chr10.2617_chr10_52514010_52518376_-_2.R 

fitVsDatCorrelation=0.82249865428761
cont.fitVsDatCorrelation=0.281871024485776

fstatistic=9947.67725206267,43,485
cont.fstatistic=3488.46199041815,43,485

residuals=-0.486831119674876,-0.0862365354470821,-0.00543382844834294,0.0768056292749745,0.713698552853116
cont.residuals=-0.526568145756884,-0.165377502121319,-0.0503658829918984,0.101938113886058,0.867543533641595

predictedValues:
Include	Exclude	Both
chr10.2617_chr10_52514010_52518376_-_2.R.tl.Lung	50.467509129582	53.0333269024273	83.7617770312819
chr10.2617_chr10_52514010_52518376_-_2.R.tl.cerebhem	64.4163761928152	48.7502964798213	75.8273311377257
chr10.2617_chr10_52514010_52518376_-_2.R.tl.cortex	65.5302521866828	50.6380390313081	99.0290565900185
chr10.2617_chr10_52514010_52518376_-_2.R.tl.heart	52.8301837751953	53.448276340318	77.6382229669387
chr10.2617_chr10_52514010_52518376_-_2.R.tl.kidney	51.2725835328103	53.8469012733594	88.6350655416575
chr10.2617_chr10_52514010_52518376_-_2.R.tl.liver	53.1685623046993	52.7769557862737	84.9447588584662
chr10.2617_chr10_52514010_52518376_-_2.R.tl.stomach	52.1529220885816	50.9127902358441	74.5606234300228
chr10.2617_chr10_52514010_52518376_-_2.R.tl.testicle	51.8773725507918	54.298073026003	90.6417475285671


diffExp=-2.56581777284522,15.6660797129940,14.8922131553747,-0.618092565122723,-2.57431774054905,0.391606518425576,1.24013185273743,-2.42070047521128
diffExpScore=1.61404158386723
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=0,1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,1,1,0,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	61.9226737907115	61.7253981751032	63.065074894266
cerebhem	57.0515990505776	54.7425430055447	56.8963283370512
cortex	62.3662259296381	59.8901715231484	55.354211612773
heart	55.9232352556615	61.9080535476023	68.9703201270857
kidney	59.4457226168465	60.745752785562	56.9223615719952
liver	58.8838223432518	57.3527451797088	57.9293483807786
stomach	64.663124810429	57.1478367503364	55.9244862389824
testicle	61.1922126788463	59.2640219839423	58.4861680889
cont.diffExp=0.197275615608277,2.30905604503281,2.47605440648971,-5.98481829194073,-1.30003016871557,1.53107716354302,7.51528806009254,1.92819069490403
cont.diffExpScore=2.40297412201593

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=-0.802329421876344
cont.tran.correlation=0.00886605183509267

tran.covariance=-0.00313878582320494
cont.tran.covariance=4.86074674104346e-05

tran.mean=53.7137763022821
cont.tran.mean=59.6390712141819

weightedLogRatios:
wLogRatio
Lung	-0.195691422522788
cerebhem	1.12188254401185
cortex	1.04505587433751
heart	-0.0462115603253574
kidney	-0.194076041397280
liver	0.0293470700114273
stomach	0.0948718665856001
testicle	-0.181132799849652

cont.weightedLogRatios:
wLogRatio
Lung	0.0131602978212229
cerebhem	0.166222366673017
cortex	0.166614272903241
heart	-0.414287779742817
kidney	-0.0886082105626071
liver	0.107026521597933
stomach	0.507469480983279
testicle	0.131208186023494

varWeightedLogRatios=0.303032261699375
cont.varWeightedLogRatios=0.0682726095051493

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.21918715802031	0.0748092121683155	43.0319617693254	1.03048027877924e-167	***
df.mm.trans1	0.639392776444493	0.0650629552413344	9.82729379679772	6.60237829136325e-21	***
df.mm.trans2	0.719913318265674	0.0610814659573264	11.7861172285654	2.27428101526499e-28	***
df.mm.exp2	0.259346882822534	0.0829933693384388	3.1249108801083	0.00188516769872061	** 
df.mm.exp3	0.0475281578799012	0.0829933693384388	0.572674157691875	0.56713071677599	   
df.mm.exp4	0.129463719758204	0.0829933693384388	1.55992847127659	0.119429040411386	   
df.mm.exp5	-0.0255000083617610	0.0829933693384388	-0.307253562122228	0.758782263402411	   
df.mm.exp6	0.0332673011478080	0.0829933693384388	0.400842879533511	0.688712331296544	   
df.mm.exp7	0.108408365122841	0.0829933693384388	1.30622923237111	0.192093748242932	   
df.mm.exp8	-0.0278169332078217	0.0829933693384388	-0.335170549521697	0.737641355307077	   
df.mm.trans1:exp2	-0.0153087389784606	0.0757622341833676	-0.202062929419542	0.839952255318976	   
df.mm.trans2:exp2	-0.34355612917385	0.0677638023045073	-5.06990631414139	5.6730622106001e-07	***
df.mm.trans1:exp3	0.213653997931177	0.0757622341833676	2.82005936379951	0.00499811666960829	** 
df.mm.trans2:exp3	-0.0937456299181463	0.0677638023045073	-1.38341749916697	0.167173319609322	   
df.mm.trans1:exp4	-0.0837107756223488	0.0757622341833676	-1.10491429568647	0.269744557101522	   
df.mm.trans2:exp4	-0.121669856297540	0.0677638023045073	-1.79549925121967	0.073196578891542	.  
df.mm.trans1:exp5	0.0413264379707701	0.0757622341833676	0.545475439263678	0.585677340594792	   
df.mm.trans2:exp5	0.0407243412242123	0.0677638023045073	0.600974854409897	0.548137489230255	   
df.mm.trans1:exp6	0.0188702407585590	0.0757622341833676	0.249071862280187	0.803410636413338	   
df.mm.trans2:exp6	-0.0381131745605770	0.0677638023045073	-0.562441499213836	0.574075114076266	   
df.mm.trans1:exp7	-0.0755578985151476	0.0757622341833676	-0.997302935025314	0.319114794738573	   
df.mm.trans2:exp7	-0.149214716899116	0.0677638023045073	-2.20198264891625	0.0281365117476978	*  
df.mm.trans1:exp8	0.0553699009830532	0.0757622341833676	0.730837752870926	0.465231168028622	   
df.mm.trans2:exp8	0.0513851465533859	0.0677638023045073	0.758297864138123	0.448641000538975	   
df.mm.trans1:probe2	0.0683036576623666	0.0414966846692194	1.64600276399988	0.10041101450079	   
df.mm.trans1:probe3	0.0370634016214092	0.0414966846692194	0.893165367711927	0.372211580967392	   
df.mm.trans1:probe4	0.129140876849460	0.0414966846692194	3.11207697383237	0.00196737766585040	** 
df.mm.trans1:probe5	0.155081382688478	0.0414966846692194	3.73719934314443	0.000208245395062494	***
df.mm.trans1:probe6	0.0899692440334887	0.0414966846692194	2.16810679577553	0.0306361328462577	*  
df.mm.trans1:probe7	0.00950029694028963	0.0414966846692194	0.228941107368430	0.819011195047677	   
df.mm.trans1:probe8	0.162277448722802	0.0414966846692194	3.91061237822627	0.000105186176167776	***
df.mm.trans1:probe9	0.174016077394945	0.0414966846692194	4.19349349910893	3.26606922978875e-05	***
df.mm.trans1:probe10	0.0440708925263844	0.0414966846692194	1.06203406073725	0.288748759433119	   
df.mm.trans1:probe11	0.0686360688306668	0.0414966846692194	1.65401331161230	0.0987718261397495	.  
df.mm.trans1:probe12	0.0659376316713172	0.0414966846692194	1.58898553455349	0.112715294709657	   
df.mm.trans2:probe2	0.0987792393081756	0.0414966846692194	2.38041279913251	0.0176786028106387	*  
df.mm.trans2:probe3	0.0706422007760954	0.0414966846692194	1.70235770252979	0.0893291063316917	.  
df.mm.trans2:probe4	0.104319330136096	0.0414966846692194	2.51391962918608	0.0122625434529705	*  
df.mm.trans2:probe5	0.0119490924557637	0.0414966846692194	0.28795294253054	0.773505767188265	   
df.mm.trans2:probe6	0.0325106286044259	0.0414966846692194	0.7834512290217	0.433744593004865	   
df.mm.trans3:probe2	-0.154451430282359	0.0414966846692194	-3.72201855433828	0.000220803123250455	***
df.mm.trans3:probe3	-0.373077688283889	0.0414966846692194	-8.9905420458956	5.52406717337977e-18	***
df.mm.trans3:probe4	-0.0745367640037745	0.0414966846692194	-1.79621009721442	0.073083325693406	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10422661541795	0.126195549346971	32.5227524794356	5.92241417420337e-124	***
df.mm.trans1	0.0362049342186864	0.109754602953767	0.329871670475062	0.741639356839454	   
df.mm.trans2	0.0122885922486636	0.103038234570098	0.119262449516287	0.905116814649175	   
df.mm.exp2	-0.0990483148618659	0.140001391971031	-0.707480929063625	0.479607471238712	   
df.mm.exp3	0.107368773521056	0.140001391971031	0.766912185725073	0.443506873577349	   
df.mm.exp4	-0.188460808258778	0.140001391971031	-1.34613524626794	0.178887740675855	   
df.mm.exp5	0.0456578087822039	0.140001391971031	0.326123963050677	0.744471236675376	   
df.mm.exp6	-0.0388518048856484	0.140001391971031	-0.277510132854162	0.78150667490147	   
df.mm.exp7	0.0864155469681062	0.140001391971031	0.617247769836368	0.537360980675254	   
df.mm.exp8	0.0228172966148282	0.140001391971031	0.162979069662032	0.870602799208894	   
df.mm.trans1:exp2	0.017118009985547	0.127803200774427	0.133940385544493	0.893505275019365	   
df.mm.trans2:exp2	-0.0210060125315677	0.114310657869469	-0.183762502316751	0.854276575646626	   
df.mm.trans1:exp3	-0.100231305526259	0.127803200774427	-0.784262873847478	0.433268743577265	   
df.mm.trans2:exp3	-0.137551849280938	0.114310657869469	-1.20331604982982	0.229441070637745	   
df.mm.trans1:exp4	0.0865543498596999	0.127803200774427	0.677247121630923	0.498572081579405	   
df.mm.trans2:exp4	0.191415599261499	0.114310657869469	1.67452101867942	0.0946729023469632	.  
df.mm.trans1:exp5	-0.086480547211663	0.127803200774427	-0.676669650584895	0.498938161296463	   
df.mm.trans2:exp5	-0.0616561280023405	0.114310657869469	-0.539373398347031	0.589876753071637	   
df.mm.trans1:exp6	-0.0114682150100378	0.127803200774427	-0.0897333943167758	0.928536121826409	   
df.mm.trans2:exp6	-0.0346229715598998	0.114310657869469	-0.302884894595179	0.762107448766842	   
df.mm.trans1:exp7	-0.043110858769343	0.127803200774427	-0.337322215000183	0.7360199480904	   
df.mm.trans2:exp7	-0.163469495583195	0.114310657869469	-1.43004596972803	0.153347699274098	   
df.mm.trans1:exp8	-0.0346837687261522	0.127803200774427	-0.271384194730530	0.786211028135898	   
df.mm.trans2:exp8	-0.0635103726437364	0.114310657869469	-0.555594498600983	0.578744278937343	   
df.mm.trans1:probe2	0.000552305034903521	0.0700006959855156	0.00788999347974773	0.993708005598158	   
df.mm.trans1:probe3	-0.0818688325102603	0.0700006959855156	-1.16954312178840	0.242759317257842	   
df.mm.trans1:probe4	0.0536581630498732	0.0700006959855156	0.766537565011868	0.443729444556788	   
df.mm.trans1:probe5	0.0205012922793757	0.0700006959855156	0.292872692060345	0.769744722342885	   
df.mm.trans1:probe6	-0.0506780308972771	0.0700006959855156	-0.723964671833596	0.469436475903744	   
df.mm.trans1:probe7	-0.0161712673962538	0.0700006959855156	-0.231015808751387	0.817399942077764	   
df.mm.trans1:probe8	-0.069741342944982	0.0700006959855156	-0.99629499340139	0.319603636842441	   
df.mm.trans1:probe9	-0.00544500279087547	0.0700006959855156	-0.0777849807665075	0.93803117333959	   
df.mm.trans1:probe10	0.00843861059123355	0.0700006959855156	0.120550381284490	0.904097095467634	   
df.mm.trans1:probe11	-0.0691478839866971	0.0700006959855156	-0.987817092575838	0.323734777041939	   
df.mm.trans1:probe12	-0.0228202490356531	0.0700006959855156	-0.326000316345069	0.744564727145502	   
df.mm.trans2:probe2	0.105908005940632	0.0700006959855156	1.51295647064060	0.130942049463486	   
df.mm.trans2:probe3	0.0536148635139472	0.0700006959855156	0.765919006363037	0.444097084826456	   
df.mm.trans2:probe4	-0.0291068910458666	0.0700006959855156	-0.415808594987246	0.677733984418704	   
df.mm.trans2:probe5	-0.0617973260632544	0.0700006959855156	-0.882810166288081	0.377776196892145	   
df.mm.trans2:probe6	-0.00681586899257582	0.0700006959855156	-0.0973685889349749	0.922473912415052	   
df.mm.trans3:probe2	-0.00590884701780745	0.0700006959855156	-0.0844112609827494	0.932764284972446	   
df.mm.trans3:probe3	0.0613448614591616	0.0700006959855156	0.876346450496077	0.381275554459014	   
df.mm.trans3:probe4	-0.089648069538357	0.0700006959855156	-1.28067397439744	0.200920294775322