chr17.10879_chr17_50794114_50797502_-_2.R 

fitVsDatCorrelation=0.931422147960609
cont.fitVsDatCorrelation=0.261681238610334

fstatistic=3789.48273669086,54,738
cont.fstatistic=527.101256544611,54,738

residuals=-1.19904991958460,-0.152518009058915,-0.0139628143056821,0.127658344424989,0.954436169573537
cont.residuals=-1.28639070648118,-0.546594736605743,-0.271054944861650,0.494785977171606,2.15677645114846

predictedValues:
Include	Exclude	Both
chr17.10879_chr17_50794114_50797502_-_2.R.tl.Lung	76.5850524018904	201.642904351185	60.7080192341641
chr17.10879_chr17_50794114_50797502_-_2.R.tl.cerebhem	67.0672650547147	135.955436401349	55.6692550333878
chr17.10879_chr17_50794114_50797502_-_2.R.tl.cortex	84.3527510345632	354.501007355477	98.0046559360824
chr17.10879_chr17_50794114_50797502_-_2.R.tl.heart	83.2648057484881	296.39402037283	85.296362128662
chr17.10879_chr17_50794114_50797502_-_2.R.tl.kidney	67.4271023124517	126.781410328840	55.7374922752741
chr17.10879_chr17_50794114_50797502_-_2.R.tl.liver	69.2660991044216	147.176283183676	54.2877009696283
chr17.10879_chr17_50794114_50797502_-_2.R.tl.stomach	89.134631016046	362.592342728092	56.2755082191716
chr17.10879_chr17_50794114_50797502_-_2.R.tl.testicle	82.0344999578731	247.10938330425	51.4838772126251


diffExp=-125.057851949295,-68.8881713466347,-270.148256320914,-213.129214624342,-59.3543080163879,-77.9101840792539,-273.457711712046,-165.074883346377
diffExpScore=0.99920256492211
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	90.6776799711796	91.6560946170305	94.4288074769835
cerebhem	92.2644802443115	123.257073675206	90.1599941883857
cortex	96.1902938936731	82.5832955741138	82.3092203688221
heart	90.2799454204436	126.326832944612	92.6087983343888
kidney	109.219638497844	104.571030151381	127.534073070450
liver	91.1937993579244	89.0362905769735	118.201897073928
stomach	124.047338755404	82.5581326149731	79.1642567245883
testicle	83.3179413700074	122.176347146242	101.249845289103
cont.diffExp=-0.978414645850947,-30.9925934308941,13.6069983195593,-36.0468875241685,4.64860834646352,2.15750878095092,41.4892061404311,-38.8584057762350
cont.diffExpScore=3.67117712533136

cont.diffExp1.5=0,0,0,0,0,0,1,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,0,0,0,0,0,1,-1
cont.diffExp1.4Score=2
cont.diffExp1.3=0,-1,0,-1,0,0,1,-1
cont.diffExp1.3Score=1.33333333333333
cont.diffExp1.2=0,-1,0,-1,0,0,1,-1
cont.diffExp1.2Score=1.33333333333333

tran.correlation=0.967300320527445
cont.tran.correlation=-0.507280122411362

tran.covariance=0.0471316960025395
cont.tran.covariance=-0.0120426916612327

tran.mean=155.705312166009
cont.tran.mean=99.9597634257075

weightedLogRatios:
wLogRatio
Lung	-4.66859667087351
cerebhem	-3.22153933196931
cortex	-7.39797950312197
heart	-6.42050913398945
kidney	-2.85827223120558
liver	-3.47805657043299
stomach	-7.28465551101684
testicle	-5.46767829314121

cont.weightedLogRatios:
wLogRatio
Lung	-0.048431092991174
cerebhem	-1.35233781785889
cortex	0.68482978819522
heart	-1.56922003318879
kidney	0.203188718125354
liver	0.107767180507962
stomach	1.87989415201658
testicle	-1.76627116498875

varWeightedLogRatios=3.32901821010309
cont.varWeightedLogRatios=1.57664312849158

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.53602139313866	0.135070257884335	40.9862354588776	2.35790787257976e-192	***
df.mm.trans1	-1.46559342447997	0.115604744066548	-12.6776235379778	1.83609609954638e-33	***
df.mm.trans2	-0.304387015720277	0.103707619272435	-2.93504968926793	0.00343849990806617	** 
df.mm.exp2	-0.440229240125134	0.134164073727762	-3.28127514239336	0.00108197497187221	** 
df.mm.exp3	0.181879173299148	0.134164073727762	1.35564736703066	0.175626090863375	   
df.mm.exp4	0.128759423113745	0.134164073727762	0.959716111296805	0.337512422759384	   
df.mm.exp5	-0.505966007968873	0.134164073727762	-3.77124809876861	0.000175431695167604	***
df.mm.exp6	-0.303535480252354	0.134164073727762	-2.26241997442826	0.0239614202974872	*  
df.mm.exp7	0.814343247279968	0.134164073727762	6.06975641580762	2.04787594043043e-09	***
df.mm.exp8	0.436877830962226	0.134164073727762	3.25629521244796	0.00118033000335595	** 
df.mm.trans1:exp2	0.307523392581748	0.121437172520317	2.53236621208633	0.0115357839501595	*  
df.mm.trans2:exp2	0.0460580658882719	0.0937183647192349	0.491451873133451	0.623253007774467	   
df.mm.trans1:exp3	-0.0852736696694253	0.121437172520317	-0.70220401133894	0.482773349671631	   
df.mm.trans2:exp3	0.38233368153562	0.0937183647192349	4.07960256968877	5.00255997234594e-05	***
df.mm.trans1:exp4	-0.0451353825854351	0.121437172520317	-0.371676823897426	0.710240168383892	   
df.mm.trans2:exp4	0.256431963167223	0.0937183647192348	2.73619758449106	0.00636477148399918	** 
df.mm.trans1:exp5	0.378611137174640	0.121437172520317	3.11775323253098	0.00189311004419525	** 
df.mm.trans2:exp5	0.0419321002280918	0.0937183647192348	0.447426716777589	0.654698281105018	   
df.mm.trans1:exp6	0.203089156995102	0.121437172520317	1.67238048103537	0.0948731698440709	.  
df.mm.trans2:exp6	-0.0113317788668842	0.0937183647192348	-0.120913109195113	0.903792770500969	   
df.mm.trans1:exp7	-0.66259723184071	0.121437172520317	-5.45629660250741	6.64123246390783e-08	***
df.mm.trans2:exp7	-0.227562399622069	0.0937183647192348	-2.42815162539178	0.0154140147310674	*  
df.mm.trans1:exp8	-0.368139860335006	0.121437172520317	-3.03152529571137	0.00251834734790690	** 
df.mm.trans2:exp8	-0.233545077733312	0.0937183647192348	-2.49198839985072	0.0129214035795221	*  
df.mm.trans1:probe2	0.0186793743441006	0.08314234838628	0.224667389202385	0.822300224568567	   
df.mm.trans1:probe3	0.0932828987729761	0.08314234838628	1.12196612897657	0.262241746497212	   
df.mm.trans1:probe4	-0.130431321555224	0.08314234838628	-1.56877119887496	0.117129898382360	   
df.mm.trans1:probe5	-0.0759535911241638	0.08314234838628	-0.913536754714732	0.361258658826671	   
df.mm.trans1:probe6	1.04997435542694	0.08314234838628	12.6286348149411	3.06527150116474e-33	***
df.mm.trans1:probe7	1.59110915771801	0.08314234838628	19.1371688267176	1.34862210659060e-66	***
df.mm.trans1:probe8	0.825040846515755	0.08314234838628	9.9232324143962	7.18706673835001e-22	***
df.mm.trans1:probe9	1.34008797588501	0.08314234838628	16.11799524424	2.66508335935406e-50	***
df.mm.trans1:probe10	1.09712117787648	0.08314234838628	13.1956962867977	7.54049751228502e-36	***
df.mm.trans1:probe11	1.72527998254185	0.08314234838628	20.7509171442474	1.06554145048987e-75	***
df.mm.trans1:probe12	-0.0722084492406188	0.08314234838628	-0.868491817252236	0.385407485818301	   
df.mm.trans1:probe13	-0.0614908846000867	0.08314234838628	-0.739585611828038	0.459786657617174	   
df.mm.trans1:probe14	-0.176200702478944	0.08314234838628	-2.11926540323728	0.0344013780374146	*  
df.mm.trans1:probe15	0.132882904462425	0.08314234838628	1.59825777165987	0.110413651097938	   
df.mm.trans1:probe16	-0.115730915909148	0.08314234838628	-1.39196111434646	0.164353506356370	   
df.mm.trans1:probe17	-0.00614614002253552	0.08314234838628	-0.0739230986594282	0.941091607402656	   
df.mm.trans2:probe2	0.439418493498437	0.08314234838628	5.28513449556291	1.65560197856646e-07	***
df.mm.trans2:probe3	-0.186394756630173	0.08314234838628	-2.24187505221985	0.0252657915770674	*  
df.mm.trans2:probe4	0.256701365723731	0.08314234838628	3.08749236347156	0.00209411089684153	** 
df.mm.trans2:probe5	0.186193270877561	0.08314234838628	2.23945166923246	0.0254236367567853	*  
df.mm.trans2:probe6	0.501904911943882	0.08314234838628	6.03669395543205	2.49031973248809e-09	***
df.mm.trans3:probe2	0.470608217662824	0.08314234838628	5.66027093048148	2.16398424027214e-08	***
df.mm.trans3:probe3	0.447554869037059	0.08314234838628	5.38299528127009	9.8509985435517e-08	***
df.mm.trans3:probe4	0.56467745462301	0.08314234838628	6.79169479312173	2.28159201949562e-11	***
df.mm.trans3:probe5	0.344888693907226	0.08314234838628	4.14817118593849	3.74135709855389e-05	***
df.mm.trans3:probe6	0.289132696822668	0.08314234838628	3.47756230650782	0.000535640681393359	***
df.mm.trans3:probe7	0.202366044904452	0.08314234838628	2.43397076017456	0.0151704127555223	*  
df.mm.trans3:probe8	0.471753712524721	0.08314234838628	5.67404844439743	2.00356874746954e-08	***
df.mm.trans3:probe9	0.135319933759626	0.08314234838628	1.62756929995444	0.104043069276842	   
df.mm.trans3:probe10	0.474046866659753	0.08314234838628	5.7016295048262	1.71644829946654e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.70247418639838	0.358200313040211	13.1280571657973	1.55708839030648e-35	***
df.mm.trans1	-0.184445583812746	0.306578636645763	-0.601625690004825	0.547608087183596	   
df.mm.trans2	-0.178533047845397	0.275027991135195	-0.64914500923521	0.516446613579577	   
df.mm.exp2	0.359837116057564	0.355797152983802	1.01135468072154	0.312178165414326	   
df.mm.exp3	0.092144228902168	0.355797152983802	0.258979668975493	0.7957231445131	   
df.mm.exp4	0.335895135799371	0.355797152983802	0.94406358505815	0.345446114971938	   
df.mm.exp5	0.0173353522033299	0.355797152983802	0.048722571436987	0.961153570226009	   
df.mm.exp6	-0.247871724221361	0.355797152983802	-0.696665844969945	0.486231306155744	   
df.mm.exp7	0.385132526027237	0.355797152983802	1.08244971270125	0.279406294743875	   
df.mm.exp8	0.133029679190504	0.355797152983802	0.373891915870839	0.708592119738984	   
df.mm.trans1:exp2	-0.342489118558348	0.322045977351617	-1.06347895221312	0.287912653605673	   
df.mm.trans2:exp2	-0.0636083827760997	0.248536932599875	-0.255931310130492	0.798075195746405	   
df.mm.trans1:exp3	-0.033127011918255	0.322045977351618	-0.102864231345719	0.91809867024057	   
df.mm.trans2:exp3	-0.196380272482652	0.248536932599875	-0.790145232856835	0.429696767245663	   
df.mm.trans1:exp4	-0.340291028930220	0.322045977351617	-1.05665356148412	0.291015488182167	   
df.mm.trans2:exp4	-0.0150661458698579	0.248536932599875	-0.0606193442248408	0.951678779186292	   
df.mm.trans1:exp5	0.168714294140766	0.322045977351617	0.523882631691933	0.600517462268409	   
df.mm.trans2:exp5	0.114487731770929	0.248536932599875	0.460646756090958	0.645187766497948	   
df.mm.trans1:exp6	0.253547388972479	0.322045977351617	0.787301835152717	0.431357961549541	   
df.mm.trans2:exp6	0.218872299237408	0.248536932599875	0.880642956955518	0.378797791027653	   
df.mm.trans1:exp7	-0.071780509640444	0.322045977351617	-0.222889011782539	0.823683563008662	   
df.mm.trans2:exp7	-0.48967331396668	0.248536932599875	-1.97022353516777	0.0491862114575036	*  
df.mm.trans1:exp8	-0.217677011137417	0.322045977351618	-0.675919050216087	0.499303791245667	   
df.mm.trans2:exp8	0.154392319374029	0.248536932599875	0.621204735082929	0.534656742662961	   
df.mm.trans1:probe2	-0.136049375676989	0.220489807936598	-0.617032492114602	0.537403605350886	   
df.mm.trans1:probe3	-0.135185904448261	0.220489807936598	-0.61311634180902	0.539988313384271	   
df.mm.trans1:probe4	0.139279863809263	0.220489807936598	0.631683909168775	0.52778905759751	   
df.mm.trans1:probe5	-0.104417390476604	0.220489807936598	-0.473570145730405	0.635946523120329	   
df.mm.trans1:probe6	-0.0646817076182232	0.220489807936598	-0.293354637221247	0.769333566559422	   
df.mm.trans1:probe7	0.0540114256733968	0.220489807936598	0.244961099013374	0.80655464835494	   
df.mm.trans1:probe8	0.305313955301232	0.220489807936598	1.38470779288367	0.166560314757604	   
df.mm.trans1:probe9	0.127226099386875	0.220489807936598	0.577015783983352	0.56410475853949	   
df.mm.trans1:probe10	0.0512556888350712	0.220489807936598	0.232462848576700	0.81624298942464	   
df.mm.trans1:probe11	-0.232397578501192	0.220489807936598	-1.05400599091645	0.292225122003102	   
df.mm.trans1:probe12	-0.139690818398367	0.220489807936598	-0.633547734952604	0.52657230824715	   
df.mm.trans1:probe13	0.0846198395762908	0.220489807936598	0.383781184119962	0.701251173553586	   
df.mm.trans1:probe14	-0.228320960026878	0.220489807936598	-1.03551707066900	0.300766683103908	   
df.mm.trans1:probe15	-0.145725126970092	0.220489807936598	-0.660915478741742	0.508872776384068	   
df.mm.trans1:probe16	0.0347125048479797	0.220489807936598	0.157433602817420	0.874946194706743	   
df.mm.trans1:probe17	0.100680708950281	0.220489807936598	0.456622960909068	0.648076431394638	   
df.mm.trans2:probe2	-0.123477826083005	0.220489807936598	-0.560016026312248	0.575638446277703	   
df.mm.trans2:probe3	-0.021973576792098	0.220489807936598	-0.0996580159315866	0.920642897297834	   
df.mm.trans2:probe4	0.205244521608070	0.220489807936598	0.930857183507945	0.352231839629130	   
df.mm.trans2:probe5	-0.00910141298812467	0.220489807936598	-0.0412781573592815	0.967085310636478	   
df.mm.trans2:probe6	-0.145054388245394	0.220489807936598	-0.657873439152815	0.510824605382953	   
df.mm.trans3:probe2	0.197366462529884	0.220489807936598	0.895127372901682	0.371010851599516	   
df.mm.trans3:probe3	0.272306073646543	0.220489807936598	1.23500526484582	0.217221583700356	   
df.mm.trans3:probe4	0.386933949297300	0.220489807936598	1.75488360626883	0.0796941515215349	.  
df.mm.trans3:probe5	0.134659494973505	0.220489807936598	0.610728886898147	0.541567116225084	   
df.mm.trans3:probe6	0.296450626142073	0.220489807936598	1.34450943069131	0.179196804560535	   
df.mm.trans3:probe7	0.484845397965954	0.220489807936598	2.19894698309761	0.0281912490996137	*  
df.mm.trans3:probe8	-0.0264289313728824	0.220489807936598	-0.119864639641221	0.904622998343634	   
df.mm.trans3:probe9	0.193369113486782	0.220489807936598	0.876997967826184	0.38077312876248	   
df.mm.trans3:probe10	0.144004171423462	0.220489807936598	0.653110330908675	0.513888560163683