chr10.2918_chr10_49224955_49237938_-_2.R 

fitVsDatCorrelation=0.77668325803611
cont.fitVsDatCorrelation=0.264094051989685

fstatistic=11834.6780439529,51,669
cont.fstatistic=5040.09004467068,51,669

residuals=-0.416031801299256,-0.0800690452617387,-0.00924403020327275,0.065166088154981,0.988010100535349
cont.residuals=-0.347962010788482,-0.123571510993395,-0.0370158804997831,0.0664457599625396,1.30786390551786

predictedValues:
Include	Exclude	Both
chr10.2918_chr10_49224955_49237938_-_2.R.tl.Lung	47.0816808088435	44.8151866208758	67.053353008662
chr10.2918_chr10_49224955_49237938_-_2.R.tl.cerebhem	57.7804434411326	64.0355053823831	73.2659235559071
chr10.2918_chr10_49224955_49237938_-_2.R.tl.cortex	47.4345676929125	53.4557216300277	84.5711613428477
chr10.2918_chr10_49224955_49237938_-_2.R.tl.heart	47.345271054781	46.9208035815458	56.7398877337675
chr10.2918_chr10_49224955_49237938_-_2.R.tl.kidney	46.4787515822159	46.4121108967841	70.2025343379055
chr10.2918_chr10_49224955_49237938_-_2.R.tl.liver	51.480955017598	47.4975163527268	65.8283971135185
chr10.2918_chr10_49224955_49237938_-_2.R.tl.stomach	48.7637598581289	47.8521658137177	61.054971394886
chr10.2918_chr10_49224955_49237938_-_2.R.tl.testicle	51.3389461680167	46.6758258772969	58.3092487985058


diffExp=2.2664941879677,-6.25506194125047,-6.02115393711516,0.424467473235161,0.0666406854317714,3.9834386648712,0.911594044411196,4.66312029071975
diffExpScore=23.6566017699177
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	50.054787303123	51.3760690992733	57.2563685902855
cerebhem	51.1108038724245	55.7513979880919	47.4456300632004
cortex	54.5393744868332	51.8343960672356	47.637538167879
heart	53.4070548700668	53.5375962436963	55.9578595766177
kidney	51.7036953327537	50.784618970868	49.0705271386834
liver	51.2030535274583	51.0718558039968	45.401440805577
stomach	51.5739980459675	54.445244796386	55.4546208540588
testicle	48.8120051839574	54.4847555078791	48.6775736336393
cont.diffExp=-1.32128179615031,-4.64059411566736,2.70497841959757,-0.130541373629462,0.919076361885715,0.131197723461426,-2.87124675041849,-5.67275032392167
cont.diffExpScore=1.54796871631168

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=0.778582342585811
cont.tran.correlation=-0.210908053142373

tran.covariance=0.00639167301628249
cont.tran.covariance=-0.000256411775378213

tran.mean=49.7105757361867
cont.tran.mean=52.2306691937507

weightedLogRatios:
wLogRatio
Lung	0.188823002599174
cerebhem	-0.422254883977382
cortex	-0.468342302540534
heart	0.0346989937384841
kidney	0.0055072265125956
liver	0.314160673759289
stomach	0.0731734046513465
testicle	0.370498300339966

cont.weightedLogRatios:
wLogRatio
Lung	-0.102293088538995
cerebhem	-0.345666526847912
cortex	0.202127235051724
heart	-0.00971429091985337
kidney	0.0706050741100014
liver	0.0100943599501680
stomach	-0.215091966858063
testicle	-0.433506131952976

varWeightedLogRatios=0.0963848696215958
cont.varWeightedLogRatios=0.0464886179872178

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.55272710220600	0.0727218004401906	48.8536735985781	7.5670378443624e-223	***
df.mm.trans1	0.226709313756690	0.0652423602771135	3.47487909379358	0.000544079274750217	***
df.mm.trans2	0.221878088103582	0.0600968501385039	3.69200860930689	0.000240624870022272	***
df.mm.exp2	0.473050024221249	0.082346962384582	5.74459592100046	1.39930432077629e-08	***
df.mm.exp3	-0.0483308446196364	0.082346962384582	-0.58691715176959	0.557457352948279	   
df.mm.exp4	0.218508206152462	0.082346962384582	2.6535065754092	0.0081551908586992	** 
df.mm.exp5	-0.023771159106479	0.082346962384582	-0.28867074653539	0.772922771269973	   
df.mm.exp6	0.165895607981573	0.082346962384582	2.01459292702014	0.0443469778864614	*  
df.mm.exp7	0.194386725921688	0.082346962384582	2.36058162065348	0.0185322480289495	*  
df.mm.exp8	0.266972874063327	0.082346962384582	3.24204884226928	0.00124564511957759	** 
df.mm.trans1:exp2	-0.268283637442334	0.0786852470823175	-3.40957990716691	0.000689705384202758	***
df.mm.trans2:exp2	-0.116159392141637	0.0686821144440044	-1.69126115411517	0.091252673653208	.  
df.mm.trans1:exp3	0.055798100872538	0.0786852470823175	0.709130401715129	0.478490605636065	   
df.mm.trans2:exp3	0.224637453709732	0.0686821144440044	3.27068343087887	0.00112792827658197	** 
df.mm.trans1:exp4	-0.212925246512271	0.0786852470823175	-2.70603771872911	0.00698268117779166	** 
df.mm.trans2:exp4	-0.172594124933358	0.0686821144440044	-2.51294134332559	0.0122069213836107	*  
df.mm.trans1:exp5	0.0108824291655484	0.0786852470823175	0.138303297874424	0.890042337040602	   
df.mm.trans2:exp5	0.0587845259369523	0.0686821144440044	0.85589278100747	0.392363527084281	   
df.mm.trans1:exp6	-0.0765676570675516	0.0786852470823175	-0.973087839292789	0.3308613484038	   
df.mm.trans2:exp6	-0.107765254669944	0.0686821144440044	-1.56904392857334	0.117110535983782	   
df.mm.trans1:exp7	-0.159283297681003	0.0786852470823175	-2.02430955722064	0.0433351632136861	*  
df.mm.trans2:exp7	-0.128817415533334	0.0686821144440044	-1.87555983935755	0.0611510360211971	.  
df.mm.trans1:exp8	-0.180407208236927	0.0786852470823175	-2.29277043571067	0.022170670569002	*  
df.mm.trans2:exp8	-0.226293559632036	0.0686821144440044	-3.29479605373143	0.00103688702809210	** 
df.mm.trans1:probe2	0.143406287964691	0.0393426235411587	3.64506164197883	0.000288065027103811	***
df.mm.trans1:probe3	0.052215061539787	0.0393426235411587	1.32718809372643	0.184899224695019	   
df.mm.trans1:probe4	0.0122385385119954	0.0393426235411587	0.311075810671139	0.755839892164186	   
df.mm.trans1:probe5	0.155649423423680	0.0393426235411587	3.95625429658612	8.42644769686067e-05	***
df.mm.trans1:probe6	0.176205049382837	0.0393426235411587	4.47873155176084	8.82864185649047e-06	***
df.mm.trans1:probe7	0.12755303717701	0.0393426235411587	3.24210806743909	0.00124539032316694	** 
df.mm.trans1:probe8	0.154156039994618	0.0393426235411587	3.91829588673327	9.83456107674046e-05	***
df.mm.trans1:probe9	-0.0128661140078935	0.0393426235411587	-0.327027352266771	0.743749492235521	   
df.mm.trans1:probe10	0.0768072848444346	0.0393426235411587	1.95226647160634	0.0513233236133209	.  
df.mm.trans1:probe11	0.0838204503569078	0.0393426235411587	2.13052518648681	0.033492410942226	*  
df.mm.trans1:probe12	0.0776178120536468	0.0393426235411587	1.97286822960462	0.0489218598728973	*  
df.mm.trans1:probe13	0.277596759206035	0.0393426235411587	7.05587818554153	4.29632549196905e-12	***
df.mm.trans1:probe14	0.177550878336162	0.0393426235411588	4.51293946247421	7.55300444475962e-06	***
df.mm.trans1:probe15	0.0436874023594291	0.0393426235411587	1.11043439474048	0.267210834959374	   
df.mm.trans1:probe16	-0.0229908909074538	0.0393426235411587	-0.584376150802491	0.559164271169817	   
df.mm.trans1:probe17	0.0669839061300742	0.0393426235411587	1.70257852936518	0.089111491993828	.  
df.mm.trans1:probe18	0.0241397672578079	0.0393426235411587	0.613577974345149	0.539702801211462	   
df.mm.trans1:probe19	0.0426912971244164	0.0393426235411587	1.08511566545008	0.27826129553357	   
df.mm.trans1:probe20	0.0648372535867757	0.0393426235411587	1.64801550458234	0.0998190203977191	.  
df.mm.trans1:probe21	0.0174423616137815	0.0393426235411587	0.443345157079674	0.657659432577441	   
df.mm.trans2:probe2	0.0865027071909951	0.0393426235411587	2.19870205403306	0.0282407314661055	*  
df.mm.trans2:probe3	0.00740856104263461	0.0393426235411587	0.188308769873571	0.850691716012649	   
df.mm.trans2:probe4	0.0363188511901145	0.0393426235411587	0.923142584838532	0.356265721016514	   
df.mm.trans2:probe5	0.0817648743435383	0.0393426235411587	2.07827711992819	0.0380644999681361	*  
df.mm.trans2:probe6	0.0394819149047499	0.0393426235411587	1.00354046962439	0.315962993397692	   
df.mm.trans3:probe2	0.596432041119649	0.0393426235411587	15.1599458154001	7.65966441832255e-45	***
df.mm.trans3:probe3	0.0160902948724962	0.0393426235411587	0.408978695985109	0.682686186373457	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.84467901928423	0.111352462715901	34.5271126072294	8.92934162789997e-151	***
df.mm.trans1	0.0648556338643322	0.0998998573506113	0.649206471203588	0.51642774052782	   
df.mm.trans2	0.127886710019577	0.0920209926581033	1.38975581903066	0.165065322989544	   
df.mm.exp2	0.29056220586191	0.126090622113217	2.30439188095220	0.0215060792353563	*  
df.mm.exp3	0.278604030776606	0.126090622113217	2.20955393912203	0.02747409152046	*  
df.mm.exp4	0.128976335722152	0.126090622113217	1.02288602879874	0.306731465931423	   
df.mm.exp5	0.175112439442230	0.126090622113217	1.38878242098763	0.165361081329675	   
df.mm.exp6	0.248737151644589	0.126090622113217	1.97268557705463	0.0489427282364403	*  
df.mm.exp7	0.119896345695193	0.126090622113217	0.950874408308796	0.342011524760744	   
df.mm.exp8	0.195927057954124	0.126090622113217	1.55385907905348	0.120690950808828	   
df.mm.trans1:exp2	-0.269684456523819	0.120483761251638	-2.23834692511437	0.0255266086343009	*  
df.mm.trans2:exp2	-0.208832202134588	0.105166848752118	-1.98572273118891	0.0474718979757235	*  
df.mm.trans1:exp3	-0.192799273912281	0.120483761251638	-1.60020962086008	0.110024188172417	   
df.mm.trans2:exp3	-0.269722567448836	0.105166848752118	-2.56471093932444	0.0105432106369842	*  
df.mm.trans1:exp4	-0.0641516364096511	0.120483761251638	-0.532450479161803	0.594590824056244	   
df.mm.trans2:exp4	-0.0877646774128404	0.105166848752118	-0.834527975823492	0.404281423468803	   
df.mm.trans1:exp5	-0.142701335625651	0.120483761251638	-1.18440306098686	0.236674161367566	   
df.mm.trans2:exp5	-0.186691389185861	0.105166848752118	-1.77519238620432	0.0763207190425796	.  
df.mm.trans1:exp6	-0.226056133768373	0.120483761251638	-1.87623735696829	0.061057834782342	.  
df.mm.trans2:exp6	-0.254676055525979	0.105166848752118	-2.42163817351093	0.0157156001972239	*  
df.mm.trans1:exp7	-0.0899968656518318	0.120483761251638	-0.746962617342824	0.455348560224732	   
df.mm.trans2:exp7	-0.0618733140110997	0.105166848752118	-0.588334772271605	0.556506171976505	   
df.mm.trans1:exp8	-0.221068919081085	0.120483761251638	-1.83484410500240	0.0669726415525651	.  
df.mm.trans2:exp8	-0.137178593182442	0.105166848752118	-1.30439006978118	0.192549182684788	   
df.mm.trans1:probe2	0.0362994558799704	0.0602418806258191	0.602561797587919	0.547004425937774	   
df.mm.trans1:probe3	-0.0280939858007523	0.0602418806258191	-0.466353067150288	0.641114612496025	   
df.mm.trans1:probe4	-0.0462174684447511	0.0602418806258191	-0.767198300660997	0.44323428725686	   
df.mm.trans1:probe5	-0.0188118232453796	0.0602418806258192	-0.312271513604059	0.754931491886827	   
df.mm.trans1:probe6	-0.00267391493967575	0.0602418806258191	-0.0443863125104652	0.96460971682776	   
df.mm.trans1:probe7	-0.0357363812058109	0.0602418806258191	-0.593214900241587	0.553237829320422	   
df.mm.trans1:probe8	0.0336684907434935	0.0602418806258191	0.558888440960514	0.576424882557977	   
df.mm.trans1:probe9	-0.0368734857956657	0.0602418806258192	-0.612090549176216	0.540685817693968	   
df.mm.trans1:probe10	-0.0261282451905111	0.0602418806258192	-0.433722269608442	0.664629956637635	   
df.mm.trans1:probe11	-0.0153324528392185	0.0602418806258192	-0.254514843825229	0.799176050053824	   
df.mm.trans1:probe12	0.0457345788513549	0.0602418806258192	0.759182455398869	0.448010824604451	   
df.mm.trans1:probe13	-0.0360401268706345	0.0602418806258192	-0.598257001545002	0.549870942900757	   
df.mm.trans1:probe14	0.0430650797616653	0.0602418806258192	0.714869444882635	0.474939098405301	   
df.mm.trans1:probe15	0.0332237469865363	0.0602418806258192	0.551505806946818	0.581471141279306	   
df.mm.trans1:probe16	0.0341306188107684	0.0602418806258192	0.566559650133836	0.571203411322729	   
df.mm.trans1:probe17	-0.00843298244667885	0.0602418806258192	-0.139985378262984	0.888713676542955	   
df.mm.trans1:probe18	-0.0390288764348692	0.0602418806258192	-0.647869489289181	0.517291639675575	   
df.mm.trans1:probe19	0.0313697620219008	0.0602418806258191	0.520730124890158	0.602727084597324	   
df.mm.trans1:probe20	0.0503708413137208	0.0602418806258192	0.836143241055	0.403372866884108	   
df.mm.trans1:probe21	0.0715111316568095	0.0602418806258192	1.18706672026040	0.235622637651635	   
df.mm.trans2:probe2	-0.0755034431644214	0.0602418806258192	-1.25333808274341	0.21052053838147	   
df.mm.trans2:probe3	0.0296336434846176	0.0602418806258192	0.491910995751964	0.622943629768014	   
df.mm.trans2:probe4	-0.0907384820406973	0.0602418806258192	-1.50623587939264	0.132478639490009	   
df.mm.trans2:probe5	-0.0742678157251428	0.0602418806258191	-1.23282697939732	0.218073359926457	   
df.mm.trans2:probe6	-0.0896631254257182	0.0602418806258192	-1.48838523124209	0.137120536526858	   
df.mm.trans3:probe2	0.0143847486254108	0.0602418806258192	0.238783193286394	0.811346829472082	   
df.mm.trans3:probe3	0.0159677958676552	0.0602418806258191	0.265061377596030	0.791043827891867