chr17.10824_chr17_70367615_70368701_+_2.R 

fitVsDatCorrelation=0.920165704179806
cont.fitVsDatCorrelation=0.271566969475674

fstatistic=10405.7271527955,47,577
cont.fstatistic=1711.90834786144,47,577

residuals=-0.373409512819888,-0.092420846790105,-0.00263033351887261,0.0808318139093168,0.808541371817796
cont.residuals=-0.747942849497475,-0.247791324735640,-0.112417899887338,0.209355649696681,1.38446613073738

predictedValues:
Include	Exclude	Both
chr17.10824_chr17_70367615_70368701_+_2.R.tl.Lung	55.4200516665222	122.623627016613	107.550756624360
chr17.10824_chr17_70367615_70368701_+_2.R.tl.cerebhem	56.6153659375509	64.2529417260043	81.416074046871
chr17.10824_chr17_70367615_70368701_+_2.R.tl.cortex	49.9359064493256	86.1735516524928	93.134389190393
chr17.10824_chr17_70367615_70368701_+_2.R.tl.heart	54.3432310468777	101.884368680772	68.9095103841108
chr17.10824_chr17_70367615_70368701_+_2.R.tl.kidney	54.5933738121158	122.443485400072	77.9850807426256
chr17.10824_chr17_70367615_70368701_+_2.R.tl.liver	56.6913851021256	146.399862762889	120.939460465656
chr17.10824_chr17_70367615_70368701_+_2.R.tl.stomach	53.3983501775911	88.1431613977963	79.1364876363362
chr17.10824_chr17_70367615_70368701_+_2.R.tl.testicle	51.9690890094179	101.529277209956	91.3934195691134


diffExp=-67.2035753500908,-7.63757578845338,-36.2376452031672,-47.5411376338945,-67.8501115879562,-89.7084776607637,-34.7448112202053,-49.5601882005381
diffExpScore=0.997509237755483
diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	63.3463542497898	66.8596647300687	52.8188417553333
cerebhem	66.2278021592113	63.5949737408256	59.4641732753901
cortex	69.9671350952764	58.4286836871377	66.7347009054113
heart	65.1531602680815	63.5626367312532	62.0141704210517
kidney	73.3821324761272	60.5431585384369	78.3152579975049
liver	68.5982378626662	58.5404619146061	58.8803459724178
stomach	72.603243579956	65.1630390146858	67.5281018214537
testicle	64.2232327625032	71.6778825246364	63.3633960637444
cont.diffExp=-3.51331048027892,2.63282841838568,11.5384514081387,1.59052353682829,12.8389739376902,10.0577759480601,7.44020456527032,-7.45464976213324
cont.diffExpScore=1.57944805793801

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

tran.correlation=0.320460128396979
cont.tran.correlation=-0.57080435736367

tran.covariance=0.00251011986904240
cont.tran.covariance=-0.00224416469078941

tran.mean=79.1510643155077
cont.tran.mean=65.7419874584539

weightedLogRatios:
wLogRatio
Lung	-3.50393873590046
cerebhem	-0.518786571831433
cortex	-2.28264205961849
heart	-2.70865017590132
kidney	-3.55709717100282
liver	-4.2806048345607
stomach	-2.11918549463572
testicle	-2.86998998044018

cont.weightedLogRatios:
wLogRatio
Lung	-0.225393026111748
cerebhem	0.169274130542124
cortex	0.74933428711132
heart	0.102922671331267
kidney	0.807668209424545
liver	0.657817266861548
stomach	0.457438476394318
testicle	-0.463129186780821

varWeightedLogRatios=1.31002678162130
cont.varWeightedLogRatios=0.217740954006278

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.46452461219460	0.0807213325675455	55.3078655937549	8.30152828055373e-233	***
df.mm.trans1	-0.460671812507165	0.0720070791551395	-6.39759059681681	3.26608151125195e-10	***
df.mm.trans2	0.312839036732274	0.0668831078254603	4.67739982341529	3.62240131059284e-06	***
df.mm.exp2	-0.346563085815174	0.0918102528361066	-3.7747754211486	0.000176724742786163	***
df.mm.exp3	-0.313038179955012	0.0918102528361067	-3.40962115106932	0.00069596324473508	***
df.mm.exp4	0.240266089560998	0.0918102528361067	2.61698538168612	0.00910356435042867	** 
df.mm.exp5	0.304946258485923	0.0918102528361066	3.32148370215571	0.000952004362365333	***
df.mm.exp6	0.0825755292653269	0.0918102528361066	0.8994151166617	0.368806783986249	   
df.mm.exp7	-0.0605301788465973	0.0918102528361066	-0.659296505311358	0.509968420791727	   
df.mm.exp8	-0.0902754800387343	0.0918102528361067	-0.983283209119224	0.325880187405055	   
df.mm.trans1:exp2	0.367902045468531	0.0868438050207343	4.23636487807844	2.64391234957885e-05	***
df.mm.trans2:exp2	-0.299729127059182	0.076910166443304	-3.89713273186235	0.000108757877433354	***
df.mm.trans1:exp3	0.208837020363685	0.0868438050207343	2.40474286351023	0.0164978336338671	*  
df.mm.trans2:exp3	-0.0397182360532917	0.076910166443304	-0.516423743310592	0.605756343451823	   
df.mm.trans1:exp4	-0.259887499212237	0.0868438050207343	-2.9925853565512	0.0028845512008028	** 
df.mm.trans2:exp4	-0.425547280937917	0.076910166443304	-5.53304329735937	4.77931628236252e-08	***
df.mm.trans1:exp5	-0.319975213558631	0.0868438050207343	-3.68449094880441	0.000250735764490248	***
df.mm.trans2:exp5	-0.306416399846057	0.076910166443304	-3.98408187130811	7.6415464160278e-05	***
df.mm.trans1:exp6	-0.05989474001606	0.0868438050207343	-0.689683507093682	0.490670515135764	   
df.mm.trans2:exp6	0.094646413629186	0.076910166443304	1.23060991811735	0.218970222446255	   
df.mm.trans1:exp7	0.0233685570782001	0.0868438050207343	0.269087208611147	0.787958764608543	   
df.mm.trans2:exp7	-0.26962721569073	0.076910166443304	-3.50574219455749	0.000490707507211321	***
df.mm.trans1:exp8	0.0259831080362728	0.0868438050207343	0.299193569766654	0.764900088887834	   
df.mm.trans2:exp8	-0.0984970388738194	0.076910166443304	-1.28067644927577	0.200821875672069	   
df.mm.trans1:probe2	-0.0708137251886395	0.0434219025103671	-1.63082962962603	0.103471956415666	   
df.mm.trans1:probe3	0.164559639739285	0.0434219025103671	3.78978419243596	0.00016662559663783	***
df.mm.trans1:probe4	-0.0413961394956061	0.0434219025103672	-0.953346977040507	0.340813497377198	   
df.mm.trans1:probe5	0.00670139848651702	0.0434219025103671	0.154332217132058	0.87740176146984	   
df.mm.trans1:probe6	-0.0744761550628225	0.0434219025103671	-1.71517484856959	0.0868500641741548	.  
df.mm.trans1:probe7	-0.0340758702658497	0.0434219025103671	-0.78476225811879	0.432915047654371	   
df.mm.trans1:probe8	-0.0472428389097986	0.0434219025103672	-1.08799560080352	0.277051293389033	   
df.mm.trans1:probe9	-0.103220274167354	0.0434219025103671	-2.37714766511461	0.0177721275790912	*  
df.mm.trans1:probe10	-0.0934713104332506	0.0434219025103672	-2.15263047055421	0.0317616657726122	*  
df.mm.trans1:probe11	0.0388073155367595	0.0434219025103672	0.893726743720961	0.37184072753083	   
df.mm.trans1:probe12	0.0844531160581539	0.0434219025103671	1.94494278637354	0.0522672993416948	.  
df.mm.trans1:probe13	0.325304812814748	0.0434219025103671	7.49172178112372	2.56605314772767e-13	***
df.mm.trans1:probe14	0.0273795778877138	0.0434219025103671	0.630547633908413	0.528585999058644	   
df.mm.trans1:probe15	0.0372991191335916	0.0434219025103671	0.858993203365199	0.390700977159257	   
df.mm.trans1:probe16	0.0236511013075678	0.0434219025103671	0.544681369083747	0.586183123745361	   
df.mm.trans1:probe17	-0.0216863270588313	0.0434219025103671	-0.499432908395794	0.617664817496084	   
df.mm.trans2:probe2	0.117249488096962	0.0434219025103672	2.70023838934667	0.00713234284507782	** 
df.mm.trans2:probe3	0.0329403153917537	0.0434219025103672	0.758610597126395	0.448395288405543	   
df.mm.trans2:probe4	0.175926860110081	0.0434219025103671	4.05156959827078	5.78408379478009e-05	***
df.mm.trans2:probe5	-0.105773044266064	0.0434219025103672	-2.43593758336155	0.0151548241292949	*  
df.mm.trans2:probe6	0.0654610313081495	0.0434219025103672	1.50755788032365	0.132215018279332	   
df.mm.trans3:probe2	0.457574256862981	0.0434219025103671	10.5378675370968	7.24216177123849e-24	***
df.mm.trans3:probe3	0.626238906226882	0.0434219025103672	14.4221894947455	1.72547622440053e-40	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.40746072411214	0.198421465653121	22.2126205428662	1.83474962615864e-79	***
df.mm.trans1	-0.253369310597769	0.177000920684844	-1.43145758574274	0.152840525347728	   
df.mm.trans2	-0.243019447667004	0.164405664030121	-1.47816955760405	0.139908049929765	   
df.mm.exp2	-0.124084460178099	0.225679189754211	-0.549826770971839	0.582650948265031	   
df.mm.exp3	-0.269237753416765	0.225679189754211	-1.19301098922765	0.233355399395149	   
df.mm.exp4	-0.182941522976997	0.225679189754211	-0.810626461288879	0.417914431788725	   
df.mm.exp5	-0.346050739433154	0.225679189754211	-1.53337460937378	0.125731677972131	   
df.mm.exp6	-0.161867574979824	0.225679189754211	-0.717246349369279	0.473512353484579	   
df.mm.exp7	-0.134987058132683	0.225679189754211	-0.598136931808814	0.549983269711552	   
df.mm.exp8	-0.0986843355021345	0.225679189754211	-0.437277072864416	0.66207403370267	   
df.mm.trans1:exp2	0.168567451372267	0.213471142348758	0.789649830499667	0.4300566717103	   
df.mm.trans2:exp2	0.0740230316964188	0.189053221297309	0.391545995294144	0.695538253120588	   
df.mm.trans1:exp3	0.36864603079954	0.213471142348758	1.72691271870961	0.0847185357101888	.  
df.mm.trans2:exp3	0.134448815225682	0.189053221297309	0.711169131650208	0.477266819599096	   
df.mm.trans1:exp4	0.211064977161122	0.213471142348758	0.988728381920093	0.323210481208289	   
df.mm.trans2:exp4	0.132371481441809	0.189053221297309	0.70018104179055	0.484096461098447	   
df.mm.trans1:exp5	0.493113862886081	0.213471142348758	2.30997903257789	0.0212413173013324	*  
df.mm.trans2:exp5	0.246811347945805	0.189053221297309	1.30551252315169	0.192238687422895	   
df.mm.trans1:exp6	0.241517066469919	0.213471142348758	1.13138040023855	0.258365017589673	   
df.mm.trans2:exp6	0.028989880320856	0.189053221297309	0.153342429829672	0.878181853718607	   
df.mm.trans1:exp7	0.27137930059661	0.213471142348758	1.27126925733711	0.204145102760511	   
df.mm.trans2:exp7	0.109283613574206	0.189053221297309	0.578057400050034	0.563450891616248	   
df.mm.trans1:exp8	0.112432005992095	0.213471142348758	0.526684800367112	0.59861489943456	   
df.mm.trans2:exp8	0.168270696588069	0.189053221297309	0.89007050730674	0.373798981153043	   
df.mm.trans1:probe2	0.0866550966444992	0.106735571174379	0.811867081339984	0.417202701656593	   
df.mm.trans1:probe3	0.0351302145304925	0.106735571174379	0.329133147871561	0.742174481775197	   
df.mm.trans1:probe4	-0.00693965649979772	0.106735571174379	-0.0650172798387903	0.9481827612214	   
df.mm.trans1:probe5	-0.0299369163732614	0.106735571174379	-0.280477408270501	0.77921178842459	   
df.mm.trans1:probe6	0.0258843779795738	0.106735571174379	0.242509387402680	0.808471658985097	   
df.mm.trans1:probe7	-0.0787594710685262	0.106735571174379	-0.737893377080946	0.460879228919859	   
df.mm.trans1:probe8	-0.0328429964343358	0.106735571174379	-0.307704320808652	0.758418367786864	   
df.mm.trans1:probe9	0.0452531984954083	0.106735571174379	0.423974856718347	0.671742163444044	   
df.mm.trans1:probe10	0.115975133298377	0.106735571174379	1.08656497569028	0.277682816331802	   
df.mm.trans1:probe11	-0.148601410926581	0.106735571174379	-1.39223886930631	0.164386406566369	   
df.mm.trans1:probe12	-0.0203227277845406	0.106735571174379	-0.190402576769261	0.84906064765373	   
df.mm.trans1:probe13	0.0108479844271764	0.106735571174379	0.101634200368437	0.919082339709583	   
df.mm.trans1:probe14	-0.0262622536294355	0.106735571174379	-0.246049684659762	0.805731271947236	   
df.mm.trans1:probe15	-0.101712273468450	0.106735571174379	-0.952936985761546	0.341021020348672	   
df.mm.trans1:probe16	0.0206283791740706	0.106735571174379	0.193266208697839	0.846818499849039	   
df.mm.trans1:probe17	-0.00447783307846295	0.106735571174379	-0.0419525845900736	0.966551009588459	   
df.mm.trans2:probe2	-0.0789443149572987	0.106735571174379	-0.73962516983512	0.459828266753487	   
df.mm.trans2:probe3	0.150097408967511	0.106735571174379	1.40625479693447	0.160186781915058	   
df.mm.trans2:probe4	0.100573030988285	0.106735571174379	0.942263482377153	0.346452098300570	   
df.mm.trans2:probe5	0.205106252027531	0.106735571174379	1.92162977881515	0.0551445891721101	.  
df.mm.trans2:probe6	-0.0334410667799114	0.106735571174379	-0.313307610686574	0.754160188798985	   
df.mm.trans3:probe2	0.118964584095237	0.106735571174379	1.11457298430416	0.26549748206289	   
df.mm.trans3:probe3	0.0484234538106299	0.106735571174379	0.453676813435684	0.650232037983503