chr18.11485_chr18_6636927_6645430_+_1.R 

fitVsDatCorrelation=0.919357890107082
cont.fitVsDatCorrelation=0.274036546335974

fstatistic=6491.36178195641,43,485
cont.fstatistic=1076.92655963086,43,485

residuals=-0.71214845808471,-0.0957496556932612,0.00279756339397362,0.103632924242025,0.718071400621365
cont.residuals=-0.713274931287278,-0.302254043164482,-0.127139482383803,0.164647786172535,1.5432456138304

predictedValues:
Include	Exclude	Both
chr18.11485_chr18_6636927_6645430_+_1.R.tl.Lung	43.2655479699423	45.0078153749954	74.0268038851112
chr18.11485_chr18_6636927_6645430_+_1.R.tl.cerebhem	46.2493651265597	58.9647484617656	74.5764709432983
chr18.11485_chr18_6636927_6645430_+_1.R.tl.cortex	45.5085062414018	49.2822717743693	113.135869248919
chr18.11485_chr18_6636927_6645430_+_1.R.tl.heart	46.099281881509	48.302370116915	87.782962641025
chr18.11485_chr18_6636927_6645430_+_1.R.tl.kidney	43.9033177648897	44.1231188780862	86.4413136521533
chr18.11485_chr18_6636927_6645430_+_1.R.tl.liver	56.4972120494503	53.2769999821767	72.9036245777891
chr18.11485_chr18_6636927_6645430_+_1.R.tl.stomach	49.390142726407	47.8275729831334	79.3013732099135
chr18.11485_chr18_6636927_6645430_+_1.R.tl.testicle	46.4821012520386	49.4613507738261	106.807936798952


diffExp=-1.74226740505311,-12.7153833352059,-3.77376553296754,-2.20308823540598,-0.219801113196482,3.22021206727366,1.56256974327358,-2.97924952178747
diffExpScore=1.43149772945244
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	58.4886216399619	59.5054076132816	63.8050600879142
cerebhem	50.9771462715149	54.762163522074	67.4460439943734
cortex	61.7813011659573	55.9582181293822	56.2431094653592
heart	55.1346137349046	60.8562206091877	62.0160265165525
kidney	58.2088082024026	71.8428276920084	69.3451117483887
liver	73.100782090873	66.8378423520927	64.6137217355131
stomach	60.6358431127789	54.4763284477215	69.3733771623554
testicle	65.6249444136876	57.834155432699	72.5293979693159
cont.diffExp=-1.01678597331971,-3.78501725055915,5.82308303657506,-5.7216068742831,-13.6340194896058,6.26293973878028,6.15951466505739,7.79078898098864
cont.diffExpScore=17.4350658984250

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=0.428306800407454
cont.tran.correlation=0.304546372515679

tran.covariance=0.00362320233852063
cont.tran.covariance=0.00334322943716079

tran.mean=48.3526077098416
cont.tran.mean=60.376576526908

weightedLogRatios:
wLogRatio
Lung	-0.149512635690405
cerebhem	-0.960757967266358
cortex	-0.307326818862118
heart	-0.179923764305485
kidney	-0.0188996894874714
liver	0.235030811513030
stomach	0.124854572917802
testicle	-0.240428900700064

cont.weightedLogRatios:
wLogRatio
Lung	-0.070274679606985
cerebhem	-0.284138324640083
cortex	0.403317626432124
heart	-0.400785148501429
kidney	-0.877395890678344
liver	0.380407133281062
stomach	0.433977748816273
testicle	0.520767574042155

varWeightedLogRatios=0.131587262417740
cont.varWeightedLogRatios=0.254519204698132

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.93027891489399	0.0913083979032138	32.0921074313456	4.91187175061075e-122	***
df.mm.trans1	0.815507209897008	0.0730971475438505	11.1564847233990	6.95517195475507e-26	***
df.mm.trans2	0.831327640418137	0.0730971475438505	11.3729149269392	9.92175744243245e-27	***
df.mm.exp2	0.329396897140685	0.0978820604377194	3.36524277960285	0.00082543513327316	***
df.mm.exp3	-0.282891369655942	0.0978820604377194	-2.89012479294856	0.0040234798785478	** 
df.mm.exp4	-0.0363549985885075	0.0978820604377194	-0.371416359912444	0.710489638812242	   
df.mm.exp5	-0.16025752019034	0.0978820604377194	-1.63725119264637	0.102226624555034	   
df.mm.exp6	0.450792086227247	0.0978820604377194	4.60546175888970	5.26531853203324e-06	***
df.mm.exp7	0.124332166218909	0.0978820604377194	1.27022424398206	0.204613792172681	   
df.mm.exp8	-0.200538913848103	0.0978820604377194	-2.04878108359500	0.0410211562486583	*  
df.mm.trans1:exp2	-0.262705819680887	0.0767850055696307	-3.42131667155586	0.000675857818906486	***
df.mm.trans2:exp2	-0.0592932651888294	0.0767850055696307	-0.77219848782925	0.440372985448805	   
df.mm.trans1:exp3	0.333433969089169	0.0767850055696307	4.34243595628578	1.71659930022392e-05	***
df.mm.trans2:exp3	0.373619637445764	0.0767850055696307	4.86578902578779	1.54396744273187e-06	***
df.mm.trans1:exp4	0.0997957116510513	0.0767850055696307	1.29967707771479	0.194329058356935	   
df.mm.trans2:exp4	0.106999479096225	0.0767850055696307	1.39349444989224	0.164108557428753	   
df.mm.trans1:exp5	0.174890753514681	0.0767850055696307	2.27766804491647	0.0231807327899672	*  
df.mm.trans2:exp5	0.140405253186483	0.0767850055696307	1.82855040700830	0.0680809786892522	.  
df.mm.trans1:exp6	-0.183957452960952	0.0767850055696307	-2.39574708103829	0.0169645826463428	*  
df.mm.trans2:exp6	-0.282123517911681	0.0767850055696307	-3.67420065699994	0.000265180098194375	***
df.mm.trans1:exp7	0.0080620387018312	0.0767850055696307	0.104994961477477	0.916423213937048	   
df.mm.trans2:exp7	-0.0635660020692491	0.0767850055696307	-0.82784394684462	0.408166038145798	   
df.mm.trans1:exp8	0.272249573618845	0.0767850055696307	3.54560856770352	0.000429698570181735	***
df.mm.trans2:exp8	0.294894336307145	0.0767850055696307	3.84051982700876	0.000139060834883229	***
df.mm.trans1:probe2	-0.0301373464538926	0.0525710995358082	-0.57326833031835	0.566728724754847	   
df.mm.trans1:probe3	-0.0300792349204816	0.0525710995358082	-0.572162940970893	0.567476693992441	   
df.mm.trans1:probe4	0.266182106681646	0.0525710995358082	5.06327828468452	5.86368863230657e-07	***
df.mm.trans1:probe5	0.122560770022967	0.0525710995358082	2.33133358642206	0.0201445548274527	*  
df.mm.trans1:probe6	0.0166022585916522	0.0525710995358082	0.315805808481213	0.752285761592904	   
df.mm.trans2:probe2	0.097197962139958	0.0525710995358082	1.84888585169790	0.0650826694529406	.  
df.mm.trans2:probe3	0.0657362335548866	0.0525710995358082	1.25042531229751	0.211747166762638	   
df.mm.trans2:probe4	-0.00705080546719507	0.0525710995358082	-0.134119421687053	0.893363777735455	   
df.mm.trans2:probe5	0.453675497638731	0.0525710995358082	8.62975097809616	8.85354888080697e-17	***
df.mm.trans2:probe6	0.114114622186975	0.0525710995358082	2.17067216007622	0.0304403629883222	*  
df.mm.trans3:probe2	-0.779463922668382	0.0525710995358082	-14.8268521973268	2.76041951605003e-41	***
df.mm.trans3:probe3	0.141532921022789	0.0525710995358082	2.69221915220521	0.00734330510248131	** 
df.mm.trans3:probe4	0.351032541102271	0.0525710995358082	6.67729121516984	6.67926672188425e-11	***
df.mm.trans3:probe5	-0.349040859046268	0.0525710995358082	-6.63940572155093	8.45804491653104e-11	***
df.mm.trans3:probe6	-0.777267830195684	0.0525710995358082	-14.7850784377500	4.23450901423896e-41	***
df.mm.trans3:probe7	-0.0386888342524268	0.0525710995358082	-0.735933518492882	0.46212692408051	   
df.mm.trans3:probe8	0.0448552747485234	0.0525710995358082	0.853230675115911	0.393952574652798	   
df.mm.trans3:probe9	-0.623766878302344	0.0525710995358082	-11.8652050995713	1.09485393932704e-28	***
df.mm.trans3:probe10	-0.696057641660419	0.0525710995358082	-13.2403097482545	2.23135196869944e-34	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9803897620496	0.223203048893110	17.8330438665099	4.52059464948719e-55	***
df.mm.trans1	0.0486509807552338	0.178685713163769	0.272271240346139	0.785529340833668	   
df.mm.trans2	0.0743042805764367	0.178685713163769	0.415837837624631	0.677712599119745	   
df.mm.exp2	-0.276017928409702	0.239272343216414	-1.15357222109056	0.249243752852211	   
df.mm.exp3	0.119455593601470	0.239272343216414	0.499245303471726	0.617832962874647	   
df.mm.exp4	-0.00816801086269537	0.239272343216414	-0.0341368783073591	0.97278204535509	   
df.mm.exp5	0.100354886377762	0.239272343216414	0.419416991653709	0.67509711612008	   
df.mm.exp6	0.32661476184557	0.239272343216414	1.36503349051987	0.172875391210622	   
df.mm.exp7	-0.135917651086963	0.239272343216414	-0.56804580612992	0.570266745822711	   
df.mm.exp8	-0.0415234966187676	0.239272343216414	-0.173540727944520	0.862298822764186	   
df.mm.trans1:exp2	0.138563114939586	0.187700668788241	0.738213219133008	0.460741936869499	   
df.mm.trans2:exp2	0.192950244539417	0.187700668788241	1.02796780525646	0.304477362848197	   
df.mm.trans1:exp3	-0.0646870784904169	0.187700668788241	-0.34462891852237	0.73052275076221	   
df.mm.trans2:exp3	-0.180917478852208	0.187700668788241	-0.963861663467565	0.335595613723997	   
df.mm.trans1:exp4	-0.0508865051957437	0.187700668788241	-0.271104549196639	0.786425967556432	   
df.mm.trans2:exp4	0.0306148609741075	0.187700668788241	0.163104698410246	0.870503939088094	   
df.mm.trans1:exp5	-0.105150432846651	0.187700668788241	-0.560202760733254	0.575599803499955	   
df.mm.trans2:exp5	0.088058705131802	0.187700668788241	0.469144333370212	0.639177291419019	   
df.mm.trans1:exp6	-0.103607929668393	0.187700668788241	-0.551984872175822	0.581212969754934	   
df.mm.trans2:exp6	-0.210412532106666	0.187700668788241	-1.12100043897046	0.262842419418538	   
df.mm.trans1:exp7	0.171971606381522	0.187700668788241	0.916201351288396	0.360016622764735	   
df.mm.trans2:exp7	0.0476167253155706	0.187700668788241	0.253684366832441	0.799846916968938	   
df.mm.trans1:exp8	0.156647137061801	0.187700668788241	0.834558225461234	0.404377366601574	   
df.mm.trans2:exp8	0.0130358295324676	0.187700668788241	0.0694500963508781	0.944659978725779	   
df.mm.trans1:probe2	0.101497770972274	0.128509862942657	0.789805300917365	0.430027447377206	   
df.mm.trans1:probe3	0.0329732984480718	0.128509862942657	0.256581850552475	0.797610390084482	   
df.mm.trans1:probe4	0.131077077687594	0.128509862942657	1.01997679155633	0.308247975192585	   
df.mm.trans1:probe5	0.210966698675968	0.128509862942657	1.64163818904783	0.101313244557220	   
df.mm.trans1:probe6	0.16014900436073	0.128509862942657	1.24620010241697	0.213292539796719	   
df.mm.trans2:probe2	0.0753850927611033	0.128509862942657	0.58660939351201	0.55773900087126	   
df.mm.trans2:probe3	0.159602029561284	0.128509862942657	1.24194381588051	0.214857518548008	   
df.mm.trans2:probe4	0.0622043611699341	0.128509862942657	0.484043479197319	0.628573501053874	   
df.mm.trans2:probe5	0.120786033452520	0.128509862942657	0.939896990680136	0.347738264467599	   
df.mm.trans2:probe6	0.0839928838976816	0.128509862942657	0.653590953833331	0.513685116154725	   
df.mm.trans3:probe2	-0.0869725909600886	0.128509862942657	-0.676777555967804	0.49886974529985	   
df.mm.trans3:probe3	0.077297048375779	0.128509862942657	0.601487283589045	0.54779651357142	   
df.mm.trans3:probe4	0.0259386098007300	0.128509862942657	0.201841393390204	0.840125351066209	   
df.mm.trans3:probe5	0.126338668068344	0.128509862942657	0.983104838612414	0.326046003144835	   
df.mm.trans3:probe6	0.036448789723877	0.128509862942657	0.283626399478312	0.776817735583364	   
df.mm.trans3:probe7	0.104632720880184	0.128509862942657	0.814199925859951	0.415930002925928	   
df.mm.trans3:probe8	0.222474241340870	0.128509862942657	1.73118417720313	0.0840548713381313	.  
df.mm.trans3:probe9	-0.132526773493769	0.128509862942657	-1.03125760512954	0.302934012608920	   
df.mm.trans3:probe10	0.151244041736666	0.128509862942657	1.17690610100607	0.239810251468916