chr7.21274_chr7_69838577_69838995_+_1.R 

fitVsDatCorrelation=0.870746356751091
cont.fitVsDatCorrelation=0.253469613308137

fstatistic=10118.4017067343,66,1014
cont.fstatistic=2603.22479500647,66,1014

residuals=-0.578064740611536,-0.097437681919336,-0.0112429403535446,0.085402255685663,0.917501035503202
cont.residuals=-0.730901354153594,-0.227403877476726,-0.0710159800841747,0.149026686604733,1.18956237562052

predictedValues:
Include	Exclude	Both
chr7.21274_chr7_69838577_69838995_+_1.R.tl.Lung	61.1720918248754	147.860618151013	58.9828186079937
chr7.21274_chr7_69838577_69838995_+_1.R.tl.cerebhem	72.0074216458851	100.810200006740	61.5518768987294
chr7.21274_chr7_69838577_69838995_+_1.R.tl.cortex	60.4311545380478	113.117776083040	68.6253039741256
chr7.21274_chr7_69838577_69838995_+_1.R.tl.heart	63.9717726261071	116.081548141137	63.1039764836233
chr7.21274_chr7_69838577_69838995_+_1.R.tl.kidney	61.8575247470307	158.047971382254	63.680074904563
chr7.21274_chr7_69838577_69838995_+_1.R.tl.liver	65.2348411575221	128.273911769154	58.0984315762911
chr7.21274_chr7_69838577_69838995_+_1.R.tl.stomach	66.3147410076585	112.412787417334	60.6183077628097
chr7.21274_chr7_69838577_69838995_+_1.R.tl.testicle	64.1001844677657	120.718377384527	64.8211940139985


diffExp=-86.6885263261381,-28.8027783608553,-52.6866215449923,-52.10977551503,-96.190446635223,-63.0390706116321,-46.0980464096751,-56.6181929167612
diffExpScore=0.997930606867587
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	72.0382609367353	75.1905221726657	62.4329928198827
cerebhem	70.3997454338765	74.5313846950455	68.8919688435143
cortex	70.1640832888055	78.4525607940381	70.021818158036
heart	65.7447284253832	71.552338060306	70.8980613598396
kidney	68.6782126161225	78.6434018356451	65.0229052147328
liver	65.0032916345587	84.5630574000986	67.4146967646673
stomach	68.5621539338513	86.5427926663997	63.3789217831498
testicle	71.0513953017364	72.4307070461304	64.6290925237433
cont.diffExp=-3.15226123593042,-4.13163926116907,-8.28847750523266,-5.80760963492276,-9.96518921952261,-19.5597657655399,-17.9806387325484,-1.37931174439403
cont.diffExpScore=0.985967845365218

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,-1,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,0,-1,-1,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.624388078201877
cont.tran.correlation=-0.351982438927257

tran.covariance=-0.00543276609410513
cont.tran.covariance=-0.000853220046576191

tran.mean=94.5258076468807
cont.tran.mean=73.3467897650874

weightedLogRatios:
wLogRatio
Lung	-4.0201301521151
cerebhem	-1.49561217027521
cortex	-2.7678522555348
heart	-2.65533104850091
kidney	-4.30934439001582
liver	-3.05365738711092
stomach	-2.35293421037005
testicle	-2.83397054918871

cont.weightedLogRatios:
wLogRatio
Lung	-0.184100100039545
cerebhem	-0.244245493971325
cortex	-0.48087180738328
heart	-0.357907725487674
kidney	-0.582232031168131
liver	-1.13272609143767
stomach	-1.01175449095625
testicle	-0.0821566437068949

varWeightedLogRatios=0.801056817526592
cont.varWeightedLogRatios=0.146946780889037

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.18954118941212	0.0768982567624014	67.4858105749608	0	***
df.mm.trans1	-1.04210411581944	0.0646650576980392	-16.1154130672188	3.41603604880015e-52	***
df.mm.trans2	-0.188685822483041	0.0574729023987082	-3.283039738868	0.00106202277509718	** 
df.mm.exp2	-0.262586551759528	0.0724936819884566	-3.62219912904053	0.000306581642366342	***
df.mm.exp3	-0.431441950480919	0.0724936819884566	-5.95144209325191	3.65754237151986e-09	***
df.mm.exp4	-0.26476384652284	0.0724936819884566	-3.65223339828427	0.000273262518876993	***
df.mm.exp5	0.00114569926825033	0.0724936819884566	0.0158041257779231	0.987393765901033	   
df.mm.exp6	-0.0626919941334857	0.0724936819884566	-0.86479252279486	0.387357211562852	   
df.mm.exp7	-0.220721986135138	0.0724936819884566	-3.04470651898029	0.00238918818074961	** 
df.mm.exp8	-0.250439941932157	0.0724936819884566	-3.4546450816505	0.000573820616146223	***
df.mm.trans1:exp2	0.425664674300455	0.063644503520835	6.68816081126483	3.72841635942662e-11	***
df.mm.trans2:exp2	-0.120443967950483	0.0451970241870385	-2.66486500199772	0.00782424689934176	** 
df.mm.trans1:exp3	0.41925565647281	0.063644503520835	6.58746055479183	7.17422666049842e-11	***
df.mm.trans2:exp3	0.163601431861609	0.0451970241870385	3.61973901610377	0.000309473263935381	***
df.mm.trans1:exp4	0.309514710295091	0.063644503520835	4.86318052891664	1.33825731580949e-06	***
df.mm.trans2:exp4	0.0227867310853752	0.0451970241870385	0.504164411158511	0.614255372082687	   
df.mm.trans1:exp5	0.00999698382141001	0.063644503520835	0.157075368152371	0.875216719618463	   
df.mm.trans2:exp5	0.0654828432490579	0.0451970241870385	1.4488308561659	0.147693985764229	   
df.mm.trans1:exp6	0.126994624343016	0.0636445035208349	1.99537457781319	0.0462692500506129	*  
df.mm.trans2:exp6	-0.079410153422441	0.0451970241870385	-1.75697747475185	0.0792234325795056	.  
df.mm.trans1:exp7	0.301443127009278	0.063644503520835	4.73635758523273	2.48554622962828e-06	***
df.mm.trans2:exp7	-0.053370376589046	0.0451970241870385	-1.18083828634788	0.237943872439377	   
df.mm.trans1:exp8	0.297196114088912	0.063644503520835	4.66962734639953	3.42276981260147e-06	***
df.mm.trans2:exp8	0.0476302543817783	0.0451970241870385	1.05383607081454	0.292209018338957	   
df.mm.trans1:probe2	-0.0291959988184345	0.0490858655792052	-0.594794417373035	0.552113596991548	   
df.mm.trans1:probe3	0.0404726622560109	0.0490858655792052	0.824527830536145	0.409833618085917	   
df.mm.trans1:probe4	0.329887310564942	0.0490858655792052	6.7206171608125	3.013731004977e-11	***
df.mm.trans1:probe5	-0.346801998713949	0.0490858655792052	-7.06521102606101	2.97733129857094e-12	***
df.mm.trans1:probe6	-0.00159026783706547	0.0490858655792052	-0.0323976733077958	0.974161293677592	   
df.mm.trans1:probe7	-0.0271436024388664	0.0490858655792052	-0.552982047246725	0.580397589833876	   
df.mm.trans1:probe8	-0.0866218366408775	0.0490858655792052	-1.76470019666057	0.0779151748159379	.  
df.mm.trans1:probe9	-0.273965006171288	0.0490858655792052	-5.58134206127458	3.06273041386470e-08	***
df.mm.trans1:probe10	0.0673774836820123	0.0490858655792052	1.37264532033751	0.170166145858541	   
df.mm.trans1:probe11	-0.0781599617989076	0.0490858655792052	-1.59231096114193	0.111626516775591	   
df.mm.trans1:probe12	-0.0625164174371338	0.0490858655792052	-1.27361342617575	0.203092315984642	   
df.mm.trans1:probe13	-0.115388418867623	0.0490858655792052	-2.35074634023581	0.0189265457045152	*  
df.mm.trans1:probe14	-0.301783867653695	0.0490858655792052	-6.14808079867178	1.12617144698471e-09	***
df.mm.trans1:probe15	-0.100427875846921	0.0490858655792052	-2.04596322509318	0.0410175324590665	*  
df.mm.trans1:probe16	-0.195252344792534	0.0490858655792052	-3.9777712481707	7.45090852143517e-05	***
df.mm.trans2:probe2	0.123761675470845	0.0490858655792052	2.52133020392892	0.0118435597128919	*  
df.mm.trans2:probe3	-0.224388664317251	0.0490858655792052	-4.5713498513167	5.4432155853945e-06	***
df.mm.trans2:probe4	-0.177875262241071	0.0490858655792052	-3.62375726988151	0.000304763326738929	***
df.mm.trans2:probe5	0.170894155746148	0.0490858655792052	3.48153493331786	0.000519794964939646	***
df.mm.trans2:probe6	-0.0253657832453001	0.0490858655792052	-0.516763490792879	0.605433955041596	   
df.mm.trans3:probe2	0.337063231496751	0.0490858655792052	6.8668083473615	1.14282398060842e-11	***
df.mm.trans3:probe3	0.0509431913635054	0.0490858655792052	1.03783830156368	0.299592759185780	   
df.mm.trans3:probe4	-0.0110039841136998	0.0490858655792052	-0.224178263617329	0.822663757048263	   
df.mm.trans3:probe5	-0.0769236329796722	0.0490858655792052	-1.56712389752092	0.117397730462179	   
df.mm.trans3:probe6	0.242223781310226	0.0490858655792052	4.93469511950182	9.37893696617996e-07	***
df.mm.trans3:probe7	0.141947462707554	0.0490858655792052	2.8918194888202	0.00391198976023717	** 
df.mm.trans3:probe8	0.111644664259298	0.0490858655792052	2.27447683649681	0.0231455199766290	*  
df.mm.trans3:probe9	0.0523083880761428	0.0490858655792052	1.06565072162653	0.286835233824882	   
df.mm.trans3:probe10	0.0566151210530386	0.0490858655792052	1.15338948157457	0.249022353433938	   
df.mm.trans3:probe11	0.512788537721002	0.0490858655792052	10.4467657169774	2.47410981912237e-24	***
df.mm.trans3:probe12	-0.163900771557889	0.0490858655792052	-3.33906246989611	0.000871214815575964	***
df.mm.trans3:probe13	0.0962793905151824	0.0490858655792052	1.96144835950434	0.0501000536368376	.  
df.mm.trans3:probe14	-0.0910637790152281	0.0490858655792052	-1.85519350510968	0.0638586645352409	.  
df.mm.trans3:probe15	0.544901840550032	0.0490858655792052	11.1009928035348	4.20420368402973e-27	***
df.mm.trans3:probe16	0.0698596424444661	0.0490858655792052	1.42321300888013	0.154982007575345	   
df.mm.trans3:probe17	-0.109646052117128	0.0490858655792052	-2.23376018377842	0.0257162731978045	*  
df.mm.trans3:probe18	0.0921680379497801	0.0490858655792052	1.87768997983864	0.0607104329981734	.  
df.mm.trans3:probe19	0.227478417382971	0.0490858655792052	4.63429573256502	4.04804890099988e-06	***
df.mm.trans3:probe20	0.306021368173236	0.0490858655792052	6.23440912291622	6.64330158127244e-10	***
df.mm.trans3:probe21	-0.0771444703631054	0.0490858655792052	-1.57162289903241	0.116349993515600	   
df.mm.trans3:probe22	0.219247339410966	0.0490858655792052	4.46660839783272	8.83939464797171e-06	***
df.mm.trans3:probe23	0.193575903833163	0.0490858655792052	3.94361801608262	8.57785308142547e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.53655806423399	0.151275394462032	29.9887372984018	5.71072816240038e-142	***
df.mm.trans1	-0.255325720794121	0.127210063310094	-2.00711888784872	0.0450020279241451	*  
df.mm.trans2	-0.195634483202862	0.113061548431530	-1.73033613918121	0.0838744885535471	.  
df.mm.exp2	-0.130258354922385	0.142610649454551	-0.913384487207581	0.361257481148891	   
df.mm.exp3	-0.0986049155109054	0.142610649454551	-0.691427434683482	0.489455235790147	   
df.mm.exp4	-0.268163116408024	0.142610649454551	-1.88038633463685	0.0603418908069612	.  
df.mm.exp5	-0.0435125527299966	0.142610649454551	-0.305114329795292	0.760341694285467	   
df.mm.exp6	-0.0620563415182954	0.142610649454551	-0.435145213598318	0.663549625700831	   
df.mm.exp7	0.0761196517932671	0.142610649454551	0.533758538260679	0.593625585839316	   
df.mm.exp8	-0.0857595265857087	0.142610649454551	-0.60135429516461	0.547738443316483	   
df.mm.trans1:exp2	0.107250622312604	0.125202413953315	0.856617847261273	0.391858509482045	   
df.mm.trans2:exp2	0.121453474597822	0.0889122582262128	1.36599246291571	0.172244206486403	   
df.mm.trans1:exp3	0.0722440817795359	0.125202413953315	0.577018281823815	0.564055183771416	   
df.mm.trans2:exp3	0.141073848453852	0.0889122582262128	1.58666365322685	0.11290061035705	   
df.mm.trans1:exp4	0.176745228440660	0.125202413953315	1.41167588435287	0.158352192364565	   
df.mm.trans2:exp4	0.218567111094711	0.0889122582262128	2.45823371776957	0.0141285528531794	*  
df.mm.trans1:exp5	-0.00425281615947948	0.125202413953315	-0.0339675252672465	0.972909731850893	   
df.mm.trans2:exp5	0.0884110978958235	0.0889122582262128	0.994363428166292	0.320283189000413	   
df.mm.trans1:exp6	-0.0407031291077506	0.125202413953315	-0.32509859692424	0.745173513663449	   
df.mm.trans2:exp6	0.179528650928256	0.0889122582262128	2.01916647389041	0.0437326621498678	*  
df.mm.trans1:exp7	-0.125576340996947	0.125202413953315	-1.00298658014510	0.316106618622329	   
df.mm.trans2:exp7	0.0644941646460686	0.0889122582262128	0.725368649190991	0.468393265380381	   
df.mm.trans1:exp8	0.0719656394721603	0.125202413953315	0.574794344612194	0.565557894548831	   
df.mm.trans2:exp8	0.0483646784561217	0.0889122582262128	0.543959622902289	0.586588837744579	   
df.mm.trans1:probe2	-0.0830712259654458	0.0965624448542136	-0.860285032041842	0.38983529540611	   
df.mm.trans1:probe3	0.142582009687435	0.0965624448542136	1.47657828985896	0.140099193336741	   
df.mm.trans1:probe4	0.0405342368706711	0.0965624448542136	0.419772272044978	0.674740745883764	   
df.mm.trans1:probe5	-0.0242105438156851	0.0965624448542136	-0.250724221535994	0.802078126219414	   
df.mm.trans1:probe6	-0.0480422319819005	0.0965624448542136	-0.497525016629735	0.618926733806537	   
df.mm.trans1:probe7	0.0302968629981618	0.0965624448542136	0.313754100198093	0.753772339801209	   
df.mm.trans1:probe8	-0.051440653955157	0.0965624448542136	-0.532719050691189	0.594344795669132	   
df.mm.trans1:probe9	0.0561655898039461	0.0965624448542136	0.58165045312122	0.560931430131001	   
df.mm.trans1:probe10	-0.0609284021168719	0.0965624448542136	-0.630974103947547	0.528199648001037	   
df.mm.trans1:probe11	-0.0527759046896319	0.0965624448542136	-0.546546897909545	0.584810299606345	   
df.mm.trans1:probe12	0.0266244738965286	0.0965624448542136	0.275722864481376	0.782817111473301	   
df.mm.trans1:probe13	0.00268895739740918	0.0965624448542136	0.0278468239020757	0.977789799746162	   
df.mm.trans1:probe14	-0.0319037159601176	0.0965624448542136	-0.330394658174662	0.741170028016162	   
df.mm.trans1:probe15	-0.0427834275759089	0.0965624448542136	-0.443064875174834	0.657813316872582	   
df.mm.trans1:probe16	-0.0449597529980022	0.0965624448542136	-0.465602885944746	0.641599761476661	   
df.mm.trans2:probe2	-0.170658309956440	0.0965624448542136	-1.76733625804621	0.077472669948619	.  
df.mm.trans2:probe3	-0.112392759482614	0.0965624448542136	-1.16393862699211	0.244722676987827	   
df.mm.trans2:probe4	0.0365821726949422	0.0965624448542136	0.378844723227261	0.704882483143961	   
df.mm.trans2:probe5	-0.185448263972522	0.0965624448542136	-1.92050091785171	0.0550748735019632	.  
df.mm.trans2:probe6	-0.174136235017859	0.0965624448542136	-1.80335362552971	0.0716294417697132	.  
df.mm.trans3:probe2	0.118475697921385	0.0965624448542136	1.22693349469615	0.220132383705705	   
df.mm.trans3:probe3	0.0307559488514687	0.0965624448542136	0.318508390067204	0.75016492175132	   
df.mm.trans3:probe4	-0.00261236671792000	0.0965624448542136	-0.0270536513637787	0.97842226527337	   
df.mm.trans3:probe5	0.260583043311452	0.0965624448542136	2.69859616442884	0.00707897564758453	** 
df.mm.trans3:probe6	-0.0304485555428818	0.0965624448542136	-0.315325027124695	0.752579765900693	   
df.mm.trans3:probe7	0.108321228933862	0.0965624448542135	1.12177388525530	0.262224161226816	   
df.mm.trans3:probe8	0.148683769665210	0.0965624448542136	1.53976807328861	0.123928882591915	   
df.mm.trans3:probe9	-0.0253561067447975	0.0965624448542135	-0.262587663175671	0.792921748906442	   
df.mm.trans3:probe10	0.115222628453544	0.0965624448542135	1.19324473015884	0.233052652730045	   
df.mm.trans3:probe11	-0.00731179975809249	0.0965624448542136	-0.0757209468870799	0.939656046903719	   
df.mm.trans3:probe12	0.0432830088710096	0.0965624448542136	0.448238535554446	0.654076811705407	   
df.mm.trans3:probe13	0.039928418501654	0.0965624448542136	0.413498421274818	0.679328911987795	   
df.mm.trans3:probe14	0.0587120026955218	0.0965624448542136	0.608021087123084	0.543309642771532	   
df.mm.trans3:probe15	0.0423274116914733	0.0965624448542136	0.438342377881771	0.661231478235044	   
df.mm.trans3:probe16	0.0549268961579554	0.0965624448542135	0.568822550432334	0.5696025291651	   
df.mm.trans3:probe17	-0.0622608723849617	0.0965624448542135	-0.644773156675567	0.519220040480726	   
df.mm.trans3:probe18	0.104091443377442	0.0965624448542136	1.07797025577175	0.281303400302833	   
df.mm.trans3:probe19	0.0792558226943772	0.0965624448542136	0.820772742591954	0.411968638360019	   
df.mm.trans3:probe20	0.0122668655682571	0.0965624448542136	0.127035573579120	0.898937429124523	   
df.mm.trans3:probe21	0.0267973789396242	0.0965624448542136	0.277513467892015	0.781442433435984	   
df.mm.trans3:probe22	0.0032428943750052	0.0965624448542135	0.0335833913474457	0.973215975915488	   
df.mm.trans3:probe23	-0.00347660942476946	0.0965624448542136	-0.0360037427596031	0.971286460862172