chr3.15264_chr3_116841942_116842959_-_0.R 

fitVsDatCorrelation=0.942057060773059
cont.fitVsDatCorrelation=0.335283816494581

fstatistic=8476.78130829886,43,485
cont.fstatistic=1064.84198806534,43,485

residuals=-0.777199449375155,-0.0945817379835395,-0.00243583029720731,0.107183568288315,0.532985555819249
cont.residuals=-1.22042557181451,-0.344434113632402,-0.0612349056764425,0.248383381693564,1.52987384134303

predictedValues:
Include	Exclude	Both
chr3.15264_chr3_116841942_116842959_-_0.R.tl.Lung	72.444628944285	122.021917631437	74.4406814216274
chr3.15264_chr3_116841942_116842959_-_0.R.tl.cerebhem	54.7022218783018	63.3898196637678	74.2279430385206
chr3.15264_chr3_116841942_116842959_-_0.R.tl.cortex	63.5990989780986	86.9977228191929	105.332132840332
chr3.15264_chr3_116841942_116842959_-_0.R.tl.heart	70.1218113981906	103.111823345511	81.1385627181642
chr3.15264_chr3_116841942_116842959_-_0.R.tl.kidney	79.5132799459427	126.308772821053	77.2835015933933
chr3.15264_chr3_116841942_116842959_-_0.R.tl.liver	87.3089980854032	123.249505353723	71.2317744331332
chr3.15264_chr3_116841942_116842959_-_0.R.tl.stomach	67.8925405528989	101.144931215956	78.9469691741442
chr3.15264_chr3_116841942_116842959_-_0.R.tl.testicle	81.5988524385688	116.167672241460	79.8437210473144


diffExp=-49.5772886871516,-8.68759778546596,-23.3986238410943,-32.9900119473207,-46.7954928751102,-35.9405072683193,-33.2523906630568,-34.5688198028908
diffExpScore=0.996243577450024
diffExp1.5=-1,0,0,0,-1,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=-1,0,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.857142857142857
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	82.7873470750469	134.075212593191	89.9355086754296
cerebhem	70.980025869421	102.217105909723	81.1986409550257
cortex	77.049334763498	91.8580287126946	82.9094157685046
heart	95.9856991654447	99.829153599632	106.120907635123
kidney	115.086540483375	80.7132192560203	101.027910495935
liver	122.141905050893	82.6343730801488	103.461747278291
stomach	81.1102541240948	107.768507863782	107.560889263236
testicle	84.7464500460996	81.8402365787838	90.3854536465782
cont.diffExp=-51.2878655181443,-31.2370800403017,-14.8086939491967,-3.84345443418724,34.3733212273551,39.5075319707439,-26.6582537396870,2.90621346731584
cont.diffExpScore=3.93139620276082

cont.diffExp1.5=-1,0,0,0,0,0,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=-1,-1,0,0,1,1,0,0
cont.diffExp1.4Score=4
cont.diffExp1.3=-1,-1,0,0,1,1,-1,0
cont.diffExp1.3Score=2.5
cont.diffExp1.2=-1,-1,0,0,1,1,-1,0
cont.diffExp1.2Score=2.5

tran.correlation=0.907659685206023
cont.tran.correlation=-0.508582147127446

tran.covariance=0.0323325719333269
cont.tran.covariance=-0.0177446733821178

tran.mean=88.7233498321118
cont.tran.mean=94.4264621357405

weightedLogRatios:
wLogRatio
Lung	-2.36888767559042
cerebhem	-0.600739710685063
cortex	-1.35001028145607
heart	-1.71314194206320
kidney	-2.13229587449274
liver	-1.60030539008537
stomach	-1.76083691410635
testicle	-1.61718849177228

cont.weightedLogRatios:
wLogRatio
Lung	-2.24542235519410
cerebhem	-1.62100222944551
cortex	-0.77919868936621
heart	-0.179965957274174
kidney	1.62074826787283
liver	1.80131707834124
stomach	-1.28956194926324
testicle	0.154312559870162

varWeightedLogRatios=0.279966758028194
cont.varWeightedLogRatios=2.15168392266140

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.87292471090393	0.0874934587532155	55.6947317015838	7.2963961681121e-213	***
df.mm.trans1	-0.804429582186768	0.0700430892499592	-11.4847815937421	3.59637256044657e-27	***
df.mm.trans2	-0.166721573955543	0.0700430892499592	-2.38027156912757	0.0176852999585480	*  
df.mm.exp2	-0.932953693697792	0.0937924683189072	-9.94700012079458	2.44141347102606e-21	***
df.mm.exp3	-0.815657907688672	0.0937924683189071	-8.6964115808886	5.33477566112077e-17	***
df.mm.exp4	-0.287131015568939	0.0937924683189071	-3.06134405795415	0.00232571674176899	** 
df.mm.exp5	0.0901524118557677	0.0937924683189071	0.9611903116702	0.336935436478804	   
df.mm.exp6	0.240704711971650	0.0937924683189071	2.56635438096396	0.0105764900287796	*  
df.mm.exp7	-0.311316357737548	0.0937924683189072	-3.31920423161303	0.000970695118650325	***
df.mm.exp8	-0.000242067771834181	0.0937924683189072	-0.00258088710290806	0.997941813515374	   
df.mm.trans1:exp2	0.652035490284037	0.0735768655670986	8.86196340736222	1.49830372045231e-17	***
df.mm.trans2:exp2	0.278056287806703	0.0735768655670986	3.77912657278298	0.000176967174795209	***
df.mm.trans1:exp3	0.685434679559237	0.0735768655670986	9.31589942404048	4.23287469570570e-19	***
df.mm.trans2:exp3	0.477339170194329	0.0735768655670986	6.48762578448002	2.15489257566154e-10	***
df.mm.trans1:exp4	0.254542376756336	0.0735768655670986	3.45954363228758	0.000588819275250088	***
df.mm.trans2:exp4	0.118744397126424	0.0735768655670986	1.61388224696980	0.107203361293617	   
df.mm.trans1:exp5	0.0029491078353942	0.0735768655670986	0.0400819987731708	0.968044247077102	   
df.mm.trans2:exp5	-0.0556236060433767	0.0735768655670986	-0.755993145598879	0.450020327415543	   
df.mm.trans1:exp6	-0.0540737149493885	0.0735768655670986	-0.734928221426826	0.462738412273937	   
df.mm.trans2:exp6	-0.230694593728458	0.0735768655670986	-3.13542296142088	0.00182020144625099	** 
df.mm.trans1:exp7	0.246419995593798	0.0735768655670986	3.34915049308638	0.000873738751153792	***
df.mm.trans2:exp7	0.123670127223813	0.0735768655670986	1.68082897077250	0.0934399827041733	.  
df.mm.trans1:exp8	0.119234735026044	0.0735768655670986	1.62054654145749	0.105764872230167	   
df.mm.trans2:exp8	-0.0489240157365082	0.0735768655670986	-0.664937482174907	0.506406528987712	   
df.mm.trans1:probe2	0.349533383038016	0.0503746362270313	6.93867805740013	1.27278695027989e-11	***
df.mm.trans1:probe3	1.39975058885650	0.0503746362270313	27.7868128426383	2.28422712141261e-102	***
df.mm.trans1:probe4	-0.042099740024017	0.0503746362270313	-0.835732884189565	0.403716716700769	   
df.mm.trans1:probe5	0.700349054977986	0.0503746362270313	13.9028111651588	3.18903842941233e-37	***
df.mm.trans1:probe6	1.02170515550298	0.0503746362270313	20.2821346619417	1.10081978685018e-66	***
df.mm.trans2:probe2	-0.343853808362203	0.0503746362270313	-6.82593134395062	2.61802983691547e-11	***
df.mm.trans2:probe3	0.998131989832148	0.0503746362270313	19.8141776217244	1.88438480932455e-64	***
df.mm.trans2:probe4	-0.163524823886736	0.0503746362270313	-3.24617379170250	0.00125058328886949	** 
df.mm.trans2:probe5	0.699551004819757	0.0503746362270313	13.886968863993	3.73584186099029e-37	***
df.mm.trans2:probe6	0.377656347452139	0.0503746362270313	7.49695433531463	3.12986287214542e-13	***
df.mm.trans3:probe2	0.630414666571555	0.0503746362270313	12.5145254395558	2.4599314041535e-31	***
df.mm.trans3:probe3	0.589405579052582	0.0503746362270313	11.7004433817887	5.00578704574759e-28	***
df.mm.trans3:probe4	0.539480595456909	0.0503746362270313	10.7093695530732	3.62550376142217e-24	***
df.mm.trans3:probe5	0.576617550436459	0.0503746362270313	11.4465849011341	5.0888657526195e-27	***
df.mm.trans3:probe6	0.0347450331025119	0.0503746362270313	0.689732685034608	0.490692186363605	   
df.mm.trans3:probe7	0.595835344712547	0.0503746362270313	11.8280823314972	1.54355851958175e-28	***
df.mm.trans3:probe8	0.652019274094935	0.0503746362270313	12.9434041202080	4.00895801566022e-33	***
df.mm.trans3:probe9	0.142392375136380	0.0503746362270313	2.82666805760419	0.00489779276692742	** 
df.mm.trans3:probe10	0.32137989085498	0.0503746362270313	6.37979576480844	4.14395652187900e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.79776755640941	0.245724947164295	19.5249510144428	4.49803967410517e-63	***
df.mm.trans1	-0.417123433722369	0.196715670524772	-2.12043825796706	0.0344760862262502	*  
df.mm.trans2	0.09277633483149	0.196715670524772	0.471626559205953	0.637405458432724	   
df.mm.exp2	-0.32298428167971	0.263415684446528	-1.22613914337844	0.220741305078963	   
df.mm.exp3	-0.368641812562547	0.263415684446528	-1.39946796766151	0.162311951297688	   
df.mm.exp4	-0.312502931815323	0.263415684446528	-1.18634899236138	0.236065329894646	   
df.mm.exp5	-0.294393403572678	0.263415684446528	-1.11760013148510	0.264291110388673	   
df.mm.exp6	-0.235176018888827	0.263415684446528	-0.8927942896907	0.372410103983489	   
df.mm.exp7	-0.417845550769575	0.263415684446528	-1.58625919199734	0.113332257849556	   
df.mm.exp8	-0.475233796962328	0.263415684446528	-1.80412110979974	0.0718326097942847	.  
df.mm.trans1:exp2	0.169107556747897	0.20664026387373	0.818366922194953	0.413549596370199	   
df.mm.trans2:exp2	0.0516823917542365	0.20664026387373	0.250108041798755	0.802609702017762	   
df.mm.trans1:exp3	0.296812503851888	0.20664026387373	1.43637303925076	0.151540925152148	   
df.mm.trans2:exp3	-0.00951489895865312	0.20664026387373	-0.0460457162620897	0.963292765074137	   
df.mm.trans1:exp4	0.460426908549049	0.20664026387373	2.22815679731419	0.0263278233712257	*  
df.mm.trans2:exp4	0.0175622622141202	0.20664026387373	0.0849895460105098	0.932304772724514	   
df.mm.trans1:exp5	0.623802538261842	0.20664026387373	3.01878504492728	0.00267155982528572	** 
df.mm.trans2:exp5	-0.213105157677359	0.20664026387373	-1.03128574113503	0.302920835652107	   
df.mm.trans1:exp6	0.624084307224118	0.20664026387373	3.02014861733564	0.00265978489629737	** 
df.mm.trans2:exp6	-0.248799178657807	0.20664026387373	-1.20402081372602	0.229168816587043	   
df.mm.trans1:exp7	0.397379705315821	0.20664026387373	1.92305070592944	0.0550588596311112	.  
df.mm.trans2:exp7	0.199430101223846	0.20664026387373	0.965107658523461	0.33497186040102	   
df.mm.trans1:exp8	0.498622418373128	0.20664026387373	2.41299739472757	0.0161917977495211	*  
df.mm.trans2:exp8	-0.0183981211474808	0.20664026387373	-0.0890345414905355	0.929091211893853	   
df.mm.trans1:probe2	0.0284147993280640	0.141476917265577	0.200844066136418	0.840904702210589	   
df.mm.trans1:probe3	0.0446452810075668	0.141476917265577	0.315565831306316	0.75246781691649	   
df.mm.trans1:probe4	0.0720048692032451	0.141476917265577	0.508951358249341	0.611017772791487	   
df.mm.trans1:probe5	0.289931316073896	0.141476917265577	2.04931886895474	0.0409683528977641	*  
df.mm.trans1:probe6	0.135101557167493	0.141476917265577	0.954937100543996	0.340085219268383	   
df.mm.trans2:probe2	-0.0163919072191561	0.141476917265577	-0.115862767835022	0.907809260123242	   
df.mm.trans2:probe3	0.0319241212138273	0.141476917265577	0.22564897391636	0.821569500675122	   
df.mm.trans2:probe4	7.61154497713999e-05	0.141476917265577	0.000538006137273388	0.999570954439274	   
df.mm.trans2:probe5	0.0785026701258493	0.141476917265577	0.554879705065145	0.579232746973742	   
df.mm.trans2:probe6	0.0316016285210224	0.141476917265577	0.223369501766147	0.823341988358976	   
df.mm.trans3:probe2	-0.0217905828996896	0.141476917265577	-0.154022177757696	0.877656316527424	   
df.mm.trans3:probe3	0.0474312297714321	0.141476917265577	0.335257727466561	0.737575638745385	   
df.mm.trans3:probe4	0.0359206855109569	0.141476917265577	0.253897852775004	0.799682073731405	   
df.mm.trans3:probe5	-0.095981347331488	0.141476917265577	-0.678424079253254	0.497826409445384	   
df.mm.trans3:probe6	0.0452104408233307	0.141476917265577	0.319560545261689	0.749439089129805	   
df.mm.trans3:probe7	0.250149553089766	0.141476917265577	1.76812979759936	0.0776678269710852	.  
df.mm.trans3:probe8	-0.0470043759269679	0.141476917265577	-0.332240600342827	0.739851128403395	   
df.mm.trans3:probe9	0.103402989159900	0.141476917265577	0.730882402291774	0.465203918045947	   
df.mm.trans3:probe10	-0.0606147354018242	0.141476917265577	-0.428442579703936	0.668519139254585