chr2.13503_chr2_33817474_33821110_-_0.R 

fitVsDatCorrelation=0.674420414897323
cont.fitVsDatCorrelation=0.270536037822402

fstatistic=11654.1246744498,56,784
cont.fstatistic=6849.28300848138,56,784

residuals=-0.439601038732218,-0.0784018416684465,-0.0144333374980001,0.0686160134491018,0.741376435996388
cont.residuals=-0.547470908580944,-0.112180569934956,-0.0161242713932757,0.0872245110064031,0.9493664158855

predictedValues:
Include	Exclude	Both
chr2.13503_chr2_33817474_33821110_-_0.R.tl.Lung	43.9830009611314	49.1628385049426	50.3548084369294
chr2.13503_chr2_33817474_33821110_-_0.R.tl.cerebhem	52.6649825516385	59.2081952053801	58.0613268981651
chr2.13503_chr2_33817474_33821110_-_0.R.tl.cortex	45.6919479150633	48.6399179656967	52.7887617606867
chr2.13503_chr2_33817474_33821110_-_0.R.tl.heart	45.3274543285622	49.2668888794514	50.1803707299503
chr2.13503_chr2_33817474_33821110_-_0.R.tl.kidney	45.2806452555307	49.4554102776772	49.4469472371085
chr2.13503_chr2_33817474_33821110_-_0.R.tl.liver	49.3569573477786	49.5269328954539	53.8380901582505
chr2.13503_chr2_33817474_33821110_-_0.R.tl.stomach	46.3476930669553	49.968229261449	51.9874696003269
chr2.13503_chr2_33817474_33821110_-_0.R.tl.testicle	47.0005966582228	52.4285452418873	54.3236008576109


diffExp=-5.17983754381125,-6.54321265374156,-2.9479700506334,-3.93943455088925,-4.17476502214649,-0.169975547675243,-3.62053619449370,-5.42794858366451
diffExpScore=0.969700348702197
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	53.694907359007	55.7772995879241	52.0507135764446
cerebhem	50.8109880656878	53.7768155933049	50.817112721261
cortex	52.9541707413756	49.4244405431619	58.4185062362672
heart	50.3185138844555	52.7674143344546	52.7996299252062
kidney	50.3717023716812	51.4137242024803	53.9644196888796
liver	53.4090828588446	50.6126835387228	52.1170139209485
stomach	49.6721901799349	51.0982993989583	53.3565647440556
testicle	52.7558350742251	50.5768320447163	49.2958751130202
cont.diffExp=-2.08239222891707,-2.9658275276171,3.52973019821371,-2.44890044999912,-1.04202183079909,2.7963993201218,-1.42610921902340,2.17900302950878
cont.diffExpScore=7.50792380070786

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=0.839614448677124
cont.tran.correlation=0.0183328521880031

tran.covariance=0.00313596038331562
cont.tran.covariance=2.92962245289567e-06

tran.mean=48.9568897698013
cont.tran.mean=51.8396812361834

weightedLogRatios:
wLogRatio
Lung	-0.427466715456035
cerebhem	-0.471072376758292
cortex	-0.240910512061469
heart	-0.321321650640555
kidney	-0.340153529599245
liver	-0.0134104933192126
stomach	-0.291369691570090
testicle	-0.426759746396505

cont.weightedLogRatios:
wLogRatio
Lung	-0.152284360642577
cerebhem	-0.224450201631269
cortex	0.271438912313804
heart	-0.187333212798363
kidney	-0.0804622501641134
liver	0.212484228584797
stomach	-0.110948123776636
testicle	0.166385618045943

varWeightedLogRatios=0.0210221008860185
cont.varWeightedLogRatios=0.0389394344755725

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.67648730061884	0.0657233867858234	55.9387986592903	6.37676681978399e-276	***
df.mm.trans1	-0.0386431615028086	0.0555463999772066	-0.695691557304628	0.486828176482272	   
df.mm.trans2	0.203818026731862	0.0499636983662844	4.07932225588405	4.97859404940464e-05	***
df.mm.exp2	0.223663793765449	0.0640561058013916	3.49168578025844	0.000506837078949713	***
df.mm.exp3	-0.0197788093614953	0.0640561058013916	-0.308773209267829	0.757576080357149	   
df.mm.exp4	0.0356940802615102	0.0640561058013916	0.557231505333482	0.577528312733297	   
df.mm.exp5	0.0532036976469844	0.0640561058013916	0.830579645474304	0.406463994189231	   
df.mm.exp6	0.0557670181762949	0.0640561058013916	0.870596447889021	0.384241023301691	   
df.mm.exp7	0.0367090613324233	0.0640561058013916	0.57307669383214	0.566757091990573	   
df.mm.exp8	0.0548055994381671	0.0640561058013916	0.855587437801698	0.392487419002523	   
df.mm.trans1:exp2	-0.0435162442620604	0.0566181334857087	-0.768591996644407	0.442367014027469	   
df.mm.trans2:exp2	-0.0377418520971003	0.0431190858304673	-0.875293419844085	0.381682345972742	   
df.mm.trans1:exp3	0.0578976798422739	0.0566181334857087	1.02259958564138	0.306812633498853	   
df.mm.trans2:exp3	0.0090853372934483	0.0431190858304673	0.210703383860442	0.833173484512977	   
df.mm.trans1:exp4	-0.00558439300860543	0.0566181334857087	-0.0986325875616337	0.921455207000357	   
df.mm.trans2:exp4	-0.033579873181364	0.0431190858304673	-0.778770526661698	0.436349887099124	   
df.mm.trans1:exp5	-0.0241272309871743	0.0566181334857087	-0.426139639401295	0.670123066583702	   
df.mm.trans2:exp5	-0.0472702596738942	0.0431190858304673	-1.09627230641550	0.273296250130327	   
df.mm.trans1:exp6	0.0595084999814817	0.0566181334857087	1.05105019042181	0.293559395090783	   
df.mm.trans2:exp6	-0.0483884208357517	0.0431190858304673	-1.12220423749247	0.262119175637621	   
df.mm.trans1:exp7	0.0156592399459743	0.0566181334857087	0.276576407272891	0.782178279066041	   
df.mm.trans2:exp7	-0.0204596958008448	0.0431190858304673	-0.474492800735314	0.635280662400373	   
df.mm.trans1:exp8	0.0115514795705419	0.0566181334857087	0.204024379812126	0.83838739500424	   
df.mm.trans2:exp8	0.00950757692340059	0.0431190858304673	0.220495790675661	0.825542455649118	   
df.mm.trans1:probe2	0.213099432240987	0.0410237351503724	5.1945399769151	2.61849251171587e-07	***
df.mm.trans1:probe3	0.224912891459008	0.0410237351503724	5.48250642304	5.65769177543332e-08	***
df.mm.trans1:probe4	0.04173384026951	0.0410237351503724	1.01730961640949	0.309319993308190	   
df.mm.trans1:probe5	0.217921338258398	0.0410237351503724	5.31207939646666	1.41325668720689e-07	***
df.mm.trans1:probe6	0.0423945992410811	0.0410237351503724	1.03341636459196	0.301727757306930	   
df.mm.trans1:probe7	0.261217375717015	0.0410237351503724	6.36746933840917	3.27056214696015e-10	***
df.mm.trans1:probe8	0.133891470228277	0.0410237351503724	3.26375620692504	0.0011471093946358	** 
df.mm.trans1:probe9	0.323605962018767	0.0410237351503724	7.88826177900646	1.02851396616535e-14	***
df.mm.trans1:probe10	0.303672953109114	0.0410237351503724	7.40237211448449	3.45392005144969e-13	***
df.mm.trans1:probe11	0.40836971328426	0.0410237351503724	9.95447420346738	4.6026774781799e-22	***
df.mm.trans1:probe12	0.410969263039718	0.0410237351503724	10.0178411725142	2.61103046111495e-22	***
df.mm.trans1:probe13	0.589244394372874	0.0410237351503724	14.3634993793958	1.05383914161915e-41	***
df.mm.trans1:probe14	0.299081855401745	0.0410237351503724	7.29045890885998	7.55683199553124e-13	***
df.mm.trans1:probe15	0.32482094873349	0.0410237351503724	7.9178784560416	8.2527681058996e-15	***
df.mm.trans2:probe2	0.0468948793244815	0.0410237351503724	1.14311578778939	0.253339460023427	   
df.mm.trans2:probe3	-0.0308834141248737	0.0410237351503724	-0.752818191997158	0.451785155191911	   
df.mm.trans2:probe4	0.0308559417020672	0.0410237351503724	0.75214852058119	0.452187497215288	   
df.mm.trans2:probe5	0.0913007276347693	0.0410237351503724	2.22555862600289	0.0263274408372765	*  
df.mm.trans2:probe6	0.158485781730112	0.0410237351503724	3.86327040063960	0.000121120274936838	***
df.mm.trans3:probe2	0.143538067431132	0.0410237351503724	3.49890293765288	0.000493521120992608	***
df.mm.trans3:probe3	0.150268202802825	0.0410237351503724	3.66295760861406	0.000266076318667599	***
df.mm.trans3:probe4	0.0241064080967263	0.0410237351503724	0.587620995708078	0.556955917332773	   
df.mm.trans3:probe5	0.0366890811271596	0.0410237351503724	0.894337899576327	0.371415594120475	   
df.mm.trans3:probe6	-0.00965005709986271	0.0410237351503724	-0.235231069635430	0.81409081958172	   
df.mm.trans3:probe7	0.0361525176838030	0.0410237351503724	0.881258558034415	0.378447972595342	   
df.mm.trans3:probe8	0.0639057028215456	0.0410237351503724	1.55777387376599	0.119690419356824	   
df.mm.trans3:probe9	0.166437261717437	0.0410237351503724	4.05709672967031	5.46589902091403e-05	***
df.mm.trans3:probe10	0.176584320074352	0.0410237351503724	4.30444276775585	1.88605547650356e-05	***
df.mm.trans3:probe11	0.0303873039306387	0.0410237351503724	0.740724944212273	0.459082014701183	   
df.mm.trans3:probe12	-0.0488206580820713	0.0410237351503724	-1.19005882577779	0.234383511005125	   
df.mm.trans3:probe13	0.140893833708419	0.0410237351503724	3.43444674630364	0.000624961523869418	***
df.mm.trans3:probe14	0.173555301700448	0.0410237351503724	4.23060701479964	2.60601087732784e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.05305473661789	0.085694808313639	47.2963860515785	7.5063519253306e-232	***
df.mm.trans1	-0.0713514614813214	0.0724253318544167	-0.98517272416157	0.324843166719134	   
df.mm.trans2	-0.0415403530462943	0.0651462099494662	-0.637648039364332	0.523888993129308	   
df.mm.exp2	-0.0677447201642128	0.0835208892362266	-0.81111109787884	0.417548036700132	   
df.mm.exp3	-0.250227467846503	0.0835208892362266	-2.99598663441874	0.00282179171274939	** 
df.mm.exp4	-0.134703882373541	0.0835208892362266	-1.61281666904373	0.107186770465127	   
df.mm.exp5	-0.181456860351391	0.0835208892362266	-2.17259253356567	0.0301098908793901	*  
df.mm.exp6	-0.103775054811154	0.0835208892362266	-1.24250418979187	0.21442209970646	   
df.mm.exp7	-0.190267207888063	0.0835208892362266	-2.27807928804397	0.022990160365869	*  
df.mm.exp8	-0.0611390447443002	0.0835208892362266	-0.732020998619607	0.464374511972298	   
df.mm.trans1:exp2	0.0125391898748422	0.073822733937083	0.16985539827784	0.865167677104362	   
df.mm.trans2:exp2	0.0312201883306384	0.0562217191719383	0.5553047610508	0.578844629414945	   
df.mm.trans1:exp3	0.236336142344530	0.073822733937083	3.20140056782444	0.00142275242560340	** 
df.mm.trans2:exp3	0.129305548336412	0.0562217191719383	2.29992163599564	0.0217145214231942	*  
df.mm.trans1:exp4	0.0697587990419339	0.073822733937083	0.944950089512905	0.344975480847797	   
df.mm.trans2:exp4	0.0792307606181695	0.0562217191719383	1.40925538715500	0.159156178223468	   
df.mm.trans1:exp5	0.117568254881300	0.073822733937083	1.59257519481058	0.111658596898840	   
df.mm.trans2:exp5	0.099995035808727	0.0562217191719383	1.77858374452977	0.0756954677002327	.  
df.mm.trans1:exp6	0.0984377153131793	0.073822733937083	1.33343361947385	0.182776834183308	   
df.mm.trans2:exp6	0.00661029332061364	0.0562217191719383	0.117575439135860	0.906434180867022	   
df.mm.trans1:exp7	0.112394268668502	0.073822733937083	1.52248857058439	0.128289930930156	   
df.mm.trans2:exp7	0.102651455486210	0.0562217191719383	1.82583273863042	0.0682556990008936	.  
df.mm.trans1:exp8	0.0434952665137727	0.073822733937083	0.589185257631375	0.555906733940681	   
df.mm.trans2:exp8	-0.0367343177371617	0.0562217191719383	-0.65338303912088	0.513700939786631	   
df.mm.trans1:probe2	0.00870089864826463	0.0534896524957987	0.162665080857424	0.870824058578928	   
df.mm.trans1:probe3	-0.00491207714477059	0.0534896524957987	-0.0918322874719817	0.926854749533856	   
df.mm.trans1:probe4	-0.0164279606921836	0.0534896524957987	-0.307124087102153	0.758830482400025	   
df.mm.trans1:probe5	0.00692635613438913	0.0534896524957987	0.129489645402597	0.897003430854274	   
df.mm.trans1:probe6	-0.0112371425385051	0.0534896524957987	-0.210080679424637	0.833659286803624	   
df.mm.trans1:probe7	0.0690809175693026	0.0534896524957987	1.29148189128222	0.196917252904145	   
df.mm.trans1:probe8	-0.0249868355442403	0.0534896524957987	-0.467134004024476	0.64053380311923	   
df.mm.trans1:probe9	-0.0233262884587726	0.0534896524957987	-0.436089736432756	0.662891676591279	   
df.mm.trans1:probe10	-0.0106050994268892	0.0534896524957987	-0.198264504106139	0.842889520033709	   
df.mm.trans1:probe11	-0.00709332744267044	0.0534896524957987	-0.132611208181388	0.89453489507856	   
df.mm.trans1:probe12	0.0218573733588193	0.0534896524957987	0.408628068027476	0.682924217013469	   
df.mm.trans1:probe13	0.0353406966535243	0.0534896524957987	0.660701556367376	0.50899781037286	   
df.mm.trans1:probe14	-0.0316624317457050	0.0534896524957987	-0.591935641163343	0.554064340497312	   
df.mm.trans1:probe15	0.0303319788158464	0.0534896524957987	0.567062551364094	0.5708340017654	   
df.mm.trans2:probe2	0.0424209389714557	0.0534896524957987	0.793068135463912	0.427978060744784	   
df.mm.trans2:probe3	0.0275842986241575	0.0534896524957987	0.515694107871126	0.606213460403841	   
df.mm.trans2:probe4	0.0482177892144784	0.0534896524957987	0.901441437075436	0.367630493166769	   
df.mm.trans2:probe5	0.110184533206797	0.0534896524957987	2.05992239742913	0.0397355298609628	*  
df.mm.trans2:probe6	-0.0313558475709396	0.0534896524957987	-0.586203987274032	0.557907168331968	   
df.mm.trans3:probe2	0.0140797956830029	0.0534896524957987	0.263224661706463	0.792446585377226	   
df.mm.trans3:probe3	0.057024205713266	0.0534896524957987	1.06607919574248	0.286716032394693	   
df.mm.trans3:probe4	0.0114621797130524	0.0534896524957987	0.214287795456378	0.830378355525046	   
df.mm.trans3:probe5	-0.0370648393265235	0.0534896524957987	-0.692934756482756	0.488555705090431	   
df.mm.trans3:probe6	0.0209513817356860	0.0534896524957987	0.391690369222938	0.695393445972905	   
df.mm.trans3:probe7	-0.0263149168536123	0.0534896524957987	-0.49196275589338	0.622883389481102	   
df.mm.trans3:probe8	-0.00650146006704964	0.0534896524957987	-0.121546126469232	0.903289627421215	   
df.mm.trans3:probe9	0.0295619480127210	0.0534896524957987	0.552666667913817	0.580649213570164	   
df.mm.trans3:probe10	-0.00527927283644105	0.0534896524957987	-0.0986970860739039	0.921404011155347	   
df.mm.trans3:probe11	0.0584271465550363	0.0534896524957987	1.09230746188948	0.275033520351566	   
df.mm.trans3:probe12	-0.0222020893068227	0.0534896524957987	-0.415072603221091	0.678202315855253	   
df.mm.trans3:probe13	0.0433348424975185	0.0534896524957987	0.81015374891288	0.418097649469199	   
df.mm.trans3:probe14	0.0312992309393025	0.0534896524957987	0.585145527759053	0.558618238359799