chr6.19987_chr6_88757394_88759225_+_2.R 

fitVsDatCorrelation=0.801303526453008
cont.fitVsDatCorrelation=0.278861492153817

fstatistic=8607.4804175394,53,715
cont.fstatistic=3332.24062015248,53,715

residuals=-0.679045734943593,-0.107750554513239,-0.00607368528366287,0.0950692424793007,0.74532651160322
cont.residuals=-0.65143088566806,-0.220445818587665,-0.0138530759232185,0.198704096906137,0.896250268684759

predictedValues:
Include	Exclude	Both
chr6.19987_chr6_88757394_88759225_+_2.R.tl.Lung	93.3885625641763	121.222146083053	80.2205166301812
chr6.19987_chr6_88757394_88759225_+_2.R.tl.cerebhem	87.3058434221607	122.259780991217	86.88874965759
chr6.19987_chr6_88757394_88759225_+_2.R.tl.cortex	108.846614177950	99.514493397125	131.445387735645
chr6.19987_chr6_88757394_88759225_+_2.R.tl.heart	82.1038942982397	119.345157695553	80.6357332516235
chr6.19987_chr6_88757394_88759225_+_2.R.tl.kidney	95.6100850704861	130.230138846804	81.8808815617831
chr6.19987_chr6_88757394_88759225_+_2.R.tl.liver	94.0719075001086	143.059503646980	76.7045274042904
chr6.19987_chr6_88757394_88759225_+_2.R.tl.stomach	82.3526341576861	116.693192284544	83.731432726908
chr6.19987_chr6_88757394_88759225_+_2.R.tl.testicle	86.5697362617772	129.586314938535	83.380169703884


diffExp=-27.8335835188765,-34.9539375690559,9.33212078082508,-37.2412633973129,-34.6200537763182,-48.9875961468718,-34.340558126858,-43.0165786767579
diffExpScore=1.06991268961491
diffExp1.5=0,0,0,0,0,-1,0,0
diffExp1.5Score=0.5
diffExp1.4=0,-1,0,-1,0,-1,-1,-1
diffExp1.4Score=0.833333333333333
diffExp1.3=0,-1,0,-1,-1,-1,-1,-1
diffExp1.3Score=0.857142857142857
diffExp1.2=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	95.4056340860145	107.32565685492	88.7159136094002
cerebhem	96.2244573666426	86.0282202831477	95.0501876708355
cortex	94.0890970224353	88.71514188404	106.019214898855
heart	102.532625481396	102.453588380337	105.255528978038
kidney	88.2699776725657	92.143025569913	97.218954769417
liver	96.5751307904263	116.293698574237	94.9067007235197
stomach	98.5030517505072	96.5333194227417	89.9968316247634
testicle	95.326899227118	105.396261342778	103.269312416279
cont.diffExp=-11.9200227689055,10.1962370834949,5.3739551383954,0.079037101058475,-3.87304789734726,-19.7185677838111,1.96973232776556,-10.0693621156601
cont.diffExpScore=2.18216550298483

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

tran.correlation=-0.345347438565104
cont.tran.correlation=0.299597929659099

tran.covariance=-0.00350303933045520
cont.tran.covariance=0.00135887949245008

tran.mean=107.010000333525
cont.tran.mean=97.6134866068263

weightedLogRatios:
wLogRatio
Lung	-1.21746582581871
cerebhem	-1.56168401153224
cortex	0.41637190262206
heart	-1.71868872753314
kidney	-1.45698763154227
liver	-1.99273995275748
stomach	-1.59814297940439
testicle	-1.88089802285460

cont.weightedLogRatios:
wLogRatio
Lung	-0.543560188262862
cerebhem	0.505232974320476
cortex	0.265524707468445
heart	0.00357024625102568
kidney	-0.193319118222161
liver	-0.866415156387863
stomach	0.0925125592129561
testicle	-0.462664702776865

varWeightedLogRatios=0.58297718511978
cont.varWeightedLogRatios=0.207451879775154

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.04016049351503	0.0895122359024681	56.3069444383755	5.0923265102742e-265	***
df.mm.trans1	-0.628206065568654	0.0772219067928577	-8.13507580502735	1.82107514240176e-15	***
df.mm.trans2	-0.221182136343328	0.0693949644837837	-3.18729374658032	0.00149862507457292	** 
df.mm.exp2	-0.138677393134606	0.0905365669226373	-1.53172798404323	0.126031884577386	   
df.mm.exp3	-0.53796284648523	0.0905365669226373	-5.94193997818489	4.39909404936886e-09	***
df.mm.exp4	-0.149551024432372	0.0905365669226373	-1.65183007833909	0.0990082609758535	.  
df.mm.exp5	0.074701602600242	0.0905365669226373	0.825098688180588	0.40959075307472	   
df.mm.exp6	0.217745021132799	0.0905365669226373	2.40505056171238	0.0164233510104614	*  
df.mm.exp7	-0.206670162639786	0.0905365669226373	-2.28272586055073	0.0227392681056772	*  
df.mm.exp8	-0.0477273964960922	0.0905365669226373	-0.527161544979663	0.598244931403954	   
df.mm.trans1:exp2	0.0713259074518124	0.0829781342168842	0.859574731644174	0.390311610387179	   
df.mm.trans2:exp2	0.147200745996373	0.0652868468688092	2.25467690746600	0.0244554022429890	*  
df.mm.trans1:exp3	0.69113364703875	0.0829781342168842	8.32910565610331	4.14604910429356e-16	***
df.mm.trans2:exp3	0.340641361923874	0.0652868468688092	5.21761087050775	2.37698880169569e-07	***
df.mm.trans1:exp4	0.0207675921118608	0.0829781342168841	0.25027788715494	0.802444351228453	   
df.mm.trans2:exp4	0.133946023689578	0.0652868468688092	2.05165404845997	0.0405670325203759	*  
df.mm.trans1:exp5	-0.0511921769877678	0.0829781342168841	-0.616935744228283	0.537473504324624	   
df.mm.trans2:exp5	-0.00302319885599866	0.0652868468688092	-0.0463064001555112	0.963078968345085	   
df.mm.trans1:exp6	-0.210454439150464	0.0829781342168841	-2.53626381379447	0.0114159132193837	*  
df.mm.trans2:exp6	-0.0521091484038121	0.0652868468688092	-0.798156916790957	0.425044446038338	   
df.mm.trans1:exp7	0.0809117248446759	0.0829781342168841	0.975096940998852	0.329841953916180	   
df.mm.trans2:exp7	0.168593584650315	0.0652868468688092	2.58235146489914	0.0100106687862393	*  
df.mm.trans1:exp8	-0.0280911960623150	0.0829781342168841	-0.338537330676677	0.735057687877276	   
df.mm.trans2:exp8	0.114449799827847	0.0652868468688092	1.75302997949692	0.0800253971502166	.  
df.mm.trans1:probe2	-0.0873949263049285	0.0543219401535824	-1.60883293302559	0.108094296333046	   
df.mm.trans1:probe3	-0.0207915715845256	0.0543219401535824	-0.382747220105584	0.702020951688791	   
df.mm.trans1:probe4	-0.0303604129102935	0.0543219401535824	-0.558897801228319	0.576406467904315	   
df.mm.trans1:probe5	-0.0666276093512022	0.0543219401535824	-1.22653221079417	0.220402216602098	   
df.mm.trans1:probe6	0.170020213631132	0.0543219401535824	3.12986268808589	0.00182001306833817	** 
df.mm.trans1:probe7	0.26107523496024	0.0543219401535824	4.80607346170096	1.87676703077779e-06	***
df.mm.trans1:probe8	0.236133489683927	0.0543219401535824	4.34692665645438	1.58089407057114e-05	***
df.mm.trans1:probe9	0.0831003116529271	0.0543219401535824	1.52977436774130	0.126514865307015	   
df.mm.trans1:probe10	-0.0111978184537152	0.0543219401535824	-0.206138043340426	0.836741763864945	   
df.mm.trans1:probe11	0.124779247089591	0.0543219401535824	2.29703222559444	0.0219050498872794	*  
df.mm.trans1:probe12	0.348616433117019	0.0543219401535824	6.41759907932944	2.51994802478664e-10	***
df.mm.trans1:probe13	0.491734699789414	0.0543219401535824	9.05223006393274	1.31114079830130e-18	***
df.mm.trans1:probe14	0.309374473208949	0.0543219401535824	5.6952029388911	1.80009055702751e-08	***
df.mm.trans1:probe15	0.649360419066101	0.0543219401535824	11.9539253795094	3.73531566921796e-30	***
df.mm.trans1:probe16	0.135120553976999	0.0543219401535824	2.48740294612043	0.0130948289598112	*  
df.mm.trans1:probe17	0.369613742236640	0.0543219401535824	6.80413367401174	2.15133998432018e-11	***
df.mm.trans1:probe18	0.282619306087286	0.0543219401535824	5.20267327139359	2.56869255234783e-07	***
df.mm.trans2:probe2	-0.0304191597600006	0.0543219401535824	-0.559979258362231	0.575668977362437	   
df.mm.trans2:probe3	0.0258044306589637	0.0543219401535824	0.475027780414467	0.63491223943951	   
df.mm.trans2:probe4	-0.0271633977156084	0.0543219401535824	-0.500044689840059	0.617197467506402	   
df.mm.trans2:probe5	-0.224792220023169	0.0543219401535824	-4.13814785310727	3.91818701106933e-05	***
df.mm.trans2:probe6	-0.0423797279521846	0.0543219401535824	-0.780158584770094	0.435555718589537	   
df.mm.trans3:probe2	-0.129176061172356	0.0543219401535824	-2.37797215650143	0.0176700109122535	*  
df.mm.trans3:probe3	0.273038620974055	0.0543219401535824	5.02630466073383	6.32571475307952e-07	***
df.mm.trans3:probe4	0.485612310143457	0.0543219401535824	8.93952441261308	3.29582743864100e-18	***
df.mm.trans3:probe5	0.384639217779643	0.0543219401535824	7.08073416914357	3.43451696645112e-12	***
df.mm.trans3:probe6	0.0739831520276913	0.0543219401535824	1.36193869030674	0.173646163096517	   
df.mm.trans3:probe7	0.0057301769913482	0.0543219401535824	0.105485499508071	0.916020161063136	   
df.mm.trans3:probe8	0.45822865433555	0.0543219401535824	8.4354250426258	1.82118973141224e-16	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.6940442557744	0.143686172029748	32.6687264993221	6.01290539020067e-144	***
df.mm.trans1	-0.161530817585671	0.123957580458537	-1.30311366991954	0.192955442847994	   
df.mm.trans2	-0.023111194387871	0.111393673773043	-0.207473132046604	0.835699449992626	   
df.mm.exp2	-0.281612184157043	0.145330441125426	-1.93773707680417	0.053048727998295	.  
df.mm.exp3	-0.382513678574585	0.145330441125426	-2.63202723126988	0.00867080420277193	** 
df.mm.exp4	-0.145366143957624	0.145330441125426	-1.00024566657833	0.317530019543712	   
df.mm.exp5	-0.321789744628961	0.145330441125426	-2.21419368259705	0.0271301696606473	*  
df.mm.exp6	0.0249797410335718	0.145330441125426	0.171882372613273	0.86357864087103	   
df.mm.exp7	-0.0883647938375147	0.145330441125426	-0.608026736540711	0.543362821657811	   
df.mm.exp8	-0.170867151922826	0.145330441125426	-1.17571480964102	0.240100191514963	   
df.mm.trans1:exp2	0.290158109881245	0.133197549447718	2.1784042655765	0.0297019192273912	*  
df.mm.trans2:exp2	0.0604198350773622	0.104799271472705	0.576529151665893	0.564439017480454	   
df.mm.trans1:exp3	0.36861821837567	0.133197549447718	2.76745495622169	0.00579567820801913	** 
df.mm.trans2:exp3	0.192076527939572	0.104799271472705	1.83280403804714	0.0672473850751722	.  
df.mm.trans1:exp4	0.217409555053887	0.133197549447718	1.63223389585876	0.103070522834034	   
df.mm.trans2:exp4	0.0989083093700962	0.104799271472705	0.943788138793093	0.345596718961763	   
df.mm.trans1:exp5	0.244052156500498	0.133197549447718	1.83225710617366	0.0673288919248096	.  
df.mm.trans2:exp5	0.169264005924873	0.104799271472705	1.61512578805434	0.106724613628608	   
df.mm.trans1:exp6	-0.0127961124093020	0.133197549447718	-0.0960686774070468	0.923492947400091	   
df.mm.trans2:exp6	0.0552714001810105	0.104799271472705	0.527402522978473	0.598077699065113	   
df.mm.trans1:exp7	0.120314689558463	0.133197549447718	0.903280053254195	0.366681492839313	   
df.mm.trans2:exp7	-0.0176147127538821	0.104799271472705	-0.168080488598338	0.866567489957454	   
df.mm.trans1:exp8	0.170041546956529	0.133197549447718	1.27661167687828	0.202153845258046	   
df.mm.trans2:exp8	0.152726581936429	0.104799271472705	1.4573248438679	0.145465771203367	   
df.mm.trans1:probe2	0.0622566380134886	0.0871982646752557	0.713966479096174	0.475480903878948	   
df.mm.trans1:probe3	-0.0168122642898372	0.0871982646752557	-0.192805032903459	0.847166412958742	   
df.mm.trans1:probe4	-0.0635879886025651	0.0871982646752557	-0.729234564923739	0.466096996754561	   
df.mm.trans1:probe5	0.0646859248918039	0.0871982646752557	0.741825827987605	0.458436468756909	   
df.mm.trans1:probe6	0.119618928068414	0.0871982646752557	1.37180399763572	0.170554739318407	   
df.mm.trans1:probe7	0.0284449379515914	0.0871982646752557	0.326209908620615	0.74436100005376	   
df.mm.trans1:probe8	-0.0526106413365381	0.0871982646752557	-0.603345049726287	0.546470511887651	   
df.mm.trans1:probe9	0.0452203850345822	0.0871982646752557	0.518592717446754	0.604205240949498	   
df.mm.trans1:probe10	0.104236567062292	0.0871982646752557	1.19539726450395	0.232328175672757	   
df.mm.trans1:probe11	-0.0301050503609664	0.0871982646752557	-0.345248273839896	0.730009271838214	   
df.mm.trans1:probe12	0.00435193850694154	0.0871982646752557	0.0499085448907619	0.960209206332788	   
df.mm.trans1:probe13	0.0292688154309492	0.0871982646752557	0.335658232878284	0.73722707190705	   
df.mm.trans1:probe14	0.0929493970297878	0.0871982646752557	1.06595466522127	0.286803945924698	   
df.mm.trans1:probe15	0.151148979174080	0.0871982646752557	1.73339434835073	0.0834567076958015	.  
df.mm.trans1:probe16	-0.0727771930755344	0.0871982646752557	-0.834617447337646	0.404211903650934	   
df.mm.trans1:probe17	0.0897322390253194	0.0871982646752557	1.02905991718414	0.303799502654312	   
df.mm.trans1:probe18	0.110207484180341	0.0871982646752557	1.26387244735633	0.206687705596774	   
df.mm.trans2:probe2	0.08614244883728	0.0871982646752557	0.987891779246895	0.323539834538745	   
df.mm.trans2:probe3	0.0183950738761184	0.0871982646752557	0.210956880215741	0.832981027109819	   
df.mm.trans2:probe4	0.00198196018947361	0.0871982646752557	0.0227293535812305	0.981872502683141	   
df.mm.trans2:probe5	-0.0749123433270092	0.0871982646752557	-0.859103602646201	0.390571286057799	   
df.mm.trans2:probe6	0.0374782816804495	0.0871982646752557	0.429805361609274	0.667466730472535	   
df.mm.trans3:probe2	-0.0334753517656722	0.0871982646752557	-0.383899288481729	0.701167226293586	   
df.mm.trans3:probe3	0.0377288293443488	0.0871982646752557	0.43267867181599	0.665378701210174	   
df.mm.trans3:probe4	-0.0698909830398726	0.0871982646752557	-0.801518049701574	0.42309808252595	   
df.mm.trans3:probe5	-0.112138943240623	0.0871982646752557	-1.28602264802232	0.19885146027927	   
df.mm.trans3:probe6	0.0148187771025267	0.0871982646752557	0.169943486349355	0.86510265361434	   
df.mm.trans3:probe7	-0.0386956702208875	0.0871982646752557	-0.443766517200752	0.657345657875405	   
df.mm.trans3:probe8	0.00994965200713937	0.0871982646752557	0.114103784567203	0.909187554463262