chr18.11370_chr18_32431884_32439360_+_2.R 

fitVsDatCorrelation=0.850529002661648
cont.fitVsDatCorrelation=0.245183039657118

fstatistic=7467.5446605728,51,669
cont.fstatistic=2188.37901852511,51,669

residuals=-0.772063643793617,-0.0959055217492323,-0.0125185529007586,0.086413189852598,1.90826020035030
cont.residuals=-0.522607219218139,-0.196990440004917,-0.0751965824878737,0.0869486218097513,1.87785634680794

predictedValues:
Include	Exclude	Both
chr18.11370_chr18_32431884_32439360_+_2.R.tl.Lung	72.8463706533953	56.8146758040223	56.385601357621
chr18.11370_chr18_32431884_32439360_+_2.R.tl.cerebhem	72.0138044842892	65.7640444934403	57.1614706119183
chr18.11370_chr18_32431884_32439360_+_2.R.tl.cortex	70.9226641912804	52.1348157531073	56.8923727086053
chr18.11370_chr18_32431884_32439360_+_2.R.tl.heart	71.4142278556863	52.0634545819349	55.8609529653806
chr18.11370_chr18_32431884_32439360_+_2.R.tl.kidney	73.2608840927803	56.1769569796512	57.7031041598082
chr18.11370_chr18_32431884_32439360_+_2.R.tl.liver	70.8238689411399	53.7491859735893	54.4320362284707
chr18.11370_chr18_32431884_32439360_+_2.R.tl.stomach	70.2786376504605	55.790054231201	54.0320671512102
chr18.11370_chr18_32431884_32439360_+_2.R.tl.testicle	72.658653013832	56.5924276862279	54.9798236497931


diffExp=16.031694849373,6.24975999084893,18.7878484381731,19.3507732737515,17.0839271131291,17.0746829675506,14.4885834192595,16.0662253276041
diffExpScore=0.992071891792186
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=0,0,1,1,1,1,0,0
diffExp1.3Score=0.8
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	63.7404618997518	67.4932510636718	63.3859612041109
cerebhem	62.1136127967545	61.255340216143	58.8367456352038
cortex	64.0122801132472	60.8662204122165	61.9045488457369
heart	61.7092757169241	71.3139675995297	65.4132751546294
kidney	60.3627854530166	57.5948360840336	57.4750445460012
liver	64.5287057266101	63.9059776078648	70.936569927178
stomach	58.9932636169756	64.5397815853636	58.7452231106228
testicle	59.3943083244733	62.6521170846398	55.6029658321872
cont.diffExp=-3.75278916391997,0.858272580611448,3.14605970103068,-9.6046918826056,2.76794936898300,0.622728118745293,-5.54651796838803,-3.2578087601665
cont.diffExpScore=1.87462397461723

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=0.339556971783752
cont.tran.correlation=0.144315834652094

tran.covariance=0.000398471112829782
cont.tran.covariance=0.000341395642169718

tran.mean=63.9565453991274
cont.tran.mean=62.779761581326

weightedLogRatios:
wLogRatio
Lung	1.03501400647702
cerebhem	0.384151808719461
cortex	1.26417726891178
heart	1.29905093690577
kidney	1.10490025424702
liver	1.13719841574739
stomach	0.955125888041834
testicle	1.03977863741374

cont.weightedLogRatios:
wLogRatio
Lung	-0.239325518223882
cerebhem	0.057354196864119
cortex	0.208333302620623
heart	-0.606805857602526
kidney	0.191369280652552
liver	0.0403625802982810
stomach	-0.370428584169917
testicle	-0.219518129612911

varWeightedLogRatios=0.0810175242637395
cont.varWeightedLogRatios=0.0836720256865089

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.73153048157046	0.0916558313518555	51.6227981546171	1.63817751530528e-235	***
df.mm.trans1	0.106244815722290	0.08118526011825	1.30867124854364	0.191095114046415	   
df.mm.trans2	-0.680550631486576	0.0741367821807705	-9.17966239520841	5.31479390847883e-19	***
df.mm.exp2	0.121117451270062	0.100047256872747	1.21060241985559	0.226475334118750	   
df.mm.exp3	-0.12167181430324	0.100047256872747	-1.21614343167847	0.224359211547399	   
df.mm.exp4	-0.0978387963190632	0.100047256872747	-0.977925826027465	0.328464501319794	   
df.mm.exp5	-0.0287110408172268	0.100047256872747	-0.28697479285959	0.774220479421986	   
df.mm.exp6	-0.0483618340566815	0.100047256872747	-0.483389905614247	0.62897707913005	   
df.mm.exp7	-0.0114477643038641	0.100047256872747	-0.114423570037756	0.908936362074214	   
df.mm.exp8	0.0187478527053246	0.100047256872747	0.187389972412443	0.851411701480842	   
df.mm.trans1:exp2	-0.132612333637875	0.0945357717998166	-1.40277411516444	0.161148103368674	   
df.mm.trans2:exp2	0.0251611322556575	0.0802162623652252	0.313666225697461	0.753872329368041	   
df.mm.trans1:exp3	0.094909148997878	0.0945357717998166	1.00394958639415	0.31576589362797	   
df.mm.trans2:exp3	0.0357101193190348	0.0802162623652252	0.445173064240346	0.656338694781894	   
df.mm.trans1:exp4	0.0779832035458339	0.0945357717998166	0.824906826920148	0.409718517282776	   
df.mm.trans2:exp4	0.0105073822003672	0.0802162623652252	0.130988179834745	0.895824037115324	   
df.mm.trans1:exp5	0.0343851540189129	0.0945357717998166	0.363726379594434	0.716177256052303	   
df.mm.trans2:exp5	0.0174230262958309	0.0802162623652252	0.217200674552795	0.828118181665963	   
df.mm.trans1:exp6	0.0202051979224724	0.0945357717998166	0.213730713123682	0.830822179429535	   
df.mm.trans2:exp6	-0.00710431291257619	0.0802162623652252	-0.088564496812756	0.929454529243247	   
df.mm.trans1:exp7	-0.0244370691176943	0.0945357717998166	-0.258495473749776	0.796104048211669	   
df.mm.trans2:exp7	-0.00675129096458504	0.0802162623652252	-0.0841636192652105	0.93295152130466	   
df.mm.trans1:exp8	-0.0213280762464399	0.0945357717998166	-0.225608527231396	0.821574810334998	   
df.mm.trans2:exp8	-0.0226673320937382	0.0802162623652252	-0.282577764475409	0.777587930985524	   
df.mm.trans1:probe2	-0.548641803007919	0.0517793747059203	-10.5957595301974	2.34200758878678e-24	***
df.mm.trans1:probe3	-0.77095795973249	0.0517793747059203	-14.8892867886321	1.62359872036142e-43	***
df.mm.trans1:probe4	-0.627017744151495	0.0517793747059203	-12.1094112803144	1.16937329102307e-30	***
df.mm.trans1:probe5	-0.313467189228185	0.0517793747059203	-6.0539006314486	2.35602167464631e-09	***
df.mm.trans1:probe6	-0.548513415526865	0.0517793747059203	-10.5932800201264	2.39575870334629e-24	***
df.mm.trans1:probe7	-0.534535533155353	0.0517793747059203	-10.3233292443417	2.77130060634028e-23	***
df.mm.trans1:probe8	0.350407582180019	0.0517793747059203	6.76731969379989	2.86908755665041e-11	***
df.mm.trans1:probe9	-0.949326973022794	0.0517793747059203	-18.3340756510575	3.85597771800745e-61	***
df.mm.trans1:probe10	-0.866080973686708	0.0517793747059203	-16.7263698838697	9.93546852380288e-53	***
df.mm.trans1:probe11	-0.922650677941493	0.0517793747059203	-17.8188841248405	2.04918963014212e-58	***
df.mm.trans1:probe12	-0.892156064461344	0.0517793747059203	-17.2299505262148	2.47373351118751e-55	***
df.mm.trans1:probe13	-0.87620053902881	0.0517793747059203	-16.9218061053299	9.7780836919355e-54	***
df.mm.trans1:probe14	-0.856479244368248	0.0517793747059203	-16.5409344788847	8.87610381716997e-52	***
df.mm.trans1:probe15	-0.839723824741599	0.0517793747059203	-16.2173419341348	3.95581887010406e-50	***
df.mm.trans1:probe16	-0.763722964998828	0.0517793747059203	-14.7495594401511	7.77239628314208e-43	***
df.mm.trans1:probe17	-0.825836489078337	0.0517793747059203	-15.9491398605845	8.98211750340087e-49	***
df.mm.trans1:probe18	-0.738335558553046	0.0517793747059203	-14.2592598451875	1.78276203907524e-40	***
df.mm.trans1:probe19	-0.847194905050173	0.0517793747059203	-16.3616287346418	7.30549757670313e-51	***
df.mm.trans1:probe20	-0.81570777055998	0.0517793747059203	-15.7535268664941	8.6333255477926e-48	***
df.mm.trans2:probe2	-0.0165084536432965	0.0517793747059203	-0.318822962560977	0.749960279025174	   
df.mm.trans2:probe3	-0.0153870328208171	0.0517793747059203	-0.297165288461041	0.766432565855	   
df.mm.trans2:probe4	-0.0181199029593338	0.0517793747059203	-0.349944414397534	0.726490553357797	   
df.mm.trans2:probe5	0.0113252627885242	0.0517793747059203	0.218721505480623	0.826933702053687	   
df.mm.trans2:probe6	-0.0731616823545602	0.0517793747059203	-1.41295028706083	0.158135405044329	   
df.mm.trans3:probe2	-0.320355309536219	0.0517793747059203	-6.18692889506042	1.06866150336099e-09	***
df.mm.trans3:probe3	-0.0114234405838020	0.0517793747059203	-0.220617584678863	0.825457517966175	   
df.mm.trans3:probe4	-0.168264589772547	0.0517793747059203	-3.24964507061358	0.00121335572458123	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29593223834716	0.168955023895922	25.4264841570708	2.57709625483682e-100	***
df.mm.trans1	-0.0927040050226416	0.149653953937955	-0.619455768345927	0.535827051833809	   
df.mm.trans2	-0.0340397126694484	0.136661046222295	-0.249081311832479	0.803374335295188	   
df.mm.exp2	-0.048355139479957	0.184423472313136	-0.262196231713141	0.793250871079676	   
df.mm.exp3	-0.0754451338928934	0.184423472313136	-0.409086397445081	0.68260718780642	   
df.mm.exp4	-0.00880351866570296	0.184423472313136	-0.0477353481923131	0.961941408856906	   
df.mm.exp5	-0.115149912579218	0.184423472313136	-0.624377749398963	0.532592397388145	   
df.mm.exp6	-0.154867777724606	0.184423472313136	-0.839740060102831	0.401354131727205	   
df.mm.exp7	-0.0461095365282601	0.184423472313136	-0.250019891448361	0.802648720156139	   
df.mm.exp8	-0.0140454262032727	0.184423472313136	-0.0761585606599181	0.939315719663926	   
df.mm.trans1:exp2	0.0225007564881426	0.174263801308416	0.129118935310726	0.897302347295827	   
df.mm.trans2:exp2	-0.0486214363312813	0.147867738744631	-0.328817068172328	0.742396864458211	   
df.mm.trans1:exp3	0.079700520030098	0.174263801308416	0.457355569152554	0.647563977878088	   
df.mm.trans2:exp3	-0.0279041269512893	0.147867738744631	-0.188710040392786	0.850377312643614	   
df.mm.trans1:exp4	-0.0235817815395823	0.174263801308416	-0.135322318017422	0.89239774494131	   
df.mm.trans2:exp4	0.0638681172740563	0.147867738744631	0.431927327869382	0.665933403093287	   
df.mm.trans1:exp5	0.0607031371610948	0.174263801308416	0.348340485547318	0.72769407894769	   
df.mm.trans2:exp5	-0.0434447836945444	0.147867738744631	-0.293808399745491	0.768995442849136	   
df.mm.trans1:exp6	0.167158396959945	0.174263801308416	0.959226160022214	0.337791357070284	   
df.mm.trans2:exp6	0.100253072384051	0.147867738744631	0.677991516169659	0.498011493481483	   
df.mm.trans1:exp7	-0.0312867576540212	0.174263801308416	-0.179536756452645	0.85757062160824	   
df.mm.trans2:exp7	0.00136373033380296	0.147867738744631	0.00922263602176355	0.992644254875375	   
df.mm.trans1:exp8	-0.0565757270583777	0.174263801308416	-0.324655646402714	0.745543196209405	   
df.mm.trans2:exp8	-0.0603847094105918	0.147867738744631	-0.408369735841276	0.683132921592972	   
df.mm.trans1:probe2	0.0222454031683448	0.0954482149332175	0.233062537459808	0.815784133012044	   
df.mm.trans1:probe3	-0.145952306367857	0.0954482149332175	-1.52912557317050	0.126705983437242	   
df.mm.trans1:probe4	0.0233397675680634	0.0954482149332175	0.244528067752692	0.806896844904464	   
df.mm.trans1:probe5	-0.0122623456807805	0.0954482149332175	-0.128471189213545	0.897814707219145	   
df.mm.trans1:probe6	-0.0958015909518764	0.0954482149332175	-1.00370228001546	0.315885028494891	   
df.mm.trans1:probe7	-0.0728349797908311	0.0954482149332175	-0.763083729138274	0.445682454371273	   
df.mm.trans1:probe8	0.0418009950445655	0.0954482149332175	0.437944230531839	0.661568073070435	   
df.mm.trans1:probe9	-0.0737729927400116	0.0954482149332175	-0.772911183217293	0.439847931629064	   
df.mm.trans1:probe10	-0.0325922479508541	0.0954482149332175	-0.341465243469016	0.732860589237623	   
df.mm.trans1:probe11	-0.133690867729215	0.0954482149332175	-1.40066388693340	0.161778249623712	   
df.mm.trans1:probe12	-0.172652976068736	0.0954482149332175	-1.80886542707516	0.0709207137010622	.  
df.mm.trans1:probe13	-0.145574308860659	0.0954482149332175	-1.52516533664368	0.127690446177102	   
df.mm.trans1:probe14	-0.0481543993557706	0.0954482149332175	-0.504508118768517	0.614070482086597	   
df.mm.trans1:probe15	-0.0957194371651298	0.0954482149332175	-1.00284156421471	0.316299891584134	   
df.mm.trans1:probe16	-0.0564110354573286	0.0954482149332175	-0.591011948173131	0.554712032955835	   
df.mm.trans1:probe17	-0.0887919711968535	0.0954482149332175	-0.93026329784144	0.352570326607530	   
df.mm.trans1:probe18	-0.00692282683129739	0.0954482149332175	-0.0725296626672495	0.942202078487152	   
df.mm.trans1:probe19	-0.111323400360527	0.0954482149332175	-1.16632249684729	0.243899684906390	   
df.mm.trans1:probe20	0.0432632550013326	0.0954482149332175	0.453264160378512	0.650505512315829	   
df.mm.trans2:probe2	-0.143862638190521	0.0954482149332175	-1.50723235936028	0.132223153394185	   
df.mm.trans2:probe3	0.0325190733345238	0.0954482149332175	0.340698601406810	0.733437452138223	   
df.mm.trans2:probe4	-0.164017959468342	0.0954482149332175	-1.71839734858427	0.0861868313491382	.  
df.mm.trans2:probe5	-0.119858163381828	0.0954482149332175	-1.25574023008905	0.209648556629910	   
df.mm.trans2:probe6	-0.103429482692728	0.0954482149332175	-1.08361882686957	0.278924217569627	   
df.mm.trans3:probe2	-0.0687550104614326	0.0954482149332175	-0.7203383584443	0.471568295471739	   
df.mm.trans3:probe3	-0.07716956113593	0.0954482149332175	-0.808496640716889	0.419092148126939	   
df.mm.trans3:probe4	0.0661401082236828	0.0954482149332175	0.692942327627177	0.488586221666219