chr2.13984_chr2_153492435_153493674_+_2.R 

fitVsDatCorrelation=0.896072732339065
cont.fitVsDatCorrelation=0.2069930244163

fstatistic=9546.13884117494,60,876
cont.fstatistic=1953.71313040838,60,876

residuals=-0.793636281768031,-0.0998272460147943,0.000858320891572551,0.101172661446398,0.84247767262911
cont.residuals=-0.775709407710281,-0.272010954289123,-0.033157225333081,0.230623703049030,1.46891678215890

predictedValues:
Include	Exclude	Both
chr2.13984_chr2_153492435_153493674_+_2.R.tl.Lung	132.792573924282	55.6292485114726	80.0573003411395
chr2.13984_chr2_153492435_153493674_+_2.R.tl.cerebhem	99.013433540935	64.5684818276863	72.357752918464
chr2.13984_chr2_153492435_153493674_+_2.R.tl.cortex	100.021224765712	50.7725145268948	73.5474331504813
chr2.13984_chr2_153492435_153493674_+_2.R.tl.heart	105.572613378188	53.8777174844818	74.6326720373183
chr2.13984_chr2_153492435_153493674_+_2.R.tl.kidney	120.212780313793	54.0771334165645	75.290117285913
chr2.13984_chr2_153492435_153493674_+_2.R.tl.liver	113.650220769332	50.4892627082272	80.8905806100531
chr2.13984_chr2_153492435_153493674_+_2.R.tl.stomach	132.706567054312	51.0148305634799	91.7510665944049
chr2.13984_chr2_153492435_153493674_+_2.R.tl.testicle	120.476447887162	52.6636866722878	83.549002624881


diffExp=77.1633254128093,34.4449517132486,49.2487102388176,51.6948958937064,66.1356468972287,63.160958061105,81.6917364908323,67.8127612148746
diffExpScore=0.997968936863202
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	89.2986820473044	106.997017427264	86.8123911378227
cerebhem	84.2622473645308	83.6046378929521	86.0591809642831
cortex	81.4872554435039	85.2116931048318	89.9981975839074
heart	84.3001828902645	92.3300610457975	91.9870223991123
kidney	86.5855336079347	75.3738061107231	86.2870195002043
liver	89.267120808097	88.7072698851742	83.1423697713808
stomach	88.682111749912	80.1031984173795	90.4402259162364
testicle	87.3859271076212	91.38958233166	82.4977673799529
cont.diffExp=-17.6983353799592,0.657609471578681,-3.72443766132791,-8.02987815553304,11.2117274972116,0.559850922922706,8.5789133325326,-4.00365522403889
cont.diffExpScore=4.04993877241105

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=-0.339358094251142
cont.tran.correlation=0.285660121838144

tran.covariance=-0.00316276029587168
cont.tran.covariance=0.000903947557172034

tran.mean=84.8461710840508
cont.tran.mean=87.1866454521844

weightedLogRatios:
wLogRatio
Lung	3.87511412375851
cerebhem	1.87321505901601
cortex	2.89271431746154
heart	2.90804332556313
kidney	3.50683001250525
liver	3.51113499481519
stomach	4.21618878656546
testicle	3.62266175829145

cont.weightedLogRatios:
wLogRatio
Lung	-0.828562016794178
cerebhem	0.0347088787964975
cortex	-0.197663601004754
heart	-0.407604926271536
kidney	0.609023292152137
liver	0.0282388063832952
stomach	0.451144888685979
testicle	-0.201262034902539

varWeightedLogRatios=0.530978281891996
cont.varWeightedLogRatios=0.210844613649811

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.87563991217284	0.0838207253086963	58.1674746217807	4.36676381709880e-303	***
df.mm.trans1	0.170597470576714	0.0722320474166061	2.36179752171186	0.0184044950838308	*  
df.mm.trans2	-0.849228829517353	0.0636664112232172	-13.33872623258	4.37732918373673e-37	***
df.mm.exp2	-0.0433954772521377	0.081558279108554	-0.532079363694988	0.594805944466523	   
df.mm.exp3	-0.28994777937807	0.081558279108554	-3.55509928050529	0.000398061163035588	***
df.mm.exp4	-0.191217190959824	0.081558279108554	-2.34454666098721	0.0192727196359870	*  
df.mm.exp5	-0.066428914802915	0.081558279108554	-0.814496278354502	0.41558206412441	   
df.mm.exp6	-0.262965996895453	0.081558279108554	-3.22427103378978	0.00130968248605231	** 
df.mm.exp7	-0.223577096959034	0.081558279108554	-2.74131699936255	0.00624399931021794	** 
df.mm.exp8	-0.194807675298769	0.081558279108554	-2.3885702031486	0.0171249202569979	*  
df.mm.trans1:exp2	-0.250137305719898	0.0751930180101616	-3.32660281950771	0.000915701085993298	***
df.mm.trans2:exp2	0.192412756271677	0.0547109568643883	3.51689619957862	0.00045905337198269	***
df.mm.trans1:exp3	0.0065418742680087	0.0751930180101616	0.0870010865520072	0.930690530872311	   
df.mm.trans2:exp3	0.198593819616425	0.0547109568643883	3.62987289929289	0.000299996452485775	***
df.mm.trans1:exp4	-0.0381721306467396	0.0751930180101616	-0.507655253863877	0.611822921172728	   
df.mm.trans2:exp4	0.159225063354767	0.0547109568643883	2.91029571552618	0.00370232844159329	** 
df.mm.trans1:exp5	-0.0330960595761189	0.0751930180101616	-0.440148041027509	0.659938466721004	   
df.mm.trans2:exp5	0.0381312234781165	0.0547109568643883	0.696957714935093	0.486014164709308	   
df.mm.trans1:exp6	0.107303173495207	0.0751930180101616	1.42703639692593	0.153925735137895	   
df.mm.trans2:exp6	0.166017575626829	0.0547109568643883	3.03444840195969	0.00248093855531226	** 
df.mm.trans1:exp7	0.222929208814001	0.0751930180101616	2.96475942465662	0.00311132768293933	** 
df.mm.trans2:exp7	0.136984367468822	0.0547109568643883	2.50378306868886	0.0124682096716338	*  
df.mm.trans1:exp8	0.0974736396735093	0.0751930180101616	1.29631237384749	0.195209283720012	   
df.mm.trans2:exp8	0.140024720580930	0.0547109568643883	2.55935426112193	0.0106535882944449	*  
df.mm.trans1:probe2	-0.404232168667505	0.0523817531932531	-7.71704160370812	3.24002764856791e-14	***
df.mm.trans1:probe3	-0.437025008610535	0.0523817531932531	-8.34307715891467	2.79647169340487e-16	***
df.mm.trans1:probe4	0.470795646078557	0.0523817531932531	8.98777947239835	1.52467641031003e-18	***
df.mm.trans1:probe5	-0.385718432240356	0.0523817531932531	-7.36360294809753	4.12900514875566e-13	***
df.mm.trans1:probe6	-0.684844365527	0.0523817531932531	-13.0741016437611	8.20530603897577e-36	***
df.mm.trans1:probe7	0.096145934370662	0.0523817531932531	1.83548523120158	0.0667726785115821	.  
df.mm.trans1:probe8	-0.43084995650833	0.0523817531932531	-8.22519160286191	7.00621139988514e-16	***
df.mm.trans1:probe9	-0.149142653563198	0.0523817531932531	-2.84722531170278	0.00451332005860018	** 
df.mm.trans1:probe10	-0.408419335453665	0.0523817531932531	-7.79697720209699	1.79687491913592e-14	***
df.mm.trans1:probe11	-0.158296607436346	0.0523817531932531	-3.0219799412276	0.00258432953336437	** 
df.mm.trans1:probe12	0.0105975251199680	0.0523817531932531	0.202313295640762	0.839718781851339	   
df.mm.trans1:probe13	0.0530903967620625	0.0523817531932531	1.01352844312398	0.311087670136418	   
df.mm.trans1:probe14	-0.0608769789865017	0.0523817531932531	-1.16217910389343	0.245479249940962	   
df.mm.trans1:probe15	0.0112780875349025	0.0523817531932531	0.215305652204768	0.829579139641461	   
df.mm.trans1:probe16	-0.398070866332331	0.0523817531932531	-7.59941853919474	7.64271131365229e-14	***
df.mm.trans1:probe17	-0.278367856717571	0.0523817531932531	-5.3142142014717	1.35967120193843e-07	***
df.mm.trans1:probe18	-0.403528339674512	0.0523817531932531	-7.70360507380818	3.57575278254812e-14	***
df.mm.trans1:probe19	-0.690384571275486	0.0523817531932531	-13.1798675910758	2.55479079271320e-36	***
df.mm.trans1:probe20	-0.224834232883247	0.0523817531932531	-4.29222427996561	1.96620774781876e-05	***
df.mm.trans1:probe21	-0.374934561280512	0.0523817531932531	-7.15773219535548	1.73517820481685e-12	***
df.mm.trans1:probe22	-0.348200849724677	0.0523817531932531	-6.64736914093067	5.2421350663142e-11	***
df.mm.trans2:probe2	-0.0631363473678987	0.0523817531932531	-1.20531184084214	0.228408427120769	   
df.mm.trans2:probe3	-0.0837679139555265	0.0523817531932531	-1.59918118140261	0.110140937040911	   
df.mm.trans2:probe4	0.0967914744109509	0.0523817531932531	1.84780898901677	0.0649669257087952	.  
df.mm.trans2:probe5	-0.076214028593744	0.0523817531932531	-1.45497284736855	0.146035008500593	   
df.mm.trans2:probe6	-0.00460662946709561	0.0523817531932531	-0.0879433998724761	0.929941761511427	   
df.mm.trans3:probe2	0.0949697946340276	0.0523817531932531	1.81303199768159	0.0701690365719242	.  
df.mm.trans3:probe3	0.187193890554208	0.0523817531932531	3.57364691219078	0.000371265739833023	***
df.mm.trans3:probe4	0.381024224165955	0.0523817531932531	7.27398761855554	7.74647572131124e-13	***
df.mm.trans3:probe5	0.246637153476222	0.0523817531932531	4.7084554914819	2.90061625905676e-06	***
df.mm.trans3:probe6	0.0812123283070894	0.0523817531932531	1.55039347399220	0.121408286436605	   
df.mm.trans3:probe7	0.113517747766833	0.0523817531932531	2.16712387132270	0.0304942635036507	*  
df.mm.trans3:probe8	0.000395827400820128	0.0523817531932531	0.00755658939783466	0.993972491855834	   
df.mm.trans3:probe9	0.217064283764973	0.0523817531932531	4.14389115545930	3.74584982150696e-05	***
df.mm.trans3:probe10	-0.0416920326317349	0.0523817531932531	-0.795926636474339	0.426290304264247	   
df.mm.trans3:probe11	0.76275165506246	0.0523817531932531	14.5613998876369	3.55628070179237e-43	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.64717371037995	0.184735366843128	25.1558420555507	1.55818758974219e-105	***
df.mm.trans1	-0.190714216170176	0.159194682797055	-1.19799363156684	0.231243524142219	   
df.mm.trans2	0.0197457762190319	0.140316583871009	0.140723039816761	0.888121094569919	   
df.mm.exp2	-0.296040608719021	0.179749084188011	-1.64696588055700	0.0999237080945575	.  
df.mm.exp3	-0.355242664863616	0.179749084188011	-1.97632531185553	0.048431280260805	*  
df.mm.exp4	-0.262932016848344	0.179749084188011	-1.46277249776320	0.143888317444768	   
df.mm.exp5	-0.375124928725995	0.179749084188011	-2.08693652276764	0.0371824580195818	*  
df.mm.exp6	-0.144617680531144	0.179749084188011	-0.804553086790022	0.421295916033563	   
df.mm.exp7	-0.337353486286075	0.179749084188011	-1.87680225359711	0.0608773964070146	.  
df.mm.exp8	-0.128343807298323	0.179749084188011	-0.714016474009293	0.475407191547383	   
df.mm.trans1:exp2	0.237987807633591	0.165720467282905	1.43607975246242	0.151336645717122	   
df.mm.trans2:exp2	0.049338644897447	0.120579351341317	0.409179883193987	0.682507677577681	   
df.mm.trans1:exp3	0.263702568891522	0.165720467282905	1.59124924769461	0.111914272987445	   
df.mm.trans2:exp3	0.127580372734533	0.120579351341317	1.05806152807538	0.290319065096938	   
df.mm.trans1:exp4	0.205329322305833	0.165720467282905	1.23901003703611	0.215673651077114	   
df.mm.trans2:exp4	0.115500834240502	0.120579351341317	0.95788236506232	0.338386363961048	   
df.mm.trans1:exp5	0.344270952868279	0.165720467282905	2.0774196362877	0.0380536863932549	*  
df.mm.trans2:exp5	0.0247837847589944	0.120579351341317	0.205539211177545	0.83719861920889	   
df.mm.trans1:exp6	0.144264183511766	0.165720467282905	0.870527255185018	0.384250816950692	   
df.mm.trans2:exp6	-0.0428414326997359	0.120579351341317	-0.355296592850852	0.722452937189244	   
df.mm.trans1:exp7	0.330424954833319	0.165720467282905	1.99386931651142	0.0464759842171844	*  
df.mm.trans2:exp7	0.0478683103057073	0.120579351341317	0.396985966280487	0.691474507139089	   
df.mm.trans1:exp8	0.106691330855154	0.165720467282905	0.643802981034439	0.519871713755482	   
df.mm.trans2:exp8	-0.0293256591653706	0.120579351341317	-0.243206310526253	0.807902516102372	   
df.mm.trans1:probe2	0.00963521881765037	0.115445939609853	0.0834608722507902	0.933504155939842	   
df.mm.trans1:probe3	-0.0214469100769572	0.115445939609853	-0.185774485871366	0.852664620605823	   
df.mm.trans1:probe4	0.064905898058144	0.115445939609853	0.56221897692974	0.574110662820428	   
df.mm.trans1:probe5	0.139730682868807	0.115445939609853	1.21035597562828	0.226468804877900	   
df.mm.trans1:probe6	0.198992311995355	0.115445939609853	1.72368393958112	0.0851177530924263	.  
df.mm.trans1:probe7	0.0756524242630664	0.115445939609853	0.655306063762243	0.512442763055345	   
df.mm.trans1:probe8	0.113412873502698	0.115445939609853	0.982389453331785	0.326179259675966	   
df.mm.trans1:probe9	0.0788728330041495	0.115445939609853	0.683201447107612	0.494660207592366	   
df.mm.trans1:probe10	0.0674679904522395	0.115445939609853	0.584411982614946	0.559093637116633	   
df.mm.trans1:probe11	0.0496609037845535	0.115445939609853	0.430165876360671	0.66718078407371	   
df.mm.trans1:probe12	0.250467969165412	0.115445939609853	2.16956932406513	0.0303077215506255	*  
df.mm.trans1:probe13	-0.0135894072226000	0.115445939609853	-0.117712301260010	0.906322603326328	   
df.mm.trans1:probe14	0.0603857799402647	0.115445939609853	0.523065429103329	0.601060996307989	   
df.mm.trans1:probe15	0.0638710842040835	0.115445939609853	0.553255354150475	0.580229732938368	   
df.mm.trans1:probe16	0.0328085065756172	0.115445939609853	0.284189350327026	0.776332436396738	   
df.mm.trans1:probe17	0.0397053794898122	0.115445939609853	0.343930497893609	0.73098109196209	   
df.mm.trans1:probe18	-0.0418220777692044	0.115445939609853	-0.362265471705123	0.71724103339385	   
df.mm.trans1:probe19	-0.101354056466663	0.115445939609853	-0.877935220668537	0.380219597206107	   
df.mm.trans1:probe20	0.100668963919622	0.115445939609853	0.872000905877076	0.383446814739046	   
df.mm.trans1:probe21	-0.028884969515431	0.115445939609853	-0.250203425196652	0.802488701810778	   
df.mm.trans1:probe22	0.0332573512234168	0.115445939609853	0.288077270935723	0.773355733742718	   
df.mm.trans2:probe2	0.0324434172073006	0.115445939609853	0.281026923224346	0.778756112007637	   
df.mm.trans2:probe3	0.00567364447129643	0.115445939609853	0.0491454657519389	0.960814573371682	   
df.mm.trans2:probe4	0.0653512596706325	0.115445939609853	0.566076727267201	0.571486602357193	   
df.mm.trans2:probe5	0.00343383735661541	0.115445939609853	0.0297441154554243	0.976277903388406	   
df.mm.trans2:probe6	-0.00691711833706601	0.115445939609853	-0.0599165146946031	0.952235792564507	   
df.mm.trans3:probe2	0.0629463648553196	0.115445939609853	0.54524537691014	0.585723560156653	   
df.mm.trans3:probe3	0.0445404785560759	0.115445939609853	0.385812430533282	0.699729263428521	   
df.mm.trans3:probe4	0.0318144644475995	0.115445939609853	0.275578894806659	0.78293651291891	   
df.mm.trans3:probe5	-0.0236394465642678	0.115445939609853	-0.204766375016366	0.837802225988464	   
df.mm.trans3:probe6	-0.140171895596993	0.115445939609853	-1.21417778806860	0.225007060678561	   
df.mm.trans3:probe7	-0.0388062222461387	0.115445939609853	-0.336141941217538	0.736844330453996	   
df.mm.trans3:probe8	-0.000709939256003019	0.115445939609853	-0.00614953854940454	0.995094809177161	   
df.mm.trans3:probe9	0.00829392526949955	0.115445939609853	0.0718425030583899	0.94274364636798	   
df.mm.trans3:probe10	-0.0623794518368881	0.115445939609853	-0.540334740638762	0.589103548224773	   
df.mm.trans3:probe11	-0.0189032206051264	0.115445939609853	-0.163740887457882	0.869972897643944