chr7.21114_chr7_132864424_132865055_+_2.R 

fitVsDatCorrelation=0.92061604113647
cont.fitVsDatCorrelation=0.284326306353432

fstatistic=7033.02580057094,51,669
cont.fstatistic=1155.66661063703,51,669

residuals=-0.550048488573687,-0.113873742102785,-0.00830167571427693,0.0849741314263536,1.63693098435367
cont.residuals=-0.938513271882667,-0.357043081168806,-0.090417754340639,0.229214176630701,1.68475693532573

predictedValues:
Include	Exclude	Both
chr7.21114_chr7_132864424_132865055_+_2.R.tl.Lung	87.2662822886054	64.8947659613149	57.8043102526569
chr7.21114_chr7_132864424_132865055_+_2.R.tl.cerebhem	84.7195637925435	61.6813803638224	52.3635366066604
chr7.21114_chr7_132864424_132865055_+_2.R.tl.cortex	86.529797517911	62.515509122924	55.6854373939194
chr7.21114_chr7_132864424_132865055_+_2.R.tl.heart	84.3201282100306	69.7672357474798	58.8127191540235
chr7.21114_chr7_132864424_132865055_+_2.R.tl.kidney	87.003289541782	66.3115444776813	64.8934424256199
chr7.21114_chr7_132864424_132865055_+_2.R.tl.liver	87.7913405347219	66.8242443620378	56.2554193886265
chr7.21114_chr7_132864424_132865055_+_2.R.tl.stomach	129.003952685254	75.2892306441215	91.582863707367
chr7.21114_chr7_132864424_132865055_+_2.R.tl.testicle	87.8097611851508	67.1693966638179	55.8127849677716


diffExp=22.3715163272905,23.0381834287211,24.0142883949871,14.5528924625507,20.6917450641007,20.9670961726841,53.7147220411325,20.6403645213329
diffExpScore=0.995024648102583
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=1,1,1,0,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	83.8260680724018	91.0509748639684	59.1150230326205
cerebhem	79.1972776986307	91.5478501107574	94.7125147279314
cortex	75.861149221559	69.2615035553229	95.9752757594653
heart	88.1548331476717	87.1946566483071	86.7612079669698
kidney	86.8023507586732	96.5310333025695	85.2666238840115
liver	80.0414270053252	82.8242847356363	83.5275341282045
stomach	79.8955682884975	86.3700611336152	65.3747042893404
testicle	92.894654950327	80.0086832228895	80.6413496184558
cont.diffExp=-7.22490679156658,-12.3505724121267,6.5996456662362,0.96017649936455,-9.72868254389624,-2.78285773031112,-6.47449284511768,12.8859717274375
cont.diffExpScore=3.08684742518036

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=0.796794732876847
cont.tran.correlation=0.298118998888814

tran.covariance=0.00697092604823972
cont.tran.covariance=0.0023320849445296

tran.mean=79.3060889436999
cont.tran.mean=84.4663985447595

weightedLogRatios:
wLogRatio
Lung	1.27982822811869
cerebhem	1.35853065528238
cortex	1.39715306737872
heart	0.822219150394322
kidney	1.17599180819634
liver	1.18396719579603
stomach	2.47206002177667
testicle	1.16324466846281

cont.weightedLogRatios:
wLogRatio
Lung	-0.369566152062303
cerebhem	-0.644082103219119
cortex	0.389855250154118
heart	0.0489936602270457
kidney	-0.479818060348333
liver	-0.150366137068790
stomach	-0.344384686218433
testicle	0.665538226903958

varWeightedLogRatios=0.233880545684765
cont.varWeightedLogRatios=0.203220178152152

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.5032341740183	0.106015908433704	42.4769663397674	4.08966296807277e-192	***
df.mm.trans1	-0.182527599473866	0.0951121678955925	-1.91907726963213	0.0553996909617793	.  
df.mm.trans2	-0.221387316173267	0.0876108969094845	-2.52693813193117	0.0117355418943355	*  
df.mm.exp2	0.0184503899669609	0.120047743195488	0.153692101790835	0.877898860844335	   
df.mm.exp3	-0.00848294721220644	0.120047743195488	-0.0706631127450074	0.943686996756905	   
df.mm.exp4	0.0207591711905933	0.120047743195488	0.172924293601994	0.862763239170417	   
df.mm.exp5	-0.0971044298759318	0.120047743195488	-0.808881760632563	0.418870725817177	   
df.mm.exp6	0.0624586535977777	0.120047743195488	0.520281780691779	0.603039320168945	   
df.mm.exp7	0.0792681852288112	0.120047743195488	0.660305501118248	0.509285079616182	   
df.mm.exp8	0.0757196541441613	0.120047743195488	0.630746169220843	0.528421913874128	   
df.mm.trans1:exp2	-0.0480679976604508	0.114709590511624	-0.419040792021480	0.67532082724848	   
df.mm.trans2:exp2	-0.0692352542352057	0.100126739325125	-0.691476170120645	0.489506233169733	   
df.mm.trans1:exp3	7.62164612642681e-06	0.114709590511624	6.64429721388854e-05	0.999947005837096	   
df.mm.trans2:exp3	-0.0288693535740784	0.100126739325125	-0.288328110639214	0.773184897906685	   
df.mm.trans1:exp4	-0.0551027260414159	0.114709590511624	-0.480367210759347	0.631123338689891	   
df.mm.trans2:exp4	0.0516383537632981	0.100126739325125	0.515729905031875	0.60621345490762	   
df.mm.trans1:exp5	0.0940861984806249	0.114709590511624	0.820212137982398	0.41238732012095	   
df.mm.trans2:exp5	0.118701464049374	0.100126739325125	1.18551213042037	0.236235934504936	   
df.mm.trans1:exp6	-0.0564599451480224	0.114709590511624	-0.492198994837323	0.622740148454284	   
df.mm.trans2:exp6	-0.033159672062648	0.100126739325125	-0.331176989145467	0.740614506986154	   
df.mm.trans1:exp7	0.311610699474237	0.114709590511624	2.71651827963469	0.00676777330126582	** 
df.mm.trans2:exp7	0.0693019473021146	0.100126739325125	0.69214225659623	0.489088149138696	   
df.mm.trans1:exp8	-0.0695111446132497	0.114709590511624	-0.605975004384708	0.544736890035957	   
df.mm.trans2:exp8	-0.041268889844954	0.100126739325125	-0.412166521381949	0.680349407791109	   
df.mm.trans1:probe2	0.171432210029562	0.0573547952558122	2.98897780499338	0.00290175133281032	** 
df.mm.trans1:probe3	-0.252998811541388	0.0573547952558122	-4.41111872883462	1.19824201837341e-05	***
df.mm.trans1:probe4	-0.468747918225833	0.0573547952558122	-8.17277641974202	1.50950655642837e-15	***
df.mm.trans1:probe5	-0.212290812383859	0.0573547952558122	-3.70136117541709	0.000232097468097389	***
df.mm.trans1:probe6	-0.587137819077537	0.0573547952558122	-10.2369438589886	6.0094107712782e-23	***
df.mm.trans1:probe7	0.166785550647912	0.0573547952558122	2.90796174764499	0.00375846518793317	** 
df.mm.trans1:probe8	-0.542397848524027	0.0573547952558122	-9.4568875384323	5.26429460724033e-20	***
df.mm.trans1:probe9	-0.248537158871330	0.0573547952558122	-4.33332832525706	1.69438689292021e-05	***
df.mm.trans1:probe10	0.320175922999217	0.0573547952558122	5.58237409045185	3.45027908410956e-08	***
df.mm.trans1:probe11	0.252093865739295	0.0573547952558122	4.3953406967092	1.28602591569285e-05	***
df.mm.trans1:probe12	0.092485023831416	0.0573547952558122	1.61250726149256	0.107323205699355	   
df.mm.trans1:probe13	0.143514734508509	0.0573547952558122	2.50222730058417	0.0125790303281971	*  
df.mm.trans1:probe14	0.284099187851246	0.0573547952558122	4.95336417093139	9.24802282746502e-07	***
df.mm.trans1:probe15	0.39650077586326	0.0573547952558122	6.91312337695215	1.10823042962423e-11	***
df.mm.trans1:probe16	0.647216697683265	0.0573547952558122	11.2844391614784	3.72937754035381e-27	***
df.mm.trans1:probe17	-0.157644906317395	0.0573547952558122	-2.74859156264566	0.00614661299218154	** 
df.mm.trans1:probe18	0.971216912449195	0.0573547952558122	16.9334910554105	8.50948448843651e-54	***
df.mm.trans1:probe19	0.888880489670255	0.0573547952558122	15.497928040815	1.62953631454891e-46	***
df.mm.trans1:probe20	0.786158523648588	0.0573547952558122	13.7069362752005	7.23746322638043e-38	***
df.mm.trans1:probe21	0.907377434797144	0.0573547952558122	15.8204284532808	3.98713300066564e-48	***
df.mm.trans2:probe2	-0.206723709570618	0.0573547952558122	-3.60429687959995	0.000336248630949991	***
df.mm.trans2:probe3	0.00921256164380683	0.0573547952558122	0.160624087362133	0.872437971139974	   
df.mm.trans2:probe4	-0.311182762250857	0.0573547952558122	-5.42557533093665	8.08256166605082e-08	***
df.mm.trans2:probe5	-0.222653172884146	0.0573547952558122	-3.88203238963847	0.000113854997793218	***
df.mm.trans2:probe6	-0.250371883132689	0.0573547952558122	-4.36531735517470	1.47033322803210e-05	***
df.mm.trans3:probe2	-0.128413572733402	0.0573547952558122	-2.23893350435051	0.0254882110343514	*  
df.mm.trans3:probe3	0.00164583184970508	0.0573547952558122	0.0286956276692192	0.977115901443984	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.93247098329814	0.260303257243255	18.9489406914671	1.99903399041035e-64	***
df.mm.trans1	-0.458421463240153	0.233531056541125	-1.96299999678811	0.0500600986216035	.  
df.mm.trans2	-0.357521756673784	0.215113016267771	-1.66201823988533	0.0969775704865285	.  
df.mm.exp2	-0.522720887450718	0.294755938426245	-1.77340239603524	0.0766169323034042	.  
df.mm.exp3	-0.857975139191818	0.294755938426245	-2.91079848559694	0.00372494107280354	** 
df.mm.exp4	-0.376600278913332	0.294755938426245	-1.27766816480126	0.201809633077408	   
df.mm.exp5	-0.272963307727667	0.294755938426245	-0.926065507568964	0.354745878098301	   
df.mm.exp6	-0.486589135082367	0.294755938426245	-1.6508204641452	0.0992445668093753	.  
df.mm.exp7	-0.201452370514567	0.294755938426245	-0.683454832462945	0.494556114117326	   
df.mm.exp8	-0.337088720856275	0.294755938426245	-1.14361977796292	0.253190402722508	   
df.mm.trans1:exp2	0.465918779224363	0.281649051433522	1.65425296784404	0.0985451934877082	.  
df.mm.trans2:exp2	0.528163162046326	0.245843447163166	2.14837193401288	0.0320425499595466	*  
df.mm.trans1:exp3	0.758135790616674	0.281649051433522	2.69177469889551	0.00728501610596916	** 
df.mm.trans2:exp3	0.584444873761585	0.245843447163166	2.37730507160392	0.0177198131899048	*  
df.mm.trans1:exp4	0.426950980964888	0.281649051433522	1.51589710241102	0.130017709178137	   
df.mm.trans2:exp4	0.333323817957558	0.245843447163166	1.35583771625335	0.175608290423453	   
df.mm.trans1:exp5	0.307852977536490	0.281649051433522	1.09303750880607	0.274770761012515	   
df.mm.trans2:exp5	0.331408339977487	0.245843447163166	1.34804626196740	0.178099760565392	   
df.mm.trans1:exp6	0.440389439327258	0.281649051433522	1.56361058944025	0.11838192958289	   
df.mm.trans2:exp6	0.391890934355738	0.245843447163166	1.59406703281231	0.111393325267464	   
df.mm.trans1:exp7	0.153428722058626	0.281649051433522	0.544751424788093	0.58610602848943	   
df.mm.trans2:exp7	0.148673958593026	0.245843447163166	0.60475054474139	0.545549811205308	   
df.mm.trans1:exp8	0.439810795538338	0.281649051433522	1.56155610430716	0.118865493406542	   
df.mm.trans2:exp8	0.207804376901961	0.245843447163166	0.845271164636903	0.398261655342123	   
df.mm.trans1:probe2	-0.0519750073827845	0.140824525716761	-0.369076388635041	0.712187565812292	   
df.mm.trans1:probe3	-0.0188966918276091	0.140824525716761	-0.134186085352887	0.893295785735906	   
df.mm.trans1:probe4	-0.09734677115379	0.140824525716761	-0.691262907922607	0.489640132686408	   
df.mm.trans1:probe5	-0.0354971527431164	0.140824525716761	-0.252066552771579	0.80106704000494	   
df.mm.trans1:probe6	-0.045697647983316	0.140824525716761	-0.324500634749002	0.745660478666087	   
df.mm.trans1:probe7	-0.187803219605448	0.140824525716761	-1.33359738759692	0.182789704589093	   
df.mm.trans1:probe8	-0.0088503471846704	0.140824525716761	-0.0628466322867014	0.949907403962453	   
df.mm.trans1:probe9	0.0856196351125777	0.140824525716761	0.60798809494863	0.543401707373176	   
df.mm.trans1:probe10	-0.170879544199471	0.140824525716761	-1.21342176250719	0.225396845392068	   
df.mm.trans1:probe11	-0.238585774387615	0.140824525716761	-1.6942061276136	0.0906915509784534	.  
df.mm.trans1:probe12	-0.145774952931578	0.140824525716761	-1.03515316092577	0.300971417004311	   
df.mm.trans1:probe13	0.0731762619174482	0.140824525716761	0.519627256296443	0.603495274539148	   
df.mm.trans1:probe14	-0.112270564774971	0.140824525716761	-0.797237300843319	0.425596080926729	   
df.mm.trans1:probe15	0.0235770092105316	0.140824525716761	0.167421186689823	0.867089250057392	   
df.mm.trans1:probe16	-0.0239671307038016	0.140824525716761	-0.170191453383671	0.864911026943161	   
df.mm.trans1:probe17	-0.176845542155632	0.140824525716761	-1.25578652763454	0.209631776361529	   
df.mm.trans1:probe18	-0.0697584832619337	0.140824525716761	-0.495357487673975	0.620510461611378	   
df.mm.trans1:probe19	-0.115698765735834	0.140824525716761	-0.821581078629285	0.411608049818568	   
df.mm.trans1:probe20	0.245949274507881	0.140824525716761	1.74649460565241	0.081183989502485	.  
df.mm.trans1:probe21	-0.0158062509737377	0.140824525716761	-0.112240754181759	0.910666184661092	   
df.mm.trans2:probe2	-0.146658348894673	0.140824525716761	-1.04142618729386	0.298053995896219	   
df.mm.trans2:probe3	0.0519065499257721	0.140824525716761	0.368590269781354	0.71254975776811	   
df.mm.trans2:probe4	-0.0627263785465411	0.140824525716761	-0.445422260272349	0.656158723673965	   
df.mm.trans2:probe5	-0.360376302883481	0.140824525716761	-2.55904503174612	0.0107148793444672	*  
df.mm.trans2:probe6	-0.0539129420020999	0.140824525716761	-0.382837731763674	0.701961687495817	   
df.mm.trans3:probe2	-0.152920354996913	0.140824525716761	-1.08589291686648	0.277917489581652	   
df.mm.trans3:probe3	0.0417923316611864	0.140824525716761	0.296768843697319	0.766735105991388