chr17.10636_chr17_48495785_48496926_+_0.R 

fitVsDatCorrelation=0.924082525973247
cont.fitVsDatCorrelation=0.318990684611891

fstatistic=6100.10961044054,43,485
cont.fstatistic=982.547422575888,43,485

residuals=-0.602280270139905,-0.108398042348687,0.0055028983523158,0.106415593909939,0.795870130502386
cont.residuals=-0.912420602715824,-0.345684749288782,-0.103585249611361,0.324577438516895,1.43424544250238

predictedValues:
Include	Exclude	Both
chr17.10636_chr17_48495785_48496926_+_0.R.tl.Lung	63.233098955551	234.767651008578	72.0144228411825
chr17.10636_chr17_48495785_48496926_+_0.R.tl.cerebhem	76.4076008560727	170.896630284361	61.7759831291744
chr17.10636_chr17_48495785_48496926_+_0.R.tl.cortex	64.6864825950328	155.940460621603	57.4868795881323
chr17.10636_chr17_48495785_48496926_+_0.R.tl.heart	69.9238078307143	142.095003265883	64.6841761030255
chr17.10636_chr17_48495785_48496926_+_0.R.tl.kidney	66.2673644893773	243.146594713876	69.5693760299068
chr17.10636_chr17_48495785_48496926_+_0.R.tl.liver	75.2008193114384	190.355194194838	79.5740853442318
chr17.10636_chr17_48495785_48496926_+_0.R.tl.stomach	71.140592084639	154.458933102005	76.1059992176346
chr17.10636_chr17_48495785_48496926_+_0.R.tl.testicle	69.5995991833068	161.827544229662	74.2979447858036


diffExp=-171.534552053027,-94.4890294282881,-91.2539780265705,-72.1711954351684,-176.879230224499,-115.154374883399,-83.3183410173656,-92.2279450463549
diffExpScore=0.998886449776044
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	83.7790257242819	88.168136283175	76.2347652654132
cerebhem	101.034773886237	72.791493258997	98.1875527587205
cortex	84.8930911668203	124.830768596011	69.6364191930503
heart	70.3694378468762	90.2466804329246	76.8392450397267
kidney	88.1657796402718	73.9540239176324	90.4742268135993
liver	67.5488912488295	84.9334730970097	102.323747131158
stomach	98.179021083721	90.3353591133603	94.0490273501535
testicle	81.3123607920364	84.2705120574361	96.7027311106567
cont.diffExp=-4.38911055889309,28.2432806272397,-39.9376774291908,-19.8772425860485,14.2117557226395,-17.3845818481803,7.84366197036078,-2.95815126539968
cont.diffExpScore=3.82561313939206

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,-1,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,1,-1,0,0,0,0,0
cont.diffExp1.3Score=2
cont.diffExp1.2=0,1,-1,-1,0,-1,0,0
cont.diffExp1.2Score=1.33333333333333

tran.correlation=-0.394123994782408
cont.tran.correlation=-0.168692534270481

tran.covariance=-0.00498238277086459
cont.tran.covariance=-0.00444984932894735

tran.mean=125.621711045434
cont.tran.mean=86.5508017591012

weightedLogRatios:
wLogRatio
Lung	-6.30004607526676
cerebhem	-3.81443883408276
cortex	-4.05601220536886
heart	-3.26319598942721
kidney	-6.2966254825613
liver	-4.44353200944442
stomach	-3.60678410271520
testicle	-3.93589880524153

cont.weightedLogRatios:
wLogRatio
Lung	-0.227419759513143
cerebhem	1.45950443474622
cortex	-1.78678182372162
heart	-1.08923089224389
kidney	0.771887568530298
liver	-0.991037396586328
stomach	0.378446449009225
testicle	-0.157807072741623

varWeightedLogRatios=1.39628881521386
cont.varWeightedLogRatios=1.13853942370089

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.53401850536281	0.100825906018492	54.8868710820001	3.31263888933364e-210	***
df.mm.trans1	-1.33547850561781	0.0807164105133936	-16.5453158425103	4.44057437461748e-49	***
df.mm.trans2	-0.0446305136825966	0.0807164105133935	-0.552929861458481	0.580566196601382	   
df.mm.exp2	0.0250686134564215	0.108084772630125	0.231934738320710	0.816686530518973	   
df.mm.exp3	-0.161087985082200	0.108084772630125	-1.49038556646140	0.136772925605419	   
df.mm.exp4	-0.294172309004071	0.108084772630125	-2.72168134183669	0.00672875304945707	** 
df.mm.exp5	0.116479825510040	0.108084772630125	1.07767100467189	0.281716314460297	   
df.mm.exp6	-0.136192354233632	0.108084772630125	-1.26005126272222	0.208256818005066	   
df.mm.exp7	-0.356098521415805	0.108084772630125	-3.29462247780642	0.00105765437362391	** 
df.mm.exp8	-0.307351026883646	0.108084772630125	-2.84361080108322	0.00464888457483794	** 
df.mm.trans1:exp2	0.164185683700707	0.0847886715020377	1.93641061703345	0.0533980107439594	.  
df.mm.trans2:exp2	-0.342606046925929	0.0847886715020377	-4.04070544869541	6.19531420653239e-05	***
df.mm.trans1:exp3	0.183812358706999	0.0847886715020377	2.16788817952621	0.0306528660815865	*  
df.mm.trans2:exp3	-0.248034049128598	0.0847886715020377	-2.92532062048686	0.00360227968977391	** 
df.mm.trans1:exp4	0.394750616978187	0.0847886715020377	4.65569998898615	4.1736889309801e-06	***
df.mm.trans2:exp4	-0.20792812594309	0.0847886715020377	-2.4523102232837	0.014545555340466	*  
df.mm.trans1:exp5	-0.069610171199029	0.0847886715020377	-0.820984336302003	0.412058538017319	   
df.mm.trans2:exp5	-0.0814115995630375	0.0847886715020377	-0.960170717630373	0.337447724715783	   
df.mm.trans1:exp6	0.309526598517281	0.0847886715020377	3.65056549458782	0.000290092263977429	***
df.mm.trans2:exp6	-0.0735121816357931	0.0847886715020377	-0.86700475822441	0.386368137312328	   
df.mm.trans1:exp7	0.473928728998864	0.0847886715020377	5.5895288911028	3.80433244621199e-08	***
df.mm.trans2:exp7	-0.0625695286704348	0.0847886715020377	-0.737946798340049	0.460903675345075	   
df.mm.trans1:exp8	0.403281953635194	0.0847886715020377	4.75631881583971	2.60416836070227e-06	***
df.mm.trans2:exp8	-0.0647140526298133	0.0847886715020377	-0.763239374829195	0.445691736620081	   
df.mm.trans1:probe2	0.120976358917553	0.0580508350032019	2.08397276130276	0.0376854862649621	*  
df.mm.trans1:probe3	-0.278038066972576	0.0580508350032019	-4.78956188928619	2.22424890377215e-06	***
df.mm.trans1:probe4	-0.223253427895592	0.0580508350032018	-3.84582629833452	0.000136172675885053	***
df.mm.trans1:probe5	-0.236433061020020	0.0580508350032019	-4.07286236290966	5.42332300335742e-05	***
df.mm.trans1:probe6	-0.210645691561965	0.0580508350032019	-3.62864188861963	0.000315151760517702	***
df.mm.trans2:probe2	0.0184340562163855	0.0580508350032019	0.317550233607643	0.750962793915543	   
df.mm.trans2:probe3	-0.0516710815599877	0.0580508350032019	-0.89010057404235	0.37385318722576	   
df.mm.trans2:probe4	-0.126386861694004	0.0580508350032019	-2.17717560284934	0.029948891459273	*  
df.mm.trans2:probe5	-0.156049197401300	0.0580508350032019	-2.68814733487801	0.00743209966278684	** 
df.mm.trans2:probe6	-0.176993887619444	0.0580508350032019	-3.04894645545894	0.00242195780039899	** 
df.mm.trans3:probe2	0.0127760940229439	0.0580508350032019	0.220084586591032	0.825897884006552	   
df.mm.trans3:probe3	0.107455735888309	0.0580508350032018	1.85106270878588	0.0647682832763887	.  
df.mm.trans3:probe4	0.366666833502082	0.0580508350032018	6.31630593223782	6.06515550973964e-10	***
df.mm.trans3:probe5	0.414327510235054	0.0580508350032018	7.13732214553333	3.49291184389924e-12	***
df.mm.trans3:probe6	-0.198743780824462	0.0580508350032019	-3.42361622900859	0.000670300027871099	***
df.mm.trans3:probe7	0.481015714651532	0.0580508350032019	8.28611189873499	1.15346362484607e-15	***
df.mm.trans3:probe8	0.0532638356380712	0.0580508350032019	0.917537803463694	0.359316930954181	   
df.mm.trans3:probe9	-0.159183077186896	0.0580508350032019	-2.74213242889815	0.00632984855091962	** 
df.mm.trans3:probe10	0.151990435388185	0.0580508350032019	2.61822995965178	0.00911513458266162	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.47389809937897	0.250026937404234	17.8936643620353	2.34776911221611e-55	***
df.mm.trans1	-0.0461528703557659	0.200159638686762	-0.230580304094136	0.817738099436429	   
df.mm.trans2	0.00745450379911883	0.200159638686762	0.0372427920435283	0.970306743034948	   
df.mm.exp2	-0.257426324172999	0.268027392442045	-0.960447817767962	0.337308447933662	   
df.mm.exp3	0.451453266055076	0.268027392442045	1.6843549532076	0.0927564626393849	.  
df.mm.exp4	-0.159020372571339	0.268027392442045	-0.593298957701583	0.55325766376238	   
df.mm.exp5	-0.296013241916205	0.268027392442045	-1.10441413923844	0.269961141966940	   
df.mm.exp6	-0.547032569060624	0.268027392442045	-2.04095769494496	0.0417958733161006	*  
df.mm.exp7	-0.0271054201909911	0.268027392442045	-0.101129291092335	0.919489619014147	   
df.mm.exp8	-0.312922316630975	0.268027392442045	-1.16750125343490	0.24358165013871	   
df.mm.trans1:exp2	0.444708391187386	0.210257985267597	2.11506065094937	0.0349341310102735	*  
df.mm.trans2:exp2	0.0657797906005288	0.210257985267597	0.312852758085789	0.754527010499837	   
df.mm.trans1:exp3	-0.438243238649932	0.210257985267597	-2.08431198507004	0.0376544939854351	*  
df.mm.trans2:exp3	-0.103739928378836	0.210257985267597	-0.493393524373428	0.621957891610766	   
df.mm.trans1:exp4	-0.0154032664898898	0.210257985267597	-0.0732588894081047	0.941630307916237	   
df.mm.trans2:exp4	0.182321556091639	0.210257985267597	0.867132612631084	0.386298158124067	   
df.mm.trans1:exp5	0.347049457231	0.210257985267597	1.65058871266795	0.099469956142395	.  
df.mm.trans2:exp5	0.120211212514598	0.210257985267597	0.571731971851649	0.567768440610227	   
df.mm.trans1:exp6	0.331701533073244	0.210257985267597	1.57759303481904	0.1153111199465	   
df.mm.trans2:exp6	0.509655218548791	0.210257985267597	2.42395178428134	0.0157173255288477	*  
df.mm.trans1:exp7	0.185715291668995	0.210257985267597	0.883273429223787	0.377526158015954	   
df.mm.trans2:exp7	0.0513887466507203	0.210257985267597	0.244408061769057	0.807018161096446	   
df.mm.trans1:exp8	0.283037674416411	0.210257985267597	1.34614470911146	0.178884691585062	   
df.mm.trans2:exp8	0.267708691677812	0.210257985267597	1.27323911782517	0.203543125605608	   
df.mm.trans1:probe2	0.0894216112415506	0.143953801783315	0.621182699823043	0.534771252976614	   
df.mm.trans1:probe3	0.0688402143893766	0.143953801783315	0.478210464305749	0.632715893640128	   
df.mm.trans1:probe4	-0.157019550312299	0.143953801783315	-1.09076348361158	0.27591856611641	   
df.mm.trans1:probe5	0.0071013183036976	0.143953801783315	0.0493305367119571	0.960676192439662	   
df.mm.trans1:probe6	-0.00134427441005220	0.143953801783315	-0.00933823486006751	0.992553114677912	   
df.mm.trans2:probe2	0.0203877659194542	0.143953801783315	0.141627144728993	0.88743333320187	   
df.mm.trans2:probe3	-0.0946052686048205	0.143953801783315	-0.657191872898393	0.511369292407332	   
df.mm.trans2:probe4	-0.0483765923607579	0.143953801783315	-0.336056371985065	0.736973693385494	   
df.mm.trans2:probe5	0.0337417168599342	0.143953801783315	0.234392676274877	0.814779059031594	   
df.mm.trans2:probe6	0.0551408250897931	0.143953801783315	0.383045285408949	0.701854013495869	   
df.mm.trans3:probe2	-0.078202490269439	0.143953801783315	-0.543247134154559	0.587209241725394	   
df.mm.trans3:probe3	-0.0196775398259052	0.143953801783315	-0.136693436242307	0.891329840382285	   
df.mm.trans3:probe4	-0.0451881533591419	0.143953801783315	-0.313907328596719	0.753726393870671	   
df.mm.trans3:probe5	-0.195847254144797	0.143953801783315	-1.36048684868771	0.174307843032817	   
df.mm.trans3:probe6	-0.185759377888476	0.143953801783315	-1.29040967023635	0.197523304640214	   
df.mm.trans3:probe7	-0.11978005021089	0.143953801783315	-0.832072850644042	0.405777324465990	   
df.mm.trans3:probe8	-0.271185728257163	0.143953801783315	-1.88383859889552	0.0601847612697885	.  
df.mm.trans3:probe9	-0.118586678215044	0.143953801783315	-0.823782885522857	0.410467835690083	   
df.mm.trans3:probe10	0.0204059012178475	0.143953801783315	0.141753124718188	0.887333872985764