chr7.21584_chr7_8921002_8924757_-_1.R 

fitVsDatCorrelation=0.870995062959037
cont.fitVsDatCorrelation=0.262516857290435

fstatistic=8108.5661376346,49,623
cont.fstatistic=2092.58671091496,49,623

residuals=-0.510073471032437,-0.0952652129864545,-0.0045645886877411,0.0775814272084744,1.70724137097906
cont.residuals=-0.85117716880862,-0.221878256883797,-0.066632994901799,0.158571977230160,1.50685891654872

predictedValues:
Include	Exclude	Both
chr7.21584_chr7_8921002_8924757_-_1.R.tl.Lung	51.728361750242	44.5747291138408	80.018599261083
chr7.21584_chr7_8921002_8924757_-_1.R.tl.cerebhem	58.3256747369964	57.7995552410374	81.7394748800736
chr7.21584_chr7_8921002_8924757_-_1.R.tl.cortex	55.5468184288826	45.5713136643359	80.2045290444422
chr7.21584_chr7_8921002_8924757_-_1.R.tl.heart	50.8425688454074	46.2860407023727	75.9533635161953
chr7.21584_chr7_8921002_8924757_-_1.R.tl.kidney	49.9859444168296	45.0990995070755	79.1462560973879
chr7.21584_chr7_8921002_8924757_-_1.R.tl.liver	48.4543247172264	46.3379138381079	77.9851194450043
chr7.21584_chr7_8921002_8924757_-_1.R.tl.stomach	52.5197904217899	49.4010373781801	81.7778228192887
chr7.21584_chr7_8921002_8924757_-_1.R.tl.testicle	51.4488231438038	50.2327323797174	75.7962468196075


diffExp=7.15363263640122,0.526119495959023,9.97550476454668,4.5565281430347,4.88684490975408,2.11641087911853,3.11875304360982,1.21609076408644
diffExpScore=0.971056343298372
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=0,0,1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	57.6849726809988	59.056811268126	59.6019229454856
cerebhem	61.6000628039841	60.4590281318488	58.122067301794
cortex	57.5830987122119	59.3726911068295	55.7812278204262
heart	60.6689688096037	59.8715387905064	60.5435491538737
kidney	63.4136357290082	49.3755516250437	52.5792285464447
liver	63.4708121979113	60.433462557288	53.8943009414623
stomach	61.2355246425327	51.8345111917799	56.3664290969377
testicle	61.5707811545385	55.8676837218166	52.689477609878
cont.diffExp=-1.37183858712711,1.14103467213526,-1.78959239461762,0.797430019097277,14.0380841039645,3.03734964062332,9.40101345075279,5.70309743272185
cont.diffExpScore=1.16656545351211

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=0,0,0,0,1,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.69011418353022
cont.tran.correlation=-0.379891288561878

tran.covariance=0.00340854797060632
cont.tran.covariance=-0.00112283541362052

tran.mean=50.2596705178654
cont.tran.mean=58.9686959452518

weightedLogRatios:
wLogRatio
Lung	0.576243579601641
cerebhem	0.0368025988384935
cortex	0.775609461890771
heart	0.364474796317628
kidney	0.397146234169536
liver	0.172315508458896
stomach	0.24062478950264
testicle	0.0939756490368668

cont.weightedLogRatios:
wLogRatio
Lung	-0.0955815279906489
cerebhem	0.0768691443506365
cortex	-0.124518534567909
heart	0.054231847925569
kidney	1.00704135010401
liver	0.202330433327317
stomach	0.671917407377034
testicle	0.395763615574189

varWeightedLogRatios=0.062769772931705
cont.varWeightedLogRatios=0.156887855062455

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.00754636606069	0.0818739513944484	36.733861195621	4.78986902999132e-158	***
df.mm.trans1	0.80947803111438	0.0709475713966933	11.4095241764978	1.62926108106983e-27	***
df.mm.trans2	0.811029291308097	0.0647036704785444	12.5345175213365	2.66406486334874e-32	***
df.mm.exp2	0.358572210923404	0.085771431434719	4.18055528426519	3.3245868533486e-05	***
df.mm.exp3	0.091010485035736	0.085771431434719	1.06108156892548	0.289063998930464	   
df.mm.exp4	0.0725406755146377	0.085771431434719	0.845744023403046	0.398020265415309	   
df.mm.exp5	-0.0116075523800625	0.085771431434719	-0.135331219100583	0.892393715441478	   
df.mm.exp6	-0.000850139451586862	0.085771431434719	-0.00991168548042604	0.992094921695427	   
df.mm.exp7	0.0962411878382996	0.085771431434719	1.12206577677964	0.262266740757368	   
df.mm.exp8	0.168291462211506	0.085771431434719	1.96209226541349	0.0501965075092201	.  
df.mm.trans1:exp2	-0.238536038784703	0.0787092635508985	-3.03059675600255	0.00254192476311080	** 
df.mm.trans2:exp2	-0.098758216710017	0.0652622736808817	-1.51325124210234	0.130722964689776	   
df.mm.trans1:exp3	-0.0197904588722991	0.0787092635508985	-0.251437479903510	0.801558807046007	   
df.mm.trans2:exp3	-0.0688991394252364	0.0652622736808817	-1.05572692367628	0.291502215631445	   
df.mm.trans1:exp4	-0.089812916707226	0.0787092635508985	-1.14107174499412	0.254278494980752	   
df.mm.trans2:exp4	-0.0348673432343309	0.0652622736808817	-0.534264917045713	0.59334885384902	   
df.mm.trans1:exp5	-0.0226568076489576	0.0787092635508985	-0.287854397650491	0.77355392618518	   
df.mm.trans2:exp5	0.0233027454620699	0.0652622736808817	0.35706303424265	0.721165529235201	   
df.mm.trans1:exp6	-0.0645344790202106	0.0787092635508985	-0.819909577460071	0.412581224920109	   
df.mm.trans2:exp6	0.0396435521995385	0.0652622736808817	0.607449755633505	0.543773816241088	   
df.mm.trans1:exp7	-0.0810573431318709	0.0787092635508985	-1.02983231547395	0.303488367698016	   
df.mm.trans2:exp7	0.00656314904979207	0.0652622736808817	0.100565743110398	0.919927545854744	   
df.mm.trans1:exp8	-0.173710088251707	0.0787092635508985	-2.20698403739193	0.0276790602021548	*  
df.mm.trans2:exp8	-0.0487916952061729	0.0652622736808817	-0.747624813759228	0.454968587355453	   
df.mm.trans1:probe2	-0.00871351637479995	0.0481993834324859	-0.180780660545278	0.856598536463968	   
df.mm.trans1:probe3	-0.0215645455205716	0.048199383432486	-0.447402933084767	0.654739670461312	   
df.mm.trans1:probe4	-0.0148512055831302	0.0481993834324859	-0.308120239835281	0.758093830149045	   
df.mm.trans1:probe5	-0.0447615670012647	0.0481993834324859	-0.928675095273019	0.353417154639714	   
df.mm.trans1:probe6	-0.0596500718546093	0.048199383432486	-1.23756918878771	0.216342061578746	   
df.mm.trans1:probe7	0.264425859311046	0.0481993834324859	5.48608385585334	5.98199414083437e-08	***
df.mm.trans1:probe8	0.523217672734796	0.0481993834324859	10.8552772976376	2.91441310498481e-25	***
df.mm.trans1:probe9	0.283564897310332	0.0481993834324859	5.8831644124147	6.57222464660568e-09	***
df.mm.trans1:probe10	0.548573927950802	0.0481993834324859	11.3813474132756	2.12939980267516e-27	***
df.mm.trans1:probe11	0.0898175213159126	0.0481993834324859	1.86345788928446	0.062868150606828	.  
df.mm.trans1:probe12	0.304705486282365	0.0481993834324859	6.32177145396836	4.93111153856186e-10	***
df.mm.trans1:probe13	0.538041113058717	0.0481993834324859	11.1628214873820	1.67365549242986e-26	***
df.mm.trans1:probe14	0.128620066714896	0.0481993834324859	2.6685002494909	0.00781742295401323	** 
df.mm.trans1:probe15	0.173332143138043	0.0481993834324859	3.59614855614976	0.000348544611415531	***
df.mm.trans1:probe16	0.132842194704272	0.048199383432486	2.75609738639806	0.00602073407168721	** 
df.mm.trans2:probe2	0.0405098868767953	0.0481993834324859	0.840464835686923	0.400970247739146	   
df.mm.trans2:probe3	-0.0857163737037075	0.0481993834324859	-1.77837075081619	0.0758305907407856	.  
df.mm.trans2:probe4	-0.145269243235255	0.048199383432486	-3.01392326810025	0.00268371164668752	** 
df.mm.trans2:probe5	-0.0173548357899449	0.0481993834324859	-0.360063439696365	0.718921721895577	   
df.mm.trans2:probe6	-0.0490722828501305	0.048199383432486	-1.01811017808697	0.309020803088399	   
df.mm.trans3:probe2	-0.415063978019035	0.0481993834324859	-8.6113960067648	5.90435952719659e-17	***
df.mm.trans3:probe3	-0.235474562454022	0.0481993834324859	-4.88542685994847	1.31385060778305e-06	***
df.mm.trans3:probe4	-0.600027492198207	0.0481993834324859	-12.4488624017085	6.30227574201716e-32	***
df.mm.trans3:probe5	0.299685330173421	0.0481993834324859	6.2176175052778	9.2506386653279e-10	***
df.mm.trans3:probe6	-0.523886837907373	0.048199383432486	-10.8691605700972	2.56465483974619e-25	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17881400491239	0.160805502720550	25.9867599939934	2.04784954345801e-101	***
df.mm.trans1	-0.0116135126155455	0.139345416838169	-0.0833433411665988	0.933605310404767	   
df.mm.trans2	-0.086919844229773	0.127082009380980	-0.683966555558589	0.494250606394032	   
df.mm.exp2	0.114274650615632	0.168460394496807	0.678347281311853	0.497803405999931	   
df.mm.exp3	0.0698173341742983	0.168460394496807	0.414443610813351	0.67869191480945	   
df.mm.exp4	0.0484619185732314	0.168460394496807	0.287675442753103	0.773690856635924	   
df.mm.exp5	0.0410044145069409	0.168460394496807	0.243406853162260	0.807770359909174	   
df.mm.exp6	0.219289632265389	0.168460394496807	1.30172811787844	0.193490462054222	   
df.mm.exp7	-0.0148988432447627	0.168460394496807	-0.0884412225749895	0.92955442008468	   
df.mm.exp8	0.132949014080844	0.168460394496807	0.789200419944186	0.430295111874089	   
df.mm.trans1:exp2	-0.0486084613212802	0.154589860126438	-0.314434991282894	0.753295953814541	   
df.mm.trans2:exp2	-0.0908086194194547	0.128179140608229	-0.708450836762942	0.478930318525842	   
df.mm.trans1:exp3	-0.0715849354550409	0.154589860126438	-0.463063589012192	0.643480498167694	   
df.mm.trans2:exp3	-0.0644828426591601	0.128179140608229	-0.503068146292598	0.6150941619011	   
df.mm.trans1:exp4	0.00197372573671905	0.154589860126438	0.0127674980435635	0.989817374574213	   
df.mm.trans2:exp4	-0.0347605552314780	0.128179140608229	-0.271187301354448	0.786336808673778	   
df.mm.trans1:exp5	0.05367779761125	0.154589860126438	0.347227156861048	0.728537934854325	   
df.mm.trans2:exp5	-0.220048902657024	0.128179140608229	-1.71672942736906	0.0865256677857689	.  
df.mm.trans1:exp6	-0.123706183254776	0.154589860126438	-0.800221846074497	0.423887304418198	   
df.mm.trans2:exp6	-0.196246548347347	0.128179140608229	-1.53103342256882	0.126268728021679	   
df.mm.trans1:exp7	0.0746296315820918	0.154589860126438	0.482758904892294	0.629436519558708	   
df.mm.trans2:exp7	-0.115544873566819	0.128179140608229	-0.901432737171905	0.367706628407916	   
df.mm.trans1:exp8	-0.0677582887689775	0.154589860126438	-0.438310046425805	0.661313484158679	   
df.mm.trans2:exp8	-0.188462793283974	0.128179140608229	-1.47030782379792	0.141983245919098	   
df.mm.trans1:probe2	-0.0876476696743629	0.0946665691794988	-0.925856619015872	0.354878999939761	   
df.mm.trans1:probe3	-0.0720776942303086	0.0946665691794988	-0.761384878051733	0.446715321390778	   
df.mm.trans1:probe4	-0.203145971249000	0.0946665691794988	-2.14591035684214	0.0322658196264149	*  
df.mm.trans1:probe5	-0.214213727075321	0.0946665691794988	-2.26282339089681	0.0239900145274298	*  
df.mm.trans1:probe6	-0.221026343804036	0.0946665691794988	-2.33478772622407	0.0198712295529352	*  
df.mm.trans1:probe7	-0.196944110860713	0.0946665691794988	-2.08039767964216	0.0378971619709103	*  
df.mm.trans1:probe8	-0.267985384422229	0.0946665691794988	-2.83083444076333	0.00479244910667625	** 
df.mm.trans1:probe9	-0.174530407972959	0.0946665691794988	-1.84363296869910	0.0657113812972712	.  
df.mm.trans1:probe10	-0.124393619531614	0.0946665691794988	-1.31401846089668	0.189323611620813	   
df.mm.trans1:probe11	-0.238963481317228	0.0946665691794988	-2.52426472606318	0.0118413759522379	*  
df.mm.trans1:probe12	-0.248502288703596	0.0946665691794988	-2.62502687968344	0.00887681776521282	** 
df.mm.trans1:probe13	-0.00343142654429281	0.0946665691794988	-0.0362475008235106	0.971096621620712	   
df.mm.trans1:probe14	-0.159382256488122	0.0946665691794988	-1.68361711921676	0.0927566378942472	.  
df.mm.trans1:probe15	-0.154775823964923	0.0946665691794988	-1.63495757062295	0.102563073519676	   
df.mm.trans1:probe16	-0.101463207316426	0.0946665691794988	-1.07179554721203	0.284226885283858	   
df.mm.trans2:probe2	-0.142050440637693	0.0946665691794988	-1.50053436887894	0.13398264022339	   
df.mm.trans2:probe3	-0.000835357322962449	0.0946665691794988	-0.0088242061606618	0.992962218378052	   
df.mm.trans2:probe4	0.0195963150008185	0.0946665691794988	0.207003540644445	0.836074732315384	   
df.mm.trans2:probe5	0.00200043226803857	0.0946665691794988	0.0211313485359918	0.983147643908476	   
df.mm.trans2:probe6	-0.0394422759587289	0.0946665691794988	-0.416644189185115	0.677082191270318	   
df.mm.trans3:probe2	-0.0079239340481082	0.0946665691794988	-0.083703614874682	0.933318971028997	   
df.mm.trans3:probe3	0.007312197964667	0.0946665691794988	0.077241607338724	0.938456142161605	   
df.mm.trans3:probe4	0.0893829539174785	0.0946665691794988	0.944187105249352	0.345440061162013	   
df.mm.trans3:probe5	-0.0201456271913029	0.0946665691794988	-0.212806140181382	0.831547786805301	   
df.mm.trans3:probe6	-0.0241824526242357	0.0946665691794988	-0.255448706273310	0.798460867699295