chr5.17819_chr5_113298721_113300513_+_2.R 

fitVsDatCorrelation=0.868559468778256
cont.fitVsDatCorrelation=0.240585052885028

fstatistic=7958.1448077495,65,991
cont.fstatistic=2063.36667942510,65,991

residuals=-0.855042459602089,-0.104381891415778,-0.00731042054773874,0.0855703170073775,1.20411434730890
cont.residuals=-0.678696005466849,-0.266597691409115,-0.0923696438923648,0.225614761937305,1.75774072262871

predictedValues:
Include	Exclude	Both
chr5.17819_chr5_113298721_113300513_+_2.R.tl.Lung	91.0676019842033	54.6067434549356	74.8456344096829
chr5.17819_chr5_113298721_113300513_+_2.R.tl.cerebhem	93.0564080278125	56.1941435112355	76.772419334382
chr5.17819_chr5_113298721_113300513_+_2.R.tl.cortex	101.528423993334	54.9456287968704	87.977708226637
chr5.17819_chr5_113298721_113300513_+_2.R.tl.heart	91.6483056325711	54.2885204498742	72.1960616398023
chr5.17819_chr5_113298721_113300513_+_2.R.tl.kidney	87.0807162911871	56.5999503873479	73.3366921727813
chr5.17819_chr5_113298721_113300513_+_2.R.tl.liver	94.3633987598678	53.6989742578159	68.2360968376283
chr5.17819_chr5_113298721_113300513_+_2.R.tl.stomach	105.649416625867	56.4186034144014	79.0332948685798
chr5.17819_chr5_113298721_113300513_+_2.R.tl.testicle	89.0351315894453	57.1514233987002	77.4564563089594


diffExp=36.4608585292677,36.862264516577,46.5827951964632,37.3597851826969,30.4807659038392,40.6644245020519,49.2308132114652,31.8837081907451
diffExpScore=0.996779651677627
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	69.3038852171977	72.2716391061862	74.6606703300457
cerebhem	67.314486434941	75.6559281186377	69.2663556064034
cortex	67.8251571183859	68.0159971648509	64.2486646305387
heart	66.1449202599778	81.4778197578484	69.5125309082011
kidney	67.179060554686	62.3746980563707	71.6185414169974
liver	66.220379350847	66.6361126849894	65.6749296656205
stomach	64.4882292935182	72.9419635307069	73.2799003808779
testicle	67.8333232821555	73.9173651740081	73.7753841352292
cont.diffExp=-2.96775388898853,-8.34144168369674,-0.190840046464984,-15.3328994978706,4.80436249831533,-0.415733334142473,-8.4537342371887,-6.08404189185258
cont.diffExpScore=1.22665226666801

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,-1,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=-0.0917137343930158
cont.tran.correlation=-0.131663704224688

tran.covariance=-0.000160923928856721
cont.tran.covariance=-0.000221637909588495

tran.mean=74.8333369109668
cont.tran.mean=69.3500603190817

weightedLogRatios:
wLogRatio
Lung	2.17664725908672
cerebhem	2.15931237135196
cortex	2.6483686672452
heart	2.22870709251783
kidney	1.83162879570843
liver	2.40458796268793
stomach	2.72665451063466
testicle	1.89183816225446

cont.weightedLogRatios:
wLogRatio
Lung	-0.178602905104535
cerebhem	-0.498564145078237
cortex	-0.0118524958627521
heart	-0.89566048834882
kidney	0.309441209186717
liver	-0.0262609913906656
stomach	-0.5208199565204
testicle	-0.365909411202162

varWeightedLogRatios=0.103799072964326
cont.varWeightedLogRatios=0.14083666457937

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.7712266115426	0.0867534477599309	54.9975445903408	2.26140190523226e-303	***
df.mm.trans1	0.171669066894586	0.0742340231423395	2.31253890908514	0.0209521349703291	*  
df.mm.trans2	-0.742942991031226	0.064910521820352	-11.4456481044385	1.41055160263026e-28	***
df.mm.exp2	0.0248412316516211	0.0819648401518942	0.303071800122909	0.761898752293429	   
df.mm.exp3	-0.0467322458593713	0.0819648401518942	-0.570149905407841	0.568705262402393	   
df.mm.exp4	0.0365540784395394	0.0819648401518942	0.445972667936627	0.65571440770088	   
df.mm.exp5	0.0114508050227803	0.0819648401518942	0.139703865725353	0.88892235870735	   
df.mm.exp6	0.111241762332129	0.0819648401518942	1.35718879126684	0.175030244758835	   
df.mm.exp7	0.126724249814822	0.0819648401518942	1.54608060700151	0.122404364625634	   
df.mm.exp8	-0.0113123059301315	0.0819648401518942	-0.138014127876879	0.89025728905019	   
df.mm.trans1:exp2	-0.00323749410083905	0.0748749042970406	-0.043238707698315	0.965519959871982	   
df.mm.trans2:exp2	0.00381393020765016	0.0513902846181272	0.0742150045673196	0.940854284123386	   
df.mm.trans1:exp3	0.155468934814549	0.0748749042970406	2.07638241776950	0.0381158800710052	*  
df.mm.trans2:exp3	0.0529189931820506	0.0513902846181272	1.02974703439148	0.303380024681432	   
df.mm.trans1:exp4	-0.0301977013605292	0.0748749042970406	-0.40330871396817	0.686808130730288	   
df.mm.trans2:exp4	-0.0423986652379599	0.0513902846181272	-0.825032699332519	0.409551572025606	   
df.mm.trans1:exp5	-0.0562174526882159	0.0748749042970406	-0.750818357846474	0.452940218719203	   
df.mm.trans2:exp5	0.0243999220121381	0.0513902846181272	0.47479639767419	0.63503671363257	   
df.mm.trans1:exp6	-0.075690599033003	0.0748749042970406	-1.01089410054838	0.312313868735865	   
df.mm.trans2:exp6	-0.128005243969165	0.0513902846181272	-2.49084520392037	0.0129067200657815	*  
df.mm.trans1:exp7	0.0217998628040505	0.0748749042970406	0.291150459672937	0.77099717438	   
df.mm.trans2:exp7	-0.0940826795507639	0.0513902846181272	-1.83074836517986	0.0674382681260858	.  
df.mm.trans1:exp8	-0.0112587749319621	0.0748749042970406	-0.150367803974704	0.880505040050996	   
df.mm.trans2:exp8	0.0568592205995395	0.0513902846181272	1.10641964764451	0.268813430913	   
df.mm.trans1:probe2	-0.207589221014162	0.0552987816411455	-3.75395650416471	0.000184188411682832	***
df.mm.trans1:probe3	-0.855197167880746	0.0552987816411455	-15.4650273025985	1.71757998770859e-48	***
df.mm.trans1:probe4	-0.96874809324005	0.0552987816411455	-17.5184346651002	4.56793250431697e-60	***
df.mm.trans1:probe5	-0.0233532821526746	0.0552987816411455	-0.422310970686891	0.672889656916066	   
df.mm.trans1:probe6	-1.03679795710442	0.0552987816411455	-18.7490198940112	2.30349875717368e-67	***
df.mm.trans1:probe7	-0.749104826669811	0.0552987816411455	-13.5464978510925	1.75992649433240e-38	***
df.mm.trans1:probe8	-0.42689224352826	0.0552987816411455	-7.71974048720502	2.84008490855738e-14	***
df.mm.trans1:probe9	-0.176896269210910	0.0552987816411455	-3.19891802244137	0.00142321170636274	** 
df.mm.trans1:probe10	-0.244315936857863	0.0552987816411455	-4.41810704697476	1.10531728233954e-05	***
df.mm.trans1:probe11	-1.15980971154758	0.0552987816411454	-20.9735129260897	4.08140064959953e-81	***
df.mm.trans1:probe12	-0.909773163259214	0.0552987816411455	-16.4519567386326	5.92730427572059e-54	***
df.mm.trans1:probe13	-1.11881183315248	0.0552987816411455	-20.2321244690864	1.84412655596534e-76	***
df.mm.trans1:probe14	-0.9720493060735	0.0552987816411455	-17.5781324149507	2.04878886058257e-60	***
df.mm.trans1:probe15	-1.10774469536934	0.0552987816411455	-20.0319909859482	3.24138165133374e-75	***
df.mm.trans1:probe16	-1.06051077193758	0.0552987816411454	-19.1778325030673	5.80800827406716e-70	***
df.mm.trans1:probe17	-0.879188877781363	0.0552987816411455	-15.8988833332125	7.21290501334082e-51	***
df.mm.trans1:probe18	-0.93252276553521	0.0552987816411455	-16.8633510153388	2.75091728592748e-56	***
df.mm.trans1:probe19	-0.9976132370805	0.0552987816411455	-18.0404198333769	3.93058014075088e-63	***
df.mm.trans1:probe20	-0.841350411481674	0.0552987816411455	-15.2146283609196	3.87827352780216e-47	***
df.mm.trans1:probe21	-0.799361098094809	0.0552987816411455	-14.4553112088827	4.06793294106297e-43	***
df.mm.trans1:probe22	-0.9215247443271	0.0552987816411455	-16.6644674073874	3.72835905701625e-55	***
df.mm.trans2:probe2	-0.170466160804266	0.0552987816411454	-3.08263863588324	0.00210847339386300	** 
df.mm.trans2:probe3	-0.141149186165756	0.0552987816411455	-2.55248274874709	0.0108448117728754	*  
df.mm.trans2:probe4	-0.109642815511594	0.0552987816411454	-1.98273474130239	0.0476728446133384	*  
df.mm.trans2:probe5	-0.101033109012617	0.0552987816411454	-1.82704041597622	0.0679943390815446	.  
df.mm.trans2:probe6	-0.0964859840013838	0.0552987816411454	-1.74481211227252	0.0813275615396671	.  
df.mm.trans3:probe2	0.400960839374793	0.0552987816411455	7.25080783834947	8.33612320355487e-13	***
df.mm.trans3:probe3	0.489754341451167	0.0552987816411455	8.85651233022395	3.74693913617671e-18	***
df.mm.trans3:probe4	-0.0586172186741305	0.0552987816411455	-1.06000922505887	0.289398681069481	   
df.mm.trans3:probe5	-0.246622765810994	0.0552987816411455	-4.45982277532662	9.14047122722268e-06	***
df.mm.trans3:probe6	-0.148417380497095	0.0552987816411455	-2.68391773005473	0.00739782971075234	** 
df.mm.trans3:probe7	0.050680941314658	0.0552987816411455	0.916492910161126	0.35963139414533	   
df.mm.trans3:probe8	0.093051792509827	0.0552987816411455	1.68270963207969	0.092746312822872	.  
df.mm.trans3:probe9	0.355241482891382	0.0552987816411455	6.42403814964093	2.05694335349549e-10	***
df.mm.trans3:probe10	0.362677200693225	0.0552987816411455	6.55850255520588	8.73707488041845e-11	***
df.mm.trans3:probe11	-0.0420926128646639	0.0552987816411455	-0.761185176516521	0.446727557054957	   
df.mm.trans3:probe12	-0.0840061965244426	0.0552987816411455	-1.51913286389545	0.129048011003044	   
df.mm.trans3:probe13	-0.124135664376309	0.0552987816411455	-2.24481734845211	0.0249997751368282	*  
df.mm.trans3:probe14	0.354572966083787	0.0552987816411455	6.41194897176477	2.21985018146334e-10	***
df.mm.trans3:probe15	0.0667023950923461	0.0552987816411455	1.20621816815428	0.228021264842973	   
df.mm.trans3:probe16	0.37785143515726	0.0552987816411455	6.83290705407001	1.45097137956151e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12605383880633	0.169910990323505	24.2836195054272	1.38853486096973e-102	***
df.mm.trans1	0.108186749078636	0.145391067600181	0.744108636550798	0.456987150599561	   
df.mm.trans2	0.167423640072060	0.127130521376299	1.31694292023311	0.188162200389320	   
df.mm.exp2	0.091632663670583	0.160532261501065	0.570805287446694	0.56826104464272	   
df.mm.exp3	0.0679358635700057	0.160532261501065	0.423191344436117	0.672247481496152	   
df.mm.exp4	0.144692590388359	0.160532261501064	0.901330293583386	0.367631725625731	   
df.mm.exp5	-0.136811987249282	0.160532261501064	-0.852239829988158	0.394286902465054	   
df.mm.exp6	0.00153835738859335	0.160532261501064	0.00958285502371217	0.992356033656697	   
df.mm.exp7	-0.0441188423664316	0.160532261501064	-0.274828510817055	0.783505276529809	   
df.mm.exp8	0.0129969209508929	0.160532261501064	0.0809614268768442	0.93548897586094	   
df.mm.trans1:exp2	-0.120758166914185	0.146646265571982	-0.82346568078756	0.410441345389558	   
df.mm.trans2:exp2	-0.0458686494159305	0.100650456874473	-0.455722217665994	0.648689581376703	   
df.mm.trans1:exp3	-0.0895036568661172	0.146646265571982	-0.610337102803236	0.541778458850978	   
df.mm.trans2:exp3	-0.128624718987355	0.100650456874473	-1.27793477527648	0.20157168063819	   
df.mm.trans1:exp4	-0.191345462372947	0.146646265571982	-1.30480964944194	0.192260579775227	   
df.mm.trans2:exp4	-0.0247935427418005	0.100650456874473	-0.246333136596905	0.805475373650876	   
df.mm.trans1:exp5	0.105672618968193	0.146646265571982	0.720595362971063	0.471328445064557	   
df.mm.trans2:exp5	-0.0104600848206003	0.100650456874473	-0.103924861798150	0.917250004722231	   
df.mm.trans1:exp6	-0.0470510650058492	0.146646265571982	-0.320847345292636	0.748393724648209	   
df.mm.trans2:exp6	-0.0827234796400471	0.100650456874473	-0.821888764431702	0.411337899033611	   
df.mm.trans1:exp7	-0.0278994104233866	0.146646265571982	-0.190249716312702	0.849152417812464	   
df.mm.trans2:exp7	0.0533511618411992	0.100650456874473	0.530063782102214	0.596186447649896	   
df.mm.trans1:exp8	-0.0344443212755461	0.146646265571982	-0.234880316530386	0.814350149308258	   
df.mm.trans2:exp8	0.00951907610602648	0.100650456874473	0.0945755876488295	0.924671059700543	   
df.mm.trans1:probe2	0.0542917491135827	0.108305444854838	0.501283653710576	0.616282837837324	   
df.mm.trans1:probe3	-0.0675053993144301	0.108305444854838	-0.623287217045343	0.533239213576805	   
df.mm.trans1:probe4	-0.0288445474603918	0.108305444854838	-0.266325922016683	0.790043612309489	   
df.mm.trans1:probe5	0.0454471987201677	0.108305444854838	0.419620627393946	0.674853566847399	   
df.mm.trans1:probe6	0.0144447816870888	0.108305444854838	0.133370780263625	0.893927230262362	   
df.mm.trans1:probe7	-0.0228172665007936	0.108305444854838	-0.210675156095573	0.833184071792391	   
df.mm.trans1:probe8	-0.070059444627689	0.108305444854838	-0.646869090668428	0.517866465128942	   
df.mm.trans1:probe9	-0.108692761297478	0.108305444854838	-1.00357614931695	0.315827933378581	   
df.mm.trans1:probe10	0.139479018485771	0.108305444854838	1.28783016101096	0.198105730737705	   
df.mm.trans1:probe11	0.0807398968346748	0.108305444854838	0.745483266726713	0.45615639666316	   
df.mm.trans1:probe12	-0.0220889103489572	0.108305444854838	-0.203950137304389	0.83843433813379	   
df.mm.trans1:probe13	-0.0354304593258436	0.108305444854838	-0.327134608729330	0.743635162483975	   
df.mm.trans1:probe14	0.060604692073411	0.108305444854838	0.559571978626184	0.57589786753244	   
df.mm.trans1:probe15	0.102024694885870	0.108305444854838	0.94200891767365	0.346417648770422	   
df.mm.trans1:probe16	-0.022113990621915	0.108305444854838	-0.204181707129816	0.838253428833656	   
df.mm.trans1:probe17	0.0745439577766735	0.108305444854838	0.688275255935512	0.491440538159549	   
df.mm.trans1:probe18	-0.0160997027110555	0.108305444854838	-0.148650907926503	0.881859342628183	   
df.mm.trans1:probe19	-0.0280860003522905	0.108305444854838	-0.259322145714226	0.795440551978056	   
df.mm.trans1:probe20	-0.0270389855281874	0.108305444854838	-0.249654904833526	0.802905973169792	   
df.mm.trans1:probe21	0.103897603020297	0.108305444854838	0.959301752183842	0.337640735401733	   
df.mm.trans1:probe22	-0.0648016662835558	0.108305444854838	-0.598323254850293	0.54976098138873	   
df.mm.trans2:probe2	-0.113217453455910	0.108305444854838	-1.04535329324999	0.296114624816189	   
df.mm.trans2:probe3	-0.0882183728742346	0.108305444854838	-0.814533128897384	0.415535290154974	   
df.mm.trans2:probe4	-0.042503143217629	0.108305444854838	-0.392437732697519	0.694819198907989	   
df.mm.trans2:probe5	0.0056359312281159	0.108305444854838	0.0520373766588534	0.958509398526332	   
df.mm.trans2:probe6	-0.0487022193001098	0.108305444854838	-0.44967470809418	0.653043346290404	   
df.mm.trans3:probe2	-0.228271329858464	0.108305444854838	-2.10766254793944	0.0353108565166156	*  
df.mm.trans3:probe3	-0.121974915211688	0.108305444854838	-1.12621221744826	0.260348350739100	   
df.mm.trans3:probe4	-0.209443830896786	0.108305444854838	-1.93382549859338	0.0534190683878298	.  
df.mm.trans3:probe5	0.035231924223724	0.108305444854838	0.325301505117728	0.745021557412856	   
df.mm.trans3:probe6	-0.0572754779170742	0.108305444854838	-0.528832857792518	0.597039821403724	   
df.mm.trans3:probe7	-0.0423054861711362	0.108305444854838	-0.390612736301838	0.696167461585453	   
df.mm.trans3:probe8	-0.116133637053126	0.108305444854838	-1.07227884257139	0.283855856042312	   
df.mm.trans3:probe9	-0.0718800506822103	0.108305444854838	-0.663679012431471	0.507050137362514	   
df.mm.trans3:probe10	0.118453406128409	0.108305444854838	1.09369760945234	0.274353395098166	   
df.mm.trans3:probe11	-0.212791135506278	0.108305444854838	-1.96473165122475	0.0497243710964626	*  
df.mm.trans3:probe12	-0.106504036877262	0.108305444854838	-0.983367336886976	0.325666694122698	   
df.mm.trans3:probe13	-0.140292425657774	0.108305444854838	-1.29534046830063	0.195504464591643	   
df.mm.trans3:probe14	-0.113943494495057	0.108305444854838	-1.05205693626738	0.293029886605941	   
df.mm.trans3:probe15	-0.189502874746122	0.108305444854838	-1.74970773630183	0.0804782676412363	.  
df.mm.trans3:probe16	0.0372609475255198	0.108305444854838	0.344035773782755	0.730892367157234