fitVsDatCorrelation=0.915624878871123
cont.fitVsDatCorrelation=0.248410987807565

fstatistic=10179.3722900070,70,1106
cont.fstatistic=1740.43020691181,70,1106

residuals=-0.815810871769017,-0.0992089271605756,-0.00496978557582905,0.0894043832499506,1.20088037877914
cont.residuals=-0.712158129554711,-0.248161223030717,-0.104536411425909,0.118962569923431,1.99999977991155

predictedValues:
Include	Exclude	Both
Lung	50.3434601782478	53.0386301123437	59.7148985796404
cerebhem	54.4377695265925	46.9038016386102	59.0957666953324
cortex	51.4474392215777	51.7692425786813	58.1583042292829
heart	52.1539851429896	58.2571351023194	56.299327388009
kidney	60.540463089775	51.896431420251	71.9561143664459
liver	53.4942706333185	53.2180364606143	63.1924275181762
stomach	54.6563844252281	58.2965470248122	58.8571968565857
testicle	52.3682506391394	52.2150731110484	61.0348203596591


diffExp=-2.69516993409586,7.53396788798234,-0.321803357103626,-6.10314995932978,8.64403166952397,0.276234172704129,-3.64016259958409,0.153177528091028
diffExpScore=6.05878631875396
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	69.860721039114	58.5956975375317	67.3723643171901
cerebhem	63.0481284414962	57.1594135126127	62.7709339160923
cortex	59.7747586957253	69.767870308187	58.4723606679415
heart	66.6696759751833	62.837523902419	61.0531278629681
kidney	63.8831415544666	64.6393513048316	62.5308120930557
liver	61.9438125042814	71.3352825605493	57.9175036195965
stomach	62.0632905219318	68.5097880758167	56.2995297833206
testicle	59.5207039757764	65.262685255003	59.745684644619
cont.diffExp=11.2650235015824,5.88871492888351,-9.99311161246168,3.83215207276437,-0.756209750364931,-9.39147005626787,-6.4464975538849,-5.74198127922654
cont.diffExpScore=4.31933245510499

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=-0.140744821100271
cont.tran.correlation=-0.628937984333154

tran.covariance=-0.000561346603824225
cont.tran.covariance=-0.00270119315490456

tran.mean=53.4398075190968
cont.tran.mean=64.0544903228079

weightedLogRatios:
wLogRatio
Lung	-0.205735941554872
cerebhem	0.584305229890802
cortex	-0.0245908520344620
heart	-0.443718965660781
kidney	0.620296595378497
liver	0.0205896004169505
stomach	-0.260054694152488
testicle	0.0115907344938161

cont.weightedLogRatios:
wLogRatio
Lung	0.73125464492453
cerebhem	0.401519673992979
cortex	-0.64431282850102
heart	0.246863812941823
kidney	-0.0489889614469525
liver	-0.592435577552314
stomach	-0.412835173384307
testicle	-0.380575868288612

varWeightedLogRatios=0.146104809974884
cont.varWeightedLogRatios=0.254216934675728

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.46826868336292	0.0754671710812007	45.9573167202883	9.27072331073931e-259	***
df.mm.trans1	0.320174444776823	0.0648392891501766	4.93796969357969	9.1092351136551e-07	***
df.mm.trans2	0.367736888730721	0.0561228786949311	6.55235257495682	8.66346170345049e-11	***
df.mm.exp2	-0.0343100509372006	0.0704391570412285	-0.487087755992291	0.626292661700395	   
df.mm.exp3	0.0238805184678341	0.0704391570412285	0.339023342568633	0.734656502129905	   
df.mm.exp4	0.188076888391552	0.0704391570412285	2.67006160055932	0.00769490458453459	** 
df.mm.exp5	-0.0238021234555483	0.0704391570412285	-0.337910396082916	0.735494839072302	   
df.mm.exp6	0.00747975619024087	0.0704391570412285	0.106187474473366	0.915452864844803	   
df.mm.exp7	0.191187082928766	0.0704391570412285	2.71421594123937	0.00674659421931323	** 
df.mm.exp8	0.00191949220888786	0.0704391570412285	0.0272503574647318	0.978264967120063	   
df.mm.trans1:exp2	0.112499533153597	0.0651052066945489	1.72796522529151	0.0842735901586557	.  
df.mm.trans2:exp2	-0.0886117366026512	0.0427636713298824	-2.07212650006341	0.0384849548016527	*  
df.mm.trans1:exp3	-0.00218855310583586	0.0651052066945489	-0.0336156387015833	0.973189716874593	   
df.mm.trans2:exp3	-0.0481048362670694	0.0427636713298824	-1.12489958815708	0.260875649032426	   
df.mm.trans1:exp4	-0.152745016436691	0.0651052066945489	-2.34612597350805	0.0191455189465465	*  
df.mm.trans2:exp4	-0.094230830519786	0.0427636713298824	-2.20352527248846	0.0277632734139894	*  
df.mm.trans1:exp5	0.208245352834886	0.0651052066945489	3.19859752249772	0.0014201872655108	** 
df.mm.trans2:exp5	0.00203163440180437	0.0427636713298824	0.0475084186793081	0.962116597775902	   
df.mm.trans1:exp6	0.0532260775070399	0.0651052066945489	0.817539490455168	0.413796269977815	   
df.mm.trans2:exp6	-0.00410290418575514	0.0427636713298824	-0.0959436843975582	0.923582680366503	   
df.mm.trans1:exp7	-0.108989774750215	0.0651052066945489	-1.67405619740303	0.0944022950701864	.  
df.mm.trans2:exp7	-0.096664737130687	0.0427636713298824	-2.26044055911401	0.0239876980339538	*  
df.mm.trans1:exp8	0.0375122881469607	0.0651052066945489	0.576179541568086	0.564611058766611	   
df.mm.trans2:exp8	-0.017568800185486	0.0427636713298824	-0.410834702426713	0.681273328063763	   
df.mm.trans1:probe2	0.0815839237034256	0.0484737809986129	1.68305261159141	0.0926471322772983	.  
df.mm.trans1:probe3	0.854704926127998	0.0484737809986129	17.6323139751045	1.63758316043381e-61	***
df.mm.trans1:probe4	0.200705529121191	0.0484737809986129	4.14049667648031	3.7290910801826e-05	***
df.mm.trans1:probe5	0.0370089887039109	0.0484737809986129	0.763484670299804	0.445337227521266	   
df.mm.trans1:probe6	-0.0141992854892341	0.0484737809986129	-0.292927128784123	0.769632800150993	   
df.mm.trans1:probe7	0.0951349330754742	0.0484737809986129	1.96260599267461	0.0499423556053675	*  
df.mm.trans1:probe8	0.345444738791553	0.0484737809986129	7.12642446442207	1.85457517036519e-12	***
df.mm.trans1:probe9	0.113775873928485	0.0484737809986129	2.34716317944624	0.0190925743138345	*  
df.mm.trans1:probe10	0.129986911719985	0.0484737809986129	2.68159217296676	0.00743636188474392	** 
df.mm.trans1:probe11	0.000275780685573208	0.0484737809986129	0.00568927531320695	0.995461665536653	   
df.mm.trans1:probe12	0.506794328196001	0.0484737809986129	10.4550195539833	1.83757439938725e-24	***
df.mm.trans1:probe13	1.36597237374455	0.0484737809986129	28.1796126814129	4.01114177541177e-132	***
df.mm.trans1:probe14	-0.000233636781114169	0.0484737809986129	-0.0048198588247295	0.996155193143622	   
df.mm.trans1:probe15	0.810086296620911	0.0484737809986129	16.7118446288333	4.48214845539673e-56	***
df.mm.trans1:probe16	0.0189709171966658	0.0484737809986129	0.391364502744456	0.695603211446849	   
df.mm.trans1:probe17	0.0275410216613485	0.0484737809986129	0.568163264634475	0.570039440593564	   
df.mm.trans1:probe18	0.0455490148793712	0.0484737809986129	0.939662925833547	0.347595656865165	   
df.mm.trans1:probe19	0.0488197787918369	0.0484737809986129	1.00713783381647	0.314088828188327	   
df.mm.trans1:probe20	0.0704898800311616	0.0484737809986129	1.45418571811386	0.146178534660872	   
df.mm.trans1:probe21	0.142764216403720	0.0484737809986129	2.94518425141635	0.00329517369769679	** 
df.mm.trans1:probe22	0.0877029266070653	0.0484737809986129	1.80928586135204	0.0706780131176049	.  
df.mm.trans1:probe23	0.125299914900897	0.0484737809986129	2.58490079213095	0.0098678387190531	** 
df.mm.trans1:probe24	0.222100316734660	0.0484737809986129	4.58186492077059	5.13209992158007e-06	***
df.mm.trans1:probe25	0.0553226466102603	0.0484737809986129	1.14129010509503	0.253996265802461	   
df.mm.trans1:probe26	0.236698284698386	0.0484737809986129	4.88301675301869	1.19837485321802e-06	***
df.mm.trans2:probe2	0.732050449361891	0.0484737809986129	15.1019878020829	5.42552913840675e-47	***
df.mm.trans2:probe3	0.485119137939874	0.0484737809986129	10.0078666847498	1.24819151259847e-22	***
df.mm.trans2:probe4	1.84644108265137	0.0484737809986129	38.0915423681147	1.68819881846407e-203	***
df.mm.trans2:probe5	0.0588842984591712	0.0484737809986129	1.21476594658164	0.224714651427062	   
df.mm.trans2:probe6	-0.0171512102758923	0.0484737809986129	-0.353824478358374	0.72353786486345	   
df.mm.trans3:probe2	-0.209745641910782	0.0484737809986129	-4.32699157337827	1.64832129085669e-05	***
df.mm.trans3:probe3	0.0580851968327375	0.0484737809986129	1.19828071250311	0.231064458972595	   
df.mm.trans3:probe4	0.364731866096685	0.0484737809986129	7.52431228971228	1.09479466249935e-13	***
df.mm.trans3:probe5	-0.364803256949343	0.0484737809986129	-7.52578506223358	1.08311820040472e-13	***
df.mm.trans3:probe6	-0.237929837257146	0.0484737809986129	-4.90842332402241	1.05601699649901e-06	***
df.mm.trans3:probe7	-0.273298584702258	0.0484737809986129	-5.63807029433248	2.18199060054051e-08	***
df.mm.trans3:probe8	-0.252974409765439	0.0484737809986129	-5.21878847809867	2.14961079676441e-07	***
df.mm.trans3:probe9	0.232885973024963	0.0484737809986129	4.80436987227439	1.76603903881114e-06	***
df.mm.trans3:probe10	0.170292530512363	0.0484737809986129	3.51308536293541	0.000460822915122692	***
df.mm.trans3:probe11	0.0121998026751172	0.0484737809986129	0.251678380018805	0.80133642547555	   
df.mm.trans3:probe12	-0.230912190390607	0.0484737809986129	-4.76365131074084	2.15396106531182e-06	***
df.mm.trans3:probe13	-0.325313060640651	0.0484737809986129	-6.71111380088053	3.07861236017199e-11	***
df.mm.trans3:probe14	-0.281124533782744	0.0484737809986129	-5.79951734713635	8.6724235763223e-09	***
df.mm.trans3:probe15	-0.0910545380268555	0.0484737809986129	-1.87842862989089	0.0605854316580987	.  
df.mm.trans3:probe16	-0.0733643718164968	0.0484737809986129	-1.51348564739763	0.130442038930305	   
df.mm.trans3:probe17	0.370151063409785	0.0484737809986129	7.63610875372765	4.82600126123601e-14	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12393665265046	0.181829621183511	22.6802246290135	8.03297266583223e-94	***
df.mm.trans1	0.171655375065698	0.156222940585640	1.09878468822956	0.272101121889359	   
df.mm.trans2	-0.0484286448395835	0.135221734518806	-0.358142461431363	0.720305045679173	   
df.mm.exp2	-0.0566795844780625	0.169715189502882	-0.333968837109303	0.738466385515861	   
df.mm.exp3	0.1602729694212	0.169715189502882	0.944364319367409	0.345189758134015	   
df.mm.exp4	0.121628267515884	0.169715189502882	0.716661059461734	0.473734561897348	   
df.mm.exp5	0.0832895263570414	0.169715189502882	0.49076058896677	0.623693141455121	   
df.mm.exp6	0.227669204473424	0.169715189502882	1.34147806769858	0.180040663215940	   
df.mm.exp7	0.21751519810321	0.169715189502882	1.28164838244791	0.200234636980754	   
df.mm.exp8	0.0677177659831719	0.169715189502882	0.399008280764533	0.689964129301674	   
df.mm.trans1:exp2	-0.0459255977975079	0.156863638861016	-0.29277401780918	0.769749806448249	   
df.mm.trans2:exp2	0.031862404009658	0.103034233918245	0.309240946411460	0.757196494715511	   
df.mm.trans1:exp3	-0.316193053487754	0.156863638861016	-2.0157192309424	0.0440708218552456	*  
df.mm.trans2:exp3	0.0142393505743523	0.103034233918245	0.138200188741645	0.890107362591546	   
df.mm.trans1:exp4	-0.168381611553567	0.156863638861016	-1.07342665754909	0.283313871519726	   
df.mm.trans2:exp4	-0.0517371311389256	0.103034233918245	-0.502135350275691	0.61567226616646	   
df.mm.trans1:exp5	-0.172737585746910	0.156863638861016	-1.10119583481012	0.271051027820545	   
df.mm.trans2:exp5	0.0148725792722164	0.103034233918245	0.144345997506201	0.885253555153317	   
df.mm.trans1:exp6	-0.347945040778609	0.156863638861016	-2.21813699659801	0.0267480247341793	*  
df.mm.trans2:exp6	-0.0309394259041318	0.103034233918245	-0.300282971276143	0.764017752097791	   
df.mm.trans1:exp7	-0.335864079392033	0.156863638861016	-2.14112130657388	0.032482324765158	*  
df.mm.trans2:exp7	-0.0611998444515915	0.103034233918245	-0.593975828462526	0.552649745210419	   
df.mm.trans1:exp8	-0.227897108504870	0.156863638861016	-1.45283578883945	0.146552999107472	   
df.mm.trans2:exp8	0.0400413983365191	0.103034233918245	0.388622274498501	0.697630384449321	   
df.mm.trans1:probe2	-0.0756742549509078	0.116792097941854	-0.647939854531793	0.517158332935499	   
df.mm.trans1:probe3	-0.182433642647736	0.116792097941854	-1.56203755102133	0.118565215200894	   
df.mm.trans1:probe4	-0.0404484655653981	0.116792097941854	-0.346328786606229	0.72916151050633	   
df.mm.trans1:probe5	-0.135603256711545	0.116792097941854	-1.16106533833356	0.245865858015959	   
df.mm.trans1:probe6	-0.149853918704809	0.116792097941854	-1.28308268577738	0.199731906453532	   
df.mm.trans1:probe7	-0.198855632012025	0.116792097941854	-1.70264628785953	0.0889152642194506	.  
df.mm.trans1:probe8	-0.125992240012967	0.116792097941854	-1.07877366905160	0.28092384493438	   
df.mm.trans1:probe9	-0.0547297733535069	0.116792097941854	-0.468608530182877	0.639441822561139	   
df.mm.trans1:probe10	-0.0638729166655604	0.116792097941854	-0.546894163142443	0.584561766861609	   
df.mm.trans1:probe11	-0.0769398579650598	0.116792097941854	-0.658776229906967	0.510176592438981	   
df.mm.trans1:probe12	-0.0439584678455546	0.116792097941854	-0.37638220924364	0.706704959949942	   
df.mm.trans1:probe13	-0.106671709777093	0.116792097941854	-0.913346978578985	0.361259136137141	   
df.mm.trans1:probe14	-0.138816476446434	0.116792097941854	-1.1885776426034	0.234861054185123	   
df.mm.trans1:probe15	-0.0448639528991636	0.116792097941854	-0.384135174294921	0.700952101538156	   
df.mm.trans1:probe16	-0.150962668709545	0.116792097941854	-1.29257605069054	0.196427694883373	   
df.mm.trans1:probe17	-0.0696666259576858	0.116792097941854	-0.596501194732969	0.550962525010133	   
df.mm.trans1:probe18	0.0813936415854471	0.116792097941854	0.69691051894598	0.486005244363602	   
df.mm.trans1:probe19	0.00146955231866500	0.116792097941854	0.0125826348234332	0.989963044092795	   
df.mm.trans1:probe20	-0.167045128816730	0.116792097941854	-1.43027766227724	0.152919782012148	   
df.mm.trans1:probe21	-0.18062233583235	0.116792097941854	-1.54652873794830	0.122262998446083	   
df.mm.trans1:probe22	-0.205101124387484	0.116792097941854	-1.75612158700664	0.0793444071536722	.  
df.mm.trans1:probe23	-0.0800657179543725	0.116792097941854	-0.685540540544395	0.493146548944618	   
df.mm.trans1:probe24	0.173326975301191	0.116792097941854	1.48406423341657	0.138076800716971	   
df.mm.trans1:probe25	0.085791967792569	0.116792097941854	0.73456996923954	0.462757142624614	   
df.mm.trans1:probe26	-0.160608058933204	0.116792097941854	-1.37516203376331	0.169359699294968	   
df.mm.trans2:probe2	0.040147769591399	0.116792097941854	0.343754160588732	0.731096514184539	   
df.mm.trans2:probe3	-0.0330641111670390	0.116792097941854	-0.283102296728160	0.77715139901564	   
df.mm.trans2:probe4	-0.0138864352205743	0.116792097941854	-0.118898756553614	0.905377158232767	   
df.mm.trans2:probe5	0.0296415177006855	0.116792097941854	0.253797287856263	0.799699340744882	   
df.mm.trans2:probe6	-0.134313640950096	0.116792097941854	-1.15002336045856	0.250382851162382	   
df.mm.trans3:probe2	0.114024090978375	0.116792097941854	0.976299706810151	0.329129453111475	   
df.mm.trans3:probe3	0.0706676272515529	0.116792097941854	0.605071991143916	0.545255286178229	   
df.mm.trans3:probe4	-0.0981827313819474	0.116792097941854	-0.840662451588364	0.400718763710675	   
df.mm.trans3:probe5	-0.122177190435795	0.116792097941854	-1.0461083633982	0.29573957202944	   
df.mm.trans3:probe6	-0.0576766304212692	0.116792097941854	-0.493840177868746	0.621517111031057	   
df.mm.trans3:probe7	-0.0425907636607184	0.116792097941854	-0.364671620865332	0.715426258986478	   
df.mm.trans3:probe8	0.0134647121039365	0.116792097941854	0.115287869138544	0.908237880279946	   
df.mm.trans3:probe9	-0.130411610842906	0.116792097941854	-1.11661330810097	0.264402219348888	   
df.mm.trans3:probe10	-0.0989007890726935	0.116792097941854	-0.846810621741997	0.397283877621405	   
df.mm.trans3:probe11	-0.173658579984479	0.116792097941854	-1.48690350669903	0.137325310737777	   
df.mm.trans3:probe12	0.0457491122598016	0.116792097941854	0.391714106228131	0.695344925650538	   
df.mm.trans3:probe13	-0.0378104923275836	0.116792097941854	-0.323741871187278	0.7461946745885	   
df.mm.trans3:probe14	0.0493061044854326	0.116792097941854	0.422169867262596	0.672983084784636	   
df.mm.trans3:probe15	-0.0322423997738738	0.116792097941854	-0.276066620448294	0.78254847234986	   
df.mm.trans3:probe16	-0.033141782439263	0.116792097941854	-0.283767335490137	0.776641799367928	   
df.mm.trans3:probe17	-0.0942027348923257	0.116792097941854	-0.806584833669358	0.420079117484623