chr4.16587_chr4_105918491_105920811_+_0.R 

fitVsDatCorrelation=0.863369856695261
cont.fitVsDatCorrelation=0.287006908041910

fstatistic=7670.10235756854,52,692
cont.fstatistic=2118.42821614582,52,692

residuals=-0.49366082568506,-0.104785458934021,-0.00441286791651265,0.0859458081761955,1.85856373886786
cont.residuals=-0.718658187998002,-0.259705782978841,-0.0551178365205978,0.22458117018051,1.80880786767412

predictedValues:
Include	Exclude	Both
chr4.16587_chr4_105918491_105920811_+_0.R.tl.Lung	61.0193408493345	64.9404116779254	98.507587288824
chr4.16587_chr4_105918491_105920811_+_0.R.tl.cerebhem	65.7708885800259	61.7202012325119	93.0348812908221
chr4.16587_chr4_105918491_105920811_+_0.R.tl.cortex	57.9809438127348	70.4879559928909	89.1395042814608
chr4.16587_chr4_105918491_105920811_+_0.R.tl.heart	59.5179927577096	75.212510541931	105.104323714451
chr4.16587_chr4_105918491_105920811_+_0.R.tl.kidney	62.4954930392853	80.4209175400788	138.207717128112
chr4.16587_chr4_105918491_105920811_+_0.R.tl.liver	83.0087980148456	72.3106876600847	138.737350460591
chr4.16587_chr4_105918491_105920811_+_0.R.tl.stomach	61.6027948741617	101.793321006629	110.989782955754
chr4.16587_chr4_105918491_105920811_+_0.R.tl.testicle	73.7001920634704	75.2926913497699	130.236115809488


diffExp=-3.92107082859086,4.05068734751394,-12.5070121801561,-15.6945177842214,-17.9254245007935,10.6981103547609,-40.1905261324672,-1.59249928629953
diffExpScore=1.36496891810752
diffExp1.5=0,0,0,0,0,0,-1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,-1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,0,-1,0
diffExp1.3Score=0.5
diffExp1.2=0,0,-1,-1,-1,0,-1,0
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	78.001126518467	77.7115729555691	77.1396515717366
cerebhem	74.0067533572451	68.6809547250456	79.3736848442983
cortex	81.0252690644477	85.6915121791782	79.6440873435932
heart	88.1020039575592	70.7433917382833	81.6132025432341
kidney	73.3945712758884	92.8252521243245	77.3763110947278
liver	83.1729569973892	74.6043999253933	73.3897157385037
stomach	82.2972409529012	78.0434893243472	88.6538378584065
testicle	72.6245859428816	82.5343876153085	74.0032193687071
cont.diffExp=0.289553562897851,5.32579863219954,-4.66624311473053,17.3586122192759,-19.4306808484361,8.56855707199598,4.25375162855403,-9.90980167242692
cont.diffExpScore=25.0230545519477

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

tran.correlation=-0.124962910208617
cont.tran.correlation=-0.422896652683957

tran.covariance=-0.00196756536331111
cont.tran.covariance=-0.00281203400186314

tran.mean=70.4546963120868
cont.tran.mean=78.9662167908893

weightedLogRatios:
wLogRatio
Lung	-0.257981149231831
cerebhem	0.264078351791991
cortex	-0.812128227375508
heart	-0.983735330465258
kidney	-1.0745849135926
liver	0.60018415301748
stomach	-2.19569381026966
testicle	-0.0921525749890938

cont.weightedLogRatios:
wLogRatio
Lung	0.0161960812004263
cerebhem	0.318664512391234
cortex	-0.247642173152882
heart	0.958667649495052
kidney	-1.03654255134220
liver	0.474744607378195
stomach	0.232654311967565
testicle	-0.556320330266626

varWeightedLogRatios=0.788284700378072
cont.varWeightedLogRatios=0.391769432537155

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.60648816160955	0.0897112459885729	40.2010709122124	3.52992255897094e-183	***
df.mm.trans1	0.476304559351545	0.0768015889374863	6.20175397333562	9.60249790253097e-10	***
df.mm.trans2	0.535855430130014	0.0693404303708249	7.72789305264358	3.85438964566126e-14	***
df.mm.exp2	0.0812867055493143	0.0902306464134652	0.900876905800198	0.367967186044955	   
df.mm.exp3	0.130826243959975	0.0902306464134652	1.44990919560177	0.147536901907744	   
df.mm.exp4	0.0571154212345582	0.0902306464134653	0.632993594801896	0.526946945415437	   
df.mm.exp5	-0.100916406049797	0.0902306464134652	-1.11842716483894	0.263772696591103	   
df.mm.exp6	0.0728085542580357	0.0902306464134653	0.806916021906836	0.41999210917284	   
df.mm.exp7	0.339686172691941	0.0902306464134652	3.76464301425252	0.000180949411791867	***
df.mm.exp8	0.057511986885193	0.0902306464134653	0.6373886165201	0.524082562762498	   
df.mm.trans1:exp2	-0.00630026493511596	0.0815241021188152	-0.0772810098041167	0.938422336357454	   
df.mm.trans2:exp2	-0.132145523662427	0.0644504617239037	-2.05034254414684	0.040707233317736	*  
df.mm.trans1:exp3	-0.18190271937568	0.0815241021188152	-2.23127534861495	0.0259824711885523	*  
df.mm.trans2:exp3	-0.0488544917291884	0.0644504617239037	-0.758016163460144	0.448699367652778	   
df.mm.trans1:exp4	-0.0820276321304587	0.0815241021188152	-1.00617645577880	0.314682389704687	   
df.mm.trans2:exp4	0.089732053460991	0.0644504617239037	1.39226393513502	0.164289723201535	   
df.mm.trans1:exp5	0.124819971797295	0.0815241021188152	1.53108060749175	0.126206468724151	   
df.mm.trans2:exp5	0.314720610792860	0.0644504617239037	4.88313973825474	1.29775664686730e-06	***
df.mm.trans1:exp6	0.234947171076530	0.0815241021188152	2.88193509612792	0.00407487401085377	** 
df.mm.trans2:exp6	0.0346932817598934	0.0644504617239038	0.538293765970432	0.590547348079206	   
df.mm.trans1:exp7	-0.330169808871192	0.0815241021188152	-4.04996559655444	5.70192268262084e-05	***
df.mm.trans2:exp7	0.109788214287041	0.0644504617239037	1.7034511677722	0.0889326572872772	.  
df.mm.trans1:exp8	0.131302541300667	0.0815241021188152	1.61059782184787	0.107723423273098	   
df.mm.trans2:exp8	0.0904009767690979	0.0644504617239037	1.40264281047919	0.161171846042195	   
df.mm.trans1:probe2	0.435082347459762	0.0546880302426916	7.95571435886385	7.28112709490534e-15	***
df.mm.trans1:probe3	0.0530646240542942	0.0546880302426916	0.97031514608968	0.33222845073732	   
df.mm.trans1:probe4	0.171530532496792	0.0546880302426916	3.13652789715745	0.0017820579145833	** 
df.mm.trans1:probe5	-0.00776175790094653	0.0546880302426916	-0.141927911217534	0.88717825695093	   
df.mm.trans1:probe6	-0.168959064308266	0.0546880302426916	-3.08950722047345	0.00208519774995112	** 
df.mm.trans1:probe7	-0.338513865461668	0.0546880302426916	-6.18990780906589	1.03117647702873e-09	***
df.mm.trans1:probe8	0.00631150775843766	0.0546880302426916	0.115409308589627	0.908154181394662	   
df.mm.trans1:probe9	0.150995298319421	0.0546880302426916	2.76103011297614	0.00591462286816075	** 
df.mm.trans1:probe10	0.120695054353469	0.0546880302426916	2.20697388108247	0.0276434332659935	*  
df.mm.trans1:probe11	0.460425829301734	0.0546880302426916	8.41913353358094	2.17911195181755e-16	***
df.mm.trans1:probe12	-0.058115848510769	0.0546880302426916	-1.06267949774138	0.288298024563648	   
df.mm.trans1:probe13	-0.0362422358435049	0.0546880302426916	-0.662708744174384	0.507737772131657	   
df.mm.trans1:probe14	-0.0514310348493746	0.0546880302426916	-0.940444090254059	0.347317975121793	   
df.mm.trans1:probe15	-0.0243388153905676	0.0546880302426916	-0.445048309155735	0.656424017562636	   
df.mm.trans1:probe16	-0.0311868259866366	0.0546880302426916	-0.570267860228963	0.568681139635685	   
df.mm.trans2:probe2	0.00733640976893861	0.0546880302426916	0.134150192215399	0.893322815991583	   
df.mm.trans2:probe3	0.056518509592318	0.0546880302426916	1.03347129785994	0.301744430433237	   
df.mm.trans2:probe4	0.0303595183139390	0.0546880302426916	0.555140095176425	0.578978239211081	   
df.mm.trans2:probe5	0.132070167047037	0.0546880302426916	2.41497392502424	0.0159945941757778	*  
df.mm.trans2:probe6	0.240613109377248	0.0546880302426916	4.39973991217946	1.25473439616191e-05	***
df.mm.trans3:probe2	-0.0321726764844013	0.0546880302426916	-0.588294665974019	0.556526473738874	   
df.mm.trans3:probe3	0.273146866741316	0.0546880302426916	4.99463713593558	7.47113042409713e-07	***
df.mm.trans3:probe4	-0.00977839578706762	0.0546880302426916	-0.178803217882845	0.85814455370148	   
df.mm.trans3:probe5	0.148082522748332	0.0546880302426916	2.70776844752277	0.00694099068612338	** 
df.mm.trans3:probe6	0.0182116810279389	0.0546880302426916	0.333010367115438	0.739227322113007	   
df.mm.trans3:probe7	-0.289793171688683	0.0546880302426916	-5.29902376082398	1.56681321385437e-07	***
df.mm.trans3:probe8	-0.206263051777381	0.0546880302426916	-3.77163066327380	0.00017605524769054	***
df.mm.trans3:probe9	-0.158064972880035	0.0546880302426916	-2.89030290867275	0.00396923169057137	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.28759825166076	0.170316684340097	25.1742703204524	8.85111649817339e-100	***
df.mm.trans1	0.0159036903542991	0.145807717145629	0.109073035814798	0.913176181799464	   
df.mm.trans2	0.0840467977013691	0.131642717268452	0.638446238768961	0.523394470872261	   
df.mm.exp2	-0.204648624123645	0.171302765374171	-1.19466036451097	0.232629054414126	   
df.mm.exp3	0.103837025210430	0.171302765374171	0.606160822819303	0.544606745300645	   
df.mm.exp4	-0.0285466600661661	0.171302765374171	-0.166644478878158	0.867698473928108	   
df.mm.exp5	0.11377798914882	0.171302765374171	0.664192366657352	0.506788440651482	   
df.mm.exp6	0.0732279077971904	0.171302765374171	0.427476507091062	0.66916527772851	   
df.mm.exp7	-0.0812455377889783	0.171302765374171	-0.474280363259263	0.635449610699476	   
df.mm.exp8	0.0302999191281057	0.171302765374171	0.176879334445784	0.859654926856773	   
df.mm.trans1:exp2	0.152081705618375	0.154773402305088	0.982608790356582	0.326143429610216	   
df.mm.trans2:exp2	0.081116370750213	0.122359118124408	0.662936869712793	0.507591739692015	   
df.mm.trans1:exp3	-0.0657992245597005	0.154773402305088	-0.425132636355682	0.670872151942916	   
df.mm.trans2:exp3	-0.00608743596434008	0.122359118124408	-0.0497505707596773	0.960335512553969	   
df.mm.trans1:exp4	0.150318670016669	0.154773402305088	0.971217714270846	0.331779219402659	   
df.mm.trans2:exp4	-0.0653984011349321	0.122359118124408	-0.534479180116668	0.593181722857919	   
df.mm.trans1:exp5	-0.174651286212293	0.154773402305088	-1.12843216994107	0.259528508546711	   
df.mm.trans2:exp5	0.0639365366662142	0.122359118124408	0.522531852519622	0.601467314604888	   
df.mm.trans1:exp6	-0.0090289180245257	0.154773402305088	-0.058336367166808	0.95349754114022	   
df.mm.trans2:exp6	-0.114032612463238	0.122359118124408	-0.93195026419932	0.351687255823663	   
df.mm.trans1:exp7	0.134859851514220	0.154773402305088	0.871337384238573	0.383872151465153	   
df.mm.trans2:exp7	0.0855075741729842	0.122359118124408	0.698824701286623	0.484896382489283	   
df.mm.trans1:exp8	-0.101719674404866	0.154773402305088	-0.657216762634427	0.511260072455822	   
df.mm.trans2:exp8	0.0299109165772267	0.122359118124408	0.244451880952712	0.806953327878119	   
df.mm.trans1:probe2	0.0664832680057402	0.103825154598930	0.64033873354257	0.52216436909765	   
df.mm.trans1:probe3	0.0446082251907698	0.103825154598930	0.429647568193746	0.667585775050346	   
df.mm.trans1:probe4	-0.00948941763291086	0.103825154598930	-0.0913980592619189	0.927202754093142	   
df.mm.trans1:probe5	0.0907234331690355	0.103825154598930	0.873809757563036	0.382524999115705	   
df.mm.trans1:probe6	-0.063103578515871	0.103825154598930	-0.607786993042642	0.543528161128135	   
df.mm.trans1:probe7	0.213275500258608	0.103825154598930	2.05417946240946	0.0403334279436802	*  
df.mm.trans1:probe8	0.0643910940141001	0.103825154598930	0.620187798061453	0.535338327919239	   
df.mm.trans1:probe9	0.120722799135398	0.103825154598930	1.16275096918221	0.245331373147040	   
df.mm.trans1:probe10	0.0494162495082293	0.103825154598930	0.47595642596557	0.634255643810657	   
df.mm.trans1:probe11	0.143113883232786	0.103825154598930	1.37841242602167	0.168521455366909	   
df.mm.trans1:probe12	0.182168308864610	0.103825154598930	1.75456814457262	0.0797757653087652	.  
df.mm.trans1:probe13	0.0256691854142913	0.103825154598930	0.247234743000864	0.804799890881427	   
df.mm.trans1:probe14	0.260389335670339	0.103825154598930	2.50796000907686	0.0123708341721211	*  
df.mm.trans1:probe15	0.0402477765578756	0.103825154598930	0.387649570215911	0.698394584945101	   
df.mm.trans1:probe16	0.0486957878795468	0.103825154598930	0.469017244112525	0.639205034695889	   
df.mm.trans2:probe2	-0.118367556410261	0.103825154598930	-1.14006626686479	0.254653133420838	   
df.mm.trans2:probe3	-0.0652656623826807	0.103825154598930	-0.628611270889005	0.529811004707161	   
df.mm.trans2:probe4	0.00906272555314183	0.103825154598930	0.0872883415213836	0.930467572615567	   
df.mm.trans2:probe5	-0.0661307536886556	0.103825154598930	-0.636943464655697	0.524372318664262	   
df.mm.trans2:probe6	-0.0389116379586864	0.103825154598930	-0.374780448042671	0.707938583006095	   
df.mm.trans3:probe2	0.0960262502846057	0.103825154598930	0.924884250406843	0.355348525782613	   
df.mm.trans3:probe3	-0.0536616120642222	0.103825154598930	-0.516845963500019	0.605428830873506	   
df.mm.trans3:probe4	-0.0741660427789204	0.103825154598930	-0.714335972485852	0.475260336860701	   
df.mm.trans3:probe5	-0.00967130534442431	0.103825154598930	-0.0931499248114195	0.925811417133429	   
df.mm.trans3:probe6	-0.171079600988959	0.103825154598930	-1.64776639774657	0.0998545612289087	.  
df.mm.trans3:probe7	0.00207644533936791	0.103825154598930	0.0199994437512672	0.984049581310187	   
df.mm.trans3:probe8	-0.131098524261971	0.103825154598930	-1.26268556756209	0.207127527248005	   
df.mm.trans3:probe9	-0.0358073441740500	0.103825154598930	-0.344881202559934	0.730288476925456