chr16.9649_chr16_18369838_18371629_+_2.R 

fitVsDatCorrelation=0.88836154995685
cont.fitVsDatCorrelation=0.269601508569185

fstatistic=9675.8374671911,51,669
cont.fstatistic=2189.54807733762,51,669

residuals=-0.500869063379187,-0.0878310707711553,-0.00553435162410619,0.0845002587259285,1.06604329755976
cont.residuals=-0.70966485943929,-0.233676606635843,-0.069081341687924,0.182774955271864,1.33237252341586

predictedValues:
Include	Exclude	Both
chr16.9649_chr16_18369838_18371629_+_2.R.tl.Lung	67.8877879907325	91.0822951792918	50.6152828302162
chr16.9649_chr16_18369838_18371629_+_2.R.tl.cerebhem	76.447832332331	91.8445597132607	61.8257068143083
chr16.9649_chr16_18369838_18371629_+_2.R.tl.cortex	71.9768245565287	95.7370059151236	55.1345700954568
chr16.9649_chr16_18369838_18371629_+_2.R.tl.heart	73.0228075716866	96.464603715509	51.7740139831403
chr16.9649_chr16_18369838_18371629_+_2.R.tl.kidney	66.4440617374083	84.205696083047	50.8202740805596
chr16.9649_chr16_18369838_18371629_+_2.R.tl.liver	65.8702079772894	90.728912464635	53.6523021146055
chr16.9649_chr16_18369838_18371629_+_2.R.tl.stomach	90.3505387723997	117.496724315257	52.5235162649913
chr16.9649_chr16_18369838_18371629_+_2.R.tl.testicle	66.422091535975	89.684994708478	55.9829098791638


diffExp=-23.1945071885593,-15.3967273809296,-23.760181358595,-23.4417961438223,-17.7616343456386,-24.8587044873457,-27.1461855428577,-23.2629031725029
diffExpScore=0.994438964959519
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=-1,0,-1,-1,0,-1,-1,-1
diffExp1.3Score=0.857142857142857
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	73.3695068197971	65.3613589826122	65.0014905950021
cerebhem	68.7425822921604	65.3386729046562	72.6125579438205
cortex	66.1623388483063	63.0210879987435	68.7662473805724
heart	71.3286093673683	64.9220824968906	77.43780543501
kidney	70.922097893932	72.0645418937596	74.590858026831
liver	67.0317957236162	57.4626865800978	74.6303505482722
stomach	67.9107358672955	69.3616586759959	63.9915247435633
testicle	69.0942436710899	59.2140485420707	75.0223695584221
cont.diffExp=8.00814783718492,3.40390938750421,3.14125084956282,6.40652687047763,-1.14244399982759,9.56910914351841,-1.45092280870043,9.88019512901915
cont.diffExpScore=1.10786165924946

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=0.928576976745003
cont.tran.correlation=0.383329242551207

tran.covariance=0.00961060461920084
cont.tran.covariance=0.00101525955286416

tran.mean=83.4791840355596
cont.tran.mean=66.9567530348995

weightedLogRatios:
wLogRatio
Lung	-1.28284935486170
cerebhem	-0.812554086360226
cortex	-1.26055988051503
heart	-1.23332387357987
kidney	-1.02218812638619
liver	-1.39211525476953
stomach	-1.21769106821031
testicle	-1.30503981352160

cont.weightedLogRatios:
wLogRatio
Lung	0.48978365752361
cerebhem	0.213548642583142
cortex	0.202729664707952
heart	0.397165687592277
kidney	-0.0682280642542973
liver	0.635864374820336
stomach	-0.0893965452852658
testicle	0.641680429619122

varWeightedLogRatios=0.034421347881096
cont.varWeightedLogRatios=0.0827284794319608

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.7253060160238	0.0765958207777826	61.6914339195178	2.78163397202605e-278	***
df.mm.trans1	-0.506882323948258	0.0642275482693172	-7.89197684804677	1.21790131692662e-14	***
df.mm.trans2	-0.285155313550899	0.0590210437714331	-4.83141766613279	1.68227554280067e-06	***
df.mm.exp2	-0.072979131236045	0.0765958207777826	-0.952782155670997	0.34104456717886	   
df.mm.exp3	0.0228061274677937	0.0765958207777826	0.297746368355502	0.765989189034947	   
df.mm.exp4	0.107693559092807	0.0765958207777826	1.40599784687005	0.160189038063565	   
df.mm.exp5	-0.104038428939852	0.0765958207777826	-1.35827813950431	0.174833300996958	   
df.mm.exp6	-0.0923280844770715	0.0765958207777826	-1.20539323868506	0.228477709042186	   
df.mm.exp7	0.503480393959044	0.0765958207777826	6.57320972406218	9.91564341890541e-11	***
df.mm.exp8	-0.138079329519775	0.0765958207777826	-1.80270056665842	0.0718851681971995	.  
df.mm.trans1:exp2	0.191731543623883	0.0663339266172797	2.89039942909	0.00397220380866414	** 
df.mm.trans2:exp2	0.0813132705456394	0.0541614242825195	1.50131337243809	0.133746339835514	   
df.mm.trans1:exp3	0.0356818930459753	0.0663339266172796	0.537913174533532	0.590815888813116	   
df.mm.trans2:exp3	0.0270353424504953	0.0541614242825195	0.499162324636668	0.617829134107107	   
df.mm.trans1:exp4	-0.0347778997805273	0.0663339266172796	-0.524285257243732	0.600253808657648	   
df.mm.trans2:exp4	-0.0502808593863195	0.0541614242825195	-0.928351867632616	0.353559895237106	   
df.mm.trans1:exp5	0.0825426802046823	0.0663339266172796	1.24435088368763	0.213806223193342	   
df.mm.trans2:exp5	0.0255375568673738	0.0541614242825195	0.471508222054196	0.637431577906712	   
df.mm.trans1:exp6	0.0621581789867389	0.0663339266172797	0.937049593722483	0.349071192857941	   
df.mm.trans2:exp6	0.088440720609345	0.0541614242825195	1.63290980215026	0.102958497581705	   
df.mm.trans1:exp7	-0.217639579296798	0.0663339266172797	-3.28096933794515	0.00108822202053683	** 
df.mm.trans2:exp7	-0.248833379406459	0.0541614242825195	-4.594291651351	5.18990888849482e-06	***
df.mm.trans1:exp8	0.116252868995452	0.0663339266172796	1.75254013931943	0.0801390486745867	.  
df.mm.trans2:exp8	0.122619361044556	0.0541614242825195	2.26396116182143	0.0238958885940845	*  
df.mm.trans1:probe2	-0.0161968919512735	0.0469051693338093	-0.345311448211718	0.729968785063511	   
df.mm.trans1:probe3	-0.132695402655913	0.0469051693338093	-2.82901446771383	0.00480902730393663	** 
df.mm.trans1:probe4	0.0884159118359823	0.0469051693338093	1.88499291425118	0.0598639690828571	.  
df.mm.trans1:probe5	0.11300847948915	0.0469051693338093	2.40929690893779	0.0162524042088372	*  
df.mm.trans1:probe6	-0.135456687481843	0.0469051693338093	-2.88788398817710	0.00400370712236916	** 
df.mm.trans1:probe7	0.0646550169845241	0.0469051693338093	1.37841986081310	0.168534471558261	   
df.mm.trans1:probe8	0.314227167316870	0.0469051693338093	6.69920121342307	4.44871411198267e-11	***
df.mm.trans1:probe9	-0.19571249379188	0.0469051693338093	-4.17251438533471	3.41003910889763e-05	***
df.mm.trans1:probe10	-0.186766510800054	0.0469051693338093	-3.98178950108667	7.58904623487406e-05	***
df.mm.trans1:probe11	-0.00389175656217953	0.0469051693338093	-0.0829707389921808	0.933899625131195	   
df.mm.trans1:probe12	0.0767925304780015	0.0469051693338093	1.63718693629466	0.102061679774273	   
df.mm.trans2:probe2	0.140268934168221	0.0469051693338093	2.99047921925133	0.0028877140440646	** 
df.mm.trans2:probe3	0.0276586666642008	0.0469051693338093	0.589672035236945	0.555609636033577	   
df.mm.trans2:probe4	0.491166474958138	0.0469051693338093	10.4714785584220	7.27040602016072e-24	***
df.mm.trans2:probe5	0.405820305705223	0.0469051693338093	8.65193136426239	3.75074588345552e-17	***
df.mm.trans2:probe6	0.224114902541959	0.0469051693338093	4.77804271309638	2.17707672538562e-06	***
df.mm.trans3:probe2	0.0126636942189126	0.0469051693338093	0.269985044266424	0.787255019273	   
df.mm.trans3:probe3	-0.137845953126019	0.0469051693338092	-2.938822204116	0.00340810369042364	** 
df.mm.trans3:probe4	-0.041434428469273	0.0469051693338093	-0.88336592869748	0.377355915566405	   
df.mm.trans3:probe5	0.172185504648559	0.0469051693338092	3.67092811078389	0.000260937851493663	***
df.mm.trans3:probe6	-0.008458394211913	0.0469051693338093	-0.180329680759007	0.856948366028445	   
df.mm.trans3:probe7	0.0860774513135775	0.0469051693338093	1.83513784378416	0.0669290596329152	.  
df.mm.trans3:probe8	-0.0186009027677709	0.0469051693338092	-0.396564025499922	0.69181544663763	   
df.mm.trans3:probe9	0.086061766425015	0.0469051693338093	1.83480344804942	0.0669786756547352	.  
df.mm.trans3:probe10	-0.0155424598499077	0.0469051693338093	-0.331359209883604	0.740476940550868	   
df.mm.trans3:probe11	-0.216953288974944	0.0469051693338093	-4.62535989223184	4.49018863812047e-06	***
df.mm.trans3:probe12	-0.0263307765989821	0.0469051693338092	-0.561361934578987	0.574738815584751	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.28031387148553	0.160645753297184	26.644425910016	3.64137912661569e-107	***
df.mm.trans1	0.0317063151716563	0.134705559250937	0.235374956668210	0.813989752376343	   
df.mm.trans2	-0.135657828973869	0.123785866392833	-1.09590725441434	0.273513714897899	   
df.mm.exp2	-0.176214400775708	0.160645753297184	-1.09691291029476	0.273074137244041	   
df.mm.exp3	-0.196161683774686	0.160645753297184	-1.22108228663723	0.222485025336807	   
df.mm.exp4	-0.210019234913713	0.160645753297184	-1.30734383326767	0.191545091284449	   
df.mm.exp5	-0.073903210194188	0.160645753297184	-0.460038368132097	0.645638155238805	   
df.mm.exp6	-0.357273841924661	0.160645753297184	-2.22398559931882	0.0264824765321042	*  
df.mm.exp7	-0.00225171914146884	0.160645753297184	-0.0140166739254122	0.988820857357753	   
df.mm.exp8	-0.302185538294727	0.160645753297184	-1.88106770389195	0.0603967705879982	.  
df.mm.trans1:exp2	0.111074826689569	0.139123303365449	0.798391240019637	0.424926806833560	   
df.mm.trans2:exp2	0.175867253527826	0.11359370152526	1.54821307137983	0.122043906832101	   
df.mm.trans1:exp3	0.0927646743808359	0.139123303365449	0.666780274309341	0.505142385082209	   
df.mm.trans2:exp3	0.159699841692149	0.11359370152526	1.40588641401598	0.160222117194576	   
df.mm.trans1:exp4	0.181808324372982	0.139123303365449	1.30681431489165	0.191724809224369	   
df.mm.trans2:exp4	0.203275812336586	0.11359370152526	1.78949897403760	0.073986712836097	.  
df.mm.trans1:exp5	0.0399768612157568	0.139123303365449	0.287348418623626	0.773934534378741	   
df.mm.trans2:exp5	0.171534100257978	0.11359370152526	1.51006700155672	0.131498475170417	   
df.mm.trans1:exp6	0.266932500905610	0.139123303365449	1.91867569593594	0.055450622690912	.  
df.mm.trans2:exp6	0.228478407437383	0.11359370152526	2.01136510536701	0.0446874892820368	*  
df.mm.trans1:exp7	-0.0750625567516448	0.139123303365449	-0.539539781875869	0.58969397854074	   
df.mm.trans2:exp7	0.0616547227383815	0.11359370152526	0.542765328627585	0.587472157176786	   
df.mm.trans1:exp8	0.242148550392954	0.139123303365449	1.74053192049989	0.0822254411626065	.  
df.mm.trans2:exp8	0.203413115882642	0.11359370152526	1.79070769903038	0.073792227394075	.  
df.mm.trans1:probe2	-0.0138822450626744	0.0983750312307823	-0.141115533982474	0.887821152210317	   
df.mm.trans1:probe3	0.0191125887790233	0.0983750312307823	0.194282924639549	0.846013334368153	   
df.mm.trans1:probe4	-0.0300461839238902	0.0983750312307823	-0.305424898452165	0.760137566223564	   
df.mm.trans1:probe5	0.116053328518332	0.0983750312307823	1.17970309199778	0.238537652815505	   
df.mm.trans1:probe6	-0.0295952983456720	0.0983750312307823	-0.300841564931686	0.763628783065887	   
df.mm.trans1:probe7	0.0662946908024595	0.0983750312307823	0.673897532463658	0.500609226623331	   
df.mm.trans1:probe8	-0.204008223602986	0.0983750312307823	-2.07378052185207	0.0384817423136637	*  
df.mm.trans1:probe9	-0.0793816068010254	0.0983750312307823	-0.8069284025415	0.419994509289701	   
df.mm.trans1:probe10	-0.0503287206976678	0.0983750312307823	-0.511600556238701	0.609099413216451	   
df.mm.trans1:probe11	-0.076664824498541	0.0983750312307823	-0.77931181865335	0.436071639760646	   
df.mm.trans1:probe12	-0.113836123828504	0.0983750312307823	-1.15716480497425	0.247618028491107	   
df.mm.trans2:probe2	0.123362547593887	0.0983750312307823	1.25400262699267	0.210279045468066	   
df.mm.trans2:probe3	0.0778251459882876	0.0983750312307823	0.791106696634375	0.429162102284226	   
df.mm.trans2:probe4	0.0336819621213561	0.0983750312307823	0.342383241966552	0.732170036517177	   
df.mm.trans2:probe5	0.106389400285082	0.0983750312307823	1.08146751217286	0.279878879302516	   
df.mm.trans2:probe6	0.29369454565946	0.0983750312307823	2.98545822029240	0.00293490226478854	** 
df.mm.trans3:probe2	-0.0227403130357211	0.0983750312307823	-0.231159398388130	0.817261649379579	   
df.mm.trans3:probe3	-0.00943039199947986	0.0983750312307823	-0.095861641734646	0.923659167117102	   
df.mm.trans3:probe4	0.0168317482619851	0.0983750312307823	0.17109776791327	0.864198626557336	   
df.mm.trans3:probe5	-0.0105744080752891	0.0983750312307823	-0.107490772231443	0.914431872862158	   
df.mm.trans3:probe6	-0.0467401103463933	0.0983750312307823	-0.475121682419024	0.634855319236144	   
df.mm.trans3:probe7	0.0461189402373095	0.0983750312307823	0.468807375817925	0.639360051412228	   
df.mm.trans3:probe8	0.0564927914251066	0.0983750312307823	0.574259450983834	0.565985273182451	   
df.mm.trans3:probe9	-0.0721175353127807	0.0983750312307823	-0.733087800944093	0.463761668479625	   
df.mm.trans3:probe10	0.0478112251217171	0.0983750312307823	0.486009758000021	0.627119394641252	   
df.mm.trans3:probe11	-0.0056305424734771	0.0983750312307823	-0.0572354834660043	0.954374702947374	   
df.mm.trans3:probe12	-0.0234472736407901	0.0983750312307823	-0.238345780910443	0.811685901826442