chr6.20086_chr6_115813207_115816121_-_2.R 

fitVsDatCorrelation=0.882909720993375
cont.fitVsDatCorrelation=0.260469738660614

fstatistic=8083.36832614625,65,991
cont.fstatistic=1900.21209714402,65,991

residuals=-0.842464110389827,-0.102137311770709,-0.00782460204256998,0.086683647618543,1.38931045219917
cont.residuals=-0.722641335253566,-0.255148249759223,-0.0818291071877834,0.159910645110827,1.94480652725847

predictedValues:
Include	Exclude	Both
chr6.20086_chr6_115813207_115816121_-_2.R.tl.Lung	62.5897635231261	66.6173179465579	79.6285567156553
chr6.20086_chr6_115813207_115816121_-_2.R.tl.cerebhem	78.3784899091785	70.8774167879762	67.6495814955288
chr6.20086_chr6_115813207_115816121_-_2.R.tl.cortex	60.1006886259147	75.338168708326	73.3966868214408
chr6.20086_chr6_115813207_115816121_-_2.R.tl.heart	62.6760199054512	73.7890947333643	78.5313286298222
chr6.20086_chr6_115813207_115816121_-_2.R.tl.kidney	61.6409965402646	75.3695254078294	82.2146182769202
chr6.20086_chr6_115813207_115816121_-_2.R.tl.liver	64.0596903088047	68.4428508659102	73.7304122965384
chr6.20086_chr6_115813207_115816121_-_2.R.tl.stomach	68.4609362898403	70.4849159632381	81.7997143450542
chr6.20086_chr6_115813207_115816121_-_2.R.tl.testicle	62.9450316598328	76.4373974905347	83.9913278371727


diffExp=-4.02755442343178,7.50107312120231,-15.2374800824114,-11.1130748279131,-13.7285288675647,-4.38316055710547,-2.02397967339779,-13.4923658307019
diffExpScore=1.24349411216261
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,-1,0,-1,0,0,-1
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	68.9935733376634	72.7715835092655	65.6708914986363
cerebhem	68.633084062008	67.7312332954593	69.468648816629
cortex	66.9508899321496	71.1583068860047	74.0925247837802
heart	66.5325798267686	77.3586770455111	68.7900478925756
kidney	68.1810542366	68.2590893447063	74.112960874019
liver	62.8042402231448	80.750693086967	70.7568579146048
stomach	64.6388483340368	58.7847766366952	76.2325227151608
testicle	67.6002435042378	67.7405149923885	66.075821737073
cont.diffExp=-3.77801017160206,0.90185076654869,-4.20741695385506,-10.8260972187425,-0.0780351081063912,-17.9464528638222,5.85407169734164,-0.140271488150731
cont.diffExpScore=1.40075913252146

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=-0.304299289838916
cont.tran.correlation=-0.228655932628246

tran.covariance=-0.00130667387466551
cont.tran.covariance=-0.000565514954166466

tran.mean=68.6380190416344
cont.tran.mean=68.6805867658504

weightedLogRatios:
wLogRatio
Lung	-0.259914753517655
cerebhem	0.433701755330674
cortex	-0.951090117121643
heart	-0.688773072360458
kidney	-0.848915086303435
liver	-0.277502504072089
stomach	-0.123558390765238
testicle	-0.823328473943115

cont.weightedLogRatios:
wLogRatio
Lung	-0.227145586789472
cerebhem	0.05584767008556
cortex	-0.258078986207669
heart	-0.644212644725323
kidney	-0.00483027788698451
liver	-1.07215654145481
stomach	0.391250717156017
testicle	-0.00873639497370168

varWeightedLogRatios=0.222790378062204
cont.varWeightedLogRatios=0.206895682793962

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.39182526805613	0.0863786810048996	50.8438565739041	2.06176713544725e-278	***
df.mm.trans1	-0.254144547259164	0.0739133391270718	-3.43841247413053	0.000609482207580114	***
df.mm.trans2	-0.163494079514676	0.0646301144560554	-2.52968884382593	0.0115704044417554	*  
df.mm.exp2	0.44996668430843	0.0816107597324579	5.51357058534368	4.48516402934069e-08	***
df.mm.exp3	0.163935863997159	0.0816107597324579	2.00875306803398	0.0448341898127254	*  
df.mm.exp4	0.117498699194444	0.0816107597324579	1.43974519511442	0.150255306743054	   
df.mm.exp5	0.076203539770463	0.0816107597324578	0.93374378599438	0.350663520463210	   
df.mm.exp6	0.127205514446628	0.0816107597324579	1.55868558096558	0.119390091049995	   
df.mm.exp7	0.119194752439860	0.0816107597324579	1.46052742102405	0.144462129938629	   
df.mm.exp8	0.0898268165829408	0.0816107597324579	1.10067369642701	0.271305975947561	   
df.mm.trans1:exp2	-0.225018900740011	0.0745514517353127	-3.01830340660457	0.00260696028027419	** 
df.mm.trans2:exp2	-0.387979396512831	0.0511682834100626	-7.58241962904175	7.7855015634249e-14	***
df.mm.trans1:exp3	-0.204516307259815	0.0745514517353127	-2.74329074081520	0.00619243642498473	** 
df.mm.trans2:exp3	-0.0409135420313326	0.0511682834100626	-0.799587934257077	0.424141190635714	   
df.mm.trans1:exp4	-0.116121525099539	0.0745514517353127	-1.55760246643910	0.119646790804763	   
df.mm.trans2:exp4	-0.0152523193195521	0.0511682834100626	-0.298081512669088	0.765703433713175	   
df.mm.trans1:exp5	-0.091478105085738	0.0745514517353127	-1.22704659609476	0.220096500326194	   
df.mm.trans2:exp5	0.0472349082616507	0.0511682834100626	0.923128647547352	0.356164865341308	   
df.mm.trans1:exp6	-0.103991947410068	0.0745514517353127	-1.39490170867874	0.163358036026145	   
df.mm.trans2:exp6	-0.100170984295837	0.0511682834100626	-1.95767724887439	0.0505482253150569	.  
df.mm.trans1:exp7	-0.0295331857203543	0.0745514517353127	-0.396145011705592	0.692083330504171	   
df.mm.trans2:exp7	-0.0627605964543618	0.0511682834100626	-1.22655270553828	0.220282094737806	   
df.mm.trans1:exp8	-0.0841667271287934	0.0745514517353127	-1.12897502556515	0.259181583646255	   
df.mm.trans2:exp8	0.0476806828026808	0.0511682834100626	0.931840578284947	0.351645876501444	   
df.mm.trans1:probe2	0.0366437025283786	0.0550598960926404	0.665524367621838	0.505870028409765	   
df.mm.trans1:probe3	-0.333268847553495	0.0550598960926404	-6.05284192677657	2.01635591656826e-09	***
df.mm.trans1:probe4	-0.158479849309681	0.0550598960926404	-2.87831726095219	0.00408393480651813	** 
df.mm.trans1:probe5	-0.163822547879211	0.0550598960926404	-2.97535156266137	0.00299753635316236	** 
df.mm.trans1:probe6	-0.164502976217066	0.0550598960926404	-2.98770952891527	0.00288001129084391	** 
df.mm.trans1:probe7	-0.290667136741301	0.0550598960926404	-5.279107978196	1.59510894766287e-07	***
df.mm.trans1:probe8	0.123718401152627	0.0550598960926404	2.24697847130815	0.0248608173946244	*  
df.mm.trans1:probe9	-0.232585224661499	0.0550598960926404	-4.22422200489019	2.61855944416397e-05	***
df.mm.trans1:probe10	0.0990348927256713	0.0550598960926404	1.79867561971133	0.0723742037539869	.  
df.mm.trans1:probe11	-0.292416743719459	0.0550598960926404	-5.31088440899809	1.34695378022614e-07	***
df.mm.trans1:probe12	-0.218215123162875	0.0550598960926404	-3.96323165586292	7.92468348464062e-05	***
df.mm.trans1:probe13	-0.352503847877241	0.0550598960926404	-6.40218875974882	2.36052266883056e-10	***
df.mm.trans1:probe14	-0.332248193414774	0.0550598960926404	-6.03430476613603	2.25274629097584e-09	***
df.mm.trans1:probe15	-0.218817788741118	0.0550598960926404	-3.97417729181596	7.57443925300728e-05	***
df.mm.trans1:probe16	-0.187278899461684	0.0550598960926404	-3.40136674334764	0.000697278004467199	***
df.mm.trans1:probe17	0.435277389865663	0.0550598960926404	7.90552508732112	7.08155465564888e-15	***
df.mm.trans1:probe18	0.295567562706973	0.0550598960926404	5.36810970746601	9.91103289288667e-08	***
df.mm.trans1:probe19	0.389771829723995	0.0550598960926404	7.07905131292273	2.74548782414854e-12	***
df.mm.trans1:probe20	0.160852424972173	0.0550598960926404	2.92140807351929	0.00356358611355691	** 
df.mm.trans1:probe21	0.561310714784732	0.0550598960926404	10.1945472951911	2.81732856236462e-23	***
df.mm.trans1:probe22	0.801629094117508	0.0550598960926404	14.5592191595991	1.16641080099546e-43	***
df.mm.trans2:probe2	-0.0802256706836774	0.0550598960926404	-1.45706178865094	0.145416102366534	   
df.mm.trans2:probe3	-0.278800654213874	0.0550598960926404	-5.0635884554664	4.90426236052309e-07	***
df.mm.trans2:probe4	-0.208720090995782	0.0550598960926404	-3.79078250791833	0.000159241887952852	***
df.mm.trans2:probe5	-0.308118762477328	0.0550598960926404	-5.59606509171223	2.83715680532667e-08	***
df.mm.trans2:probe6	0.229799635846997	0.0550598960926404	4.17363003120003	3.26119972564199e-05	***
df.mm.trans3:probe2	0.181450413205696	0.0550598960926404	3.29550954655633	0.00101727260848334	** 
df.mm.trans3:probe3	0.465364103393190	0.0550598960926404	8.45196116262545	1.01311571463514e-16	***
df.mm.trans3:probe4	0.0829962698860278	0.0550598960926404	1.50738152041521	0.132031592739866	   
df.mm.trans3:probe5	0.249697636183556	0.0550598960926404	4.53501829650078	6.4640231295078e-06	***
df.mm.trans3:probe6	0.292637451213636	0.0550598960926404	5.31489290719442	1.31844219856492e-07	***
df.mm.trans3:probe7	0.479646419797033	0.0550598960926404	8.7113571553061	1.24136419986969e-17	***
df.mm.trans3:probe8	1.36204670721586	0.0550598960926404	24.7375459068097	1.34695181165400e-105	***
df.mm.trans3:probe9	0.164393106667348	0.0550598960926404	2.98571407382881	0.00289869842750717	** 
df.mm.trans3:probe10	0.535032452884809	0.0550598960926404	9.71728046824855	2.20928580163041e-21	***
df.mm.trans3:probe11	0.218401105509278	0.0550598960926404	3.96660947455857	7.81499486616434e-05	***
df.mm.trans3:probe12	0.0746545232441388	0.0550598960926404	1.35587838957287	0.175446741803618	   
df.mm.trans3:probe13	0.236843845065175	0.0550598960926404	4.30156723628167	1.86381525245199e-05	***
df.mm.trans3:probe14	1.29412979424595	0.0550598960926404	23.5040362602306	1.92299434691949e-97	***
df.mm.trans3:probe15	0.267576740151723	0.0550598960926404	4.85973928649475	1.36576144493557e-06	***
df.mm.trans3:probe16	0.546107670391223	0.0550598960926404	9.9184290045222	3.5866229049071e-22	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.09281254175176	0.177613341811768	23.0433845791228	1.99535707899794e-94	***
df.mm.trans1	0.109111615566816	0.151981889675776	0.717925114627696	0.472972665896042	   
df.mm.trans2	0.193531410103389	0.13289356212288	1.45628883003708	0.145629529443326	   
df.mm.exp2	-0.133236566415188	0.167809459408820	-0.7939753032074	0.427399884470772	   
df.mm.exp3	-0.173131340380593	0.167809459408820	-1.03171383180973	0.302457914800530	   
df.mm.exp4	-0.0215977094030192	0.16780945940882	-0.128703766039806	0.897618169739771	   
df.mm.exp5	-0.196796223311556	0.16780945940882	-1.17273617354382	0.241183435759632	   
df.mm.exp6	-0.0645434548960274	0.16780945940882	-0.384623459984968	0.700598933081018	   
df.mm.exp7	-0.427772788107704	0.16780945940882	-2.54915777462555	0.0109480661613577	*  
df.mm.exp8	-0.0981899890927357	0.167809459408820	-0.585127855358402	0.558594952449797	   
df.mm.trans1:exp2	0.127997899620629	0.153293987886624	0.834983167867589	0.40392843126372	   
df.mm.trans2:exp2	0.0614584458842196	0.105213111678765	0.584132955518546	0.559263801973972	   
df.mm.trans1:exp3	0.143077344693002	0.153293987886624	0.93335261653459	0.3508652833331	   
df.mm.trans2:exp3	0.150712867246971	0.105213111678765	1.43245328307679	0.152329487094722	   
df.mm.trans1:exp4	-0.0147239009264208	0.153293987886624	-0.096050087347917	0.923500211783814	   
df.mm.trans2:exp4	0.08272491653834	0.105213111678765	0.786260526073164	0.43190283693719	   
df.mm.trans1:exp5	0.184949592225309	0.153293987886624	1.20650258222846	0.227911697986194	   
df.mm.trans2:exp5	0.132781283130777	0.105213111678765	1.26202220438251	0.207237729508957	   
df.mm.trans1:exp6	-0.0294473146579228	0.153293987886624	-0.192096996522147	0.847705582089666	   
df.mm.trans2:exp6	0.168584457491928	0.105213111678765	1.60231414889285	0.109404830176292	   
df.mm.trans1:exp7	0.362575025321703	0.153293987886624	2.36522664926601	0.0182108274939220	*  
df.mm.trans2:exp7	0.214330166164862	0.105213111678765	2.03710509788222	0.0419048131890848	*  
df.mm.trans1:exp8	0.0777882140432676	0.153293987886624	0.507444650085036	0.611955770149574	   
df.mm.trans2:exp8	0.0265488963489032	0.105213111678765	0.252334484982841	0.800834859916022	   
df.mm.trans1:probe2	-0.0426320942469558	0.113215113162794	-0.376558332681737	0.706582465234353	   
df.mm.trans1:probe3	0.0047662414950176	0.113215113162794	0.0420989862737154	0.966428268516657	   
df.mm.trans1:probe4	-0.0106890207539609	0.113215113162794	-0.0944133734035224	0.92479987830523	   
df.mm.trans1:probe5	-0.0300883770768664	0.113215113162794	-0.2657629024634	0.790477092657483	   
df.mm.trans1:probe6	-0.0793109435767256	0.113215113162794	-0.700533182903621	0.483758823642116	   
df.mm.trans1:probe7	0.0181336056878722	0.113215113162794	0.160169478979345	0.872780211995251	   
df.mm.trans1:probe8	0.0921160140664876	0.113215113162794	0.813637079830786	0.416048333711812	   
df.mm.trans1:probe9	0.120490352549915	0.113215113162794	1.06426031988026	0.287470031903519	   
df.mm.trans1:probe10	0.224127943394404	0.113215113162794	1.97966452652065	0.048017582469218	*  
df.mm.trans1:probe11	0.0373968178752971	0.113215113162794	0.3303164818775	0.741230657030595	   
df.mm.trans1:probe12	0.0598718216886687	0.113215113162794	0.528832414825908	0.597040128603261	   
df.mm.trans1:probe13	0.179489240893125	0.113215113162794	1.58538233879636	0.113198515424513	   
df.mm.trans1:probe14	0.0654350155709579	0.113215113162794	0.577970676731717	0.563415219298096	   
df.mm.trans1:probe15	0.189626226371775	0.113215113162794	1.67491972647776	0.0942654772604159	.  
df.mm.trans1:probe16	-0.026242594540656	0.113215113162794	-0.231794093628837	0.816745798212046	   
df.mm.trans1:probe17	-0.0683606262337406	0.113215113162794	-0.603811843878509	0.546106930581117	   
df.mm.trans1:probe18	0.175160408973593	0.113215113162794	1.54714687889528	0.122147099098656	   
df.mm.trans1:probe19	-0.0510297474343444	0.113215113162794	-0.450732645216438	0.652280849000028	   
df.mm.trans1:probe20	0.0768484177100545	0.113215113162794	0.678782324755114	0.497434276951949	   
df.mm.trans1:probe21	0.191035302313386	0.113215113162794	1.68736573215886	0.0918477335670434	.  
df.mm.trans1:probe22	0.0932457057392492	0.113215113162794	0.823615356062664	0.410356308180584	   
df.mm.trans2:probe2	0.093930786060279	0.113215113162794	0.829666494483068	0.406927174011319	   
df.mm.trans2:probe3	0.0389563159963499	0.113215113162794	0.344091128013395	0.730850752041957	   
df.mm.trans2:probe4	0.0252565223493449	0.113215113162794	0.223084371368583	0.82351581491718	   
df.mm.trans2:probe5	-0.0623723674416108	0.113215113162794	-0.550919092859312	0.581813312643319	   
df.mm.trans2:probe6	-0.0741762607896544	0.113215113162794	-0.65517984938102	0.512504016498366	   
df.mm.trans3:probe2	-0.276204739041960	0.113215113162794	-2.43964547952887	0.0148761529085865	*  
df.mm.trans3:probe3	-0.205037031094229	0.113215113162794	-1.81103940424812	0.0704374422926412	.  
df.mm.trans3:probe4	-0.168472654903165	0.113215113162794	-1.48807566584256	0.137049061611730	   
df.mm.trans3:probe5	-0.337177678541816	0.113215113162794	-2.97820378501042	0.00297002908308447	** 
df.mm.trans3:probe6	-0.199770250105071	0.113215113162794	-1.76451928125372	0.077952606577244	.  
df.mm.trans3:probe7	-0.345776160586161	0.113215113162794	-3.05415196722865	0.00231727752712293	** 
df.mm.trans3:probe8	-0.110890555382037	0.113215113162794	-0.979467778498667	0.327587947190422	   
df.mm.trans3:probe9	-0.18255741392581	0.113215113162794	-1.61248272272014	0.107175325273024	   
df.mm.trans3:probe10	-0.236199613960098	0.113215113162794	-2.08629049039118	0.037207526426469	*  
df.mm.trans3:probe11	-0.285897956499124	0.113215113162794	-2.52526317831814	0.0117161856819369	*  
df.mm.trans3:probe12	-0.214538237898324	0.113215113162794	-1.89496112228264	0.0583883983231605	.  
df.mm.trans3:probe13	-0.251978608154418	0.113215113162794	-2.22566229114742	0.0262611435208366	*  
df.mm.trans3:probe14	-0.0515047720708252	0.113215113162794	-0.454928415756346	0.649260379718662	   
df.mm.trans3:probe15	-0.302491128201948	0.113215113162794	-2.67182639977572	0.00766772752098948	** 
df.mm.trans3:probe16	-0.2042998696986	0.113215113162794	-1.80452824707982	0.0714520262638525	.