chr10.2790_chr10_57861806_57897935_+_2.R 

fitVsDatCorrelation=0.709471686640594
cont.fitVsDatCorrelation=0.282464666193872

fstatistic=11054.9661200173,51,669
cont.fstatistic=5960.45447916981,51,669

residuals=-0.429179411619442,-0.083605928866508,-0.00845005096043349,0.0657593226829473,0.873280562436434
cont.residuals=-0.559968420344681,-0.125314854440995,-0.0240884256510977,0.111169582487008,0.855920462005276

predictedValues:
Include	Exclude	Both
chr10.2790_chr10_57861806_57897935_+_2.R.tl.Lung	54.7401937751324	47.4523586293234	52.1078895529236
chr10.2790_chr10_57861806_57897935_+_2.R.tl.cerebhem	58.406296966547	64.3554329873672	62.4789250409438
chr10.2790_chr10_57861806_57897935_+_2.R.tl.cortex	50.8801658716368	45.1071640200755	54.9781974105594
chr10.2790_chr10_57861806_57897935_+_2.R.tl.heart	53.3470183276478	47.4658669504745	49.8457261133599
chr10.2790_chr10_57861806_57897935_+_2.R.tl.kidney	55.6890127998788	44.5035810167342	53.0576458049796
chr10.2790_chr10_57861806_57897935_+_2.R.tl.liver	55.1096219137902	50.5570559791105	53.0444066032469
chr10.2790_chr10_57861806_57897935_+_2.R.tl.stomach	53.729864501972	49.142273210464	51.965774820988
chr10.2790_chr10_57861806_57897935_+_2.R.tl.testicle	55.0856228484926	52.573285820205	54.7338807154598


diffExp=7.28783514580897,-5.94913602082022,5.77300185156131,5.88115137717328,11.1854317831446,4.55256593467971,4.58759129150805,2.51233702828763
diffExpScore=1.29590121408354
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=0,0,0,0,1,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	56.2490648960989	53.600635699579	50.7154573051884
cerebhem	51.9260623649129	56.9482685378485	51.2377341962929
cortex	54.6545249497003	57.495753130393	48.9556601969815
heart	57.5634011218348	55.4002439820481	53.713240441653
kidney	52.9981783691809	59.4770172320549	53.487056575019
liver	53.453445898004	54.2080449677186	54.2805070823797
stomach	52.7469567847212	55.8691268634157	57.0783096903242
testicle	53.1171655905455	52.354287382823	52.1165206016743
cont.diffExp=2.64842919651994,-5.02220617293562,-2.84122818069275,2.16315713978666,-6.478838862874,-0.754599069714622,-3.12217007869449,0.762878207722437
cont.diffExpScore=1.74380675029209

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=0.754890835662096
cont.tran.correlation=-0.238545126119735

tran.covariance=0.00340965562793328
cont.tran.covariance=-0.000341343305163709

tran.mean=52.3840509761782
cont.tran.mean=54.8788861106799

weightedLogRatios:
wLogRatio
Lung	0.561653058474124
cerebhem	-0.399234815005562
cortex	0.465982752874772
heart	0.457699121510331
kidney	0.876152648748414
liver	0.341974089979638
stomach	0.351583946182946
testicle	0.186047940467257

cont.weightedLogRatios:
wLogRatio
Lung	0.193187660207364
cerebhem	-0.368918881998586
cortex	-0.204052791970734
heart	0.154504052464594
kidney	-0.46455023626929
liver	-0.0558741190921976
stomach	-0.229693283056666
testicle	0.0573627923552436

varWeightedLogRatios=0.133537959620775
cont.varWeightedLogRatios=0.0583659289543161

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.84403037422521	0.0728870178760732	52.7395753899693	1.66254598543949e-240	***
df.mm.trans1	0.0950314380694916	0.0645605567942159	1.47197364440955	0.141498245106471	   
df.mm.trans2	0.0103353982437378	0.0589554302043322	0.175308673143705	0.860890142421582	   
df.mm.exp2	0.188007660674304	0.0795600900955483	2.36309009263960	0.0184083395702808	*  
df.mm.exp3	-0.177430542266282	0.0795600900955483	-2.23014506460709	0.0260687807978468	*  
df.mm.exp4	0.0188880849439468	0.0795600900955483	0.237406530350368	0.812414107535868	   
df.mm.exp5	-0.0650345569885336	0.0795600900955483	-0.817426889668298	0.413975527918595	   
df.mm.exp6	0.0522893172282017	0.0795600900955483	0.657230492894169	0.511258761508589	   
df.mm.exp7	0.0190951766689995	0.0795600900955483	0.2400094902616	0.810396420578469	   
df.mm.exp8	0.0596058544010551	0.0795600900955483	0.749192897211038	0.454004290826264	   
df.mm.trans1:exp2	-0.123182195407809	0.0751772188138264	-1.63855749589333	0.101775626534263	   
df.mm.trans2:exp2	0.116687467013443	0.0637899854568019	1.82924429560284	0.0678079711573888	.  
df.mm.trans1:exp3	0.104305477823937	0.0751772188138264	1.38746124783155	0.165763147246414	   
df.mm.trans2:exp3	0.126745392113178	0.0637899854568018	1.98691677393483	0.0473390697963132	*  
df.mm.trans1:exp4	-0.0446682411740048	0.0751772188138264	-0.594172568216763	0.552597563784963	   
df.mm.trans2:exp4	-0.0186034542281971	0.0637899854568019	-0.291635969109842	0.770655375439168	   
df.mm.trans1:exp5	0.0822191842047802	0.0751772188138264	1.09367153377133	0.274492696825217	   
df.mm.trans2:exp5	0.000877983710569449	0.0637899854568018	0.0137636606166655	0.989022637850487	   
df.mm.trans1:exp6	-0.0455632334548204	0.0751772188138264	-0.606077667859143	0.544668758964715	   
df.mm.trans2:exp6	0.0110869711552050	0.0637899854568018	0.173804259019827	0.862071874227401	   
df.mm.trans1:exp7	-0.0377244372880018	0.0751772188138264	-0.501806769167997	0.61596855153464	   
df.mm.trans2:exp7	0.0158982177346793	0.0637899854568019	0.249227486428029	0.803261316649539	   
df.mm.trans1:exp8	-0.0533153441540779	0.0751772188138264	-0.709195484952847	0.478450248643148	   
df.mm.trans2:exp8	0.0428760306916646	0.0637899854568018	0.672143603492432	0.501724336738989	   
df.mm.trans1:probe2	0.00580625005697564	0.0411762585548345	0.141009656067791	0.887904761746681	   
df.mm.trans1:probe3	-0.0454350778279524	0.0411762585548345	-1.10342900065693	0.270237654453315	   
df.mm.trans1:probe4	0.259304223546736	0.0411762585548345	6.29742071396362	5.48121277238727e-10	***
df.mm.trans1:probe5	0.278096025459005	0.0411762585548345	6.75379539616657	3.13106215392910e-11	***
df.mm.trans1:probe6	0.255700499533502	0.0411762585548345	6.20990125154246	9.30916759274348e-10	***
df.mm.trans1:probe7	-0.00301480698726578	0.0411762585548345	-0.0732171181422654	0.941655229452754	   
df.mm.trans1:probe8	0.237494804204185	0.0411762585548345	5.76776065965082	1.22789944851782e-08	***
df.mm.trans1:probe9	-0.0157579968407562	0.0411762585548345	-0.382696179638839	0.702066602048918	   
df.mm.trans1:probe10	0.00523598834512946	0.0411762585548345	0.127160371750549	0.898851679603228	   
df.mm.trans1:probe11	-0.0492839960948099	0.0411762585548345	-1.19690321132937	0.231768242168963	   
df.mm.trans1:probe12	0.0922663563632737	0.0411762585548345	2.24076590738331	0.0253685839830698	*  
df.mm.trans1:probe13	0.229444277340810	0.0411762585548345	5.57224685762207	3.64757355108552e-08	***
df.mm.trans1:probe14	0.0201897152523045	0.0411762585548345	0.490324180994197	0.624065286529518	   
df.mm.trans1:probe15	-0.0580783887367261	0.0411762585548345	-1.41048241814839	0.158862071781284	   
df.mm.trans1:probe16	-0.0528739873718725	0.0411762585548345	-1.28408916272614	0.199555277688418	   
df.mm.trans1:probe17	0.0900727084461058	0.0411762585548345	2.18749132649233	0.0290520544039436	*  
df.mm.trans1:probe18	0.139695475870864	0.0411762585548345	3.39262188391476	0.000733066913916078	***
df.mm.trans1:probe19	0.123684527002669	0.0411762585548345	3.00378255197610	0.00276602330287191	** 
df.mm.trans1:probe20	0.0123277519567362	0.0411762585548345	0.299389803479092	0.764735627923081	   
df.mm.trans2:probe2	-0.0704202679623783	0.0411762585548345	-1.71021531421072	0.087689713235888	.  
df.mm.trans2:probe3	-0.0429462225741092	0.0411762585548345	-1.04298506181463	0.297331950994898	   
df.mm.trans2:probe4	0.0116217461831811	0.0411762585548345	0.282243860687450	0.777843822269245	   
df.mm.trans2:probe5	-0.0372094618385915	0.0411762585548345	-0.903663012243806	0.366499389730012	   
df.mm.trans2:probe6	0.192558796488459	0.0411762585548345	4.67645199556022	3.53215261057983e-06	***
df.mm.trans3:probe2	-0.105309616121954	0.0411762585548345	-2.55753241838893	0.0107611285697480	*  
df.mm.trans3:probe3	-0.14475167713214	0.0411762585548345	-3.51541597543094	0.000468697337024092	***
df.mm.trans3:probe4	0.265737924112499	0.0411762585548345	6.45366853228337	2.09635511808044e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.06314254094684	0.0992131741852595	40.9536593735	1.90323940889067e-184	***
df.mm.trans1	-0.0402864674812288	0.087879267850037	-0.458429712340984	0.646792629272512	   
df.mm.trans2	-0.0508166730721043	0.0802496183336023	-0.633232582625583	0.526798174159568	   
df.mm.exp2	-0.0296317442341253	0.108296501995258	-0.273616817608963	0.78446356011508	   
df.mm.exp3	0.0767084988700393	0.108296501995258	0.708319266613044	0.478993732873021	   
df.mm.exp4	-0.00130817097411703	0.108296501995258	-0.0120795311945931	0.990365764202569	   
df.mm.exp5	-0.00871177896548692	0.108296501995258	-0.0804437706202956	0.9359083738342	   
df.mm.exp6	-0.107644346379189	0.108296501995258	-0.99397805465501	0.320592923303089	   
df.mm.exp7	-0.141025767429041	0.108296501995258	-1.30221904522102	0.193289661233857	   
df.mm.exp8	-0.108067618056015	0.108296501995258	-0.997886506627394	0.318695185973187	   
df.mm.trans1:exp2	-0.0503368434864652	0.102330575763451	-0.491904234007484	0.622948407521196	   
df.mm.trans2:exp2	0.0902141023934872	0.086830372854072	1.03896942311997	0.299194309540225	   
df.mm.trans1:exp3	-0.105465905838474	0.102330575763451	-1.03063923027532	0.303082485503471	   
df.mm.trans2:exp3	-0.00655834046947277	0.086830372854072	-0.0755304883982795	0.939815220704824	   
df.mm.trans1:exp4	0.0244057226582535	0.102330575763451	0.238498830639536	0.811567257047717	   
df.mm.trans2:exp4	0.0343312406525497	0.086830372854072	0.395382854225987	0.692686402470764	   
df.mm.trans1:exp5	-0.0508200953428743	0.102330575763451	-0.496626692107653	0.619615469268594	   
df.mm.trans2:exp5	0.112740823816849	0.086830372854072	1.29840308305853	0.194596261072692	   
df.mm.trans1:exp6	0.0566660344555873	0.102330575763451	0.553754672372579	0.579931780585576	   
df.mm.trans2:exp6	0.118912746875499	0.086830372854072	1.36948331519139	0.171307765739093	   
df.mm.trans1:exp7	0.07674242995911	0.102330575763451	0.749946234412958	0.453550734813817	   
df.mm.trans2:exp7	0.182476774700768	0.086830372854072	2.10153162658232	0.035967719008273	*  
df.mm.trans1:exp8	0.0507783463118587	0.102330575763451	0.496218710126665	0.619903100367093	   
df.mm.trans2:exp8	0.0845405223761081	0.086830372854072	0.97362846199207	0.330592950375896	   
df.mm.trans1:probe2	-0.00215531358999852	0.0560487646681336	-0.0384542568022721	0.969336974228832	   
df.mm.trans1:probe3	-0.0260092568606016	0.0560487646681336	-0.464046924398836	0.642765084598383	   
df.mm.trans1:probe4	0.00195892894142082	0.0560487646681336	0.0349504391937930	0.972129685835785	   
df.mm.trans1:probe5	0.0955730913429249	0.0560487646681336	1.70517748087431	0.0886255573025398	.  
df.mm.trans1:probe6	-0.0887467186298636	0.0560487646681336	-1.58338402559513	0.113806550273909	   
df.mm.trans1:probe7	0.0524530928836506	0.0560487646681336	0.935847439175992	0.349689428611638	   
df.mm.trans1:probe8	-0.00528590658132969	0.0560487646681336	-0.0943090648407276	0.924891887695862	   
df.mm.trans1:probe9	0.0125605048147645	0.0560487646681336	0.224099583445516	0.822748237054347	   
df.mm.trans1:probe10	0.051355164504679	0.0560487646681336	0.916258633151943	0.35986141952856	   
df.mm.trans1:probe11	0.00869065462520975	0.0560487646681336	0.155055239427084	0.87682453773882	   
df.mm.trans1:probe12	0.0304199069395967	0.0560487646681336	0.542740007201119	0.587489583954327	   
df.mm.trans1:probe13	0.0243783105908414	0.0560487646681336	0.434948222948109	0.663740280298511	   
df.mm.trans1:probe14	-0.0114166323534782	0.0560487646681336	-0.203691061187101	0.838656870270588	   
df.mm.trans1:probe15	0.0653664612887103	0.0560487646681336	1.16624267592242	0.243931924264212	   
df.mm.trans1:probe16	0.0468993564230028	0.0560487646681336	0.836759859038736	0.403026354278371	   
df.mm.trans1:probe17	-0.0431431529395391	0.0560487646681336	-0.769743154822252	0.441723964104519	   
df.mm.trans1:probe18	0.00594157496989608	0.0560487646681336	0.106007242176992	0.915608383699956	   
df.mm.trans1:probe19	-0.00598034406922423	0.0560487646681336	-0.106698945188784	0.915059806657969	   
df.mm.trans1:probe20	-0.0464594804274282	0.0560487646681336	-0.828911764648449	0.407449975806996	   
df.mm.trans2:probe2	-0.122962807333740	0.0560487646681336	-2.19385401376473	0.0285891522940631	*  
df.mm.trans2:probe3	-0.0835746110747334	0.0560487646681336	-1.49110531819178	0.136405191801166	   
df.mm.trans2:probe4	-0.0371221263834403	0.0560487646681336	-0.662318368714127	0.50799529765071	   
df.mm.trans2:probe5	-0.00737146247978825	0.0560487646681336	-0.131518732365198	0.895404510311607	   
df.mm.trans2:probe6	-0.056618390743662	0.0560487646681336	-1.01016304425086	0.312782391257707	   
df.mm.trans3:probe2	-0.0525990046562302	0.0560487646681336	-0.938450739595609	0.348351499777669	   
df.mm.trans3:probe3	-0.0916887172845219	0.0560487646681336	-1.63587400770407	0.102336305790325	   
df.mm.trans3:probe4	-0.0389469118504435	0.0560487646681336	-0.69487547283244	0.487374605349848