chr4.17383_chr4_135780060_135785148_+_2.R 

fitVsDatCorrelation=0.920678438867184
cont.fitVsDatCorrelation=0.239037798195539

fstatistic=8671.47107651485,55,761
cont.fstatistic=1389.57009147043,55,761

residuals=-0.672258150552866,-0.114885889764555,-0.00234429723363088,0.103785616487384,0.844076978338772
cont.residuals=-1.04488776467849,-0.376882657671447,0.0597247139989638,0.339425800775847,1.13266718467759

predictedValues:
Include	Exclude	Both
chr4.17383_chr4_135780060_135785148_+_2.R.tl.Lung	84.6792232481158	164.967699869956	91.857745531166
chr4.17383_chr4_135780060_135785148_+_2.R.tl.cerebhem	81.7019052682957	118.493616695911	114.501187073948
chr4.17383_chr4_135780060_135785148_+_2.R.tl.cortex	123.508792668482	127.781463348710	147.222560571236
chr4.17383_chr4_135780060_135785148_+_2.R.tl.heart	74.501557082393	189.671529407405	89.5246115190446
chr4.17383_chr4_135780060_135785148_+_2.R.tl.kidney	82.7008764574497	177.317385884309	90.274547047886
chr4.17383_chr4_135780060_135785148_+_2.R.tl.liver	65.3765131086112	160.148523849463	74.464841088008
chr4.17383_chr4_135780060_135785148_+_2.R.tl.stomach	66.0387253524268	166.413196157479	81.2551356228398
chr4.17383_chr4_135780060_135785148_+_2.R.tl.testicle	78.772829815463	155.884099446157	102.772224828099


diffExp=-80.2884766218405,-36.7917114276155,-4.27267068022823,-115.169972325012,-94.616509426859,-94.772010740852,-100.374470805052,-77.1112696306935
diffExpScore=0.998345458616857
diffExp1.5=-1,0,0,-1,-1,-1,-1,-1
diffExp1.5Score=0.857142857142857
diffExp1.4=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	103.631048858642	108.431451164934	117.082815053416
cerebhem	92.5335349258923	87.0038233331627	104.441469039661
cortex	97.169420586385	89.0707521350523	117.075062314536
heart	92.650530541504	99.1349933464369	111.039582345402
kidney	106.734119562994	106.434424228128	89.3837511877121
liver	99.5408311348225	97.6645404303423	100.889889824607
stomach	112.283934054353	100.569967685960	93.457228207976
testicle	108.369752974042	111.300996135727	102.900699835701
cont.diffExp=-4.80040230629257,5.5297115927296,8.09866845133267,-6.48446280493296,0.299695334865604,1.87629070448017,11.7139663683929,-2.93124316168428
cont.diffExpScore=2.91803849546053

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=-0.529235357365501
cont.tran.correlation=0.694048960873315

tran.covariance=-0.0166527023736878
cont.tran.covariance=0.00450609691870621

tran.mean=119.872371103789
cont.tran.mean=100.782757568649

weightedLogRatios:
wLogRatio
Lung	-3.18255532881986
cerebhem	-1.70609472094113
cortex	-0.164376941942547
heart	-4.46496879094927
kidney	-3.65840902482108
liver	-4.1465223906783
stomach	-4.29986012377067
testicle	-3.21331127441903

cont.weightedLogRatios:
wLogRatio
Lung	-0.211167872854468
cerebhem	0.277086197608127
cortex	0.394479580339089
heart	-0.308653867703594
kidney	0.0131281935518120
liver	0.0873648662370875
stomach	0.51408001978395
testicle	-0.125409663420594

varWeightedLogRatios=2.18431149638639
cont.varWeightedLogRatios=0.0869593578602824

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.2916056212558	0.091047133828843	47.1360869999869	6.26117351498733e-228	***
df.mm.trans1	-0.255790027597208	0.0788288167496494	-3.24487970445586	0.00122635700507937	** 
df.mm.trans2	0.904112761430948	0.0704157194479845	12.8396438823409	2.69298946894089e-34	***
df.mm.exp2	-0.587027610582497	0.091678195832654	-6.40313223063459	2.66258604871098e-10	***
df.mm.exp3	-0.349690456522049	0.091678195832654	-3.81432524218039	0.000147622175261665	***
df.mm.exp4	0.0372213989109571	0.091678195832654	0.406000560688385	0.684856321494336	   
df.mm.exp5	0.0659370840784306	0.091678195832654	0.7192231858357	0.472224210384668	   
df.mm.exp6	-0.0784411900455374	0.091678195832654	-0.855614460266223	0.39248037441243	   
df.mm.exp7	-0.117257713737541	0.091678195832654	-1.27901419386109	0.201281926280103	   
df.mm.exp8	-0.241213046705323	0.091678195832654	-2.63108413635915	0.00868353570368382	** 
df.mm.trans1:exp2	0.551234659039643	0.0847048254578396	6.50771258969196	1.38344400798558e-10	***
df.mm.trans2:exp2	0.256137005788212	0.0656120756683237	3.90380891290518	0.000103090715654826	***
df.mm.trans1:exp3	0.72713253231772	0.0847048254578396	8.58431061497952	5.1157920465145e-17	***
df.mm.trans2:exp3	0.0942622473522463	0.0656120756683237	1.43666004149529	0.151225590358435	   
df.mm.trans1:exp4	-0.165271646743325	0.0847048254578396	-1.95114795231573	0.0514060460423789	.  
df.mm.trans2:exp4	0.102322688196967	0.0656120756683237	1.55951000108912	0.119291466409915	   
df.mm.trans1:exp5	-0.08957715750476	0.0847048254578396	-1.05752130437180	0.290609310578928	   
df.mm.trans2:exp5	0.00625448695389707	0.0656120756683237	0.0953252414313821	0.924081589156193	   
df.mm.trans1:exp6	-0.180266016206694	0.0847048254578396	-2.12816702274439	0.0336439707848326	*  
df.mm.trans2:exp6	0.0487931523850798	0.0656120756683237	0.743661161273644	0.457311026311022	   
df.mm.trans1:exp7	-0.131371241975431	0.0847048254578396	-1.55092984685765	0.121334249261485	   
df.mm.trans2:exp7	0.125981846437761	0.0656120756683237	1.92010152330210	0.0552185756236705	.  
df.mm.trans1:exp8	0.168910911381782	0.0847048254578396	1.99411202925924	0.0464962205419564	*  
df.mm.trans2:exp8	0.184576129187313	0.0656120756683237	2.81314266173144	0.00503245170940023	** 
df.mm.trans1:probe2	-0.0875729853908153	0.0554523249126532	-1.57924821959688	0.114694676779112	   
df.mm.trans1:probe3	0.200035648609604	0.0554523249126532	3.6073446681396	0.000329555144367353	***
df.mm.trans1:probe4	-0.00557334109035885	0.0554523249126532	-0.100506896674536	0.919968380060247	   
df.mm.trans1:probe5	0.510014595378	0.0554523249126532	9.19735279235559	3.45409271008132e-19	***
df.mm.trans1:probe6	-0.124240311712717	0.0554523249126532	-2.24048877857541	0.0253469974610776	*  
df.mm.trans1:probe7	-0.0687523926672524	0.0554523249126532	-1.23984689146125	0.215414208236772	   
df.mm.trans1:probe8	0.427560242971299	0.0554523249126532	7.71041148671004	3.92636471358968e-14	***
df.mm.trans1:probe9	1.09195088132928	0.0554523249126532	19.6917060384626	4.41535443214901e-70	***
df.mm.trans1:probe10	0.839675625729781	0.0554523249126532	15.1422979478753	1.80439668455726e-45	***
df.mm.trans1:probe11	0.995776155120724	0.0554523249126532	17.9573382484727	2.20081327781945e-60	***
df.mm.trans1:probe12	0.793840521166287	0.0554523249126532	14.3157301775303	2.44898055785821e-41	***
df.mm.trans1:probe13	1.09701483034147	0.0554523249126532	19.7830268085144	1.33629022831271e-70	***
df.mm.trans1:probe14	0.792879367583468	0.0554523249126532	14.2983972057508	2.98033359131315e-41	***
df.mm.trans1:probe15	0.838248905737424	0.0554523249126532	15.1165691800625	2.43700477767435e-45	***
df.mm.trans1:probe16	0.590938887629226	0.0554523249126532	10.6567017444274	8.23302917798395e-25	***
df.mm.trans1:probe17	0.837867060376698	0.0554523249126532	15.1096831683159	2.64100853936184e-45	***
df.mm.trans1:probe18	0.953574973828445	0.0554523249126532	17.1963028661194	3.07203595699105e-56	***
df.mm.trans1:probe19	0.744589184584246	0.0554523249126532	13.4275557563565	4.79808995346265e-37	***
df.mm.trans1:probe20	0.857703184194399	0.0554523249126532	15.4673980855704	3.95446834650544e-47	***
df.mm.trans2:probe2	-0.283948221629685	0.0554523249126532	-5.12058280833042	3.86241015417206e-07	***
df.mm.trans2:probe3	-0.339752763319914	0.0554523249126532	-6.12693451275635	1.43637285266775e-09	***
df.mm.trans2:probe4	-0.0360674061037975	0.0554523249126532	-0.650421892330209	0.515616050464048	   
df.mm.trans2:probe5	-0.29726503910439	0.0554523249126532	-5.36073175601984	1.09990768807348e-07	***
df.mm.trans2:probe6	-0.302528178227498	0.0554523249126532	-5.45564462272829	6.60374117238252e-08	***
df.mm.trans3:probe2	-0.337801942131674	0.0554523249126532	-6.09175436131432	1.77219006008534e-09	***
df.mm.trans3:probe3	-0.726629606361548	0.0554523249126532	-13.1036815409654	1.60321047362983e-35	***
df.mm.trans3:probe4	-0.70111942661399	0.0554523249126532	-12.6436434850725	2.13573030342572e-33	***
df.mm.trans3:probe5	-0.557011795767662	0.0554523249126532	-10.0448772282325	2.22997607366730e-22	***
df.mm.trans3:probe6	0.0443709855730405	0.0554523249126532	0.800164567363628	0.423865166350026	   
df.mm.trans3:probe7	-1.04077045018353	0.0554523249126532	-18.7687432731254	7.01055309244325e-65	***
df.mm.trans3:probe8	-0.0385355160048561	0.0554523249126532	-0.69493057442688	0.487310931465525	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.6631171217822	0.226499444841477	20.5877640232003	3.31451915862649e-75	***
df.mm.trans1	-0.0157609831060471	0.1961037374869	-0.0803706411108046	0.935963607088809	   
df.mm.trans2	0.0107520780972222	0.175174337646520	0.0613792992836572	0.95107325417715	   
df.mm.exp2	-0.219177292773873	0.228069348115889	-0.9610116159165	0.336851567934597	   
df.mm.exp3	-0.261001872434454	0.228069348115889	-1.14439697658026	0.252818856286284	   
df.mm.exp4	-0.148643231738791	0.228069348115889	-0.651745764903319	0.514761972851984	   
df.mm.exp5	0.280857361345216	0.228069348115889	1.23145597453326	0.218532785536568	   
df.mm.exp6	0.00400308476873315	0.228069348115889	0.0175520507328283	0.986000809393355	   
df.mm.exp7	0.230306924834297	0.228069348115889	1.00981094889293	0.312906835498507	   
df.mm.exp8	0.199949116997668	0.228069348115889	0.876703154761806	0.380924554541061	   
df.mm.trans1:exp2	0.105911426997752	0.210721580513027	0.502613100850411	0.61538172994461	   
df.mm.trans2:exp2	-0.000988829999987528	0.163224234402692	-0.00605810775346006	0.995167946622997	   
df.mm.trans1:exp3	0.196620947049980	0.210721580513027	0.933084056086153	0.351072517298133	   
df.mm.trans2:exp3	0.0643147073356046	0.163224234402692	0.394026705476427	0.693671706478246	   
df.mm.trans1:exp4	0.0366409264194783	0.210721580513027	0.173883122603160	0.86200356028471	   
df.mm.trans2:exp4	0.0590075353921059	0.163224234402692	0.361512097808516	0.717817009731472	   
df.mm.trans1:exp5	-0.251353467510939	0.210721580513027	-1.19282261882712	0.23331062214802	   
df.mm.trans2:exp5	-0.299446487606527	0.163224234402692	-1.83457124919184	0.0669594708205162	.  
df.mm.trans1:exp6	-0.0442721459483016	0.210721580513027	-0.210097825958384	0.83364755861731	   
df.mm.trans2:exp6	-0.108582721815568	0.163224234402692	-0.665236520869095	0.506100782308096	   
df.mm.trans1:exp7	-0.150113120396087	0.210721580513027	-0.712376587298836	0.476449957527476	   
df.mm.trans2:exp7	-0.305571430507924	0.163224234402692	-1.87209596434097	0.0615766530217181	.  
df.mm.trans1:exp8	-0.155237082865684	0.210721580513027	-0.736692855509818	0.461536194480678	   
df.mm.trans2:exp8	-0.173829095570714	0.163224234402691	-1.06497111906716	0.287226778091482	   
df.mm.trans1:probe2	-0.0163416729726019	0.137949656180239	-0.118461135932449	0.905733548334935	   
df.mm.trans1:probe3	-0.0211631372072629	0.137949656180239	-0.153412033007259	0.878114026265482	   
df.mm.trans1:probe4	-0.187081353437094	0.137949656180239	-1.35615672135247	0.175451546130519	   
df.mm.trans1:probe5	-0.0188570318209943	0.137949656180239	-0.136695025874921	0.89130801556925	   
df.mm.trans1:probe6	0.069501795169258	0.137949656180239	0.503819995596435	0.614533690317223	   
df.mm.trans1:probe7	-0.00267171451940943	0.137949656180239	-0.0193673155366091	0.984553160560194	   
df.mm.trans1:probe8	0.188658766808898	0.137949656180239	1.36759142452957	0.171843745682013	   
df.mm.trans1:probe9	0.00610689265121347	0.137949656180240	0.0442689950834995	0.964701598876719	   
df.mm.trans1:probe10	0.051650363495988	0.137949656180239	0.374414586640968	0.708200196773506	   
df.mm.trans1:probe11	0.142902244466122	0.137949656180239	1.03590141811888	0.300577190230085	   
df.mm.trans1:probe12	-0.0620091735672086	0.137949656180239	-0.4495058217919	0.653194804866777	   
df.mm.trans1:probe13	-0.00650569439355894	0.137949656180239	-0.0471599174198656	0.962398146327649	   
df.mm.trans1:probe14	-0.0375037876989541	0.137949656180239	-0.271865756953777	0.785798980138977	   
df.mm.trans1:probe15	-0.107932538921422	0.137949656180239	-0.782405276751117	0.434219815630439	   
df.mm.trans1:probe16	-0.0577624980720303	0.137949656180239	-0.418721580549358	0.67553776971782	   
df.mm.trans1:probe17	-0.110713262612225	0.137949656180239	-0.802562802096235	0.422478039275609	   
df.mm.trans1:probe18	-0.00744819437119704	0.137949656180239	-0.0539921198604912	0.956955613032032	   
df.mm.trans1:probe19	0.157118154066899	0.137949656180239	1.13895285002823	0.255081217070659	   
df.mm.trans1:probe20	-0.162484478694370	0.137949656180239	-1.17785345171194	0.239223240676949	   
df.mm.trans2:probe2	0.00768452198841289	0.137949656180239	0.055705263798357	0.955591227606378	   
df.mm.trans2:probe3	0.115239289526915	0.137949656180239	0.835372067737149	0.403770440421636	   
df.mm.trans2:probe4	-0.0456566945409037	0.137949656180239	-0.330966352545675	0.74076100963041	   
df.mm.trans2:probe5	0.0270301283731161	0.137949656180239	0.195941977106486	0.844707889814002	   
df.mm.trans2:probe6	0.0671885718348905	0.137949656180239	0.487051390306509	0.626362167897499	   
df.mm.trans3:probe2	0.133028111775380	0.137949656180239	0.964323619636799	0.335190032095653	   
df.mm.trans3:probe3	0.0792759883725338	0.137949656180239	0.574673330602252	0.565682088216649	   
df.mm.trans3:probe4	0.210206401062927	0.137949656180239	1.52379068482983	0.127976442901019	   
df.mm.trans3:probe5	0.0977588798612438	0.137949656180239	0.708656205228348	0.478754867341394	   
df.mm.trans3:probe6	0.0174018824586664	0.137949656180239	0.126146617110302	0.89964918666214	   
df.mm.trans3:probe7	0.163197147695053	0.137949656180239	1.18301960449851	0.237170827955068	   
df.mm.trans3:probe8	0.137317897627288	0.137949656180239	0.99542036877478	0.319848257079389