fitVsDatCorrelation=0.849280016376797
cont.fitVsDatCorrelation=0.282646868095447

fstatistic=9418.11205327507,43,485
cont.fstatistic=2845.10801920458,43,485

residuals=-0.417365893800071,-0.0823360622702259,-0.00227244447100912,0.0798067395722896,0.754469903319366
cont.residuals=-0.493150325484558,-0.164939994639651,-0.0496676519400594,0.111533785688117,1.51025498785049

predictedValues:
Include	Exclude	Both
Lung	49.1612625075383	74.3746000219571	50.2836848611101
cerebhem	51.897531423365	79.239716586389	56.7765249851533
cortex	54.6631636823742	104.072373447975	49.3198681374641
heart	59.6545842777396	93.021645978932	50.0244754780514
kidney	53.8285108281269	74.7762921497763	50.3387120237196
liver	56.0955656961076	81.0460015731893	52.7732892972379
stomach	58.4567497799855	131.706263070667	50.2169284902264
testicle	52.2460417654826	79.9170591122464	51.8410720446958


diffExp=-25.2133375144188,-27.342185163024,-49.4092097656008,-33.3670617011924,-20.9477813216495,-24.9504358770817,-73.2495132906813,-27.6710173467638
diffExpScore=0.996468309779647
diffExp1.5=-1,-1,-1,-1,0,0,-1,-1
diffExp1.5Score=0.857142857142857
diffExp1.4=-1,-1,-1,-1,0,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	57.4533124475847	56.7207902443549	60.5580619267357
cerebhem	62.864956559227	59.364445242054	61.2467988979846
cortex	51.1972272850283	65.2798657040619	54.4264428083936
heart	58.8733940155072	59.6090672749371	57.3233356009497
kidney	52.284970847901	54.0645540156282	60.092171042142
liver	56.5866547272523	61.0208022148886	60.8850162234078
stomach	57.9083939578632	63.1298477443165	57.1122070330572
testicle	61.9759241994225	57.3604518270656	63.2976137121396
cont.diffExp=0.732522203229834,3.50051131717302,-14.0826384190335,-0.735673259429952,-1.77958316772713,-4.43414748763634,-5.2214537864533,4.6154723723569
cont.diffExpScore=1.90720025270934

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,-1,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.648646717838546
cont.tran.correlation=-0.173132918627439

tran.covariance=0.00856938219335341
cont.tran.covariance=-0.000645922701154131

tran.mean=72.1348351188657
cont.tran.mean=58.4809161441933

weightedLogRatios:
wLogRatio
Lung	-1.69830858364071
cerebhem	-1.76090877469658
cortex	-2.78365361415391
heart	-1.91507755567701
kidney	-1.36414509781095
liver	-1.54949373718841
stomach	-3.63452186988085
testicle	-1.7717076720728

cont.weightedLogRatios:
wLogRatio
Lung	0.0518991294021436
cerebhem	0.235610052412055
cortex	-0.985888848307866
heart	-0.0506870201447895
kidney	-0.132990418709135
liver	-0.307311111729348
stomach	-0.35413354710806
testicle	0.316377979250764

varWeightedLogRatios=0.581716426925047
cont.varWeightedLogRatios=0.168879239931180

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.39624349055479	0.073740316341671	59.6179092883881	2.23752230796551e-225	***
df.mm.trans1	-0.493832512067178	0.0590329795214556	-8.36536654714666	6.42337911233601e-16	***
df.mm.trans2	-0.0167253171080317	0.0590329795214556	-0.28332158131969	0.777051228167808	   
df.mm.exp2	-0.00391386177633662	0.0790491813086334	-0.0495117306915001	0.96053187178205	   
df.mm.exp3	0.461409824983431	0.0790491813086334	5.83699688402766	9.73560809246288e-09	***
df.mm.exp4	0.422351037730167	0.0790491813086334	5.34288946119723	1.40969157907775e-07	***
df.mm.exp5	0.0949899647654797	0.0790491813086334	1.20165652816325	0.230083064294522	   
df.mm.exp6	0.169528718377895	0.0790491813086334	2.14459802835908	0.0324812175435066	*  
df.mm.exp7	0.745969349125546	0.0790491813086334	9.4367751414533	1.60447640582787e-19	***
df.mm.exp8	0.102230952242162	0.0790491813086335	1.29325757142278	0.196537627628085	   
df.mm.trans1:exp2	0.0580791207502164	0.062011279696347	0.93658961780203	0.349435820053059	   
df.mm.trans2:exp2	0.0672770209566986	0.0620112796963471	1.0849158618583	0.278497904955466	   
df.mm.trans1:exp3	-0.355325732831188	0.0620112796963471	-5.73001774146776	1.76532852546922e-08	***
df.mm.trans2:exp3	-0.125437755324242	0.0620112796963471	-2.02282158888638	0.0436397145368786	*  
df.mm.trans1:exp4	-0.228886005171221	0.0620112796963471	-3.69103824807382	0.000248675413511282	***
df.mm.trans2:exp4	-0.198633305193223	0.0620112796963471	-3.20318023053028	0.00144858430606246	** 
df.mm.trans1:exp5	-0.00429266289339734	0.0620112796963471	-0.0692239043351046	0.94483992686468	   
df.mm.trans2:exp5	-0.089603565888869	0.0620112796963471	-1.44495592298101	0.149116029697524	   
df.mm.trans1:exp6	-0.037577917793168	0.0620112796963471	-0.605985201034024	0.544808074374031	   
df.mm.trans2:exp6	-0.0836262902499971	0.062011279696347	-1.34856578770013	0.178105851912195	   
df.mm.trans1:exp7	-0.572788154289535	0.0620112796963471	-9.23683815419272	7.94718595516539e-19	***
df.mm.trans2:exp7	-0.174509671916409	0.0620112796963471	-2.8141601458789	0.00508924092450994	** 
df.mm.trans1:exp8	-0.0413727859211344	0.0620112796963471	-0.667181617985084	0.504973428667603	   
df.mm.trans2:exp8	-0.0303561024690325	0.0620112796963471	-0.489525496291616	0.624691035198622	   
df.mm.trans1:probe2	-0.122504289305411	0.0424562208868142	-2.88542613418185	0.00408298281878049	** 
df.mm.trans1:probe3	0.0278252197415999	0.0424562208868143	0.65538616392119	0.512529896337832	   
df.mm.trans1:probe4	0.0691131610247428	0.0424562208868142	1.62786888661133	0.104202095510515	   
df.mm.trans1:probe5	0.0135667624858622	0.0424562208868143	0.3195471052883	0.749449272635586	   
df.mm.trans1:probe6	-0.104881054232732	0.0424562208868143	-2.47033419465993	0.0138415965422563	*  
df.mm.trans2:probe2	-0.182447794163874	0.0424562208868142	-4.29731592574547	2.0900675426353e-05	***
df.mm.trans2:probe3	-0.22803982107291	0.0424562208868142	-5.37117567955119	1.21608453963611e-07	***
df.mm.trans2:probe4	-0.165681498485118	0.0424562208868142	-3.90240805762753	0.000108703967450899	***
df.mm.trans2:probe5	-0.42768695151918	0.0424562208868142	-10.0735991707639	8.45394521267707e-22	***
df.mm.trans2:probe6	-0.122602933982348	0.0424562208868142	-2.88774957877669	0.00405345973854136	** 
df.mm.trans3:probe2	0.00317934423566950	0.0424562208868142	0.0748852387061356	0.940336898220469	   
df.mm.trans3:probe3	0.03404008120295	0.0424562208868142	0.801768986780497	0.423079248910127	   
df.mm.trans3:probe4	0.0137461041479577	0.0424562208868143	0.323771260390886	0.746250784211772	   
df.mm.trans3:probe5	0.0183620846803678	0.0424562208868142	0.432494562559395	0.665574241543168	   
df.mm.trans3:probe6	-0.0339741835206083	0.0424562208868142	-0.800216854231596	0.423976947551005	   
df.mm.trans3:probe7	-0.000446194250638872	0.0424562208868142	-0.0105095140669349	0.991619096823617	   
df.mm.trans3:probe8	0.0824129013198485	0.0424562208868142	1.94112663817998	0.0528218481591695	.  
df.mm.trans3:probe9	0.179617873945191	0.0424562208868142	4.23066090653808	2.78664272036231e-05	***
df.mm.trans3:probe10	0.0230120529707665	0.0424562208868142	0.542018401310736	0.588054758405682	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.96988634281608	0.133979480908289	29.6305547379566	6.81270387200426e-111	***
df.mm.trans1	0.0801410887487948	0.107257580996907	0.747183443854715	0.455314915723837	   
df.mm.trans2	0.0847808630754924	0.107257580996907	0.790441685216987	0.42965618620857	   
df.mm.exp2	0.124261907097229	0.143625208073198	0.86518173769259	0.387366784400207	   
df.mm.exp3	0.132008043644317	0.143625208073198	0.919114725160497	0.358492445865410	   
df.mm.exp4	0.128978309225035	0.143625208073198	0.898019999102815	0.369620466484409	   
df.mm.exp5	-0.134502693692414	0.143625208073198	-0.936483890932746	0.349490172756960	   
df.mm.exp6	0.0524900267704725	0.143625208073198	0.365465279212832	0.714923452057252	   
df.mm.exp7	0.173527281833356	0.143625208073198	1.20819516407537	0.227560970910592	   
df.mm.exp8	0.0427425642272999	0.143625208073198	0.297597927276919	0.766137486463769	   
df.mm.trans1:exp2	-0.0342456886231690	0.112668882862933	-0.303949837372849	0.761296465106408	   
df.mm.trans2:exp2	-0.0787072393913399	0.112668882862933	-0.698571223849723	0.485154854881541	   
df.mm.trans1:exp3	-0.247295328208575	0.112668882862933	-2.19488577435726	0.0286450588208763	*  
df.mm.trans2:exp3	0.00853479527736572	0.112668882862933	0.0757511307514137	0.93964833223806	   
df.mm.trans1:exp4	-0.104561695682760	0.112668882862933	-0.928044132735072	0.353846294159665	   
df.mm.trans2:exp4	-0.0793114257422464	0.112668882862933	-0.703933719115089	0.481811875887230	   
df.mm.trans1:exp5	0.0402389986273715	0.112668882862933	0.357143850235245	0.721139463121802	   
df.mm.trans2:exp5	0.086540656512834	0.112668882862933	0.76809722714758	0.442803234016915	   
df.mm.trans1:exp6	-0.0676895121203684	0.112668882862933	-0.600782668651434	0.548265398734488	   
df.mm.trans2:exp6	0.0205839846823612	0.112668882862933	0.182694495226359	0.855114076620026	   
df.mm.trans1:exp7	-0.165637594950533	0.112668882862933	-1.47012725023678	0.142175702168224	   
df.mm.trans2:exp7	-0.0664744157395311	0.112668882862933	-0.589998001670085	0.555466759907445	   
df.mm.trans1:exp8	0.0330307654841341	0.112668882862933	0.293166708010390	0.76952012432496	   
df.mm.trans2:exp8	-0.0315283054286747	0.112668882862933	-0.279831526039275	0.7797260669842	   
df.mm.trans1:probe2	0.0560276957879463	0.0771391108411693	0.726320217811536	0.467992860806322	   
df.mm.trans1:probe3	-0.00525220633836216	0.0771391108411693	-0.0680874627810599	0.945744070915947	   
df.mm.trans1:probe4	0.00448599514519006	0.0771391108411692	0.0581546130914939	0.953649425012595	   
df.mm.trans1:probe5	-0.0580669455254048	0.0771391108411693	-0.752756220446533	0.451961625246186	   
df.mm.trans1:probe6	0.0179291247569796	0.0771391108411693	0.232425867519992	0.816305304478252	   
df.mm.trans2:probe2	-0.0854869526164445	0.0771391108411693	-1.10821801916363	0.268316935388831	   
df.mm.trans2:probe3	-0.0483351443378287	0.0771391108411693	-0.626597115402997	0.531218172319374	   
df.mm.trans2:probe4	-0.0496612675876393	0.0771391108411692	-0.643788436839682	0.520016941101656	   
df.mm.trans2:probe5	-0.0629426659484207	0.0771391108411693	-0.81596307323299	0.414921811987752	   
df.mm.trans2:probe6	-0.0179962317330086	0.0771391108411693	-0.23329581501222	0.815630137833384	   
df.mm.trans3:probe2	-0.0367269507290097	0.0771391108411692	-0.476113223610149	0.634208115065107	   
df.mm.trans3:probe3	-0.0645021007411587	0.0771391108411692	-0.836178950441491	0.403466010662326	   
df.mm.trans3:probe4	-0.0430939767815641	0.0771391108411693	-0.558652754894923	0.576656555480585	   
df.mm.trans3:probe5	-0.137787001132366	0.0771391108411693	-1.78621453669685	0.0746891043200172	.  
df.mm.trans3:probe6	-0.065040563643787	0.0771391108411692	-0.843159364096207	0.399554951264583	   
df.mm.trans3:probe7	0.077080197626726	0.0771391108411692	0.999236273094143	0.318178518709264	   
df.mm.trans3:probe8	-0.108652886712777	0.0771391108411692	-1.40853174904356	0.159614399610166	   
df.mm.trans3:probe9	0.0161394472016519	0.0771391108411693	0.209225216957496	0.834360276956018	   
df.mm.trans3:probe10	0.0505269493718332	0.0771391108411693	0.655010782738591	0.512771342188657