chr14.7291_chr14_46108151_46108532_+_1.R 

fitVsDatCorrelation=0.922944466183503
cont.fitVsDatCorrelation=0.275960185767830

fstatistic=6517.20864167874,43,485
cont.fstatistic=1035.80992531062,43,485

residuals=-0.92173450740045,-0.088485895878902,-0.00438940434497942,0.0968300434402573,0.792162726870484
cont.residuals=-0.743033136788648,-0.282747993895338,-0.0815580280944866,0.162694122535006,1.91261103067132

predictedValues:
Include	Exclude	Both
chr14.7291_chr14_46108151_46108532_+_1.R.tl.Lung	48.6597580793748	99.4448012701649	88.2205115599534
chr14.7291_chr14_46108151_46108532_+_1.R.tl.cerebhem	55.480207114305	77.027062033963	66.4288056466507
chr14.7291_chr14_46108151_46108532_+_1.R.tl.cortex	47.8379983017646	76.2941340431136	81.2330254237561
chr14.7291_chr14_46108151_46108532_+_1.R.tl.heart	46.2811835858493	76.594907268179	92.5643218659312
chr14.7291_chr14_46108151_46108532_+_1.R.tl.kidney	47.9023377563942	102.280003837184	99.1551702530655
chr14.7291_chr14_46108151_46108532_+_1.R.tl.liver	47.2164105578778	89.3194161529284	91.9801157287023
chr14.7291_chr14_46108151_46108532_+_1.R.tl.stomach	47.3202333832345	78.8312516871987	83.085620853101
chr14.7291_chr14_46108151_46108532_+_1.R.tl.testicle	50.2055896748607	78.2108977607392	86.70183785173


diffExp=-50.7850431907901,-21.5468549196580,-28.456135741349,-30.3137236823297,-54.3776660807901,-42.1030055950506,-31.5110183039642,-28.0053080858785
diffExpScore=0.99652896799947
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,-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	78.5278476179819	71.3047339009522	77.6073216400918
cerebhem	63.9470414852052	63.2643890499445	77.3294066828622
cortex	68.6532172925814	63.8081509689359	87.8637212525489
heart	67.5617867601791	78.8617379128914	63.4501829504062
kidney	65.5510949887968	66.5097153077884	70.696788392543
liver	73.6383803071252	58.7681682481796	69.2810531548143
stomach	78.393357399182	75.4260913674861	70.1841023895525
testicle	68.9368781755121	78.4712940026761	75.7064226686252
cont.diffExp=7.22311371702965,0.682652435260636,4.84506632364558,-11.2999511527122,-0.958620318991606,14.8702120589456,2.96726603169584,-9.534415827164
cont.diffExpScore=5.34758235474693

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

tran.correlation=-0.219346766849605
cont.tran.correlation=0.176099394805541

tran.covariance=-0.0015068172262472
cont.tran.covariance=0.00152315303945713

tran.mean=66.8066370316957
cont.tran.mean=70.1014927990886

weightedLogRatios:
wLogRatio
Lung	-3.03213368127711
cerebhem	-1.37161548116482
cortex	-1.91434467086082
heart	-2.05882575206622
kidney	-3.22265308085414
liver	-2.66049921165890
stomach	-2.09871093575838
testicle	-1.83420052713352

cont.weightedLogRatios:
wLogRatio
Lung	0.416376964080042
cerebhem	0.0445694291989921
cortex	0.306834863447277
heart	-0.663521183900776
kidney	-0.0608322025418074
liver	0.94430578752427
stomach	0.167557379080713
testicle	-0.556764882266091

varWeightedLogRatios=0.40545577588653
cont.varWeightedLogRatios=0.271208799579087

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.33523177100228	0.0936450390543356	46.2943025576277	4.26239794233370e-180	***
df.mm.trans1	-0.483164771156055	0.0749677509812433	-6.44496820075263	2.7940025934443e-10	***
df.mm.trans2	0.320537947247517	0.0749677509812433	4.27567778214015	2.29558328972008e-05	***
df.mm.exp2	0.159436759809747	0.100386925878659	1.58822235479611	0.112887731749274	   
df.mm.exp3	-0.199521160576683	0.100386925878659	-1.98752137123762	0.0474265879100418	*  
df.mm.exp4	-0.359253309610941	0.100386925878659	-3.57868623295807	0.000380047003428578	***
df.mm.exp5	-0.104423104744859	0.100386925878659	-1.04020622039047	0.298762371308931	   
df.mm.exp6	-0.179227617925869	0.100386925878659	-1.78536812794234	0.0748263967005182	.  
df.mm.exp7	-0.200239769724927	0.100386925878659	-1.99467976504194	0.0466370191904857	*  
df.mm.exp8	-0.191555318859923	0.100386925878659	-1.90816998511800	0.0569594034300886	.  
df.mm.trans1:exp2	-0.0282627969257517	0.0787499836869041	-0.358892733719407	0.719831516500836	   
df.mm.trans2:exp2	-0.414882673839169	0.0787499836869042	-5.26835250517216	2.0741794931139e-07	***
df.mm.trans1:exp3	0.182489062135161	0.0787499836869042	2.31732190397276	0.0209016741734766	*  
df.mm.trans2:exp3	-0.0654855133810289	0.0787499836869042	-0.831562246938204	0.406065295661636	   
df.mm.trans1:exp4	0.309136420375731	0.0787499836869041	3.92554265921885	9.9059260895932e-05	***
df.mm.trans2:exp4	0.0981811702738555	0.0787499836869041	1.24674527761436	0.213092684060377	   
df.mm.trans1:exp5	0.0887350472157483	0.0787499836869042	1.12679448377466	0.260386571869200	   
df.mm.trans2:exp5	0.132534563560511	0.0787499836869042	1.68297893352518	0.0930227267243442	.  
df.mm.trans1:exp6	0.149116765710989	0.0787499836869042	1.89354662349964	0.0588801055315331	.  
df.mm.trans2:exp6	0.0718437791755054	0.0787499836869041	0.912302146767972	0.362062928869825	   
df.mm.trans1:exp7	0.172325375153748	0.0787499836869042	2.18825918540991	0.0291270551675586	*  
df.mm.trans2:exp7	-0.0320534461428451	0.0787499836869042	-0.407027971844209	0.684167014607668	   
df.mm.trans1:exp8	0.222829321773442	0.0787499836869042	2.82957927533511	0.00485418132206001	** 
df.mm.trans2:exp8	-0.0486384148677641	0.0787499836869042	-0.61763079292996	0.537108621129305	   
df.mm.trans1:probe2	0.0227263430225191	0.0539164280856016	0.421510545662207	0.67356906196827	   
df.mm.trans1:probe3	-0.0662734068903226	0.0539164280856016	-1.22918763804424	0.219597478025038	   
df.mm.trans1:probe4	-0.00959401574314043	0.0539164280856016	-0.177942346772458	0.858842549569607	   
df.mm.trans1:probe5	0.405190080670751	0.0539164280856016	7.51515067035676	2.76358482188747e-13	***
df.mm.trans1:probe6	0.172516852444160	0.0539164280856016	3.19970848532956	0.00146577603215186	** 
df.mm.trans2:probe2	-0.377412374545132	0.0539164280856016	-6.99995136817901	8.56746223846893e-12	***
df.mm.trans2:probe3	-0.0984920380041259	0.0539164280856016	-1.82675376506309	0.0683512713591213	.  
df.mm.trans2:probe4	0.0673621420266645	0.0539164280856016	1.24938065110166	0.212128494710264	   
df.mm.trans2:probe5	-0.131619396794832	0.0539164280856016	-2.44117426669777	0.0149961500192787	*  
df.mm.trans2:probe6	-0.358510158698449	0.0539164280856016	-6.64936776095873	7.94971262059753e-11	***
df.mm.trans3:probe2	-0.152734694230302	0.0539164280856016	-2.83280439104403	0.00480627923528224	** 
df.mm.trans3:probe3	0.092383424007826	0.0539164280856016	1.71345594075986	0.0872677035570779	.  
df.mm.trans3:probe4	0.200979558725954	0.0539164280856016	3.7276126379675	0.000216094868485205	***
df.mm.trans3:probe5	1.36329578773909	0.0539164280856016	25.2853506091803	1.34772987026558e-90	***
df.mm.trans3:probe6	0.270212400987243	0.0539164280856016	5.01168958296412	7.57449041789463e-07	***
df.mm.trans3:probe7	-0.0838933568684817	0.0539164280856016	-1.55598877461405	0.120363030907950	   
df.mm.trans3:probe8	1.17565548693476	0.0539164280856016	21.8051441588120	5.64020372931803e-74	***
df.mm.trans3:probe9	0.248283781161431	0.0539164280856016	4.60497458710058	5.27714038768876e-06	***
df.mm.trans3:probe10	-0.0418369259493273	0.0539164280856016	-0.7759587835252	0.438151537417908	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20793336083061	0.233829484927574	17.9957346360061	7.78265287443707e-56	***
df.mm.trans1	0.149576920988859	0.187192730924609	0.799053041483221	0.424650788222937	   
df.mm.trans2	0.0410182274741350	0.187192730924609	0.219122971664187	0.826646439849566	   
df.mm.exp2	-0.321450699617957	0.25066381955428	-1.28239767585744	0.200315764084223	   
df.mm.exp3	-0.369592309577917	0.25066381955428	-1.47445415231887	0.141008148042708	   
df.mm.exp4	0.151729373050820	0.25066381955428	0.605310225147847	0.54525601328828	   
df.mm.exp5	-0.156976453647978	0.25066381955428	-0.626242965287558	0.531450207262827	   
df.mm.exp6	-0.144159006365102	0.25066381955428	-0.575108951189844	0.565484309223592	   
df.mm.exp7	0.155016373355083	0.25066381955428	0.618423407218188	0.53658658715337	   
df.mm.exp8	-0.00969310145690772	0.25066381955428	-0.0386697269440144	0.969169621621317	   
df.mm.trans1:exp2	0.116052655029554	0.196636878039842	0.590187640214874	0.555339731353078	   
df.mm.trans2:exp2	0.201810576874328	0.196636878039842	1.02631092847923	0.305256636014807	   
df.mm.trans1:exp3	0.235206997237751	0.196636878039842	1.19614896036995	0.232222872469586	   
df.mm.trans2:exp3	0.258510530563067	0.196636878039842	1.31465945320128	0.189245695901567	   
df.mm.trans1:exp4	-0.302140143054724	0.196636878039842	-1.53653854793964	0.125058445284655	   
df.mm.trans2:exp4	-0.0509959262312233	0.196636878039842	-0.259340601516723	0.795482496413985	   
df.mm.trans1:exp5	-0.0236469402537558	0.196636878039842	-0.120256894278826	0.904329449727761	   
df.mm.trans2:exp5	0.0873617661539238	0.196636878039842	0.444279664246005	0.657038570400376	   
df.mm.trans1:exp6	0.0798720591385842	0.196636878039842	0.406190638982788	0.684781689158686	   
df.mm.trans2:exp6	-0.0492033609790434	0.196636878039842	-0.25022448214965	0.802519710246083	   
df.mm.trans1:exp7	-0.156730485246015	0.196636878039842	-0.797055398806007	0.425808873972177	   
df.mm.trans2:exp7	-0.0988258381814127	0.196636878039842	-0.502580386581345	0.615487442157894	   
df.mm.trans1:exp8	-0.120568930296054	0.196636878039842	-0.6131552305851	0.540061126284003	   
df.mm.trans2:exp8	0.105463258533906	0.196636878039842	0.536335094338395	0.591972885107218	   
df.mm.trans1:probe2	-0.0056215256406437	0.134628067174767	-0.0417559707913367	0.966710418525505	   
df.mm.trans1:probe3	0.0331219244122591	0.134628067174767	0.246025402483584	0.805766651373322	   
df.mm.trans1:probe4	0.0341908726206894	0.134628067174767	0.253965412548816	0.799629909295308	   
df.mm.trans1:probe5	-0.0270915822411138	0.134628067174767	-0.201232794985795	0.840600915396803	   
df.mm.trans1:probe6	0.0604887394413591	0.134628067174767	0.449302591285335	0.653414097732011	   
df.mm.trans2:probe2	0.0126461462128109	0.134628067174767	0.0939339506107169	0.925200416825516	   
df.mm.trans2:probe3	0.195880049438564	0.134628067174767	1.45497186098856	0.146323949617817	   
df.mm.trans2:probe4	0.283347348758645	0.134628067174767	2.10466773166117	0.0358341440456703	*  
df.mm.trans2:probe5	-0.0868159228599911	0.134628067174767	-0.64485752994798	0.519324416273954	   
df.mm.trans2:probe6	-0.116879525001813	0.134628067174767	-0.868166107221057	0.38573277537016	   
df.mm.trans3:probe2	-0.0142116333981743	0.134628067174767	-0.105562188453062	0.915973370523794	   
df.mm.trans3:probe3	-0.137871383899483	0.134628067174767	-1.02409094026809	0.306302834216030	   
df.mm.trans3:probe4	0.0939631653763856	0.134628067174767	0.6979463298274	0.485545231768254	   
df.mm.trans3:probe5	-0.142899673347133	0.134628067174767	-1.06144042877499	0.289018053227278	   
df.mm.trans3:probe6	-0.0463709364226991	0.134628067174767	-0.344437362845763	0.730666691985992	   
df.mm.trans3:probe7	-0.101933945481651	0.134628067174767	-0.757152261194731	0.449326319332965	   
df.mm.trans3:probe8	-0.0430739470363433	0.134628067174767	-0.319947748937277	0.749145722634988	   
df.mm.trans3:probe9	-0.00624692357846852	0.134628067174767	-0.046401346387593	0.963009462954559	   
df.mm.trans3:probe10	-0.070022077443447	0.134628067174767	-0.520115002115777	0.60322074316844