chr7.22173_chr7_134028067_134030236_-_2.R 

fitVsDatCorrelation=0.937116900287845
cont.fitVsDatCorrelation=0.311978927957511

fstatistic=11585.5101604166,57,807
cont.fstatistic=1551.17507776471,57,807

residuals=-0.956155958218956,-0.080903327477135,-0.00666169692896981,0.0777980491828475,0.703680912518536
cont.residuals=-0.771191054984077,-0.242381347646639,-0.104316737764148,0.0971479608824018,1.70945696367106

predictedValues:
Include	Exclude	Both
chr7.22173_chr7_134028067_134030236_-_2.R.tl.Lung	56.0747776085239	50.124807948928	98.4694954721928
chr7.22173_chr7_134028067_134030236_-_2.R.tl.cerebhem	58.6661119171177	52.3746429836423	67.4260252627624
chr7.22173_chr7_134028067_134030236_-_2.R.tl.cortex	60.5357542120414	51.2157131166207	89.6781188474999
chr7.22173_chr7_134028067_134030236_-_2.R.tl.heart	57.1500009730862	52.7917214257138	90.0690972373875
chr7.22173_chr7_134028067_134030236_-_2.R.tl.kidney	53.8960298917058	53.2247204906987	102.400101908191
chr7.22173_chr7_134028067_134030236_-_2.R.tl.liver	57.0369717946359	56.6984394835336	109.098014252452
chr7.22173_chr7_134028067_134030236_-_2.R.tl.stomach	59.3860219305964	51.5927657190525	92.2547269686835
chr7.22173_chr7_134028067_134030236_-_2.R.tl.testicle	61.1981778100151	53.3125206376088	105.876245672918


diffExp=5.94996965959592,6.29146893347536,9.32004109542066,4.35827954737238,0.6713094010071,0.338532311102263,7.79325621154389,7.88565717240635
diffExpScore=0.977068698273265
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	65.8937817104956	70.0639583583704	64.8265620799755
cerebhem	61.9263931229685	62.1672661274886	66.8243877079456
cortex	62.5658358159744	59.8029171128732	75.5847459689265
heart	59.7071938819456	80.1715594878048	90.0682684225704
kidney	63.2837340037682	68.0013530158569	63.1363010080657
liver	61.6615146840725	60.1237270703247	58.3773096597233
stomach	69.3971240366029	88.8703611738062	57.1132278879024
testicle	58.6168558147671	78.2581943472125	60.4536570578089
cont.diffExp=-4.1701766478748,-0.240873004520019,2.76291870310126,-20.4643656058592,-4.71761901208873,1.53778761374786,-19.4732371372034,-19.6413385324454
cont.diffExpScore=1.11621728307910

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,-1,0,0,0,-1
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=0,0,0,-1,0,0,-1,-1
cont.diffExp1.2Score=0.75

tran.correlation=-0.140861059725007
cont.tran.correlation=0.304436019100849

tran.covariance=-0.000209880456484660
cont.tran.covariance=0.00202243501742618

tran.mean=55.32994862147
cont.tran.mean=66.9069856102708

weightedLogRatios:
wLogRatio
Lung	0.445382562883204
cerebhem	0.455476504583105
cortex	0.672034763873036
heart	0.317777370607824
kidney	0.0498946590837414
liver	0.0240544151249404
stomach	0.564639775020832
testicle	0.558012296094587

cont.weightedLogRatios:
wLogRatio
Lung	-0.258879768869098
cerebhem	-0.0160249593258816
cortex	0.185792141395498
heart	-1.24865724350015
kidney	-0.300796213891023
liver	0.103775378730800
stomach	-1.07924168683561
testicle	-1.21824778026875

varWeightedLogRatios=0.0571850323448781
cont.varWeightedLogRatios=0.367897250435779

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.80998988214123	0.0713704951394581	39.3718703597404	5.06707873433493e-190	***
df.mm.trans1	1.11665267696623	0.0617001303357875	18.0980602616092	1.04844108313263e-61	***
df.mm.trans2	1.10111505830921	0.0547962603681059	20.0947117725229	3.88937317580306e-73	***
df.mm.exp2	0.467798378127264	0.0708858916804078	6.59931570355853	7.47598029762743e-11	***
df.mm.exp3	0.191598389659397	0.0708858916804078	2.70291288036873	0.00701796332480234	** 
df.mm.exp4	0.160001312628105	0.0708858916804078	2.25716724210055	0.0242642454027106	*  
df.mm.exp5	-0.0187632892601186	0.0708858916804078	-0.264697090144732	0.791310408966873	   
df.mm.exp6	0.0377443230229988	0.0708858916804078	0.532465941081348	0.594549896439445	   
df.mm.exp7	0.151431459563426	0.0708858916804078	2.13627078638104	0.0329576061239153	*  
df.mm.exp8	0.0765623496445194	0.0708858916804078	1.08007881158784	0.280429769616354	   
df.mm.trans1:exp2	-0.422622242193761	0.0654940580764966	-6.45283335016653	1.89177022344943e-10	***
df.mm.trans2:exp2	-0.42389187062619	0.0495088542922375	-8.56194062023875	5.57206414397506e-17	***
df.mm.trans1:exp3	-0.115050334903930	0.0654940580764966	-1.75665301987475	0.079356205995385	.  
df.mm.trans2:exp3	-0.170068062064688	0.0495088542922375	-3.43510397273227	0.000622568948027859	***
df.mm.trans1:exp4	-0.141008019794014	0.0654940580764966	-2.15298950676285	0.0316152351285968	*  
df.mm.trans2:exp4	-0.108162979565133	0.0495088542922375	-2.18471990740638	0.0291964022299081	*  
df.mm.trans1:exp5	-0.0208660068970251	0.0654940580764966	-0.318593892481876	0.750116921120899	   
df.mm.trans2:exp5	0.0787701943828724	0.0495088542922375	1.59103246295931	0.111993896586302	   
df.mm.trans1:exp6	-0.0207307519141968	0.0654940580764966	-0.316528743569125	0.751683069137377	   
df.mm.trans2:exp6	0.0854863108191341	0.0495088542922375	1.72668731767718	0.0846065218398282	.  
df.mm.trans1:exp7	-0.094058696354507	0.0654940580764966	-1.43614091288476	0.151349765115222	   
df.mm.trans2:exp7	-0.122566050324471	0.0495088542922375	-2.47563899582479	0.0135040163058067	*  
df.mm.trans1:exp8	0.0108689506507287	0.0654940580764966	0.165953232551782	0.868235321114823	   
df.mm.trans2:exp8	-0.0149071914628782	0.0495088542922375	-0.301101523676655	0.763414641446422	   
df.mm.trans1:probe2	0.187501957959254	0.0439347497944097	4.2677370153844	2.20910808054167e-05	***
df.mm.trans1:probe3	-0.105279330117186	0.0439347497944097	-2.3962656122963	0.0167897920702127	*  
df.mm.trans1:probe4	-0.0615688323552524	0.0439347497944097	-1.40136981872801	0.161487964934378	   
df.mm.trans1:probe5	-0.0659175092973259	0.0439347497944097	-1.50035016941677	0.133914836876953	   
df.mm.trans1:probe6	0.0433208454952748	0.0439347497944097	0.986026908039591	0.324415439485608	   
df.mm.trans1:probe7	0.728908314012923	0.0439347497944097	16.5907013792911	2.07096534663071e-53	***
df.mm.trans1:probe8	0.674861672573383	0.0439347497944097	15.3605443465904	6.66879764647594e-47	***
df.mm.trans1:probe9	0.0330442495067989	0.0439347497944097	0.752121035431583	0.452197582640756	   
df.mm.trans1:probe10	0.42705023059602	0.0439347497944097	9.720101573228	3.40658583839197e-21	***
df.mm.trans1:probe11	0.194895267184541	0.0439347497944097	4.43601632185327	1.04347702358963e-05	***
df.mm.trans1:probe12	0.146535216993918	0.0439347497944096	3.33529194270188	0.000891013154792248	***
df.mm.trans1:probe13	0.0477804307092397	0.0439347497944096	1.08753164483298	0.27712662008577	   
df.mm.trans1:probe14	0.201208113359657	0.0439347497944097	4.5797031803117	5.39136849927114e-06	***
df.mm.trans1:probe15	0.0463912424407762	0.0439347497944096	1.05591229397827	0.291324286811591	   
df.mm.trans1:probe16	0.137730422624162	0.0439347497944097	3.13488578559487	0.00178145596981567	** 
df.mm.trans1:probe17	0.202052126674924	0.0439347497944097	4.59891378966345	4.92894341589881e-06	***
df.mm.trans1:probe18	0.0809479902827096	0.0439347497944097	1.84245934394759	0.0657746568040349	.  
df.mm.trans1:probe19	0.0330848457891266	0.0439347497944097	0.753045048485434	0.451642463388395	   
df.mm.trans1:probe20	0.0288631432741224	0.0439347497944097	0.656954766083475	0.511397305060382	   
df.mm.trans1:probe21	0.0198962620775416	0.0439347497944097	0.452859346431813	0.65077174949332	   
df.mm.trans2:probe2	0.0472630615833453	0.0439347497944097	1.07575579249934	0.28235799212866	   
df.mm.trans2:probe3	0.0235075044992361	0.0439347497944097	0.53505493053308	0.592759271233911	   
df.mm.trans2:probe4	-0.0499011700748408	0.0439347497944097	-1.13580184952346	0.256376696827165	   
df.mm.trans2:probe5	-0.037960450656176	0.0439347497944097	-0.86401881958609	0.387834283265339	   
df.mm.trans2:probe6	0.0682577605402555	0.0439347497944097	1.55361668974250	0.120667938357087	   
df.mm.trans3:probe2	0.0430243767816735	0.0439347497944097	0.979278975822187	0.327735630180931	   
df.mm.trans3:probe3	-0.972237282318523	0.0439347497944097	-22.1291184510679	3.56417066882181e-85	***
df.mm.trans3:probe4	-1.08800410911115	0.0439347497944097	-24.7640902520763	3.74151684919526e-101	***
df.mm.trans3:probe5	-0.783474584518473	0.0439347497944097	-17.8326857028821	3.19210307961978e-60	***
df.mm.trans3:probe6	0.247724537775037	0.0439347497944097	5.63846474451888	2.37273235209575e-08	***
df.mm.trans3:probe7	-1.0459504464203	0.0439347497944097	-23.8069057253033	2.60124121547767e-95	***
df.mm.trans3:probe8	-0.734940201633537	0.0439347497944097	-16.7279933326729	3.75329043827225e-54	***
df.mm.trans3:probe9	0.391760438882526	0.0439347497944097	8.91686969234486	3.16025188604311e-18	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21162230758	0.194284486430625	21.6776047586480	1.78296939122426e-82	***
df.mm.trans1	-0.0987800521827923	0.167959856682621	-0.588117030663154	0.556618285501856	   
df.mm.trans2	0.0463840362542373	0.149166168500497	0.310955471475309	0.755914774131795	   
df.mm.exp2	-0.212030199422393	0.192965300764614	-1.09879962139429	0.272183181169341	   
df.mm.exp3	-0.363717838911788	0.192965300764614	-1.88488726973490	0.0598041581434622	.  
df.mm.exp4	-0.292683740304416	0.192965300764614	-1.51676876176532	0.129716682017298	   
df.mm.exp5	-0.0438771311004677	0.192965300764614	-0.227383529197255	0.820183118600628	   
df.mm.exp6	-0.114599902332391	0.192965300764614	-0.593888651888682	0.552753020618611	   
df.mm.exp7	0.416251201161837	0.192965300764614	2.15712980267678	0.0312901555184534	*  
df.mm.exp8	0.0634216033853335	0.192965300764614	0.328668434863828	0.742491582739618	   
df.mm.trans1:exp2	0.149932593904324	0.178287672136588	0.840958839764637	0.400620097992161	   
df.mm.trans2:exp2	0.0924502768712502	0.134772812086295	0.685971268537858	0.492928248014602	   
df.mm.trans1:exp3	0.311893136721857	0.178287672136588	1.74938139571934	0.080605234678847	.  
df.mm.trans2:exp3	0.205363764108501	0.134772812086295	1.52377739196394	0.127956089454988	   
df.mm.trans1:exp4	0.194092176398688	0.178287672136588	1.08864608569230	0.27663498385724	   
df.mm.trans2:exp4	0.427444056709752	0.134772812086295	3.17158965590226	0.0015733956193495	** 
df.mm.trans1:exp5	0.00346138288408717	0.178287672136588	0.0194145946413802	0.984515166747613	   
df.mm.trans2:exp5	0.0139962176488026	0.134772812086295	0.103850453456746	0.91731382945144	   
df.mm.trans1:exp6	0.0482158120090044	0.178287672136588	0.270438283428064	0.786892238633118	   
df.mm.trans2:exp6	-0.0384040565751689	0.134772812086295	-0.284954034724591	0.775752468586727	   
df.mm.trans1:exp7	-0.364449852482307	0.178287672136588	-2.04416742960835	0.0412611064824248	*  
df.mm.trans2:exp7	-0.178481025072614	0.134772812086295	-1.32431031385122	0.185774942049268	   
df.mm.trans1:exp8	-0.180443383852919	0.178287672136588	-1.01209119896231	0.311797798266855	   
df.mm.trans2:exp8	0.0471834249224987	0.134772812086295	0.350096018567062	0.726357990487092	   
df.mm.trans1:probe2	0.215054299160326	0.119599006334282	1.79812780851409	0.0725303982366334	.  
df.mm.trans1:probe3	0.126580281045470	0.119599006334282	1.05837234710525	0.290202411685641	   
df.mm.trans1:probe4	0.0195793131827834	0.119599006334282	0.163707992088653	0.870002015302389	   
df.mm.trans1:probe5	0.0459531471152218	0.119599006334282	0.384226830336547	0.700911553159737	   
df.mm.trans1:probe6	0.154823518422514	0.119599006334282	1.29452177879956	0.195855533969846	   
df.mm.trans1:probe7	-0.0575485251073353	0.119599006334282	-0.481178956842549	0.630519752289541	   
df.mm.trans1:probe8	0.179481419802180	0.119599006334282	1.50069323569901	0.133826052176535	   
df.mm.trans1:probe9	0.148365621293199	0.119599006334282	1.24052553479009	0.215141608875232	   
df.mm.trans1:probe10	0.0572032026920835	0.119599006334282	0.478291621689558	0.632572200713235	   
df.mm.trans1:probe11	0.153792928102263	0.119599006334282	1.28590473128521	0.198845150883126	   
df.mm.trans1:probe12	0.00560062478238277	0.119599006334282	0.0468283554691825	0.962661615143916	   
df.mm.trans1:probe13	0.0102103881966364	0.119599006334282	0.0853718480578187	0.931986948491748	   
df.mm.trans1:probe14	0.138454532358733	0.119599006334282	1.15765620971590	0.247346872379632	   
df.mm.trans1:probe15	0.0838910616043434	0.119599006334282	0.701436108673561	0.483233114993972	   
df.mm.trans1:probe16	0.104698081845912	0.119599006334282	0.875409295235102	0.381611716278606	   
df.mm.trans1:probe17	0.0338613323971864	0.119599006334282	0.283123860599169	0.777154500867195	   
df.mm.trans1:probe18	0.107811901953728	0.119599006334282	0.901444796726752	0.367620820961778	   
df.mm.trans1:probe19	0.249408286835634	0.119599006334282	2.08537089462543	0.0373493319449204	*  
df.mm.trans1:probe20	0.251680856903108	0.119599006334282	2.10437247446399	0.0356547078641857	*  
df.mm.trans1:probe21	0.227152392890710	0.119599006334282	1.89928327879091	0.0578835839630965	.  
df.mm.trans2:probe2	-0.087251897950836	0.119599006334282	-0.729536980490999	0.465884945083657	   
df.mm.trans2:probe3	-0.0387902917961435	0.119599006334282	-0.324336238109903	0.745767534671696	   
df.mm.trans2:probe4	0.0598314158836148	0.119599006334282	0.500266830949954	0.617023596237215	   
df.mm.trans2:probe5	0.0582274961614669	0.119599006334282	0.486856019511731	0.62649256899019	   
df.mm.trans2:probe6	-0.12098414398412	0.119599006334282	-1.01158151469893	0.312041385325379	   
df.mm.trans3:probe2	-0.0974371007057147	0.119599006334282	-0.814698246182545	0.415485407558321	   
df.mm.trans3:probe3	0.0700431376560844	0.119599006334282	0.585649829400022	0.558274592479526	   
df.mm.trans3:probe4	0.180656051810759	0.119599006334282	1.51051465516211	0.131303592789058	   
df.mm.trans3:probe5	0.0472694021968756	0.119599006334282	0.395232399044825	0.692775831653802	   
df.mm.trans3:probe6	-0.0517656810628255	0.119599006334282	-0.432827016289242	0.665256092450943	   
df.mm.trans3:probe7	0.0932916579742772	0.119599006334282	0.780037065805754	0.435597773631117	   
df.mm.trans3:probe8	-0.0535891734785253	0.119599006334282	-0.448073735067182	0.654220247837187	   
df.mm.trans3:probe9	-0.0760660671618722	0.119599006334282	-0.636009190153852	0.524950759109674