chr13.6504_chr13_59349842_59353202_-_2.R 

fitVsDatCorrelation=0.91028816476315
cont.fitVsDatCorrelation=0.264511676550038

fstatistic=12245.3608641951,51,669
cont.fstatistic=2245.72807548262,51,669

residuals=-0.455924015085192,-0.0936592191588717,-0.00141080595694131,0.0775138217352617,0.68717643081873
cont.residuals=-0.844721311443373,-0.249311568042327,-0.0612226168543715,0.255809559988744,0.954370841196587

predictedValues:
Include	Exclude	Both
chr13.6504_chr13_59349842_59353202_-_2.R.tl.Lung	54.5204394784698	112.113169447977	101.068916046481
chr13.6504_chr13_59349842_59353202_-_2.R.tl.cerebhem	62.0058071475265	94.9734091780127	70.7923214932713
chr13.6504_chr13_59349842_59353202_-_2.R.tl.cortex	52.5266542295648	107.118983187068	107.985644583993
chr13.6504_chr13_59349842_59353202_-_2.R.tl.heart	53.6378190722464	111.722333394355	101.160639123424
chr13.6504_chr13_59349842_59353202_-_2.R.tl.kidney	53.3434656492475	98.9946575895308	73.0196678154907
chr13.6504_chr13_59349842_59353202_-_2.R.tl.liver	55.4933206878359	111.194649757332	85.2897157822348
chr13.6504_chr13_59349842_59353202_-_2.R.tl.stomach	56.9370617435102	124.706966294847	117.930838422660
chr13.6504_chr13_59349842_59353202_-_2.R.tl.testicle	55.4393166362628	109.038961743139	85.3915354570573


diffExp=-57.5927299695067,-32.9676020304861,-54.5923289575035,-58.084514322109,-45.6511919402834,-55.7013290694963,-67.7699045513368,-53.5996451068762
diffExpScore=0.997657856084647
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	65.755973629092	79.64237971158	72.1914098476915
cerebhem	71.4884334681583	59.5586072294176	67.0615811470302
cortex	66.5415145788081	60.350338273876	74.6430561015133
heart	69.9210431720808	63.5270837727302	60.318082054975
kidney	70.4990890534572	63.5662720664565	69.1882326905817
liver	63.6278321966533	71.4255748701689	63.7021583256484
stomach	65.8788753689587	71.1873171206715	67.6770538315165
testicle	69.7448299658406	61.7878454537855	63.8838597865042
cont.diffExp=-13.886406082488,11.9298262387407,6.19117630493211,6.3939593993506,6.93281698700062,-7.79774267351553,-5.30844175171279,7.95698451205504
cont.diffExpScore=4.95052921511932

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

tran.correlation=-0.263937268124455
cont.tran.correlation=-0.702982295229617

tran.covariance=-0.00120885901839079
cont.tran.covariance=-0.0030322532783442

tran.mean=82.1104384523078
cont.tran.mean=67.1564381207334

weightedLogRatios:
wLogRatio
Lung	-3.14257798409726
cerebhem	-1.85061692216524
cortex	-3.07682753487574
heart	-3.19123136375329
kidney	-2.65003903905151
liver	-3.03290740019623
stomach	-3.47631170497889
testicle	-2.94477615769293

cont.weightedLogRatios:
wLogRatio
Lung	-0.820365184781413
cerebhem	0.762843020382534
cortex	0.405188818506541
heart	0.402725383916779
kidney	0.435168093015133
liver	-0.486795755415315
stomach	-0.327545520062027
testicle	0.506868953588026

varWeightedLogRatios=0.241000361653636
cont.varWeightedLogRatios=0.325065432723152

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.89905419829452	0.0764543913937848	50.9984335394433	1.08272028462010e-232	***
df.mm.trans1	-0.0778676080930154	0.0685910540977756	-1.13524437140179	0.256679497823411	   
df.mm.trans2	0.901325566557902	0.0631814404237921	14.2656697997422	1.66149565983783e-40	***
df.mm.exp2	0.318792602970003	0.0865735839064934	3.68233112902343	0.000249758080228171	***
df.mm.exp3	-0.149019197395951	0.0865735839064934	-1.72130100975032	0.0856585247573427	.  
df.mm.exp4	-0.0207205628534259	0.0865735839064933	-0.239340476834202	0.810914887029616	   
df.mm.exp5	0.178806698334282	0.0865735839064934	2.06537248737915	0.0392723876957588	*  
df.mm.exp6	0.179209201496344	0.0865735839064933	2.07002174808779	0.0388335049870334	*  
df.mm.exp7	-0.00446699322782212	0.0865735839064934	-0.0515976470680346	0.958864696277915	   
df.mm.exp8	0.157465465654216	0.0865735839064933	1.81886273559255	0.069379318647424	.  
df.mm.trans1:exp2	-0.190140226304156	0.0827239237881058	-2.29849138673830	0.0218413040373108	*  
df.mm.trans2:exp2	-0.484704456581564	0.0722073604843339	-6.71267379572372	4.08024631049327e-11	***
df.mm.trans1:exp3	0.111764270105583	0.0827239237881058	1.35105136443798	0.177135714401606	   
df.mm.trans2:exp3	0.103450603812271	0.0722073604843338	1.43268779136049	0.152414180491013	   
df.mm.trans1:exp4	0.00439929416907618	0.0827239237881057	0.0531804339980876	0.957604020801143	   
df.mm.trans2:exp4	0.0172283871632	0.0722073604843338	0.238595996968174	0.811491935561067	   
df.mm.trans1:exp5	-0.200630876552127	0.0827239237881058	-2.42530657837309	0.0155590798949248	*  
df.mm.trans2:exp5	-0.303249616040396	0.0722073604843338	-4.19970504400572	3.0345536797648e-05	***
df.mm.trans1:exp6	-0.161522203616135	0.0827239237881058	-1.95254523987363	0.0512901803314265	.  
df.mm.trans2:exp6	-0.187435737172916	0.0722073604843338	-2.5957982110921	0.0096442314137094	** 
df.mm.trans1:exp7	0.0478378034053098	0.0827239237881058	0.578282571893526	0.563267966021891	   
df.mm.trans2:exp7	0.110924906144374	0.0722073604843338	1.53619943175239	0.124962262391127	   
df.mm.trans1:exp8	-0.140752104697134	0.0827239237881058	-1.70146794605229	0.0893197964475874	.  
df.mm.trans2:exp8	-0.185269002751834	0.0722073604843338	-2.56579109815307	0.0105107630925964	*  
df.mm.trans1:probe2	-0.0234535984612295	0.0413619618940529	-0.567033027139888	0.570881943794411	   
df.mm.trans1:probe3	0.0157129561645154	0.0413619618940529	0.379889044063324	0.704148347068081	   
df.mm.trans1:probe4	0.198173463832094	0.0413619618940529	4.79120077378601	2.04347172543511e-06	***
df.mm.trans1:probe5	0.0300748691899262	0.0413619618940529	0.727114184451933	0.467410368506491	   
df.mm.trans1:probe6	0.208347449979347	0.0413619618940529	5.03717523150911	6.0846082586001e-07	***
df.mm.trans1:probe7	0.00867711700270645	0.0413619618940529	0.209784947458067	0.833899391161076	   
df.mm.trans1:probe8	0.245117741720657	0.0413619618940529	5.9261633272744	4.96475990767756e-09	***
df.mm.trans1:probe9	0.121016279259736	0.0413619618940529	2.92578673056453	0.00355229208902175	** 
df.mm.trans1:probe10	0.20580148336828	0.0413619618940529	4.97562189857997	8.27980258483077e-07	***
df.mm.trans1:probe11	0.178548285086139	0.0413619618940529	4.31672669549583	1.82316688375420e-05	***
df.mm.trans1:probe12	0.219457402080400	0.0413619618940529	5.30577835361223	1.52724589712217e-07	***
df.mm.trans1:probe13	0.298408792352625	0.0413619618940529	7.21457055438975	1.47059371441183e-12	***
df.mm.trans1:probe14	0.43223574168927	0.0413619618940529	10.4500783303371	8.82799557227887e-24	***
df.mm.trans1:probe15	0.141470196525105	0.0413619618940529	3.42029705668885	0.00066354672999103	***
df.mm.trans1:probe16	0.450086988413020	0.0413619618940529	10.8816644037800	1.6695490976898e-25	***
df.mm.trans1:probe17	0.123691870286764	0.0413619618940529	2.99047396744856	0.00288776303669085	** 
df.mm.trans1:probe18	0.453073133234286	0.0413619618940529	10.9538598385351	8.50471231876478e-26	***
df.mm.trans1:probe19	0.322859009488557	0.0413619618940529	7.80569863478787	2.28632834974151e-14	***
df.mm.trans1:probe20	0.263009100682818	0.0413619618940529	6.35871918639899	3.76830343632802e-10	***
df.mm.trans1:probe21	0.365029577177631	0.0413619618940529	8.82524813771262	9.46743999745024e-18	***
df.mm.trans2:probe2	-0.254593054440419	0.0413619618940529	-6.15524609525414	1.29176862309666e-09	***
df.mm.trans2:probe3	-0.194163365293211	0.0413619618940529	-4.69424941182804	3.24714896276649e-06	***
df.mm.trans2:probe4	-0.141745889481902	0.0413619618940529	-3.42696243096445	0.00064774720957057	***
df.mm.trans2:probe5	0.000372290519085619	0.0413619618940529	0.00900079449904304	0.992821185311539	   
df.mm.trans2:probe6	-0.137708641192844	0.0413619618940529	-3.32935467484787	0.000918239223018708	***
df.mm.trans3:probe2	0.00358511534491548	0.0413619618940529	0.0866766270443993	0.930954490827478	   
df.mm.trans3:probe3	-0.323713721876072	0.0413619618940529	-7.82636284771144	1.96719176751508e-14	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30192162682454	0.178105542914968	24.1537773413283	3.66117798045782e-93	***
df.mm.trans1	-0.0666655778504376	0.159787380508628	-0.417214285873082	0.676655525485117	   
df.mm.trans2	0.0505419645293418	0.147185328974367	0.343389962039926	0.731412993950744	   
df.mm.exp2	-0.133290647274682	0.20167886870412	-0.660905369665828	0.508900523084287	   
df.mm.exp3	-0.298900733836962	0.20167886870412	-1.48206272554749	0.138794469408969	   
df.mm.exp4	0.0150252362224197	0.20167886870412	0.0745007958392657	0.940634178536336	   
df.mm.exp5	-0.113323814144889	0.20167886870412	-0.561902270044684	0.574370805149471	   
df.mm.exp6	-0.0166872787936921	0.20167886870412	-0.0827418306187235	0.934081572791516	   
df.mm.exp7	-0.0457904955794815	0.20167886870412	-0.227046570985382	0.820456891048731	   
df.mm.exp8	-0.0726925973334217	0.20167886870412	-0.360437351719123	0.718633868665601	   
df.mm.trans1:exp2	0.216875793607079	0.192710831774861	1.12539493296593	0.260825292623801	   
df.mm.trans2:exp2	-0.157294889119226	0.168211804542133	-0.935100182459708	0.350074073457734	   
df.mm.trans1:exp3	0.310776245492699	0.192710831774861	1.61265582546897	0.107290900708883	   
df.mm.trans2:exp3	0.0215209269575356	0.168211804542133	0.127939457139259	0.898235333270857	   
df.mm.trans1:exp4	0.0463908939173974	0.192710831774861	0.240728004181907	0.809839685494378	   
df.mm.trans2:exp4	-0.241105264758112	0.168211804542133	-1.43334331032469	0.152226913447580	   
df.mm.trans1:exp5	0.182973082046741	0.192710831774861	0.949469629504295	0.342724671116876	   
df.mm.trans2:exp5	-0.112139529203611	0.168211804542133	-0.66665671597098	0.505221273411024	   
df.mm.trans1:exp6	-0.0162122540353474	0.192710831774861	-0.084127362670967	0.932980336726695	   
df.mm.trans2:exp6	-0.0922030841452533	0.168211804542133	-0.548136823073903	0.583780805878037	   
df.mm.trans1:exp7	0.0476578092356129	0.192710831774861	0.247302182221341	0.804750244708303	   
df.mm.trans2:exp7	-0.0664411918055127	0.168211804542133	-0.394985310254315	0.692979629653368	   
df.mm.trans1:exp8	0.131585372328827	0.192710831774861	0.682812538957616	0.494961677507361	   
df.mm.trans2:exp8	-0.181147092659371	0.168211804542133	-1.07689881309131	0.281913647811795	   
df.mm.trans1:probe2	-0.0389062470051542	0.0963554158874303	-0.403778517759785	0.68650462616159	   
df.mm.trans1:probe3	0.0200710513622267	0.0963554158874303	0.208302264873988	0.835056363235152	   
df.mm.trans1:probe4	-0.0823955951115124	0.0963554158874303	-0.85512157622539	0.392789977435026	   
df.mm.trans1:probe5	-0.0793523614902565	0.0963554158874303	-0.823538155685633	0.410495504649851	   
df.mm.trans1:probe6	-0.0236110356319536	0.0963554158874303	-0.245041084764117	0.806499763715195	   
df.mm.trans1:probe7	-0.0264585587595486	0.0963554158874303	-0.274593374081427	0.783713430016807	   
df.mm.trans1:probe8	-0.0412364167115461	0.0963554158874303	-0.427961587127823	0.668816818771654	   
df.mm.trans1:probe9	-0.0370191998430384	0.0963554158874303	-0.384194282200878	0.700956538457732	   
df.mm.trans1:probe10	0.0700421934709489	0.0963554158874303	0.72691496192365	0.467532328340356	   
df.mm.trans1:probe11	-0.107720172919905	0.0963554158874303	-1.11794621950209	0.263991250953604	   
df.mm.trans1:probe12	-0.142883281651401	0.0963554158874303	-1.48287753558480	0.138577860757476	   
df.mm.trans1:probe13	0.0161983914919594	0.0963554158874303	0.168110856486610	0.866546876813777	   
df.mm.trans1:probe14	-0.117458105988269	0.0963554158874303	-1.21900886324327	0.223270470992858	   
df.mm.trans1:probe15	-0.0847409198538814	0.0963554158874303	-0.879461928251985	0.379466650957289	   
df.mm.trans1:probe16	-0.078683752730846	0.0963554158874303	-0.816599170956517	0.414448209009249	   
df.mm.trans1:probe17	-0.233809855473038	0.0963554158874303	-2.42653568893515	0.0155069456186041	*  
df.mm.trans1:probe18	-0.00503484039783911	0.0963554158874303	-0.0522528012719201	0.95834285919142	   
df.mm.trans1:probe19	0.0103136672665825	0.0963554158874303	0.107037753629041	0.914791118641407	   
df.mm.trans1:probe20	-0.0492247914336782	0.0963554158874303	-0.510866887764631	0.609612806639508	   
df.mm.trans1:probe21	-0.151422849440264	0.0963554158874303	-1.57150325226313	0.116538601523156	   
df.mm.trans2:probe2	0.09036773809681	0.0963554158874303	0.937858419939616	0.348655627043166	   
df.mm.trans2:probe3	0.120325034806674	0.0963554158874303	1.24876254955142	0.212188731098556	   
df.mm.trans2:probe4	-0.0149796304213540	0.0963554158874303	-0.155462256930678	0.876503800986095	   
df.mm.trans2:probe5	0.119024384701468	0.0963554158874303	1.23526408562774	0.217165857723757	   
df.mm.trans2:probe6	-0.0889926128948334	0.0963554158874303	-0.923587035302731	0.35603435362722	   
df.mm.trans3:probe2	-0.00537803783897521	0.0963554158874303	-0.0558145879963639	0.955506178107445	   
df.mm.trans3:probe3	-0.0140528177464071	0.0963554158874303	-0.145843569009392	0.884088812473783