chr2.13122_chr2_165787320_165788204_+_2.R 

fitVsDatCorrelation=0.800383151268281
cont.fitVsDatCorrelation=0.249285604730122

fstatistic=7006.90809026956,54,738
cont.fstatistic=2676.61854287811,54,738

residuals=-0.769580848854933,-0.113938839684568,0.000120782776672168,0.113557177268877,1.1847210665591
cont.residuals=-0.97373193237669,-0.197426538751025,0.0154473239742668,0.197950410763301,1.61947217773498

predictedValues:
Include	Exclude	Both
chr2.13122_chr2_165787320_165788204_+_2.R.tl.Lung	104.295591781143	130.677932573808	109.382580950344
chr2.13122_chr2_165787320_165788204_+_2.R.tl.cerebhem	99.555642803906	185.083313904942	78.5957057320291
chr2.13122_chr2_165787320_165788204_+_2.R.tl.cortex	93.4515039493895	98.7440085946605	102.848346698789
chr2.13122_chr2_165787320_165788204_+_2.R.tl.heart	108.217102342894	99.1221122343191	130.502708012048
chr2.13122_chr2_165787320_165788204_+_2.R.tl.kidney	114.434174587945	122.409458111362	123.056794457318
chr2.13122_chr2_165787320_165788204_+_2.R.tl.liver	93.98786575671	123.968171133724	85.5657828661236
chr2.13122_chr2_165787320_165788204_+_2.R.tl.stomach	99.8419490562346	122.512920521522	93.100697125781
chr2.13122_chr2_165787320_165788204_+_2.R.tl.testicle	103.102272396556	111.030446858388	117.808588813744


diffExp=-26.3823407926651,-85.5276711010361,-5.292504645271,9.09499010857505,-7.97528352341739,-29.9803053770136,-22.6709714652875,-7.92817446183216
diffExpScore=1.09675650920705
diffExp1.5=0,-1,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,-1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,0,0,0,-1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,-1,0,0,0,-1,-1,0
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	110.082671319110	104.191094029979	129.628918358816
cerebhem	124.386472146757	108.479281135348	112.789144049896
cortex	111.409401995741	107.313713683456	121.613102393104
heart	113.237528110602	128.426471175050	96.784502803058
kidney	111.608024038849	110.510912501543	118.817175296783
liver	121.118204838239	118.588096987829	112.350308017744
stomach	111.684622487111	110.371075556168	109.583985987802
testicle	111.232711926078	113.304899467295	131.945092179580
cont.diffExp=5.8915772891307,15.9071910114096,4.09568831228556,-15.1889430644488,1.09711153730618,2.53010785041020,1.31354693094320,-2.07218754121648
cont.diffExpScore=3.30012685949169

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=-0.0769712689243955
cont.tran.correlation=0.148360654413195

tran.covariance=-0.000626562389450332
cont.tran.covariance=0.000500525801004155

tran.mean=113.152154162969
cont.tran.mean=113.496573837447

weightedLogRatios:
wLogRatio
Lung	-1.07340778622952
cerebhem	-3.04511087364483
cortex	-0.251476452149218
heart	0.407352667398959
kidney	-0.321612161098012
liver	-1.29614254254869
stomach	-0.962959747096962
testicle	-0.346172429247663

cont.weightedLogRatios:
wLogRatio
Lung	0.257078402896016
cerebhem	0.650643532624844
cortex	0.175833187983128
heart	-0.60321689845383
kidney	0.0465290916690615
liver	0.101040990946138
stomach	0.055720863659407
testicle	-0.0871370404174218

varWeightedLogRatios=1.07877514552555
cont.varWeightedLogRatios=0.123258686751708

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29029853798743	0.107443462607239	39.9307545929590	1.37751051024827e-186	***
df.mm.trans1	-0.0339938973152962	0.0946481237920641	-0.359160815379485	0.719577521915498	   
df.mm.trans2	0.47757509198888	0.0854038923746413	5.59195932070521	3.16277372007394e-08	***
df.mm.exp2	0.632092509089679	0.113732563656558	5.55770914474793	3.81990344094269e-08	***
df.mm.exp3	-0.328395392784192	0.113732563656558	-2.88743506895578	0.00399754550232738	** 
df.mm.exp4	-0.416015222965545	0.113732563656558	-3.65783738263212	0.000272439256848892	***
df.mm.exp5	-0.0903877984343083	0.113732563656558	-0.794739831129238	0.427020356698636	   
df.mm.exp6	0.0887918555601152	0.113732563656558	0.780707413122621	0.435224939718042	   
df.mm.exp7	0.0530100435436481	0.113732563656558	0.46609380672825	0.641285890840488	   
df.mm.exp8	-0.248648496060429	0.113732563656558	-2.18625596809091	0.0291103014332626	*  
df.mm.trans1:exp2	-0.678604893384297	0.107277355634515	-6.32570489243085	4.36775608639858e-10	***
df.mm.trans2:exp2	-0.284022206037144	0.0877723818827851	-3.23589493579471	0.00126672570570391	** 
df.mm.trans1:exp3	0.218608923871222	0.107277355634515	2.03779187674987	0.0419268888571821	*  
df.mm.trans2:exp3	0.0481903561828004	0.087772381882785	0.54903780835247	0.583145576193661	   
df.mm.trans1:exp4	0.452925542894729	0.107277355634515	4.22200510271532	2.72365709154691e-05	***
df.mm.trans2:exp4	0.139632003846588	0.087772381882785	1.59084214021967	0.112073346437013	   
df.mm.trans1:exp5	0.183158465373546	0.107277355634515	1.70733575869964	0.0881802669657303	.  
df.mm.trans2:exp5	0.0250236715960102	0.087772381882785	0.285097328558633	0.775649565063104	   
df.mm.trans1:exp6	-0.192855265628684	0.107277355634515	-1.79772575943917	0.07262912133955	.  
df.mm.trans2:exp6	-0.141502773392453	0.0877723818827851	-1.61215601487745	0.107355527215674	   
df.mm.trans1:exp7	-0.096650713627322	0.107277355634515	-0.90094235689965	0.367912877374305	   
df.mm.trans2:exp7	-0.117529311558582	0.087772381882785	-1.33902383685492	0.180975189008313	   
df.mm.trans1:exp8	0.237140831232732	0.107277355634515	2.21053949204948	0.0273737530708153	*  
df.mm.trans2:exp8	0.0857171896991658	0.0877723818827851	0.97658497878793	0.329094661287637	   
df.mm.trans1:probe2	0.287557566702433	0.0626364810704327	4.59089594096265	5.18707653486388e-06	***
df.mm.trans1:probe3	0.705143075607262	0.0626364810704327	11.257705789927	3.04920259638345e-27	***
df.mm.trans1:probe4	0.342232550017563	0.0626364810704327	5.4637895387631	6.37723527699444e-08	***
df.mm.trans1:probe5	-0.171044020256380	0.0626364810704327	-2.73074121236227	0.00647001095089892	** 
df.mm.trans1:probe6	0.319991465671311	0.0626364810704327	5.10870758067476	4.13412415232203e-07	***
df.mm.trans1:probe7	0.189002484277134	0.0626364810704327	3.01745055033674	0.00263677047328012	** 
df.mm.trans1:probe8	0.393691876186298	0.0626364810704327	6.28534472975269	5.59301245565124e-10	***
df.mm.trans1:probe9	0.403170430882359	0.0626364810704327	6.43667115381221	2.19781671178001e-10	***
df.mm.trans1:probe10	0.199815254378459	0.0626364810704327	3.19007790609715	0.00148250556867711	** 
df.mm.trans1:probe11	0.847758751083341	0.0626364810704327	13.5345845838636	1.92739483336235e-37	***
df.mm.trans1:probe12	0.444540860899071	0.0626364810704327	7.09715573579555	2.99437564624381e-12	***
df.mm.trans1:probe13	0.548102587900295	0.0626364810704327	8.75053289286752	1.43075707252466e-17	***
df.mm.trans1:probe14	0.55058465429567	0.0626364810704327	8.79015942285384	1.04027755403801e-17	***
df.mm.trans1:probe15	0.594284001892987	0.0626364810704327	9.48782549301795	3.16877356835936e-20	***
df.mm.trans1:probe16	0.578670662097356	0.0626364810704327	9.2385563845239	2.61191704769918e-19	***
df.mm.trans1:probe17	0.748705649132881	0.0626364810704327	11.9531882433016	3.15144992704501e-30	***
df.mm.trans1:probe18	0.343989815702667	0.0626364810704327	5.49184452612945	5.47631359303779e-08	***
df.mm.trans1:probe19	0.974342593451916	0.0626364810704327	15.5555129662584	2.11030749069002e-47	***
df.mm.trans1:probe20	0.798107911576822	0.0626364810704327	12.741902130156	9.3547369489253e-34	***
df.mm.trans1:probe21	0.792286698582924	0.0626364810704327	12.6489656673405	2.47836098634481e-33	***
df.mm.trans1:probe22	0.664025432179586	0.0626364810704327	10.6012569804634	1.54090924398596e-24	***
df.mm.trans2:probe2	0.161712505149941	0.0626364810704327	2.58176229549199	0.0100214205108056	*  
df.mm.trans2:probe3	0.170870257291309	0.0626364810704327	2.72796706282351	0.00652411644213668	** 
df.mm.trans2:probe4	0.284417510033341	0.0626364810704327	4.54076450612739	6.54427022192725e-06	***
df.mm.trans2:probe5	0.443247153569595	0.0626364810704327	7.07650152107329	3.44321361696184e-12	***
df.mm.trans2:probe6	0.0932360711276524	0.0626364810704327	1.48852664668073	0.137039230524781	   
df.mm.trans3:probe2	-0.0341549770579389	0.0626364810704327	-0.545288887150809	0.585719602419084	   
df.mm.trans3:probe3	-0.0527690385008335	0.0626364810704327	-0.84246492777103	0.399800697851226	   
df.mm.trans3:probe4	-0.29913076418381	0.0626364810704327	-4.77566362400607	2.16081692489972e-06	***
df.mm.trans3:probe5	0.190914385546771	0.0626364810704327	3.04797431599157	0.00238613562693828	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.49494935986478	0.173567004428819	25.8974876858475	1.06985687990639e-105	***
df.mm.trans1	0.255715078757170	0.152897076497326	1.67246545594769	0.0948564184962655	.  
df.mm.trans2	0.125748062745565	0.137963701153364	0.911457591339775	0.362351937111581	   
df.mm.exp2	0.301650131164947	0.183726584204005	1.64184259165241	0.101048655780291	   
df.mm.exp3	0.105341055031561	0.183726584204005	0.573357717871648	0.566577178015783	   
df.mm.exp4	0.529574874436845	0.183726584204005	2.88240744653926	0.00406116229286442	** 
df.mm.exp5	0.159738847266164	0.183726584204005	0.869437854942073	0.384890361118145	   
df.mm.exp6	0.368018944719285	0.183726584204005	2.00307944717813	0.0455340012848665	*  
df.mm.exp7	0.240053478622143	0.183726584204005	1.30657999038177	0.191762415244358	   
df.mm.exp8	0.0765386511000272	0.183726584204005	0.416589964003472	0.677099484430555	   
df.mm.trans1:exp2	-0.179488342534992	0.173298670842291	-1.03571678687792	0.300673536069131	   
df.mm.trans2:exp2	-0.261317589401683	0.141789821598238	-1.84299258195083	0.065730953232172	.  
df.mm.trans1:exp3	-0.0933609735352187	0.173298670842291	-0.538728733933461	0.590236502996789	   
df.mm.trans2:exp3	-0.0758112623374448	0.141789821598238	-0.534673515227747	0.593036680928034	   
df.mm.trans1:exp4	-0.501318884157829	0.173298670842291	-2.89280282255628	0.00393062670078212	** 
df.mm.trans2:exp4	-0.320444998334636	0.141789821598238	-2.26000001073856	0.0241119569654386	*  
df.mm.trans1:exp5	-0.145977540874936	0.173298670842290	-0.842346569453967	0.399866882775482	   
df.mm.trans2:exp5	-0.100851231218988	0.141789821598238	-0.711272713952277	0.477140001162613	   
df.mm.trans1:exp6	-0.272483617449724	0.173298670842290	-1.5723352990843	0.116301406581511	   
df.mm.trans2:exp6	-0.238589481562872	0.141789821598238	-1.68269822807815	0.0928565008240126	.  
df.mm.trans1:exp7	-0.225606090960814	0.173298670842290	-1.30183393712307	0.193379352123013	   
df.mm.trans2:exp7	-0.182432031584825	0.141789821598238	-1.28663700629900	0.198624285998716	   
df.mm.trans1:exp8	-0.0661457815075266	0.173298670842290	-0.381686606054366	0.702803705770495	   
df.mm.trans2:exp8	0.00731710360668312	0.141789821598238	0.051605281142226	0.958857175558127	   
df.mm.trans1:probe2	-0.027492209207008	0.101184624206489	-0.271703427498078	0.785926053314504	   
df.mm.trans1:probe3	-0.105596874426765	0.101184624206489	-1.04360593573261	0.297009467709362	   
df.mm.trans1:probe4	-0.0154089884440070	0.101184624206489	-0.152285869170810	0.879003092303485	   
df.mm.trans1:probe5	0.0310330101014303	0.101184624206489	0.306696895351421	0.759160603993381	   
df.mm.trans1:probe6	0.0048031230499365	0.101184624206489	0.0474689023910857	0.96215235711478	   
df.mm.trans1:probe7	-0.108019035365448	0.101184624206489	-1.06754396937831	0.28607535156878	   
df.mm.trans1:probe8	-0.127253320046933	0.101184624206489	-1.25763495239400	0.208921609380846	   
df.mm.trans1:probe9	-0.110418038252479	0.101184624206489	-1.09125313374834	0.27551765120363	   
df.mm.trans1:probe10	-0.108210092739822	0.101184624206489	-1.06943217498141	0.285224628238759	   
df.mm.trans1:probe11	0.0684576481052161	0.101184624206489	0.676561766593249	0.498896029128848	   
df.mm.trans1:probe12	-0.161856772235191	0.101184624206489	-1.59961825726493	0.110111282877497	   
df.mm.trans1:probe13	-0.0808046119437113	0.101184624206489	-0.79858587781887	0.424787490234554	   
df.mm.trans1:probe14	-0.107978920550414	0.101184624206489	-1.06714751769063	0.286254189247187	   
df.mm.trans1:probe15	-0.106117003023311	0.101184624206489	-1.04874632737437	0.294638207748684	   
df.mm.trans1:probe16	0.0500278700109349	0.101184624206489	0.494421661425973	0.621155557704723	   
df.mm.trans1:probe17	-0.139982458827024	0.101184624206489	-1.38343606970720	0.166949523297486	   
df.mm.trans1:probe18	-0.0573113698835698	0.101184624206489	-0.566403940648272	0.571291415535174	   
df.mm.trans1:probe19	-0.135126695691589	0.101184624206489	-1.33544692932628	0.182141844506774	   
df.mm.trans1:probe20	-0.0413679774497092	0.101184624206489	-0.408836597201656	0.682778189938051	   
df.mm.trans1:probe21	-0.0120918166112629	0.101184624206489	-0.119502510446518	0.90490977371012	   
df.mm.trans1:probe22	-0.0439710034151691	0.101184624206489	-0.43456210624884	0.664007335735846	   
df.mm.trans2:probe2	0.117705863487473	0.101184624206489	1.16327816020020	0.245092643087594	   
df.mm.trans2:probe3	0.133322397526967	0.101184624206489	1.31761518681825	0.188041384156312	   
df.mm.trans2:probe4	0.0205316423979503	0.101184624206489	0.20291267135658	0.839259219298454	   
df.mm.trans2:probe5	0.0639158804405163	0.101184624206489	0.63167581973801	0.527794341696826	   
df.mm.trans2:probe6	-0.054654219815182	0.101184624206489	-0.540143527179074	0.589261008600353	   
df.mm.trans3:probe2	0.0197387252769068	0.101184624206489	0.195076331327037	0.84538688344125	   
df.mm.trans3:probe3	-0.0863571501658108	0.101184624206489	-0.853461193763793	0.393680516776466	   
df.mm.trans3:probe4	-0.0837359599074182	0.101184624206489	-0.827556168381246	0.408189411061975	   
df.mm.trans3:probe5	0.0916713505179728	0.101184624206489	0.905981034538389	0.365241578875712