chr1.735_chr1_34917817_34924305_+_2.R 

fitVsDatCorrelation=0.852702253413513
cont.fitVsDatCorrelation=0.244770674241405

fstatistic=8822.78177515927,49,623
cont.fstatistic=2552.15051026784,49,623

residuals=-1.04810082041234,-0.085290069271511,-0.0138172124470325,0.0728270601392281,1.02558124289994
cont.residuals=-0.714500367196891,-0.181158642088491,-0.0312641929646864,0.117871608279964,1.89012739648472

predictedValues:
Include	Exclude	Both
chr1.735_chr1_34917817_34924305_+_2.R.tl.Lung	68.327752438062	59.2642955771235	51.4562762464977
chr1.735_chr1_34917817_34924305_+_2.R.tl.cerebhem	70.2577050674593	58.5171503646137	45.8898403390489
chr1.735_chr1_34917817_34924305_+_2.R.tl.cortex	62.0665231334958	65.0179350400677	48.130297334288
chr1.735_chr1_34917817_34924305_+_2.R.tl.heart	65.4634512619098	63.5949593689322	49.5086807752575
chr1.735_chr1_34917817_34924305_+_2.R.tl.kidney	67.9570016938708	58.4825092791695	51.2993252109443
chr1.735_chr1_34917817_34924305_+_2.R.tl.liver	63.6963566260726	62.2400038627936	47.1674643022822
chr1.735_chr1_34917817_34924305_+_2.R.tl.stomach	75.8274401353448	72.7567695573893	60.4000046645512
chr1.735_chr1_34917817_34924305_+_2.R.tl.testicle	67.9994866637501	58.4882090408256	49.0441632130771


diffExp=9.06345686093857,11.7405547028456,-2.95141190657188,1.86849189297761,9.47449241470126,1.45635276327891,3.07067057795548,9.51127762292452
diffExpScore=1.11083864374580
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	63.5411783130826	64.989776982918	66.8775858806472
cerebhem	65.8790745995334	58.0751423496455	65.858843320355
cortex	67.8891499080233	60.1967236227241	63.106790770717
heart	62.239029779788	56.98092167909	63.3351042002485
kidney	65.4198947088071	59.9194895365981	74.3012530113905
liver	66.7733156498267	58.0407027635783	61.8751072169276
stomach	66.3826663351061	62.4816582004918	66.8379648061159
testicle	65.232163448537	67.5552972757027	58.229829066664
cont.diffExp=-1.44859866983544,7.80393224988788,7.69242628529921,5.25810810069807,5.50040517220899,8.73261288624843,3.90100813461428,-2.32313382716568
cont.diffExpScore=1.18117530293028

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=0.363301568872428
cont.tran.correlation=-0.0394553297994224

tran.covariance=0.00141573826304302
cont.tran.covariance=-3.53788296429876e-05

tran.mean=64.99734681943
cont.tran.mean=63.2247615720908

weightedLogRatios:
wLogRatio
Lung	0.591032217290996
cerebhem	0.760792639164166
cortex	-0.192860689517810
heart	0.120667444075119
kidney	0.622181238798457
liver	0.0958150846892649
stomach	0.178076675052802
testicle	0.624421614703598

cont.weightedLogRatios:
wLogRatio
Lung	-0.0938406210594946
cerebhem	0.52006507845073
cortex	0.500003597990146
heart	0.360728978270106
kidney	0.363322796698864
liver	0.579028222166594
stomach	0.252253954111998
testicle	-0.146814847553824

varWeightedLogRatios=0.116813674257959
cont.varWeightedLogRatios=0.075783714020628

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20562274837488	0.088206652500679	47.6792013883817	4.7358245741472e-210	***
df.mm.trans1	0.00148688912264229	0.0789322040679038	0.0188375472369066	0.984976732000201	   
df.mm.trans2	-0.204969516310091	0.0729798311885426	-2.80857756138891	0.00513205842865219	** 
df.mm.exp2	0.129655513747918	0.100080790185538	1.29550849376341	0.195624681013610	   
df.mm.exp3	0.0633674582732107	0.100080790185538	0.633163049129959	0.526859519723841	   
df.mm.exp4	0.0662875796708925	0.100080790185538	0.66234069043623	0.507997841320236	   
df.mm.exp5	-0.0156652981494154	0.100080790185538	-0.156526523425463	0.87566872300794	   
df.mm.exp6	0.0658303679431153	0.100080790185538	0.657772263998652	0.510927438743224	   
df.mm.exp7	0.149002406256897	0.100080790185538	1.48882124112593	0.137040459658252	   
df.mm.exp8	0.0300135530603202	0.100080790185538	0.299893246293106	0.764358617554019	   
df.mm.trans1:exp2	-0.101801546369844	0.0951958199160004	-1.06939092976637	0.285307706104938	   
df.mm.trans2:exp2	-0.142342660325993	0.0836386066419197	-1.70187747071639	0.0892772412563663	.  
df.mm.trans1:exp3	-0.159476710162415	0.0951958199160003	-1.67524908449904	0.094387172770411	.  
df.mm.trans2:exp3	0.0292886707795960	0.0836386066419197	0.350181237535304	0.72632104425539	   
df.mm.trans1:exp4	-0.109111604265642	0.0951958199160004	-1.14618062391731	0.252160469742181	   
df.mm.trans2:exp4	0.00423960585261716	0.0836386066419197	0.0506895801213917	0.959589127080903	   
df.mm.trans1:exp5	0.0102244597936525	0.0951958199160003	0.107404503713235	0.914502659962065	   
df.mm.trans2:exp5	0.00238599452469624	0.0836386066419197	0.0285274303398113	0.977250626424257	   
df.mm.trans1:exp6	-0.13601901832065	0.0951958199160004	-1.42883393872411	0.153553143430893	   
df.mm.trans2:exp6	-0.0168394524863392	0.0836386066419197	-0.201335880192667	0.840501665676104	   
df.mm.trans1:exp7	-0.0448581875515942	0.0951958199160004	-0.471220139615127	0.637648499344629	   
df.mm.trans2:exp7	0.0561125213246149	0.0836386066419197	0.670892588692305	0.502537601199023	   
df.mm.trans1:exp8	-0.0348294124720792	0.0951958199160004	-0.365871237863304	0.714585357654884	   
df.mm.trans2:exp8	-0.0431954004713403	0.0836386066419197	-0.516452894250999	0.60572139474201	   
df.mm.trans1:probe2	-0.176420915807323	0.0475979099580002	-3.70648450663053	0.000228868416945812	***
df.mm.trans1:probe3	-0.0245287027759617	0.0475979099580002	-0.515331509253358	0.606504188773365	   
df.mm.trans1:probe4	-0.0634496477041497	0.0475979099580002	-1.33303432356876	0.183007799645006	   
df.mm.trans1:probe5	-0.074302817135491	0.0475979099580002	-1.56105209663733	0.119019266327175	   
df.mm.trans1:probe6	0.866833379281038	0.0475979099580002	18.2115849213951	9.83315416885245e-60	***
df.mm.trans1:probe7	0.0456781694770399	0.0475979099580002	0.959667546691562	0.337594848586548	   
df.mm.trans1:probe8	0.250103314800576	0.0475979099580002	5.25450203635546	2.04099179648262e-07	***
df.mm.trans1:probe9	0.302490045949656	0.0475979099580002	6.35511194118753	4.02422485525781e-10	***
df.mm.trans1:probe10	0.218323047338160	0.0475979099580002	4.58682004169522	5.4424230395983e-06	***
df.mm.trans1:probe11	0.265739193965144	0.0475979099580002	5.58300131664666	3.53176243451518e-08	***
df.mm.trans1:probe12	0.0647613195682853	0.0475979099580002	1.36059166516828	0.174134793471004	   
df.mm.trans1:probe13	0.184564203883812	0.0475979099580002	3.8775694993051	0.000116711958410241	***
df.mm.trans1:probe14	-0.293500661617486	0.0475979099580002	-6.16625103657844	1.25753927016419e-09	***
df.mm.trans1:probe15	-0.186322024407227	0.0475979099580002	-3.91450012346415	0.000100580385519610	***
df.mm.trans1:probe16	-0.232321488820640	0.0475979099580002	-4.88091786016733	1.34311746280881e-06	***
df.mm.trans1:probe17	-0.233964775872001	0.0475979099580002	-4.91544221329147	1.1341259669072e-06	***
df.mm.trans1:probe18	-0.291169993518272	0.0475979099580002	-6.11728527103809	1.6818030930263e-09	***
df.mm.trans1:probe19	-0.243971330619478	0.0475979099580002	-5.12567318259888	3.96132980330112e-07	***
df.mm.trans2:probe2	0.269650275325027	0.0475979099580002	5.66517049935518	2.24543304613318e-08	***
df.mm.trans2:probe3	0.180516279779445	0.0475979099580002	3.79252534278774	0.000163643536437698	***
df.mm.trans2:probe4	0.143610166639068	0.0475979099580002	3.01715278603173	0.00265569547121250	** 
df.mm.trans2:probe5	0.122113640801681	0.0475979099580002	2.56552527011022	0.0105348177079978	*  
df.mm.trans2:probe6	0.0162937871578051	0.0475979099580002	0.342321483699232	0.732224423485293	   
df.mm.trans3:probe2	-0.0414049392879188	0.0475979099580002	-0.869889861224034	0.384695625229658	   
df.mm.trans3:probe3	-0.142818097858829	0.0475979099580002	-3.00051195493352	0.00280297561854721	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04735194864022	0.163713529321213	24.7221592828723	1.49443468916909e-94	***
df.mm.trans1	0.0901045772402544	0.146499944604056	0.615048541375077	0.538747246523362	   
df.mm.trans2	0.0520808533126078	0.135452206771486	0.38449615959724	0.700741950720769	   
df.mm.exp2	-0.0610094074475113	0.185752195713402	-0.328445148188950	0.74268547489533	   
df.mm.exp3	0.0476114880480339	0.185752195713402	0.256317228796013	0.79779050964463	   
df.mm.exp4	-0.0977952299543315	0.185752195713402	-0.5264822285343	0.598740578830701	   
df.mm.exp5	-0.157353864661900	0.185752195713402	-0.847117117822292	0.397255141110138	   
df.mm.exp6	0.0142758353370392	0.185752195713402	0.076854194278626	0.938764211807445	   
df.mm.exp7	0.00498348199371025	0.185752195713402	0.0268286572579701	0.978604987571423	   
df.mm.exp8	0.203447101168504	0.185752195713402	1.09526081447997	0.273825622961586	   
df.mm.trans1:exp2	0.0971420928001117	0.176685581112548	0.549802039240728	0.582652198072978	   
df.mm.trans2:exp2	-0.0514828431047648	0.155235133549047	-0.331644273610900	0.740269417491812	   
df.mm.trans1:exp3	0.0185765654795542	0.176685581112548	0.105139114140395	0.916299263629683	   
df.mm.trans2:exp3	-0.124223542426446	0.155235133549047	-0.800228270407595	0.423883585730290	   
df.mm.trans1:exp4	0.0770893482325215	0.176685581112548	0.436308088906338	0.66276436999799	   
df.mm.trans2:exp4	-0.0337182459629309	0.155235133549047	-0.217207568879873	0.828117699998264	   
df.mm.trans1:exp5	0.186492104206524	0.176685581112548	1.05550267900882	0.291604625998321	   
df.mm.trans2:exp5	0.0761257044120276	0.155235133549047	0.490389660327604	0.624030853863133	   
df.mm.trans1:exp6	0.0353395259782457	0.176685581112548	0.200013638666614	0.841535184039453	   
df.mm.trans2:exp6	-0.127361279471872	0.155235133549047	-0.820441072585102	0.412278507588593	   
df.mm.trans1:exp7	0.0387643181398929	0.176685581112548	0.219397179417829	0.82641252871211	   
df.mm.trans2:exp7	-0.0443404174542855	0.155235133549047	-0.285633905421777	0.775253473099336	   
df.mm.trans1:exp8	-0.177182622544541	0.176685581112548	-1.00281314088486	0.316340357751319	   
df.mm.trans2:exp8	-0.164730600107778	0.155235133549047	-1.06116828285996	0.289024627746012	   
df.mm.trans1:probe2	-0.0472586319241714	0.0883427905562738	-0.534946107391388	0.59287799844767	   
df.mm.trans1:probe3	-0.000395103870721258	0.0883427905562738	-0.00447239518056178	0.996432988519967	   
df.mm.trans1:probe4	0.054453062946143	0.0883427905562738	0.616383777366153	0.537866410983516	   
df.mm.trans1:probe5	-0.00943248011565579	0.0883427905562738	-0.106771362510304	0.915004739098697	   
df.mm.trans1:probe6	0.0289994378299678	0.0883427905562738	0.328260378094977	0.742825095678142	   
df.mm.trans1:probe7	0.0114375427402090	0.0883427905562738	0.129467754733459	0.89702932551931	   
df.mm.trans1:probe8	-0.0054100686263032	0.0883427905562738	-0.0612395034415063	0.951188109828165	   
df.mm.trans1:probe9	0.126915406868167	0.0883427905562738	1.43662438178611	0.151326725993347	   
df.mm.trans1:probe10	-0.0641852968058641	0.0883427905562738	-0.726548215216028	0.467775647159059	   
df.mm.trans1:probe11	0.158553207616158	0.0883427905562738	1.79474982189023	0.0731782132360394	.  
df.mm.trans1:probe12	-0.0642085113822646	0.0883427905562738	-0.7268109936075	0.467614748454105	   
df.mm.trans1:probe13	-0.0106421273882262	0.0883427905562738	-0.120464016601866	0.904154421527412	   
df.mm.trans1:probe14	0.00619905110994384	0.0883427905562738	0.0701704244444835	0.944080531776165	   
df.mm.trans1:probe15	-0.0495912175895894	0.0883427905562738	-0.561349910698147	0.574760882343984	   
df.mm.trans1:probe16	-0.0377813290031040	0.0883427905562738	-0.427667371216189	0.669041092831415	   
df.mm.trans1:probe17	0.126469194703327	0.0883427905562738	1.43157346408213	0.152767390276214	   
df.mm.trans1:probe18	-0.0235179798594544	0.0883427905562738	-0.266212779915228	0.790163420888397	   
df.mm.trans1:probe19	0.112492081568545	0.0883427905562738	1.27335893353842	0.20336560708505	   
df.mm.trans2:probe2	0.162922106381974	0.0883427905562738	1.84420375851941	0.0656280593680164	.  
df.mm.trans2:probe3	0.101396692835444	0.0883427905562738	1.14776420573737	0.251506462856459	   
df.mm.trans2:probe4	0.0955119305537145	0.0883427905562738	1.08115138713978	0.280048101836526	   
df.mm.trans2:probe5	0.159599704505535	0.0883427905562738	1.80659568823413	0.0713077297741596	.  
df.mm.trans2:probe6	0.153744171211567	0.0883427905562738	1.74031372841492	0.0822977070926521	.  
df.mm.trans3:probe2	0.00354329568879881	0.0883427905562738	0.0401084872516196	0.968019485000779	   
df.mm.trans3:probe3	0.0364361822602755	0.0883427905562738	0.412440925069781	0.680158158293303