chr11.4708_chr11_61965250_61976891_-_2.R 

fitVsDatCorrelation=0.888236894016986
cont.fitVsDatCorrelation=0.28036584591672

fstatistic=6703.35456133542,52,692
cont.fstatistic=1525.06869209555,52,692

residuals=-0.739719184891566,-0.121424918978409,-0.000769375016682798,0.121341211894153,0.752590403901967
cont.residuals=-0.967545437709577,-0.318087630127969,-0.0569946677946331,0.254119986860461,1.74936933235860

predictedValues:
Include	Exclude	Both
chr11.4708_chr11_61965250_61976891_-_2.R.tl.Lung	128.997868321052	104.201977951932	92.0611512381342
chr11.4708_chr11_61965250_61976891_-_2.R.tl.cerebhem	76.6939753005906	70.7556062246948	95.2976455277032
chr11.4708_chr11_61965250_61976891_-_2.R.tl.cortex	104.752730153691	79.347263083512	115.117669107285
chr11.4708_chr11_61965250_61976891_-_2.R.tl.heart	103.580297221145	92.4813195102026	74.6989460023646
chr11.4708_chr11_61965250_61976891_-_2.R.tl.kidney	126.013866086894	90.9538654214821	69.8067719990787
chr11.4708_chr11_61965250_61976891_-_2.R.tl.liver	114.094343414493	76.4635928992112	60.6275172363725
chr11.4708_chr11_61965250_61976891_-_2.R.tl.stomach	96.5796621443362	75.8179461238183	68.1418541868688
chr11.4708_chr11_61965250_61976891_-_2.R.tl.testicle	104.827399852100	74.7729819143507	76.5472318656774


diffExp=24.7958903691200,5.93836907589576,25.405467070179,11.0989777109427,35.0600006654123,37.6307505152818,20.7617160205179,30.0544179377492
diffExpScore=0.994784756179732
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,1,0,1
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,1,0,1,1,0,1
diffExp1.3Score=0.8
diffExp1.2=1,0,1,0,1,1,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	105.797593239164	103.050107724100	114.055513281103
cerebhem	101.450486695292	96.750352545286	96.9159196028976
cortex	100.729359914342	112.831859813677	99.0162281903506
heart	105.204992400626	130.816627343047	120.341441210046
kidney	111.559506316091	102.240443503969	116.733166818017
liver	105.686681147038	96.424352947969	90.7857288173758
stomach	104.812900904269	105.112075307505	105.065819905507
testicle	108.475584163231	107.787088651042	105.598858895959
cont.diffExp=2.74748551506408,4.70013415000604,-12.1024998993353,-25.6116349424214,9.31906281212208,9.26232819906912,-0.299174403236137,0.688495512189235
cont.diffExpScore=5.26446423513606

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

tran.correlation=0.752635312798357
cont.tran.correlation=-0.080152027017238

tran.covariance=0.0162281710172328
cont.tran.covariance=-0.000232028325070917

tran.mean=95.0209184764691
cont.tran.mean=106.170625788541

weightedLogRatios:
wLogRatio
Lung	1.01461159720448
cerebhem	0.346504865047528
cortex	1.25349174487634
heart	0.519516037495347
kidney	1.52370413645430
liver	1.81572519828750
stomach	1.07689063416120
testicle	1.514750355148

cont.weightedLogRatios:
wLogRatio
Lung	0.12230989676203
cerebhem	0.21801293589608
cortex	-0.52977047633075
heart	-1.03819393035829
kidney	0.407450113303086
liver	0.423253232384992
stomach	-0.0132641629722188
testicle	0.0298199449356195

varWeightedLogRatios=0.255873725555643
cont.varWeightedLogRatios=0.249875779384232

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.53844418314087	0.101388859415335	44.7627501612314	1.62207543141745e-206	***
df.mm.trans1	-0.0030784547485217	0.0863295030092171	-0.0356593590975848	0.97156426025468	   
df.mm.trans2	0.0739784026276022	0.0781253630906184	0.94691915276981	0.344010512963460	   
df.mm.exp2	-0.941624122113839	0.101388859415335	-9.28725431515619	2.01100148800500e-19	***
df.mm.exp3	-0.704192185583145	0.101388859415336	-6.94545919190637	8.71138747692143e-12	***
df.mm.exp4	-0.129786126312679	0.101388859415335	-1.28008271383166	0.200944991842811	   
df.mm.exp5	0.117339381298878	0.101388859415336	1.15732026157037	0.247540854766884	   
df.mm.exp6	-0.0145824194998793	0.101388859415335	-0.143826645096608	0.885679234860763	   
df.mm.exp7	-0.306562367543958	0.101388859415336	-3.02362970953383	0.00258997450839817	** 
df.mm.exp8	-0.354810105632045	0.101388859415336	-3.49949794955853	0.00049600414953892	***
df.mm.trans1:exp2	0.421651398935708	0.0906849527255439	4.64962914202346	3.98326746446122e-06	***
df.mm.trans2:exp2	0.554524784019556	0.0716927500293532	7.73474003706814	3.66818707399515e-14	***
df.mm.trans1:exp3	0.495998927983338	0.0906849527255439	5.46947330374057	6.31095080695369e-08	***
df.mm.trans2:exp3	0.431695028856211	0.0716927500293533	6.02146003158564	2.80520177767073e-09	***
df.mm.trans1:exp4	-0.0896626227938941	0.0906849527255438	-0.98872657589905	0.323142517344344	   
df.mm.trans2:exp4	0.0104616877917806	0.0716927500293533	0.145923929372179	0.884023936830437	   
df.mm.trans1:exp5	-0.140743311675715	0.0906849527255439	-1.55200292270838	0.121118724026921	   
df.mm.trans2:exp5	-0.253318088130122	0.0716927500293533	-3.53338500791790	0.000437551504460462	***
df.mm.trans1:exp6	-0.108187780126808	0.0906849527255439	-1.19300696394732	0.233275521004187	   
df.mm.trans2:exp6	-0.294933974166208	0.0716927500293533	-4.11386052349021	4.35930673863599e-05	***
df.mm.trans1:exp7	0.0171346702062014	0.0906849527255439	0.188947225435063	0.850189580396767	   
df.mm.trans2:exp7	-0.0114337229824814	0.0716927500293533	-0.159482276489604	0.873335457154321	   
df.mm.trans1:exp8	0.147329412735449	0.0906849527255439	1.62462909564874	0.10469696528176	   
df.mm.trans2:exp8	0.0229356096055430	0.0716927500293533	0.319915327507348	0.749129082879012	   
df.mm.trans1:probe2	0.0520719908291616	0.0620877427925875	0.83868390904651	0.401936317841404	   
df.mm.trans1:probe3	0.105812670745281	0.0620877427925875	1.70424412268879	0.0887843912117295	.  
df.mm.trans1:probe4	1.02939034534572	0.0620877427925875	16.5796065220883	3.09426582782065e-52	***
df.mm.trans1:probe5	0.655244334563156	0.0620877427925875	10.5535215984915	3.03123603562643e-24	***
df.mm.trans1:probe6	0.646598168610879	0.0620877427925875	10.4142643866267	1.08054255158646e-23	***
df.mm.trans1:probe7	1.41528383606536	0.0620877427925875	22.7948991605848	3.14938570182821e-86	***
df.mm.trans1:probe8	0.615777439249318	0.0620877427925875	9.91785836548136	9.08782975349894e-22	***
df.mm.trans1:probe9	0.562643505591134	0.0620877427925875	9.0620705518434	1.29600835471351e-18	***
df.mm.trans1:probe10	0.605712565250147	0.0620877427925875	9.75575110329928	3.73396796035006e-21	***
df.mm.trans1:probe11	0.654243759128549	0.0620877427925875	10.5374060917972	3.51372509795689e-24	***
df.mm.trans1:probe12	0.433537408309363	0.0620877427925875	6.98265694337855	6.80568519781679e-12	***
df.mm.trans1:probe13	0.470000526972961	0.0620877427925875	7.56994063293719	1.19601099787690e-13	***
df.mm.trans1:probe14	0.524719501376764	0.0620877427925875	8.45125749102622	1.69868837672666e-16	***
df.mm.trans1:probe15	0.339717727706700	0.0620877427925875	5.47157478154058	6.2396285422697e-08	***
df.mm.trans2:probe2	0.151397391886414	0.0620877427925875	2.43844251823066	0.0150011864291291	*  
df.mm.trans2:probe3	-0.0737595734307125	0.0620877427925875	-1.18798928924049	0.235245205225403	   
df.mm.trans2:probe4	0.314033634336204	0.0620877427925875	5.05790064530573	5.43556705505771e-07	***
df.mm.trans2:probe5	0.00810544417441021	0.0620877427925875	0.130548217890406	0.89617064872708	   
df.mm.trans2:probe6	0.142759513191354	0.0620877427925875	2.29931878290795	0.0217837932770207	*  
df.mm.trans3:probe2	-0.242047501557697	0.0620877427925875	-3.89847481436537	0.000106221431978637	***
df.mm.trans3:probe3	-0.209639249278595	0.0620877427925875	-3.37649977031574	0.000775196499209772	***
df.mm.trans3:probe4	-0.229283768540380	0.0620877427925875	-3.69289908486983	0.000239183994361612	***
df.mm.trans3:probe5	0.0751970929870729	0.0620877427925875	1.21114232221775	0.226254310620829	   
df.mm.trans3:probe6	-0.243927898265028	0.0620877427925875	-3.92876093240983	9.3951018022887e-05	***
df.mm.trans3:probe7	0.505951057814944	0.0620877427925875	8.14896846073375	1.71809216462934e-15	***
df.mm.trans3:probe8	-0.154368344733094	0.0620877427925875	-2.48629339367003	0.0131429493317949	*  
df.mm.trans3:probe9	-0.328439834372456	0.0620877427925875	-5.28993034051268	1.64355269306449e-07	***
df.mm.trans3:probe10	-0.0423524281217523	0.0620877427925875	-0.682138312923315	0.495379750483044	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.55290481612556	0.211853522986257	21.4908147476071	7.11190143117856e-79	***
df.mm.trans1	0.166364641803144	0.180386774795782	0.922266291370236	0.356711124113374	   
df.mm.trans2	0.0935252767146603	0.163244102959349	0.572916724213615	0.566887186379729	   
df.mm.exp2	0.0578032098707906	0.211853522986257	0.272845167056959	0.785053638250434	   
df.mm.exp3	0.182994466818246	0.211853522986257	0.863778256971054	0.388009003940118	   
df.mm.exp4	0.179316425717104	0.211853522986257	0.846417011100337	0.39761275248922	   
df.mm.exp5	0.0219369260679199	0.211853522986257	0.103547610437156	0.917558366060899	   
df.mm.exp6	0.160677735601485	0.211853522986257	0.758437874133952	0.448447118612123	   
df.mm.exp7	0.0925591841514528	0.211853522986257	0.436901793497469	0.662318867823003	   
df.mm.exp8	0.146977596402851	0.211853522986257	0.693769895024995	0.488059335485387	   
df.mm.trans1:exp2	-0.0997601171731109	0.189487551468034	-0.526473197844557	0.59872816865274	   
df.mm.trans2:exp2	-0.120884586782125	0.149803062721842	-0.806956710935783	0.419968681555774	   
df.mm.trans1:exp3	-0.232084922306192	0.189487551468034	-1.22480300425089	0.221066211266857	   
df.mm.trans2:exp3	-0.092311075171208	0.149803062721842	-0.6162162074257	0.537954477147766	   
df.mm.trans1:exp4	-0.184933441213186	0.189487551468034	-0.97596617709414	0.329422271494396	   
df.mm.trans2:exp4	0.0592647728930629	0.149803062721842	0.395617898701491	0.692508864950764	   
df.mm.trans1:exp5	0.0310934404511261	0.189487551468034	0.164092259413524	0.869706430618264	   
df.mm.trans2:exp5	-0.0298249502702416	0.149803062721842	-0.199094395857722	0.842247387597748	   
df.mm.trans1:exp6	-0.161726627795629	0.189487551468034	-0.853494736422896	0.393680338098028	   
df.mm.trans2:exp6	-0.227134294640963	0.149803062721842	-1.51621929828438	0.129920560580613	   
df.mm.trans1:exp7	-0.101910090562536	0.189487551468034	-0.537819449209192	0.590874620010231	   
df.mm.trans2:exp7	-0.0727473720555755	0.149803062721842	-0.485620058320533	0.627390289687197	   
df.mm.trans1:exp8	-0.121980250452579	0.189487551468034	-0.643737541107849	0.519958925046859	   
df.mm.trans2:exp8	-0.102035069120768	0.149803062721842	-0.681128057509938	0.496018323657402	   
df.mm.trans1:probe2	0.0980676612484293	0.129733257881829	0.755917664056174	0.449955798534774	   
df.mm.trans1:probe3	-0.273822354339416	0.129733257881829	-2.11065657958605	0.0351600154317011	*  
df.mm.trans1:probe4	-0.220463096584452	0.129733257881829	-1.69935682017071	0.0897013991940689	.  
df.mm.trans1:probe5	-0.272938577570275	0.129733257881829	-2.10384431892467	0.0357522934231296	*  
df.mm.trans1:probe6	-0.0178834064382023	0.129733257881829	-0.137847509036518	0.890401035741267	   
df.mm.trans1:probe7	0.129407072456359	0.129733257881829	0.997485722390728	0.318877463236368	   
df.mm.trans1:probe8	-0.196654162702744	0.129733257881829	-1.51583461260081	0.130017817036744	   
df.mm.trans1:probe9	-0.0994330854238493	0.129733257881829	-0.766442522505838	0.443674413181426	   
df.mm.trans1:probe10	-0.0908821895204955	0.129733257881829	-0.700531159120956	0.483831115526794	   
df.mm.trans1:probe11	-0.0859186376790004	0.129733257881829	-0.662271487526057	0.508017739668235	   
df.mm.trans1:probe12	-0.0694565433127553	0.129733257881829	-0.535379627759149	0.592559382278042	   
df.mm.trans1:probe13	-0.149502686067389	0.129733257881829	-1.15238519796957	0.249560758637686	   
df.mm.trans1:probe14	-0.105359085608400	0.129733257881829	-0.812120864985671	0.417001556345282	   
df.mm.trans1:probe15	-0.0887030828406839	0.129733257881829	-0.683734335273392	0.494371816695111	   
df.mm.trans2:probe2	-0.080502916987201	0.129733257881829	-0.62052644249888	0.53511555519192	   
df.mm.trans2:probe3	-0.211672125459391	0.129733257881829	-1.63159492727915	0.103219816505960	   
df.mm.trans2:probe4	-0.124944539721290	0.129733257881829	-0.963087968045167	0.335839796125393	   
df.mm.trans2:probe5	0.120970869100293	0.129733257881829	0.932458423348024	0.351424878225162	   
df.mm.trans2:probe6	0.116712870720204	0.129733257881829	0.89963724511193	0.368626276515624	   
df.mm.trans3:probe2	-0.0156851307567465	0.129733257881829	-0.120902928153039	0.90380301901691	   
df.mm.trans3:probe3	-0.0823369605569732	0.129733257881829	-0.634663477209306	0.525857684611956	   
df.mm.trans3:probe4	0.0075421235525178	0.129733257881829	0.0581356213176096	0.95365738362951	   
df.mm.trans3:probe5	-0.0873750826697612	0.129733257881829	-0.673497945679813	0.500855430369414	   
df.mm.trans3:probe6	-0.0711972035179378	0.129733257881829	-0.548796851943622	0.583321890207509	   
df.mm.trans3:probe7	-0.191556309341945	0.129733257881829	-1.47653972828177	0.140253961626643	   
df.mm.trans3:probe8	-0.187057624273442	0.129733257881829	-1.44186330727799	0.149793335498830	   
df.mm.trans3:probe9	-0.131226607376489	0.129733257881829	-1.01151092263497	0.312125497423763	   
df.mm.trans3:probe10	-0.00220166674489442	0.129733257881829	-0.0169707196199441	0.986464866446922