chr6.20540_chr6_94480425_94487353_-_2.R 

fitVsDatCorrelation=0.86835191872262
cont.fitVsDatCorrelation=0.268830999255654

fstatistic=9474.44735933143,63,945
cont.fstatistic=2500.89543001436,63,945

residuals=-0.853932113723354,-0.100747580227389,-0.00584544920251452,0.0928227281799572,0.811517384425326
cont.residuals=-0.603131344716,-0.219292339550626,-0.0544733296504319,0.150314146215212,1.52846963730237

predictedValues:
Include	Exclude	Both
chr6.20540_chr6_94480425_94487353_-_2.R.tl.Lung	55.6424396819649	61.428992981525	79.5848803780596
chr6.20540_chr6_94480425_94487353_-_2.R.tl.cerebhem	68.7335127345093	51.5132449187628	70.6845456115083
chr6.20540_chr6_94480425_94487353_-_2.R.tl.cortex	81.096755314819	53.4479664231762	93.774111882073
chr6.20540_chr6_94480425_94487353_-_2.R.tl.heart	59.0938803521592	56.7815661049151	70.3547805852383
chr6.20540_chr6_94480425_94487353_-_2.R.tl.kidney	56.5507568854264	53.0582629889299	71.5479609906387
chr6.20540_chr6_94480425_94487353_-_2.R.tl.liver	61.2863546125933	58.8133754877725	73.7134485617173
chr6.20540_chr6_94480425_94487353_-_2.R.tl.stomach	57.9348492146044	68.6906429286825	83.1627523510343
chr6.20540_chr6_94480425_94487353_-_2.R.tl.testicle	58.4987759548738	53.129963472481	70.247289868204


diffExp=-5.78655329956012,17.2202678157465,27.6487888916428,2.31231424724407,3.49249389649652,2.47297912482076,-10.7557937140781,5.36881248239283
diffExpScore=1.74661910943956
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,1,1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,1,1,0,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	66.1529493482864	65.6084823791676	63.5788695121169
cerebhem	64.6486891416095	62.8446836896013	59.6906266467472
cortex	65.7802681638456	72.145853150718	67.3148751070918
heart	59.2581437771839	73.9979806514702	68.4052139669818
kidney	64.0555098551297	79.0897571470457	58.7630835202195
liver	72.1121015263798	65.5144527980861	65.0556082179412
stomach	60.9197925740151	70.7349896555155	69.6288932333427
testicle	60.7819225171033	70.8510834725409	60.6965764451448
cont.diffExp=0.54446696911883,1.80400545200818,-6.36558498687238,-14.7398368742862,-15.0342472919160,6.59764872829369,-9.81519708150046,-10.0691609554376
cont.diffExpScore=1.351351456209

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,-1,-1,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.422063071500094
cont.tran.correlation=-0.46733870648188

tran.covariance=-0.00542727907536782
cont.tran.covariance=-0.00222865605164832

tran.mean=59.7313337535747
cont.tran.mean=67.1560412404811

weightedLogRatios:
wLogRatio
Lung	-0.402511427782669
cerebhem	1.17840490211322
cortex	1.74577745647883
heart	0.162024346743574
kidney	0.255200013144618
liver	0.168662997579540
stomach	-0.705777277971679
testicle	0.387068486082038

cont.weightedLogRatios:
wLogRatio
Lung	0.0346103103771787
cerebhem	0.117587491786783
cortex	-0.390956011773952
heart	-0.931403768857891
kidney	-0.899239279509437
liver	0.405896796990372
stomach	-0.625052484360365
testicle	-0.641346567165268

varWeightedLogRatios=0.627707431800057
cont.varWeightedLogRatios=0.248248852936318

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.52212816639775	0.0778602836214819	45.236518576282	1.09928839092359e-238	***
df.mm.trans1	0.589567714990198	0.066776817211955	8.82892805625735	5.04736013402184e-18	***
df.mm.trans2	0.60994462458273	0.0585429367384324	10.4187568742569	3.8957436071474e-24	***
df.mm.exp2	0.153844886557566	0.0742787611099841	2.07118272112493	0.0386126738181641	*  
df.mm.exp3	0.0734587110794888	0.0742787611099841	0.988959831609455	0.32293593995772	   
df.mm.exp4	0.104784362790610	0.0742787611099841	1.41069077115404	0.158664904125042	   
df.mm.exp5	-0.0238428156511322	0.074278761109984	-0.320991024821057	0.748288165667709	   
df.mm.exp6	0.129737255709082	0.0742787611099841	1.74662654263957	0.0810270288893492	.  
df.mm.exp7	0.108128512314400	0.074278761109984	1.45571238263243	0.145804265558270	   
df.mm.exp8	0.0297211480295759	0.0742787611099841	0.400129829650335	0.689151324883149	   
df.mm.trans1:exp2	0.057445792737243	0.0680652346904331	0.84398140987116	0.398893386478622	   
df.mm.trans2:exp2	-0.329887851163817	0.0476898420065476	-6.91736095746606	8.47259061477295e-12	***
df.mm.trans1:exp3	0.303238027071988	0.068065234690433	4.45510881511159	9.38806078634935e-06	***
df.mm.trans2:exp3	-0.212632042769984	0.0476898420065476	-4.45864431131457	9.23770783021152e-06	***
df.mm.trans1:exp4	-0.0446032048016828	0.0680652346904331	-0.655300830218867	0.512433570811043	   
df.mm.trans2:exp4	-0.183454552243252	0.0476898420065476	-3.84682658873277	0.000127685238686249	***
df.mm.trans1:exp5	0.0400351887604277	0.068065234690433	0.58818850684217	0.556546309564039	   
df.mm.trans2:exp5	-0.122648495007145	0.0476898420065476	-2.57179495353133	0.0102691179922813	*  
df.mm.trans1:exp6	-0.0331262513877648	0.068065234690433	-0.486683863479294	0.62659523503485	   
df.mm.trans2:exp6	-0.173249874489696	0.0476898420065476	-3.63284647631899	0.000295399615484235	***
df.mm.trans1:exp7	-0.0677556362546787	0.068065234690433	-0.995451445408769	0.319771567295658	   
df.mm.trans2:exp7	0.00360255362742865	0.0476898420065476	0.075541320244551	0.939800011116198	   
df.mm.trans1:exp8	0.0203384684200802	0.068065234690433	0.298808466797793	0.765151885517029	   
df.mm.trans2:exp8	-0.174862017177771	0.0476898420065476	-3.66665121586612	0.000259452605680459	***
df.mm.trans1:probe2	-0.313796042017073	0.0493179479608434	-6.36271489369784	3.08497282423555e-10	***
df.mm.trans1:probe3	0.428664178758833	0.0493179479608434	8.69184944797736	1.55430604412884e-17	***
df.mm.trans1:probe4	-0.330799774880164	0.0493179479608434	-6.70749267878718	3.40480484112907e-11	***
df.mm.trans1:probe5	-0.425551458912694	0.0493179479608434	-8.62873409190841	2.59608536078486e-17	***
df.mm.trans1:probe6	-0.122288546614951	0.0493179479608434	-2.47959519143098	0.0133265403218379	*  
df.mm.trans1:probe7	-0.24518743894214	0.0493179479608434	-4.97156611497318	7.88440768084903e-07	***
df.mm.trans1:probe8	0.0300239272336082	0.0493179479608434	0.608782978104565	0.542814606116274	   
df.mm.trans1:probe9	0.00305876006515752	0.0493179479608434	0.0620212355061094	0.950559035908223	   
df.mm.trans1:probe10	-0.0428968636241345	0.0493179479608434	-0.86980228086929	0.384629338120785	   
df.mm.trans1:probe11	-0.242627265073095	0.0493179479608434	-4.91965450926166	1.02231563882720e-06	***
df.mm.trans1:probe12	-0.0396209975959991	0.0493179479608434	-0.80337887593086	0.421957802020535	   
df.mm.trans1:probe13	-0.329285334509600	0.0493179479608434	-6.67678498649296	4.16029345790075e-11	***
df.mm.trans1:probe14	-0.199249983141486	0.0493179479608434	-4.04011098149669	5.77639029462979e-05	***
df.mm.trans1:probe15	-0.286440980914606	0.0493179479608434	-5.80804743015725	8.62959180618046e-09	***
df.mm.trans1:probe16	-0.186771696636671	0.0493179479608434	-3.78709383417495	0.000162044878249240	***
df.mm.trans1:probe17	-0.208268542208075	0.0493179479608434	-4.22297664074411	2.64398591914957e-05	***
df.mm.trans1:probe18	-0.218815190309722	0.0493179479608434	-4.43682674071219	1.02036474510207e-05	***
df.mm.trans1:probe19	-0.158878674097282	0.0493179479608434	-3.22151834507441	0.00131870962674445	** 
df.mm.trans1:probe20	-0.0908625238974966	0.0493179479608434	-1.84238249267058	0.0657323463851288	.  
df.mm.trans1:probe21	-0.145129542099133	0.0493179479608434	-2.94273277984639	0.00333280725956585	** 
df.mm.trans1:probe22	-0.214264046595816	0.0493179479608434	-4.34454504810162	1.54662594453247e-05	***
df.mm.trans2:probe2	-0.125089594924315	0.0493179479608434	-2.53639091033616	0.0113601531185143	*  
df.mm.trans2:probe3	-0.0623579153014535	0.0493179479608434	-1.26440612149888	0.206396052264987	   
df.mm.trans2:probe4	-0.0696884066478211	0.0493179479608434	-1.41304351720293	0.157972205857182	   
df.mm.trans2:probe5	-0.0522579310094246	0.0493179479608434	-1.05961284218304	0.289591521500713	   
df.mm.trans2:probe6	0.0255764702471144	0.0493179479608434	0.518603699152713	0.604158538885864	   
df.mm.trans3:probe2	-0.474553244619712	0.0493179479608434	-9.62232339829892	5.67292603998785e-21	***
df.mm.trans3:probe3	-0.372002519233503	0.0493179479608434	-7.54294399127999	1.07770585628922e-13	***
df.mm.trans3:probe4	-0.499226340756208	0.0493179479608434	-10.1226097475218	6.16489002753526e-23	***
df.mm.trans3:probe5	-0.75844879609251	0.0493179479608434	-15.3787581895072	8.4549113584846e-48	***
df.mm.trans3:probe6	-0.25498557448229	0.0493179479608434	-5.17023892974499	2.85355595383958e-07	***
df.mm.trans3:probe7	-0.430553225952973	0.0493179479608434	-8.73015289068426	1.13678853199633e-17	***
df.mm.trans3:probe8	0.60913488563016	0.0493179479608434	12.3511806718680	1.35066405453597e-32	***
df.mm.trans3:probe9	-0.144062233456682	0.0493179479608434	-2.92109139599769	0.00357105275765798	** 
df.mm.trans3:probe10	-0.554903364944767	0.0493179479608434	-11.2515501534115	1.19160484732956e-27	***
df.mm.trans3:probe11	-0.515486405606057	0.0493179479608434	-10.4523084783522	2.83802491836643e-24	***
df.mm.trans3:probe12	-0.318587340693369	0.0493179479608434	-6.45986611094028	1.67494594206175e-10	***
df.mm.trans3:probe13	-0.289962882209031	0.0493179479608434	-5.87945959226143	5.70458834298982e-09	***
df.mm.trans3:probe14	-0.823792223843092	0.0493179479608434	-16.7037003343519	4.42770647003081e-55	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17584846902591	0.151213354818003	27.6156062673954	1.49641040579236e-123	***
df.mm.trans1	0.0644196854303767	0.129688026873592	0.49672808649612	0.619496342951604	   
df.mm.trans2	0.0354441438471023	0.113696912641017	0.311742359786078	0.75530509610387	   
df.mm.exp2	-0.00293412023397756	0.144257638641156	-0.0203394444940017	0.983776883711516	   
df.mm.exp3	0.0322351938146108	0.144257638641156	0.223455715192985	0.82322914049933	   
df.mm.exp4	-0.0629013194021762	0.144257638641156	-0.436034583642704	0.662911226510911	   
df.mm.exp5	0.233426303696389	0.144257638641156	1.61812092513896	0.105970236069670	   
df.mm.exp6	0.0618569376367372	0.144257638641156	0.428794885452184	0.668170226615312	   
df.mm.exp7	-0.0980744297777131	0.144257638641156	-0.679856059627284	0.496762109186508	   
df.mm.exp8	0.0385920882872359	0.144257638641156	0.267521974231357	0.789125672400333	   
df.mm.trans1:exp2	-0.0200675263166095	0.132190277318427	-0.151807884238489	0.879370865715171	   
df.mm.trans2:exp2	-0.0401045277365072	0.092618992188733	-0.433005442931022	0.665109623587115	   
df.mm.trans1:exp3	-0.0378847517155309	0.132190277318427	-0.286592573100305	0.774487121823761	   
df.mm.trans2:exp3	0.0627496222135607	0.0926189921887329	0.677502753276495	0.498252852498101	   
df.mm.trans1:exp4	-0.0471649376424869	0.132190277318427	-0.356795814331136	0.72132432288391	   
df.mm.trans2:exp4	0.183234131588951	0.092618992188733	1.97836455848676	0.0481776491787007	*  
df.mm.trans1:exp5	-0.265645730104605	0.132190277318427	-2.00957086628015	0.0447605159701767	*  
df.mm.trans2:exp5	-0.0465479219110771	0.092618992188733	-0.502574264857307	0.615380720986717	   
df.mm.trans1:exp6	0.0243954608780926	0.132190277318427	0.184548072467747	0.853623114059147	   
df.mm.trans2:exp6	-0.063291158081429	0.092618992188733	-0.683349673601052	0.49455341683337	   
df.mm.trans1:exp7	0.0156630775764483	0.132190277318427	0.118488877504344	0.905705488540362	   
df.mm.trans2:exp7	0.17330979129439	0.0926189921887329	1.87121223410886	0.0616244789463697	.  
df.mm.trans1:exp8	-0.123269145877900	0.132190277318427	-0.932512953134687	0.351309678811638	   
df.mm.trans2:exp8	0.0382831780658128	0.092618992188733	0.413340473277897	0.67945095670585	   
df.mm.trans1:probe2	-0.0241333225821978	0.0957809555402293	-0.251963685746082	0.801123919244916	   
df.mm.trans1:probe3	-0.0308439581301076	0.0957809555402293	-0.322026001475342	0.747504217832083	   
df.mm.trans1:probe4	-0.0629999263918735	0.0957809555402293	-0.657750030123814	0.510858951293634	   
df.mm.trans1:probe5	-0.0321057378823724	0.0957809555402293	-0.335199598931623	0.73754891812515	   
df.mm.trans1:probe6	-0.150455143085959	0.0957809555402293	-1.57082524639009	0.116557988908029	   
df.mm.trans1:probe7	-0.0453994993184172	0.0957809555402293	-0.473992967206814	0.635614414529872	   
df.mm.trans1:probe8	-0.137143166453977	0.0957809555402293	-1.43184170256451	0.152519782692715	   
df.mm.trans1:probe9	-0.154420033333574	0.0957809555402293	-1.61222063887967	0.107247828908684	   
df.mm.trans1:probe10	-0.105300336471119	0.0957809555402293	-1.09938699063084	0.271879297322572	   
df.mm.trans1:probe11	-0.0900486100849107	0.0957809555402293	-0.940151511091253	0.347380096360154	   
df.mm.trans1:probe12	-0.0981967214690137	0.0957809555402293	-1.02522177728504	0.305520912040573	   
df.mm.trans1:probe13	-0.0642132140973057	0.0957809555402293	-0.670417346904994	0.50275558035445	   
df.mm.trans1:probe14	-0.147060977934230	0.0957809555402293	-1.53538850291029	0.125023147152915	   
df.mm.trans1:probe15	-0.0073345546942418	0.0957809555402293	-0.0765763366305234	0.938976793103766	   
df.mm.trans1:probe16	-0.0790494512006168	0.0957809555402293	-0.825314915212084	0.409401114562622	   
df.mm.trans1:probe17	-0.064862141585958	0.0957809555402293	-0.677192467125838	0.498449586410547	   
df.mm.trans1:probe18	-0.157252604030764	0.0957809555402293	-1.64179406170902	0.100965408145227	   
df.mm.trans1:probe19	-0.045153998846913	0.0957809555402293	-0.471429822267201	0.637442640059805	   
df.mm.trans1:probe20	-0.0425592948485073	0.0957809555402293	-0.444339844058372	0.656898634685824	   
df.mm.trans1:probe21	-0.0421585898396335	0.0957809555402293	-0.440156287874225	0.659924569662585	   
df.mm.trans1:probe22	-0.158061166228139	0.0957809555402293	-1.65023584632909	0.0992270047901312	.  
df.mm.trans2:probe2	-0.0314081545760496	0.0957809555402293	-0.327916488188070	0.74304744308612	   
df.mm.trans2:probe3	-0.213731897719538	0.0957809555402293	-2.23146549868954	0.0258841761772523	*  
df.mm.trans2:probe4	-0.0941590198543903	0.0957809555402293	-0.983066198528811	0.325826474949597	   
df.mm.trans2:probe5	-0.173031440933443	0.0957809555402293	-1.80653283272757	0.0711531595924669	.  
df.mm.trans2:probe6	-0.0394219013486725	0.0957809555402293	-0.411583922151568	0.680737747558949	   
df.mm.trans3:probe2	-0.0382237181704722	0.0957809555402293	-0.399074304018796	0.68992861959052	   
df.mm.trans3:probe3	-0.0816779632102674	0.0957809555402293	-0.852757865585936	0.394009669350062	   
df.mm.trans3:probe4	-0.234485177145757	0.0957809555402293	-2.44813988149523	0.0145405194806545	*  
df.mm.trans3:probe5	-0.193000793801291	0.0957809555402293	-2.01502263902794	0.0441850601595934	*  
df.mm.trans3:probe6	-0.0115770358752798	0.0957809555402293	-0.120869914170122	0.903819766622475	   
df.mm.trans3:probe7	-0.0954494385528114	0.0957809555402293	-0.996538800583601	0.319243523041066	   
df.mm.trans3:probe8	-0.231294111299286	0.0957809555402293	-2.41482359405090	0.0159318409505251	*  
df.mm.trans3:probe9	-0.131225436940512	0.0957809555402293	-1.37005771346053	0.170994290779478	   
df.mm.trans3:probe10	-0.169777880216953	0.0957809555402293	-1.77256406828854	0.076623084002053	.  
df.mm.trans3:probe11	-0.0953114413734833	0.0957809555402293	-0.995098042569132	0.319943310841341	   
df.mm.trans3:probe12	-0.121951225389749	0.0957809555402293	-1.27323041101347	0.203249403253174	   
df.mm.trans3:probe13	-0.167176399000370	0.0957809555402293	-1.74540333260879	0.0812396978942129	.  
df.mm.trans3:probe14	-0.156885108540310	0.0957809555402293	-1.63795722913222	0.101763513194576