chr17.10221_chr17_26757900_26759379_+_0.R 

fitVsDatCorrelation=0.915710528915905
cont.fitVsDatCorrelation=0.339444836555978

fstatistic=9854.27108668076,43,485
cont.fstatistic=1789.21045737132,43,485

residuals=-0.523767428886031,-0.0861122721978151,0.0068533566903355,0.0830253194959504,0.49604352354356
cont.residuals=-0.906777589382636,-0.280973726851891,0.0362691895090816,0.287718054803088,0.880657543639726

predictedValues:
Include	Exclude	Both
chr17.10221_chr17_26757900_26759379_+_0.R.tl.Lung	101.39750486806	126.936015292201	83.46944694942
chr17.10221_chr17_26757900_26759379_+_0.R.tl.cerebhem	102.540639829478	70.0182029601554	82.9984249540207
chr17.10221_chr17_26757900_26759379_+_0.R.tl.cortex	143.271144735485	96.6280632435037	104.698884953635
chr17.10221_chr17_26757900_26759379_+_0.R.tl.heart	139.716384452693	130.021233087851	99.3406362900003
chr17.10221_chr17_26757900_26759379_+_0.R.tl.kidney	98.9873510321117	110.861825802380	79.7081256042024
chr17.10221_chr17_26757900_26759379_+_0.R.tl.liver	106.817053957931	110.920183336021	79.4550860343666
chr17.10221_chr17_26757900_26759379_+_0.R.tl.stomach	104.137321480505	114.425235252360	90.4782172075796
chr17.10221_chr17_26757900_26759379_+_0.R.tl.testicle	148.114686813457	161.936765288527	128.887119659710


diffExp=-25.5385104241407,32.522436869323,46.6430814919816,9.69515136484173,-11.8744747702679,-4.10312937809002,-10.2879137718547,-13.8220784750699
diffExpScore=6.3746467035604
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,1,1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,1,1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,1,1,0,0,0,0,0
diffExp1.2Score=1.5

cont.predictedValues:
Include	Exclude	Both
Lung	98.8072971268677	123.668803951162	109.173515148745
cerebhem	90.808358718394	116.872813253889	121.823924396461
cortex	85.9158857894652	138.737991836812	96.9851219722674
heart	97.5779262419137	101.437358602182	98.7933109124835
kidney	89.1221946958424	112.491140925257	116.808175424863
liver	104.841645647808	111.244634260583	97.6507523943896
stomach	102.170765910456	108.870290182766	128.826563404553
testicle	107.891228435452	118.652601788586	106.496455403091
cont.diffExp=-24.8615068242946,-26.0644545354951,-52.8221060473463,-3.85943236026833,-23.3689462294146,-6.40298861277535,-6.69952427231011,-10.7613733531338
cont.diffExpScore=0.993583175897676

cont.diffExp1.5=0,0,-1,0,0,0,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,0,-1,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,-1,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=-1,-1,-1,0,-1,0,0,0
cont.diffExp1.2Score=0.8

tran.correlation=0.477039668485028
cont.tran.correlation=-0.437546582675588

tran.covariance=0.0182535144538087
cont.tran.covariance=-0.00333799545592539

tran.mean=116.670600714545
cont.tran.mean=106.819433585465

weightedLogRatios:
wLogRatio
Lung	-1.0628287455118
cerebhem	1.69368956855915
cortex	1.87789376844917
heart	0.352655131257782
kidney	-0.526995917004399
liver	-0.176780128418772
stomach	-0.44211590255394
testicle	-0.449895593679654

cont.weightedLogRatios:
wLogRatio
Lung	-1.05605684642755
cerebhem	-1.16955195719144
cortex	-2.24896166905511
heart	-0.178436484583936
kidney	-1.07268390961254
liver	-0.277557249250868
stomach	-0.295862461348762
testicle	-0.449583829928869

varWeightedLogRatios=1.16509477611439
cont.varWeightedLogRatios=0.485287928361631

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.14550667646786	0.083113030580753	61.9097467691117	2.11547175152353e-232	***
df.mm.trans1	-0.546805604034455	0.0665363274210293	-8.21815127508274	1.89939716403488e-15	***
df.mm.trans2	-0.367646551401093	0.0665363274210293	-5.52550111572428	5.37015685835719e-08	***
df.mm.exp2	-0.578058141320984	0.0890966753254242	-6.48798778640882	2.15013548811315e-10	***
df.mm.exp3	-0.153731274009997	0.0890966753254242	-1.72544344049311	0.0850845632072826	.  
df.mm.exp4	0.170506629408307	0.0890966753254242	1.91372605976075	0.0562434812909495	.  
df.mm.exp5	-0.113345811889712	0.0890966753254242	-1.27216657047773	0.203923547566208	   
df.mm.exp6	-0.0335144046584731	0.0890966753254242	-0.376157747032224	0.706964112357353	   
df.mm.exp7	-0.157728025207145	0.0890966753254242	-1.77030203013800	0.0773049898521785	.  
df.mm.exp8	0.188004859157155	0.0890966753254243	2.11012204967772	0.0353593642379401	*  
df.mm.trans1:exp2	0.589268863430375	0.0698931824739352	8.43099201628323	3.94388577674711e-16	***
df.mm.trans2:exp2	-0.0168697510503792	0.0698931824739352	-0.241364757666748	0.809374433066175	   
df.mm.trans1:exp3	0.499421741910026	0.0698931824739352	7.14550009360744	3.30982477974218e-12	***
df.mm.trans2:exp3	-0.119082660097724	0.0698931824739352	-1.70378076777564	0.0890626055150277	.  
df.mm.trans1:exp4	0.150059429072951	0.0698931824739352	2.14698234879936	0.0322898318454923	*  
df.mm.trans2:exp4	-0.146492003748364	0.0698931824739352	-2.09594124295304	0.0366050958236039	*  
df.mm.trans1:exp5	0.089289402478488	0.0698931824739352	1.27751233121751	0.202032604319476	   
df.mm.trans2:exp5	-0.0220527177154127	0.0698931824739352	-0.315520297328522	0.752502362190619	   
df.mm.trans1:exp6	0.085583515660449	0.0698931824739352	1.22449018103254	0.221361795917078	   
df.mm.trans2:exp6	-0.101357864631711	0.0698931824739352	-1.45018242186225	0.147654024286479	   
df.mm.trans1:exp7	0.184389968209094	0.0698931824739352	2.63816815435264	0.00860342432587404	** 
df.mm.trans2:exp7	0.0539665247936735	0.0698931824739352	0.772128595143008	0.440414336784588	   
df.mm.trans1:exp8	0.190933541389796	0.0698931824739352	2.73179063581774	0.0065288192758472	** 
df.mm.trans2:exp8	0.0555179192449642	0.0698931824739352	0.794325244320762	0.427394602412041	   
df.mm.trans1:probe2	0.0935249864669188	0.0478525908209988	1.9544393493085	0.0512234715790779	.  
df.mm.trans1:probe3	0.112666648979034	0.0478525908209988	2.35445243498902	0.0189477516170382	*  
df.mm.trans1:probe4	-0.00613069480197465	0.0478525908209988	-0.128116256545181	0.898110076202571	   
df.mm.trans1:probe5	0.107811787007389	0.0478525908209988	2.25299790790175	0.0247053451306461	*  
df.mm.trans1:probe6	0.0176858579016218	0.0478525908209988	0.369590394128896	0.711849025923061	   
df.mm.trans2:probe2	0.0490012775210342	0.0478525908209988	1.02400469191589	0.306343527915581	   
df.mm.trans2:probe3	0.309000697213611	0.0478525908209988	6.45734519097374	2.59154663437035e-10	***
df.mm.trans2:probe4	0.143578538872387	0.0478525908209988	3.00043396624999	0.00283474586564199	** 
df.mm.trans2:probe5	0.406288412764951	0.0478525908209988	8.49041620932805	2.52975357219156e-16	***
df.mm.trans2:probe6	0.145299359016138	0.0478525908209988	3.03639482258455	0.00252310735756198	** 
df.mm.trans3:probe2	0.690588832118548	0.0478525908209988	14.4315870942457	1.55367690002342e-39	***
df.mm.trans3:probe3	0.962163288270193	0.0478525908209988	20.1068170346165	7.56839301570059e-66	***
df.mm.trans3:probe4	0.691231695547819	0.0478525908209988	14.4450213392519	1.35570796000754e-39	***
df.mm.trans3:probe5	-0.221398203270760	0.0478525908209988	-4.62667118900499	4.77460059902088e-06	***
df.mm.trans3:probe6	0.0440422929508534	0.0478525908209988	0.920374261774069	0.357834761929223	   
df.mm.trans3:probe7	-0.090382116413588	0.0478525908209988	-1.88876119062557	0.0595202432905157	.  
df.mm.trans3:probe8	-0.0810117402548236	0.0478525908209988	-1.69294366020562	0.0911083632967332	.  
df.mm.trans3:probe9	0.00497487763857568	0.0478525908209988	0.103962555699128	0.917242039527904	   
df.mm.trans3:probe10	-0.0659475359104176	0.0478525908209988	-1.37813929776773	0.168795728897741	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.73609021389954	0.194551450503864	24.3436386705607	4.05626873498229e-86	***
df.mm.trans1	-0.167962859337692	0.155748610302257	-1.07842284442687	0.281381149701146	   
df.mm.trans2	0.0939596379645529	0.155748610302257	0.603277536680475	0.546606086999231	   
df.mm.exp2	-0.250579161929850	0.208557999852880	-1.20148429744537	0.230149765868343	   
df.mm.exp3	0.093558346590906	0.208557999852880	0.44859629770569	0.653923256322273	   
df.mm.exp4	-0.110777162901102	0.20855799985288	-0.531157581963988	0.595552741628933	   
df.mm.exp5	-0.265490207005521	0.208557999852880	-1.27298021266411	0.203634909227469	   
df.mm.exp6	0.0649453839267636	0.208557999852880	0.311402027122321	0.755628819480985	   
df.mm.exp7	-0.259504283089648	0.208557999852880	-1.24427872952706	0.213997977633327	   
df.mm.exp8	0.0713717670346313	0.208557999852880	0.342215436880762	0.732337014153091	   
df.mm.trans1:exp2	0.166159040084270	0.163606355533187	1.01560260017261	0.310325033281431	   
df.mm.trans2:exp2	0.194058382822576	0.163606355533187	1.18612985534789	0.236151763667355	   
df.mm.trans1:exp3	-0.233361060763536	0.163606355533187	-1.42635694073755	0.154408705335494	   
df.mm.trans2:exp3	0.0214218005561615	0.163606355533187	0.130935014635273	0.895881007989427	   
df.mm.trans1:exp4	0.0982570056008224	0.163606355533187	0.600569612840567	0.5484072156167	   
df.mm.trans2:exp4	-0.0873884421792851	0.163606355533187	-0.534138431813907	0.593490500153944	   
df.mm.trans1:exp5	0.162327149595243	0.163606355533187	0.992181196544746	0.321603882202782	   
df.mm.trans2:exp5	0.170757621804276	0.163606355533187	1.04371019846865	0.297139426378750	   
df.mm.trans1:exp6	-0.0056657684254956	0.163606355533187	-0.0346304910162631	0.972388635460837	   
df.mm.trans2:exp6	-0.170820751823063	0.163606355533187	-1.04409606378899	0.296961066421533	   
df.mm.trans1:exp7	0.292978412510987	0.163606355533187	1.79075202522653	0.0739566126920782	.  
df.mm.trans2:exp7	0.132054401971381	0.163606355533187	0.807147140103573	0.419977362624513	   
df.mm.trans1:exp8	0.0165803488771056	0.163606355533187	0.101342938806203	0.919320113280064	   
df.mm.trans2:exp8	-0.112778912542692	0.163606355533187	-0.689330877001382	0.490944725723208	   
df.mm.trans1:probe2	0.0528086920397829	0.112013614345921	0.471448871176488	0.637532224696415	   
df.mm.trans1:probe3	0.129584157686488	0.112013614345921	1.15686078378210	0.247898712816209	   
df.mm.trans1:probe4	0.207601466166013	0.112013614345921	1.85335923118146	0.064437978352387	.  
df.mm.trans1:probe5	-0.108961691555257	0.112013614345921	-0.972754001301671	0.331160470796183	   
df.mm.trans1:probe6	0.119673056047393	0.112013614345921	1.06837956034361	0.285880800230911	   
df.mm.trans2:probe2	-0.0763711594565658	0.112013614345921	-0.68180247465916	0.49568930822005	   
df.mm.trans2:probe3	0.0847755143566006	0.112013614345921	0.756832237327834	0.449517869328075	   
df.mm.trans2:probe4	-0.205249309671357	0.112013614345921	-1.83236038645718	0.0675107097267379	.  
df.mm.trans2:probe5	-0.0258875494089116	0.112013614345921	-0.231110740958376	0.817326234359303	   
df.mm.trans2:probe6	0.0236477769776898	0.112013614345921	0.211115203413226	0.832886051974258	   
df.mm.trans3:probe2	-0.00995303740683074	0.112013614345921	-0.0888556044276342	0.92923334494307	   
df.mm.trans3:probe3	-0.0386703225709753	0.112013614345921	-0.345228772384341	0.730072062478599	   
df.mm.trans3:probe4	0.0742011807099161	0.112013614345921	0.66243001927219	0.508010323641262	   
df.mm.trans3:probe5	0.0017529662506963	0.112013614345921	0.0156495820702899	0.987520385239465	   
df.mm.trans3:probe6	0.220438969834088	0.112013614345921	1.96796586844637	0.0496412259168946	*  
df.mm.trans3:probe7	0.098363443234124	0.112013614345921	0.878138285319117	0.380303488464051	   
df.mm.trans3:probe8	0.0151042586499418	0.112013614345921	0.134843061159483	0.892791899904404	   
df.mm.trans3:probe9	-0.0484219126273371	0.112013614345921	-0.432285958363957	0.665725725007843	   
df.mm.trans3:probe10	-0.00430048507296515	0.112013614345921	-0.0383925212848178	0.969390521378112