chr16.9574_chr16_37405482_37410251_+_2.R 

fitVsDatCorrelation=0.891749589900293
cont.fitVsDatCorrelation=0.253072949484585

fstatistic=5791.84246012698,52,692
cont.fstatistic=1256.83382874881,52,692

residuals=-0.682519079642918,-0.120213502141789,-0.00605262203527237,0.0957417269153667,1.16154903485589
cont.residuals=-0.76153600881767,-0.259806267300933,-0.124932192709450,0.115145945707308,1.66734625358608

predictedValues:
Include	Exclude	Both
chr16.9574_chr16_37405482_37410251_+_2.R.tl.Lung	60.7531102211574	51.0243258754398	55.9471996979886
chr16.9574_chr16_37405482_37410251_+_2.R.tl.cerebhem	62.3955176960592	56.6211741246388	49.7070234875022
chr16.9574_chr16_37405482_37410251_+_2.R.tl.cortex	59.1108980998723	50.2169073297671	57.3245761927219
chr16.9574_chr16_37405482_37410251_+_2.R.tl.heart	63.8465168831741	48.9704545168349	61.6222213774942
chr16.9574_chr16_37405482_37410251_+_2.R.tl.kidney	115.988300126731	52.1557418980384	183.09384574417
chr16.9574_chr16_37405482_37410251_+_2.R.tl.liver	141.689917873822	51.0181659050615	214.283126901244
chr16.9574_chr16_37405482_37410251_+_2.R.tl.stomach	59.4836479933813	52.9259098979027	54.1619202527307
chr16.9574_chr16_37405482_37410251_+_2.R.tl.testicle	60.577030478402	50.2216105279204	50.3348013811014


diffExp=9.72878434571761,5.77434357142037,8.89399077010524,14.8760623663392,63.832558228693,90.6717519687603,6.55773809547861,10.3554199504815
diffExpScore=0.995276125783917
diffExp1.5=0,0,0,0,1,1,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=0,0,0,0,1,1,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,0,1,1,1,0,0
diffExp1.3Score=0.75
diffExp1.2=0,0,0,1,1,1,0,1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	69.6407910386198	56.1024458184627	78.801659719318
cerebhem	66.8409507206048	77.0647289449916	65.8644170945831
cortex	67.423911434077	57.9005764984777	68.2761191681318
heart	64.1031771223023	57.8782724997443	61.7064209768764
kidney	64.1280541490882	63.8299880393059	58.2024020908878
liver	63.1204345522902	65.4422268245719	51.4757374545122
stomach	63.9617455383012	56.2766240472324	64.5365691098437
testicle	64.7437851438943	67.0400577045573	64.4877187610816
cont.diffExp=13.5383452201571,-10.2237782243869,9.52333493559924,6.22490462255801,0.298066109782361,-2.32179227228174,7.68512149106875,-2.29627256066307
cont.diffExpScore=2.22433723103012

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

tran.correlation=-0.0407316526592999
cont.tran.correlation=-0.106328769771820

tran.covariance=-0.000353903905532938
cont.tran.covariance=-0.000487442750984265

tran.mean=64.8124518405127
cont.tran.mean=64.0936106297826

weightedLogRatios:
wLogRatio
Lung	0.701476720334338
cerebhem	0.396690085888887
cortex	0.651909038500539
heart	1.06738598714698
kidney	3.47984601545254
liver	4.53825283287701
stomach	0.470422315135077
testicle	0.75179001579735

cont.weightedLogRatios:
wLogRatio
Lung	0.893924625409725
cerebhem	-0.60824357117264
cortex	0.629625522381875
heart	0.419784743568932
kidney	0.0193739415366252
liver	-0.150384545686705
stomach	0.524092415283203
testicle	-0.145957963124617

varWeightedLogRatios=2.50450672196524
cont.varWeightedLogRatios=0.249678692899248

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.43459385511572	0.106481975901152	41.6464271778014	1.07786372863046e-190	***
df.mm.trans1	0.120026028553980	0.0944433700031925	1.27087828981455	0.204199005328742	   
df.mm.trans2	-0.544791049479212	0.0860683564305224	-6.32974849379153	4.41362658098138e-10	***
df.mm.exp2	0.24901782512228	0.116088378350221	2.14507109722070	0.0322946103504538	*  
df.mm.exp3	-0.0676747493358354	0.116088378350221	-0.582958865457412	0.560110908895244	   
df.mm.exp4	-0.0880357569926116	0.116088378350221	-0.758351165239137	0.448498977522195	   
df.mm.exp5	-0.516987627228209	0.116088378350221	-4.45339692547464	9.85271156167252e-06	***
df.mm.exp6	-0.496187798522651	0.116088378350221	-4.27422456557820	2.18710176826000e-05	***
df.mm.exp7	0.0479039855700748	0.116088378350221	0.412651001339307	0.679990143555475	   
df.mm.exp8	0.0869521031503155	0.116088378350221	0.749016433738048	0.454101822136212	   
df.mm.trans1:exp2	-0.222342662087069	0.109974111823045	-2.02177274634263	0.0435841983710653	*  
df.mm.trans2:exp2	-0.144937305685802	0.0929450885172364	-1.55938638606948	0.119362148822677	   
df.mm.trans1:exp3	0.040271779794957	0.109974111823045	0.366193271556097	0.714332787868263	   
df.mm.trans2:exp3	0.0517240217707821	0.0929450885172365	0.556500860840968	0.578048417155665	   
df.mm.trans1:exp4	0.137699508461334	0.109974111823045	1.25210839331806	0.210953395552424	   
df.mm.trans2:exp4	0.0469504072438095	0.0929450885172364	0.505141347356969	0.613620397806546	   
df.mm.trans1:exp5	1.16365867430509	0.109974111823045	10.5812054765896	2.35105220147293e-24	***
df.mm.trans2:exp5	0.538919409167587	0.0929450885172364	5.79825591394908	1.01882723693667e-08	***
df.mm.trans1:exp6	1.34301051351366	0.109974111823045	12.2120605590763	3.37128127988593e-31	***
df.mm.trans2:exp6	0.496067065085465	0.0929450885172364	5.33720579537101	1.28081633919804e-07	***
df.mm.trans1:exp7	-0.0690208123963004	0.109974111823045	-0.627609636960368	0.530466732062012	   
df.mm.trans2:exp7	-0.0113134734396718	0.0929450885172364	-0.121722122385991	0.90315442975183	   
df.mm.trans1:exp8	-0.0898545949030644	0.109974111823045	-0.817052244510468	0.414179770586414	   
df.mm.trans2:exp8	-0.102809177438576	0.0929450885172364	-1.10612813521083	0.269055451157904	   
df.mm.trans1:probe2	-0.422298927879031	0.0602353017870775	-7.01082115221722	5.64127589044753e-12	***
df.mm.trans1:probe3	-0.505726907474478	0.0602353017870775	-8.39585579337088	2.60889201355004e-16	***
df.mm.trans1:probe4	-0.564252474832146	0.0602353017870775	-9.36747153399665	1.02688249533017e-19	***
df.mm.trans1:probe5	-0.41071744852695	0.0602353017870775	-6.81855052339197	2.00578380332667e-11	***
df.mm.trans1:probe6	-0.567136383364854	0.0602353017870775	-9.41534891565072	6.86127991353317e-20	***
df.mm.trans1:probe7	-0.415317160713269	0.0602353017870775	-6.89491292301234	1.21622295052016e-11	***
df.mm.trans1:probe8	-0.227018996924389	0.0602353017870775	-3.768869586258	0.000177974012944455	***
df.mm.trans1:probe9	-0.203871435050953	0.0602353017870775	-3.38458394002254	0.000753087476267097	***
df.mm.trans1:probe10	-0.797425790718607	0.0602353017870775	-13.2385124181395	8.01463183336524e-36	***
df.mm.trans1:probe11	-0.752791187942615	0.0602353017870775	-12.4975083648392	1.84037840301858e-32	***
df.mm.trans1:probe12	-0.814802136918279	0.0602353017870775	-13.5269868788651	3.66992479701701e-37	***
df.mm.trans1:probe13	-0.629151727375207	0.0602353017870775	-10.4449003941104	8.17797052743994e-24	***
df.mm.trans1:probe14	-0.829670252727417	0.0602353017870775	-13.7738208012998	2.54503346941633e-38	***
df.mm.trans1:probe15	-0.869797499149404	0.0602353017870775	-14.4399957059069	1.66130497200226e-41	***
df.mm.trans1:probe16	-0.656054892748255	0.0602353017870775	-10.8915349186314	1.31936910598895e-25	***
df.mm.trans1:probe17	-0.483439927492672	0.0602353017870775	-8.02585714937657	4.32476707026004e-15	***
df.mm.trans1:probe18	-0.413485930833620	0.0602353017870775	-6.86451164958431	1.48509610205849e-11	***
df.mm.trans1:probe19	-0.615720629412233	0.0602353017870775	-10.2219232102250	6.13032381710682e-23	***
df.mm.trans1:probe20	-0.533384968795603	0.0602353017870775	-8.85502276855916	6.96935876452837e-18	***
df.mm.trans1:probe21	-0.482975463707002	0.0602353017870775	-8.01814632579157	4.58047789363657e-15	***
df.mm.trans2:probe2	0.166916681605111	0.0602353017870775	2.77107736913372	0.00573713558844337	** 
df.mm.trans2:probe3	0.0996638870657224	0.0602353017870775	1.65457603944642	0.0984639835007483	.  
df.mm.trans2:probe4	0.0776112753530052	0.0602353017870775	1.28846827442401	0.198013622433189	   
df.mm.trans2:probe5	0.0278852837771840	0.0602353017870775	0.462939222513637	0.643553477157547	   
df.mm.trans2:probe6	0.052919785396924	0.0602353017870775	0.878551012892527	0.379949710941917	   
df.mm.trans3:probe2	-0.0326562454546588	0.0602353017870775	-0.542144630902549	0.587893405584704	   
df.mm.trans3:probe3	0.0628193304476197	0.0602353017870775	1.04289890784770	0.297359309408533	   
df.mm.trans3:probe4	0.0281551119441052	0.0602353017870775	0.467418791120682	0.640347431483656	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81547869457081	0.22764429630812	16.7607041180004	3.56274025717074e-53	***
df.mm.trans1	0.366767762752881	0.201907358718647	1.816515084346	0.0697239098202121	.  
df.mm.trans2	0.18881819695766	0.184002694054166	1.02617082824926	0.305169633317072	   
df.mm.exp2	0.455767465389544	0.248181506545475	1.83642799068122	0.066723173137096	.  
df.mm.exp3	0.142571182300331	0.248181506545475	0.574463360646121	0.565840979550567	   
df.mm.exp4	0.192852165885305	0.248181506545475	0.77706098479972	0.43738833589273	   
df.mm.exp5	0.349582591581894	0.248181506545475	1.40857631355316	0.159409622296853	   
df.mm.exp6	0.481505878203188	0.248181506545475	1.94013601136296	0.0527690749489112	.  
df.mm.exp7	0.117736574314685	0.248181506545475	0.474397048972351	0.635366457172677	   
df.mm.exp8	0.305657407903694	0.248181506545475	1.23158817173063	0.218521321702447	   
df.mm.trans1:exp2	-0.49680201174113	0.235110018256121	-2.11306185685346	0.0349529080931812	*  
df.mm.trans2:exp2	-0.138301170078235	0.198704232258460	-0.69601522074448	0.486652988639922	   
df.mm.trans1:exp3	-0.174921932050885	0.235110018256121	-0.744000333751542	0.457128821766865	   
df.mm.trans2:exp3	-0.111023250030496	0.198704232258460	-0.558736211949852	0.576522513752743	   
df.mm.trans1:exp4	-0.275708711901213	0.235110018256121	-1.17267955634653	0.241327852362091	   
df.mm.trans2:exp4	-0.161689519860795	0.198704232258460	-0.813719557067516	0.416085526228128	   
df.mm.trans1:exp5	-0.432051134973927	0.235110018256121	-1.83765514621016	0.0665417903758334	.  
df.mm.trans2:exp5	-0.220538888752890	0.198704232258460	-1.10988521103078	0.267434024096430	   
df.mm.trans1:exp6	-0.579811790952024	0.235110018256121	-2.46612966666693	0.0138995640117490	*  
df.mm.trans2:exp6	-0.327517567050166	0.19870423225846	-1.64826668927794	0.0997518667344597	.  
df.mm.trans1:exp7	-0.202801869433182	0.235110018256121	-0.862582849244033	0.388665693882944	   
df.mm.trans2:exp7	-0.114636737869146	0.19870423225846	-0.576921470500108	0.564180129928725	   
df.mm.trans1:exp8	-0.378570168069068	0.235110018256121	-1.61018305760440	0.107813930738970	   
df.mm.trans2:exp8	-0.127546500037428	0.19870423225846	-0.641891209803345	0.521156388954667	   
df.mm.trans1:probe2	0.0680086989228203	0.128775060494329	0.528120108519112	0.597585284880523	   
df.mm.trans1:probe3	0.123153936926452	0.128775060494329	0.956349284199137	0.339229772974525	   
df.mm.trans1:probe4	0.134061497206607	0.128775060494329	1.04105171212489	0.298215126444219	   
df.mm.trans1:probe5	0.0884504823337102	0.128775060494329	0.6868603438754	0.4924008416802	   
df.mm.trans1:probe6	-0.0142365526636049	0.128775060494329	-0.110553647646951	0.912002360444016	   
df.mm.trans1:probe7	0.0430967044386650	0.128775060494329	0.334666543919526	0.73797805258842	   
df.mm.trans1:probe8	-0.0659341604142951	0.128775060494329	-0.512010323747421	0.60880714247617	   
df.mm.trans1:probe9	0.0693917425960324	0.128775060494329	0.538860104818885	0.590156691747175	   
df.mm.trans1:probe10	0.0140931930226862	0.128775060494329	0.109440391397112	0.912884926397917	   
df.mm.trans1:probe11	0.180576479173653	0.128775060494329	1.40226281766418	0.161285202643451	   
df.mm.trans1:probe12	0.0807447394327781	0.128775060494329	0.627021560873846	0.530851912903871	   
df.mm.trans1:probe13	0.160467534315763	0.128775060494329	1.24610723302925	0.213146719045093	   
df.mm.trans1:probe14	-0.0108911035308545	0.128775060494329	-0.0845746333882259	0.932624027592957	   
df.mm.trans1:probe15	0.050697471692049	0.128775060494329	0.393690140757352	0.693931093761232	   
df.mm.trans1:probe16	0.0881158071512951	0.128775060494329	0.684261430847284	0.494039182352507	   
df.mm.trans1:probe17	0.402280782151791	0.128775060494329	3.12390287845764	0.00185919206808773	** 
df.mm.trans1:probe18	0.0425553228596162	0.128775060494329	0.330462456754118	0.741150580904249	   
df.mm.trans1:probe19	0.0615609643089907	0.128775060494329	0.47805036217942	0.632765338372953	   
df.mm.trans1:probe20	-0.0201566132731341	0.128775060494329	-0.156525752702106	0.875664274950014	   
df.mm.trans1:probe21	0.0315634853754007	0.128775060494329	0.245105575988378	0.806447352509785	   
df.mm.trans2:probe2	0.0603850172182356	0.128775060494329	0.468918569996671	0.639275531203779	   
df.mm.trans2:probe3	-0.0703603811908023	0.128775060494329	-0.546382047274602	0.584979463013538	   
df.mm.trans2:probe4	0.000574455184565462	0.128775060494329	0.00446091954731219	0.99644199862199	   
df.mm.trans2:probe5	0.0266268753984927	0.128775060494329	0.206770435954602	0.83624997047994	   
df.mm.trans2:probe6	0.211599208658469	0.128775060494329	1.64316916525764	0.100802192602312	   
df.mm.trans3:probe2	0.039162525774823	0.128775060494329	0.304115762978404	0.761131122379211	   
df.mm.trans3:probe3	0.0148624969286381	0.128775060494329	0.115414404556173	0.908150143888528	   
df.mm.trans3:probe4	-0.0705474035491836	0.128775060494329	-0.547834365430489	0.583982298229915