.mm.trans1:probe7	0.0345066171354935	0.0457563303354253	0.754138648850035	0.451006522802383	   
df.mm.trans1:probe8	0.237921219282382	0.0457563303354253	5.19974433129267	2.58683909666177e-07	***
df.mm.trans1:probe9	0.294832711270677	0.0457563303354253	6.44353926788601	2.10564725740707e-10	***
df.mm.trans1:probe10	0.277539571173419	0.0457563303354253	6.06559942938745	2.09897158074208e-09	***
df.mm.trans1:probe11	0.0319785499047385	0.0457563303354253	0.698887993646208	0.484842216538447	   
df.mm.trans1:probe12	-0.0748407184212332	0.0457563303354253	-1.63563637801807	0.102342103110972	   
df.mm.trans1:probe13	-0.0659915946069714	0.0457563303354253	-1.44223966658182	0.149659010863962	   
df.mm.trans1:probe14	-0.142473176126469	0.0457563303354253	-3.11373694267094	0.00191872481741736	** 
df.mm.trans1:probe15	-0.0601554480265382	0.0457563303354253	-1.31469126972284	0.189022080268143	   
df.mm.trans1:probe16	-0.0158271469761677	0.0457563303354253	-0.345900706200515	0.729515919147862	   
df.mm.trans1:probe17	0.0866175374704307	0.0457563303354253	1.89301757451843	0.0587465274588789	.  
df.mm.trans1:probe18	0.102657559878682	0.0457563303354253	2.24357065188864	0.0251558560126598	*  
df.mm.trans1:probe19	0.144225094268904	0.0457563303354253	3.15202493756897	0.00168699838616883	** 
df.mm.trans1:probe20	0.175925502107260	0.0457563303354253	3.84483416431357	0.000131041545133066	***
df.mm.trans1:probe21	0.0692956282324135	0.0457563303354253	1.51444898934047	0.130340147864039	   
df.mm.trans1:probe22	0.108203669138230	0.0457563303354253	2.36478031225457	0.0182985991379799	*  
df.mm.trans2:probe2	0.0258930527997829	0.0457563303354253	0.565890066138807	0.571640531954075	   
df.mm.trans2:probe3	-0.0451481325937218	0.0457563303354253	-0.98670789949183	0.324109234766254	   
df.mm.trans2:probe4	0.0592954029338843	0.0457563303354253	1.29589507067565	0.19541677492535	   
df.mm.trans2:probe5	0.0907048739627854	0.0457563303354253	1.98234590269492	0.0478110110979819	*  
df.mm.trans2:probe6	-0.072198071757882	0.0457563303354253	-1.57788160083251	0.115021327050720	   
df.mm.trans3:probe2	0.314896700331307	0.0457563303354253	6.88203573195006	1.26124684428763e-11	***
df.mm.trans3:probe3	0.56226941433306	0.0457563303354253	12.2883415302591	1.04075071092773e-31	***
df.mm.trans3:probe4	0.714834135039022	0.0457563303354253	15.6226281653008	9.57478614472798e-48	***
df.mm.trans3:probe5	-0.0656700319832809	0.0457563303354253	-1.43521194776492	0.151650368380416	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.11412236304015	0.153342752604411	26.8295846602783	3.35532094691489e-111	***
df.mm.trans1	0.197173051723336	0.135081311407217	1.45966195966914	0.144808521506630	   
df.mm.trans2	0.0630315343795182	0.121887992271165	0.517126693163438	0.605222666573233	   
df.mm.exp2	0.159722053384136	0.162318525005150	0.984003849092815	0.325436100100299	   
df.mm.exp3	-0.0599447047859905	0.162318525005150	-0.369302917113673	0.712007885135702	   
df.mm.exp4	-0.00145063226888759	0.162318525005150	-0.00893694831715336	0.992871857053945	   
df.mm.exp5	0.260549682071758	0.162318525005150	1.60517526920290	0.108883037806017	   
df.mm.exp6	0.233649068570554	0.162318525005150	1.43944795311035	0.150447657492107	   
df.mm.exp7	0.166148249959882	0.162318525005150	1.02359388710938	0.306362533618629	   
df.mm.exp8	0.290928721588290	0.162318525005150	1.7923322158026	0.0734892607233809	.  
df.mm.trans1:exp2	-0.184599924812052	0.153105685594412	-1.20570261055537	0.228318597820321	   
df.mm.trans2:exp2	-0.122376672282227	0.125268288213609	-0.976916616546625	0.328930548448869	   
df.mm.trans1:exp3	-0.0241479163782847	0.153105685594412	-0.157720572456429	0.874720131546429	   
df.mm.trans2:exp3	0.0119163139707726	0.125268288213609	0.0951263415562344	0.924240297871682	   
df.mm.trans1:exp4	0.0139148455257673	0.153105685594412	0.0908839242105534	0.927609485221572	   
df.mm.trans2:exp4	-0.05598243550683	0.125268288213609	-0.446900299390759	0.655078164663756	   
df.mm.trans1:exp5	-0.252175719656824	0.153105685594412	-1.64706959560507	0.0999694400441296	.  
df.mm.trans2:exp5	-0.236867472058169	0.125268288213609	-1.89088136699257	0.0590316241574526	.  
df.mm.trans1:exp6	-0.229857633502833	0.153105685594412	-1.50130044230848	0.133705581523352	   
df.mm.trans2:exp6	-0.191042604560273	0.125268288213609	-1.52506757523903	0.127670650379943	   
df.mm.trans1:exp7	-0.152109293452172	0.153105685594412	-0.99349212840548	0.320795807722140	   
df.mm.trans2:exp7	-0.175500561326794	0.125268288213609	-1.40099752163555	0.161635158990881	   
df.mm.trans1:exp8	-0.309573880732596	0.153105685594412	-2.02196201617672	0.0435406155651678	*  
df.mm.trans2:exp8	-0.199124733021094	0.125268288213609	-1.58958612639093	0.112356397925955	   
df.mm.trans1:probe2	-0.00214170701275087	0.0893944609352816	-0.0239579386725246	0.98089263540134	   
df.mm.trans1:probe3	-0.23571628970562	0.0893944609352816	-2.63681090796297	0.00854497658647885	** 
df.mm.trans1:probe4	-0.100015276745055	0.0893944609352816	-1.11880843285651	0.263585897105748	   
df.mm.trans1:probe5	-0.165573749629015	0.0893944609352816	-1.8521701221386	0.0644005435937573	.  
df.mm.trans1:probe6	-0.050637117456497	0.0893944609352816	-0.566445805777121	0.571262977654076	   
df.mm.trans1:probe7	-0.0670656652280162	0.0893944609352816	-0.750221708664582	0.453360292339578	   
df.mm.trans1:probe8	-0.158382952562987	0.0893944609352816	-1.77173116662844	0.07685178282626	.  
df.mm.trans1:probe9	-0.203991368407596	0.0893944609352816	-2.28192402832742	0.0227776832029478	*  
df.mm.trans1:probe10	-0.171876170306025	0.0893944609352816	-1.92267136585182	0.0549059058647731	.  
df.mm.trans1:probe11	-0.11396959943826	0.0893944609352816	-1.27490672515795	0.202743486007203	   
df.mm.trans1:probe12	-0.159467871550625	0.0893944609352816	-1.78386747771849	0.0748559981743545	.  
df.mm.trans1:probe13	-0.0847374328058025	0.0893944609352816	-0.947904735027704	0.343488232052804	   
df.mm.trans1:probe14	-0.0655063439591115	0.0893944609352816	-0.73277855555878	0.46392617474575	   
df.mm.trans1:probe15	-0.0340174226325699	0.0893944609352816	-0.380531660202049	0.70366030079162	   
df.mm.trans1:probe16	0.0067304856743736	0.0893944609352816	0.075289739475539	0.940004588750928	   
df.mm.trans1:probe17	-0.102384862708849	0.0893944609352816	-1.14531551102447	0.252449752639233	   
df.mm.trans1:probe18	-0.101176156193	0.0893944609352816	-1.13179446617222	0.258088471485389	   
df.mm.trans1:probe19	-0.117167867885088	0.0893944609352816	-1.31068375668055	0.190372351087488	   
df.mm.trans1:probe20	-0.0716313001842596	0.0893944609352816	-0.80129461529074	0.423219012560799	   
df.mm.trans1:probe21	-0.108592685918229	0.0893944609352816	-1.21475855195152	0.22484671196428	   
df.mm.trans1:probe22	0.0010103498669486	0.0893944609352816	0.0113021529116894	0.990985433162813	   
df.mm.trans2:probe2	0.15419553868706	0.0893944609352816	1.72488918299638	0.0849661205072147	.  
df.mm.trans2:probe3	-0.0298869745121736	0.0893944609352816	-0.33432691689713	0.738227867154814	   
df.mm.trans2:probe4	0.139300287088308	0.0893944609352816	1.55826530671913	0.119599074587597	   
df.mm.trans2:probe5	0.0526085509090132	0.0893944609352816	0.588499000481695	0.556377464520083	   
df.mm.trans2:probe6	0.0918156116079525	0.0893944609352816	1.02708390035960	0.304717472033476	   
df.mm.trans3:probe2	-0.0199555010974509	0.0893944609352816	-0.223229726860794	0.823418489849846	   
df.mm.trans3:probe3	-0.0854756099440747	0.0893944609352816	-0.956162261618827	0.339303363859348	   
df.mm.trans3:probe4	-0.175304010849180	0.0893944609352816	-1.96101647702863	0.0502527813491972	.  
df.mm.trans3:probe5	-0.114786890942613	0.0893944609352816	-1.28404925474873	0.199527686897528