chr1.451_chr1_171554448_171560585_+_2.R 

fitVsDatCorrelation=0.700098429487307
cont.fitVsDatCorrelation=0.265129558584841

fstatistic=15734.7400561834,49,623
cont.fstatistic=8623.38013912223,49,623

residuals=-0.328309383917952,-0.0655260590389723,-0.00678335849588052,0.0618326576664306,0.724822790371101
cont.residuals=-0.506280413064872,-0.0985167856614715,-0.0182813672783502,0.0677845942969576,0.964968757988926

predictedValues:
Include	Exclude	Both
chr1.451_chr1_171554448_171560585_+_2.R.tl.Lung	48.1967019273722	50.4903172613319	50.1454221005777
chr1.451_chr1_171554448_171560585_+_2.R.tl.cerebhem	55.4543149347375	48.6132989129122	51.5387121526916
chr1.451_chr1_171554448_171560585_+_2.R.tl.cortex	48.7270738697867	46.9658956381365	46.6150965049557
chr1.451_chr1_171554448_171560585_+_2.R.tl.heart	50.1633243395027	46.4727676281365	48.9123289384819
chr1.451_chr1_171554448_171560585_+_2.R.tl.kidney	48.4312738532883	45.0139497753282	48.1862975126168
chr1.451_chr1_171554448_171560585_+_2.R.tl.liver	54.1219134229082	46.9701565244068	51.8494523067107
chr1.451_chr1_171554448_171560585_+_2.R.tl.stomach	52.4883045211124	49.1174986857874	52.3581443210546
chr1.451_chr1_171554448_171560585_+_2.R.tl.testicle	52.8687912771775	47.7232522981413	48.9089458916372


diffExp=-2.29361533395976,6.84101602182537,1.76117823165016,3.69055671136619,3.41732407796010,7.15175689850145,3.370805835325,5.14553897903613
diffExpScore=1.11923825704607
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	49.2431933229417	47.6070370158643	46.1407956468672
cerebhem	48.5228091633768	47.7314700284184	47.3580771174085
cortex	49.0867557012143	46.0201534037248	47.7652169147543
heart	48.509421616933	49.2905346767633	46.2673343082276
kidney	51.2805136875736	46.1380927009206	48.410654567113
liver	48.6363757885726	46.38789537824	47.9766125995002
stomach	49.1132370644097	45.5303548873982	50.8779452416879
testicle	49.8850245831808	44.8216440640316	51.4358257819228
cont.diffExp=1.63615630707732,0.791339134958406,3.06660229748947,-0.781113059830261,5.14242098665301,2.24848041033259,3.58288217701152,5.06338051914916
cont.diffExpScore=1.02584929995341

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=0.172912545433263
cont.tran.correlation=-0.488696836877531

tran.covariance=0.000356480092892767
cont.tran.covariance=-0.000279241814103957

tran.mean=49.4886771793791
cont.tran.mean=47.9877820677227

weightedLogRatios:
wLogRatio
Lung	-0.181246788702077
cerebhem	0.520030686815049
cortex	0.142386723922839
heart	0.296277064222770
kidney	0.281245988685598
liver	0.55562202379088
stomach	0.260681419222034
testicle	0.401039774735131

cont.weightedLogRatios:
wLogRatio
Lung	0.131103196379259
cerebhem	0.0636972920338747
cortex	0.249094056743352
heart	-0.0621349003254034
kidney	0.410479899276005
liver	0.182739428880428
stomach	0.292108722130796
testicle	0.412728571153179

varWeightedLogRatios=0.0543423203123587
cont.varWeightedLogRatios=0.0274272825101492

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98991255806881	0.054597825163335	73.0782324411746	8.79889498158488e-308	***
df.mm.trans1	-0.0383789398874949	0.0459880992089718	-0.83454068656109	0.404296257193487	   
df.mm.trans2	-0.0568411882847467	0.0423774265892682	-1.34130816473743	0.180309174236767	   
df.mm.exp2	0.0749784951622557	0.0553440715112655	1.35477013372595	0.175981874917148	   
df.mm.exp3	0.0115871244633223	0.0553440715112655	0.209365233654046	0.834231572331535	   
df.mm.exp4	-0.0180237711270166	0.0553440715112655	-0.325667603319495	0.744785208092062	   
df.mm.exp5	-0.070101449246861	0.0553440715112655	-1.26664785102758	0.205754580672760	   
df.mm.exp6	0.0102622703839538	0.0553440715112655	0.185426733229500	0.852954693682965	   
df.mm.exp7	0.0145532857714875	0.0553440715112655	0.262960157684191	0.792668226796195	   
df.mm.exp8	0.0611266858172974	0.0553440715112655	1.10448480113818	0.269809279482120	   
df.mm.trans1:exp2	0.0652904383113623	0.048308266089033	1.35153760623556	0.177013808939636	   
df.mm.trans2:exp2	-0.112862942003057	0.040055098222493	-2.81769230413918	0.00499042753282713	** 
df.mm.trans1:exp3	-0.000642911201874476	0.048308266089033	-0.0133085133026629	0.989385916775896	   
df.mm.trans2:exp3	-0.0839469913647861	0.0400550982224930	-2.09578792937888	0.0365039458409388	*  
df.mm.trans1:exp4	0.05801734599443	0.048308266089033	1.20098175098032	0.230214680781016	   
df.mm.trans2:exp4	-0.0648913107388176	0.040055098222493	-1.62005121990633	0.105727306886127	   
df.mm.trans1:exp5	0.0749566143232382	0.048308266089033	1.55163122984153	0.121258308318115	   
df.mm.trans2:exp5	-0.0447076945120325	0.0400550982224930	-1.11615490901298	0.264786140951991	   
df.mm.trans1:exp6	0.105686293511964	0.048308266089033	2.18774760653140	0.0290588216013461	*  
df.mm.trans2:exp6	-0.0825314184149721	0.040055098222493	-2.06044728579960	0.0397704538320013	*  
df.mm.trans1:exp7	0.0707464940103493	0.048308266089033	1.4644800929092	0.143567233274515	   
df.mm.trans2:exp7	-0.0421195062477924	0.040055098222493	-1.05153920766421	0.293418705652169	   
df.mm.trans1:exp8	0.0313959280285509	0.048308266089033	0.649907988224783	0.515991218573251	   
df.mm.trans2:exp8	-0.117489517632962	0.040055098222493	-2.93319759148625	0.00347820209296004	** 
df.mm.trans1:probe2	-0.0324826348691206	0.0330744088136565	-0.982107799783512	0.326427978172222	   
df.mm.trans1:probe3	-0.150169864735290	0.0330744088136565	-4.54036429135763	6.74244311040226e-06	***
df.mm.trans1:probe4	-0.177079796677619	0.0330744088136565	-5.3539822185575	1.21138778132418e-07	***
df.mm.trans1:probe5	-0.155599076668739	0.0330744088136565	-4.70451573436714	3.13680148188885e-06	***
df.mm.trans1:probe6	-0.112257817965486	0.0330744088136565	-3.39409900258398	0.000732249322973395	***
df.mm.trans1:probe7	-0.150352833841084	0.0330744088136565	-4.54589633599142	6.57328176166554e-06	***
df.mm.trans1:probe8	-0.207072747072333	0.0330744088136565	-6.26081476585099	7.13367008471764e-10	***
df.mm.trans1:probe9	-0.122551202064539	0.0330744088136566	-3.70531799237896	0.000229901370905637	***
df.mm.trans1:probe10	-0.219390554279620	0.0330744088136566	-6.63324189755534	7.13463441736729e-11	***
df.mm.trans1:probe11	-0.151502778721607	0.0330744088136565	-4.58066475428735	5.59969701023913e-06	***
df.mm.trans1:probe12	-0.198887225706036	0.0330744088136566	-6.01332670302833	3.09754101521217e-09	***
df.mm.trans2:probe2	-0.0647205654870512	0.0330744088136565	-1.95681700167919	0.0508152459983914	.  
df.mm.trans2:probe3	-0.0665189313946257	0.0330744088136565	-2.01119033659522	0.0447355938105905	*  
df.mm.trans2:probe4	0.0241309835391722	0.0330744088136565	0.729596821371102	0.465910883507386	   
df.mm.trans2:probe5	0.0295991825541185	0.0330744088136565	0.894927033189988	0.371171711807175	   
df.mm.trans2:probe6	-0.103127297788947	0.0330744088136565	-3.11803903646391	0.00190451689219838	** 
df.mm.trans3:probe2	-0.0604358381148017	0.0330744088136565	-1.82726888499508	0.0681375759070303	.  
df.mm.trans3:probe3	-0.120135508006466	0.0330744088136565	-3.63227982949954	0.000304055898809514	***
df.mm.trans3:probe4	-0.0520403775830547	0.0330744088136565	-1.57343334165861	0.116126185827333	   
df.mm.trans3:probe5	0.0565455608990871	0.0330744088136565	1.70964691213889	0.0878291373362413	.  
df.mm.trans3:probe6	0.129798466998210	0.0330744088136565	3.92443800672306	9.66140266604764e-05	***
df.mm.trans3:probe7	-0.062522621376682	0.0330744088136565	-1.89036247719313	0.0591733525063787	.  
df.mm.trans3:probe8	0.389739256396412	0.0330744088136565	11.7837104388541	4.47468858326188e-29	***
df.mm.trans3:probe9	-0.022956620269875	0.0330744088136565	-0.694090116597825	0.487884423194099	   
df.mm.trans3:probe10	-0.0556443568559671	0.0330744088136566	-1.68239913733519	0.0929925458168372	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87139480023596	0.0737261968183383	52.5104368230888	5.75741417154024e-231	***
df.mm.trans1	0.0336386467653662	0.0621000496528719	0.541684699986556	0.588229396382846	   
df.mm.trans2	-0.0196781073044613	0.0572243763195388	-0.343876308840475	0.73105533656844	   
df.mm.exp2	-0.0381667209658393	0.0747338908969558	-0.510701644297688	0.609740900805825	   
df.mm.exp3	-0.0716832613108712	0.0747338908969558	-0.959180104909956	0.33784011196521	   
df.mm.exp4	0.0169996764373276	0.0747338908969558	0.227469441685660	0.820133355814857	   
df.mm.exp5	-0.0388244220518053	0.0747338908969558	-0.51950221761285	0.603595080627157	   
df.mm.exp6	-0.0773576223919244	0.0747338908969558	-1.03510765281292	0.301020255385003	   
df.mm.exp7	-0.144975938887803	0.0747338908969558	-1.93989550320213	0.0528433497708548	.  
df.mm.exp8	-0.155977172392004	0.0747338908969558	-2.08710091927459	0.0372848683096549	*  
df.mm.trans1:exp2	0.0234295489591271	0.0652330880026421	0.359166638840984	0.719592127294337	   
df.mm.trans2:exp2	0.0407770635664909	0.0540884192052496	0.753896382361518	0.451196276908769	   
df.mm.trans1:exp3	0.0685013669792463	0.0652330880026421	1.05010155239733	0.294078592051259	   
df.mm.trans2:exp3	0.0377820925484286	0.0540884192052495	0.698524621417696	0.48510982418193	   
df.mm.trans1:exp4	-0.0320127886429017	0.0652330880026421	-0.490744645441361	0.623779870845458	   
df.mm.trans2:exp4	0.0177518050135156	0.0540884192052496	0.328199738028075	0.742870919950783	   
df.mm.trans1:exp5	0.0793641004965231	0.0652330880026421	1.21662338740286	0.224208249706090	   
df.mm.trans2:exp5	0.00748274995007627	0.0540884192052495	0.138342921831038	0.890014108024086	   
df.mm.trans1:exp6	0.06495819492213	0.0652330880026421	0.995785987005536	0.319740741387246	   
df.mm.trans2:exp6	0.0514155853428658	0.0540884192052496	0.950583990035997	0.342184216422525	   
df.mm.trans1:exp7	0.142333379908280	0.0652330880026421	2.18192000817922	0.0294883571034284	*  
df.mm.trans2:exp7	0.100374596009488	0.0540884192052495	1.85575022314104	0.0639612079287057	.  
df.mm.trans1:exp8	0.168926870128499	0.0652330880026421	2.58958873940870	0.00983363014788045	** 
df.mm.trans2:exp8	0.0956877347958429	0.0540884192052496	1.76909838005685	0.0773666603931226	.  
df.mm.trans1:probe2	-0.0424802404607251	0.0446620422434584	-0.951148633758393	0.341897775716508	   
df.mm.trans1:probe3	-0.0228941324188792	0.0446620422434584	-0.512608274697346	0.608407054428454	   
df.mm.trans1:probe4	-0.0582176857032282	0.0446620422434584	-1.30351597864415	0.192880154046744	   
df.mm.trans1:probe5	-0.0116795713476903	0.0446620422434584	-0.261510015238970	0.793785657640473	   
df.mm.trans1:probe6	0.00711365142311208	0.0446620422434584	0.159277343036278	0.87350199176495	   
df.mm.trans1:probe7	-0.0268988027901702	0.0446620422434584	-0.60227435735118	0.547210661771759	   
df.mm.trans1:probe8	0.0487123314985142	0.0446620422434584	1.09068750669701	0.27583206319328	   
df.mm.trans1:probe9	-0.0542534917344556	0.0446620422434584	-1.21475617793546	0.224919302960346	   
df.mm.trans1:probe10	-0.0130458505687962	0.0446620422434584	-0.292101523205804	0.770306246378796	   
df.mm.trans1:probe11	0.0184606918664553	0.0446620422434584	0.413341865690417	0.67949839449295	   
df.mm.trans1:probe12	-0.0265874024235064	0.0446620422434584	-0.595301985488597	0.55185780087397	   
df.mm.trans2:probe2	0.0439081584749543	0.0446620422434584	0.983120257591569	0.325929889692712	   
df.mm.trans2:probe3	0.0268783939450084	0.0446620422434584	0.601817395597159	0.547514634905803	   
df.mm.trans2:probe4	-0.000599001956144623	0.0446620422434584	-0.0134118801124093	0.989303482452873	   
df.mm.trans2:probe5	0.0304976268695218	0.0446620422434584	0.682853388192045	0.494953327608244	   
df.mm.trans2:probe6	0.079537124216723	0.0446620422434584	1.78086626185064	0.0754214694180252	.  
df.mm.trans3:probe2	-0.0412583733618863	0.0446620422434584	-0.923790567770765	0.355953015510292	   
df.mm.trans3:probe3	-0.0851702877642301	0.0446620422434584	-1.90699492199565	0.0569809187657877	.  
df.mm.trans3:probe4	-0.0742060778319577	0.0446620422434584	-1.66150211912503	0.0971157266709515	.  
df.mm.trans3:probe5	-0.0759099908659964	0.0446620422434584	-1.69965337572791	0.0896953086778728	.  
df.mm.trans3:probe6	-0.026585971230675	0.0446620422434584	-0.595269940540371	0.551879203856307	   
df.mm.trans3:probe7	-0.0470158702490776	0.0446620422434584	-1.05270309836680	0.292885207744712	   
df.mm.trans3:probe8	-0.0744959130840972	0.0446620422434584	-1.66799163992570	0.0958198832734517	.  
df.mm.trans3:probe9	-0.0603683572047731	0.0446620422434584	-1.35167032612834	0.176971351415266	   
df.mm.trans3:probe10	-0.0515675875195644	0.0446620422434584	-1.15461776777835	0.248689674143528