chr12.5740_chr12_53613680_53615403_+_2.R 

fitVsDatCorrelation=0.901731421172185
cont.fitVsDatCorrelation=0.268634935931501

fstatistic=12056.8417926205,67,1037
cont.fstatistic=2416.07514459375,67,1037

residuals=-0.550489765693809,-0.0945214936137621,-0.00161492718734733,0.0914690451093933,0.744696532992167
cont.residuals=-0.784581404772613,-0.249098382203004,-0.0761692768293116,0.21500725939984,1.20403770806486

predictedValues:
Include	Exclude	Both
chr12.5740_chr12_53613680_53615403_+_2.R.tl.Lung	61.3528850588747	48.4652714853318	55.6635577513177
chr12.5740_chr12_53613680_53615403_+_2.R.tl.cerebhem	59.92030107064	50.1847830863957	58.4385866885882
chr12.5740_chr12_53613680_53615403_+_2.R.tl.cortex	53.9787398762906	46.3440972722085	59.4400501964612
chr12.5740_chr12_53613680_53615403_+_2.R.tl.heart	53.5831953870347	48.9556904642812	60.9341925555538
chr12.5740_chr12_53613680_53615403_+_2.R.tl.kidney	68.0263216760272	47.9448211279475	65.8609100167256
chr12.5740_chr12_53613680_53615403_+_2.R.tl.liver	61.5886749480528	45.632707147793	71.9348590235926
chr12.5740_chr12_53613680_53615403_+_2.R.tl.stomach	53.4651196863746	46.7611239079478	61.5826789771652
chr12.5740_chr12_53613680_53615403_+_2.R.tl.testicle	56.057608251717	47.8626205278981	70.9084445961908


diffExp=12.8876135735429,9.73551798424435,7.63464260408217,4.62750492275349,20.0815005480798,15.9559678002598,6.70399577842681,8.19498772381889
diffExpScore=0.988482146241172
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,1,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,1,1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,0,0,1,1,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	69.3885186688713	55.0428350907783	59.8905455121976
cerebhem	64.28826679796	54.3445556747644	63.2152001500434
cortex	66.0506570328487	59.6142742207816	62.3410016021975
heart	61.9171272690433	71.969763838797	63.5889985067047
kidney	71.0927017554193	53.1810216323083	67.1441659882389
liver	74.2977368994651	51.688710683545	61.4060708749452
stomach	63.1634168664611	68.5082026511074	63.3840745040637
testicle	62.6463279653523	63.3755180712425	62.6059465859423
cont.diffExp=14.3456835780930,9.9437111231956,6.43638281206711,-10.0526365697537,17.911680123111,22.6090262159202,-5.34478578464626,-0.72919010589014
cont.diffExpScore=1.55690122135564

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,0,0,1,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,0,0,1,1,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=1,0,0,0,1,1,0,0
cont.diffExp1.2Score=0.75

tran.correlation=0.111093254719191
cont.tran.correlation=-0.813732700949816

tran.covariance=0.000311229478782766
cont.tran.covariance=-0.00685436815753275

tran.mean=53.132747560926
cont.tran.mean=63.1606021949216

weightedLogRatios:
wLogRatio
Lung	0.942882867284384
cerebhem	0.709987718301936
cortex	0.5966178129533
heart	0.355505900475795
kidney	1.41510913966216
liver	1.19058286547956
stomach	0.524124670057115
testicle	0.623860274160443

cont.weightedLogRatios:
wLogRatio
Lung	0.955139138323222
cerebhem	0.685466014364351
cortex	0.424375226127326
heart	-0.632040528415857
kidney	1.19563050354332
liver	1.49732181958588
stomach	-0.340048805871046
testicle	-0.0479485243704724

varWeightedLogRatios=0.129327166302548
cont.varWeightedLogRatios=0.572096991979622

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.48566128029689	0.0701937650850554	49.6577050123075	2.32429895618618e-276	***
df.mm.trans1	0.532461382529442	0.0580249588437961	9.1764198224222	2.3462469615866e-19	***
df.mm.trans2	0.390340476614735	0.0528399467467152	7.38722312658274	3.07748886540703e-13	***
df.mm.exp2	-0.0374131963725169	0.0671180769812447	-0.557423544523952	0.577358380114899	   
df.mm.exp3	-0.238448061614999	0.0671180769812447	-3.55266527796424	0.000398503182865314	***
df.mm.exp4	-0.215807370958221	0.0671180769812447	-3.21533900648741	0.00134315649511515	** 
df.mm.exp5	-0.0757636031947965	0.0671180769812447	-1.12881069605090	0.259238765505644	   
df.mm.exp6	-0.312822259369342	0.0671180769812447	-4.66077506148986	3.5603400055862e-06	***
df.mm.exp7	-0.274463013251746	0.0671180769812447	-4.08925621227856	4.6624594274837e-05	***
df.mm.exp8	-0.344838828888092	0.0671180769812447	-5.13779363768799	3.31998362766585e-07	***
df.mm.trans1:exp2	0.0137863638365012	0.0564882791604539	0.244057068853900	0.807234897321418	   
df.mm.trans2:exp2	0.07227756174225	0.0433245323963047	1.66828255827672	0.0955615321988365	.  
df.mm.trans1:exp3	0.110396128386356	0.0564882791604539	1.95431919731132	0.0509319383697829	.  
df.mm.trans2:exp3	0.193694504717061	0.0433245323963047	4.47078119494215	8.65166841150604e-06	***
df.mm.trans1:exp4	0.0804006747033001	0.0564882791604539	1.42331605597196	0.154945332734302	   
df.mm.trans2:exp4	0.225875494118246	0.0433245323963047	5.21357027127456	2.23512724190525e-07	***
df.mm.trans1:exp5	0.179016120668664	0.0564882791604539	3.16908433624208	0.00157395960759101	** 
df.mm.trans2:exp5	0.0649669034105019	0.0433245323963047	1.49954078710479	0.134037759737394	   
df.mm.trans1:exp6	0.316658068485011	0.0564882791604539	5.605730484116	2.65772178239928e-08	***
df.mm.trans2:exp6	0.252599491186186	0.0433245323963047	5.83040317378547	7.37906054242943e-09	***
df.mm.trans1:exp7	0.136850289775111	0.0564882791604539	2.42263159382835	0.0155790211193318	*  
df.mm.trans2:exp7	0.238667695630122	0.0433245323963047	5.5088348893635	4.55545153789482e-08	***
df.mm.trans1:exp8	0.254576513359595	0.0564882791604539	4.50671390849906	7.33258240435535e-06	***
df.mm.trans2:exp8	0.332326174292801	0.0433245323963047	7.67062345307957	3.93200628681435e-14	***
df.mm.trans1:probe2	-0.0323251539611569	0.0443944350423156	-0.72813527034065	0.466695164338304	   
df.mm.trans1:probe3	-0.0201032306353445	0.0443944350423156	-0.452832221339965	0.650764283034419	   
df.mm.trans1:probe4	0.00898806435699082	0.0443944350423156	0.202459257526842	0.839597419416154	   
df.mm.trans1:probe5	0.0399702840981971	0.0443944350423156	0.900344470204396	0.368145996020457	   
df.mm.trans1:probe6	0.196631369421506	0.0443944350423156	4.4291895872553	1.04622278462718e-05	***
df.mm.trans1:probe7	0.650837811188758	0.0443944350423156	14.6603467431987	2.29820935469253e-44	***
df.mm.trans1:probe8	0.624606794251779	0.0443944350423156	14.0694840165534	2.82062181836558e-41	***
df.mm.trans1:probe9	0.602540169453211	0.0443944350423156	13.5724256627860	9.60977723825525e-39	***
df.mm.trans1:probe10	0.650502851290872	0.0443944350423156	14.6528016556767	2.51975192367678e-44	***
df.mm.trans1:probe11	0.657282656640237	0.0443944350423156	14.8055191154867	3.88858075785712e-45	***
df.mm.trans1:probe12	0.56184972187167	0.0443944350423156	12.6558592611017	3.05781541046804e-34	***
df.mm.trans2:probe2	-0.0820180489173344	0.0443944350423156	-1.84748491199757	0.0649615606120896	.  
df.mm.trans2:probe3	0.0597565825793677	0.0443944350423156	1.34603768518305	0.178584539728378	   
df.mm.trans2:probe4	-0.00671790209634121	0.0443944350423156	-0.151323067630839	0.879750305040102	   
df.mm.trans2:probe5	-0.00817547091312585	0.0443944350423156	-0.184155309225880	0.853927621958244	   
df.mm.trans2:probe6	0.201909750991550	0.0443944350423156	4.54808695727504	6.05214488220948e-06	***
df.mm.trans3:probe2	0.0650975537883165	0.0443944350423156	1.46634490846132	0.142857510960107	   
df.mm.trans3:probe3	-0.198028316856528	0.0443944350423156	-4.46065631126452	9.06257834361442e-06	***
df.mm.trans3:probe4	0.286547128852879	0.0443944350423156	6.45457315944556	1.66338548020281e-10	***
df.mm.trans3:probe5	-0.189251276878112	0.0443944350423156	-4.26295045083292	2.20131347490721e-05	***
df.mm.trans3:probe6	0.125786241253947	0.0443944350423156	2.83337857850992	0.00469516377202061	** 
df.mm.trans3:probe7	-0.0334871321591478	0.0443944350423156	-0.754309231038277	0.450834811144087	   
df.mm.trans3:probe8	-0.0125249944602060	0.0443944350423156	-0.282129831098593	0.777900255328822	   
df.mm.trans3:probe9	-0.275150943600621	0.0443944350423156	-6.19787014607472	8.24586509608056e-10	***
df.mm.trans3:probe10	-0.411171413598645	0.0443944350423156	-9.2617782658283	1.12428661098822e-19	***
df.mm.trans3:probe11	-0.692672914140763	0.0443944350423156	-15.6026969029007	1.85121574752298e-49	***
df.mm.trans3:probe12	-0.716751070861557	0.0443944350423156	-16.1450657087621	1.76865479210221e-52	***
df.mm.trans3:probe13	-0.668813034780953	0.0443944350423156	-15.0652448700712	1.57476330308438e-46	***
df.mm.trans3:probe14	-0.702970256186248	0.0443944350423156	-15.8346480930818	9.62931196070307e-51	***
df.mm.trans3:probe15	-0.511020960674657	0.0443944350423156	-11.5109238396111	6.11686252031162e-29	***
df.mm.trans3:probe16	-0.676861293930175	0.0443944350423156	-15.2465346903280	1.64484708247742e-47	***
df.mm.trans3:probe17	-0.756906178062352	0.0443944350423156	-17.0495733832605	1.19757732796027e-57	***
df.mm.trans3:probe18	-0.725475763024499	0.0443944350423156	-16.3415924165494	1.37504543987316e-53	***
df.mm.trans3:probe19	-0.720866983286904	0.0443944350423156	-16.2377780593399	5.31245529522044e-53	***
df.mm.trans3:probe20	-0.464376062637684	0.0443944350423156	-10.4602313824932	2.05328188912106e-24	***
df.mm.trans3:probe21	-0.404937463294717	0.0443944350423156	-9.12135637966204	3.75990727287115e-19	***
df.mm.trans3:probe22	-0.572794202567196	0.0443944350423156	-12.9023874731422	1.97800047917492e-35	***
df.mm.trans3:probe23	-0.474197049610742	0.0443944350423156	-10.6814525099542	2.44120226692139e-25	***
df.mm.trans3:probe24	-0.524475341610388	0.0443944350423156	-11.8139884224334	2.62854655962769e-30	***
df.mm.trans3:probe25	-0.462382159090228	0.0443944350423156	-10.4153180156364	3.15025720106749e-24	***
df.mm.trans3:probe26	-0.342269227063669	0.0443944350423156	-7.70973268918564	2.94458194935194e-14	***
df.mm.trans3:probe27	-0.38749972584839	0.0443944350423156	-8.72856531407677	1.01464098646553e-17	***
df.mm.trans3:probe28	-0.448396208531573	0.0443944350423156	-10.1002796432520	6.08650499378777e-23	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.05752359926847	0.156405509812514	25.9423315977315	9.28954044086196e-115	***
df.mm.trans1	0.193712523556155	0.12929101692746	1.49826746018124	0.134368102995886	   
df.mm.trans2	-0.0606795388453779	0.117737790519882	-0.515378610193395	0.606398322923057	   
df.mm.exp2	-0.143137669216063	0.149552271988358	-0.957107955051369	0.338735843262119	   
df.mm.exp3	-0.0096167807225138	0.149552271988358	-0.0643038089268375	0.948740713740354	   
df.mm.exp4	0.0942879782560297	0.149552271988358	0.630468377393622	0.528527093577682	   
df.mm.exp5	-0.124470222412600	0.149552271988358	-0.832285733661672	0.405439303056005	   
df.mm.exp6	-0.0195032804148929	0.149552271988358	-0.130411127531457	0.896266455899539	   
df.mm.exp7	0.0681516683185707	0.149552271988358	0.455704667087077	0.648697771359258	   
df.mm.exp8	-0.00559202658623424	0.149552271988358	-0.0373917862422682	0.970179818336248	   
df.mm.trans1:exp2	0.066793391104331	0.125866992457474	0.5306664583004	0.595763554863735	   
df.mm.trans2:exp2	0.130370404246288	0.0965355764663988	1.35049076224936	0.177153250829617	   
df.mm.trans1:exp3	-0.0396826576300329	0.125866992457474	-0.315274535883109	0.752616648710375	   
df.mm.trans2:exp3	0.0894001245248587	0.0965355764663988	0.926084743027108	0.354617408900071	   
df.mm.trans1:exp4	-0.208212561063178	0.125866992457474	-1.65422687074632	0.0983840506358251	.  
df.mm.trans2:exp4	0.173846403932333	0.0965355764663988	1.80085322215736	0.0720164697095524	.  
df.mm.trans1:exp5	0.148733489571489	0.125866992457474	1.18167191149610	0.237606815985082	   
df.mm.trans2:exp5	0.0900601169378714	0.0965355764663988	0.932921522141826	0.351077647037995	   
df.mm.trans1:exp6	0.0878623559387455	0.125866992457474	0.698057165133511	0.485297937630365	   
df.mm.trans2:exp6	-0.0433690258776019	0.0965355764663988	-0.449254331564461	0.653342072402844	   
df.mm.trans1:exp7	-0.162147798590634	0.125866992457474	-1.28824718399002	0.197947294141088	   
df.mm.trans2:exp7	0.150690114570022	0.0965355764663987	1.56098010791362	0.118833552330931	   
df.mm.trans1:exp8	-0.0966243224538463	0.125866992457474	-0.767670066371785	0.442857980344792	   
df.mm.trans2:exp8	0.146557961216316	0.0965355764663988	1.51817564654341	0.129274917066254	   
df.mm.trans1:probe2	0.0144009829798668	0.0989195299214716	0.145582808483817	0.884279032264718	   
df.mm.trans1:probe3	-0.0412473627346376	0.0989195299214716	-0.416978960245588	0.676780098954903	   
df.mm.trans1:probe4	-0.0986845860430556	0.0989195299214716	-0.997624898959765	0.318694009034613	   
df.mm.trans1:probe5	0.0564727929975932	0.0989195299214717	0.570896293607791	0.568193633390511	   
df.mm.trans1:probe6	-0.0778435099073318	0.0989195299214716	-0.786937725736553	0.431498126607471	   
df.mm.trans1:probe7	0.0783150145067151	0.0989195299214716	0.79170427284568	0.428714182154155	   
df.mm.trans1:probe8	-0.101917276574450	0.0989195299214716	-1.03030490192743	0.303107136690805	   
df.mm.trans1:probe9	-0.117832962043007	0.0989195299214716	-1.19120018197165	0.233847578665347	   
df.mm.trans1:probe10	0.0208158559094597	0.0989195299214716	0.210432216226508	0.833371689751803	   
df.mm.trans1:probe11	-0.146373224862231	0.0989195299214716	-1.47972018243952	0.139251652131214	   
df.mm.trans1:probe12	-0.0466939660708096	0.0989195299214717	-0.472039910701942	0.636997611893684	   
df.mm.trans2:probe2	0.107823348037382	0.0989195299214716	1.09001072005679	0.275961640822371	   
df.mm.trans2:probe3	0.127029076269607	0.0989195299214716	1.2841657898137	0.199370774985254	   
df.mm.trans2:probe4	0.0783797060945483	0.0989195299214717	0.792358254803383	0.428333035957083	   
df.mm.trans2:probe5	0.0704745404712995	0.0989195299214716	0.712443139663588	0.476350672121458	   
df.mm.trans2:probe6	-0.000606859229460778	0.0989195299214716	-0.00613487781373951	0.995106286361576	   
df.mm.trans3:probe2	-0.188810368926954	0.0989195299214716	-1.90872691243926	0.0565731729910599	.  
df.mm.trans3:probe3	-0.0113898232742396	0.0989195299214716	-0.115142310960045	0.908354614343417	   
df.mm.trans3:probe4	-0.0627222795220501	0.0989195299214716	-0.634073772609341	0.526172516598648	   
df.mm.trans3:probe5	-0.136581236819504	0.0989195299214716	-1.38073075082271	0.167659265881999	   
df.mm.trans3:probe6	0.0351316012429214	0.0989195299214716	0.355153337978972	0.722546940174436	   
df.mm.trans3:probe7	-0.067997591120804	0.0989195299214716	-0.68740309597897	0.491982452714014	   
df.mm.trans3:probe8	-0.102361483911985	0.0989195299214716	-1.03479549481529	0.301005676442421	   
df.mm.trans3:probe9	-0.0442215345456551	0.0989195299214716	-0.447045538740033	0.6549355316048	   
df.mm.trans3:probe10	-0.163270136999628	0.0989195299214716	-1.65053490578900	0.0991363848699615	.  
df.mm.trans3:probe11	-0.270957150140471	0.0989195299214716	-2.73916738540482	0.0062651172672027	** 
df.mm.trans3:probe12	0.00336829093893729	0.0989195299214716	0.0340508182925177	0.972843179850413	   
df.mm.trans3:probe13	-0.121947090009285	0.0989195299214716	-1.23279083620892	0.217933210321996	   
df.mm.trans3:probe14	-0.198288672564022	0.0989195299214716	-2.00454523713806	0.0452712992729687	*  
df.mm.trans3:probe15	-0.102475693005179	0.0989195299214716	-1.03595006048382	0.300466949452387	   
df.mm.trans3:probe16	-0.0601661781532366	0.0989195299214716	-0.608233563190203	0.543165766875556	   
df.mm.trans3:probe17	-0.0457660353218394	0.0989195299214716	-0.462659247958126	0.643705696801011	   
df.mm.trans3:probe18	-0.0612581018708365	0.0989195299214716	-0.619272068108966	0.535873165228456	   
df.mm.trans3:probe19	-0.0449879545458451	0.0989195299214716	-0.454793452633259	0.649353030240684	   
df.mm.trans3:probe20	-0.0493995829732313	0.0989195299214716	-0.499391606616486	0.617609461681121	   
df.mm.trans3:probe21	-0.106137889162735	0.0989195299214716	-1.07297203339921	0.283533284811214	   
df.mm.trans3:probe22	-0.061260743181367	0.0989195299214716	-0.619298769717158	0.535855584827191	   
df.mm.trans3:probe23	-0.287008333875467	0.0989195299214716	-2.90143244820625	0.00379312840326829	** 
df.mm.trans3:probe24	-0.107729560912099	0.0989195299214717	-1.08906260470123	0.276379300156352	   
df.mm.trans3:probe25	-0.189178321102983	0.0989195299214716	-1.91244662457620	0.0560941983450785	.  
df.mm.trans3:probe26	-0.077665109733922	0.0989195299214716	-0.78513423785553	0.432554200243272	   
df.mm.trans3:probe27	-0.108690044489326	0.0989195299214716	-1.09877235138107	0.272122408961059	   
df.mm.trans3:probe28	-0.113290992929079	0.0989195299214717	-1.14528438437805	0.252355615053394