chr10.2591_chr10_60735160_60757341_-_0.R 

fitVsDatCorrelation=0.799952668964075
cont.fitVsDatCorrelation=0.299491905615708

fstatistic=11118.4187442372,45,531
cont.fstatistic=4390.81602256804,45,531

residuals=-0.483823194230733,-0.081019006571042,-0.00831790150497223,0.077198027638095,0.592171415444441
cont.residuals=-0.59707263991312,-0.142207581674852,-0.0285766649426463,0.119489214431166,0.947481284460204

predictedValues:
Include	Exclude	Both
chr10.2591_chr10_60735160_60757341_-_0.R.tl.Lung	61.4048973363703	48.0943752365133	68.5924952742671
chr10.2591_chr10_60735160_60757341_-_0.R.tl.cerebhem	63.0442983295891	53.0159064968415	63.5312791083454
chr10.2591_chr10_60735160_60757341_-_0.R.tl.cortex	59.1616893175273	49.1548095004969	63.5966497839575
chr10.2591_chr10_60735160_60757341_-_0.R.tl.heart	64.3746883920464	50.7217475699642	65.7192720041236
chr10.2591_chr10_60735160_60757341_-_0.R.tl.kidney	61.1825906021137	62.6081529041588	81.4283330966516
chr10.2591_chr10_60735160_60757341_-_0.R.tl.liver	68.2862354732633	47.2579859711878	60.5800063446295
chr10.2591_chr10_60735160_60757341_-_0.R.tl.stomach	74.7609528138496	45.6335342723173	66.7852395821214
chr10.2591_chr10_60735160_60757341_-_0.R.tl.testicle	60.0427863946403	50.2291706705525	66.9442435886929


diffExp=13.3105220998570,10.0283918327476,10.0068798170304,13.6529408220822,-1.42556230204516,21.0282495020755,29.1274185415323,9.81361572408774
diffExpScore=1.01737452535768
diffExp1.5=0,0,0,0,0,0,1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,1,1,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,0,0,0,1,1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,1,1,0,1,1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	62.4582408081614	64.6768722942095	62.1105731415373
cerebhem	66.8222348611824	56.522464947684	62.3492297408574
cortex	61.378207916953	60.5753245970034	62.4956899943791
heart	63.9437949018135	54.9324666291663	65.8480001121941
kidney	65.2299576744891	59.8114376137147	60.3789819640341
liver	60.6729016225568	62.1061766536237	66.9384313734558
stomach	63.7227301989385	61.3914209018035	58.1779084097261
testicle	65.5808123718182	59.9285437726085	63.5448565400032
cont.diffExp=-2.21863148604807,10.2997699134984,0.802883319949672,9.01132827264724,5.4185200607744,-1.43327503106686,2.33130929713503,5.65226859920977
cont.diffExpScore=1.20424370499863

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=-0.464740963370494
cont.tran.correlation=-0.566105853633842

tran.covariance=-0.00370039446936791
cont.tran.covariance=-0.000972551302698965

tran.mean=57.4358638300895
cont.tran.mean=61.8595992353579

weightedLogRatios:
wLogRatio
Lung	0.97615582028491
cerebhem	0.702894761157436
cortex	0.738904804368851
heart	0.964317628989981
kidney	-0.0950191386559351
liver	1.48694637341457
stomach	2.00791793006115
testicle	0.714885708707725

cont.weightedLogRatios:
wLogRatio
Lung	-0.144925884060575
cerebhem	0.689400201675659
cortex	0.054123507671029
heart	0.620063843314602
kidney	0.358556837659322
liver	-0.0961288680269578
stomach	0.154150294320247
testicle	0.372978599188805

varWeightedLogRatios=0.380004394576318
cont.varWeightedLogRatios=0.0971780112425806

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.42583110235616	0.0697674546521956	49.1035701307236	1.45848479698787e-199	***
df.mm.trans1	0.675246595830476	0.0555430803221891	12.1571686682404	3.57302845041258e-30	***
df.mm.trans2	0.432807786040357	0.0555430803221891	7.79228994016476	3.48738085123410e-14	***
df.mm.exp2	0.200425555429639	0.0740574404295855	2.70635272117195	0.00702153396396133	** 
df.mm.exp3	0.060216401155862	0.0740574404295855	0.8131040015232	0.416522849376549	   
df.mm.exp4	0.143211373304727	0.0740574404295855	1.93378777978282	0.0536703136913222	.  
df.mm.exp5	0.088563210527802	0.0740574404295855	1.19587188017940	0.232280302296808	   
df.mm.exp6	0.212893266389769	0.0740574404295855	2.8747046232605	0.00420622336792519	** 
df.mm.exp7	0.170984788580370	0.0740574404295855	2.30881309951488	0.0213370438326092	*  
df.mm.exp8	0.0453215798942874	0.0740574404295855	0.611978750972085	0.540813758562503	   
df.mm.trans1:exp2	-0.174077521193198	0.0573646466893065	-3.03457845972662	0.00252671284401539	** 
df.mm.trans2:exp2	-0.102998795647372	0.0573646466893065	-1.7955099803059	0.0731409435764543	.  
df.mm.trans1:exp3	-0.0974318018089589	0.0573646466893065	-1.69846425336951	0.0900059793474571	.  
df.mm.trans2:exp3	-0.0384069371542507	0.0573646466893065	-0.669522770048025	0.503453071248358	   
df.mm.trans1:exp4	-0.095980447995454	0.0573646466893065	-1.67316376086643	0.0948840337085165	.  
df.mm.trans2:exp4	-0.0900218398495468	0.0573646466893065	-1.56929128034407	0.117175758684407	   
df.mm.trans1:exp5	-0.092190121914751	0.0573646466893065	-1.60708950957309	0.108629165867693	   
df.mm.trans2:exp5	0.175167065859616	0.0573646466893065	3.05357177232069	0.00237472260015401	** 
df.mm.trans1:exp6	-0.106674643680118	0.0573646466893065	-1.85958861139475	0.0634965806170049	.  
df.mm.trans2:exp6	-0.23043684279225	0.0573646466893065	-4.01705329138210	6.74361624413015e-05	***
df.mm.trans1:exp7	0.0258213457433239	0.0573646466893065	0.450126466971466	0.652802961586855	   
df.mm.trans2:exp7	-0.223507172949916	0.0573646466893065	-3.896252933631	0.000110177129715071	***
df.mm.trans1:exp8	-0.0677537583855442	0.0573646466893065	-1.18110652284684	0.238089145536099	   
df.mm.trans2:exp8	-0.00189086423263746	0.0573646466893065	-0.0329621873708837	0.973717127453898	   
df.mm.trans1:probe2	0.0993022383066768	0.0405629306743791	2.44810314875497	0.0146836303826664	*  
df.mm.trans1:probe3	-0.0242275504333199	0.0405629306743791	-0.597283037259012	0.550573027785118	   
df.mm.trans1:probe4	-0.0072544807463064	0.0405629306743791	-0.178845084063134	0.858127559902206	   
df.mm.trans1:probe5	0.120991679579561	0.0405629306743791	2.98281405135216	0.00298736276846675	** 
df.mm.trans1:probe6	0.106602222652108	0.0405629306743791	2.62807003536955	0.00883555718301675	** 
df.mm.trans2:probe2	0.0247865614449768	0.0405629306743791	0.611064364257902	0.541418454589995	   
df.mm.trans2:probe3	0.0478548280986625	0.0405629306743791	1.17976751933482	0.23862096562014	   
df.mm.trans2:probe4	0.0154877646419048	0.0405629306743791	0.381820652117906	0.702747147387099	   
df.mm.trans2:probe5	0.105856999271138	0.0405629306743791	2.60969800532685	0.00931790606734698	** 
df.mm.trans2:probe6	0.0674880192378794	0.0405629306743791	1.66378558244824	0.0967452821650002	.  
df.mm.trans3:probe2	-0.457949907974599	0.0405629306743791	-11.2898624522674	1.21702647796916e-26	***
df.mm.trans3:probe3	-0.283285281765866	0.0405629306743791	-6.98384650852653	8.62268362889798e-12	***
df.mm.trans3:probe4	-0.173739903014932	0.0405629306743790	-4.2832186956519	2.18685182953459e-05	***
df.mm.trans3:probe5	-0.536693195107291	0.0405629306743791	-13.2311247285267	9.64471594798707e-35	***
df.mm.trans3:probe6	-0.329430542718077	0.0405629306743791	-8.12146798175402	3.24094778110371e-15	***
df.mm.trans3:probe7	-0.413958618971357	0.0405629306743791	-10.2053429594235	1.89170271736821e-22	***
df.mm.trans3:probe8	-0.468476210389821	0.0405629306743791	-11.5493679229082	1.10735225527426e-27	***
df.mm.trans3:probe9	-0.244418301902836	0.0405629306743791	-6.0256568704297	3.15111500241062e-09	***
df.mm.trans3:probe10	-0.368944772063609	0.0405629306743790	-9.09561429437463	1.87859932197369e-18	***
df.mm.trans3:probe11	-0.358634692222958	0.0405629306743791	-8.84143936989947	1.39311994053148e-17	***
df.mm.trans3:probe12	-0.0328968196353313	0.0405629306743791	-0.811006973322813	0.417725016580757	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.13394511807256	0.110930079367137	37.2662233873533	2.62442913330183e-150	***
df.mm.trans1	0.0140083064361083	0.0883133595621552	0.158620468132562	0.874028208874482	   
df.mm.trans2	0.00877805196963436	0.0883133595621552	0.0993966486288673	0.920860856302315	   
df.mm.exp2	-0.0710628959869082	0.117751146082874	-0.603500673674071	0.546433369294447	   
df.mm.exp3	-0.0891407456686657	0.117751146082874	-0.75702656521006	0.449369716134015	   
df.mm.exp4	-0.198225642134135	0.117751146082874	-1.68342855868786	0.0928799158151565	.  
df.mm.exp5	-0.00651094851412855	0.117751146082874	-0.0552941413372412	0.955924927672686	   
df.mm.exp6	-0.144416276960404	0.117751146082874	-1.22645325981595	0.220571721408483	   
df.mm.exp7	0.0333201005980346	0.117751146082874	0.282970499281458	0.777309727862726	   
df.mm.exp8	-0.0502956134298371	0.117751146082874	-0.427134810173643	0.66945427872533	   
df.mm.trans1:exp2	0.138600591984957	0.0912096455551613	1.51958261805913	0.129211089109121	   
df.mm.trans2:exp2	-0.0637026121186952	0.0912096455551612	-0.698419687204787	0.485220517888518	   
df.mm.trans1:exp3	0.0716974115402384	0.0912096455551612	0.786072691148412	0.432175537236718	   
df.mm.trans2:exp3	0.0236246940280006	0.0912096455551612	0.259015303526347	0.795723810158876	   
df.mm.trans1:exp4	0.221731948623802	0.0912096455551612	2.43101425593968	0.0153865463505448	*  
df.mm.trans2:exp4	0.0349265165095184	0.0912096455551612	0.382925690555345	0.701928100788	   
df.mm.trans1:exp5	0.0499315990929922	0.0912096455551612	0.54743770561848	0.584308068772679	   
df.mm.trans2:exp5	-0.0716958213003419	0.0912096455551612	-0.786055256151413	0.432185742128491	   
df.mm.trans1:exp6	0.115415257723182	0.0912096455551612	1.26538434636699	0.206288551443912	   
df.mm.trans2:exp6	0.1038580470493	0.0912096455551612	1.13867394634803	0.255352624816196	   
df.mm.trans1:exp7	-0.0132769560240733	0.0912096455551612	-0.145565262788394	0.884319830920365	   
df.mm.trans2:exp7	-0.0854536768594081	0.0912096455551612	-0.936892982527029	0.349239518739645	   
df.mm.trans1:exp8	0.09908058589982	0.0912096455551612	1.08629504365192	0.277841358785769	   
df.mm.trans2:exp8	-0.0259551481261555	0.0912096455551612	-0.284565825995437	0.776087737050977	   
df.mm.trans1:probe2	-0.0765680283555246	0.064494958881676	-1.18719400218548	0.235681926615995	   
df.mm.trans1:probe3	-0.0991083903740332	0.064494958881676	-1.53668429428508	0.124966264526907	   
df.mm.trans1:probe4	-0.0224872441011395	0.064494958881676	-0.348666694127135	0.727477752027281	   
df.mm.trans1:probe5	0.00857457353538557	0.064494958881676	0.132949515498052	0.894283673007245	   
df.mm.trans1:probe6	-0.0526051973229291	0.064494958881676	-0.815648203132277	0.415067081907392	   
df.mm.trans2:probe2	0.0346906550431266	0.064494958881676	0.537881652219842	0.590884115814232	   
df.mm.trans2:probe3	0.069365033649784	0.064494958881676	1.07551093686319	0.282634467940194	   
df.mm.trans2:probe4	0.224530578933787	0.064494958881676	3.48136633974318	0.00053994156765075	***
df.mm.trans2:probe5	0.0299605443799809	0.064494958881676	0.464540871092689	0.642450693582299	   
df.mm.trans2:probe6	0.121702311896994	0.064494958881676	1.88700503120364	0.0597048727138997	.  
df.mm.trans3:probe2	-0.0617155292623911	0.064494958881676	-0.956904699724144	0.339050811899911	   
df.mm.trans3:probe3	-0.0592907848544007	0.064494958881676	-0.919308824790122	0.358351474202257	   
df.mm.trans3:probe4	-0.000346429236082789	0.064494958881676	-0.00537141572131795	0.995716268270933	   
df.mm.trans3:probe5	-0.0368215342188681	0.064494958881676	-0.570921120927013	0.568294644852855	   
df.mm.trans3:probe6	0.00559016224132762	0.064494958881676	0.0866759563578212	0.930961762376124	   
df.mm.trans3:probe7	0.0628136853511537	0.064494958881676	0.973931706296507	0.330533904710479	   
df.mm.trans3:probe8	-0.00588332173422502	0.064494958881676	-0.0912214200340636	0.927351056204744	   
df.mm.trans3:probe9	0.00620325005237297	0.064494958881676	0.0961819366960697	0.923412364996625	   
df.mm.trans3:probe10	-0.0659024260603954	0.064494958881676	-1.02182290217909	0.307330026990611	   
df.mm.trans3:probe11	-0.0848956474596907	0.064494958881676	-1.31631446754532	0.188636419799456	   
df.mm.trans3:probe12	-0.0935343300585054	0.064494958881676	-1.4502579997005	0.147576992345662