chr13.6709_chr13_9025676_9054301_+_2.R 

fitVsDatCorrelation=0.910576021664724
cont.fitVsDatCorrelation=0.268186350370567

fstatistic=7872.02277598593,56,784
cont.fstatistic=1437.75308269991,56,784

residuals=-0.676727407865262,-0.0934989147265867,-0.00911041494604712,0.075702021859583,1.44668155759819
cont.residuals=-0.732495021003956,-0.314132712451434,-0.086554059295131,0.222080138842404,1.70989459979758

predictedValues:
Include	Exclude	Both
chr13.6709_chr13_9025676_9054301_+_2.R.tl.Lung	61.274007837551	99.0499741450586	78.0461628413875
chr13.6709_chr13_9025676_9054301_+_2.R.tl.cerebhem	72.4901880114837	74.2820951374747	91.2744199305166
chr13.6709_chr13_9025676_9054301_+_2.R.tl.cortex	60.335478939901	92.8199764424281	80.6146795489818
chr13.6709_chr13_9025676_9054301_+_2.R.tl.heart	60.1896501590233	91.6888080418243	71.578772019285
chr13.6709_chr13_9025676_9054301_+_2.R.tl.kidney	60.6518022239259	102.098756856033	76.5811266002741
chr13.6709_chr13_9025676_9054301_+_2.R.tl.liver	60.1574431424896	97.7853715978883	76.7917179477917
chr13.6709_chr13_9025676_9054301_+_2.R.tl.stomach	64.864308999809	94.4820711946493	83.8921485373512
chr13.6709_chr13_9025676_9054301_+_2.R.tl.testicle	63.6594994675671	107.294199450413	98.0263094719275


diffExp=-37.7759663075075,-1.79190712599097,-32.4844975025271,-31.4991578828011,-41.4469546321073,-37.6279284553988,-29.6177621948402,-43.6346999828462
diffExpScore=0.99610711467198
diffExp1.5=-1,0,-1,-1,-1,-1,0,-1
diffExp1.5Score=0.857142857142857
diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	75.4086906305902	67.519628425635	72.8730205804352
cerebhem	81.217762917072	78.2715543871618	64.0468297644906
cortex	78.8512174396843	74.263969713807	73.1494048313782
heart	75.0681125476084	57.672772506723	60.2590060985701
kidney	73.123189152135	86.4086044288475	71.7644264108602
liver	70.937359593517	79.1584740638183	74.8916163594321
stomach	75.4886821896134	78.907399955837	69.1417620003582
testicle	68.4697678660232	89.7620556995228	85.9685248149508
cont.diffExp=7.88906220495514,2.94620852991018,4.58724772587735,17.3953400408855,-13.2854152767125,-8.22111447030132,-3.41871776622364,-21.2922878334996
cont.diffExpScore=5.48869219070088

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=0,0,0,1,0,0,0,-1
cont.diffExp1.3Score=2
cont.diffExp1.2=0,0,0,1,0,0,0,-1
cont.diffExp1.2Score=2

tran.correlation=-0.709726248116697
cont.tran.correlation=-0.402772390897827

tran.covariance=-0.00516278776986054
cont.tran.covariance=-0.00288536353999285

tran.mean=78.94522697797
cont.tran.mean=75.6580775948497

weightedLogRatios:
wLogRatio
Lung	-2.09180590582535
cerebhem	-0.104894563384857
cortex	-1.85877528386157
heart	-1.81321591705001
kidney	-2.27353738973631
liver	-2.10835147595571
stomach	-1.63998383380175
testicle	-2.30451605169193

cont.weightedLogRatios:
wLogRatio
Lung	0.471595429194595
cerebhem	0.161790278385638
cortex	0.259981736113102
heart	1.10363000396587
kidney	-0.730472917580361
liver	-0.473337804057844
stomach	-0.192499923454030
testicle	-1.18103862406333

varWeightedLogRatios=0.508053318602678
cont.varWeightedLogRatios=0.525944973474577

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.3042725832315	0.090001849647896	47.8242680574968	1.16885920584334e-234	***
df.mm.trans1	-0.318906072926684	0.0784446801331517	-4.06536265283222	5.27960144633517e-05	***
df.mm.trans2	0.264687926843137	0.0700034117927289	3.7810718087119	0.000168018562910973	***
df.mm.exp2	-0.276229181888119	0.0915862010115857	-3.01605677315053	0.00264356862341327	** 
df.mm.exp3	-0.112778343319199	0.0915862010115857	-1.23139012289561	0.218546232063907	   
df.mm.exp4	-0.00857758096885349	0.0915862010115857	-0.093655822319439	0.925406493232848	   
df.mm.exp5	0.0390594755795722	0.0915862010115857	0.426477735162650	0.66987684200783	   
df.mm.exp6	-0.0150363926554458	0.0915862010115857	-0.164177490597560	0.8696337365926	   
df.mm.exp7	-0.06250416664405	0.0915862010115857	-0.68246270675801	0.49514800403404	   
df.mm.exp8	-0.109792524917099	0.0915862010115857	-1.19878894095858	0.230972240862225	   
df.mm.trans1:exp2	0.444324659495674	0.0855216634532874	5.19546324935983	2.6059606781054e-07	***
df.mm.trans2:exp2	-0.0115253881971047	0.0667064374507543	-0.172777750357502	0.862870720351592	   
df.mm.trans1:exp3	0.0973429105029246	0.0855216634532874	1.13822517678335	0.255374169149393	   
df.mm.trans2:exp3	0.0478157112184159	0.0667064374507543	0.716808048004597	0.473706026109628	   
df.mm.trans1:exp4	-0.00927774308721259	0.0855216634532874	-0.108484128027750	0.913639428067104	   
df.mm.trans2:exp4	-0.0686466090402102	0.0667064374507543	-1.02908522271015	0.303757003690044	   
df.mm.trans1:exp5	-0.0492658628011599	0.0855216634532874	-0.576062962433715	0.564737948975157	   
df.mm.trans2:exp5	-0.00874343834735109	0.0667064374507543	-0.131073381842733	0.89575088371608	   
df.mm.trans1:exp6	-0.00335416688357263	0.0855216634532874	-0.0392200846912280	0.96872490542389	   
df.mm.trans2:exp6	0.00218687172975969	0.0667064374507543	0.0327835185528253	0.97385556561077	   
df.mm.trans1:exp7	0.119445963391413	0.0855216634532874	1.39667493086889	0.162906586421895	   
df.mm.trans2:exp7	0.0152897482208208	0.0667064374507543	0.229209485697814	0.818765879991026	   
df.mm.trans1:exp8	0.147985346829413	0.0855216634532874	1.73038433601381	0.0839551347043903	.  
df.mm.trans2:exp8	0.18974260180115	0.0667064374507543	2.8444421416033	0.00456480625246011	** 
df.mm.trans1:probe2	0.175659733360096	0.0543480686296431	3.23212466954688	0.00128007868148666	** 
df.mm.trans1:probe3	0.274421950111872	0.0543480686296431	5.04934134793143	5.51688044944864e-07	***
df.mm.trans1:probe4	0.282542517173529	0.0543480686296431	5.19875911504649	2.56169666453719e-07	***
df.mm.trans1:probe5	-0.093913521254111	0.0543480686296431	-1.7280010793776	0.0843818001217356	.  
df.mm.trans1:probe6	-0.109851630364783	0.0543480686296431	-2.02126097825796	0.0435921127096232	*  
df.mm.trans1:probe7	-0.28841510625416	0.0543480686296431	-5.30681427190312	1.45321501569220e-07	***
df.mm.trans1:probe8	-0.159267063184812	0.0543480686296431	-2.93050088440388	0.00348247253624204	** 
df.mm.trans1:probe9	-0.131761168591458	0.0543480686296431	-2.42439468252956	0.0155590456404115	*  
df.mm.trans1:probe10	-0.138806103948605	0.0543480686296431	-2.554020914607	0.0108367256374166	*  
df.mm.trans1:probe11	0.756120538557218	0.0543480686296431	13.9125558207014	1.73744363745635e-39	***
df.mm.trans1:probe12	1.51685622396332	0.0543480686296431	27.9100299644499	1.40790646808963e-119	***
df.mm.trans1:probe13	0.0287597108232840	0.0543480686296431	0.529176317548064	0.596832935549287	   
df.mm.trans1:probe14	0.263241296667844	0.0543480686296431	4.8436182426594	1.53649688484210e-06	***
df.mm.trans1:probe15	1.14588251954470	0.054348068629643	21.0841442656106	1.6052620575713e-78	***
df.mm.trans1:probe16	-0.0900228146386686	0.0543480686296431	-1.65641239713839	0.0980384613750843	.  
df.mm.trans1:probe17	0.188240363494341	0.0543480686296431	3.46360723095998	0.000561901602651998	***
df.mm.trans1:probe18	-0.0712321548862152	0.0543480686296431	-1.31066580068612	0.190354502732753	   
df.mm.trans1:probe19	-0.0175836958097004	0.0543480686296431	-0.323538559015319	0.746373692444872	   
df.mm.trans1:probe20	0.102102057031368	0.0543480686296431	1.87866946527845	0.0606604718258644	.  
df.mm.trans1:probe21	-0.0370469164748034	0.0543480686296431	-0.681660221033079	0.495655136520939	   
df.mm.trans1:probe22	0.173760848162210	0.0543480686296431	3.19718533783252	0.00144343418158810	** 
df.mm.trans2:probe2	-0.283308969306918	0.054348068629643	-5.21286177136371	2.38037676017016e-07	***
df.mm.trans2:probe3	-0.156487534193360	0.054348068629643	-2.87935777920187	0.00409338568745528	** 
df.mm.trans2:probe4	0.401433706804817	0.0543480686296431	7.38634724152577	3.86604346155379e-13	***
df.mm.trans2:probe5	0.287119041029663	0.054348068629643	5.28296677819862	1.64832916727373e-07	***
df.mm.trans2:probe6	0.0978757821948374	0.0543480686296431	1.80090635532636	0.0721018261818114	.  
df.mm.trans3:probe2	0.271665336253147	0.054348068629643	4.99861987928992	7.1263171646603e-07	***
df.mm.trans3:probe3	-0.169169600440336	0.0543480686296431	-3.11270675675981	0.00192110671120286	** 
df.mm.trans3:probe4	0.0555408209275463	0.054348068629643	1.02194654433870	0.307121432611001	   
df.mm.trans3:probe5	0.109296558385824	0.0543480686296431	2.01104769942479	0.0446622828269087	*  
df.mm.trans3:probe6	0.144999285647502	0.054348068629643	2.66797494931430	0.00778856545274317	** 
df.mm.trans3:probe7	0.338389919675128	0.054348068629643	6.22634673517285	7.77675738862216e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.41416820001152	0.209765504006250	21.0433465737055	2.7823597108615e-78	***
df.mm.trans1	-0.0393249128513314	0.182829441051652	-0.215090702160062	0.829752541142783	   
df.mm.trans2	-0.192074876402696	0.163155546406063	-1.17725005759018	0.239452979721869	   
df.mm.exp2	0.351081301072986	0.213458119920562	1.64473153424025	0.100426051055644	   
df.mm.exp3	0.136062272991045	0.213458119920562	0.637419054574639	0.524038018170031	   
df.mm.exp4	0.0279066171659836	0.213458119920562	0.130735795744706	0.89601785243855	   
df.mm.exp5	0.231221507906502	0.213458119920562	1.08321720435161	0.279045048090936	   
df.mm.exp6	0.0705847258074862	0.213458119920562	0.330672479612179	0.740980242534932	   
df.mm.exp7	0.209476440680919	0.213458119920562	0.981346789519533	0.326724487877289	   
df.mm.exp8	0.0229510144097863	0.213458119920562	0.107519987613155	0.914403977784693	   
df.mm.trans1:exp2	-0.276869851417017	0.199323623991222	-1.38904684689663	0.165212923285168	   
df.mm.trans2:exp2	-0.203315401254339	0.155471354500612	-1.30773544687641	0.191346389402136	   
df.mm.trans1:exp3	-0.0914220484805257	0.199323623991222	-0.458661380171132	0.646604470387651	   
df.mm.trans2:exp3	-0.0408547158091799	0.155471354500612	-0.262779699452730	0.792789421157628	   
df.mm.trans1:exp4	-0.032433277187247	0.199323623991222	-0.162716674209553	0.87078344787075	   
df.mm.trans2:exp4	-0.185539782419628	0.155471354500612	-1.19340172352391	0.233073081515839	   
df.mm.trans1:exp5	-0.261998495117196	0.199323623991222	-1.31443774636936	0.189083339236821	   
df.mm.trans2:exp5	0.0154474041440462	0.155471354500612	0.0993585229489033	0.92087901295883	   
df.mm.trans1:exp6	-0.131710026156764	0.199323623991222	-0.660784825799496	0.508944428944093	   
df.mm.trans2:exp6	0.0884487710367181	0.155471354500612	0.56890718757049	0.569582059917518	   
df.mm.trans1:exp7	-0.208416229160717	0.199323623991222	-1.04561729807750	0.296060028608128	   
df.mm.trans2:exp7	-0.0536197752773659	0.155471354500613	-0.344885239146448	0.730273148228597	   
df.mm.trans1:exp8	-0.119481240309146	0.199323623991222	-0.599433413444299	0.549057084460507	   
df.mm.trans2:exp8	0.261792982182910	0.155471354500612	1.68386634968102	0.0926055636434127	.  
df.mm.trans1:probe2	-0.157994175477083	0.126667952408352	-1.24730977704401	0.21265639419681	   
df.mm.trans1:probe3	0.0181574890094414	0.126667952408352	0.143347142384566	0.886052860460082	   
df.mm.trans1:probe4	-0.0880083286655669	0.126667952408352	-0.694795542141913	0.487389294689207	   
df.mm.trans1:probe5	-0.218707561683793	0.126667952408352	-1.72662111864510	0.0846296518427979	.  
df.mm.trans1:probe6	-0.105017532452787	0.126667952408352	-0.829077366895704	0.407312971531206	   
df.mm.trans1:probe7	-0.08857058708011	0.126667952408352	-0.699234379305164	0.484612957913197	   
df.mm.trans1:probe8	-0.210917014412531	0.126667952408352	-1.66511742238145	0.0962888565555006	.  
df.mm.trans1:probe9	-0.155072587282770	0.126667952408352	-1.22424484121167	0.22122763050098	   
df.mm.trans1:probe10	-0.0228218332651979	0.126667952408352	-0.180170539045464	0.857065245160751	   
df.mm.trans1:probe11	-0.0762382334363647	0.126667952408352	-0.601874680902617	0.547431567689323	   
df.mm.trans1:probe12	-0.0148365487948144	0.126667952408352	-0.117129459446730	0.906787463589847	   
df.mm.trans1:probe13	0.0526329354882995	0.126667952408352	0.415518957144121	0.677875738723382	   
df.mm.trans1:probe14	0.0315826698055937	0.126667952408352	0.249334335995087	0.803167481619222	   
df.mm.trans1:probe15	0.0138526098795048	0.126667952408352	0.109361599490033	0.912943675176533	   
df.mm.trans1:probe16	-0.00389143012365723	0.126667952408352	-0.0307215049242451	0.975499459637787	   
df.mm.trans1:probe17	-0.0308412880926271	0.126667952408352	-0.243481381882617	0.807696199976913	   
df.mm.trans1:probe18	-0.124292568726749	0.126667952408352	-0.981247161287134	0.326773572468964	   
df.mm.trans1:probe19	-0.0208923644394085	0.126667952408352	-0.164938045039646	0.869035263971689	   
df.mm.trans1:probe20	-0.118544895845036	0.126667952408352	-0.935871257023803	0.349627653668853	   
df.mm.trans1:probe21	-0.0143043754157295	0.126667952408352	-0.112928133310429	0.910116440660834	   
df.mm.trans1:probe22	-0.170976373620801	0.126667952408352	-1.34979977468655	0.177469809036925	   
df.mm.trans2:probe2	-0.0394794865021465	0.126667952408352	-0.311676992889824	0.755368878006343	   
df.mm.trans2:probe3	0.0723127858010316	0.126667952408352	0.570884619401676	0.568241450263503	   
df.mm.trans2:probe4	-0.196963647802953	0.126667952408352	-1.5549603830966	0.12035906674689	   
df.mm.trans2:probe5	0.126324203262020	0.126667952408352	0.997286218496499	0.318933300666271	   
df.mm.trans2:probe6	-0.0879685494023373	0.126667952408352	-0.694481498514671	0.487586043319187	   
df.mm.trans3:probe2	0.135864088822643	0.126667952408352	1.07260034001848	0.283780514371244	   
df.mm.trans3:probe3	0.0295955439709182	0.126667952408352	0.233646659697380	0.815320294093522	   
df.mm.trans3:probe4	0.136204673612238	0.126667952408352	1.07528914001184	0.282576098245601	   
df.mm.trans3:probe5	0.0641701427679821	0.126667952408352	0.50660124797084	0.612577070831272	   
df.mm.trans3:probe6	0.116056846651913	0.126667952408352	0.916228962774805	0.359828553599999	   
df.mm.trans3:probe7	0.259759206058589	0.126667952408352	2.05070975822817	0.0406271621284373	*