chr6.20471_chr6_17716710_17721119_+_2.R 

fitVsDatCorrelation=0.76836214592152
cont.fitVsDatCorrelation=0.291426322321291

fstatistic=14264.459952758,54,738
cont.fstatistic=6377.75282115887,54,738

residuals=-0.415543326651576,-0.0793688406368487,-0.00663115581363808,0.0714881390704322,0.521143603477031
cont.residuals=-0.438313428913028,-0.119678487621479,-0.0252598829742769,0.0869792321859697,0.88089880273532

predictedValues:
Include	Exclude	Both
chr6.20471_chr6_17716710_17721119_+_2.R.tl.Lung	49.0919754702222	52.1257225094237	63.1556515557838
chr6.20471_chr6_17716710_17721119_+_2.R.tl.cerebhem	55.9114240374657	54.9972093749174	61.2003649145018
chr6.20471_chr6_17716710_17721119_+_2.R.tl.cortex	55.5118668182662	49.2871558526485	82.6812264837917
chr6.20471_chr6_17716710_17721119_+_2.R.tl.heart	51.0528359627272	52.07098084783	71.9559868576086
chr6.20471_chr6_17716710_17721119_+_2.R.tl.kidney	49.3834387442877	52.9110040604919	62.9858002153955
chr6.20471_chr6_17716710_17721119_+_2.R.tl.liver	51.337741865026	56.0894830776724	61.7240688742989
chr6.20471_chr6_17716710_17721119_+_2.R.tl.stomach	50.3047839999371	55.0078861496111	61.6727293784823
chr6.20471_chr6_17716710_17721119_+_2.R.tl.testicle	49.1220898419607	48.9727167123536	61.6346075155556


diffExp=-3.03374703920155,0.914214662548318,6.22471096561767,-1.01814488510279,-3.52756531620422,-4.75174121264639,-4.70310214967394,0.149373129607049
diffExpScore=2.263409192675
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	52.9410191447694	53.6165241533306	52.2640819733705
cerebhem	54.0146577369418	57.3801954986078	53.3178168158384
cortex	54.1776806576436	55.6937033048272	50.699867415959
heart	52.8023341072368	55.7175031378712	49.7877982405179
kidney	53.8216787273005	60.9057002085617	52.7968560767942
liver	53.6042432981832	56.9671812126526	51.2068597244891
stomach	52.7287736983757	53.4775774731157	57.4026490782122
testicle	50.8286172064895	61.8162614287286	58.3350212753071
cont.diffExp=-0.675505008561188,-3.36553776166595,-1.51602264718365,-2.91516903063434,-7.08402148126123,-3.36293791446941,-0.748803774739969,-10.9876442222391
cont.diffExpScore=0.96841005451633

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,-1
cont.diffExp1.2Score=0.5

tran.correlation=0.0632894037171409
cont.tran.correlation=-0.300170668271808

tran.covariance=0.00016980022568139
cont.tran.covariance=-0.000321225263714742

tran.mean=52.0736447078026
cont.tran.mean=55.0308531871647

weightedLogRatios:
wLogRatio
Lung	-0.235275259711268
cerebhem	0.0662010774626172
cortex	0.470634540060055
heart	-0.0778560274077186
kidney	-0.271438916531174
liver	-0.352555987124039
stomach	-0.354179669636627
testicle	0.0118554306849198

cont.weightedLogRatios:
wLogRatio
Lung	-0.0504050324277741
cerebhem	-0.242952356749581
cortex	-0.110559731948713
heart	-0.214602422171230
kidney	-0.500475321600403
liver	-0.244121727495063
stomach	-0.0560127807768515
testicle	-0.787977367037827

varWeightedLogRatios=0.0774170665661385
cont.varWeightedLogRatios=0.0635779823544761

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.85821724282857	0.0632341994498554	61.0147242535763	1.53650884443448e-290	***
df.mm.trans1	0.0361409442587155	0.0557036993427903	0.648806895863608	0.516665038219683	   
df.mm.trans2	0.101477412656942	0.0502631489451661	2.01892270553219	0.0438563904804411	*  
df.mm.exp2	0.215146226805397	0.0669355532638763	3.21422945377381	0.00136485838755696	** 
df.mm.exp3	-0.202484028868792	0.0669355532638763	-3.02505946384801	0.00257213121843339	** 
df.mm.exp4	-0.092337529876028	0.0669355532638763	-1.37949901619564	0.168158793646858	   
df.mm.exp5	0.0235653545773270	0.0669355532638763	0.352060354000908	0.724893505888679	   
df.mm.exp6	0.140948776792303	0.0669355532638763	2.10573857866907	0.0355643089151611	*  
df.mm.exp7	0.101983104359641	0.0669355532638763	1.52360142535312	0.128036682339394	   
df.mm.exp8	-0.0374031445090506	0.0669355532638763	-0.558793386850756	0.576472244577537	   
df.mm.trans1:exp2	-0.0850730909821154	0.0631364397426721	-1.34744834090822	0.178249406990593	   
df.mm.trans2:exp2	-0.161522322613003	0.0516570870621862	-3.12681825087346	0.00183644854864123	** 
df.mm.trans1:exp3	0.325385254245437	0.0631364397426721	5.15368391964488	3.28230020496416e-07	***
df.mm.trans2:exp3	0.146489004486299	0.0516570870621862	2.83579684448624	0.00469628688461978	** 
df.mm.trans1:exp4	0.131503036527048	0.0631364397426721	2.08283896055939	0.0376096593061284	*  
df.mm.trans2:exp4	0.0912867928590906	0.0516570870621862	1.76716880588325	0.077613202911331	.  
df.mm.trans1:exp5	-0.0176458237485771	0.063136439742672	-0.279487152276830	0.779949304984821	   
df.mm.trans2:exp5	-0.00861256219403204	0.0516570870621862	-0.166725665031480	0.867631579503287	   
df.mm.trans1:exp6	-0.0962181753123634	0.063136439742672	-1.5239721419916	0.127944053857345	   
df.mm.trans2:exp6	-0.0676589903739964	0.0516570870621862	-1.30977169294402	0.190680648980799	   
df.mm.trans1:exp7	-0.077578511499449	0.063136439742672	-1.22874384136386	0.219559529777377	   
df.mm.trans2:exp7	-0.0481650859823304	0.0516570870621862	-0.932400348559103	0.351434594722325	   
df.mm.trans1:exp8	0.0380163840198391	0.063136439742672	0.602130626541251	0.54727213358596	   
df.mm.trans2:exp8	-0.0249920553556683	0.0516570870621862	-0.483806903892629	0.628666436782026	   
df.mm.trans1:probe2	0.0415663062922354	0.0368637387583408	1.12756621255164	0.259869618444337	   
df.mm.trans1:probe3	0.146151172781510	0.0368637387583408	3.96463239227034	8.06605643744652e-05	***
df.mm.trans1:probe4	-0.0857156927264267	0.0368637387583408	-2.32520345503568	0.0203313555038788	*  
df.mm.trans1:probe5	0.0750120844844483	0.0368637387583408	2.03484744117215	0.0422231513842447	*  
df.mm.trans1:probe6	0.103977721491709	0.0368637387583408	2.82059620087187	0.00492212291070294	** 
df.mm.trans1:probe7	0.118851811451679	0.0368637387583408	3.22408457348315	0.00131937999601296	** 
df.mm.trans1:probe8	0.136792457015345	0.0368637387583409	3.71075918023628	0.000222170541515444	***
df.mm.trans1:probe9	0.044602585905032	0.0368637387583408	1.20993115205766	0.226692717913026	   
df.mm.trans1:probe10	-0.0276398252089738	0.0368637387583408	-0.749783558042386	0.45362401732249	   
df.mm.trans1:probe11	0.0615353118687392	0.0368637387583408	1.66926399604045	0.095489174028778	.  
df.mm.trans1:probe12	-0.0521552793265686	0.0368637387583408	-1.41481252535102	0.157545252393909	   
df.mm.trans1:probe13	-0.0975956573477118	0.0368637387583408	-2.64747040411439	0.00828271344630075	** 
df.mm.trans1:probe14	-0.0614432117867643	0.0368637387583408	-1.66676560371571	0.095985320244311	.  
df.mm.trans1:probe15	-0.108963085788163	0.0368637387583408	-2.95583382093897	0.00321762596433524	** 
df.mm.trans1:probe16	-0.00965216850789728	0.0368637387583408	-0.261833683533073	0.79352272947656	   
df.mm.trans1:probe17	-0.0768776968240202	0.0368637387583408	-2.08545577343605	0.0373709658002776	*  
df.mm.trans1:probe18	-0.160753539486579	0.0368637387583408	-4.36074974761496	1.48043353065837e-05	***
df.mm.trans1:probe19	-0.0296375365655368	0.0368637387583408	-0.803975330875276	0.421670110487881	   
df.mm.trans1:probe20	-0.109041020202412	0.0368637387583408	-2.95794794221029	0.00319590086947455	** 
df.mm.trans1:probe21	0.056985009717198	0.0368637387583408	1.54582827560605	0.122574731318912	   
df.mm.trans1:probe22	0.0161101061770396	0.0368637387583408	0.437017695970799	0.662226355723451	   
df.mm.trans2:probe2	-0.0777634786529626	0.0368637387583409	-2.10948431364325	0.0352389629964511	*  
df.mm.trans2:probe3	-0.0781568380483985	0.0368637387583408	-2.12015494577892	0.0343260563486654	*  
df.mm.trans2:probe4	0.218678429412874	0.0368637387583409	5.93207408631051	4.59666701764348e-09	***
df.mm.trans2:probe5	-0.0142941145551428	0.0368637387583408	-0.387755421359928	0.69830884801249	   
df.mm.trans2:probe6	-0.114861256232451	0.0368637387583408	-3.11583306797556	0.00190531679550859	** 
df.mm.trans3:probe2	0.425643880458876	0.0368637387583408	11.5464110477011	1.81769427515649e-28	***
df.mm.trans3:probe3	0.123311895143611	0.0368637387583408	3.34507294422789	0.000864198745284107	***
df.mm.trans3:probe4	-0.00655282359996941	0.0368637387583409	-0.177757976284670	0.858961830004429	   
df.mm.trans3:probe5	0.206430744003741	0.0368637387583408	5.59983200176716	3.02804219901706e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03330509536321	0.0945124405654436	42.6748592167653	1.85042458024358e-201	***
df.mm.trans1	-0.0772287492483745	0.0832570447513249	-0.92759416910658	0.353921383838776	   
df.mm.trans2	-0.0043052614370946	0.0751253739059224	-0.05730768731329	0.954315609034168	   
df.mm.exp2	0.0679577288274247	0.100044636519577	0.679274083964778	0.497177193655924	   
df.mm.exp3	0.0914864374996018	0.100044636519577	0.91445619357815	0.360775855571523	   
df.mm.exp4	0.0843534060047583	0.100044636519577	0.843157703794066	0.399413436042814	   
df.mm.exp5	0.133825072629345	0.100044636519577	1.33765364426266	0.181421436737751	   
df.mm.exp6	0.093503674129021	0.100044636519577	0.9346195596475	0.350290094322848	   
df.mm.exp7	-0.10039309159849	0.100044636519577	-1.00348299610090	0.315956778945374	   
df.mm.exp8	-0.00830302298700524	0.100044636519577	-0.0829931846009607	0.933879463742545	   
df.mm.trans1:exp2	-0.0478807270769772	0.094366324878122	-0.507392092876534	0.612031319458	   
df.mm.trans2:exp2	-0.000115818202771931	0.0772088112639179	-0.00150006457651624	0.998803527455882	   
df.mm.trans1:exp3	-0.0683958574034552	0.094366324878122	-0.724790940961103	0.468810020705382	   
df.mm.trans2:exp3	-0.0534766505575718	0.0772088112639179	-0.692623674450524	0.488763630780565	   
df.mm.trans1:exp4	-0.0869764572469369	0.094366324878122	-0.921689568384385	0.356991725179911	   
df.mm.trans2:exp4	-0.0459163759843007	0.0772088112639179	-0.594703832796333	0.5522237394252	   
df.mm.trans1:exp5	-0.117327183856483	0.0943663248781219	-1.24331623604094	0.214146147939290	   
df.mm.trans2:exp5	-0.00635560981887099	0.0772088112639179	-0.082317156744533	0.934416831989096	   
df.mm.trans1:exp6	-0.0810538907286152	0.0943663248781219	-0.858928127521123	0.390659062305311	   
df.mm.trans2:exp6	-0.0328856473206999	0.0772088112639179	-0.425931273676641	0.670282115823754	   
df.mm.trans1:exp7	0.0963759410749806	0.0943663248781219	1.02129590401506	0.307448930972036	   
df.mm.trans2:exp7	0.0977982379871628	0.0772088112639179	1.26667198194343	0.205672220072019	   
df.mm.trans1:exp8	-0.0324158977571693	0.0943663248781219	-0.343511287517404	0.731311687471081	   
df.mm.trans2:exp8	0.150612175743758	0.0772088112639179	1.95071227335608	0.0514694136736204	.  
df.mm.trans1:probe2	0.0303717338983800	0.0550980632115158	0.551230517519029	0.581642505997713	   
df.mm.trans1:probe3	0.0242288847468943	0.0550980632115158	0.439741133075441	0.660253349389237	   
df.mm.trans1:probe4	0.0107790870236599	0.0550980632115158	0.195634590317270	0.844950035298036	   
df.mm.trans1:probe5	-0.0256065113991794	0.0550980632115158	-0.464744310537353	0.642251653538488	   
df.mm.trans1:probe6	-0.0210563761934387	0.0550980632115158	-0.38216182141658	0.7024513598858	   
df.mm.trans1:probe7	-0.0257252219173400	0.0550980632115158	-0.466898842135041	0.640710059189366	   
df.mm.trans1:probe8	0.0689050288448113	0.0550980632115159	1.25058894684359	0.211480838010016	   
df.mm.trans1:probe9	0.0459746271596827	0.0550980632115158	0.834414577935177	0.404317414335502	   
df.mm.trans1:probe10	-0.0229136278976258	0.0550980632115158	-0.415869933751803	0.67762607967789	   
df.mm.trans1:probe11	0.0483042413810874	0.0550980632115158	0.876695814073398	0.380937159713858	   
df.mm.trans1:probe12	0.0177137967086792	0.0550980632115158	0.321495814484036	0.747925715172426	   
df.mm.trans1:probe13	0.0171088615434938	0.0550980632115158	0.310516568936636	0.756255835170366	   
df.mm.trans1:probe14	-0.00588752174527687	0.0550980632115158	-0.106855330334848	0.914932791468056	   
df.mm.trans1:probe15	-0.0420979075613778	0.0550980632115158	-0.764054217291963	0.445079140194279	   
df.mm.trans1:probe16	0.0233630465419695	0.0550980632115158	0.424026638691112	0.671669951263137	   
df.mm.trans1:probe17	0.0265269804187487	0.0550980632115158	0.481450324613305	0.630339198903866	   
df.mm.trans1:probe18	0.111716896259939	0.0550980632115158	2.02760114872040	0.0429598239160353	*  
df.mm.trans1:probe19	-0.00220278758732947	0.0550980632115158	-0.0399794014332807	0.968120361101955	   
df.mm.trans1:probe20	0.00576219803852529	0.0550980632115158	0.104580772946679	0.91673688718194	   
df.mm.trans1:probe21	0.104146061224019	0.0550980632115158	1.89019459403161	0.0591235244838825	.  
df.mm.trans1:probe22	-0.0356547506370971	0.0550980632115158	-0.64711440945251	0.517759122059824	   
df.mm.trans2:probe2	-0.0855148291069148	0.0550980632115159	-1.55204782387054	0.121079489779025	   
df.mm.trans2:probe3	-0.126954381964907	0.0550980632115158	-2.30415325993478	0.0214909285508651	*  
df.mm.trans2:probe4	-0.100240717395139	0.0550980632115159	-1.81931471910955	0.0692685018060179	.  
df.mm.trans2:probe5	-0.0929916243169416	0.0550980632115158	-1.68774760666189	0.0918822108539206	.  
df.mm.trans2:probe6	-0.112866243304458	0.0550980632115158	-2.04846117496320	0.0408680925774745	*  
df.mm.trans3:probe2	-0.0428418686355138	0.0550980632115158	-0.777556707774795	0.437079571824645	   
df.mm.trans3:probe3	-0.0287613545988813	0.0550980632115158	-0.522003005595121	0.601824872356625	   
df.mm.trans3:probe4	0.119153141059102	0.0550980632115159	2.16256496352123	0.0308952128438365	*  
df.mm.trans3:probe5	-0.0248585112868997	0.0550980632115158	-0.451168513700207	0.65200063854686