chr2.13651_chr2_29195860_29196950_+_2.R 

fitVsDatCorrelation=0.802001684355674
cont.fitVsDatCorrelation=0.249039003375146

fstatistic=15995.0348787295,52,692
cont.fstatistic=6076.02463384425,52,692

residuals=-0.339397801823814,-0.0759497490332409,-0.00591991788684298,0.0710672376418509,0.475331188100692
cont.residuals=-0.390381518072561,-0.131399287404104,-0.0346762063974652,0.0990216527594643,0.663852203829796

predictedValues:
Include	Exclude	Both
chr2.13651_chr2_29195860_29196950_+_2.R.tl.Lung	52.0261189847553	50.5095774782312	59.4490072273985
chr2.13651_chr2_29195860_29196950_+_2.R.tl.cerebhem	59.1188554594647	54.5205068378944	52.9708647224282
chr2.13651_chr2_29195860_29196950_+_2.R.tl.cortex	49.9635750250944	48.7717625718916	58.5478075555889
chr2.13651_chr2_29195860_29196950_+_2.R.tl.heart	50.7970489698442	49.0301067835125	57.5429999620162
chr2.13651_chr2_29195860_29196950_+_2.R.tl.kidney	52.1418078295212	49.084684649796	54.0551489385793
chr2.13651_chr2_29195860_29196950_+_2.R.tl.liver	54.0930553348151	49.4584755752883	58.5318085244264
chr2.13651_chr2_29195860_29196950_+_2.R.tl.stomach	52.2278583720465	47.3478488624096	60.1928846387792
chr2.13651_chr2_29195860_29196950_+_2.R.tl.testicle	52.964404102448	51.4057793421468	62.9579411985265


diffExp=1.51654150652406,4.59834862157032,1.19181245320279,1.76694218633168,3.05712317972517,4.63457975952676,4.88000950963688,1.55862476030126
diffExpScore=0.958684484191166
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	53.0485608364856	52.6889056035262	57.605275403492
cerebhem	52.1708129014172	52.3476556539115	52.3535303020335
cortex	53.4497941084856	51.9923753524555	52.0520252364764
heart	53.6859366526103	55.1741126107108	55.4916551928355
kidney	54.8977021212139	52.6300071522367	60.7537492223032
liver	52.8962333565397	51.3663815450376	48.7871061216329
stomach	54.5147050563058	49.7233275994814	49.8778639072231
testicle	56.5817220166229	54.8312635070264	54.1544076126303
cont.diffExp=0.359655232959426,-0.176842752494338,1.45741875603011,-1.48817595810053,2.26769496897720,1.52985181150210,4.79137745682446,1.75045850959645
cont.diffExpScore=1.20276291061753

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=0.823959092342742
cont.tran.correlation=0.323758579491031

tran.covariance=0.00177391734376109
cont.tran.covariance=0.000267483095970688

tran.mean=51.4663416361975
cont.tran.mean=53.2499685046292

weightedLogRatios:
wLogRatio
Lung	0.116466565747907
cerebhem	0.327055136769682
cortex	0.0941378885218633
heart	0.138433406128981
kidney	0.237073464934447
liver	0.353445373324109
stomach	0.383213489772919
testicle	0.118124325348816

cont.weightedLogRatios:
wLogRatio
Lung	0.0269922830496071
cerebhem	-0.0133876559532061
cortex	0.109613008516987
heart	-0.109284273222146
kidney	0.168081252340715
liver	0.116032866080696
stomach	0.363612797811045
testicle	0.126329538259859

varWeightedLogRatios=0.0142547031766070
cont.varWeightedLogRatios=0.0195911764450308

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.83062749896008	0.063468470218751	60.35481059741	6.42907250906788e-278	***
df.mm.trans1	-0.0235167025660383	0.0570039678233558	-0.412545011584317	0.680067773927025	   
df.mm.trans2	0.103756981587811	0.0524228902623422	1.97923046723627	0.0481860005347614	*  
df.mm.exp2	0.319594851537758	0.0718063770931445	4.4507864687727	9.96986558106185e-06	***
df.mm.exp3	-0.0601878219255245	0.0718063770931445	-0.838196053916649	0.402210021451265	   
df.mm.exp4	-0.0210496266810959	0.0718063770931445	-0.29314425171167	0.769499778662053	   
df.mm.exp5	0.0687194147324011	0.0718063770931445	0.957009913524267	0.338896466021939	   
df.mm.exp6	0.0334789804761163	0.0718063770931445	0.466239654908208	0.64119069517526	   
df.mm.exp7	-0.0732066523831033	0.0718063770931445	-1.01950070936099	0.308321605409316	   
df.mm.exp8	-0.0218861597836201	0.0718063770931445	-0.304794095867422	0.760614622402091	   
df.mm.trans1:exp2	-0.191790815347164	0.0687493719387503	-2.78971007208667	0.0054206751891828	** 
df.mm.trans2:exp2	-0.243180919719999	0.0598386475776204	-4.06394411579163	5.3783049862068e-05	***
df.mm.trans1:exp3	0.0197361817255331	0.0687493719387503	0.287074356739088	0.774141323812668	   
df.mm.trans2:exp3	0.0251763601546863	0.0598386475776204	0.420737452697748	0.674077437989848	   
df.mm.trans1:exp4	-0.00285799221194362	0.0687493719387503	-0.0415711755809180	0.966852543112527	   
df.mm.trans2:exp4	-0.00867881106992981	0.0598386475776204	-0.145036885378667	0.884723981759904	   
df.mm.trans1:exp5	-0.0664982148704325	0.0687493719387503	-0.967255598053704	0.333754194910570	   
df.mm.trans2:exp5	-0.0973353215160665	0.0598386475776204	-1.62662970264839	0.104271012331488	   
df.mm.trans1:exp6	0.00548094929741879	0.0687493719387503	0.0797236271816684	0.936480116563907	   
df.mm.trans2:exp6	-0.0545085115565413	0.0598386475776204	-0.91092485815016	0.362652160579901	   
df.mm.trans1:exp7	0.0770768095552757	0.0687493719387503	1.12112747188359	0.262622509666939	   
df.mm.trans2:exp7	0.00856506907732716	0.0598386475776204	0.143136073826149	0.886224383433315	   
df.mm.trans1:exp8	0.0397603463436699	0.0687493719387503	0.578337593819663	0.563224379399934	   
df.mm.trans2:exp8	0.0394737931534322	0.0598386475776204	0.659670543225902	0.509684753341153	   
df.mm.trans1:probe2	0.50275396000924	0.0343746859693752	14.6257033579056	2.08082723149589e-42	***
df.mm.trans1:probe3	0.083981098425658	0.0343746859693751	2.44310881852064	0.0148102876709973	*  
df.mm.trans1:probe4	-0.0379978925093277	0.0343746859693752	-1.10540333497681	0.269369027021735	   
df.mm.trans1:probe5	0.142418701277333	0.0343746859693752	4.14312734098037	3.85020901920512e-05	***
df.mm.trans1:probe6	0.406226972742556	0.0343746859693751	11.8176198934434	1.74694426713339e-29	***
df.mm.trans1:probe7	0.0620651541766434	0.0343746859693751	1.80554825233714	0.0714233964192681	.  
df.mm.trans1:probe8	0.169652359347026	0.0343746859693752	4.9353864497314	1.00323719446332e-06	***
df.mm.trans1:probe9	0.526135422017271	0.0343746859693751	15.3058975574646	9.20088257839972e-46	***
df.mm.trans1:probe10	0.0995929731037552	0.0343746859693751	2.89727659454064	0.00388310074856659	** 
df.mm.trans1:probe11	0.252027385232246	0.0343746859693752	7.33177273115398	6.3615066364331e-13	***
df.mm.trans1:probe12	0.333802438634784	0.0343746859693751	9.7107051081768	5.51293235714905e-21	***
df.mm.trans1:probe13	0.266057368197269	0.0343746859693752	7.73992141875282	3.53319979922144e-14	***
df.mm.trans1:probe14	0.121569548039852	0.0343746859693751	3.53660097864342	0.000432352617396716	***
df.mm.trans1:probe15	0.141926311625956	0.0343746859693751	4.12880314753712	4.0918725128991e-05	***
df.mm.trans1:probe16	0.233567788894972	0.0343746859693752	6.79476138641849	2.34184190069461e-11	***
df.mm.trans1:probe17	0.0933116209599207	0.0343746859693752	2.7145446810206	0.00680206352840481	** 
df.mm.trans1:probe18	0.116032982049824	0.0343746859693751	3.37553576935071	0.000777872825830418	***
df.mm.trans1:probe19	0.0545908760832118	0.0343746859693752	1.58811272143250	0.112717625035685	   
df.mm.trans1:probe20	-0.0589597682395404	0.0343746859693752	-1.71520892705954	0.0867545941328096	.  
df.mm.trans1:probe21	0.0557494103382561	0.0343746859693752	1.62181584401742	0.105298281024067	   
df.mm.trans1:probe22	0.0513723958751669	0.0343746859693752	1.49448335094422	0.135505172328763	   
df.mm.trans2:probe2	-0.0836699019437153	0.0343746859693751	-2.43405574725127	0.0151826272176627	*  
df.mm.trans2:probe3	-0.0093703888224526	0.0343746859693752	-0.272595619660375	0.785245400494358	   
df.mm.trans2:probe4	-0.0216316904811591	0.0343746859693752	-0.629291290120614	0.529366060295516	   
df.mm.trans2:probe5	0.0599461629874355	0.0343746859693751	1.74390430914314	0.0816198223661457	.  
df.mm.trans2:probe6	-0.0552677646675028	0.0343746859693752	-1.60780420559308	0.108334196113824	   
df.mm.trans3:probe2	0.171597118909936	0.0343746859693751	4.9919617902201	7.5717296176304e-07	***
df.mm.trans3:probe3	0.351156318276124	0.0343746859693752	10.2155498551746	6.49067522510949e-23	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.91520729557353	0.102907345845775	38.0459457329816	9.02312570847347e-172	***
df.mm.trans1	0.0768525760031334	0.0924258456389645	0.831505251283675	0.405975085557133	   
df.mm.trans2	0.0642731476791155	0.0849981176459512	0.756171424252441	0.4498037590058	   
df.mm.exp2	0.0724124818689369	0.116426371330331	0.621959449921226	0.534173389134264	   
df.mm.exp3	0.0955976622609736	0.116426371330331	0.821099731689989	0.411872238380686	   
df.mm.exp4	0.0954137967927831	0.116426371330331	0.819520489237531	0.412771677226782	   
df.mm.exp5	-0.0200693681334349	0.116426371330331	-0.172378198376492	0.863190611939153	   
df.mm.exp6	0.137851477630698	0.116426371330331	1.18402279531309	0.236810577331831	   
df.mm.exp7	0.113368879334512	0.116426371330331	0.973738836305874	0.330526477859733	   
df.mm.exp8	0.166108667819889	0.116426371330331	1.42672717462436	0.154109566342188	   
df.mm.trans1:exp2	-0.0890970194332015	0.111469763969364	-0.799293155924225	0.424394707212135	   
df.mm.trans2:exp2	-0.0789102412430939	0.097021976108609	-0.813323376909574	0.416312421376871	   
df.mm.trans1:exp3	-0.088062613093197	0.111469763969364	-0.790013452593295	0.42979053819327	   
df.mm.trans2:exp3	-0.108905495795566	0.097021976108609	-1.12248276280885	0.262046538015699	   
df.mm.trans1:exp4	-0.0834704531211782	0.111469763969364	-0.748816989906957	0.454221961255389	   
df.mm.trans2:exp4	-0.0493248414839995	0.097021976108609	-0.508388341098969	0.611343054357373	   
df.mm.trans1:exp5	0.0543331238010524	0.111469763969364	0.487424767634612	0.626111717451713	   
df.mm.trans2:exp5	0.0189508898679741	0.097021976108609	0.195325746063551	0.845195279009252	   
df.mm.trans1:exp6	-0.140727080647794	0.111469763969364	-1.26246863397388	0.207205484009206	   
df.mm.trans2:exp6	-0.163272488228948	0.097021976108609	-1.68284026750987	0.0928570945474996	.  
df.mm.trans1:exp7	-0.0861061327689249	0.111469763969364	-0.772461784278919	0.440104711469056	   
df.mm.trans2:exp7	-0.171299601679619	0.097021976108609	-1.76557527016211	0.0779080836061587	.  
df.mm.trans1:exp8	-0.101630403589501	0.111469763969364	-0.911730678979755	0.362228005599385	   
df.mm.trans2:exp8	-0.126253048114967	0.097021976108609	-1.30128299977765	0.193594684036274	   
df.mm.trans1:probe2	-0.0225558987520144	0.055734881984682	-0.404699856693221	0.685823209136678	   
df.mm.trans1:probe3	-0.0296376213188356	0.055734881984682	-0.531760726199816	0.595062388684998	   
df.mm.trans1:probe4	-0.0421002455610408	0.055734881984682	-0.755366191904944	0.450286311759847	   
df.mm.trans1:probe5	-0.0544049710439387	0.0557348819846821	-0.976138624620953	0.329336880474748	   
df.mm.trans1:probe6	0.0175108766946667	0.055734881984682	0.314181641211321	0.753477778547873	   
df.mm.trans1:probe7	-0.0440088222168025	0.055734881984682	-0.789610036832907	0.430026012956821	   
df.mm.trans1:probe8	-0.0797812934481599	0.055734881984682	-1.43144276272239	0.152754825102338	   
df.mm.trans1:probe9	-0.038642516551641	0.055734881984682	-0.693327323493056	0.488336796589945	   
df.mm.trans1:probe10	-0.0175459840285705	0.055734881984682	-0.314811539986624	0.752999647662314	   
df.mm.trans1:probe11	-0.0579954568698323	0.055734881984682	-1.04055942714244	0.298443483044854	   
df.mm.trans1:probe12	-0.0168567242823788	0.055734881984682	-0.302444782910128	0.762403903966218	   
df.mm.trans1:probe13	0.0476727800198324	0.0557348819846821	0.855349079826428	0.392653971683119	   
df.mm.trans1:probe14	-0.0273105863838306	0.055734881984682	-0.490008867181894	0.624282935273322	   
df.mm.trans1:probe15	0.0400375778437487	0.055734881984682	0.718357632025712	0.472779267244833	   
df.mm.trans1:probe16	-0.0149306770736850	0.0557348819846821	-0.267887479833339	0.788865759963524	   
df.mm.trans1:probe17	-0.00394767727753306	0.055734881984682	-0.0708295619719447	0.943553867997813	   
df.mm.trans1:probe18	-0.0721053547044655	0.055734881984682	-1.29372041595571	0.196193650640645	   
df.mm.trans1:probe19	-0.000782282572849953	0.0557348819846821	-0.0140357805559712	0.988805480613228	   
df.mm.trans1:probe20	-0.0390217613902715	0.0557348819846821	-0.700131766691389	0.484080324643073	   
df.mm.trans1:probe21	-0.00372873114515796	0.055734881984682	-0.0669012118153	0.946679672297829	   
df.mm.trans1:probe22	-0.0611680129462789	0.0557348819846821	-1.09748169850059	0.272812612557107	   
df.mm.trans2:probe2	-0.0846063643529335	0.055734881984682	-1.51801459588963	0.129467420608091	   
df.mm.trans2:probe3	-0.0115310766799911	0.055734881984682	-0.206891559995771	0.836155409704779	   
df.mm.trans2:probe4	-0.0312031271690393	0.0557348819846821	-0.559849165512094	0.575763495986259	   
df.mm.trans2:probe5	0.0477896253854612	0.055734881984682	0.85744552932929	0.391495561071863	   
df.mm.trans2:probe6	-0.0561288247078725	0.0557348819846821	-1.00706815389505	0.314254027225753	   
df.mm.trans3:probe2	-0.0187402091193549	0.055734881984682	-0.336238428288147	0.73679300668878	   
df.mm.trans3:probe3	0.0105836024746377	0.0557348819846821	0.189891897098597	0.849449529164202