chr9.25173_chr9_119775471_119777073_+_1.R 

fitVsDatCorrelation=0.855533917013232
cont.fitVsDatCorrelation=0.237038357664462

fstatistic=7818.73745178633,64,968
cont.fstatistic=2209.84861817848,64,968

residuals=-0.813477959403197,-0.113371733546402,-0.00234721955841983,0.0994463450084478,0.832146448106916
cont.residuals=-0.856539866657684,-0.256281500477932,-0.0551170216189822,0.19300032016717,1.38118303834119

predictedValues:
Include	Exclude	Both
chr9.25173_chr9_119775471_119777073_+_1.R.tl.Lung	66.4188441101313	122.763380026555	67.9442918038863
chr9.25173_chr9_119775471_119777073_+_1.R.tl.cerebhem	70.3035733355115	123.433020339811	71.5090216981641
chr9.25173_chr9_119775471_119777073_+_1.R.tl.cortex	63.1712452805859	104.443622639490	70.7509999721091
chr9.25173_chr9_119775471_119777073_+_1.R.tl.heart	63.0515706638425	101.364306947458	89.361493260438
chr9.25173_chr9_119775471_119777073_+_1.R.tl.kidney	68.0612037705011	120.533396201821	66.837829297164
chr9.25173_chr9_119775471_119777073_+_1.R.tl.liver	65.6332194339583	118.245030358317	63.9291613712876
chr9.25173_chr9_119775471_119777073_+_1.R.tl.stomach	70.5347827379416	108.671443781767	77.908958343213
chr9.25173_chr9_119775471_119777073_+_1.R.tl.testicle	63.6693560226149	102.460065547557	69.7421716666111


diffExp=-56.344535916424,-53.1294470042992,-41.2723773589039,-38.3127362836152,-52.4721924313197,-52.6118109243588,-38.1366610438252,-38.7907095249426
diffExpScore=0.997312337099235
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
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	80.17860153287	77.2991490648769	81.4418939599372
cerebhem	78.8549281747612	81.0417307492914	80.3383589773178
cortex	80.1774565946049	73.8522851175353	78.6112787523026
heart	79.4678709821436	82.0162856904991	73.5616968874552
kidney	82.0645701772466	79.47032173227	76.2115096344241
liver	78.4191356738457	81.360054729462	71.8614292816532
stomach	89.7193116865593	65.896508706246	80.1487100629554
testicle	80.9493977065715	73.9458916679686	75.0323745123271
cont.diffExp=2.87945246799316,-2.18680257453018,6.32517147706953,-2.54841470835555,2.59424844497661,-2.94091905561635,23.8228029803132,7.00350603860284
cont.diffExpScore=1.39923933024859

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=0,0,0,0,0,0,1,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,0,0,1,0
cont.diffExp1.2Score=0.5

tran.correlation=0.629512039142978
cont.tran.correlation=-0.859711213501037

tran.covariance=0.00248110625446696
cont.tran.covariance=-0.00277028129027928

tran.mean=89.5473788248664
cont.tran.mean=79.044593749172

weightedLogRatios:
wLogRatio
Lung	-2.76616719166844
cerebhem	-2.55222665913136
cortex	-2.21092859343775
heart	-2.08011883793905
kidney	-2.57536239815462
liver	-2.63634330502634
stomach	-1.93299568146594
testicle	-2.08938479311488

cont.weightedLogRatios:
wLogRatio
Lung	0.159679749151577
cerebhem	-0.119847496579084
cortex	0.356900621231781
heart	-0.138606327433182
kidney	0.141065238195720
liver	-0.161273580249980
stomach	1.34006274521722
testicle	0.393505550570767

varWeightedLogRatios=0.0972320296347637
cont.varWeightedLogRatios=0.24244704045951

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.90664427882108	0.0897114895195128	54.6935995054887	2.40556327354301e-298	***
df.mm.trans1	-0.908041184844369	0.0755315740585605	-12.0220079637206	3.96429124479455e-31	***
df.mm.trans2	-0.0582745524812348	0.0672047105601957	-0.867120057440585	0.38609112346031	   
df.mm.exp2	0.011146226707597	0.0849623155533731	0.131190241638306	0.895652075113318	   
df.mm.exp3	-0.252221467680085	0.084962315553373	-2.96862751488499	0.00306502880211136	** 
df.mm.exp4	-0.517567309841438	0.0849623155533731	-6.09172792043672	1.60969855121674e-09	***
df.mm.exp5	0.0225135453507955	0.0849623155533731	0.264982718563650	0.791079188734996	   
df.mm.exp6	0.0115138999284844	0.0849623155533731	0.135517727518283	0.892230723729159	   
df.mm.exp7	-0.1986573758258	0.0849623155533731	-2.33818222269383	0.0195806354573045	*  
df.mm.exp8	-0.24918012711148	0.084962315553373	-2.93283116742441	0.00343801446055795	** 
df.mm.trans1:exp2	0.0456955875663518	0.0747568707686161	0.611256023647467	0.541173611368926	   
df.mm.trans2:exp2	-0.00570632617598855	0.0535902431914922	-0.106480691934879	0.91522303634157	   
df.mm.trans1:exp3	0.202089872501635	0.0747568707686161	2.70329496705572	0.00698586450177186	** 
df.mm.trans2:exp3	0.0906101342443356	0.0535902431914922	1.69079535467980	0.0911978140403542	.  
df.mm.trans1:exp4	0.465539470284224	0.0747568707686161	6.2273803798602	7.05987957392403e-10	***
df.mm.trans2:exp4	0.326029573553235	0.0535902431914922	6.08374872247256	1.68882122301357e-09	***
df.mm.trans1:exp5	0.00191299740782266	0.0747568707686161	0.0255895864574599	0.979589966321769	   
df.mm.trans2:exp5	-0.0408454469024297	0.0535902431914922	-0.762180659574133	0.446137855240504	   
df.mm.trans1:exp6	-0.0234127514011877	0.0747568707686161	-0.313185278630158	0.75420736782466	   
df.mm.trans2:exp6	-0.0490136629950381	0.0535902431914923	-0.914600495838379	0.360629158692096	   
df.mm.trans1:exp7	0.258782523086366	0.0747568707686161	3.46165536927485	0.00056033068640074	***
df.mm.trans2:exp7	0.0767276657527252	0.0535902431914922	1.43174692226264	0.152539054886022	   
df.mm.trans1:exp8	0.206902693731761	0.0747568707686161	2.76767461779073	0.00575347869536493	** 
df.mm.trans2:exp8	0.0683944823730389	0.0535902431914922	1.27624877776067	0.202173708483985	   
df.mm.trans1:probe2	0.170157896589839	0.0570965031692025	2.98018069662839	0.00295278055977842	** 
df.mm.trans1:probe3	0.154239629534153	0.0570965031692025	2.70138486549818	0.00702580890769436	** 
df.mm.trans1:probe4	0.291481857553732	0.0570965031692025	5.10507371510898	3.98029188689913e-07	***
df.mm.trans1:probe5	0.489677565216614	0.0570965031692025	8.57631445073757	3.84218944223666e-17	***
df.mm.trans1:probe6	-0.109789501160636	0.0570965031692025	-1.92287609690002	0.0547887443886056	.  
df.mm.trans1:probe7	0.0821053725522902	0.0570965031692025	1.43801052595069	0.150754265863272	   
df.mm.trans1:probe8	0.0582154565384329	0.0570965031692025	1.01959758141255	0.308174105176589	   
df.mm.trans1:probe9	0.194974138428188	0.0570965031692025	3.41481750380391	0.000664744549651578	***
df.mm.trans1:probe10	0.153366470992629	0.0570965031692025	2.68609218568316	0.00735309126632864	** 
df.mm.trans1:probe11	0.314014088628902	0.0570965031692025	5.49970788400715	4.86836022937853e-08	***
df.mm.trans1:probe12	1.08252689374525	0.0570965031692025	18.9596005649809	2.04949930200593e-68	***
df.mm.trans1:probe13	0.93555234850614	0.0570965031692025	16.3854578928184	1.92085901761472e-53	***
df.mm.trans1:probe14	0.250768043915307	0.0570965031692025	4.39200353780282	1.24685612430955e-05	***
df.mm.trans1:probe15	0.979392633695807	0.0570965031692025	17.1532857413952	8.61835370746896e-58	***
df.mm.trans1:probe16	0.479893239567706	0.0570965031692025	8.40494974176558	1.51566673565521e-16	***
df.mm.trans2:probe2	-0.311617505889001	0.0570965031692025	-5.45773363677876	6.12755542627513e-08	***
df.mm.trans2:probe3	-0.252264081469254	0.0570965031692025	-4.41820544984484	1.1075340338815e-05	***
df.mm.trans2:probe4	-0.197877961773249	0.0570965031692025	-3.46567566820771	0.000552121218415715	***
df.mm.trans2:probe5	-0.0405738965097768	0.0570965031692025	-0.710619639692087	0.477491140954429	   
df.mm.trans2:probe6	-0.226662564877122	0.0570965031692025	-3.96981517774248	7.72487927300645e-05	***
df.mm.trans3:probe2	0.170157896589839	0.0570965031692025	2.98018069662839	0.00295278055977842	** 
df.mm.trans3:probe3	0.154239629534153	0.0570965031692025	2.70138486549818	0.00702580890769436	** 
df.mm.trans3:probe4	0.489677565216615	0.0570965031692025	8.57631445073757	3.84218944223666e-17	***
df.mm.trans3:probe5	-0.109789501160636	0.0570965031692025	-1.92287609690002	0.0547887443886056	.  
df.mm.trans3:probe6	0.0821053725522902	0.0570965031692025	1.43801052595069	0.150754265863272	   
df.mm.trans3:probe7	0.058215456538433	0.0570965031692025	1.01959758141255	0.308174105176589	   
df.mm.trans3:probe8	0.194974138428188	0.0570965031692025	3.41481750380391	0.000664744549651578	***
df.mm.trans3:probe9	0.153366470992629	0.0570965031692025	2.68609218568316	0.00735309126632864	** 
df.mm.trans3:probe10	0.394468212986667	0.0570965031692025	6.90879810656148	8.8492221004499e-12	***
df.mm.trans3:probe11	0.368846248148172	0.0570965031692025	6.46004969963074	1.65496766559315e-10	***
df.mm.trans3:probe12	1.01077521350446	0.0570965031692025	17.7029267538344	5.73627839449941e-61	***
df.mm.trans3:probe13	0.63486148462709	0.0570965031692025	11.1190957307090	4.09963200453951e-27	***
df.mm.trans3:probe14	0.293756573161494	0.0570965031692025	5.14491355610626	3.23977511158885e-07	***
df.mm.trans3:probe15	-0.00467974217275904	0.0570965031692025	-0.0819619751299106	0.934693894887716	   
df.mm.trans3:probe16	0.167405921958918	0.0570965031692025	2.93198204210194	0.00344734427952765	** 
df.mm.trans3:probe17	0.195197528414145	0.0570965031692025	3.41873000235562	0.00065537334662234	***
df.mm.trans3:probe18	0.235321989260648	0.0570965031692025	4.12147813261494	4.08694285341835e-05	***
df.mm.trans3:probe19	0.0853257825691998	0.0570965031692025	1.49441345499463	0.135393482372913	   
df.mm.trans3:probe20	0.815625730318897	0.0570965031692025	14.2850382255781	3.78371310200657e-42	***
df.mm.trans3:probe21	0.455699413145105	0.0570965031692025	7.98121404728874	4.08224786452236e-15	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.48475711789198	0.168334683016511	26.6419078797452	9.67390618217646e-118	***
df.mm.trans1	-0.0181728071709400	0.141727482677906	-0.128223593812359	0.897998662507643	   
df.mm.trans2	-0.144111289599202	0.126102951917951	-1.14280663067244	0.253401452802447	   
df.mm.exp2	0.0442770214204906	0.159423330652815	0.277732382325616	0.781277105767297	   
df.mm.exp3	-0.0102556523717355	0.159423330652815	-0.0643296833013094	0.948721000273479	   
df.mm.exp4	0.152096363009925	0.159423330652815	0.954040806870068	0.340301160949172	   
df.mm.exp5	0.117327718283594	0.159423330652815	0.735950740730071	0.461938967954962	   
df.mm.exp6	0.154162923815072	0.159423330652815	0.967003531941015	0.333783798625398	   
df.mm.exp7	-0.0311620899757534	0.159423330652815	-0.195467563299231	0.845067949895947	   
df.mm.exp8	0.0471883869369797	0.159423330652815	0.295994235873447	0.767297969283	   
df.mm.trans1:exp2	-0.0609238748001584	0.140273829043983	-0.434321036328562	0.664152121338548	   
df.mm.trans2:exp2	0.00300424770411158	0.100556758657490	0.0298761390504289	0.976171994014128	   
df.mm.trans1:exp3	0.0102413724214976	0.140273829043983	0.0730098585837162	0.941813355207848	   
df.mm.trans2:exp3	-0.0353603437576762	0.100556758657490	-0.351645620142933	0.725180577272889	   
df.mm.trans1:exp4	-0.161000227091757	0.140273829043983	-1.14775669979946	0.251352591699835	   
df.mm.trans2:exp4	-0.0928614767999773	0.100556758657490	-0.923473250726751	0.355990761478833	   
df.mm.trans1:exp5	-0.0940780051692792	0.140273829043983	-0.670673965417888	0.502588232901783	   
df.mm.trans2:exp5	-0.0896270251816276	0.100556758657490	-0.891307818372604	0.372985542135609	   
df.mm.trans1:exp6	-0.176351614234968	0.140273829043983	-1.25719541155231	0.208986125668908	   
df.mm.trans2:exp6	-0.102961446708768	0.100556758657490	-1.02391373870223	0.306131827040888	   
df.mm.trans1:exp7	0.143591462336687	0.140273829043983	1.02365112092052	0.306255832705509	   
df.mm.trans2:exp7	-0.128435395896559	0.100556758657490	-1.27724279910441	0.201822804235760	   
df.mm.trans1:exp8	-0.037620812621861	0.140273829043983	-0.268195520705896	0.788605963164173	   
df.mm.trans2:exp8	-0.0915377021778314	0.100556758657490	-0.910308798731486	0.362886290334175	   
df.mm.trans1:probe2	-0.233562444197568	0.107135906602558	-2.18005756990524	0.0294934977785075	*  
df.mm.trans1:probe3	-0.10550161259033	0.107135906602558	-0.984745599640176	0.324995113293407	   
df.mm.trans1:probe4	-0.208527117587488	0.107135906602558	-1.94637936243972	0.0518982155787496	.  
df.mm.trans1:probe5	-0.303793421551381	0.107135906602558	-2.83558921733274	0.00466941394889714	** 
df.mm.trans1:probe6	-0.0965003775288933	0.107135906602558	-0.900728622075142	0.367956660641159	   
df.mm.trans1:probe7	-0.132255443119712	0.107135906602558	-1.23446421758804	0.217329560206040	   
df.mm.trans1:probe8	-0.124778393761938	0.107135906602558	-1.16467389616469	0.244437963143505	   
df.mm.trans1:probe9	-0.16623855278083	0.107135906602558	-1.55166048482256	0.121070345774446	   
df.mm.trans1:probe10	-0.205094489258618	0.107135906602558	-1.91433941955106	0.0558713767062503	.  
df.mm.trans1:probe11	-0.079227242975684	0.107135906602558	-0.739502240547543	0.459781370342305	   
df.mm.trans1:probe12	-0.273748969957905	0.107135906602558	-2.55515614362075	0.0107659574780074	*  
df.mm.trans1:probe13	-0.147307822468376	0.107135906602558	-1.37496220585357	0.169461317219347	   
df.mm.trans1:probe14	-0.0804579730633814	0.107135906602558	-0.750989799916999	0.452841315855987	   
df.mm.trans1:probe15	-0.00986274228809315	0.107135906602558	-0.0920582333305019	0.926670819856144	   
df.mm.trans1:probe16	-0.138317463990721	0.107135906602558	-1.29104674965637	0.196995699148666	   
df.mm.trans2:probe2	0.0179036643815359	0.107135906602558	0.167111708383195	0.867317055754167	   
df.mm.trans2:probe3	0.0742977306317585	0.107135906602558	0.693490473808942	0.488168167258344	   
df.mm.trans2:probe4	0.0253245189762906	0.107135906602558	0.236377511325283	0.813189752257596	   
df.mm.trans2:probe5	0.104463528313798	0.107135906602558	0.975056184490284	0.329775988642005	   
df.mm.trans2:probe6	-0.0319872286643508	0.107135906602558	-0.298566836074984	0.765334651512354	   
df.mm.trans3:probe2	0.122927562474579	0.107135906602558	1.14739835012180	0.251500524821888	   
df.mm.trans3:probe3	0.0831669572085223	0.107135906602558	0.776275292251426	0.437776085610137	   
df.mm.trans3:probe4	0.151765112898799	0.107135906602558	1.41656628213174	0.156931493602265	   
df.mm.trans3:probe5	0.0505367156636275	0.107135906602558	0.471706613274889	0.637242518574272	   
df.mm.trans3:probe6	0.0761095634710663	0.107135906602558	0.71040201072279	0.477625987078827	   
df.mm.trans3:probe7	0.00214337545710116	0.107135906602558	0.0200061354318159	0.984042601039228	   
df.mm.trans3:probe8	0.0124406843788024	0.107135906602558	0.116120587143147	0.907581057976103	   
df.mm.trans3:probe9	0.132823173954495	0.107135906602558	1.23976338247857	0.215363386521403	   
df.mm.trans3:probe10	0.0223164608304749	0.107135906602558	0.208300480559353	0.835038175347617	   
df.mm.trans3:probe11	0.140631090709658	0.107135906602558	1.31264199995392	0.189614735255427	   
df.mm.trans3:probe12	0.0665230131494093	0.107135906602558	0.620921736315628	0.534797290635751	   
df.mm.trans3:probe13	0.122716041530665	0.107135906602558	1.14542402656754	0.252316651463401	   
df.mm.trans3:probe14	0.0270356035194589	0.107135906602558	0.252348669804539	0.800825142993904	   
df.mm.trans3:probe15	0.0405121120459591	0.107135906602558	0.378137576193263	0.705411300896081	   
df.mm.trans3:probe16	0.0681574548253682	0.107135906602558	0.636177514959684	0.524811113600085	   
df.mm.trans3:probe17	0.126278058121758	0.107135906602558	1.17867167158263	0.238818585145449	   
df.mm.trans3:probe18	-0.0419015421029	0.107135906602558	-0.391106431369849	0.695804633872838	   
df.mm.trans3:probe19	0.132275404063657	0.107135906602558	1.23465053181805	0.217260212590763	   
df.mm.trans3:probe20	0.290323112050181	0.107135906602558	2.70985817226704	0.00685016567707866	** 
df.mm.trans3:probe21	-0.00102742088553634	0.107135906602558	-0.00958988371048896	0.992350473104549