chr18.11560_chr18_12119626_12129021_-_2.R 

fitVsDatCorrelation=0.759904095890801
cont.fitVsDatCorrelation=0.261870292795345

fstatistic=9042.81278988706,52,692
cont.fstatistic=4095.05280082881,52,692

residuals=-0.459655480692207,-0.0801756596830286,-0.00933727990255659,0.0679877510452234,1.18472322414819
cont.residuals=-0.48306358036095,-0.166896239809892,-0.0308994635994266,0.142254481784243,1.44481519885519

predictedValues:
Include	Exclude	Both
chr18.11560_chr18_12119626_12129021_-_2.R.tl.Lung	56.6235057850606	48.5469497846817	60.9050755143684
chr18.11560_chr18_12119626_12129021_-_2.R.tl.cerebhem	55.343560869947	54.5805465541368	67.8427314426564
chr18.11560_chr18_12119626_12129021_-_2.R.tl.cortex	51.7084623020018	46.9156324203249	57.4998243208028
chr18.11560_chr18_12119626_12129021_-_2.R.tl.heart	52.5207136370557	49.1529877047991	58.2549948036482
chr18.11560_chr18_12119626_12129021_-_2.R.tl.kidney	55.9414621704265	46.983552559027	62.4027737487639
chr18.11560_chr18_12119626_12129021_-_2.R.tl.liver	56.0017115586007	48.940366902019	61.9898617495002
chr18.11560_chr18_12119626_12129021_-_2.R.tl.stomach	52.5011519400792	49.549444289401	59.2068315118433
chr18.11560_chr18_12119626_12129021_-_2.R.tl.testicle	55.9063830679548	50.442521991172	61.6099611998666


diffExp=8.07655600037894,0.763014315810231,4.79282988167694,3.36772593225668,8.9579096113995,7.0613446565817,2.95170765067822,5.46386107678276
diffExpScore=0.976434518702002
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	55.8852123307615	55.4152943404449	51.3392069105913
cerebhem	54.7148796621944	52.3702591980341	51.7938633944102
cortex	55.3759771431604	56.8607927116301	50.6927969263271
heart	53.7886052293404	53.9844048349616	54.950374344549
kidney	55.1766880595262	55.6379963571183	56.1335640627482
liver	55.95884215608	60.0572841185615	56.661789914234
stomach	56.945234892695	53.7012189910788	59.5707104195316
testicle	56.7580525953761	52.1315863982419	54.7961309592545
cont.diffExp=0.469917990316617,2.34462046416031,-1.48481556846974,-0.195799605621183,-0.461308297592169,-4.09844196248148,3.24401590161621,4.62646619713416
cont.diffExpScore=3.10862407577167

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=0.209207794489968
cont.tran.correlation=0.0182878958782294

tran.covariance=0.00037528967649244
cont.tran.covariance=1.53066483655493e-05

tran.mean=51.978684596043
cont.tran.mean=55.2976455637003

weightedLogRatios:
wLogRatio
Lung	0.609335237312921
cerebhem	0.0556230433548415
cortex	0.379061854674261
heart	0.260313393238214
kidney	0.687047928178151
liver	0.533457024321345
stomach	0.22751589729441
testicle	0.408522954624175

cont.weightedLogRatios:
wLogRatio
Lung	0.0339378175803315
cerebhem	0.174321806690690
cortex	-0.106565066487631
heart	-0.0144865598878475
kidney	-0.0334256551353364
liver	-0.286967481788254
stomach	0.235365850050112
testicle	0.339790115213723

varWeightedLogRatios=0.0446009796938474
cont.varWeightedLogRatios=0.0404014073721796

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.59758696225255	0.0815261530225018	44.128010814635	2.45582538992831e-203	***
df.mm.trans1	0.277162773228236	0.0723089947353028	3.83303314121333	0.000138101576029704	***
df.mm.trans2	0.306734962896511	0.0658968049509504	4.65477746796413	3.88797662279753e-06	***
df.mm.exp2	-0.0135934560554354	0.0888811352101498	-0.152939721385141	0.878490394984866	   
df.mm.exp3	-0.0674485587428322	0.0888811352101498	-0.758862480585531	0.448193218992361	   
df.mm.exp4	-0.0183236160164074	0.0888811352101498	-0.206158663175073	0.836727613842799	   
df.mm.exp5	-0.0691453539798327	0.0888811352101497	-0.777953092254572	0.43686256229206	   
df.mm.exp6	-0.0206250917538281	0.0888811352101498	-0.232052523913677	0.816565825058667	   
df.mm.exp7	-0.0268698247804895	0.0888811352101498	-0.302311899110635	0.762505148939952	   
df.mm.exp8	0.0140504562936691	0.0888811352101497	0.158081422570136	0.874438756907011	   
df.mm.trans1:exp2	-0.00927042189020412	0.0841998487830685	-0.110100220180779	0.912361815058896	   
df.mm.trans2:exp2	0.130739618607043	0.071161860446508	1.83721473534717	0.0666068395169232	.  
df.mm.trans1:exp3	-0.0233541878004869	0.0841998487830685	-0.277366148966090	0.781581808984883	   
df.mm.trans2:exp3	0.033268126029004	0.071161860446508	0.467499385489106	0.640289811091473	   
df.mm.trans1:exp4	-0.0568929422841145	0.0841998487830685	-0.675689364130485	0.499463621736525	   
df.mm.trans2:exp4	0.0307298817497557	0.071161860446508	0.431830780658906	0.665998918749354	   
df.mm.trans1:exp5	0.0570269840502105	0.0841998487830685	0.677281311955015	0.498453839699505	   
df.mm.trans2:exp5	0.0364115823965022	0.071161860446508	0.51167271580642	0.609043318482868	   
df.mm.trans1:exp6	0.00958315001513055	0.0841998487830685	0.113814337598402	0.909417984428492	   
df.mm.trans2:exp6	0.0286962802209902	0.071161860446508	0.403253653585420	0.686886195197702	   
df.mm.trans1:exp7	-0.0487192597000853	0.0841998487830685	-0.578614574779167	0.56303753474262	   
df.mm.trans2:exp7	0.0473095038497437	0.071161860446508	0.664815444015913	0.506390028593523	   
df.mm.trans1:exp8	-0.0267960909425563	0.0841998487830685	-0.31824393190531	0.750395929097972	   
df.mm.trans2:exp8	0.0242526868825420	0.071161860446508	0.340810185826615	0.733349924993683	   
df.mm.trans1:probe2	0.353226479448756	0.0461181565170105	7.65916303090888	6.3235094952215e-14	***
df.mm.trans1:probe3	0.269126689264076	0.0461181565170105	5.83559078656601	8.23505862867472e-09	***
df.mm.trans1:probe4	-0.0269151619345349	0.0461181565170105	-0.583613135633626	0.55967078665664	   
df.mm.trans1:probe5	0.381670161131926	0.0461181565170105	8.27591972352902	6.55451045023988e-16	***
df.mm.trans1:probe6	0.611503577018787	0.0461181565170105	13.2594974127649	6.41249837839008e-36	***
df.mm.trans1:probe7	-0.0327580388750799	0.0461181565170105	-0.710306771759128	0.477753221878233	   
df.mm.trans1:probe8	0.216330707964322	0.0461181565170105	4.69079261406578	3.28004104193084e-06	***
df.mm.trans1:probe9	0.256827280805212	0.0461181565170105	5.56889737581948	3.67095758919706e-08	***
df.mm.trans1:probe10	0.0966890803499804	0.0461181565170105	2.09655128591961	0.0363958026268172	*  
df.mm.trans1:probe11	0.0531499793235162	0.0461181565170105	1.15247406526131	0.249524283815332	   
df.mm.trans1:probe12	0.00802963282773517	0.0461181565170105	0.174110012935436	0.86182992609333	   
df.mm.trans1:probe13	0.0663623587887311	0.0461181565170105	1.43896382250781	0.150612918495720	   
df.mm.trans1:probe14	-0.0389984660329627	0.0461181565170105	-0.845620661757768	0.398056688065424	   
df.mm.trans1:probe15	0.0784040100570474	0.0461181565170105	1.70006817224206	0.0895674544326033	.  
df.mm.trans1:probe16	0.24272067917379	0.0461181565170105	5.26301781130959	1.89261836396637e-07	***
df.mm.trans1:probe17	0.30486588194639	0.0461181565170105	6.61053920995176	7.65327308566043e-11	***
df.mm.trans1:probe18	0.262124423743729	0.0461181565170105	5.68375762476641	1.94270502236898e-08	***
df.mm.trans1:probe19	0.348767031533102	0.0461181565170105	7.56246688664714	1.26124541915512e-13	***
df.mm.trans1:probe20	0.225448426742342	0.0461181565170105	4.88849606681885	1.26409540762378e-06	***
df.mm.trans1:probe21	0.36528676853425	0.0461181565170105	7.92067151252058	9.43250337087713e-15	***
df.mm.trans2:probe2	-0.0456266488318196	0.0461181565170105	-0.98934242558005	0.322841431706045	   
df.mm.trans2:probe3	-0.0237792745704707	0.0461181565170105	-0.515616329150099	0.606287089336429	   
df.mm.trans2:probe4	-0.0954725617844337	0.0461181565170105	-2.07017298597395	0.0388065520780379	*  
df.mm.trans2:probe5	-0.0786874660167871	0.0461181565170105	-1.70621447081831	0.0884168412505077	.  
df.mm.trans2:probe6	0.0256603646682756	0.0461181565170105	0.55640482200998	0.578114018162322	   
df.mm.trans3:probe2	0.227351391807929	0.0461181565170105	4.92975888409745	1.03155878637752e-06	***
df.mm.trans3:probe3	-0.124663842609590	0.0461181565170105	-2.70314019519856	0.00703734236133314	** 
df.mm.trans3:probe4	-0.391360290617269	0.0461181565170105	-8.48603500603752	1.29612778908726e-16	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04144039888514	0.121041354076113	33.3889225689247	2.34607619475310e-146	***
df.mm.trans1	-0.0478830997204512	0.107356698558166	-0.446018742784906	0.655723225563528	   
df.mm.trans2	0.00702667791184675	0.097836561702489	0.0718205728980354	0.942765452213612	   
df.mm.exp2	-0.0864978283039732	0.131961248738049	-0.655479007141531	0.512377245261822	   
df.mm.exp3	0.0292673871832878	0.131961248738049	0.221787740440265	0.824544557395301	   
df.mm.exp4	-0.132374348255928	0.131961248738049	-1.00313046081201	0.316148553945652	   
df.mm.exp5	-0.0980277281192096	0.131961248738049	-0.742852383231082	0.457823154000093	   
df.mm.exp6	-0.0168854897572092	0.131961248738049	-0.127957941582743	0.898219432844373	   
df.mm.exp7	-0.161339007960539	0.131961248738049	-1.22262413779371	0.221887930037279	   
df.mm.exp8	-0.110751722879680	0.131961248738049	-0.83927458961478	0.401605075518177	   
df.mm.trans1:exp2	0.0653337165617082	0.125010972943782	0.522623854716251	0.601403310598681	   
df.mm.trans2:exp2	0.0299810591643130	0.105653555671189	0.283767630666582	0.776673341581146	   
df.mm.trans1:exp3	-0.0384213203010136	0.125010972943782	-0.307343582697272	0.75867433589383	   
df.mm.trans2:exp3	-0.00351696637362278	0.105653555671189	-0.0332877237427594	0.973454742232105	   
df.mm.trans1:exp4	0.0941361870265224	0.125010972943782	0.753023393145305	0.451691954484437	   
df.mm.trans2:exp4	0.106213926977293	0.105653555671189	1.00530385657676	0.315101949494317	   
df.mm.trans1:exp5	0.0852684671853059	0.125010972943782	0.68208786138839	0.495411629994163	   
df.mm.trans2:exp5	0.102038456910865	0.105653555671189	0.96578346334529	0.334489934798860	   
df.mm.trans1:exp6	0.018202141523693	0.125010972943782	0.145604350522723	0.884276134359	   
df.mm.trans2:exp6	0.0973287050390106	0.105653555671189	0.921206147968306	0.357263846060092	   
df.mm.trans1:exp7	0.180129215329507	0.125010972943782	1.44090723468341	0.150063206180140	   
df.mm.trans2:exp7	0.129919082458025	0.105653555671189	1.22967070660977	0.219238351013774	   
df.mm.trans1:exp8	0.126249457875193	0.125010972943782	1.00990700977880	0.312892830985748	   
df.mm.trans2:exp8	0.0496671259964334	0.105653555671189	0.470094221447745	0.638435813973643	   
df.mm.trans1:probe2	0.134846426966925	0.0684713298169772	1.96938524967117	0.0493073793854945	*  
df.mm.trans1:probe3	0.00596062309428924	0.0684713298169772	0.0870528308742052	0.930654701700254	   
df.mm.trans1:probe4	0.00122300974855779	0.0684713298169772	0.0178616327713639	0.985754385300362	   
df.mm.trans1:probe5	0.099943350395435	0.0684713298169772	1.459637934046	0.144843403250971	   
df.mm.trans1:probe6	0.134551374417696	0.0684713298169773	1.96507610962646	0.0498050543233373	*  
df.mm.trans1:probe7	0.0384071465387553	0.0684713298169772	0.56092304094892	0.57503157753371	   
df.mm.trans1:probe8	0.0293444264885894	0.0684713298169773	0.428565161024718	0.668373070130178	   
df.mm.trans1:probe9	0.0282490662593629	0.0684713298169773	0.412567805165639	0.680051078873894	   
df.mm.trans1:probe10	0.0421819314291311	0.0684713298169773	0.61605246373749	0.538062475586506	   
df.mm.trans1:probe11	-0.0644074914689679	0.0684713298169772	-0.940649051816696	0.347212970998759	   
df.mm.trans1:probe12	-0.0360531791703567	0.0684713298169772	-0.52654416478731	0.598678900230581	   
df.mm.trans1:probe13	0.0956365860281137	0.0684713298169772	1.39673913569006	0.162939806906609	   
df.mm.trans1:probe14	0.094501521550461	0.0684713298169773	1.38016191306729	0.167982495404158	   
df.mm.trans1:probe15	0.00361062132391643	0.0684713298169772	0.0527318708949799	0.957960763495645	   
df.mm.trans1:probe16	0.0837367066177335	0.0684713298169772	1.22294552831908	0.221766585938290	   
df.mm.trans1:probe17	0.0288511783730032	0.0684713298169772	0.421361443543187	0.67362201573756	   
df.mm.trans1:probe18	0.0132306911114567	0.0684713298169772	0.193229650232033	0.846835818290672	   
df.mm.trans1:probe19	-0.00664830351553084	0.0684713298169772	-0.097096164676539	0.922678140766818	   
df.mm.trans1:probe20	-0.00157833112786903	0.0684713298169773	-0.0230509781552058	0.98161625412457	   
df.mm.trans1:probe21	0.0179753471591775	0.0684713298169772	0.262523704552333	0.792995846244512	   
df.mm.trans2:probe2	0.0427682719553312	0.0684713298169773	0.624615763556077	0.532429153078595	   
df.mm.trans2:probe3	-0.090222788750748	0.0684713298169773	-1.31767250602423	0.188049367308345	   
df.mm.trans2:probe4	-0.0797805431050723	0.0684713298169773	-1.16516713372333	0.244352826862862	   
df.mm.trans2:probe5	-0.103874528592485	0.0684713298169772	-1.51705142678169	0.129710375000608	   
df.mm.trans2:probe6	-0.105004911679085	0.0684713298169773	-1.53356027931342	0.125594904589235	   
df.mm.trans3:probe2	-0.0711388779638727	0.0684713298169773	-1.03895861456212	0.299186861957068	   
df.mm.trans3:probe3	-0.109886326707439	0.0684713298169773	-1.60485165106569	0.108982699808264	   
df.mm.trans3:probe4	-0.0674917934899998	0.0684713298169773	-0.985694212021357	0.324627698558369