chr19.12108_chr19_45480162_45480816_+_0.R 

fitVsDatCorrelation=0.777157518097565
cont.fitVsDatCorrelation=0.290107137550355

fstatistic=6557.7987950966,43,485
cont.fstatistic=2829.31863622316,43,485

residuals=-0.510445172218164,-0.0795388017194339,-0.00346005704704991,0.0698245141715122,1.48673628952678
cont.residuals=-0.593197483566876,-0.177879788229990,-0.0299900990332828,0.139990239729606,1.34397586359593

predictedValues:
Include	Exclude	Both
chr19.12108_chr19_45480162_45480816_+_0.R.tl.Lung	50.9071383488911	55.1651662213882	66.3662371552585
chr19.12108_chr19_45480162_45480816_+_0.R.tl.cerebhem	51.48849681261	53.1053483206892	66.138089074557
chr19.12108_chr19_45480162_45480816_+_0.R.tl.cortex	62.6915299580296	55.2721970654116	88.6787307239759
chr19.12108_chr19_45480162_45480816_+_0.R.tl.heart	51.9317639183248	58.9241774831078	62.7846603426996
chr19.12108_chr19_45480162_45480816_+_0.R.tl.kidney	52.691078904743	61.4031293281321	68.5944221367179
chr19.12108_chr19_45480162_45480816_+_0.R.tl.liver	52.0659929746545	61.816341844256	60.2394049469541
chr19.12108_chr19_45480162_45480816_+_0.R.tl.stomach	50.5321813648174	54.7481181195177	63.366539769077
chr19.12108_chr19_45480162_45480816_+_0.R.tl.testicle	50.0060645999174	57.2113235742017	66.3201665128139


diffExp=-4.25802787249718,-1.61685150807921,7.41933289261807,-6.99241356478297,-8.71205042338904,-9.75034886960144,-4.2159367547003,-7.20525897428433
diffExpScore=1.38089935200342
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	62.2823347574904	50.5334864202309	57.0670272145645
cerebhem	63.2394827348477	57.0993086432523	62.6429966607734
cortex	51.532533287782	55.405695787001	53.8242724514779
heart	56.8833978977059	56.1863777318279	58.8123465352508
kidney	61.3198332591779	56.2486371230622	57.5166126473683
liver	57.8698488953672	55.4673443178363	58.2754823400719
stomach	57.3839220747622	48.7835908211356	55.4861356812526
testicle	65.1722683006165	53.5657933084903	61.8309283643962
cont.diffExp=11.7488483372595,6.14017409159538,-3.87316249921893,0.69702016587798,5.07119613611565,2.40250457753091,8.60033125362659,11.6064749921262
cont.diffExpScore=1.15546896557995

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

tran.correlation=-0.104658146162324
cont.tran.correlation=-0.0505662328243486

tran.covariance=-0.000362246824930608
cont.tran.covariance=-0.000234605058967652

tran.mean=54.9975030524183
cont.tran.mean=56.8108659600366

weightedLogRatios:
wLogRatio
Lung	-0.318917797191292
cerebhem	-0.122341746976845
cortex	0.513303319560155
heart	-0.506937135706538
kidney	-0.618325040579882
liver	-0.693204265849224
stomach	-0.317539617155903
testicle	-0.535663767770369

cont.weightedLogRatios:
wLogRatio
Lung	0.841843429858526
cerebhem	0.418338175524471
cortex	-0.288314448237350
heart	0.0497462687956135
kidney	0.351582605780801
liver	0.1711768743902
stomach	0.644378813390278
testicle	0.799981788917542

varWeightedLogRatios=0.149001394478365
cont.varWeightedLogRatios=0.151549696076295

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.92709117684793	0.0881818722057972	44.5339963715327	1.74981308387730e-173	***
df.mm.trans1	-0.00766757130382803	0.0705942002197068	-0.108614748519916	0.913552982956266	   
df.mm.trans2	0.179464197183698	0.0705942002197067	2.54219463674297	0.0113258162017523	*  
df.mm.exp2	-0.0232551634248196	0.0945304434528392	-0.246007133526476	0.805780785242203	   
df.mm.exp3	-0.079670118097335	0.0945304434528392	-0.842798522754018	0.399756564822336	   
df.mm.exp4	0.141324919826089	0.0945304434528391	1.49502017195757	0.135559528515202	   
df.mm.exp5	0.108549325995149	0.0945304434528392	1.14830018806908	0.251410704991538	   
df.mm.exp6	0.233206651744514	0.0945304434528392	2.46700050508977	0.0139694792336783	*  
df.mm.exp7	0.0312710125744805	0.0945304434528392	0.330803616615651	0.740935693276789	   
df.mm.exp8	0.0192556987539102	0.0945304434528392	0.203698385943961	0.838674642664394	   
df.mm.trans1:exp2	0.0346104269491409	0.0741557808914779	0.466725945476739	0.640905545205207	   
df.mm.trans2:exp2	-0.0147988991044308	0.0741557808914779	-0.199565009315834	0.841904436891294	   
df.mm.trans1:exp3	0.287893311888043	0.0741557808914779	3.88227739533019	0.000117813131808052	***
df.mm.trans2:exp3	0.0816084272772686	0.0741557808914779	1.10049987062637	0.271660286396623	   
df.mm.trans1:exp4	-0.121397451649597	0.0741557808914779	-1.63705985143969	0.102266611228101	   
df.mm.trans2:exp4	-0.0754051371849999	0.0741557808914779	-1.01684772621234	0.309732851366742	   
df.mm.trans1:exp5	-0.0741063218565759	0.0741557808914779	-0.999333038715155	0.318131704585863	   
df.mm.trans2:exp5	-0.00142023325985097	0.0741557808914779	-0.0191520235209900	0.984727706523459	   
df.mm.trans1:exp6	-0.210697798452097	0.0741557808914779	-2.84128622096826	0.00468234125715353	** 
df.mm.trans2:exp6	-0.119370598475078	0.0741557808914779	-1.60972748233572	0.108108007006708	   
df.mm.trans1:exp7	-0.0386637808391223	0.0741557808914779	-0.521385930730123	0.602335940967758	   
df.mm.trans2:exp7	-0.0388597240901483	0.0741557808914779	-0.524028250029717	0.60049827632961	   
df.mm.trans1:exp8	-0.0371145650169244	0.0741557808914779	-0.500494561189223	0.616953916522866	   
df.mm.trans2:exp8	0.0171644372480993	0.0741557808914779	0.231464587679527	0.817051513274757	   
df.mm.trans1:probe2	0.0622591410547577	0.0507709924545913	1.22627386317967	0.220690666469656	   
df.mm.trans1:probe3	0.095769481920641	0.0507709924545913	1.8863031288249	0.0598512984487774	.  
df.mm.trans1:probe4	0.0166734218007239	0.0507709924545912	0.328404488362845	0.742747587977949	   
df.mm.trans1:probe5	-0.0496447539030283	0.0507709924545913	-0.977817283115546	0.328652176239152	   
df.mm.trans1:probe6	0.0442155217689112	0.0507709924545913	0.870881572946537	0.384249672777349	   
df.mm.trans2:probe2	-0.276355546144526	0.0507709924545913	-5.44317794046854	8.3249144608989e-08	***
df.mm.trans2:probe3	-0.238514070962403	0.0507709924545913	-4.69784141359314	3.42902524039633e-06	***
df.mm.trans2:probe4	-0.385421592190373	0.0507709924545912	-7.5913740022925	1.63670933940487e-13	***
df.mm.trans2:probe5	-0.357781787093877	0.0507709924545913	-7.04697248953466	6.31153657036917e-12	***
df.mm.trans2:probe6	-0.281505670110295	0.0507709924545913	-5.54461625626225	4.84664498700762e-08	***
df.mm.trans3:probe2	-0.201811694999157	0.0507709924545912	-3.9749409109868	8.11088612960956e-05	***
df.mm.trans3:probe3	0.238775376602424	0.0507709924545913	4.70298816427473	3.34736260071582e-06	***
df.mm.trans3:probe4	0.187139466904618	0.0507709924545912	3.68595250667973	0.000253555843034982	***
df.mm.trans3:probe5	0.0916616879809672	0.0507709924545913	1.80539484358018	0.0716328884613044	.  
df.mm.trans3:probe6	0.0557599317222872	0.0507709924545912	1.09826357584319	0.272634330007886	   
df.mm.trans3:probe7	-0.0249311034413306	0.0507709924545912	-0.491050149622909	0.623613095948874	   
df.mm.trans3:probe8	0.0914422678145645	0.0507709924545912	1.80107308117621	0.0723123984839716	.  
df.mm.trans3:probe9	0.395178705037728	0.0507709924545913	7.78355288979567	4.29255741107212e-14	***
df.mm.trans3:probe10	0.129791840968912	0.0507709924545913	2.55641725114977	0.0108791685050039	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97940815902774	0.134099344215386	29.6750754622344	4.25861887383747e-111	***
df.mm.trans1	0.14832152182765	0.107353537842555	1.3816174558232	0.167725289581165	   
df.mm.trans2	-0.076382682861182	0.107353537842555	-0.711505968002706	0.477112820331774	   
df.mm.exp2	0.0441813964549171	0.143753700826756	0.307340932447799	0.758715807384863	   
df.mm.exp3	-0.038916358446097	0.143753700826756	-0.270715523998905	0.786725004849368	   
df.mm.exp4	-0.0147615031003897	0.143753700826756	-0.102686073579277	0.918254569541434	   
df.mm.exp5	0.0837237697255774	0.143753700826756	0.582411230069663	0.560560368516244	   
df.mm.exp6	-0.00127805572973714	0.143753700826756	-0.00889059358045595	0.992910081824071	   
df.mm.exp7	-0.0890625737591194	0.143753700826756	-0.619549780262372	0.5358451726923	   
df.mm.exp8	0.0234534719320333	0.143753700826756	0.163150387065848	0.87046798614851	   
df.mm.trans1:exp2	-0.0289303978864752	0.112769680871817	-0.25654411418757	0.797639507588174	   
df.mm.trans2:exp2	0.0779743983501286	0.112769680871817	0.691448248743035	0.489614730065159	   
df.mm.trans1:exp3	-0.150548153346867	0.112769680871817	-1.33500558113659	0.18250081226849	   
df.mm.trans2:exp3	0.130962544994603	0.112769680871817	1.16132761910948	0.246079926311087	   
df.mm.trans1:exp4	-0.0759128095252111	0.112769680871817	-0.673166838270119	0.501161787433877	   
df.mm.trans2:exp4	0.120799627545998	0.112769680871817	1.07120660989817	0.284609304636716	   
df.mm.trans1:exp5	-0.0992982692581677	0.112769680871817	-0.880540482960471	0.379002701282424	   
df.mm.trans2:exp5	0.023421828182423	0.112769680871817	0.207696146706721	0.8355534082724	   
df.mm.trans1:exp6	-0.0722032741337079	0.112769680871817	-0.640272044538107	0.522298114089545	   
df.mm.trans2:exp6	0.0944362986831215	0.112769680871817	0.837426318430977	0.402765439482351	   
df.mm.trans1:exp7	0.00714890028024668	0.112769680871817	0.0633938149419143	0.9494790304244	   
df.mm.trans2:exp7	0.0538203624473598	0.112769680871817	0.477259153624248	0.633392581557032	   
df.mm.trans1:exp8	0.0219027393672694	0.112769680871817	0.194225426532561	0.846080698546356	   
df.mm.trans2:exp8	0.0348209939155289	0.112769680871817	0.308779750428746	0.757621664543229	   
df.mm.trans1:probe2	-0.0318490412792888	0.077208122520191	-0.412508946464277	0.680148704518639	   
df.mm.trans1:probe3	0.0548225137439513	0.077208122520191	0.710061479990197	0.478007269003726	   
df.mm.trans1:probe4	0.0148656247517946	0.077208122520191	0.192539648246297	0.847400107908783	   
df.mm.trans1:probe5	-0.0458329603433507	0.077208122520191	-0.593628738108023	0.553037202619085	   
df.mm.trans1:probe6	0.0711643188969993	0.077208122520191	0.921720624386233	0.357132583477743	   
df.mm.trans2:probe2	0.0783884529298943	0.077208122520191	1.01528764553748	0.310474944431629	   
df.mm.trans2:probe3	0.0361844283185557	0.077208122520191	0.468660901695841	0.639522609728248	   
df.mm.trans2:probe4	0.103393275279291	0.077208122520191	1.33915023321869	0.181149024872363	   
df.mm.trans2:probe5	0.13465486814428	0.077208122520191	1.74405054479943	0.0817838188058534	.  
df.mm.trans2:probe6	-0.0388492199822050	0.077208122520191	-0.503175297029732	0.615069462271813	   
df.mm.trans3:probe2	0.0202103441005512	0.077208122520191	0.261764480741853	0.793614155589596	   
df.mm.trans3:probe3	-0.00124140846224689	0.077208122520191	-0.0160787287881821	0.987178195307103	   
df.mm.trans3:probe4	-0.0758895804683364	0.077208122520191	-0.982922236562484	0.326135780254955	   
df.mm.trans3:probe5	0.0104236422320635	0.077208122520191	0.135007067803488	0.892662296488636	   
df.mm.trans3:probe6	-0.0423999131683208	0.077208122520191	-0.549163893439225	0.583145723328049	   
df.mm.trans3:probe7	0.0103775339570597	0.077208122520191	0.134409873188483	0.893134233013104	   
df.mm.trans3:probe8	-0.046310689572736	0.077208122520191	-0.599816289544213	0.548908797806324	   
df.mm.trans3:probe9	-0.0317964411398614	0.077208122520191	-0.411827669187865	0.680647681997102	   
df.mm.trans3:probe10	0.0854214628978142	0.077208122520191	1.10637922681613	0.269110879904595