chr2.14481_chr2_137932153_138026992_+_2.R 

fitVsDatCorrelation=0.698743525347619
cont.fitVsDatCorrelation=0.217644157022211

fstatistic=14385.6374393087,79,1313
cont.fstatistic=7720.33458508157,79,1313

residuals=-0.556986128318862,-0.0789630944123729,-0.00714562243146432,0.0675767691944436,1.03696003951530
cont.residuals=-0.439321747646259,-0.124280783629996,-0.0145905748040954,0.0917250803671675,1.26713534185335

predictedValues:
Include	Exclude	Both
chr2.14481_chr2_137932153_138026992_+_2.R.tl.Lung	56.9789957970401	57.4415903772064	50.5617984403037
chr2.14481_chr2_137932153_138026992_+_2.R.tl.cerebhem	62.2366406916016	73.3793452065428	54.4541364348817
chr2.14481_chr2_137932153_138026992_+_2.R.tl.cortex	55.2986709510099	53.8700198498522	51.2933096276817
chr2.14481_chr2_137932153_138026992_+_2.R.tl.heart	57.081878229166	51.8512869068768	52.6324702161169
chr2.14481_chr2_137932153_138026992_+_2.R.tl.kidney	56.8632512387869	55.4950580937249	51.4245897484257
chr2.14481_chr2_137932153_138026992_+_2.R.tl.liver	56.4451859828021	54.82273727215	53.6448248894931
chr2.14481_chr2_137932153_138026992_+_2.R.tl.stomach	58.2861430664671	52.9575984238414	52.9899060848458
chr2.14481_chr2_137932153_138026992_+_2.R.tl.testicle	57.5335527251058	54.7492831908352	51.8951366084712


diffExp=-0.462594580166282,-11.1427045149412,1.42865110115770,5.23059132228923,1.36819314506203,1.62244871065212,5.32854464262566,2.7842695342706
diffExpScore=4.10316597832921
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	58.0889810036315	52.9144368394268	55.4307896694642
cerebhem	56.6507491148284	52.072011979732	53.9990657451647
cortex	56.6635272653145	55.4814320169622	53.9491128879668
heart	56.7793083554051	52.8920246130449	56.2242850724353
kidney	55.5096201959827	55.7491589033285	56.0455173925481
liver	56.778946566503	55.6328367261354	54.4627357438497
stomach	57.1785854708254	54.470836328263	54.2849307581243
testicle	56.3615289974522	51.5082847017612	55.2121569609931
cont.diffExp=5.17454416420463,4.57873713509634,1.18209524835228,3.88728374236018,-0.239538707345851,1.1461098403676,2.70774914256242,4.85324429569099
cont.diffExpScore=0.978554229601287

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=0.868690209874738
cont.tran.correlation=-0.274639414637685

tran.covariance=0.0032698996129281
cont.tran.covariance=-0.000108953787240097

tran.mean=57.2057023751881
cont.tran.mean=55.2957668174123

weightedLogRatios:
wLogRatio
Lung	-0.0327214467114451
cerebhem	-0.693923451928292
cortex	0.104690218116587
heart	0.384084980631945
kidney	0.0981146788724137
liver	0.117204965183595
stomach	0.385162141804829
testicle	0.199783507247778

cont.weightedLogRatios:
wLogRatio
Lung	0.374629043433647
cerebhem	0.336669343929976
cortex	0.084889926959235
heart	0.283940894966557
kidney	-0.0173044848055253
liver	0.0821586775057806
stomach	0.195119558475773
testicle	0.358984854394773

varWeightedLogRatios=0.116167497605763
cont.varWeightedLogRatios=0.0220929621146422

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.11775374578335	0.0623487511895018	66.0438848769868	0	***
df.mm.trans1	-0.0372255077304163	0.0537296810488535	-0.692829493935933	0.488539081894444	   
df.mm.trans2	-0.0765621505636962	0.0459208141932654	-1.66726465784060	0.0957002652993853	.  
df.mm.exp2	0.258972588943663	0.0570639083359705	4.53829042726852	6.19029608576841e-06	***
df.mm.exp3	-0.108492344577822	0.0570639083359705	-1.90124279499155	0.0574889730973328	.  
df.mm.exp4	-0.140721779826480	0.0570639083359705	-2.46603823555099	0.0137887107973136	*  
df.mm.exp5	-0.0534281975948672	0.0570639083359704	-0.936287035937013	0.349297594676702	   
df.mm.exp6	-0.115264972057462	0.0570639083359705	-2.01992775151021	0.0435936563680213	*  
df.mm.exp7	-0.105500496205282	0.0570639083359705	-1.84881301126687	0.0647094994767317	.  
df.mm.exp8	-0.064347496830338	0.0570639083359704	-1.12763914542068	0.259678365807320	   
df.mm.trans1:exp2	-0.170711389129023	0.0533463490592263	-3.20005758856141	0.00140687593853294	** 
df.mm.trans2:exp2	-0.0140987053616843	0.0326678337088764	-0.431577602828725	0.666119292878544	   
df.mm.trans1:exp3	0.0785585142467857	0.0533463490592262	1.47261275855201	0.141095143024573	   
df.mm.trans2:exp3	0.0442978377649922	0.0326678337088764	1.35600781367256	0.17532983504125	   
df.mm.trans1:exp4	0.142525771964992	0.0533463490592262	2.67170620817474	0.0076400152716502	** 
df.mm.trans2:exp4	0.038332922016637	0.0326678337088764	1.17341487526372	0.240842288214179	   
df.mm.trans1:exp5	0.0513947767665078	0.0533463490592262	0.963416947417495	0.335515721192086	   
df.mm.trans2:exp5	0.0189535590442197	0.0326678337088764	0.580190263398754	0.561885869131433	   
df.mm.trans1:exp6	0.105852274569125	0.0533463490592262	1.98424590315647	0.0474363456953640	*  
df.mm.trans2:exp6	0.068601381701767	0.0326678337088764	2.09996727401997	0.0359221208400076	*  
df.mm.trans1:exp7	0.128182172924702	0.0533463490592262	2.40282934418607	0.0164068388686291	*  
df.mm.trans2:exp7	0.0242234478418489	0.0326678337088764	0.741507626667849	0.458518312931733	   
df.mm.trans1:exp8	0.0740330949678729	0.0533463490592262	1.38778184961973	0.165438938738412	   
df.mm.trans2:exp8	0.0163431610542729	0.0326678337088764	0.500282975599699	0.61695967756596	   
df.mm.trans1:probe2	0.0194223270933771	0.0405194608368489	0.479333305336438	0.631781355745902	   
df.mm.trans1:probe3	0.0358215377086708	0.0405194608368489	0.884057610068055	0.376826962523716	   
df.mm.trans1:probe4	-0.0860297633351546	0.0405194608368489	-2.12317147263021	0.0339261155432056	*  
df.mm.trans1:probe5	-0.175726420120295	0.0405194608368489	-4.33684003910752	1.55600741615585e-05	***
df.mm.trans1:probe6	0.0937373238701992	0.0405194608368489	2.31339020644010	0.0208550577054427	*  
df.mm.trans1:probe7	-0.0452194416211216	0.0405194608368488	-1.11599317185382	0.264629253161645	   
df.mm.trans1:probe8	-0.137820414018064	0.0405194608368489	-3.40133879305543	0.000690720516781374	***
df.mm.trans1:probe9	-0.0404023211186913	0.0405194608368489	-0.99710905042322	0.318895300461388	   
df.mm.trans1:probe10	-0.0187158799712559	0.0405194608368489	-0.461898544174	0.644230597222733	   
df.mm.trans1:probe11	-0.124142180771849	0.0405194608368489	-3.0637668470394	0.00223020195842145	** 
df.mm.trans1:probe12	-0.270823669205665	0.0405194608368489	-6.6837925187636	3.43258641801345e-11	***
df.mm.trans1:probe13	-0.230023782901733	0.0405194608368489	-5.6768717586821	1.68652079080603e-08	***
df.mm.trans1:probe14	-0.286138445180256	0.0405194608368489	-7.06175351968255	2.65791708355621e-12	***
df.mm.trans1:probe15	-0.21308087995448	0.0405194608368489	-5.2587293994964	1.69183024294825e-07	***
df.mm.trans1:probe16	-0.179972678731475	0.0405194608368489	-4.44163557496811	9.67803964700832e-06	***
df.mm.trans1:probe17	-0.0344500684087782	0.0405194608368489	-0.850210434622784	0.395363108487812	   
df.mm.trans1:probe18	-0.06861490906023	0.0405194608368489	-1.69338159104602	0.0906200548977528	.  
df.mm.trans1:probe19	0.0596403635810043	0.0405194608368489	1.47189430336069	0.141289059051898	   
df.mm.trans1:probe20	-0.0769597381069714	0.0405194608368489	-1.89932779255995	0.0577403757333947	.  
df.mm.trans1:probe21	0.0141294942147563	0.0405194608368489	0.348708840713565	0.7273638133557	   
df.mm.trans1:probe22	-0.0491575584194131	0.0405194608368489	-1.21318392209969	0.225277716603240	   
df.mm.trans1:probe23	0.0233477535801763	0.0405194608368489	0.576210865050396	0.564571416264381	   
df.mm.trans1:probe24	0.0059967703605792	0.0405194608368489	0.147997289123000	0.882367631432518	   
df.mm.trans1:probe25	0.0208006077190157	0.0405194608368488	0.513348580889788	0.607793871530298	   
df.mm.trans1:probe26	-0.0732089979352601	0.0405194608368489	-1.80676140361382	0.0710283404944583	.  
df.mm.trans1:probe27	0.128845363301261	0.0405194608368489	3.17983903635972	0.00150800499898440	** 
df.mm.trans1:probe28	-0.0818721028559585	0.0405194608368489	-2.02056249429417	0.0435277551914604	*  
df.mm.trans1:probe29	-0.182261405092903	0.0405194608368489	-4.49812019530015	7.46141797371097e-06	***
df.mm.trans1:probe30	0.0645085397887597	0.0405194608368489	1.59203845402836	0.111616768312932	   
df.mm.trans1:probe31	-0.0299856996244876	0.0405194608368489	-0.740032048926433	0.459412845672636	   
df.mm.trans1:probe32	-0.0296114365220066	0.0405194608368489	-0.730795423000238	0.465034502105753	   
df.mm.trans2:probe2	0.130597969084722	0.0405194608368489	3.22309246933401	0.00129930859026760	** 
df.mm.trans2:probe3	0.0949241633430726	0.0405194608368489	2.34268081022311	0.0192944343411763	*  
df.mm.trans2:probe4	-0.0124735659081572	0.0405194608368489	-0.307841359449027	0.758251873356947	   
df.mm.trans2:probe5	-0.00891836642165135	0.0405194608368489	-0.220100816680682	0.825826854054583	   
df.mm.trans2:probe6	0.0448722357264745	0.0405194608368489	1.10742430426584	0.268313354512373	   
df.mm.trans3:probe2	-0.202228484685195	0.0405194608368488	-4.99089771948018	6.8163995115086e-07	***
df.mm.trans3:probe3	0.343998565904681	0.0405194608368489	8.48971232094591	5.52434132942446e-17	***
df.mm.trans3:probe4	-0.115990300231985	0.0405194608368489	-2.86258251804038	0.00426875091862759	** 
df.mm.trans3:probe5	-0.0708792787992355	0.0405194608368489	-1.74926510213524	0.080478842175032	.  
df.mm.trans3:probe6	-0.239715535606199	0.0405194608368489	-5.91605936148586	4.20102932541712e-09	***
df.mm.trans3:probe7	-0.0857310603693504	0.0405194608368489	-2.11579963303425	0.0345496880414192	*  
df.mm.trans3:probe8	0.00587031717412027	0.0405194608368489	0.144876487813030	0.884830625439186	   
df.mm.trans3:probe9	-0.106945234802838	0.0405194608368489	-2.63935483330964	0.0084047485487997	** 
df.mm.trans3:probe10	-0.197888316292831	0.0405194608368489	-4.88378453725299	1.16855184000928e-06	***
df.mm.trans3:probe11	-0.133304947066731	0.0405194608368489	-3.28989933018807	0.00102881898052333	** 
df.mm.trans3:probe12	0.241686314662112	0.0405194608368489	5.96469720155604	3.1468433320869e-09	***
df.mm.trans3:probe13	-0.195059942827812	0.0405194608368488	-4.81398169667702	1.65102576104384e-06	***
df.mm.trans3:probe14	-0.180185873028377	0.0405194608368488	-4.44689710344106	9.4475287212412e-06	***
df.mm.trans3:probe15	-0.103839482300160	0.0405194608368489	-2.56270641700462	0.0104966677534289	*  
df.mm.trans3:probe16	-0.173358946693274	0.0405194608368489	-4.27841198063572	2.01880123192601e-05	***
df.mm.trans3:probe17	-0.158832409009215	0.0405194608368489	-3.91990430595195	9.31539742029194e-05	***
df.mm.trans3:probe18	-0.265039367485186	0.0405194608368488	-6.54103884926712	8.73065616317893e-11	***
df.mm.trans3:probe19	0.137709376940946	0.0405194608368489	3.3985984536031	0.000697616075222505	***
df.mm.trans3:probe20	-0.115660752800428	0.0405194608368489	-2.85444945247753	0.00437888873058521	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10362409903005	0.085066392762086	48.2402505359206	5.10368177648399e-293	***
df.mm.trans1	-0.00421617010644075	0.0733068435836285	-0.0575140041547545	0.954144501986842	   
df.mm.trans2	-0.133025376826933	0.0626527066155068	-2.12321835740148	0.0339221806662909	*  
df.mm.exp2	-0.0149509184585965	0.0778559433258494	-0.192033103959998	0.847745968735627	   
df.mm.exp3	0.0496209134866085	0.0778559433258494	0.637342653199008	0.524012788424191	   
df.mm.exp4	-0.0374412387497459	0.0778559433258494	-0.480904053696243	0.630664814535439	   
df.mm.exp5	-0.00426249561802101	0.0778559433258493	-0.0547484936401226	0.956347163971174	   
df.mm.exp6	0.0449055031753855	0.0778559433258494	0.576776816991905	0.564189099569387	   
df.mm.exp7	0.0340812331118396	0.0778559433258494	0.437747353072326	0.661641436545847	   
df.mm.exp8	-0.0531706756611709	0.0778559433258494	-0.682936631293985	0.494767364058582	   
df.mm.trans1:exp2	-0.0101198607596261	0.0727838392095907	-0.139039941689316	0.88943990880398	   
df.mm.trans2:exp2	-0.00109768495974442	0.0445708168960681	-0.0246278851541815	0.980355519154243	   
df.mm.trans1:exp3	-0.0744661580070229	0.0727838392095907	-1.02311390572003	0.306442453776036	   
df.mm.trans2:exp3	-0.00224871671356642	0.0445708168960681	-0.0504526699344565	0.959769339037167	   
df.mm.trans1:exp4	0.0146372184222587	0.0727838392095907	0.201105335761541	0.840647358070785	   
df.mm.trans2:exp4	0.0370175930402218	0.0445708168960681	0.830534318600012	0.406387742675338	   
df.mm.trans1:exp5	-0.0411571519536675	0.0727838392095907	-0.565471022147514	0.571850040864968	   
df.mm.trans2:exp5	0.0564486091593121	0.0445708168960681	1.26649258619022	0.205561411391747	   
df.mm.trans1:exp6	-0.0677158953672937	0.0727838392095907	-0.930369929680363	0.352350602745491	   
df.mm.trans2:exp6	0.00519190260257858	0.0445708168960681	0.116486592890708	0.907284724877097	   
df.mm.trans1:exp7	-0.049877774871938	0.0727838392095907	-0.685286396177981	0.493284177847736	   
df.mm.trans2:exp7	-0.00509199765902761	0.0445708168960681	-0.114245105062833	0.909060963893884	   
df.mm.trans1:exp8	0.0229815012805488	0.0727838392095907	0.315750055645877	0.752242410757946	   
df.mm.trans2:exp8	0.0262371286390389	0.0445708168960681	0.588661605647022	0.556189547916135	   
df.mm.trans1:probe2	-0.0274649769864097	0.0552832944412802	-0.496804274491671	0.619410248649529	   
df.mm.trans1:probe3	-0.0711051048840928	0.0552832944412802	-1.28619514453174	0.198601720861366	   
df.mm.trans1:probe4	-0.0456039382354037	0.0552832944412802	-0.824913542079921	0.409570515309294	   
df.mm.trans1:probe5	-0.0423248797181228	0.0552832944412802	-0.76559980995848	0.444052055859719	   
df.mm.trans1:probe6	-0.0810591437476273	0.0552832944412802	-1.46625024009242	0.142819570649072	   
df.mm.trans1:probe7	-0.082567780941061	0.0552832944412802	-1.49353944578613	0.135536293052859	   
df.mm.trans1:probe8	-0.0764051004882522	0.0552832944412802	-1.38206489429473	0.167186846328904	   
df.mm.trans1:probe9	-0.0973290297710773	0.0552832944412802	-1.76055046564666	0.078547303561122	.  
df.mm.trans1:probe10	-0.100436349863719	0.0552832944412802	-1.81675768202272	0.0694821348516195	.  
df.mm.trans1:probe11	-0.100411270864280	0.0552832944412802	-1.81630403685390	0.0695516994754522	.  
df.mm.trans1:probe12	-0.106540339305083	0.0552832944412802	-1.92717059252405	0.0541739223090014	.  
df.mm.trans1:probe13	-0.0843027042845287	0.0552832944412802	-1.52492186177638	0.127519408324236	   
df.mm.trans1:probe14	0.0119030238432156	0.0552832944412802	0.215309596931828	0.829559386551228	   
df.mm.trans1:probe15	-0.150472008488566	0.0552832944412802	-2.72183504997864	0.00657791159188014	** 
df.mm.trans1:probe16	-0.0800708102462145	0.0552832944412802	-1.44837262423394	0.147751617580521	   
df.mm.trans1:probe17	-0.106141810664423	0.0552832944412802	-1.91996174861038	0.0550791888164742	.  
df.mm.trans1:probe18	-0.0470836920503108	0.0552832944412802	-0.851680286534322	0.394546871059781	   
df.mm.trans1:probe19	-0.0574805161328551	0.0552832944412802	-1.03974476763335	0.2986499181845	   
df.mm.trans1:probe20	-0.0486627622698628	0.0552832944412802	-0.880243530376985	0.378888478665999	   
df.mm.trans1:probe21	-0.0459088238159159	0.0552832944412802	-0.830428509731425	0.406447520266505	   
df.mm.trans1:probe22	-0.0269422010967225	0.0552832944412802	-0.487347965945471	0.626093115191079	   
df.mm.trans1:probe23	-0.0705066174993858	0.0552832944412802	-1.27536931747574	0.202404157709227	   
df.mm.trans1:probe24	-0.0913387527149184	0.0552832944412802	-1.65219445834464	0.0987340105265985	.  
df.mm.trans1:probe25	-0.0201200918056939	0.0552832944412802	-0.36394523895577	0.715957497505295	   
df.mm.trans1:probe26	-0.0499082750139614	0.0552832944412802	-0.902773170780769	0.366811877256195	   
df.mm.trans1:probe27	-0.156948004592906	0.0552832944412802	-2.83897705770068	0.00459556172803318	** 
df.mm.trans1:probe28	-0.0456186202744928	0.0552832944412802	-0.825179120302737	0.409419797995908	   
df.mm.trans1:probe29	-0.0225375820368487	0.0552832944412802	-0.407674366454178	0.683579219680187	   
df.mm.trans1:probe30	0.0137847143971476	0.0552832944412802	0.249346833188265	0.803131507940088	   
df.mm.trans1:probe31	-0.0498994232984409	0.0552832944412802	-0.902613055223078	0.366896847640204	   
df.mm.trans1:probe32	0.0130420575345527	0.0552832944412802	0.235913175333744	0.813536836993556	   
df.mm.trans2:probe2	-0.0139425151150834	0.0552832944412802	-0.252201234676646	0.800924995676368	   
df.mm.trans2:probe3	-0.0163923789880211	0.0552832944412802	-0.296515957554455	0.766882977064042	   
df.mm.trans2:probe4	0.0271422822808124	0.0552832944412802	0.490967163862528	0.623531719021202	   
df.mm.trans2:probe5	-0.061129343659284	0.0552832944412802	-1.10574712084522	0.269038551022171	   
df.mm.trans2:probe6	0.0143366316826714	0.0552832944412802	0.259330270157818	0.795421078475675	   
df.mm.trans3:probe2	0.0358099836350678	0.0552832944412802	0.647754154251857	0.517257216158104	   
df.mm.trans3:probe3	0.0468372238621566	0.0552832944412802	0.847222010473803	0.397025786003074	   
df.mm.trans3:probe4	0.025538545160734	0.0552832944412802	0.461957729162832	0.644188164288882	   
df.mm.trans3:probe5	0.0439757511394186	0.0552832944412802	0.795461840396071	0.426488675586014	   
df.mm.trans3:probe6	0.0086891901878063	0.0552832944412802	0.157175694314593	0.875130548651117	   
df.mm.trans3:probe7	0.105207556279446	0.0552832944412802	1.90306235080099	0.0572509455991116	.  
df.mm.trans3:probe8	0.0230848641899440	0.0552832944412802	0.41757396014928	0.676326910631507	   
df.mm.trans3:probe9	0.101322783381342	0.0552832944412802	1.83279206504169	0.0670598336915651	.  
df.mm.trans3:probe10	0.0772908672086151	0.0552832944412802	1.39808721585343	0.162322996384888	   
df.mm.trans3:probe11	0.00212194492645417	0.0552832944412802	0.0383831127992566	0.969388059996071	   
df.mm.trans3:probe12	0.0805581544381691	0.0552832944412802	1.45718802130606	0.145303595120452	   
df.mm.trans3:probe13	0.0432972523390725	0.0552832944412802	0.783188715083925	0.433657540354321	   
df.mm.trans3:probe14	0.0617295902552498	0.0552832944412802	1.11660476965273	0.264367643750272	   
df.mm.trans3:probe15	0.0625010967205777	0.0552832944412802	1.13056027778815	0.258446683926937	   
df.mm.trans3:probe16	0.0137051582537627	0.0552832944412802	0.247907770191225	0.804244530997786	   
df.mm.trans3:probe17	0.0491316780823596	0.0552832944412802	0.888725582997688	0.374313361694653	   
df.mm.trans3:probe18	0.0674606979201202	0.0552832944412802	1.22027275331383	0.222580610583124	   
df.mm.trans3:probe19	0.0658467197925184	0.0552832944412802	1.19107807264378	0.233838206827765	   
df.mm.trans3:probe20	0.0609440918357457	0.0552832944412802	1.10239616599691	0.270491500119374