chr5.18805_chr5_98394279_98395088_+_2.R 

fitVsDatCorrelation=0.925339864441717
cont.fitVsDatCorrelation=0.294193930420781

fstatistic=8725.43931018901,51,669
cont.fstatistic=1362.03576577476,51,669

residuals=-0.790534078035682,-0.0956393942752962,-0.0044728812107881,0.0888500015190479,0.972772226473721
cont.residuals=-0.852559952985617,-0.310746064987104,-0.106012639689725,0.264399377372174,1.34535045570788

predictedValues:
Include	Exclude	Both
chr5.18805_chr5_98394279_98395088_+_2.R.tl.Lung	69.4307566241487	103.605755260032	83.9883558750143
chr5.18805_chr5_98394279_98395088_+_2.R.tl.cerebhem	64.8273156596808	80.7281195547792	62.2746609165041
chr5.18805_chr5_98394279_98395088_+_2.R.tl.cortex	64.5477351468124	88.2911487287423	67.1513635088039
chr5.18805_chr5_98394279_98395088_+_2.R.tl.heart	68.0815461807943	97.3519650916807	70.1481174466747
chr5.18805_chr5_98394279_98395088_+_2.R.tl.kidney	72.5267660070258	104.493323720125	85.2580566803809
chr5.18805_chr5_98394279_98395088_+_2.R.tl.liver	72.3938092382264	111.547006353942	74.3159356523389
chr5.18805_chr5_98394279_98395088_+_2.R.tl.stomach	67.424949841139	91.0147882260074	78.412669742384
chr5.18805_chr5_98394279_98395088_+_2.R.tl.testicle	66.8700867929371	92.45770955088	69.9096196318983


diffExp=-34.1749986358829,-15.9008038950983,-23.7434135819299,-29.2704189108864,-31.9665577130991,-39.1531971157156,-23.5898383848684,-25.5876227579428
diffExpScore=0.99554341087473
diffExp1.5=0,0,0,0,0,-1,0,0
diffExp1.5Score=0.5
diffExp1.4=-1,0,0,-1,-1,-1,0,0
diffExp1.4Score=0.8
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	75.5849480297995	70.0907151707477	75.9737948486378
cerebhem	72.8374922020889	69.0771469216935	96.2943938186852
cortex	63.6900781735783	69.8322779563882	73.2289308724786
heart	73.4962842898218	72.5628149783549	69.9584618395657
kidney	77.9588675366974	70.9194933801476	70.7331788302202
liver	68.9035428371252	70.6734828060712	96.6169434028637
stomach	79.0161890886554	85.0241269854061	95.4408940724983
testicle	70.7323721289935	90.4231255794222	82.5969323970487
cont.diffExp=5.49423285905176,3.76034528039546,-6.14219978280991,0.933469311466908,7.03937415654984,-1.76993996894602,-6.00793789675073,-19.6907534504287
cont.diffExpScore=2.92452713211074

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=0.932900732674507
cont.tran.correlation=0.201607945610658

tran.covariance=0.00432243623646966
cont.tran.covariance=0.00154782396184169

tran.mean=82.2245488735596
cont.tran.mean=73.801434879062

weightedLogRatios:
wLogRatio
Lung	-1.77735210804572
cerebhem	-0.939169045408666
cortex	-1.35443444356665
heart	-1.57338575825818
kidney	-1.63103504422626
liver	-1.94472171669130
stomach	-1.30833555613979
testicle	-1.41417780511231

cont.weightedLogRatios:
wLogRatio
Lung	0.323565822264039
cerebhem	0.225900846955043
cortex	-0.386689453359485
heart	0.0548466407532324
kidney	0.407773637360965
liver	-0.10767512011277
stomach	-0.322903380633949
testicle	-1.07613147894335

varWeightedLogRatios=0.0966667097389801
cont.varWeightedLogRatios=0.235910397594913

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.54332967535982	0.09266043720587	49.0320336527832	1.12440457444194e-223	***
df.mm.trans1	-0.411929691763978	0.083130307432256	-4.95522877862162	9.16288967177328e-07	***
df.mm.trans2	0.206912330053776	0.0765740173486114	2.70212191051418	0.00706453969053372	** 
df.mm.exp2	-0.0189852642290535	0.104924596076333	-0.180941980612836	0.856467918307998	   
df.mm.exp3	-0.00914899726634703	0.104924596076333	-0.0871959255358114	0.930541869947713	   
df.mm.exp4	0.0981854819750718	0.104924596076333	0.935771836601988	0.349728332247079	   
df.mm.exp5	0.0371515826906988	0.104924596076333	0.354078872637936	0.723391342026545	   
df.mm.exp6	0.237996805161034	0.104924596076333	2.26826515479641	0.0236309539191249	*  
df.mm.exp7	-0.0901930524067386	0.104924596076333	-0.859598757388809	0.390318174436625	   
df.mm.exp8	0.0320551595988556	0.104924596076333	0.305506628546231	0.760075354958624	   
df.mm.trans1:exp2	-0.0496176316371004	0.100258923076250	-0.494894919221952	0.620836786601036	   
df.mm.trans2:exp2	-0.23052065663719	0.0875131626841273	-2.63412553685482	0.00863053084511502	** 
df.mm.trans1:exp3	-0.0637759212206485	0.100258923076250	-0.636112171004921	0.524920869149309	   
df.mm.trans2:exp3	-0.150804022031185	0.0875131626841273	-1.72321531305527	0.0853116638528014	.  
df.mm.trans1:exp4	-0.117809235094346	0.100258923076250	-1.17504987565794	0.240392808113209	   
df.mm.trans2:exp4	-0.160445445504382	0.0875131626841273	-1.83338643677522	0.0671892618215244	.  
df.mm.trans1:exp5	0.00647414898098407	0.100258923076250	0.0645742920663562	0.948532244005697	   
df.mm.trans2:exp5	-0.0286212821578392	0.0875131626841273	-0.327051168989809	0.743731486867354	   
df.mm.trans1:exp6	-0.196205965544920	0.100258923076250	-1.95699255013642	0.0507638551878713	.  
df.mm.trans2:exp6	-0.164143602437128	0.0875131626841273	-1.87564473049150	0.0611393516890993	.  
df.mm.trans1:exp7	0.0608782291511838	0.100258923076250	0.6072100844818	0.543917529330431	   
df.mm.trans2:exp7	-0.0393778272866954	0.0875131626841273	-0.449964623365596	0.652881711206142	   
df.mm.trans1:exp8	-0.069633373958713	0.100258923076250	-0.694535427093656	0.487587614193588	   
df.mm.trans2:exp8	-0.145896694655477	0.0875131626841273	-1.66714000706478	0.0959545913354501	.  
df.mm.trans1:probe2	-0.212029918757146	0.0501294615381248	-4.22964684342145	2.66671957565796e-05	***
df.mm.trans1:probe3	-0.220015644965388	0.0501294615381248	-4.38894889780653	1.32331024031676e-05	***
df.mm.trans1:probe4	-0.205443341660352	0.0501294615381248	-4.09825550398356	4.6739739770812e-05	***
df.mm.trans1:probe5	-0.139952416891532	0.0501294615381248	-2.79181967245139	0.00539086396463932	** 
df.mm.trans1:probe6	0.0256323434635347	0.0501294615381248	0.511322936194729	0.609293658540653	   
df.mm.trans1:probe7	-0.144918298235609	0.0501294615381248	-2.89088080719548	0.00396620082076657	** 
df.mm.trans1:probe8	0.310556974891892	0.0501294615381248	6.19509895704156	1.01753173078522e-09	***
df.mm.trans1:probe9	-0.193941938431405	0.0501294615381248	-3.86882149699348	0.000120059123689783	***
df.mm.trans1:probe10	0.961118594545304	0.0501294615381248	19.1727292704780	1.25028113197811e-65	***
df.mm.trans1:probe11	0.445022829000570	0.0501294615381248	8.87747075962743	6.22725589023022e-18	***
df.mm.trans1:probe12	0.65569366833038	0.0501294615381248	13.0800062121495	5.63338313422697e-35	***
df.mm.trans1:probe13	0.823356445936352	0.0501294615381248	16.4246018343957	3.48836011095123e-51	***
df.mm.trans1:probe14	1.25601629197127	0.0501294615381248	25.0554514936498	3.14523418097173e-98	***
df.mm.trans1:probe15	0.202889497061793	0.0501294615381248	4.04731052033125	5.78633880331389e-05	***
df.mm.trans1:probe16	-0.210011336035111	0.0501294615381248	-4.18937945055309	3.17225317358305e-05	***
df.mm.trans1:probe17	-0.192156101363850	0.0501294615381248	-3.83319699569704	0.000138419997017088	***
df.mm.trans1:probe18	-0.181617409524611	0.0501294615381248	-3.62296749161142	0.000313310957200351	***
df.mm.trans1:probe19	-0.126359930747877	0.0501294615381248	-2.52067201343818	0.0119445321729292	*  
df.mm.trans1:probe20	-0.062472158824586	0.0501294615381248	-1.24621643456262	0.213121153476712	   
df.mm.trans1:probe21	-0.177048995417683	0.0501294615381248	-3.53183517207805	0.000441039577940717	***
df.mm.trans2:probe2	-0.483590031974647	0.0501294615381248	-9.64682278916689	1.04851339274561e-20	***
df.mm.trans2:probe3	-0.0367417724496883	0.0501294615381248	-0.732937704143205	0.463853152832013	   
df.mm.trans2:probe4	-0.125470246075865	0.0501294615381248	-2.50292427299348	0.0125545211945055	*  
df.mm.trans2:probe5	-0.29339937468429	0.0501294615381248	-5.85283315802529	7.56975613617908e-09	***
df.mm.trans2:probe6	-0.0476406946692912	0.0501294615381248	-0.95035320962024	0.342276004046513	   
df.mm.trans3:probe2	0.560466768477933	0.0501294615381248	11.1803867682018	1.00463258990546e-26	***
df.mm.trans3:probe3	-0.283369203329617	0.0501294615381248	-5.65274779810088	2.33885432569279e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10698344863592	0.233581600651601	17.5826496486840	3.56671011378072e-57	***
df.mm.trans1	0.202724820982263	0.209557723427793	0.96739369786156	0.33369684449405	   
df.mm.trans2	0.203448094257227	0.193030403049718	1.05396917295368	0.292277530038279	   
df.mm.exp2	-0.288614345641094	0.264497511972456	-1.09117981295472	0.275586599885908	   
df.mm.exp3	-0.138124464898496	0.264497511972456	-0.522214609386871	0.601693781900387	   
df.mm.exp4	0.0891267665826116	0.264497511972456	0.336966370375132	0.736247934220764	   
df.mm.exp5	0.114152911670178	0.264497511972456	0.431584066023521	0.666182787546899	   
df.mm.exp6	-0.324635111632369	0.264497511972456	-1.22736546446673	0.220116970523411	   
df.mm.exp7	0.00942165300969812	0.264497511972456	0.0356209513633506	0.971595227745242	   
df.mm.exp8	0.104771834029757	0.264497511972456	0.396116520145820	0.692145373986333	   
df.mm.trans1:exp2	0.251588007823768	0.252736123829476	0.995457254039068	0.319873833573368	   
df.mm.trans2:exp2	0.274047962924777	0.220606174913963	1.24224973771317	0.214579713385234	   
df.mm.trans1:exp3	-0.0331039065390646	0.252736123829476	-0.130982093249955	0.895828850164593	   
df.mm.trans2:exp3	0.134430468615806	0.220606174913963	0.609368566715025	0.542487053892932	   
df.mm.trans1:exp4	-0.117149078749184	0.252736123829476	-0.463523286557272	0.643140091147286	   
df.mm.trans2:exp4	-0.0544645004131711	0.220606174913963	-0.246885656915145	0.805072456873918	   
df.mm.trans1:exp5	-0.0832287265734486	0.252736123829476	-0.329310766155470	0.742023878477274	   
df.mm.trans2:exp5	-0.102397908095847	0.220606174913963	-0.464166101133761	0.642679748166797	   
df.mm.trans1:exp6	0.232085545089825	0.252736123829476	0.918291938537506	0.358796992585035	   
df.mm.trans2:exp6	0.332915213721477	0.220606174913963	1.50909290663017	0.131747154415276	   
df.mm.trans1:exp7	0.0349739404083839	0.252736123829476	0.138381248704998	0.88998075749607	   
df.mm.trans2:exp7	0.183723076024620	0.220606174913963	0.83281021529099	0.405248977004685	   
df.mm.trans1:exp8	-0.171125648966086	0.252736123829476	-0.677092163847326	0.498581537773924	   
df.mm.trans2:exp8	0.149937880519008	0.220606174913963	0.679663117215482	0.496952892147586	   
df.mm.trans1:probe2	-0.0436053023576363	0.126368061914738	-0.345065847310829	0.730153327874516	   
df.mm.trans1:probe3	-0.0266441443894634	0.126368061914738	-0.210845556905355	0.833071993602262	   
df.mm.trans1:probe4	0.0731319962926164	0.126368061914738	0.578722148496345	0.562971448522659	   
df.mm.trans1:probe5	-0.0895609962748791	0.126368061914738	-0.708731264196383	0.478738144690958	   
df.mm.trans1:probe6	0.0759620890792344	0.126368061914738	0.60111778188453	0.547965153524324	   
df.mm.trans1:probe7	0.0203246092379621	0.126368061914738	0.160836598504418	0.872270653641689	   
df.mm.trans1:probe8	0.116421476165107	0.126368061914738	0.921288768705322	0.357231785381332	   
df.mm.trans1:probe9	-0.0254758935977326	0.126368061914738	-0.201600730530484	0.8402901568692	   
df.mm.trans1:probe10	-0.0255101162783382	0.126368061914738	-0.201871548014641	0.840078513735421	   
df.mm.trans1:probe11	-0.145200966914502	0.126368061914738	-1.14903215824004	0.250953357293587	   
df.mm.trans1:probe12	0.0662324310306597	0.126368061914738	0.524123184506441	0.600366461176923	   
df.mm.trans1:probe13	0.0449107097833518	0.126368061914738	0.355396047884738	0.722404931924376	   
df.mm.trans1:probe14	0.069624039035353	0.126368061914738	0.550962307883847	0.581843456506742	   
df.mm.trans1:probe15	-0.114753656276695	0.126368061914738	-0.908090656277698	0.364157321221477	   
df.mm.trans1:probe16	0.117663954854861	0.126368061914738	0.931120989528587	0.352126861603403	   
df.mm.trans1:probe17	-0.0123465484690468	0.126368061914738	-0.0977030768848629	0.922197336240144	   
df.mm.trans1:probe18	-0.106887675354990	0.126368061914738	-0.845844066415358	0.397942166543557	   
df.mm.trans1:probe19	0.201711749605201	0.126368061914738	1.59622412933181	0.110910997376053	   
df.mm.trans1:probe20	0.124370244042369	0.126368061914738	0.98419048419278	0.325377659848185	   
df.mm.trans1:probe21	0.0528054474102322	0.126368061914738	0.417870200825432	0.676176105798597	   
df.mm.trans2:probe2	0.0103619360778325	0.126368061914738	0.0819980612255002	0.934672781149336	   
df.mm.trans2:probe3	-0.098422628732584	0.126368061914738	-0.778856834878031	0.436339454286310	   
df.mm.trans2:probe4	-0.189352865460074	0.126368061914738	-1.49842343540754	0.13449495959669	   
df.mm.trans2:probe5	-0.100785154444808	0.126368061914738	-0.79755242675803	0.425413249611633	   
df.mm.trans2:probe6	-0.167572166882651	0.126368061914738	-1.32606423129060	0.185270979667353	   
df.mm.trans3:probe2	-0.381580721600918	0.126368061914738	-3.01959779883603	0.00262747164861776	** 
df.mm.trans3:probe3	-0.166722941412214	0.126368061914738	-1.31934397731528	0.187505496933182