chr4.16524_chr4_96711638_96714493_+_1.R 

fitVsDatCorrelation=0.877416507750551
cont.fitVsDatCorrelation=0.334778658350835

fstatistic=10134.5681062059,42,462
cont.fstatistic=2618.62345342757,42,462

residuals=-0.58784420763941,-0.0757703902644286,-0.00715212764910954,0.0788523398776747,0.578774914615618
cont.residuals=-0.590907280289965,-0.190239651446789,-0.0195642361565463,0.142334700638096,1.05944277813545

predictedValues:
Include	Exclude	Both
chr4.16524_chr4_96711638_96714493_+_1.R.tl.Lung	55.9180225756832	67.8670307798815	66.6572517733549
chr4.16524_chr4_96711638_96714493_+_1.R.tl.cerebhem	62.4385321654834	62.879468576119	62.7218291261068
chr4.16524_chr4_96711638_96714493_+_1.R.tl.cortex	57.4929794994061	68.1210774637579	65.7795264291725
chr4.16524_chr4_96711638_96714493_+_1.R.tl.heart	57.4670295477369	73.8984808631449	69.5852681944753
chr4.16524_chr4_96711638_96714493_+_1.R.tl.kidney	55.0100325619615	72.3964642466181	70.4828077103802
chr4.16524_chr4_96711638_96714493_+_1.R.tl.liver	57.4568832408117	78.9998999354981	73.032937007543
chr4.16524_chr4_96711638_96714493_+_1.R.tl.stomach	57.029619462972	69.9978018106726	66.6754381036666
chr4.16524_chr4_96711638_96714493_+_1.R.tl.testicle	62.4222123914634	70.756568553142	73.386444570974


diffExp=-11.9490082041983,-0.440936410635580,-10.6280979643518,-16.4314513154080,-17.3864316846566,-21.5430166946864,-12.9681823477007,-8.33435616167863
diffExpScore=0.990067686805758
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,-1,-1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,0,0,-1,-1,-1,-1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	63.1644203666696	58.4740048759058	69.7965693917283
cerebhem	59.868046015608	62.9446343871136	64.6428826182614
cortex	66.2248704519205	59.2245036179757	69.572359109966
heart	68.4591004549593	70.4909154854534	64.9530005979973
kidney	75.0772518396808	56.1631158174638	65.680030454422
liver	72.927202131822	58.5734942913435	62.4463122754605
stomach	66.8561323574939	68.6376419091293	61.2485838556473
testicle	76.3658286616592	73.8508711018875	64.3793189537843
cont.diffExp=4.69041549076371,-3.0765883715056,7.00036683394476,-2.0318150304941,18.914136022217,14.3537078404785,-1.78150955163541,2.51495755977164
cont.diffExpScore=1.30732798868866

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=0,0,0,0,1,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,1,1,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.407931624984468
cont.tran.correlation=0.164041851818285

tran.covariance=-0.00132295928654834
cont.tran.covariance=0.00118966586977574

tran.mean=64.384506479647
cont.tran.mean=66.0813771103804

weightedLogRatios:
wLogRatio
Lung	-0.798033293680765
cerebhem	-0.0291174549251125
cortex	-0.701644683523707
heart	-1.05042355019265
kidney	-1.13834570671461
liver	-1.34059021793528
stomach	-0.849490198624272
testicle	-0.525933417186951

cont.weightedLogRatios:
wLogRatio
Lung	0.316903996750631
cerebhem	-0.206323612778593
cortex	0.462210444743237
heart	-0.124033921023741
kidney	1.21135688750843
liver	0.916141978583145
stomach	-0.110864555284308
testicle	0.144625631145776

varWeightedLogRatios=0.164397988844329
cont.varWeightedLogRatios=0.265772306764467

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.16160565727081	0.0727926706012982	57.1706687348354	1.12840236176173e-211	***
df.mm.trans1	-0.157265844987038	0.0584817668106212	-2.68914319049738	0.0074226737692387	** 
df.mm.trans2	0.0824385515556852	0.0584817668106212	1.40964536558278	0.159317216432587	   
df.mm.exp2	0.0948194929114339	0.0785228936123637	1.20753946459895	0.227842275493042	   
df.mm.exp3	0.0447676238291895	0.0785228936123637	0.5701219322124	0.56887213944205	   
df.mm.exp4	0.0694775219529145	0.0785228936123637	0.88480593055953	0.376721559815100	   
df.mm.exp5	-0.00756902942085647	0.0785228936123637	-0.09639264515928	0.923250531902368	   
df.mm.exp6	0.0876976121418185	0.0785228936123637	1.11684131986713	0.264642665279443	   
df.mm.exp7	0.0503247183936453	0.0785228936123637	0.640892306415482	0.521910423960367	   
df.mm.exp8	0.0555540618567932	0.0785228936123637	0.70748872463923	0.47961954299612	   
df.mm.trans1:exp2	0.0154763589044764	0.062077798070539	0.249305861121083	0.803234997470786	   
df.mm.trans2:exp2	-0.171150157343616	0.062077798070539	-2.75702687052685	0.00606394960373951	** 
df.mm.trans1:exp3	-0.0169915147778057	0.062077798070539	-0.273713232523136	0.78442733325096	   
df.mm.trans2:exp3	-0.0410313121369542	0.062077798070539	-0.660965971929776	0.50896359326868	   
df.mm.trans1:exp4	-0.0421528734107129	0.062077798070539	-0.679032999250628	0.497456975081801	   
df.mm.trans2:exp4	0.0156643880144454	0.062077798070539	0.252334787980816	0.800894521788581	   
df.mm.trans1:exp5	-0.00880212739904849	0.062077798070539	-0.141791875237692	0.887306210472074	   
df.mm.trans2:exp5	0.0721761301274931	0.062077798070539	1.16267220118664	0.245562735032062	   
df.mm.trans1:exp6	-0.0605495379383303	0.062077798070539	-0.975381534466282	0.329881466852972	   
df.mm.trans2:exp6	0.064198612565316	0.062077798070539	1.03416381638355	0.301600607086542	   
df.mm.trans1:exp7	-0.0306406814589248	0.062077798070539	-0.493585185223673	0.621833719393059	   
df.mm.trans2:exp7	-0.0194112406618579	0.062077798070539	-0.312692158310785	0.754655648275025	   
df.mm.trans1:exp8	0.0544803823538542	0.062077798070539	0.87761460694769	0.380609046151206	   
df.mm.trans2:exp8	-0.0138590490222713	0.062077798070539	-0.223252909301378	0.823437351770352	   
df.mm.trans1:probe2	0.0223490913296725	0.0416430529137692	0.536682345935372	0.591745388934468	   
df.mm.trans1:probe3	0.188826237357071	0.0416430529137692	4.53439947710069	7.36999300389851e-06	***
df.mm.trans1:probe4	-0.0621692886793448	0.0416430529137692	-1.49290900472834	0.136143575536420	   
df.mm.trans1:probe5	0.252041494171347	0.0416430529137692	6.05242595189294	2.95365168673753e-09	***
df.mm.trans1:probe6	-0.107843683908285	0.0416430529137692	-2.58971608377508	0.00990877621782686	** 
df.mm.trans2:probe2	-0.179199552282716	0.0416430529137692	-4.3032280235022	2.05599079819389e-05	***
df.mm.trans2:probe3	0.141890855547143	0.0416430529137692	3.40731155904824	0.000713433209294208	***
df.mm.trans2:probe4	-0.164108272460493	0.0416430529137692	-3.94083192700387	9.37607298807913e-05	***
df.mm.trans2:probe5	0.132428337558058	0.0416430529137692	3.18008234968457	0.00157133967773183	** 
df.mm.trans2:probe6	-0.32841908396214	0.0416430529137692	-7.88652754739673	2.26229663245979e-14	***
df.mm.trans3:probe2	-0.266306653711703	0.0416430529137692	-6.39498391876186	3.93692624006738e-10	***
df.mm.trans3:probe3	0.121114188556873	0.0416430529137692	2.90838879674998	0.00380800886832619	** 
df.mm.trans3:probe4	-0.102891299934432	0.0416430529137692	-2.47079147024812	0.013841279736029	*  
df.mm.trans3:probe5	0.699803013564242	0.0416430529137692	16.8047961088092	8.37923728602994e-50	***
df.mm.trans3:probe6	0.0754769521963418	0.0416430529137692	1.81247403624881	0.0705619559220753	.  
df.mm.trans3:probe7	0.128834867026094	0.0416430529137692	3.09379015253454	0.00209600026634938	** 
df.mm.trans3:probe8	0.610044445676592	0.0416430529137692	14.6493689341129	3.48138024596215e-40	***
df.mm.trans3:probe9	-0.251006187363561	0.0416430529137692	-6.027564498773	3.40699739938749e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9654899563318	0.142981238962554	27.7343376313192	2.17714913403544e-100	***
df.mm.trans1	0.194143245549338	0.114871393043143	1.69009220142757	0.0916848810624917	.  
df.mm.trans2	0.085462533712472	0.114871393043143	0.743984480804322	0.457264076706776	   
df.mm.exp2	0.0967818111722327	0.154236690629404	0.627488898894864	0.530648810439816	   
df.mm.exp3	0.0632854897736537	0.154236690629404	0.410314105647627	0.681765757466225	   
df.mm.exp4	0.339317787134204	0.154236690629404	2.19998098863199	0.0283026467234792	*  
df.mm.exp5	0.193244309705776	0.154236690629404	1.25290752101326	0.210873129468354	   
df.mm.exp6	0.256698198242705	0.154236690629404	1.66431344704803	0.0967278403124227	.  
df.mm.exp7	0.347704795438326	0.154236690629404	2.25435850587446	0.0246412790119677	*  
df.mm.exp8	0.504052075794471	0.154236690629404	3.26804260216912	0.00116368828839331	** 
df.mm.trans1:exp2	-0.150380077632388	0.121934810288917	-1.23328258170142	0.218097459820109	   
df.mm.trans2:exp2	-0.0231085848054318	0.121934810288917	-0.189515895835467	0.849771729395496	   
df.mm.trans1:exp3	-0.0159705845428459	0.121934810288917	-0.130976416865739	0.89585097628871	   
df.mm.trans2:exp3	-0.050532415440558	0.121934810288917	-0.414421569368293	0.678757741531127	   
df.mm.trans1:exp4	-0.25882246758023	0.121934810288917	-2.12262984595594	0.0343161309639678	*  
df.mm.trans2:exp4	-0.152416238365833	0.121934810288917	-1.24998134662851	0.211939157061855	   
df.mm.trans1:exp5	-0.0204678754961869	0.121934810288917	-0.167859165464641	0.866767528921682	   
df.mm.trans2:exp5	-0.233566364831578	0.121934810288917	-1.91550193319001	0.0560455209077359	.  
df.mm.trans1:exp6	-0.112977659475490	0.121934810288917	-0.926541479072272	0.354648402182013	   
df.mm.trans2:exp6	-0.254998214217325	0.121934810288917	-2.09126674829872	0.0370497813795465	*  
df.mm.trans1:exp7	-0.290902936745724	0.121934810288917	-2.38572509406008	0.0174474396626262	*  
df.mm.trans2:exp7	-0.18744598970401	0.121934810288917	-1.53726396309527	0.124913242257359	   
df.mm.trans1:exp8	-0.314257922247505	0.121934810288917	-2.57726174750992	0.0102676693383716	*  
df.mm.trans2:exp8	-0.270586565714144	0.121934810288917	-2.2191084323911	0.0269644035569417	*  
df.mm.trans1:probe2	0.0231513549821115	0.0817963573888676	0.283036503349993	0.77727562820216	   
df.mm.trans1:probe3	0.0281221338520512	0.0817963573888676	0.343806677335959	0.73114809246341	   
df.mm.trans1:probe4	-0.0819733719497102	0.0817963573888676	-1.00216408855471	0.316788942752500	   
df.mm.trans1:probe5	-0.12455181587535	0.0817963573888676	-1.52270614305254	0.128516393157132	   
df.mm.trans1:probe6	-0.0531287230584047	0.0817963573888676	-0.64952431568347	0.516322322914975	   
df.mm.trans2:probe2	0.172034146993542	0.0817963573888676	2.10320058845255	0.0359884375036785	*  
df.mm.trans2:probe3	-0.0552437931695239	0.0817963573888676	-0.675382069972746	0.499770937952092	   
df.mm.trans2:probe4	0.0513523141725464	0.0817963573888676	0.627806858542766	0.530440656304885	   
df.mm.trans2:probe5	0.118089404137053	0.0817963573888676	1.44370003636769	0.149501109498809	   
df.mm.trans2:probe6	-0.0217850070297015	0.0817963573888676	-0.266332239296837	0.790102168103064	   
df.mm.trans3:probe2	0.0149699931765813	0.0817963573888676	0.183015401351096	0.854866220224505	   
df.mm.trans3:probe3	-0.0516151479803406	0.0817963573888676	-0.631020128866587	0.528339406912642	   
df.mm.trans3:probe4	0.0599537517946507	0.0817963573888676	0.732963590415462	0.463952357331103	   
df.mm.trans3:probe5	-0.0434827621002062	0.0817963573888676	-0.531597781224964	0.595260104931249	   
df.mm.trans3:probe6	0.102139862489038	0.0817963573888676	1.24870918155261	0.212403833001454	   
df.mm.trans3:probe7	0.000555148654038916	0.0817963573888676	0.00678696059042931	0.994587760083633	   
df.mm.trans3:probe8	-0.0787623438256425	0.0817963573888676	-0.962907717897496	0.336097547163779	   
df.mm.trans3:probe9	0.000643612452846864	0.0817963573888676	0.00786847328404944	0.993725327873361