chr19.12139_chr19_9365160_9367879_+_2.R 

fitVsDatCorrelation=0.786773115894158
cont.fitVsDatCorrelation=0.232951356666745

fstatistic=8313.2888885853,53,715
cont.fstatistic=3340.94599536282,53,715

residuals=-0.962523559621343,-0.111322946867601,-0.00424666700421506,0.106381832595070,0.784576402843675
cont.residuals=-0.840077608760052,-0.158660236046724,0.00307610964545606,0.164680996351271,1.37215117860096

predictedValues:
Include	Exclude	Both
chr19.12139_chr19_9365160_9367879_+_2.R.tl.Lung	94.7988221550942	90.4180453082318	71.5474966694252
chr19.12139_chr19_9365160_9367879_+_2.R.tl.cerebhem	78.1665888896482	104.864431836759	64.187382833878
chr19.12139_chr19_9365160_9367879_+_2.R.tl.cortex	87.3148467372362	83.9105724670073	72.2708371912517
chr19.12139_chr19_9365160_9367879_+_2.R.tl.heart	92.594697469139	85.163892215243	81.7163520569049
chr19.12139_chr19_9365160_9367879_+_2.R.tl.kidney	101.223780130948	100.443438186737	78.9523482756365
chr19.12139_chr19_9365160_9367879_+_2.R.tl.liver	98.5054987303967	96.7096038440357	62.9045726917915
chr19.12139_chr19_9365160_9367879_+_2.R.tl.stomach	99.0773257637991	87.0013383669561	63.0559173269917
chr19.12139_chr19_9365160_9367879_+_2.R.tl.testicle	91.7762391045177	83.6600461180135	68.7805421259123


diffExp=4.38077684686242,-26.6978429471109,3.40427427022897,7.43080525389605,0.780341944211443,1.79589488636103,12.075987396843,8.1161929865042
diffExpScore=5.26451647665998
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,-1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	91.82854189913	87.3094900533036	91.5208670186863
cerebhem	87.978363256564	92.6070648188804	87.226289730742
cortex	92.6868132276385	91.698757971266	95.6656467009909
heart	92.0703729188765	87.8798499800216	77.7205784464223
kidney	88.6141943779823	86.6596912765749	96.4754014467883
liver	94.2669794133077	95.67283487022	96.5694801882554
stomach	92.1474545288064	101.454504838790	84.1999917311515
testicle	85.0179315172343	97.4944430315507	88.6635800899364
cont.diffExp=4.51905184582647,-4.62870156231651,0.98805525637249,4.19052293885485,1.95450310140738,-1.40585545691236,-9.30705030998377,-12.4765115143164
cont.diffExpScore=2.29932918932438

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=-0.163642407414212
cont.tran.correlation=-0.0516058121015819

tran.covariance=-0.00130756565286093
cont.tran.covariance=-0.000118968483427974

tran.mean=92.2268229577351
cont.tran.mean=91.5867054987592

weightedLogRatios:
wLogRatio
Lung	0.214238563000423
cerebhem	-1.32390845986485
cortex	0.176957113298782
heart	0.375307196065939
kidney	0.035703349812861
liver	0.084287223659752
stomach	0.588914861809629
testicle	0.414168951031563

cont.weightedLogRatios:
wLogRatio
Lung	0.226820095200045
cerebhem	-0.230875228073222
cortex	0.0484839875499082
heart	0.209587618799559
kidney	0.0997653388499594
liver	-0.0674079386458132
stomach	-0.439871626182735
testicle	-0.617750635537372

varWeightedLogRatios=0.351077019188445
cont.varWeightedLogRatios=0.0951821760394715

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.6670235078868	0.0958312698197335	48.7004243673892	2.81998945705286e-229	***
df.mm.trans1	-0.113055010438125	0.085100022249894	-1.32849566250573	0.184438266783706	   
df.mm.trans2	-0.215952863852388	0.0774095366809332	-2.78974494760902	0.005415400042957	** 
df.mm.exp2	0.0638647023232504	0.104359558727314	0.611967922268869	0.540753526581477	   
df.mm.exp3	-0.166987887224706	0.104359558727314	-1.60012067184987	0.110013551344766	   
df.mm.exp4	-0.216284038576115	0.104359558727314	-2.07248901024249	0.0385776732726325	*  
df.mm.exp5	0.0722446615035006	0.104359558727314	0.69226683577948	0.488994526113144	   
df.mm.exp6	0.234366895680909	0.104359558727314	2.24576357488533	0.0250238016632993	*  
df.mm.exp7	0.131962876159992	0.104359558727314	1.26450205203343	0.206461907368079	   
df.mm.exp8	-0.0706452690310205	0.104359558727314	-0.676941047782817	0.498662304689135	   
df.mm.trans1:exp2	-0.256779382780152	0.099094297066455	-2.59126297255986	0.00975749094568123	** 
df.mm.trans2:exp2	0.0843598245306256	0.0834448657097715	1.01096483064682	0.312375252959543	   
df.mm.trans1:exp3	0.0847514166698866	0.099094297066455	0.855260284182148	0.392693563235715	   
df.mm.trans2:exp3	0.092295641757576	0.0834448657097715	1.10606735324601	0.269069378291421	   
df.mm.trans1:exp4	0.192758931176927	0.099094297066455	1.94520710962467	0.052141689844441	.  
df.mm.trans2:exp4	0.156417718573123	0.0834448657097715	1.87450380850462	0.0612685140791653	.  
df.mm.trans1:exp5	-0.00666793538233378	0.099094297066455	-0.067288790371681	0.946370611968504	   
df.mm.trans2:exp5	0.0329062397309109	0.0834448657097715	0.394347087157665	0.693442393699002	   
df.mm.trans1:exp6	-0.196011509046395	0.099094297066455	-1.97803016771939	0.0483088019139709	*  
df.mm.trans2:exp6	-0.167098046008252	0.0834448657097715	-2.00249643386608	0.0456085167712766	*  
df.mm.trans1:exp7	-0.0878192472387874	0.099094297066455	-0.886218983721068	0.37579749994504	   
df.mm.trans2:exp7	-0.170483237814465	0.0834448657097715	-2.04306443978735	0.0414121993953661	*  
df.mm.trans1:exp8	0.0382417152107604	0.099094297066455	0.385912371779726	0.69967636468886	   
df.mm.trans2:exp8	-0.00703707741879988	0.0834448657097715	-0.0843320599649049	0.932816029088977	   
df.mm.trans1:probe2	-0.381172250082457	0.0542761818234154	-7.02282727481789	5.06850775210253e-12	***
df.mm.trans1:probe3	-0.110163213658558	0.0542761818234154	-2.02967876437897	0.0427590389577979	*  
df.mm.trans1:probe4	-0.553160139841398	0.0542761818234154	-10.1915816709634	7.2327469366525e-23	***
df.mm.trans1:probe5	0.155099102904873	0.0542761818234154	2.85759052487295	0.00439277218896585	** 
df.mm.trans1:probe6	-0.109884407475523	0.0542761818234154	-2.02454195899458	0.0432856577558866	*  
df.mm.trans1:probe7	0.475167633736050	0.0542761818234154	8.75462528447528	1.46639632398895e-17	***
df.mm.trans1:probe8	0.0251732011293248	0.0542761818234154	0.463798304958599	0.64293341960562	   
df.mm.trans1:probe9	-0.289675627787513	0.0542761818234154	-5.3370671638244	1.26954981002967e-07	***
df.mm.trans1:probe10	-0.270679496154030	0.0542761818234154	-4.98707696563239	7.7014974109777e-07	***
df.mm.trans1:probe11	0.227230506068845	0.0542761818234154	4.18656026336058	3.18545038001079e-05	***
df.mm.trans1:probe12	-0.00780353956766413	0.0542761818234154	-0.143774659629753	0.885718924951636	   
df.mm.trans1:probe13	0.135112917819034	0.0542761818234154	2.48935929683882	0.0130236146793683	*  
df.mm.trans1:probe14	0.0756246043371828	0.0542761818234154	1.39332948259373	0.163953158305596	   
df.mm.trans1:probe15	0.313419264498013	0.0542761818234155	5.7745267623597	1.15078209102159e-08	***
df.mm.trans1:probe16	0.142847374773610	0.0542761818234154	2.63186115851619	0.00867500204001364	** 
df.mm.trans1:probe17	-0.0815965489478603	0.0542761818234154	-1.50335830942070	0.133188215509924	   
df.mm.trans1:probe18	0.0131122127673943	0.0542761818234154	0.241583183025920	0.809172420548595	   
df.mm.trans1:probe19	0.0201650679927787	0.0542761818234154	0.371527018211867	0.710355103970188	   
df.mm.trans1:probe20	0.105287787907018	0.0542761818234154	1.93985251669998	0.0527905326800845	.  
df.mm.trans1:probe21	0.042059048722089	0.0542761818234154	0.774908022434699	0.43865019847121	   
df.mm.trans1:probe22	0.0163371683394861	0.0542761818234154	0.301000692949223	0.763501475010382	   
df.mm.trans2:probe2	0.0839144591609623	0.0542761818234154	1.54606415451207	0.122531547738391	   
df.mm.trans2:probe3	0.133039390016777	0.0542761818234154	2.45115602364244	0.0144781655848471	*  
df.mm.trans2:probe4	0.0125504811960724	0.0542761818234154	0.231233678833649	0.817199421123775	   
df.mm.trans2:probe5	0.198529254992663	0.0542761818234154	3.65776015045728	0.000273115498639829	***
df.mm.trans2:probe6	0.105698611479062	0.0542761818234154	1.94742164846721	0.0518753036881564	.  
df.mm.trans3:probe2	-0.305713360728110	0.0542761818234154	-5.63255097277719	2.55343319824836e-08	***
df.mm.trans3:probe3	0.111831319313420	0.0542761818234155	2.06041242321092	0.0397203931845688	*  
df.mm.trans3:probe4	-0.0767344200084354	0.0542761818234154	-1.41377704603626	0.157862613828658	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.62626998902411	0.150985757550817	30.6404396286653	2.5144201814021e-132	***
df.mm.trans1	-0.0508608717207163	0.134078274775670	-0.379337158132538	0.704550139731434	   
df.mm.trans2	-0.126147259129932	0.121961626506818	-1.03431925879473	0.301336637962533	   
df.mm.exp2	0.0641352463624748	0.164422396382229	0.390063931518072	0.6966054429651	   
df.mm.exp3	0.0140604543754858	0.164422396382229	0.0855142285044906	0.931876523544565	   
df.mm.exp4	0.172588355644182	0.164422396382229	1.04966451919950	0.294227037447529	   
df.mm.exp5	-0.0958224877872618	0.164422396382229	-0.582782454797129	0.560223502053939	   
df.mm.exp6	0.063987287550986	0.164422396382229	0.389164061337704	0.69727065900389	   
df.mm.exp7	0.236990382104221	0.164422396382229	1.44135097966396	0.149923357488481	   
df.mm.exp8	0.06499304133979	0.164422396382229	0.395280951803563	0.692753455947227	   
df.mm.trans1:exp2	-0.106967497436867	0.156126779282888	-0.685132287543391	0.493482495404956	   
df.mm.trans2:exp2	-0.00522897684105366	0.131470513608093	-0.039773000785872	0.968285204160403	   
df.mm.trans1:exp3	-0.00475740728810663	0.156126779282888	-0.0304714368025656	0.975699576322288	   
df.mm.trans2:exp3	0.0349892171552283	0.131470513608093	0.266137373278464	0.790210138320101	   
df.mm.trans1:exp4	-0.169958311204998	0.156126779282888	-1.08859166880685	0.276700776788616	   
df.mm.trans2:exp4	-0.166076978390447	0.131470513608093	-1.26322605603805	0.206919710946693	   
df.mm.trans1:exp5	0.0601913767562928	0.156126779282888	0.385528844140384	0.699960310721739	   
df.mm.trans2:exp5	0.0883521784644057	0.131470513608093	0.672030374261558	0.501781447957313	   
df.mm.trans1:exp6	-0.0377794877587161	0.156126779282888	-0.241979549775141	0.808865395262358	   
df.mm.trans2:exp6	0.0274879502964974	0.131470513608093	0.209080724963452	0.834444773762119	   
df.mm.trans1:exp7	-0.233523484733956	0.156126779282888	-1.49572985369045	0.135165304354465	   
df.mm.trans2:exp7	-0.0868390754567926	0.131470513608093	-0.660521306820599	0.509132091903745	   
df.mm.trans1:exp8	-0.142054011308516	0.156126779282888	-0.909863201950297	0.363201266362630	   
df.mm.trans2:exp8	0.0453431773306913	0.131470513608093	0.344892372337247	0.730276712559491	   
df.mm.trans1:probe2	-0.100429080961791	0.0855141588438681	-1.17441465038737	0.240619975661758	   
df.mm.trans1:probe3	-0.0204318836366107	0.0855141588438681	-0.238929832355777	0.811228459251015	   
df.mm.trans1:probe4	0.0495251896949732	0.0855141588438681	0.57914607784889	0.562673018369138	   
df.mm.trans1:probe5	-0.122428390908021	0.0855141588438681	-1.43167391883665	0.152674131829068	   
df.mm.trans1:probe6	-0.0258847595210107	0.0855141588438681	-0.302695598845463	0.762209891298429	   
df.mm.trans1:probe7	0.0184908822706580	0.0855141588438681	0.216231820796118	0.828868725575202	   
df.mm.trans1:probe8	-0.0618016927049048	0.0855141588438681	-0.722707134589751	0.470096152699698	   
df.mm.trans1:probe9	-0.149327693907044	0.0855141588438681	-1.74623355858165	0.0811998290544862	.  
df.mm.trans1:probe10	-0.0791378612344884	0.0855141588438681	-0.925435767648471	0.355051517465257	   
df.mm.trans1:probe11	-0.00132234293427642	0.0855141588438681	-0.0154634384779573	0.987666766630386	   
df.mm.trans1:probe12	-0.103577106072089	0.0855141588438681	-1.21122756128843	0.22620837519741	   
df.mm.trans1:probe13	-0.0260498847163361	0.0855141588438681	-0.30462656791021	0.760739226846908	   
df.mm.trans1:probe14	-0.102410192320497	0.0855141588438681	-1.19758170699518	0.231476762959981	   
df.mm.trans1:probe15	-0.0592349927170096	0.0855141588438682	-0.692692222175288	0.488727635707089	   
df.mm.trans1:probe16	-0.067172634793427	0.0855141588438681	-0.785514769736213	0.43241203026377	   
df.mm.trans1:probe17	-0.0953837533545581	0.0855141588438681	-1.11541474118584	0.265047481833860	   
df.mm.trans1:probe18	-0.16570279968013	0.0855141588438681	-1.93772355268875	0.0530503820494545	.  
df.mm.trans1:probe19	-0.0910155108015629	0.0855141588438681	-1.06433264423192	0.287537336581287	   
df.mm.trans1:probe20	-0.123164389993299	0.0855141588438681	-1.44028066999025	0.150225724500252	   
df.mm.trans1:probe21	-0.0602546438021721	0.0855141588438681	-0.704615991279118	0.481278685960793	   
df.mm.trans1:probe22	-0.0559212636429483	0.0855141588438681	-0.653941574108791	0.513359710070229	   
df.mm.trans2:probe2	-0.140407125330092	0.0855141588438681	-1.64191669810431	0.101046992654523	   
df.mm.trans2:probe3	-0.0605988572367239	0.0855141588438681	-0.708641212823777	0.478778125975235	   
df.mm.trans2:probe4	-0.0898341270926	0.0855141588438681	-1.05051757869266	0.293835141298047	   
df.mm.trans2:probe5	-0.0118644780269796	0.0855141588438681	-0.138742848989976	0.889692423754368	   
df.mm.trans2:probe6	-0.00393107964664648	0.0855141588438681	-0.0459699270833483	0.96334705526691	   
df.mm.trans3:probe2	0.0390264902583058	0.0855141588438681	0.456374602591373	0.648259219827698	   
df.mm.trans3:probe3	0.106277400938848	0.0855141588438681	1.24280472819582	0.214347207863867	   
df.mm.trans3:probe4	0.123916680628816	0.0855141588438681	1.44907793404205	0.147754245240578