chr7.21812_chr7_4440701_4444477_+_2.R 

fitVsDatCorrelation=0.908118052018213
cont.fitVsDatCorrelation=0.249958793257477

fstatistic=7383.95496464226,66,1014
cont.fstatistic=1368.35038586727,66,1014

residuals=-0.877843095385071,-0.124948769361712,0.00473576408003454,0.116513743603330,1.10748934448166
cont.residuals=-1.09276969658153,-0.374402222374682,-0.118892516388789,0.334063879938046,1.96152793152954

predictedValues:
Include	Exclude	Both
chr7.21812_chr7_4440701_4444477_+_2.R.tl.Lung	126.334418070119	176.025812675698	121.615080966408
chr7.21812_chr7_4440701_4444477_+_2.R.tl.cerebhem	169.958476741905	236.558482917054	135.425701512533
chr7.21812_chr7_4440701_4444477_+_2.R.tl.cortex	181.582378095447	137.742698077466	238.516780466166
chr7.21812_chr7_4440701_4444477_+_2.R.tl.heart	132.687833021891	158.767658281271	148.948291945473
chr7.21812_chr7_4440701_4444477_+_2.R.tl.kidney	127.908515465745	167.578499333905	125.450499493922
chr7.21812_chr7_4440701_4444477_+_2.R.tl.liver	109.377702805334	173.680384792670	104.351137939088
chr7.21812_chr7_4440701_4444477_+_2.R.tl.stomach	126.90120749939	156.527915659004	143.019058454726
chr7.21812_chr7_4440701_4444477_+_2.R.tl.testicle	124.497106133713	168.498160032613	129.63413931666


diffExp=-49.6913946055786,-66.6000061751489,43.8396800179805,-26.0798252593794,-39.6699838681599,-64.302681987335,-29.6267081596140,-44.0010538989
diffExpScore=1.31277285982142
diffExp1.5=0,0,0,0,0,-1,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,-1,0,0
diffExp1.4Score=0.5
diffExp1.3=-1,-1,1,0,-1,-1,0,-1
diffExp1.3Score=1.2
diffExp1.2=-1,-1,1,0,-1,-1,-1,-1
diffExp1.2Score=1.16666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	150.204602289925	134.502364402117	134.100579740892
cerebhem	166.420936740917	155.660411763002	142.130432244183
cortex	145.494744658569	152.237916858110	167.083773070417
heart	151.60127849907	171.793755806414	148.090169839842
kidney	158.615143678058	129.998138704751	165.054679831653
liver	133.801255077326	127.307583491649	155.130678489114
stomach	156.801480957129	145.713561153827	131.084419230455
testicle	151.546638058861	133.361446210264	147.912039229334
cont.diffExp=15.7022378878082,10.7605249779146,-6.74317219954145,-20.1924773073446,28.6170049733078,6.49367158567647,11.0879198033022,18.1851918485973
cont.diffExpScore=1.81452110100463

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

tran.correlation=0.161669067951101
cont.tran.correlation=0.331508776001633

tran.covariance=0.00209486295036439
cont.tran.covariance=0.00239631836624799

tran.mean=154.664203100202
cont.tran.mean=147.816328646874

weightedLogRatios:
wLogRatio
Lung	-1.66007676218779
cerebhem	-1.75268818876607
cortex	1.39916976089597
heart	-0.893215196352932
kidney	-1.3470047529455
liver	-2.27783561201934
stomach	-1.03828395542853
testicle	-1.50582842295180

cont.weightedLogRatios:
wLogRatio
Lung	0.547311916625112
cerebhem	0.339638693557905
cortex	-0.226649366060616
heart	-0.635678983126282
kidney	0.988237667040564
liver	0.242353501768836
stomach	0.368031776986572
testicle	0.633652199142478

varWeightedLogRatios=1.23365789901126
cont.varWeightedLogRatios=0.257996521154118

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.33632389264692	0.106757651919086	49.9853996104319	8.01405291182639e-276	***
df.mm.trans1	-0.700321060217108	0.091263571497515	-7.67361005849061	3.91694528033793e-14	***
df.mm.trans2	-0.0842080087701116	0.0797128219377586	-1.05639226818319	0.291040651839721	   
df.mm.exp2	0.484624286616145	0.100450353335013	4.82451549971029	1.61874991846844e-06	***
df.mm.exp3	-0.556044970497982	0.100450353335013	-5.5355203046774	3.95116345047908e-08	***
df.mm.exp4	-0.256860264607464	0.100450353335013	-2.55708672074858	0.0106999048311527	*  
df.mm.exp5	-0.0678462416924012	0.100450353335013	-0.675420637557408	0.499562548341088	   
df.mm.exp6	-0.0044399086077586	0.100450353335013	-0.0442000297694427	0.964753660632838	   
df.mm.exp7	-0.275036805310067	0.100450353335013	-2.73803721120611	0.00628894053195398	** 
df.mm.exp8	-0.122211035059900	0.100450353335013	-1.21663121136382	0.224027669684614	   
df.mm.trans1:exp2	-0.188002636544713	0.0916378597591746	-2.05158257775537	0.0404666787488297	*  
df.mm.trans2:exp2	-0.189059471406827	0.0623171535310367	-3.03382713577679	0.00247623369530704	** 
df.mm.trans1:exp3	0.918821892352973	0.0916378597591746	10.0266625035509	1.26453719658670e-22	***
df.mm.trans2:exp3	0.310801761399665	0.0623171535310367	4.98741909392367	7.19544568951617e-07	***
df.mm.trans1:exp4	0.305927011204453	0.0916378597591746	3.33843470382692	0.000873164087184983	***
df.mm.trans2:exp4	0.153671482247854	0.0623171535310367	2.46595798332347	0.0138293318861948	*  
df.mm.trans1:exp5	0.0802290244674906	0.0916378597591746	0.87550085388652	0.381508780548402	   
df.mm.trans2:exp5	0.0186674886995608	0.0623171535310367	0.299556183840515	0.764577100347381	   
df.mm.trans1:exp6	-0.139685538341194	0.0916378597591746	-1.52432126534044	0.127740286399303	   
df.mm.trans2:exp6	-0.00897399751230265	0.0623171535310367	-0.144005253831647	0.885524934824097	   
df.mm.trans1:exp7	0.279513192662293	0.0916378597591746	3.05019337418898	0.00234636052760354	** 
df.mm.trans2:exp7	0.157640526986998	0.0623171535310367	2.52964903007783	0.0115682057008081	*  
df.mm.trans1:exp8	0.107561004104132	0.0916378597591746	1.17376163505786	0.240766127840454	   
df.mm.trans2:exp8	0.0785052178906431	0.0623171535310367	1.25976899525014	0.208042576409163	   
df.mm.trans1:probe2	-0.395715981925010	0.0682285453141761	-5.7998595764109	8.86175707076726e-09	***
df.mm.trans1:probe3	-0.314011202937215	0.0682285453141761	-4.60234351313322	4.70677747810802e-06	***
df.mm.trans1:probe4	0.43826202546444	0.0682285453141761	6.42344085523659	2.04482142168490e-10	***
df.mm.trans1:probe5	0.210435595408291	0.0682285453141761	3.08427498254997	0.00209572913334153	** 
df.mm.trans1:probe6	0.214818070882869	0.0682285453141762	3.14850726911564	0.00168902297599766	** 
df.mm.trans1:probe7	0.284668171097064	0.0682285453141762	4.17227378637834	3.27415630507301e-05	***
df.mm.trans1:probe8	0.283423968008858	0.0682285453141761	4.1540379719919	3.54170865162831e-05	***
df.mm.trans1:probe9	0.30592629643648	0.0682285453141762	4.48384609444276	8.16703130420113e-06	***
df.mm.trans1:probe10	0.515402903616528	0.0682285453141762	7.55406554900475	9.39927889082433e-14	***
df.mm.trans1:probe11	1.15469856960327	0.0682285453141762	16.9239804877293	8.8410097398936e-57	***
df.mm.trans1:probe12	0.917355694348746	0.0682285453141761	13.4453356747464	4.82302166456211e-38	***
df.mm.trans1:probe13	1.15798017909289	0.0682285453141761	16.9720777976530	4.67389323953206e-57	***
df.mm.trans1:probe14	1.40051117357156	0.0682285453141761	20.5267629131258	1.40568688860238e-78	***
df.mm.trans1:probe15	0.923441197565177	0.0682285453141761	13.5345285952229	1.72933762511514e-38	***
df.mm.trans1:probe16	1.49649666199751	0.0682285453141762	21.9335859368905	1.41316846978199e-87	***
df.mm.trans1:probe17	-0.154007699800174	0.0682285453141761	-2.25723264494363	0.0242056495887182	*  
df.mm.trans1:probe18	-0.345321976572913	0.0682285453141761	-5.06125368762865	4.94379621329098e-07	***
df.mm.trans1:probe19	-0.176526180179318	0.0682285453141761	-2.58727750044292	0.00981196993645494	** 
df.mm.trans1:probe20	0.127186961040241	0.0682285453141761	1.86413121450230	0.0625920885105985	.  
df.mm.trans1:probe21	0.0705213652462789	0.0682285453141761	1.03360499511670	0.301567309487803	   
df.mm.trans1:probe22	-0.201288652033845	0.0682285453141762	-2.95021169082469	0.00324854329285062	** 
df.mm.trans2:probe2	-0.176754850152599	0.0682285453141761	-2.59062902980982	0.00971757089406483	** 
df.mm.trans2:probe3	-0.325300267264601	0.0682285453141761	-4.76780306199805	2.13470711322969e-06	***
df.mm.trans2:probe4	-0.339187900807083	0.0682285453141762	-4.97134885765487	7.80285367792961e-07	***
df.mm.trans2:probe5	-0.568333946544327	0.0682285453141761	-8.32985583859783	2.60364037811072e-16	***
df.mm.trans2:probe6	-0.464583478469021	0.0682285453141761	-6.80922444307914	1.67804343644463e-11	***
df.mm.trans3:probe2	0.0312520530115876	0.0682285453141762	0.458049528502761	0.647014998874491	   
df.mm.trans3:probe3	0.0160190516466761	0.0682285453141761	0.234785185187699	0.814422868207125	   
df.mm.trans3:probe4	0.177569540766953	0.0682285453141762	2.60256964221189	0.00938780688304383	** 
df.mm.trans3:probe5	0.0101700481462521	0.0682285453141761	0.149058551657835	0.881537070426395	   
df.mm.trans3:probe6	-0.0844558749151824	0.0682285453141761	-1.2378378364405	0.216062728429431	   
df.mm.trans3:probe7	0.780462732734034	0.0682285453141762	11.4389472784476	1.38788205310259e-28	***
df.mm.trans3:probe8	-0.0734547822453185	0.0682285453141762	-1.07659897931396	0.281915532473586	   
df.mm.trans3:probe9	0.829935077432871	0.0682285453141762	12.1640447353995	7.11232626776862e-32	***
df.mm.trans3:probe10	-0.170638019217630	0.0682285453141761	-2.50097695080384	0.0125419417728716	*  
df.mm.trans3:probe11	0.466398747493349	0.0682285453141762	6.83583015504279	1.40565057684858e-11	***
df.mm.trans3:probe12	0.150247379340453	0.0682285453141762	2.20211904927183	0.0278814060069591	*  
df.mm.trans3:probe13	0.169335011197767	0.0682285453141762	2.48187925476089	0.0132302012294534	*  
df.mm.trans3:probe14	0.332925102042274	0.0682285453141762	4.87955738333909	1.23413053377735e-06	***
df.mm.trans3:probe15	0.807110419453722	0.0682285453141762	11.8295123505444	2.44762039561420e-30	***
df.mm.trans3:probe16	-0.0721329111660331	0.0682285453141761	-1.05722481453882	0.290660798301056	   
df.mm.trans3:probe17	0.863381132169388	0.0682285453141762	12.6542509179072	3.49860398344255e-34	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.98534407139307	0.246871926396691	20.1940501869065	1.74230500560826e-76	***
df.mm.trans1	0.057281358701439	0.211042611938580	0.271420819593105	0.786122637270208	   
df.mm.trans2	-0.0904146086073414	0.184332060105686	-0.490498552207916	0.623887274432705	   
df.mm.exp2	0.190461911549439	0.232286602311543	0.819943594051934	0.412440954015625	   
df.mm.exp3	-0.127900859946731	0.232286602311543	-0.55061660325631	0.582017805331304	   
df.mm.exp4	0.154737181254785	0.232286602311543	0.666147680128584	0.50546822998817	   
df.mm.exp5	-0.187265935660527	0.232286602311543	-0.806184832861629	0.420325308575244	   
df.mm.exp6	-0.316296289794817	0.232286602311543	-1.3616639386313	0.173606420560761	   
df.mm.exp7	0.145791755683653	0.232286602311543	0.627637385164887	0.530382823132993	   
df.mm.exp8	-0.0976513097558272	0.232286602311543	-0.420391485277559	0.67428855786944	   
df.mm.trans1:exp2	-0.0879399492555799	0.21190813551017	-0.414990906525906	0.678236347103276	   
df.mm.trans2:exp2	-0.0443669028352143	0.144105415051893	-0.307878110057403	0.758238311070369	   
df.mm.trans1:exp3	0.0960424467821991	0.21190813551017	0.453226802977510	0.650482401384444	   
df.mm.trans2:exp3	0.251763621480048	0.144105415051893	1.74707953472385	0.080926280909063	.  
df.mm.trans1:exp4	-0.145481654649572	0.21190813551017	-0.68653171007014	0.492534833484313	   
df.mm.trans2:exp4	0.0899757038360341	0.144105415051893	0.624374204145165	0.532522312846383	   
df.mm.trans1:exp5	0.241748343832559	0.21190813551017	1.14081671876612	0.254215565093908	   
df.mm.trans2:exp5	0.153204290272567	0.144105415051893	1.06314041160354	0.287971397419396	   
df.mm.trans1:exp6	0.200653437755352	0.21190813551017	0.946888788730445	0.343921041251844	   
df.mm.trans2:exp6	0.261320587310427	0.144105415051893	1.81339880403748	0.0700658693715046	.  
df.mm.trans1:exp7	-0.102809582870971	0.21190813551017	-0.485161094091346	0.627666875197609	   
df.mm.trans2:exp7	-0.0657307490320292	0.144105415051893	-0.456129625721278	0.648394440551368	   
df.mm.trans1:exp8	0.106546349365642	0.21190813551017	0.50279499231651	0.615217597477477	   
df.mm.trans2:exp8	0.089132614462383	0.144105415051893	0.618523699683917	0.536369091485805	   
df.mm.trans1:probe2	-0.109176631176185	0.157775223734977	-0.691975765216312	0.489110962647325	   
df.mm.trans1:probe3	0.150752708085103	0.157775223734977	0.955490377490007	0.339557501087181	   
df.mm.trans1:probe4	0.0120476367019239	0.157775223734977	0.076359496863468	0.939148155259789	   
df.mm.trans1:probe5	0.036937330401779	0.157775223734977	0.234113630311338	0.814944023568271	   
df.mm.trans1:probe6	-0.0418734615507463	0.157775223734977	-0.265399475022031	0.790755685580968	   
df.mm.trans1:probe7	-0.255467635124373	0.157775223734977	-1.61918727843793	0.105717931071221	   
df.mm.trans1:probe8	-0.151902462415018	0.157775223734977	-0.962777670784208	0.335888644025048	   
df.mm.trans1:probe9	-0.136154822313265	0.157775223734977	-0.862967068530172	0.388359630655651	   
df.mm.trans1:probe10	-0.136963392539982	0.157775223734977	-0.868091892362307	0.385549428644031	   
df.mm.trans1:probe11	0.0615749078277527	0.157775223734977	0.390269817846580	0.696419006756108	   
df.mm.trans1:probe12	-0.275163970970423	0.157775223734977	-1.7440252306828	0.0814577141259839	.  
df.mm.trans1:probe13	0.000452624661793592	0.157775223734977	0.00286879429531908	0.997711600739177	   
df.mm.trans1:probe14	-0.291225472784565	0.157775223734977	-1.84582512951306	0.0652089516092825	.  
df.mm.trans1:probe15	0.0875677368625671	0.157775223734977	0.555015767302344	0.57900628345824	   
df.mm.trans1:probe16	0.125304246527258	0.157775223734977	0.794194700289176	0.427267916980389	   
df.mm.trans1:probe17	0.0963653372659142	0.157775223734977	0.61077610910433	0.541484688547777	   
df.mm.trans1:probe18	-0.159530814778198	0.157775223734977	-1.01112716560726	0.312196780177557	   
df.mm.trans1:probe19	-0.109037836885164	0.157775223734977	-0.691096068849949	0.489663349456808	   
df.mm.trans1:probe20	-0.0214750123406111	0.157775223734977	-0.136111436461556	0.89176020334721	   
df.mm.trans1:probe21	-0.0551970411635188	0.157775223734977	-0.349846064907099	0.726526874727506	   
df.mm.trans1:probe22	-0.0222889303697596	0.157775223734977	-0.141270155364821	0.88768458562786	   
df.mm.trans2:probe2	0.204522444873965	0.157775223734977	1.29629000062464	0.195170535667893	   
df.mm.trans2:probe3	0.113917675506641	0.157775223734977	0.722025124160146	0.470445498144004	   
df.mm.trans2:probe4	-0.0346533227079275	0.157775223734977	-0.219637290872339	0.826197852269072	   
df.mm.trans2:probe5	-0.0476619504266931	0.157775223734977	-0.302087674467528	0.762647176300741	   
df.mm.trans2:probe6	-0.083121595356372	0.157775223734977	-0.526835541022561	0.598423027741167	   
df.mm.trans3:probe2	-0.0657213468377656	0.157775223734977	-0.416550490514031	0.677095386406042	   
df.mm.trans3:probe3	-0.0360560892168058	0.157775223734977	-0.228528208442734	0.819281707159687	   
df.mm.trans3:probe4	0.0960204570381974	0.157775223734977	0.608590213121725	0.542932396815602	   
df.mm.trans3:probe5	-0.123843994500972	0.157775223734977	-0.784939431992182	0.432672428600594	   
df.mm.trans3:probe6	0.118077192715379	0.157775223734977	0.748388688161324	0.454399288227278	   
df.mm.trans3:probe7	-0.141265793473720	0.157775223734977	-0.895361072097175	0.370806592036023	   
df.mm.trans3:probe8	-0.274024220626673	0.157775223734977	-1.73680134396112	0.0827259243748393	.  
df.mm.trans3:probe9	-0.0899368842245956	0.157775223734977	-0.570031733091802	0.568782447110927	   
df.mm.trans3:probe10	-0.00730241552878971	0.157775223734977	-0.0462836645445419	0.96309327255864	   
df.mm.trans3:probe11	-0.184499240319886	0.157775223734977	-1.16938031176428	0.242525244247588	   
df.mm.trans3:probe12	-0.136563074305498	0.157775223734977	-0.865554623043286	0.386939184298734	   
df.mm.trans3:probe13	0.0149137303274325	0.157775223734977	0.0945251730555862	0.924710661676097	   
df.mm.trans3:probe14	-0.0217437615472881	0.157775223734977	-0.137814804077301	0.890414144956623	   
df.mm.trans3:probe15	-0.0327475389626553	0.157775223734977	-0.207558184279067	0.83561563370587	   
df.mm.trans3:probe16	0.116804013179431	0.157775223734977	0.740319109771206	0.459277766261231	   
df.mm.trans3:probe17	-0.143663069895701	0.157775223734977	-0.910555323546993	0.362746115477962