chr11.4845_chr11_60277699_60280407_+_2.R 

fitVsDatCorrelation=0.904416919867033
cont.fitVsDatCorrelation=0.23681510612883

fstatistic=7303.48032972732,59,853
cont.fstatistic=1396.77051804545,59,853

residuals=-0.86823690283037,-0.130749773581107,0.00774657512549014,0.130118641587737,0.89672552260389
cont.residuals=-1.05010739123338,-0.337678344021148,-0.105240657998042,0.280745735682304,1.57390273746845

predictedValues:
Include	Exclude	Both
chr11.4845_chr11_60277699_60280407_+_2.R.tl.Lung	145.347788563621	88.5734822542694	161.981444803311
chr11.4845_chr11_60277699_60280407_+_2.R.tl.cerebhem	91.513036297174	102.470625334997	94.394139631479
chr11.4845_chr11_60277699_60280407_+_2.R.tl.cortex	102.226916277613	95.6071978687776	130.396949358935
chr11.4845_chr11_60277699_60280407_+_2.R.tl.heart	91.2654382987301	83.7562238099647	113.610644167167
chr11.4845_chr11_60277699_60280407_+_2.R.tl.kidney	79.4862436918834	85.7187923539617	80.4467714031934
chr11.4845_chr11_60277699_60280407_+_2.R.tl.liver	69.8614143825542	92.3039281693977	68.4685697980705
chr11.4845_chr11_60277699_60280407_+_2.R.tl.stomach	95.0897529798	76.6798131545215	98.4020950270869
chr11.4845_chr11_60277699_60280407_+_2.R.tl.testicle	86.4383725209072	75.0034375165649	82.937449193817


diffExp=56.7743063093517,-10.9575890378232,6.61971840883578,7.50921448876545,-6.23254866207832,-22.4425137868436,18.4099398252785,11.4349350043423
diffExpScore=2.25999710475803
diffExp1.5=1,0,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=1,0,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,0,0,0,-1,0,0
diffExp1.3Score=2
diffExp1.2=1,0,0,0,0,-1,1,0
diffExp1.2Score=1.5

cont.predictedValues:
Include	Exclude	Both
Lung	105.254554824921	87.64721511677	93.851153923664
cerebhem	103.797936237833	83.2525552532426	98.67007403858
cortex	107.145442904738	80.3792467586054	119.771830122015
heart	99.2780040751098	97.0541224024076	99.2368958467872
kidney	96.937367064923	93.875247920054	105.674152111529
liver	102.70560011703	111.697295287729	103.298270287430
stomach	96.515891269998	83.3009842423717	113.275736529369
testicle	113.129551785231	106.417875824729	102.142064278208
cont.diffExp=17.6073397081512,20.5453809845906,26.7661961461324,2.22388167270213,3.062119144869,-8.99169517069926,13.2149070276263,6.7116759605024
cont.diffExpScore=1.20676199856354

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=0,0,1,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=1,1,1,0,0,0,0,0
cont.diffExp1.2Score=0.75

tran.correlation=0.0595106067648259
cont.tran.correlation=0.224471774790695

tran.covariance=0.00120619022034651
cont.tran.covariance=0.00131842931702601

tran.mean=91.3339039671711
cont.tran.mean=98.0243056928558

weightedLogRatios:
wLogRatio
Lung	2.34348783902406
cerebhem	-0.517185504130909
cortex	0.307535431804750
heart	0.383873959994207
kidney	-0.333153795418837
liver	-1.22176642847495
stomach	0.956967069839356
testicle	0.622715871996227

cont.weightedLogRatios:
wLogRatio
Lung	0.835650304020591
cerebhem	0.999646683234148
cortex	1.30219876746699
heart	0.103910492406134
kidney	0.146304646633356
liver	-0.39225510694999
stomach	0.66203626109271
testicle	0.287327168034513

varWeightedLogRatios=1.15722814367929
cont.varWeightedLogRatios=0.307863163225417

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97640400871678	0.0995679394008496	39.9365903587521	1.80298302941974e-197	***
df.mm.trans1	0.668702291600291	0.0859844369528062	7.77701541463041	2.13897327512340e-14	***
df.mm.trans2	0.488594550979884	0.0759668614050813	6.43168010291396	2.100159659987e-10	***
df.mm.exp2	0.22309848232115	0.0977176734224777	2.2830924489636	0.0226701335772371	*  
df.mm.exp3	-0.05862027159917	0.0977176734224777	-0.599894262174336	0.54873602716689	   
df.mm.exp4	-0.166574690836003	0.0977176734224777	-1.70465264881846	0.088623366767891	.  
df.mm.exp5	0.063580175329239	0.0977176734224777	0.650651751135674	0.515446530170702	   
df.mm.exp6	0.169745242178334	0.0977176734224777	1.73709868678974	0.0827307231337824	.  
df.mm.exp7	-0.0700825368416817	0.0977176734224777	-0.717194079505794	0.473450691172751	   
df.mm.exp8	-0.0166011872177517	0.0977176734224777	-0.169889300843025	0.865137478506337	   
df.mm.trans1:exp2	-0.685746459323429	0.0903224775817976	-7.59220160565685	8.25090271737583e-14	***
df.mm.trans2:exp2	-0.0773548223695381	0.066707570606945	-1.15961084575136	0.24653176941918	   
df.mm.trans1:exp3	-0.293314128969743	0.0903224775817976	-3.24741013336478	0.00121008575821838	** 
df.mm.trans2:exp3	0.135035864879906	0.0667075706069449	2.02429594799016	0.043250907906769	*  
df.mm.trans1:exp4	-0.298782556505047	0.0903224775817976	-3.30795350730348	0.000979141793092298	***
df.mm.trans2:exp4	0.110652657472085	0.0667075706069449	1.65877210735306	0.0975293421622054	.  
df.mm.trans1:exp5	-0.667125616292084	0.0903224775817976	-7.38604203685537	3.60149551977404e-13	***
df.mm.trans2:exp5	-0.0963406085444324	0.0667075706069449	-1.44422301198902	0.149043446670756	   
df.mm.trans1:exp6	-0.902361169376192	0.0903224775817976	-9.99043863205589	2.66570209630166e-22	***
df.mm.trans2:exp6	-0.128491058316855	0.0667075706069449	-1.92618404699448	0.0544134203416341	.  
df.mm.trans1:exp7	-0.354225661690514	0.0903224775817976	-3.92178858656442	9.49533886778664e-05	***
df.mm.trans2:exp7	-0.0741114971317747	0.0667075706069449	-1.11099079845757	0.266885366948764	   
df.mm.trans1:exp8	-0.503096521496359	0.0903224775817976	-5.57000355798196	3.41483016145608e-08	***
df.mm.trans2:exp8	-0.149697382198830	0.0667075706069449	-2.24408385490275	0.0250827472037841	*  
df.mm.trans1:probe2	0.243394086293192	0.0618395730266315	3.93589532366877	8.96381782065809e-05	***
df.mm.trans1:probe3	-0.0359208682320891	0.0618395730266315	-0.58087186689694	0.561480249036758	   
df.mm.trans1:probe4	0.8623105207972	0.0618395730266315	13.9443155667624	5.80479739618312e-40	***
df.mm.trans1:probe5	-0.0286668140064839	0.0618395730266315	-0.463567463412764	0.643075931128689	   
df.mm.trans1:probe6	0.0358737726372933	0.0618395730266315	0.58011028992461	0.561993444933992	   
df.mm.trans1:probe7	-0.0904211472850811	0.0618395730266315	-1.46218906210981	0.144057720397942	   
df.mm.trans1:probe8	0.120209776201815	0.0618395730266315	1.94389725411018	0.052236355034758	.  
df.mm.trans1:probe9	0.756938108669886	0.0618395730266315	12.2403514711188	7.47005524447941e-32	***
df.mm.trans1:probe10	0.0500476262304258	0.0618395730266315	0.809313903394394	0.418560297626474	   
df.mm.trans1:probe11	0.327146904085188	0.0618395730266315	5.2902516636766	1.553698236548e-07	***
df.mm.trans1:probe12	0.307216158415664	0.0618395730266315	4.96795406855995	8.1755708803745e-07	***
df.mm.trans1:probe13	0.354480655399987	0.0618395730266315	5.73226233705282	1.37460351620081e-08	***
df.mm.trans1:probe14	0.338827032375115	0.0618395730266315	5.47912955720437	5.62809973355323e-08	***
df.mm.trans1:probe15	0.22231959154669	0.0618395730266315	3.59510230529805	0.000342876503369319	***
df.mm.trans1:probe16	0.373221568974231	0.0618395730266315	6.0353193061909	2.363920176412e-09	***
df.mm.trans1:probe17	1.10742760238196	0.0618395730266315	17.9080732317644	3.98591334990155e-61	***
df.mm.trans1:probe18	1.19231042917260	0.0618395730266315	19.2807028059384	4.92363812483107e-69	***
df.mm.trans1:probe19	1.00046276571916	0.0618395730266315	16.1783582381512	1.51251151166242e-51	***
df.mm.trans1:probe20	1.21136625607867	0.0618395730266315	19.5888521991733	7.67169239694502e-71	***
df.mm.trans1:probe21	1.19180710147521	0.0618395730266315	19.2725635567043	5.49384326387567e-69	***
df.mm.trans1:probe22	1.14838845743733	0.0618395730266315	18.5704460951368	6.52861039491763e-65	***
df.mm.trans2:probe2	0.0220119690562374	0.0618395730266315	0.355952798166279	0.721963928052713	   
df.mm.trans2:probe3	0.0730539904076882	0.0618395730266315	1.18134694067547	0.237794184591403	   
df.mm.trans2:probe4	0.0576405472203814	0.0618395730266315	0.932098078936577	0.35154958136179	   
df.mm.trans2:probe5	-0.0204347062757388	0.0618395730266315	-0.330447079040123	0.741143318206635	   
df.mm.trans2:probe6	0.169071491753052	0.0618395730266315	2.73403394425509	0.00638600468428298	** 
df.mm.trans3:probe2	0.127438781686954	0.0618395730266315	2.06079659754559	0.0396251321809523	*  
df.mm.trans3:probe3	-0.136858595464597	0.0618395730266315	-2.21312322783435	0.0271531665330015	*  
df.mm.trans3:probe4	-0.602864985531255	0.0618395730266315	-9.74885426960546	2.31479302846160e-21	***
df.mm.trans3:probe5	0.34845592772541	0.0618395730266315	5.63483721945081	2.3804085694368e-08	***
df.mm.trans3:probe6	0.120803640021466	0.0618395730266315	1.95350055165227	0.0510868723536288	.  
df.mm.trans3:probe7	0.123144180029563	0.0618395730266315	1.99134913134879	0.0467610679029456	*  
df.mm.trans3:probe8	-0.289791986394694	0.0618395730266315	-4.68618996237075	3.23854242499205e-06	***
df.mm.trans3:probe9	0.223102967638507	0.0618395730266315	3.60777018208755	0.000326807176670562	***
df.mm.trans3:probe10	-0.375620538468839	0.0618395730266315	-6.07411274180493	1.87626429620895e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.45330512202271	0.226733277647492	19.6411623747017	3.77573954849703e-71	***
df.mm.trans1	0.163536166373064	0.195801312493744	0.835214862915125	0.403830610890785	   
df.mm.trans2	0.00145750278383049	0.172989574582076	0.0084253793175205	0.993279569482592	   
df.mm.exp2	-0.115448316284605	0.222519904624807	-0.518822423905239	0.604019146799966	   
df.mm.exp3	-0.312636835817983	0.222519904624807	-1.40498368604428	0.160390190578851	   
df.mm.exp4	-0.0123085841766182	0.222519904624807	-0.0553145310635102	0.95590083872849	   
df.mm.exp5	-0.132320029849349	0.222519904624807	-0.594643567156185	0.55223944293656	   
df.mm.exp6	0.122046977009614	0.222519904624807	0.548476673200982	0.583508158812483	   
df.mm.exp7	-0.325648484373924	0.222519904624807	-1.46345777436407	0.143710543585620	   
df.mm.exp8	0.181551047751827	0.222519904624807	0.815886776771463	0.414792663688433	   
df.mm.trans1:exp2	0.101512656844558	0.205679775142443	0.493547101431123	0.621753114110821	   
df.mm.trans2:exp2	0.0640073003135903	0.151904581119463	0.421365174387023	0.673594600390906	   
df.mm.trans1:exp3	0.330442278906980	0.205679775142443	1.60658615402576	0.108515270558082	   
df.mm.trans2:exp3	0.226073015696804	0.151904581119463	1.48825673347542	0.137052783373298	   
df.mm.trans1:exp4	-0.0461491269991879	0.205679775142443	-0.224373674889655	0.822520285306455	   
df.mm.trans2:exp4	0.114257531894824	0.151904581119463	0.752166465637844	0.452158455789107	   
df.mm.trans1:exp5	0.0500033515644698	0.205679775142443	0.243112632391008	0.807976594401746	   
df.mm.trans2:exp5	0.200966942796288	0.151904581119463	1.32298144871773	0.186196083902009	   
df.mm.trans1:exp6	-0.146562080543869	0.205679775142443	-0.712574099433782	0.476304210067783	   
df.mm.trans2:exp6	0.120425676603371	0.151904581119463	0.792771855304775	0.428131193252929	   
df.mm.trans1:exp7	0.238974407661851	0.205679775142443	1.16187606436438	0.245610804595220	   
df.mm.trans2:exp7	0.274789011018462	0.151904581119463	1.8089580247903	0.0708094902415213	.  
df.mm.trans1:exp8	-0.109399157638075	0.205679775142443	-0.531890690576218	0.594940200645497	   
df.mm.trans2:exp8	0.0125026828226524	0.151904581119463	0.0823061604232977	0.93442256476459	   
df.mm.trans1:probe2	0.110412101863137	0.140819315585133	0.784069297626908	0.433217117636275	   
df.mm.trans1:probe3	0.166178254070096	0.140819315585133	1.18008139280887	0.23829683026884	   
df.mm.trans1:probe4	0.239963067647257	0.140819315585133	1.70404938165025	0.0887360563718192	.  
df.mm.trans1:probe5	0.164885049184173	0.140819315585133	1.17089795884209	0.2419667867966	   
df.mm.trans1:probe6	0.121474915622664	0.140819315585133	0.86262964081249	0.388583584947697	   
df.mm.trans1:probe7	0.0310520601280085	0.140819315585133	0.220509949213862	0.825526790762103	   
df.mm.trans1:probe8	0.0223366189985160	0.140819315585133	0.15861899985597	0.874006603378677	   
df.mm.trans1:probe9	0.163953879858027	0.140819315585133	1.16428544746696	0.244633882708742	   
df.mm.trans1:probe10	-0.0542600322429153	0.140819315585133	-0.38531668768204	0.700098862376682	   
df.mm.trans1:probe11	0.154637669702467	0.140819315585133	1.09812825790209	0.272458356762515	   
df.mm.trans1:probe12	0.0649460938403255	0.140819315585133	0.461201601289292	0.644771547356489	   
df.mm.trans1:probe13	-0.113494144601683	0.140819315585133	-0.805955803222671	0.420492955514207	   
df.mm.trans1:probe14	0.0136862680988682	0.140819315585133	0.0971902756521643	0.922598121216554	   
df.mm.trans1:probe15	0.0533874549734962	0.140819315585133	0.379120255993721	0.704692916740072	   
df.mm.trans1:probe16	0.00815943905640488	0.140819315585133	0.0579426126487035	0.953807910508842	   
df.mm.trans1:probe17	-0.0122795523657096	0.140819315585133	-0.0872007672717733	0.930532395156863	   
df.mm.trans1:probe18	-0.0866966806546563	0.140819315585133	-0.615659011652017	0.538283824643713	   
df.mm.trans1:probe19	0.0608673756685203	0.140819315585133	0.432237405895661	0.665678119345218	   
df.mm.trans1:probe20	0.107730346598623	0.140819315585133	0.765025352885589	0.444468048329069	   
df.mm.trans1:probe21	-0.0609042434162185	0.140819315585133	-0.432499214778520	0.665487941466097	   
df.mm.trans1:probe22	0.109258761221958	0.140819315585133	0.77587908141696	0.438035445002993	   
df.mm.trans2:probe2	0.163882954308907	0.140819315585133	1.16378178396863	0.244837874699068	   
df.mm.trans2:probe3	0.133753958728439	0.140819315585133	0.949826791677432	0.342469298638135	   
df.mm.trans2:probe4	0.0352775376067528	0.140819315585133	0.250516326259416	0.802248406964767	   
df.mm.trans2:probe5	-0.0331206728257767	0.140819315585133	-0.235199785541874	0.814110123612388	   
df.mm.trans2:probe6	-0.00287836523235452	0.140819315585133	-0.0204401308186617	0.983697050916883	   
df.mm.trans3:probe2	0.0136940004128850	0.140819315585133	0.0972451851223939	0.922554529364731	   
df.mm.trans3:probe3	-0.047687937492982	0.140819315585133	-0.338646280837461	0.73495955380245	   
df.mm.trans3:probe4	-0.0939262962908576	0.140819315585133	-0.666998670605483	0.5049533635343	   
df.mm.trans3:probe5	-0.182954258560372	0.140819315585133	-1.29921280898263	0.194221953451980	   
df.mm.trans3:probe6	-0.055846693702673	0.140819315585133	-0.396584044387793	0.69177341304857	   
df.mm.trans3:probe7	-0.068498291559855	0.140819315585133	-0.486426817764529	0.626789526966565	   
df.mm.trans3:probe8	-0.223313590540984	0.140819315585133	-1.58581647420363	0.113151566755737	   
df.mm.trans3:probe9	-0.119629622924273	0.140819315585133	-0.849525666469743	0.395827216571049	   
df.mm.trans3:probe10	0.0122753624721680	0.140819315585133	0.0871710135868887	0.93055603806998