chr12.5930_chr12_71945850_71996176_+_2.R 

fitVsDatCorrelation=0.77964468308738
cont.fitVsDatCorrelation=0.239108562054382

fstatistic=11733.3582610969,56,784
cont.fstatistic=4872.12973511229,56,784

residuals=-0.393475169795422,-0.0833440947257166,-0.00303745866643965,0.0731699550837415,0.800465477862088
cont.residuals=-0.540605023549513,-0.145361770315123,-0.0274779734915224,0.118588867283942,0.898114217675654

predictedValues:
Include	Exclude	Both
chr12.5930_chr12_71945850_71996176_+_2.R.tl.Lung	49.5973019340424	42.7969871102465	57.2750335742593
chr12.5930_chr12_71945850_71996176_+_2.R.tl.cerebhem	55.2136442222432	41.3731656316329	68.2232730625653
chr12.5930_chr12_71945850_71996176_+_2.R.tl.cortex	50.9844123519954	45.7553904591213	62.2088538916854
chr12.5930_chr12_71945850_71996176_+_2.R.tl.heart	51.5355550467643	46.9986716612881	53.9711819525248
chr12.5930_chr12_71945850_71996176_+_2.R.tl.kidney	49.3074573905033	44.4920797872973	60.6805846024211
chr12.5930_chr12_71945850_71996176_+_2.R.tl.liver	53.4971637615588	49.8462485554181	52.9387917972664
chr12.5930_chr12_71945850_71996176_+_2.R.tl.stomach	53.3975379734924	43.6038899335594	56.7835246528158
chr12.5930_chr12_71945850_71996176_+_2.R.tl.testicle	52.331864329517	44.4751423450501	52.9179548000918


diffExp=6.80031482379595,13.8404785906103,5.22902189287409,4.53688338547611,4.81537760320594,3.65091520614067,9.793648039933,7.85672198446697
diffExpScore=0.982615758650696
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=0,1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,1,0,0,0,0,1,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	51.0816920600493	50.179402493976	50.2341825864162
cerebhem	53.9962879731006	49.9920868551569	52.7035367421231
cortex	54.3489668513296	51.4499127853849	54.0733671194049
heart	53.8831557636606	47.7352911639299	53.8153085548172
kidney	51.0918043261867	47.3010709359527	53.5491684568136
liver	52.7705454995069	50.0233758519404	52.5409266797653
stomach	52.6365735043839	54.1585607708059	52.1405609056389
testicle	53.5624460344384	50.6252262173328	46.950321960119
cont.diffExp=0.902289566073229,4.00420111794367,2.89905406594468,6.14786459973069,3.790733390234,2.74716964756644,-1.52198726642195,2.93721981710562
cont.diffExpScore=1.08923102713048

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=-0.0419626396367312
cont.tran.correlation=0.194329841572070

tran.covariance=-0.000116081145813737
cont.tran.covariance=0.000210217116198175

tran.mean=48.4504070308582
cont.tran.mean=51.552274942946

weightedLogRatios:
wLogRatio
Lung	0.564835036035392
cerebhem	1.11590688007076
cortex	0.419576359080605
heart	0.359044636805316
kidney	0.395302140845971
liver	0.278803428405865
stomach	0.785440725061293
testicle	0.630571933829298

cont.weightedLogRatios:
wLogRatio
Lung	0.0699408401539302
cerebhem	0.304379814170656
cortex	0.217514151543143
heart	0.475652497063683
kidney	0.300276131940303
liver	0.210601714738509
stomach	-0.113382755441890
testicle	0.222922273925898

varWeightedLogRatios=0.0757935793216746
cont.varWeightedLogRatios=0.0302910789309118

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.79806174549650	0.0667027939287166	56.9400698500782	9.02947484810137e-281	***
df.mm.trans1	0.152447594452767	0.0578408993756934	2.63563665327151	0.00856392710856398	** 
df.mm.trans2	-0.0378597082093686	0.0515340806386585	-0.73465380075041	0.462770038227404	   
df.mm.exp2	-0.101482482399860	0.0670343757264783	-1.51388718549332	0.130457470656158	   
df.mm.exp3	0.0117928930168963	0.0670343757264783	0.175923067666284	0.860399811537586	   
df.mm.exp4	0.191401734219437	0.0670343757264783	2.85527734308167	0.00441349092444634	** 
df.mm.exp5	-0.0247765891764399	0.0670343757264783	-0.369610202346574	0.71177275912591	   
df.mm.exp6	0.306896176473901	0.0670343757264783	4.57819101241631	5.45245030556273e-06	***
df.mm.exp7	0.101125453133804	0.0670343757264783	1.50856112312329	0.131813837699760	   
df.mm.exp8	0.171253862055880	0.0670343757264783	2.55471704181524	0.0108152616059514	*  
df.mm.trans1:exp2	0.208756147318407	0.0621524397797633	3.35877639008433	0.000820688988653622	***
df.mm.trans2:exp2	0.0676472744376096	0.0478144473399325	1.41478733313965	0.157527898525341	   
df.mm.trans1:exp3	0.0157906170727453	0.0621524397797633	0.254062706608128	0.79951373041934	   
df.mm.trans2:exp3	0.0550490105174676	0.0478144473399325	1.15130496283062	0.249957785282065	   
df.mm.trans1:exp4	-0.153066211261479	0.0621524397797633	-2.46275466906639	0.0140013686630210	*  
df.mm.trans2:exp4	-0.0977501009240798	0.0478144473399325	-2.04436329106001	0.0412512330025677	*  
df.mm.trans1:exp5	0.0189154885527989	0.0621524397797633	0.304340241828411	0.760949451590946	   
df.mm.trans2:exp5	0.063620074710455	0.0478144473399325	1.33056174963508	0.183720122290240	   
df.mm.trans1:exp6	-0.231203973540119	0.0621524397797633	-3.71995008336581	0.000213475866497971	***
df.mm.trans2:exp6	-0.154420643042956	0.0478144473399325	-3.22958125909344	0.00129136706772682	** 
df.mm.trans1:exp7	-0.0272972493561084	0.0621524397797633	-0.439198355733677	0.660638836862959	   
df.mm.trans2:exp7	-0.0824467935884677	0.0478144473399325	-1.72430715349107	0.0850465821285153	.  
df.mm.trans1:exp8	-0.117584851666531	0.0621524397797633	-1.89187829284244	0.0588754701323267	.  
df.mm.trans2:exp8	-0.132791133435388	0.0478144473399325	-2.77721778297093	0.00561353629232177	** 
df.mm.trans1:probe2	-0.0295348254062697	0.0406883228452785	-0.7258796465654	0.468129183849151	   
df.mm.trans1:probe3	-0.0889161258166694	0.0406883228452785	-2.18529837552612	0.0291622547159388	*  
df.mm.trans1:probe4	0.0191766118578165	0.0406883228452785	0.471305045694253	0.637554031707237	   
df.mm.trans1:probe5	-0.0296273702927043	0.0406883228452785	-0.728154129266163	0.46673666030086	   
df.mm.trans1:probe6	-0.0892688621915503	0.0406883228452785	-2.19396760419456	0.0285305372246174	*  
df.mm.trans1:probe7	-0.165808400497505	0.0406883228452785	-4.0750856487255	5.06817976955193e-05	***
df.mm.trans1:probe8	0.0309233460040854	0.0406883228452785	0.760005422727168	0.447479820164781	   
df.mm.trans1:probe9	-0.0553372950070506	0.0406883228452785	-1.36002890110453	0.17421172357712	   
df.mm.trans1:probe10	-0.261830219842722	0.0406883228452785	-6.43502119363233	2.14782546765015e-10	***
df.mm.trans1:probe11	-0.268372383304025	0.0406883228452785	-6.59580844176199	7.77485036252164e-11	***
df.mm.trans1:probe12	-0.272297659737055	0.0406883228452785	-6.69228025869963	4.18264921678651e-11	***
df.mm.trans1:probe13	-0.248654871812783	0.0406883228452785	-6.11120966470696	1.55715230074043e-09	***
df.mm.trans1:probe14	-0.186658108435136	0.0406883228452785	-4.5875105038102	5.22078476464706e-06	***
df.mm.trans1:probe15	-0.205607578163591	0.0406883228452785	-5.05323306014442	5.40907915092974e-07	***
df.mm.trans1:probe16	0.0414999484372937	0.0406883228452785	1.01994738380104	0.308068043285949	   
df.mm.trans1:probe17	0.0428473694248572	0.0406883228452785	1.05306305172097	0.292636536853291	   
df.mm.trans1:probe18	0.103828325956257	0.0406883228452785	2.55179665062810	0.0109055619422298	*  
df.mm.trans1:probe19	-0.0206741042975009	0.0406883228452785	-0.508109031087771	0.611519808875918	   
df.mm.trans1:probe20	0.0993868330465703	0.0406883228452785	2.44263774214776	0.0148001523435697	*  
df.mm.trans1:probe21	0.234311148596567	0.0406883228452785	5.75868289011465	1.21696641960415e-08	***
df.mm.trans2:probe2	-0.0199696598360995	0.0406883228452785	-0.490795846072008	0.62370819027542	   
df.mm.trans2:probe3	-0.00211483344800469	0.0406883228452785	-0.051976422229212	0.958560720287059	   
df.mm.trans2:probe4	-0.0712723185213533	0.0406883228452785	-1.75166518394905	0.080222386330166	.  
df.mm.trans2:probe5	0.0412830384663111	0.0406883228452785	1.01461637097440	0.310601745335957	   
df.mm.trans2:probe6	-0.000206871827289919	0.0406883228452785	-0.00508430460691561	0.995944622727714	   
df.mm.trans3:probe2	0.127522356585828	0.0406883228452785	3.13412664047974	0.00178786313645086	** 
df.mm.trans3:probe3	0.406214436329253	0.0406883228452785	9.98356304519911	3.54926695975154e-22	***
df.mm.trans3:probe4	-0.0362259076571305	0.0406883228452785	-0.890326883093293	0.373563514831503	   
df.mm.trans3:probe5	-0.08429117753772	0.0406883228452785	-2.07163067050578	0.0386264180172415	*  
df.mm.trans3:probe6	0.404461516742159	0.0406883228452785	9.94048140740932	5.21455560767693e-22	***
df.mm.trans3:probe7	0.244452618602928	0.0406883228452785	6.00793056849465	2.87526762311140e-09	***
df.mm.trans3:probe8	0.0195876192823394	0.0406883228452785	0.481406406374214	0.630362019814564	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97734832036991	0.103426454007279	38.4558124760808	1.24047992490252e-182	***
df.mm.trans1	0.0241041286653961	0.0896855853656279	0.268762572793934	0.788183103688984	   
df.mm.trans2	-0.056501575413602	0.079906506265354	-0.707096055807662	0.479716899971541	   
df.mm.exp2	0.00376240189005846	0.103940590335549	0.0361976190236403	0.971133998714476	   
df.mm.exp3	0.013357529635964	0.103940590335549	0.128511196567599	0.897777396237393	   
df.mm.exp4	-0.0654041381758566	0.103940590335549	-0.629245398402242	0.529371717808272	   
df.mm.exp5	-0.122778299415487	0.103940590335549	-1.18123534818423	0.237867442296735	   
df.mm.exp6	-0.0154838907514764	0.103940590335549	-0.148968662786022	0.881616669586788	   
df.mm.exp7	0.069049013433171	0.103940590335549	0.664312307735236	0.506685772805458	   
df.mm.exp8	0.123873052671149	0.103940590335549	1.19176783844745	0.233712919215086	   
df.mm.trans1:exp2	0.051726744770692	0.0963708725783135	0.536746668228592	0.591594891908402	   
df.mm.trans2:exp2	-0.00750230549668869	0.0741390044916539	-0.101192422910578	0.919423584266576	   
df.mm.trans1:exp3	0.04864191827355	0.0963708725783135	0.504736721502882	0.61388560036562	   
df.mm.trans2:exp3	0.0116466038962043	0.0741390044916539	0.157091452415111	0.875213219838343	   
df.mm.trans1:exp4	0.118795901924834	0.0963708725783135	1.23269509496551	0.21805905439521	   
df.mm.trans2:exp4	0.0154704855842565	0.0741390044916539	0.208668644667303	0.834761119880089	   
df.mm.trans1:exp5	0.122976242454104	0.0963708725783135	1.27607272990260	0.202307715933489	   
df.mm.trans2:exp5	0.0637066023961071	0.0741390044916539	0.85928591613715	0.390445447301374	   
df.mm.trans1:exp6	0.0480109191172859	0.0963708725783135	0.498189108729621	0.618490478653725	   
df.mm.trans2:exp6	0.0123696703533695	0.0741390044916539	0.166844300623999	0.867535581704898	   
df.mm.trans1:exp7	-0.0390639778384355	0.0963708725783135	-0.405350463198214	0.685330505002865	   
df.mm.trans2:exp7	0.00726240754901529	0.0741390044916539	0.0979566369795651	0.921991763931829	   
df.mm.trans1:exp8	-0.0764510201405886	0.0963708725783135	-0.79330007184964	0.427843029328348	   
df.mm.trans2:exp8	-0.115027692376927	0.0741390044916539	-1.55151385111836	0.121182150051453	   
df.mm.trans1:probe2	-0.0885627289232233	0.0630895454827237	-1.40376235469116	0.160785597449131	   
df.mm.trans1:probe3	-0.0686522307940042	0.0630895454827236	-1.08817126940317	0.276853903920775	   
df.mm.trans1:probe4	-0.133300247006841	0.0630895454827237	-2.11287378894406	0.0349270721772237	*  
df.mm.trans1:probe5	-0.0753118298554615	0.0630895454827236	-1.19372915558703	0.232945007315289	   
df.mm.trans1:probe6	-0.144445087740248	0.0630895454827237	-2.28952493848291	0.0223138022300689	*  
df.mm.trans1:probe7	-0.0951319996320622	0.0630895454827236	-1.50788849252548	0.131985909744247	   
df.mm.trans1:probe8	-0.113047526957632	0.0630895454827236	-1.7918583196734	0.0735411997541612	.  
df.mm.trans1:probe9	-0.114980570513783	0.0630895454827237	-1.82249800080219	0.0687602423459281	.  
df.mm.trans1:probe10	-0.0443665088878455	0.0630895454827237	-0.703230757939043	0.482120727514137	   
df.mm.trans1:probe11	-0.174724723937923	0.0630895454827237	-2.76947190855527	0.00574741865058898	** 
df.mm.trans1:probe12	-0.0648959640888272	0.0630895454827237	-1.02863261404535	0.303969585023471	   
df.mm.trans1:probe13	-0.132654594553281	0.0630895454827236	-2.10263988333229	0.0358155338624678	*  
df.mm.trans1:probe14	-0.0929893197339944	0.0630895454827236	-1.47392597335257	0.140902921238689	   
df.mm.trans1:probe15	-0.105076353043588	0.0630895454827236	-1.66551133376546	0.0962102809769148	.  
df.mm.trans1:probe16	-0.120261064397036	0.0630895454827236	-1.90619639873564	0.056990135625386	.  
df.mm.trans1:probe17	-0.0942789542728958	0.0630895454827237	-1.49436730842692	0.135482000844971	   
df.mm.trans1:probe18	-0.0611288229414294	0.0630895454827236	-0.968921593486022	0.332883118796882	   
df.mm.trans1:probe19	-0.0439837293061188	0.0630895454827236	-0.697163515279456	0.485907140988544	   
df.mm.trans1:probe20	-0.055989359807842	0.0630895454827236	-0.887458601570906	0.375104208748473	   
df.mm.trans1:probe21	-0.148980872875961	0.0630895454827237	-2.36141934033679	0.0184487948464046	*  
df.mm.trans2:probe2	-0.0444455070272072	0.0630895454827236	-0.704482916894339	0.481341292438309	   
df.mm.trans2:probe3	-0.0342542969719887	0.0630895454827236	-0.542947277712896	0.5873204219993	   
df.mm.trans2:probe4	0.0250302965539393	0.0630895454827237	0.396742381997244	0.691665412305507	   
df.mm.trans2:probe5	-0.0073757084172525	0.0630895454827236	-0.116908567985678	0.906962449564418	   
df.mm.trans2:probe6	-0.0123443429435216	0.0630895454827237	-0.195663843336801	0.844923985978743	   
df.mm.trans3:probe2	-0.0923963829479212	0.0630895454827236	-1.46452763672585	0.143450698742873	   
df.mm.trans3:probe3	0.00680934633749933	0.0630895454827237	0.107931453387376	0.914077681494759	   
df.mm.trans3:probe4	-0.0285023181889955	0.0630895454827237	-0.4517756146587	0.651555591929569	   
df.mm.trans3:probe5	-0.0852234378553	0.0630895454827236	-1.3508329661154	0.177138676925239	   
df.mm.trans3:probe6	0.0332058388596511	0.0630895454827237	0.526328706374087	0.598808700738386	   
df.mm.trans3:probe7	-0.0481933066891728	0.0630895454827236	-0.763887365496554	0.445164191060869	   
df.mm.trans3:probe8	-0.0117409445882179	0.0630895454827237	-0.186099685746397	0.852414728165672