chr10.2522_chr10_42586831_42618074_+_2.R 

fitVsDatCorrelation=0.907178810070627
cont.fitVsDatCorrelation=0.272643803580264

fstatistic=9751.62548150744,63,945
cont.fstatistic=1852.79439743083,63,945

residuals=-0.72983347374799,-0.101794895326091,-0.000109078786779117,0.103932514412837,0.655001585801514
cont.residuals=-0.746858241243766,-0.301586197388707,-0.0658840282455713,0.228203528386033,1.66200479819862

predictedValues:
Include	Exclude	Both
chr10.2522_chr10_42586831_42618074_+_2.R.tl.Lung	75.4728362451663	56.9425265926353	89.278690601643
chr10.2522_chr10_42586831_42618074_+_2.R.tl.cerebhem	69.5667556316348	86.4210111163835	81.65308136455
chr10.2522_chr10_42586831_42618074_+_2.R.tl.cortex	93.5362130078952	58.155168333005	112.073892002850
chr10.2522_chr10_42586831_42618074_+_2.R.tl.heart	84.8082053831154	62.3399487076578	94.7970498519982
chr10.2522_chr10_42586831_42618074_+_2.R.tl.kidney	73.7835554290703	61.1526896302857	92.0874931158924
chr10.2522_chr10_42586831_42618074_+_2.R.tl.liver	66.6805379856512	60.2194370308359	74.0134570816898
chr10.2522_chr10_42586831_42618074_+_2.R.tl.stomach	81.1462459479647	58.8721364645288	91.7822997915373
chr10.2522_chr10_42586831_42618074_+_2.R.tl.testicle	78.2253276691023	64.059601230146	91.359380215866


diffExp=18.5303096525311,-16.8542554847488,35.3810446748902,22.4682566754576,12.6308657987846,6.46110095481525,22.2741094834360,14.1657264389562
diffExpScore=1.28183105185798
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,1,1,0,0,1,0
diffExp1.3Score=0.8
diffExp1.2=1,-1,1,1,1,0,1,1
diffExp1.2Score=1.16666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	82.1428787849565	100.718798652070	77.2448779861913
cerebhem	72.1820243853967	88.2681117702571	74.0325924385724
cortex	78.3828037676243	85.62224933116	79.4282387041134
heart	74.7506302358466	79.045170248005	77.3830719790476
kidney	83.8866248906463	95.5239316016548	74.3684640883624
liver	80.6841069620665	98.6292609425664	76.5766855255502
stomach	82.6820052922178	84.8784248849144	78.7783677246419
testicle	80.5818711621198	82.7845916418459	74.8052578657066
cont.diffExp=-18.5759198671131,-16.0860873848604,-7.2394455635357,-4.29454001215836,-11.6373067110085,-17.9451539804999,-2.19641959269653,-2.20272047972611
cont.diffExpScore=0.987681329837997

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=-1,-1,0,0,0,-1,0,0
cont.diffExp1.2Score=0.75

tran.correlation=-0.408234402971489
cont.tran.correlation=0.478220179277682

tran.covariance=-0.006023576623845
cont.tran.covariance=0.00217183862608636

tran.mean=70.7113872753174
cont.tran.mean=84.4227177845842

weightedLogRatios:
wLogRatio
Lung	1.17845204291240
cerebhem	-0.9438711348805
cortex	2.04385355455313
heart	1.31934037636296
kidney	0.789962736114948
liver	0.422852908079048
stomach	1.35920854609132
testicle	0.85100172696942

cont.weightedLogRatios:
wLogRatio
Lung	-0.919544900264757
cerebhem	-0.88115957587223
cortex	-0.389208829925526
heart	-0.242556973013715
kidney	-0.583873106884134
liver	-0.901902130236526
stomach	-0.116095987363725
testicle	-0.118734683118497

varWeightedLogRatios=0.77295434685324
cont.varWeightedLogRatios=0.122604093483373

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.75249191361605	0.0810490984853497	46.2989963336155	2.96861186694057e-245	***
df.mm.trans1	0.540890323578231	0.0693215561453916	7.80262812398202	1.60158764850612e-14	***
df.mm.trans2	0.299156821213294	0.0608547800885469	4.91591327383001	1.04153835847898e-06	***
df.mm.exp2	0.424985649201496	0.0771066848735421	5.51165764548805	4.58555600403243e-08	***
df.mm.exp3	0.00825258865233164	0.0771066848735421	0.107028186542661	0.914789335379482	   
df.mm.exp4	0.147204034073287	0.0771066848735421	1.90909561621936	0.0565524014443231	.  
df.mm.exp5	0.0177181605480277	0.0771066848735421	0.229787606315667	0.818306543213411	   
df.mm.exp6	0.119608943243592	0.0771066848735421	1.55121366506361	0.121185305134129	   
df.mm.exp7	0.0781490514184706	0.0771066848735421	1.01351849773646	0.311071987287404	   
df.mm.exp8	0.130554007540051	0.0771066848735421	1.69316068709431	0.0907544648528375	.  
df.mm.trans1:exp2	-0.506471651627149	0.0702690473311961	-7.20760663283249	1.16445062892447e-12	***
df.mm.trans2:exp2	-0.00779727249891148	0.0491780887636887	-0.158551759430486	0.874055913698283	   
df.mm.trans1:exp3	0.206323270871748	0.0702690473311961	2.93618995429515	0.00340326023347585	** 
df.mm.trans2:exp3	0.0128197119318757	0.0491780887636887	0.260679344280279	0.79439658881088	   
df.mm.trans1:exp4	-0.0305845410478425	0.0702690473311961	-0.435249120479601	0.663480997694425	   
df.mm.trans2:exp4	-0.0566440362785733	0.0491780887636887	-1.15181451135200	0.249688616312169	   
df.mm.trans1:exp5	-0.0403550866200525	0.0702690473311961	-0.574293919623652	0.565905594798887	   
df.mm.trans2:exp5	0.053613230999581	0.0491780887636887	1.09018533146345	0.275909417550512	   
df.mm.trans1:exp6	-0.243468623907067	0.0702690473311961	-3.46480610103531	0.000554461600095055	***
df.mm.trans2:exp6	-0.0636562225354567	0.0491780887636887	-1.29440212370470	0.195842765132549	   
df.mm.trans1:exp7	-0.00566882586612803	0.0702690473311961	-0.0806731566945742	0.935718966477232	   
df.mm.trans2:exp7	-0.0448235915702304	0.0491780887636887	-0.911454525726231	0.362288374850184	   
df.mm.trans1:exp8	-0.0947333358756902	0.0702690473311961	-1.34815170368239	0.177932643408443	   
df.mm.trans2:exp8	-0.0127825420818358	0.0491780887636887	-0.259923522918075	0.79497938415012	   
df.mm.trans1:probe2	-0.486674631779514	0.0514316763124036	-9.4625465602828	2.3133480965652e-20	***
df.mm.trans1:probe3	-0.444408713445863	0.0514316763124036	-8.64075887292607	2.35492997835633e-17	***
df.mm.trans1:probe4	0.0394220226106432	0.0514316763124037	0.766493053253563	0.443574410798407	   
df.mm.trans1:probe5	-0.191428563081090	0.0514316763124037	-3.72199735272722	0.000209328501896582	***
df.mm.trans1:probe6	0.0966022409339402	0.0514316763124036	1.87826351113201	0.0606528286680907	.  
df.mm.trans1:probe7	0.171382323906987	0.0514316763124037	3.33223289993477	0.000894938409670857	***
df.mm.trans1:probe8	-0.477355262775224	0.0514316763124036	-9.28134754690276	1.11309641912053e-19	***
df.mm.trans1:probe9	-0.444901734604618	0.0514316763124036	-8.6503448167277	2.17866036465507e-17	***
df.mm.trans1:probe10	0.162562595350454	0.0514316763124036	3.16074853098361	0.00162373429887029	** 
df.mm.trans1:probe11	0.150624646317602	0.0514316763124036	2.92863575751811	0.00348629111629883	** 
df.mm.trans1:probe12	0.0179211143586745	0.0514316763124036	0.348445075945396	0.727583565762683	   
df.mm.trans1:probe13	0.269423610442972	0.0514316763124036	5.23847616411437	1.99652632495062e-07	***
df.mm.trans1:probe14	-0.0644519633875416	0.0514316763124036	-1.25315696490332	0.210458560847962	   
df.mm.trans1:probe15	0.122717243655152	0.0514316763124037	2.38602457578379	0.0172276548895391	*  
df.mm.trans1:probe16	0.580612341346205	0.0514316763124036	11.2890028670168	8.19003218238654e-28	***
df.mm.trans1:probe17	0.566695482378421	0.0514316763124036	11.0184136121916	1.20412237192064e-26	***
df.mm.trans1:probe18	0.499815896024312	0.0514316763124036	9.71805571703236	2.42179335988808e-21	***
df.mm.trans1:probe19	0.267200971953664	0.0514316763124037	5.19526080251877	2.50447583890005e-07	***
df.mm.trans1:probe20	0.174190840043483	0.0514316763124036	3.38683963916365	0.00073618312943487	***
df.mm.trans1:probe21	0.0841100414049242	0.0514316763124037	1.63537429528891	0.102303620427517	   
df.mm.trans2:probe2	-0.0884697838414858	0.0514316763124036	-1.72014194723320	0.0857340317193557	.  
df.mm.trans2:probe3	0.0433444011897911	0.0514316763124036	0.842756921367111	0.399577650863595	   
df.mm.trans2:probe4	-0.00490009332424597	0.0514316763124036	-0.0952738404729817	0.92411751881395	   
df.mm.trans2:probe5	-0.062752210346615	0.0514316763124036	-1.22010820657388	0.222728247703529	   
df.mm.trans2:probe6	-0.0889542161265074	0.0514316763124036	-1.72956089523869	0.0840352971135	.  
df.mm.trans3:probe2	-0.340932198775175	0.0514316763124037	-6.6288369973458	5.67967457056453e-11	***
df.mm.trans3:probe3	0.306654280689375	0.0514316763124037	5.96236216036808	3.50844527210753e-09	***
df.mm.trans3:probe4	-0.179237435719213	0.0514316763124036	-3.48496196450021	0.000514820500337712	***
df.mm.trans3:probe5	-0.527041060840099	0.0514316763124036	-10.2474019636998	1.94015794490583e-23	***
df.mm.trans3:probe6	-0.271794652431186	0.0514316763124036	-5.2845769750973	1.56483469466055e-07	***
df.mm.trans3:probe7	0.469571933776765	0.0514316763124036	9.13001417500988	4.05656587509033e-19	***
df.mm.trans3:probe8	-0.592249773094753	0.0514316763124036	-11.5152726016033	8.33471023912168e-29	***
df.mm.trans3:probe9	0.853487637874678	0.0514316763124036	16.5945910977209	1.81283263182602e-54	***
df.mm.trans3:probe10	-0.529739092891594	0.0514316763124036	-10.2998605309670	1.18943168461684e-23	***
df.mm.trans3:probe11	-0.116180762323056	0.0514316763124037	-2.25893400046611	0.0241146924386193	*  
df.mm.trans3:probe12	-0.47839361695391	0.0514316763124037	-9.30153654817852	9.35494294094994e-20	***
df.mm.trans3:probe13	0.101718606260338	0.0514316763124037	1.97774238666623	0.0482479501514937	*  
df.mm.trans3:probe14	-0.50683504479759	0.0514316763124036	-9.85453092601918	7.1137260044197e-22	***
df.mm.trans3:probe15	0.299327787990100	0.0514316763124037	5.81991118026055	8.05859591170875e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.59354828784444	0.185334350953965	24.7851963988339	7.03318060595777e-105	***
df.mm.trans1	-0.213441600580907	0.158517069966513	-1.34648969114807	0.178467502468121	   
df.mm.trans2	0.0324722774732266	0.139156158192133	0.233351350706248	0.8155391229864	   
df.mm.exp2	-0.218747490230843	0.176319264030222	-1.24063295882036	0.215049287581032	   
df.mm.exp3	-0.237116229225157	0.176319264030222	-1.34481181355495	0.179008681590589	   
df.mm.exp4	-0.338402948635115	0.176319264030222	-1.91926248386059	0.0552520213299148	.  
df.mm.exp5	0.00599900198871664	0.176319264030222	0.0340235199013103	0.972865580292226	   
df.mm.exp6	-0.0301950741565515	0.176319264030222	-0.171252269697405	0.864062070782889	   
df.mm.exp7	-0.184228520652264	0.176319264030222	-1.04485758641033	0.296355988962985	   
df.mm.exp8	-0.183184529455375	0.176319264030222	-1.03893655899095	0.299099989571623	   
df.mm.trans1:exp2	0.0894783801891284	0.160683690783245	0.556860374273024	0.577754681859779	   
df.mm.trans2:exp2	0.0867939354467177	0.112455157830315	0.771809289331863	0.440420345980263	   
df.mm.trans1:exp3	0.190260637613342	0.160683690783245	1.18406937683548	0.236683338675736	   
df.mm.trans2:exp3	0.074728938627572	0.112455157830315	0.664522108806531	0.506518319262512	   
df.mm.trans1:exp4	0.244100436908113	0.160683690783245	1.51913635863265	0.129062654337375	   
df.mm.trans2:exp4	0.0960899509277515	0.112455157830315	0.854473487758944	0.393059252875275	   
df.mm.trans1:exp5	0.0150070262903445	0.160683690783244	0.0933948319035586	0.92560969273082	   
df.mm.trans2:exp5	-0.0589546552720283	0.112455157830315	-0.524250344843995	0.600227367420183	   
df.mm.trans1:exp6	0.0122765350748918	0.160683690783245	0.0764018738619364	0.939115550502248	   
df.mm.trans2:exp6	0.00923059379839129	0.112455157830315	0.0820824404721344	0.934598538241439	   
df.mm.trans1:exp7	0.190770353615949	0.160683690783244	1.18724154695507	0.235430703078415	   
df.mm.trans2:exp7	0.0131159957576506	0.112455157830315	0.116633118575508	0.907175573416481	   
df.mm.trans1:exp8	0.163998074910923	0.160683690783244	1.02062676125699	0.307692514737078	   
df.mm.trans2:exp8	-0.0129059798169752	0.112455157830315	-0.114765565812901	0.90865532265502	   
df.mm.trans1:probe2	0.0893862309506568	0.117608419167756	0.760032585959317	0.447424674209499	   
df.mm.trans1:probe3	0.00394003051729401	0.117608419167756	0.0335012624536171	0.973281933598201	   
df.mm.trans1:probe4	0.163214808430468	0.117608419167756	1.38778167061033	0.165530660080021	   
df.mm.trans1:probe5	-0.0371639574664539	0.117608419167756	-0.315997423734124	0.752074242102401	   
df.mm.trans1:probe6	0.00668471763954776	0.117608419167756	0.0568387678947773	0.954685646823583	   
df.mm.trans1:probe7	0.201444735725906	0.117608419167756	1.71284281475263	0.0870694788480763	.  
df.mm.trans1:probe8	-0.101470100970437	0.117608419167756	-0.862779226933584	0.388477762882717	   
df.mm.trans1:probe9	-0.0685668210294004	0.117608419167756	-0.583009460671324	0.560026026236532	   
df.mm.trans1:probe10	0.124037957246307	0.117608419167756	1.05466902900361	0.291846289007767	   
df.mm.trans1:probe11	0.0366349189403999	0.117608419167756	0.311499118852572	0.755489918236586	   
df.mm.trans1:probe12	0.0764440603807692	0.117608419167756	0.649987993391272	0.515857948161697	   
df.mm.trans1:probe13	-0.0286843518740869	0.117608419167756	-0.243897095778251	0.807363457141657	   
df.mm.trans1:probe14	-0.0210072560523166	0.117608419167756	-0.178620341987184	0.858274106064426	   
df.mm.trans1:probe15	0.126262534041094	0.117608419167756	1.07358414418439	0.28328316628824	   
df.mm.trans1:probe16	0.0496915954476896	0.117608419167756	0.422517331661519	0.672743569400214	   
df.mm.trans1:probe17	-0.0742765775842666	0.117608419167756	-0.631558336638459	0.527828255307311	   
df.mm.trans1:probe18	0.0595485481063591	0.117608419167756	0.506328956104915	0.612743840701168	   
df.mm.trans1:probe19	0.181050585987041	0.117608419167756	1.53943558861030	0.124032750094055	   
df.mm.trans1:probe20	0.0973346481237319	0.117608419167756	0.827616328937254	0.408096761722069	   
df.mm.trans1:probe21	0.136218539549952	0.117608419167756	1.15823799447258	0.247059553604501	   
df.mm.trans2:probe2	-0.182444060215142	0.117608419167756	-1.55128401101034	0.121168453620082	   
df.mm.trans2:probe3	-0.0936264430401418	0.117608419167756	-0.796086230073322	0.426181848414524	   
df.mm.trans2:probe4	-0.0746463245815125	0.117608419167756	-0.634702218682469	0.525776274277803	   
df.mm.trans2:probe5	-0.132231660510801	0.117608419167756	-1.12433838875248	0.261155008962244	   
df.mm.trans2:probe6	0.195498320028551	0.117608419167756	1.66228167517236	0.0967879413520453	.  
df.mm.trans3:probe2	-0.0782980009692403	0.117608419167756	-0.665751665767708	0.505732310884359	   
df.mm.trans3:probe3	-0.267512785408512	0.117608419167756	-2.27460574082654	0.0231529701244518	*  
df.mm.trans3:probe4	-0.190216454463457	0.117608419167756	-1.61737106756051	0.106131929510663	   
df.mm.trans3:probe5	-0.0565228788013028	0.117608419167756	-0.480602317430004	0.630910407405539	   
df.mm.trans3:probe6	0.0180562065694378	0.117608419167756	0.153528180186510	0.878014546782192	   
df.mm.trans3:probe7	-0.0851924566532628	0.117608419167756	-0.724373792761765	0.469015580420545	   
df.mm.trans3:probe8	0.0339763054816487	0.117608419167756	0.288893479923279	0.772726253251907	   
df.mm.trans3:probe9	-0.0830811451303415	0.117608419167756	-0.706421748700109	0.480100029109862	   
df.mm.trans3:probe10	-0.0328851335399218	0.117608419167756	-0.279615471176555	0.77983372380042	   
df.mm.trans3:probe11	-0.0855367281837973	0.117608419167756	-0.727301062195116	0.467221686332189	   
df.mm.trans3:probe12	-0.0111201278868336	0.117608419167756	-0.0945521414667764	0.924690609091218	   
df.mm.trans3:probe13	-0.136931761558175	0.117608419167756	-1.16430237330931	0.244595346302552	   
df.mm.trans3:probe14	-0.106920728268984	0.117608419167756	-0.909124780569267	0.36351606272596	   
df.mm.trans3:probe15	0.098210355667602	0.117608419167756	0.835062288589351	0.403893700249824