chr4.16881_chr4_133859423_133861906_+_1.R 

fitVsDatCorrelation=0.890589604662045
cont.fitVsDatCorrelation=0.312344928027178

fstatistic=6778.47511830725,38,370
cont.fstatistic=1546.20227672447,38,370

residuals=-0.459100807439262,-0.0922575400993176,-0.00749450112365112,0.0864519601485761,0.913593620651055
cont.residuals=-0.637178817439633,-0.257414213763374,-0.058352786217286,0.203172454417739,1.16513883467519

predictedValues:
Include	Exclude	Both
chr4.16881_chr4_133859423_133861906_+_1.R.tl.Lung	73.2525337280586	47.971512493494	76.3260206524413
chr4.16881_chr4_133859423_133861906_+_1.R.tl.cerebhem	58.3937933344045	47.7713262311653	67.9968249705796
chr4.16881_chr4_133859423_133861906_+_1.R.tl.cortex	59.7857867581459	47.1539663658038	55.6657632649623
chr4.16881_chr4_133859423_133861906_+_1.R.tl.heart	120.647789733706	47.4441624539719	134.409666202993
chr4.16881_chr4_133859423_133861906_+_1.R.tl.kidney	85.0155021879325	49.4267977799945	89.9733278890234
chr4.16881_chr4_133859423_133861906_+_1.R.tl.liver	84.3303647160207	47.1410429070932	84.28221487797
chr4.16881_chr4_133859423_133861906_+_1.R.tl.stomach	85.8011600588086	49.4408273754621	88.5353996905627
chr4.16881_chr4_133859423_133861906_+_1.R.tl.testicle	60.0173240989171	45.0820994290444	58.1356663259645


diffExp=25.2810212345645,10.6224671032391,12.6318203923421,73.2036272797345,35.588704407938,37.1893218089275,36.3603326833465,14.9352246698727
diffExpScore=0.995948341673665
diffExp1.5=1,0,0,1,1,1,1,0
diffExp1.5Score=0.833333333333333
diffExp1.4=1,0,0,1,1,1,1,0
diffExp1.4Score=0.833333333333333
diffExp1.3=1,0,0,1,1,1,1,1
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	68.8837331437956	62.1553565419742	63.8924628555394
cerebhem	71.7334609120633	55.98435464548	62.5781689424359
cortex	58.354789173135	59.8674250220868	73.6200205929122
heart	66.5140407083898	56.333184634634	59.7028529141462
kidney	74.576466412793	64.6109469851881	65.7729183352914
liver	65.4146602725991	53.0447261571328	66.0695974260373
stomach	70.9628653608703	62.1546176769765	58.7832052057646
testicle	60.1924112536195	62.2292086086552	68.3561736088318
cont.diffExp=6.72837660182147,15.7491062665832,-1.51263584895185,10.1808560737558,9.96551942760492,12.3699341154664,8.80824768389375,-2.03679735503570
cont.diffExpScore=1.09956909118083

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=0,1,0,0,0,1,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.32320832208747
cont.tran.correlation=0.174764526005556

tran.covariance=0.00287557099538942
cont.tran.covariance=0.00083753440394493

tran.mean=63.0422493532515
cont.tran.mean=63.3132654693371

weightedLogRatios:
wLogRatio
Lung	1.72804311817877
cerebhem	0.796473567237149
cortex	0.94277558902352
heart	4.03775106635091
kidney	2.26246346467991
liver	2.4101088735764
stomach	2.30226797177596
testicle	1.13073076358966

cont.weightedLogRatios:
wLogRatio
Lung	0.429738884741821
cerebhem	1.02847873043662
cortex	-0.104394730877915
heart	0.683513730106426
kidney	0.608205409759822
liver	0.854361845121625
stomach	0.556088149179402
testicle	-0.136912666917305

varWeightedLogRatios=1.12252970461908
cont.varWeightedLogRatios=0.175494643930140

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97500149351731	0.087629338416856	45.3615371898402	3.2397217474863e-153	***
df.mm.trans1	0.429656039231967	0.0721662488766268	5.95369782855825	6.09394809039292e-09	***
df.mm.trans2	-0.102953785725106	0.0721662488766268	-1.42661960858064	0.154532826410818	   
df.mm.exp2	-0.115332088411700	0.0986492597703035	-1.16911255776516	0.243110918017840	   
df.mm.exp3	0.0953145145973107	0.0986492597703035	0.96619594327664	0.334577236773168	   
df.mm.exp4	-0.0779696535887966	0.0986492597703035	-0.790372414048949	0.429816582109484	   
df.mm.exp5	0.0143068197552338	0.0986492597703035	0.145027137441741	0.884768413640287	   
df.mm.exp6	0.024208879270937	0.0986492597703035	0.245403557282694	0.806280033258995	   
df.mm.exp7	0.0399003348480036	0.0986492597703035	0.404466642131002	0.686103309197042	   
df.mm.exp8	0.0108328895820687	0.0986492597703035	0.109812173018755	0.912617871104194	   
df.mm.trans1:exp2	-0.111371143365649	0.0817956451260996	-1.36157790789417	0.174159636692372	   
df.mm.trans2:exp2	0.111150333270158	0.0817956451260995	1.35887837425579	0.175012900309112	   
df.mm.trans1:exp3	-0.298459398963767	0.0817956451260996	-3.64884216639712	0.000301269086078527	***
df.mm.trans2:exp3	-0.112503731773806	0.0817956451260996	-1.37542446911893	0.169832021661754	   
df.mm.trans1:exp4	0.576932288394616	0.0817956451260996	7.05333746686872	8.63930315952054e-12	***
df.mm.trans2:exp4	0.0669158008661605	0.0817956451260996	0.818085128652024	0.413835107612595	   
df.mm.trans1:exp5	0.134613961344575	0.0817956451260996	1.64573506495424	0.100667473918161	   
df.mm.trans2:exp5	0.0155785775353888	0.0817956451260996	0.190457297712760	0.849055255585732	   
df.mm.trans1:exp6	0.116620281730007	0.0817956451260996	1.42575172003619	0.154783120752368	   
df.mm.trans2:exp6	-0.0416722033994563	0.0817956451260996	-0.509467262410031	0.61072845515275	   
df.mm.trans1:exp7	0.118219354610350	0.0817956451260996	1.44530132903895	0.149219669366762	   
df.mm.trans2:exp7	-0.00973113190229958	0.0817956451260996	-0.118968826363527	0.905364617707809	   
df.mm.trans1:exp8	-0.210112471497035	0.0817956451260996	-2.56874887621605	0.0105980295322344	*  
df.mm.trans2:exp8	-0.0729549753136322	0.0817956451260996	-0.891917597827631	0.37301676826815	   
df.mm.trans1:probe2	-0.434133711542352	0.0477583675257594	-9.09021254355468	6.11248671981411e-18	***
df.mm.trans1:probe3	-0.230778596303634	0.0477583675257594	-4.83221283012154	1.98174813308553e-06	***
df.mm.trans1:probe4	-0.143353738134671	0.0477583675257594	-3.00164652942441	0.00286747173617132	** 
df.mm.trans1:probe5	-0.318312566042015	0.0477583675257594	-6.66506378950928	9.6254542717112e-11	***
df.mm.trans1:probe6	-0.0916130363452624	0.0477583675257593	-1.91826147105739	0.0558471865421858	.  
df.mm.trans2:probe2	-0.0310835214919065	0.0477583675257593	-0.650849748478965	0.515547455785007	   
df.mm.trans2:probe3	0.104736693993634	0.0477583675257593	2.1930543152913	0.0289253458597953	*  
df.mm.trans2:probe4	-0.0715640877975065	0.0477583675257594	-1.4984617671219	0.134865978672962	   
df.mm.trans2:probe5	0.0111122429916971	0.0477583675257593	0.232676357409065	0.816141382175731	   
df.mm.trans2:probe6	-0.0290453183630096	0.0477583675257594	-0.608172344821951	0.543446309634655	   
df.mm.trans3:probe2	0.00277560320896591	0.0477583675257593	0.0581176315850587	0.953686304139412	   
df.mm.trans3:probe3	0.093953565963263	0.0477583675257593	1.96726921020882	0.0498988999952829	*  
df.mm.trans3:probe4	-0.017573130081112	0.0477583675257593	-0.367959186871989	0.713114073545392	   
df.mm.trans3:probe5	0.0873947807084858	0.0477583675257593	1.82993651659780	0.0680638390256662	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.28009048286664	0.183033645747811	23.3841732506594	6.66472665383117e-75	***
df.mm.trans1	0.0239777707953194	0.150735494190289	0.159071829260398	0.873699074581395	   
df.mm.trans2	-0.201895427418657	0.150735494190290	-1.33940203336437	0.181261980962372	   
df.mm.exp2	-0.0432425456021448	0.206051238001911	-0.20986307105684	0.83389005466419	   
df.mm.exp3	-0.345098696878815	0.206051238001911	-1.67481981775628	0.0948145250182369	.  
df.mm.exp4	-0.0655386120469751	0.206051238001911	-0.318069489329481	0.750611669662766	   
df.mm.exp5	0.0891450142925726	0.206051238001911	0.432635179273934	0.665531884647502	   
df.mm.exp6	-0.243682504674064	0.206051238001911	-1.18263062642606	0.237714962571387	   
df.mm.exp7	0.113069987118997	0.206051238001911	0.548746943796323	0.583510096663397	   
df.mm.exp8	-0.201216771557287	0.206051238001911	-0.976537552059847	0.329436180341627	   
df.mm.trans1:exp2	0.0837798072928512	0.170848661010139	0.49037438629899	0.624159664081875	   
df.mm.trans2:exp2	-0.061322185591043	0.170848661010139	-0.358926931170996	0.71985447876255	   
df.mm.trans1:exp3	0.179220072462561	0.170848661010139	1.04899898777623	0.294863290309577	   
df.mm.trans2:exp3	0.307594229841981	0.170848661010139	1.80039005294707	0.0726133967823991	.  
df.mm.trans1:exp4	0.0305316194530961	0.170848661010139	0.178705640843648	0.858266649882072	   
df.mm.trans2:exp4	-0.0328146028868153	0.170848661010139	-0.19206824737636	0.847794113819278	   
df.mm.trans1:exp5	-0.00974007692858476	0.170848661010139	-0.0570099693553157	0.954568027740528	   
df.mm.trans2:exp5	-0.0503981614487379	0.170848661010139	-0.294987160863654	0.76816913702281	   
df.mm.trans1:exp6	0.192008844764868	0.170848661010139	1.12385337777668	0.261803854511347	   
df.mm.trans2:exp6	0.0851809505452565	0.170848661010139	0.498575464634172	0.618374748010999	   
df.mm.trans1:exp7	-0.0833333264294262	0.170848661010139	-0.48776107425554	0.626007976562591	   
df.mm.trans2:exp7	-0.113081874580198	0.170848661010139	-0.66188329432378	0.508458023184779	   
df.mm.trans1:exp8	0.0663430005748704	0.170848661010139	0.388314430927458	0.698006689519507	   
df.mm.trans2:exp8	0.202404251294341	0.170848661010139	1.18469907869122	0.236896857353614	   
df.mm.trans1:probe2	-0.139331391841968	0.0997541266558526	-1.39674814980493	0.163326528293425	   
df.mm.trans1:probe3	-0.215351251720398	0.0997541266558526	-2.15882048131553	0.0315064993213082	*  
df.mm.trans1:probe4	-0.049233527315753	0.0997541266558526	-0.493548778043103	0.621917709203993	   
df.mm.trans1:probe5	-0.244476414109976	0.0997541266558526	-2.45078998038255	0.0147164055093403	*  
df.mm.trans1:probe6	-0.139737584441053	0.0997541266558526	-1.40082008760541	0.16210598676274	   
df.mm.trans2:probe2	-0.0111179989302645	0.0997541266558526	-0.111454025041201	0.91131675626186	   
df.mm.trans2:probe3	0.0814004787374241	0.0997541266558526	0.816011141255862	0.415018779722822	   
df.mm.trans2:probe4	0.236762016718725	0.0997541266558526	2.37345586248821	0.0181325372769750	*  
df.mm.trans2:probe5	0.215275367218955	0.0997541266558526	2.15805976590468	0.0315660392782940	*  
df.mm.trans2:probe6	0.0435415435246601	0.0997541266558526	0.436488644473592	0.662736828496012	   
df.mm.trans3:probe2	0.0588295574741262	0.0997541266558526	0.5897456019748	0.555721170764077	   
df.mm.trans3:probe3	0.0544428917315246	0.0997541266558526	0.545770822287385	0.585552369931185	   
df.mm.trans3:probe4	0.111171295221758	0.0997541266558526	1.11445309531198	0.265808237684989	   
df.mm.trans3:probe5	0.0506991355766044	0.0997541266558526	0.508240984871876	0.611587224719295