chr19.12032_chr19_6540957_6542063_+_2.R 

fitVsDatCorrelation=0.819550039886296
cont.fitVsDatCorrelation=0.252458757476173

fstatistic=9070.42079720737,52,692
cont.fstatistic=3172.25639784112,52,692

residuals=-0.692221197416808,-0.0870136921835257,-0.00313492629568235,0.0733442005000487,1.12527128710683
cont.residuals=-0.567930048281862,-0.195019920280566,-0.0557987054419413,0.159509619310766,1.58129931492217

predictedValues:
Include	Exclude	Both
chr19.12032_chr19_6540957_6542063_+_2.R.tl.Lung	57.6569045337721	48.8822770109381	71.1252256166398
chr19.12032_chr19_6540957_6542063_+_2.R.tl.cerebhem	71.6479372167931	56.4609886822984	105.624560427791
chr19.12032_chr19_6540957_6542063_+_2.R.tl.cortex	61.5852007728981	49.2968189496597	90.2722388565096
chr19.12032_chr19_6540957_6542063_+_2.R.tl.heart	56.8844860179596	50.010675045006	66.6742038894726
chr19.12032_chr19_6540957_6542063_+_2.R.tl.kidney	58.0494468308504	51.7979107213547	68.5220450178345
chr19.12032_chr19_6540957_6542063_+_2.R.tl.liver	63.0975117117718	51.0781538122623	67.2692754165008
chr19.12032_chr19_6540957_6542063_+_2.R.tl.stomach	55.8180024076848	47.7427656579632	75.9565872355824
chr19.12032_chr19_6540957_6542063_+_2.R.tl.testicle	61.7778428486591	53.0514610730242	71.6583426535109


diffExp=8.774627522834,15.1869485344947,12.2883818232384,6.87381097295366,6.2515361094957,12.0193578995095,8.07523674972161,8.72638177563495
diffExpScore=0.987373144515432
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=0,1,1,0,0,1,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	59.2836723504458	60.2759752364054	57.3574299881024
cerebhem	59.6453723435802	54.6407056406958	64.4399513947677
cortex	60.5405428167722	59.5647703870011	54.7304849622422
heart	62.1839451184803	65.5472396446271	52.4273723541686
kidney	61.0057192642578	66.4360942063697	54.4287742174957
liver	65.1274394715752	62.2714155781291	61.8346250269093
stomach	61.7194292656959	59.987289213321	66.5417227082922
testicle	63.6295889825372	56.673245360502	64.5201752494588
cont.diffExp=-0.992302885959624,5.00466670288443,0.97577242977112,-3.36329452614681,-5.43037494211186,2.85602389344604,1.73214005237487,6.95634362203515
cont.diffExpScore=3.12518585963283

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=0.856454746223572
cont.tran.correlation=0.191857849127574

tran.covariance=0.00363136406176717
cont.tran.covariance=0.000430908705148807

tran.mean=55.927398955806
cont.tran.mean=61.1582778050247

weightedLogRatios:
wLogRatio
Lung	0.655751571867698
cerebhem	0.98922285686524
cortex	0.892282504369917
heart	0.512134879325852
kidney	0.456273984589845
liver	0.853543481196275
stomach	0.616318564289615
testicle	0.616348186895668

cont.weightedLogRatios:
wLogRatio
Lung	-0.067903192771709
cerebhem	0.354458184339748
cortex	0.0665426053907455
heart	-0.218937039251981
kidney	-0.35418977215176
liver	0.186276318980673
stomach	0.116949096344319
testicle	0.474126246092539

varWeightedLogRatios=0.0364062256056625
cont.varWeightedLogRatios=0.0774869721235226

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.29804548540395	0.0872517532222322	37.799188710899	1.89021691683688e-170	***
df.mm.trans1	0.502855652731953	0.0783648340044925	6.41685341543792	2.58060220841984e-10	***
df.mm.trans2	0.616785253908215	0.0720671077875563	8.55848490168929	7.35737435304432e-17	***
df.mm.exp2	-0.0340594648979231	0.0987140901981689	-0.345031442112758	0.73017557719025	   
df.mm.exp3	-0.164031670552818	0.098714090198169	-1.66168446899043	0.0970289682103528	.  
df.mm.exp4	0.0739581653277629	0.098714090198169	0.74921589389409	0.453981691142795	   
df.mm.exp5	0.102006636356467	0.098714090198169	1.03335436867917	0.301799087874101	   
df.mm.exp6	0.189851782590701	0.098714090198169	1.92324907426663	0.0548587849543805	.  
df.mm.exp7	-0.121720803314403	0.098714090198169	-1.23306412559796	0.217970545152818	   
df.mm.exp8	0.143414753766613	0.098714090198169	1.45282961610351	0.146724354566461	   
df.mm.trans1:exp2	0.251313823083168	0.0945115458732311	2.65908065264593	0.00801663745695325	** 
df.mm.trans2:exp2	0.178194501297147	0.0822617418318075	2.16618925552882	0.0306374644879385	*  
df.mm.trans1:exp3	0.229943259077368	0.0945115458732312	2.43296474470740	0.0152280516939883	*  
df.mm.trans2:exp3	0.172476327700153	0.0822617418318075	2.0966773114627	0.0363845991792688	*  
df.mm.trans1:exp4	-0.0874455207928461	0.0945115458732311	-0.925236382337215	0.35516549920058	   
df.mm.trans2:exp4	-0.0511365792594215	0.0822617418318075	-0.621632585460875	0.534388220317312	   
df.mm.trans1:exp5	-0.0952214634000188	0.0945115458732312	-1.00751143704431	0.314041221701205	   
df.mm.trans2:exp5	-0.0440717189408823	0.0822617418318075	-0.535749887608646	0.592303565844165	   
df.mm.trans1:exp6	-0.0996804538236285	0.0945115458732312	-1.05469075659106	0.291934874726716	   
df.mm.trans2:exp6	-0.145909792615948	0.0822617418318075	-1.77372602824622	0.0765481888928383	.  
df.mm.trans1:exp7	0.0893072385057063	0.0945115458732311	0.944934692164432	0.345022027274937	   
df.mm.trans2:exp7	0.0981334567460466	0.0822617418318075	1.19294163435890	0.233301090551041	   
df.mm.trans1:exp8	-0.0743799895049023	0.0945115458732312	-0.786993682281618	0.431555006858657	   
df.mm.trans2:exp8	-0.0615672451246629	0.0822617418318075	-0.748431090245371	0.454454466881248	   
df.mm.trans1:probe2	0.155385455909063	0.0472557729366156	3.28817933244859	0.00105938592513306	** 
df.mm.trans1:probe3	0.160611005482462	0.0472557729366156	3.39875946369326	0.000715730244704278	***
df.mm.trans1:probe4	0.171795979415378	0.0472557729366156	3.63544957027385	0.000298079147833984	***
df.mm.trans1:probe5	0.169986175765369	0.0472557729366156	3.59715152672186	0.000344630611847369	***
df.mm.trans1:probe6	0.0300693484135419	0.0472557729366156	0.636310582706457	0.524784412407947	   
df.mm.trans1:probe7	0.155612776690575	0.0472557729366156	3.29298976654766	0.00104170328889626	** 
df.mm.trans1:probe8	0.268296391233513	0.0472557729366156	5.67753682906383	2.01140239943506e-08	***
df.mm.trans1:probe9	0.0603324989874026	0.0472557729366156	1.27672229736517	0.202128567494191	   
df.mm.trans1:probe10	0.0997328046185962	0.0472557729366156	2.11048933116316	0.0351744553760977	*  
df.mm.trans1:probe11	0.752983440400301	0.0472557729366156	15.9342106499936	6.31017389780331e-49	***
df.mm.trans1:probe12	0.487284984999068	0.0472557729366156	10.3116498729725	2.7357068072524e-23	***
df.mm.trans1:probe13	0.428537473762969	0.0472557729366156	9.06846819197664	1.22977887919195e-18	***
df.mm.trans1:probe14	0.672449872171852	0.0472557729366156	14.2300047250060	1.71111855099476e-40	***
df.mm.trans1:probe15	0.556759732591718	0.0472557729366156	11.7818352762636	2.48897711946617e-29	***
df.mm.trans1:probe16	0.494917601467071	0.0472557729366156	10.4731669955099	6.32096440269299e-24	***
df.mm.trans1:probe17	0.275348467984232	0.0472557729366156	5.82676889770818	8.66071466850621e-09	***
df.mm.trans1:probe18	0.279622782672981	0.0472557729366156	5.91721953311483	5.14997895893301e-09	***
df.mm.trans1:probe19	0.170114974764009	0.0472557729366156	3.59987709844859	0.000341104498538802	***
df.mm.trans1:probe20	0.359623116274074	0.0472557729366156	7.61014144782772	8.98162191172714e-14	***
df.mm.trans1:probe21	0.247030523498430	0.0472557729366156	5.22752053658658	2.27760222201467e-07	***
df.mm.trans1:probe22	0.343726288443187	0.0472557729366156	7.27374174800248	9.49564458832904e-13	***
df.mm.trans2:probe2	-0.0697118984812371	0.0472557729366156	-1.47520385656885	0.140612568642882	   
df.mm.trans2:probe3	-0.00964057549781777	0.0472557729366156	-0.204008418415856	0.838406901162889	   
df.mm.trans2:probe4	-0.0506265626092194	0.0472557729366156	-1.07133074888279	0.284394235551881	   
df.mm.trans2:probe5	-0.0611262000957048	0.0472557729366156	-1.29351815232593	0.196263510854799	   
df.mm.trans2:probe6	-0.0376373398788263	0.0472557729366156	-0.796460147404835	0.426037782177076	   
df.mm.trans3:probe2	-0.410541011220347	0.0472557729366156	-8.68763720722561	2.65628865628461e-17	***
df.mm.trans3:probe3	-0.0491919712442793	0.0472557729366156	-1.04097273597156	0.298251753292481	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07724885114868	0.147337420168854	27.672867126871	4.56519734949467e-114	***
df.mm.trans1	0.00286371554720218	0.132330549791640	0.0216406230587814	0.982740866903277	   
df.mm.trans2	0.00711138996995362	0.121695912670139	0.0584357339036462	0.953418421758917	   
df.mm.exp2	-0.208503250801997	0.166693262278286	-1.25081990688929	0.211422931155342	   
df.mm.exp3	0.0559915604235747	0.166693262278286	0.335895762421997	0.73705128963006	   
df.mm.exp4	0.221473978209143	0.166693262278286	1.32863185459412	0.184407413803822	   
df.mm.exp5	0.178350020919589	0.166693262278286	1.06992939295797	0.285024138146286	   
df.mm.exp6	0.0514198344361102	0.166693262278286	0.308469782961398	0.757817725274339	   
df.mm.exp7	-0.113062828118040	0.166693262278286	-0.678268734877161	0.497828058557295	   
df.mm.exp8	-0.108562277165624	0.166693262278286	-0.651269737491758	0.515088589459023	   
df.mm.trans1:exp2	0.214585887877910	0.159596647985568	1.34455134607411	0.179210716639872	   
df.mm.trans2:exp2	0.110348776623243	0.138911051898571	0.794384428848875	0.427244005160983	   
df.mm.trans1:exp3	-0.0350122189208437	0.159596647985568	-0.219379412805774	0.826419197819182	   
df.mm.trans2:exp3	-0.0678608657296143	0.138911051898571	-0.488520278279688	0.625336135943484	   
df.mm.trans1:exp4	-0.173711057148783	0.159596647985568	-1.08843800506695	0.276780713556632	   
df.mm.trans2:exp4	-0.137636483289769	0.138911051898571	-0.990824570173629	0.322117570899156	   
df.mm.trans1:exp5	-0.149716331011468	0.159596647985568	-0.938091951812212	0.348524451392515	   
df.mm.trans2:exp5	-0.0810431284011065	0.138911051898571	-0.583417426428256	0.559802421026749	   
df.mm.trans1:exp6	0.0425921949320174	0.159596647985568	0.266873994345226	0.789645688227799	   
df.mm.trans2:exp6	-0.0188509366220706	0.138911051898571	-0.135705088719902	0.892093890141353	   
df.mm.trans1:exp7	0.153327680116307	0.159596647985568	0.96071992771535	0.337028568815593	   
df.mm.trans2:exp7	0.108261917733767	0.138911051898571	0.77936144211777	0.436033278357193	   
df.mm.trans1:exp8	0.179306946597071	0.159596647985568	1.12350070543640	0.261614508245318	   
df.mm.trans2:exp8	0.0469309096873191	0.138911051898571	0.337848638001724	0.735579717360568	   
df.mm.trans1:probe2	0.0598766593456401	0.0797983239927839	0.750349836308024	0.453299080951994	   
df.mm.trans1:probe3	0.0627897085831945	0.0797983239927839	0.786854979421278	0.43163615266328	   
df.mm.trans1:probe4	-0.0412595012417708	0.0797983239927839	-0.517047215747312	0.605288413066842	   
df.mm.trans1:probe5	0.00943573332217798	0.0797983239927839	0.118244755654658	0.905908042730582	   
df.mm.trans1:probe6	0.0260735212381339	0.0797983239927839	0.326742717559979	0.743961289963226	   
df.mm.trans1:probe7	0.00985405393049602	0.0797983239927839	0.123486978641144	0.90175734216351	   
df.mm.trans1:probe8	7.04096547069613e-05	0.0797983239927839	0.000882345031624077	0.999296244923195	   
df.mm.trans1:probe9	0.00268325372763854	0.0797983239927839	0.033625439650602	0.973185532184257	   
df.mm.trans1:probe10	-0.0256241193054011	0.0797983239927839	-0.321110996112127	0.748223233195125	   
df.mm.trans1:probe11	-0.0702532118835012	0.0797983239927839	-0.880384554064746	0.378956664516512	   
df.mm.trans1:probe12	-0.0291840760755632	0.0797983239927839	-0.365722920173139	0.71468360828664	   
df.mm.trans1:probe13	-0.00303542759266822	0.0797983239927839	-0.0380387386700341	0.969667766094761	   
df.mm.trans1:probe14	-0.0137407550203051	0.0797983239927839	-0.172193529046395	0.863335730614335	   
df.mm.trans1:probe15	-0.0353734250947110	0.0797983239927839	-0.443285313835786	0.657697927643891	   
df.mm.trans1:probe16	-0.020656130368693	0.0797983239927839	-0.258854188097496	0.795824723642625	   
df.mm.trans1:probe17	0.0781396036592206	0.0797983239927839	0.979213594339233	0.327816657936465	   
df.mm.trans1:probe18	0.0213357526718612	0.0797983239927839	0.267370937186482	0.78926323917174	   
df.mm.trans1:probe19	-0.0486991890418117	0.0797983239927839	-0.61027834426968	0.541877800552716	   
df.mm.trans1:probe20	0.0209730084165929	0.0797983239927839	0.262825174354407	0.792763560060747	   
df.mm.trans1:probe21	0.0189063451112551	0.0797983239927839	0.236926594009228	0.81278385722984	   
df.mm.trans1:probe22	0.0332218260815508	0.0797983239927839	0.416322353894989	0.677303227764578	   
df.mm.trans2:probe2	0.188909487673161	0.0797983239927839	2.36733653316132	0.0181907886538736	*  
df.mm.trans2:probe3	-0.00172560708234646	0.0797983239927839	-0.0216246030744018	0.982753641390517	   
df.mm.trans2:probe4	0.0644164826930435	0.0797983239927839	0.807241048055954	0.419804989589892	   
df.mm.trans2:probe5	-0.0382340462985071	0.0797983239927839	-0.479133450245955	0.631995063621733	   
df.mm.trans2:probe6	-0.0822060535811352	0.0797983239927839	-1.03017268368405	0.303288871290165	   
df.mm.trans3:probe2	-0.103918667927424	0.0797983239927839	-1.30226629743278	0.193258632843216	   
df.mm.trans3:probe3	-0.0098460310554908	0.0797983239927839	-0.123386439248789	0.901836922557467