chr1.1195_chr1_52678766_52681576_+_0.R 

fitVsDatCorrelation=0.869792459467035
cont.fitVsDatCorrelation=0.248610650509920

fstatistic=9693.55767712627,63,945
cont.fstatistic=2504.37134088396,63,945

residuals=-0.906790017276404,-0.0977815135427012,-0.00699843644312343,0.0916057946723253,1.34389800488844
cont.residuals=-0.680291218911488,-0.218540358098656,-0.0556074328336673,0.154255226531788,1.95439089180807

predictedValues:
Include	Exclude	Both
chr1.1195_chr1_52678766_52681576_+_0.R.tl.Lung	78.8843691249298	60.5359036130954	88.8799259865138
chr1.1195_chr1_52678766_52681576_+_0.R.tl.cerebhem	77.5635067354514	58.4106943626408	64.6756042486603
chr1.1195_chr1_52678766_52681576_+_0.R.tl.cortex	69.6553691547068	56.8706942755437	66.2943708828905
chr1.1195_chr1_52678766_52681576_+_0.R.tl.heart	77.996464720159	57.3864891985863	81.4692750441557
chr1.1195_chr1_52678766_52681576_+_0.R.tl.kidney	71.378122511622	59.2919044586808	70.4964729087165
chr1.1195_chr1_52678766_52681576_+_0.R.tl.liver	78.724243750815	60.9534496722731	79.3727012682154
chr1.1195_chr1_52678766_52681576_+_0.R.tl.stomach	73.2419656109799	60.1753864956961	72.6949181301302
chr1.1195_chr1_52678766_52681576_+_0.R.tl.testicle	77.1719098308982	60.1194404915675	80.5132900654791


diffExp=18.3484655118344,19.1528123728105,12.7846748791632,20.6099755215727,12.0862180529412,17.7707940785419,13.0665791152838,17.0524693393307
diffExpScore=0.992416888464656
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=1,1,0,1,0,0,0,0
diffExp1.3Score=0.75
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	72.2140564290919	66.4025734578105	72.2019658335081
cerebhem	72.5533972059597	71.7678403527774	77.9871115158321
cortex	75.356117332626	61.6110777632763	70.7189633865564
heart	71.94590964987	65.5497274773857	70.8290582996163
kidney	65.0405222123265	72.5835739831149	70.6757022326127
liver	75.2333719471172	75.2250494322962	64.1399617995092
stomach	76.2580784719947	69.2104147945525	77.3043798362522
testicle	74.0546137686623	72.9407465898131	63.7916709736268
cont.diffExp=5.81148297128144,0.785556853182328,13.7450395693498,6.39618217248427,-7.54305177078842,0.00832251482094648,7.04766367744223,1.11386717884913
cont.diffExpScore=1.49660046441048

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

tran.correlation=0.43365550588388
cont.tran.correlation=-0.196069080563134

tran.covariance=0.000544812049843654
cont.tran.covariance=-0.000697493430577362

tran.mean=67.3974946254779
cont.tran.mean=71.1216919292922

weightedLogRatios:
wLogRatio
Lung	1.12136272311683
cerebhem	1.1937486967545
cortex	0.839947635476885
heart	1.28978238665509
kidney	0.774583366524735
liver	1.08426080213879
stomach	0.824440501050639
testicle	1.05403932600626

cont.weightedLogRatios:
wLogRatio
Lung	0.355537139386308
cerebhem	0.0465813136829572
cortex	0.850146829721443
heart	0.393776969734167
kidney	-0.464136303737374
liver	0.000477975968546523
stomach	0.415586838238869
testicle	0.0651262684859366

varWeightedLogRatios=0.0356234222963832
cont.varWeightedLogRatios=0.15043236158616

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.8275446737791	0.0786835359407891	48.6447975172265	1.50696462316203e-259	***
df.mm.trans1	0.663812371944149	0.0662924644423926	10.0133910773674	1.68029073809023e-22	***
df.mm.trans2	0.256031450444706	0.0590209234045014	4.33797771495352	1.59262380410193e-05	***
df.mm.exp2	0.265278544543639	0.0747119950657505	3.55068211349704	0.000403186302638236	***
df.mm.exp3	0.106301541557212	0.0747119950657505	1.42281760062305	0.155119054458731	   
df.mm.exp4	0.0223130186762964	0.0747119950657505	0.298653765793026	0.76526989595775	   
df.mm.exp5	0.110968091525033	0.0747119950657505	1.4852781193619	0.137803663844185	   
df.mm.exp6	0.117973694210653	0.0747119950657505	1.57904623088742	0.114660102079429	   
df.mm.exp7	0.120827060881811	0.0747119950657505	1.61723777788931	0.106160691556070	   
df.mm.exp8	0.0700130992119381	0.0747119950657505	0.937106540259336	0.348943182925854	   
df.mm.trans1:exp2	-0.282164600270933	0.0658195038404323	-4.28694511212043	1.99729886968210e-05	***
df.mm.trans2:exp2	-0.301016185992819	0.0474280003545087	-6.34680323317075	3.40690498043437e-10	***
df.mm.trans1:exp3	-0.230724854929426	0.0658195038404323	-3.50541771765368	0.000477307003044893	***
df.mm.trans2:exp3	-0.168758009363671	0.0474280003545087	-3.55819364304335	0.000391981247275627	***
df.mm.trans1:exp4	-0.0336326153896672	0.0658195038404323	-0.510982511676228	0.609482656121415	   
df.mm.trans2:exp4	-0.0757407600389378	0.0474280003545087	-1.59696296434175	0.110608278233807	   
df.mm.trans1:exp5	-0.210959774807352	0.0658195038404323	-3.20512557066347	0.00139530151190907	** 
df.mm.trans2:exp5	-0.131731950410613	0.0474280003545087	-2.77751432541875	0.00558607582469123	** 
df.mm.trans1:exp6	-0.120005631758817	0.0658195038404323	-1.82325336346730	0.068580826202095	.  
df.mm.trans2:exp6	-0.111099878654811	0.0474280003545087	-2.34249552636366	0.0193619870277547	*  
df.mm.trans1:exp7	-0.195041601767428	0.0658195038404323	-2.96327973301457	0.0031201196791669	** 
df.mm.trans2:exp7	-0.126800291538129	0.0474280003545087	-2.67353231404104	0.00763515096551824	** 
df.mm.trans1:exp8	-0.091960668964487	0.0658195038404323	-1.39716442085965	0.162692096136518	   
df.mm.trans2:exp8	-0.0769164780643725	0.0474280003545087	-1.62175249830158	0.105189918779218	   
df.mm.trans1:probe2	-0.185557023400313	0.0499935019958403	-3.71162283081814	0.000217969471172245	***
df.mm.trans1:probe3	0.371233634302297	0.0499935019958403	7.42563772254213	2.50412707726684e-13	***
df.mm.trans1:probe4	-0.490930030329794	0.0499935019958403	-9.81987679860159	9.72194608620848e-22	***
df.mm.trans1:probe5	-0.449930360836808	0.0499935019958403	-8.99977682848152	1.21747472990720e-18	***
df.mm.trans1:probe6	0.134269635463270	0.0499935019958403	2.68574174848648	0.00736377494659066	** 
df.mm.trans1:probe7	-0.276687117446288	0.0499935019958403	-5.53446160801678	4.04360508408677e-08	***
df.mm.trans1:probe8	-0.119652416206224	0.0499935019958403	-2.39335936530672	0.0168891274934515	*  
df.mm.trans1:probe9	-0.417121067891855	0.0499935019958403	-8.34350568052948	2.53661188568810e-16	***
df.mm.trans1:probe10	-0.0467158283919126	0.0499935019958403	-0.934438007479443	0.350316704063080	   
df.mm.trans1:probe11	-0.558600575254028	0.0499935019958403	-11.1734636093408	2.59628745328129e-27	***
df.mm.trans1:probe12	-0.37986299635769	0.0499935019958403	-7.59824739601751	7.21403470310899e-14	***
df.mm.trans1:probe13	-0.39749449433703	0.0499935019958403	-7.95092318937976	5.26119966369385e-15	***
df.mm.trans1:probe14	-0.447439113814710	0.0499935019958403	-8.94994541194451	1.84756580356384e-18	***
df.mm.trans1:probe15	-0.110161081932792	0.0499935019958403	-2.20350800673971	0.0277995481558345	*  
df.mm.trans1:probe16	-0.449943534453508	0.0499935019958403	-9.00004033506085	1.21478635424736e-18	***
df.mm.trans2:probe2	0.245198407425752	0.0499935019958403	4.90460555146054	1.10177975501505e-06	***
df.mm.trans2:probe3	0.210563746398071	0.0499935019958403	4.21182229673751	2.77547019449621e-05	***
df.mm.trans2:probe4	0.0927116807051514	0.0499935019958403	1.85447462177916	0.0639826437984227	.  
df.mm.trans2:probe5	-0.0935299574286023	0.0499935019958403	-1.87084228339084	0.0616758116333087	.  
df.mm.trans2:probe6	0.0562294594074788	0.0499935019958403	1.12473535885038	0.260986789826981	   
df.mm.trans3:probe2	0.611509858475795	0.0499935019958403	12.2317868135478	4.82635419625569e-32	***
df.mm.trans3:probe3	-0.0638277132408526	0.0499935019958403	-1.27672018747883	0.202014688459983	   
df.mm.trans3:probe4	-0.255541519422567	0.0499935019958403	-5.11149467872503	3.86796273412965e-07	***
df.mm.trans3:probe5	-0.539689765996212	0.0499935019958403	-10.7951982647888	1.06540672687088e-25	***
df.mm.trans3:probe6	0.43587995425043	0.0499935019958403	8.71873217216704	1.2480701751221e-17	***
df.mm.trans3:probe7	-0.63224967798989	0.0499935019958403	-12.6466371178098	5.56682135825421e-34	***
df.mm.trans3:probe8	-0.171107928267897	0.0499935019958403	-3.42260336717627	0.000646857593742704	***
df.mm.trans3:probe9	-0.370883350981501	0.0499935019958403	-7.41863114555087	2.63251580978818e-13	***
df.mm.trans3:probe10	-0.57944690711324	0.0499935019958403	-11.5904444373881	3.87230310157818e-29	***
df.mm.trans3:probe11	-0.622953536516581	0.0499935019958403	-12.4606901226566	4.16789333622066e-33	***
df.mm.trans3:probe12	-0.581953867023595	0.0499935019958403	-11.6405901525365	2.31737720969207e-29	***
df.mm.trans3:probe13	-0.408710623633075	0.0499935019958403	-8.17527493207181	9.44225631503506e-16	***
df.mm.trans3:probe14	-0.251675922393011	0.0499935019958403	-5.03417268936174	5.74544525881104e-07	***
df.mm.trans3:probe15	-0.549144574078642	0.0499935019958403	-10.9843190045845	1.68362864768229e-26	***
df.mm.trans3:probe16	-0.04093251166176	0.0499935019958403	-0.81875663891611	0.413131692831718	   
df.mm.trans3:probe17	-0.403139614273284	0.0499935019958403	-8.0638402628171	2.22741953760608e-15	***
df.mm.trans3:probe18	-0.234770043434815	0.0499935019958403	-4.69601116269769	3.04556187370592e-06	***
df.mm.trans3:probe19	-0.141387005333476	0.0499935019958403	-2.82810764777471	0.00478138135199572	** 
df.mm.trans3:probe20	-0.40201892280415	0.0499935019958403	-8.04142352015268	2.64399625724273e-15	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17245700412534	0.154459680973383	27.0132437010818	1.49045720120211e-119	***
df.mm.trans1	0.143973496718176	0.130135393462957	1.10633620022180	0.268862582240071	   
df.mm.trans2	0.026325592059132	0.115860997994218	0.227217031743898	0.820304129748893	   
df.mm.exp2	0.00531244383750872	0.146663349387664	0.036222027245994	0.971112972072579	   
df.mm.exp3	-0.0115502185098004	0.146663349387664	-0.0787532710661786	0.937245545212068	   
df.mm.exp4	0.00255103626580674	0.146663349387664	0.0173938224952423	0.986126108911991	   
df.mm.exp5	0.00574404342475942	0.146663349387664	0.0391648182640137	0.968767255230776	   
df.mm.exp6	0.284108335545455	0.146663349387664	1.93714610181507	0.053025226067924	.  
df.mm.exp7	0.0276208431152543	0.146663349387664	0.188328189902756	0.850659821684153	   
df.mm.exp8	0.242924379252011	0.146663349387664	1.65634004859596	0.097984943559522	.  
df.mm.trans1:exp2	-0.000624354217159607	0.129206948359184	-0.0048322031058574	0.996145494614762	   
df.mm.trans2:exp2	0.0723882133005662	0.0931035154479542	0.777502470795873	0.437056971647122	   
df.mm.trans1:exp3	0.054140611522764	0.129206948359184	0.419022445853746	0.675294951864588	   
df.mm.trans2:exp3	-0.0633439059255636	0.0931035154479543	-0.680359980187573	0.496443201659365	   
df.mm.trans1:exp4	-0.00627116877920999	0.129206948359184	-0.0485358477918438	0.961299451348403	   
df.mm.trans2:exp4	-0.0154777961677882	0.0931035154479543	-0.166242875935661	0.868001379514385	   
df.mm.trans1:exp5	-0.110368263455823	0.129206948359184	-0.85419758656484	0.393212002039794	   
df.mm.trans2:exp5	0.0832587865045682	0.0931035154479543	0.894260395045025	0.371410280235413	   
df.mm.trans1:exp6	-0.24314814146974	0.129206948359184	-1.88185035369622	0.0601634697204542	.  
df.mm.trans2:exp6	-0.159359868529752	0.0931035154479543	-1.71164179744465	0.0872908192912762	.  
df.mm.trans1:exp7	0.0268677996007662	0.129206948359184	0.207943922072024	0.835317559189026	   
df.mm.trans2:exp7	0.0137946983693145	0.0931035154479543	0.14816517188361	0.88224402541502	   
df.mm.trans1:exp8	-0.217756248685914	0.129206948359184	-1.68532924468250	0.0922552127102042	.  
df.mm.trans2:exp8	-0.149012770879363	0.0931035154479543	-1.60050638434445	0.109820520253125	   
df.mm.trans1:probe2	-0.228213210321207	0.0981397223280691	-2.32539082960025	0.0202619979906310	*  
df.mm.trans1:probe3	-0.147634610537016	0.098139722328069	-1.50433083602470	0.132830331258112	   
df.mm.trans1:probe4	-0.00735650148797904	0.0981397223280691	-0.0749594691473362	0.940262824703157	   
df.mm.trans1:probe5	-0.0972479212600293	0.0981397223280691	-0.99091294486183	0.321981740585636	   
df.mm.trans1:probe6	-0.0443797875302949	0.0981397223280691	-0.45221024145492	0.651221312340435	   
df.mm.trans1:probe7	-0.0596487888847401	0.0981397223280691	-0.607794555249927	0.543469771389895	   
df.mm.trans1:probe8	-0.0529171114063436	0.0981397223280691	-0.539201764087411	0.589874651220858	   
df.mm.trans1:probe9	-0.105561699804806	0.0981397223280691	-1.07562664027035	0.282368805221631	   
df.mm.trans1:probe10	-0.0518192393961808	0.0981397223280691	-0.528014937957083	0.597612928526018	   
df.mm.trans1:probe11	-0.009871651607739	0.0981397223280691	-0.100587727105435	0.919899075509153	   
df.mm.trans1:probe12	-0.0573213477998618	0.0981397223280691	-0.584078968638647	0.55930657651178	   
df.mm.trans1:probe13	-0.0483511964625075	0.0981397223280691	-0.492677127217411	0.622355189760385	   
df.mm.trans1:probe14	-0.0771983561157184	0.0981397223280691	-0.786616818189619	0.431703424607657	   
df.mm.trans1:probe15	-0.0367050965214809	0.0981397223280691	-0.374008563003472	0.708481843179421	   
df.mm.trans1:probe16	-0.116442862684116	0.0981397223280691	-1.18650083698893	0.235722775356205	   
df.mm.trans2:probe2	0.0575866736649174	0.098139722328069	0.586782520867669	0.557489923816209	   
df.mm.trans2:probe3	0.00986771788869967	0.098139722328069	0.100547644262871	0.919930887084251	   
df.mm.trans2:probe4	0.0197171296770642	0.0981397223280691	0.200908757527887	0.840813152121868	   
df.mm.trans2:probe5	-0.0994434433966903	0.0981397223280691	-1.01328433622691	0.311183730076656	   
df.mm.trans2:probe6	-0.0669444503519089	0.098139722328069	-0.682134091719984	0.495321320945916	   
df.mm.trans3:probe2	-0.0526935502158466	0.0981397223280691	-0.53692377526501	0.59144665349675	   
df.mm.trans3:probe3	-0.0554877288783934	0.0981397223280691	-0.565395209626788	0.571939188999051	   
df.mm.trans3:probe4	-0.0747706675171952	0.098139722328069	-0.7618797541249	0.446321873525332	   
df.mm.trans3:probe5	-0.0642004761610711	0.0981397223280691	-0.654174218533621	0.513158732891868	   
df.mm.trans3:probe6	-0.000248666619842402	0.0981397223280691	-0.00253380195035747	0.997978855471055	   
df.mm.trans3:probe7	-0.0133656585398561	0.0981397223280691	-0.136190099409252	0.891700007870085	   
df.mm.trans3:probe8	-0.0275294429464395	0.0981397223280691	-0.280512745434636	0.77914554560588	   
df.mm.trans3:probe9	-0.095836366456749	0.0981397223280691	-0.97652983097282	0.329051883137462	   
df.mm.trans3:probe10	-0.234055087715169	0.098139722328069	-2.3849169547551	0.0172792903787551	*  
df.mm.trans3:probe11	-0.0470386238733177	0.0981397223280691	-0.479302597943708	0.63183426946329	   
df.mm.trans3:probe12	-0.0954654472478581	0.098139722328069	-0.972750329664973	0.330926324552376	   
df.mm.trans3:probe13	-0.123550908479910	0.0981397223280691	-1.25892865344467	0.208366998130026	   
df.mm.trans3:probe14	-0.0882008760031774	0.0981397223280691	-0.898727588695764	0.369026710346674	   
df.mm.trans3:probe15	0.0394351338025065	0.0981397223280691	0.401826425294742	0.68790263148507	   
df.mm.trans3:probe16	-0.0103463866644231	0.0981397223280691	-0.105425065600210	0.916060900251663	   
df.mm.trans3:probe17	-0.133455606596271	0.0981397223280691	-1.35985310973415	0.174200750589737	   
df.mm.trans3:probe18	-0.0198041286170008	0.0981397223280691	-0.201795237924131	0.840120234758098	   
df.mm.trans3:probe19	-0.130550009012134	0.0981397223280691	-1.33024636625444	0.183758047479853	   
df.mm.trans3:probe20	-0.0386118975302256	0.0981397223280691	-0.393438014845312	0.694084743385494