chr5.18825_chr5_73865057_73875511_+_2.R 

fitVsDatCorrelation=0.787522767704157
cont.fitVsDatCorrelation=0.238938182450463

fstatistic=13287.6211472028,59,853
cont.fstatistic=5343.68076655534,59,853

residuals=-0.451101492216606,-0.0808331557174838,-0.00487883438344436,0.0744277800152126,0.763919512943115
cont.residuals=-0.557572912081467,-0.145750739336634,-0.0179805372255239,0.127277279719664,1.04283297176568

predictedValues:
Include	Exclude	Both
chr5.18825_chr5_73865057_73875511_+_2.R.tl.Lung	54.1015911511902	61.2787671385678	68.2211056318393
chr5.18825_chr5_73865057_73875511_+_2.R.tl.cerebhem	56.8705766374815	66.3725006899099	70.0370924024094
chr5.18825_chr5_73865057_73875511_+_2.R.tl.cortex	53.3400784807224	72.8543001768141	76.9114477979984
chr5.18825_chr5_73865057_73875511_+_2.R.tl.heart	51.6349705752292	61.4887229282064	65.1265392223793
chr5.18825_chr5_73865057_73875511_+_2.R.tl.kidney	51.2543091380288	55.7945458211321	60.4062405310854
chr5.18825_chr5_73865057_73875511_+_2.R.tl.liver	53.0373487489357	52.3311065361901	59.3785199705773
chr5.18825_chr5_73865057_73875511_+_2.R.tl.stomach	52.2379042202806	57.4794295322858	64.729776254443
chr5.18825_chr5_73865057_73875511_+_2.R.tl.testicle	53.8770142398273	60.363874119349	66.5697025574004


diffExp=-7.1771759873776,-9.5019240524284,-19.5142216960917,-9.85375235297723,-4.54023668310336,0.70624221274565,-5.24152531200517,-6.48685987952165
diffExpScore=1.00658821313365
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=0,0,-1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,-1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	56.2378187020545	52.4750286573419	58.9502768337394
cerebhem	58.2381587030782	64.0048429215	56.8874435898103
cortex	57.7377748254134	59.5592786193647	60.9058856679835
heart	57.9555512447287	57.5044913593469	55.7114796790614
kidney	59.8449370353652	59.9126376803066	59.5618123694433
liver	56.7454921003758	63.2770323501434	58.907829174177
stomach	57.2423508658147	58.2929706670612	58.6405637003521
testicle	56.8181501861037	58.87652515788	58.4703954072597
cont.diffExp=3.76279004471261,-5.76668421842179,-1.82150379395124,0.451059885381781,-0.0677006449414179,-6.53154024976759,-1.05061980124648,-2.05837497177620
cont.diffExpScore=1.52743908834018

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=0.448044622314503
cont.tran.correlation=0.367216751393028

tran.covariance=0.00153303708319642
cont.tran.covariance=0.000459081367251115

tran.mean=57.1448150083844
cont.tran.mean=58.4201900672424

weightedLogRatios:
wLogRatio
Lung	-0.504899938829244
cerebhem	-0.636255158507909
cortex	-1.28842758508053
heart	-0.7041247402094
kidney	-0.337743484787615
liver	0.0531429154495839
stomach	-0.382821007575364
testicle	-0.45969828678467

cont.weightedLogRatios:
wLogRatio
Lung	0.276659405273857
cerebhem	-0.388223039370504
cortex	-0.126460858250634
heart	0.0316889598281493
kidney	-0.00462689527443636
liver	-0.445922196188572
stomach	-0.0737755126445552
testicle	-0.144398086177308

varWeightedLogRatios=0.145859998705032
cont.varWeightedLogRatios=0.0533856969682966

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03561375576960	0.064146656021047	62.9123013746107	0	***
df.mm.trans1	-0.056316364149358	0.0551964355639522	-1.02028987151006	0.307880285459147	   
df.mm.trans2	0.0817948897094752	0.0487755536784812	1.67696486335452	0.093915736528082	.  
df.mm.exp2	0.103492912399722	0.0625453028830286	1.65468720478137	0.0983558342328836	.  
df.mm.exp3	0.0389517976029834	0.0625453028830286	0.622777343901117	0.533597303609336	   
df.mm.exp4	0.00317780381870454	0.0625453028830286	0.0508080330931906	0.959490384506474	   
df.mm.exp5	-0.0261596246942286	0.0625453028830286	-0.418250827614538	0.675869011318879	   
df.mm.exp6	-0.0388882397874534	0.0625453028830286	-0.621761155432914	0.534265069164964	   
df.mm.exp7	-0.0465288332877835	0.0625453028830286	-0.743922103547906	0.457128469578254	   
df.mm.exp8	0.00530217318242213	0.0625453028830286	0.084773323303561	0.932461493965142	   
df.mm.trans1:exp2	-0.0535784082773812	0.0574590742183892	-0.93246208725434	0.351361620971746	   
df.mm.trans2:exp2	-0.0236434946698687	0.0421381175813038	-0.561095180017225	0.574880016959127	   
df.mm.trans1:exp3	-0.0531274042379177	0.0574590742183892	-0.924612952098606	0.355428761581734	   
df.mm.trans2:exp3	0.134076354383901	0.0421381175813038	3.181830657841	0.00151642881853923	** 
df.mm.trans1:exp4	-0.049842233273811	0.0574590742183892	-0.867438850204439	0.385945557427018	   
df.mm.trans2:exp4	0.000242580424945583	0.0421381175813038	0.0057567931096479	0.995408115060238	   
df.mm.trans1:exp5	-0.0279042767971656	0.0574590742183892	-0.485637424144838	0.627348971796247	   
df.mm.trans2:exp5	-0.0675976625708757	0.0421381175813038	-1.60419274639995	0.109041736227843	   
df.mm.trans1:exp6	0.0190210018185823	0.0574590742183892	0.331035647151008	0.740698861490609	   
df.mm.trans2:exp6	-0.118954201435521	0.0421381175813038	-2.82295954977115	0.00486900766040507	** 
df.mm.trans1:exp7	0.0114736024390685	0.0574590742183892	0.199683036929204	0.841776072582949	   
df.mm.trans2:exp7	-0.0174774369143266	0.0421381175813038	-0.41476548829227	0.678417876341094	   
df.mm.trans1:exp8	-0.00946183489256834	0.0574590742183892	-0.164670855235258	0.86924207136594	   
df.mm.trans2:exp8	-0.0203447645219617	0.0421381175813038	-0.482811423237103	0.629353522519172	   
df.mm.trans1:probe2	0.0181778684374185	0.0400277462465162	0.454131699683207	0.649849566192444	   
df.mm.trans1:probe3	-0.0490240072586072	0.0400277462465162	-1.22475062564568	0.221007305020373	   
df.mm.trans1:probe4	-0.0201465283438682	0.0400277462465162	-0.503314081682069	0.61487340845832	   
df.mm.trans1:probe5	0.076857251443086	0.0400277462465162	1.92009939729682	0.0551785469474185	.  
df.mm.trans1:probe6	0.295695589828476	0.0400277462465163	7.38726552345452	3.57049747176939e-13	***
df.mm.trans1:probe7	0.108867532755828	0.0400277462465163	2.71980171167651	0.00666493610989535	** 
df.mm.trans1:probe8	0.286133558911468	0.0400277462465163	7.14838045462955	1.88672936650165e-12	***
df.mm.trans1:probe9	-0.11950180753138	0.0400277462465163	-2.98547429564013	0.00291228893346758	** 
df.mm.trans1:probe10	-0.0624291646277253	0.0400277462465163	-1.55964725676152	0.119214177464552	   
df.mm.trans1:probe11	-0.0204593075200102	0.0400277462465163	-0.511128140815343	0.609393599578884	   
df.mm.trans1:probe12	-0.0447750179141306	0.0400277462465162	-1.11859952439934	0.263625937873585	   
df.mm.trans1:probe13	0.0434920792171291	0.0400277462465162	1.08654828951091	0.277543432403619	   
df.mm.trans1:probe14	0.112429122016349	0.0400277462465162	2.80877972304358	0.00508661106428842	** 
df.mm.trans1:probe15	0.13988800242164	0.0400277462465163	3.49477588770852	0.000498906234106589	***
df.mm.trans1:probe16	-0.089395380607283	0.0400277462465162	-2.23333534835385	0.025785461483714	*  
df.mm.trans1:probe17	-0.00673096988369708	0.0400277462465162	-0.168157603534396	0.866499154791127	   
df.mm.trans1:probe18	-0.190311755278342	0.0400277462465163	-4.75449589657838	2.3346840225699e-06	***
df.mm.trans1:probe19	0.108731003886256	0.0400277462465163	2.71639085589834	0.00673339319778668	** 
df.mm.trans1:probe20	-0.124008690198841	0.0400277462465162	-3.0980682608288	0.00201168629205359	** 
df.mm.trans1:probe21	-0.0933708166462184	0.0400277462465162	-2.33265235747178	0.0198981395478516	*  
df.mm.trans2:probe2	-0.0101048209076663	0.0400277462465163	-0.252445412375566	0.800757619345696	   
df.mm.trans2:probe3	-0.00789554034754816	0.0400277462465162	-0.197251683842563	0.843677578148703	   
df.mm.trans2:probe4	-0.0454320678360017	0.0400277462465163	-1.13501438617608	0.256688121287296	   
df.mm.trans2:probe5	0.0258119085572659	0.0400277462465162	0.644850409470965	0.519197516882931	   
df.mm.trans2:probe6	0.0040414618677556	0.0400277462465163	0.100966510651529	0.91960077151097	   
df.mm.trans3:probe2	0.282151480587891	0.0400277462465163	7.04889750350226	3.7224107831861e-12	***
df.mm.trans3:probe3	0.158600867670637	0.0400277462465163	3.96227323651631	8.04461225217975e-05	***
df.mm.trans3:probe4	0.240510800711910	0.0400277462465162	6.00860211391097	2.76956365365543e-09	***
df.mm.trans3:probe5	0.129672528350156	0.0400277462465163	3.23956606378861	0.00124344680085265	** 
df.mm.trans3:probe6	0.305407905984399	0.0400277462465162	7.62990511890185	6.27847286460187e-14	***
df.mm.trans3:probe7	0.0737676466475829	0.0400277462465163	1.84291281835542	0.0656885596879621	.  
df.mm.trans3:probe8	-0.207066667078219	0.0400277462465163	-5.17307833928423	2.87167205910706e-07	***
df.mm.trans3:probe9	0.485590473986346	0.0400277462465163	12.1313468661406	2.33305234748117e-31	***
df.mm.trans3:probe10	0.124817175226655	0.0400277462465163	3.11826637597709	0.00188024371824727	** 
df.mm.trans3:probe11	0.184826514172512	0.0400277462465163	4.617459924779	4.4826653445749e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9874684960724	0.101071058912484	39.452129412487	1.55289889746942e-194	***
df.mm.trans1	0.0326028252172651	0.0869688700345172	0.374879255121118	0.707843452197915	   
df.mm.trans2	-0.0167574892094982	0.0768519696133385	-0.218048923063513	0.827443144870395	   
df.mm.exp2	0.269192347129752	0.098547927273334	2.73158811735447	0.00643317764813502	** 
df.mm.exp3	0.120321366732296	0.098547927273334	1.22094264244210	0.222445109754943	   
df.mm.exp4	0.178120640183292	0.098547927273334	1.80745191818448	0.0710440261402066	.  
df.mm.exp5	0.184397110047633	0.098547927273334	1.87114143493030	0.0616676156860927	.  
df.mm.exp6	0.196892089108690	0.098547927273334	1.99793232142353	0.0460411061980762	*  
df.mm.exp7	0.128116318088965	0.098547927273334	1.30004071758526	0.193938187602237	   
df.mm.exp8	0.133545162504010	0.098547927273334	1.35512908489295	0.17573517605812	   
df.mm.trans1:exp2	-0.234241020641162	0.0905339395007296	-2.58732826532169	0.0098367079830642	** 
df.mm.trans2:exp2	-0.0705710078052559	0.0663938610162895	-1.06291465393075	0.28812147461353	   
df.mm.trans1:exp3	-0.0939991922998839	0.0905339395007296	-1.03827573193284	0.299435924838008	   
df.mm.trans2:exp3	0.00631331735874989	0.0663938610162894	0.095088872105223	0.924266569557075	   
df.mm.trans1:exp4	-0.148033742513555	0.0905339395007295	-1.63511875579392	0.102393093091525	   
df.mm.trans2:exp4	-0.0865949966538578	0.0663938610162895	-1.30426210086821	0.192496047199091	   
df.mm.trans1:exp5	-0.122229737076317	0.0905339395007296	-1.35009851278296	0.177342509359506	   
df.mm.trans2:exp5	-0.0518470593621974	0.0663938610162895	-0.780901405168723	0.43507714522024	   
df.mm.trans1:exp6	-0.187905331481750	0.0905339395007296	-2.07552363807427	0.0382371786471927	*  
df.mm.trans2:exp6	-0.00970707569135684	0.0663938610162895	-0.146204416233230	0.883794563221065	   
df.mm.trans1:exp7	-0.110411755136770	0.0905339395007296	-1.21956203105334	0.222968049603555	   
df.mm.trans2:exp7	-0.0229722155711313	0.0663938610162895	-0.345999091173432	0.729428717126794	   
df.mm.trans1:exp8	-0.123278803515593	0.0905339395007296	-1.36168606155264	0.173656508301792	   
df.mm.trans2:exp8	-0.0184401173066226	0.0663938610162895	-0.277738288214605	0.781280567528427	   
df.mm.trans1:probe2	-0.0131931179308325	0.0630687077076657	-0.209186431914618	0.83435264944062	   
df.mm.trans1:probe3	0.0243814715699318	0.0630687077076657	0.386585875248056	0.699159230883623	   
df.mm.trans1:probe4	0.047747218838867	0.0630687077076657	0.757066706680967	0.449218960924674	   
df.mm.trans1:probe5	-0.0683212602352788	0.0630687077076657	-1.08328302130368	0.278988928765118	   
df.mm.trans1:probe6	0.00354535031724332	0.0630687077076657	0.0562140948515487	0.955184417109418	   
df.mm.trans1:probe7	0.0575586413308588	0.0630687077076657	0.912633910268988	0.361692974293376	   
df.mm.trans1:probe8	0.0682759024180519	0.0630687077076657	1.08256384028863	0.279307989934634	   
df.mm.trans1:probe9	0.0513397029487176	0.0630687077076657	0.814028141922392	0.415856009125017	   
df.mm.trans1:probe10	0.0244951561565180	0.0630687077076657	0.388388426635555	0.697825521586929	   
df.mm.trans1:probe11	-0.00237244358790115	0.0630687077076657	-0.0376168098908547	0.970002006539113	   
df.mm.trans1:probe12	0.0285807044195396	0.0630687077076657	0.453167750828447	0.650543201638311	   
df.mm.trans1:probe13	0.0437941928497789	0.0630687077076657	0.694388619040245	0.487627604099197	   
df.mm.trans1:probe14	-0.0427089922411616	0.0630687077076657	-0.677181978091657	0.498474109208279	   
df.mm.trans1:probe15	-0.0812854923572775	0.0630687077076657	-1.28884030308738	0.197803035561625	   
df.mm.trans1:probe16	0.0497977584393564	0.0630687077076657	0.789579495907505	0.429992807892708	   
df.mm.trans1:probe17	0.0247653841462597	0.0630687077076657	0.392673086961787	0.694659054496499	   
df.mm.trans1:probe18	0.0100895017349293	0.0630687077076657	0.159976351215184	0.872937577115233	   
df.mm.trans1:probe19	0.0105811868830211	0.0630687077076657	0.167772375043210	0.86680212357534	   
df.mm.trans1:probe20	0.0738910883849193	0.0630687077076657	1.17159667718922	0.241686167019427	   
df.mm.trans1:probe21	-0.00638147615852637	0.0630687077076657	-0.101182922410676	0.91942903104096	   
df.mm.trans2:probe2	0.009988349751119	0.0630687077076657	0.158372513313840	0.874200756661742	   
df.mm.trans2:probe3	-0.0378888078041323	0.0630687077076657	-0.600754465744778	0.548163123950235	   
df.mm.trans2:probe4	-0.0909324406684381	0.0630687077076657	-1.44179964951756	0.149725928513546	   
df.mm.trans2:probe5	-0.0628853289222106	0.0630687077076657	-0.997092396655643	0.319002424798824	   
df.mm.trans2:probe6	0.00536711192685234	0.0630687077076657	0.0850994434788457	0.932202301679695	   
df.mm.trans3:probe2	0.120368991994448	0.0630687077076657	1.90853747237661	0.0566571849516979	.  
df.mm.trans3:probe3	0.0869812599987528	0.0630687077076657	1.37915082075133	0.168209747679377	   
df.mm.trans3:probe4	0.0408757936343806	0.0630687077076657	0.648115287597883	0.517084801653005	   
df.mm.trans3:probe5	0.088885075885646	0.0630687077076657	1.40933719932306	0.159099938932127	   
df.mm.trans3:probe6	0.0342201170958751	0.0630687077076657	0.542584719739164	0.587557490350722	   
df.mm.trans3:probe7	0.124409730040438	0.0630687077076657	1.97260629815182	0.0488630132276677	*  
df.mm.trans3:probe8	0.0791428609496394	0.0630687077076657	1.25486733161682	0.209870623710157	   
df.mm.trans3:probe9	0.0861296907318745	0.0630687077076657	1.36564857379194	0.172409280576471	   
df.mm.trans3:probe10	0.148973221565513	0.0630687077076657	2.36207823150633	0.0183965411826155	*  
df.mm.trans3:probe11	-0.00280134123544033	0.0630687077076657	-0.0444172924618185	0.964582173395404