chr5.18536_chr5_27699221_27706255_+_2.R 

fitVsDatCorrelation=0.869114025161991
cont.fitVsDatCorrelation=0.219114889802342

fstatistic=11675.8510702030,62,922
cont.fstatistic=2989.39560454502,62,922

residuals=-0.534576572878466,-0.071800375198687,0.00131690450770046,0.0710111086648067,2.2649062177867
cont.residuals=-0.51497311722854,-0.186411777019168,-0.073461529650645,0.119496025603890,2.29415971449762

predictedValues:
Include	Exclude	Both
chr5.18536_chr5_27699221_27706255_+_2.R.tl.Lung	51.5952239633176	49.8192540273882	46.5024852433748
chr5.18536_chr5_27699221_27706255_+_2.R.tl.cerebhem	52.329461136181	70.4613216640274	45.8724028394462
chr5.18536_chr5_27699221_27706255_+_2.R.tl.cortex	54.9432213823173	46.4967438258904	51.5273609032517
chr5.18536_chr5_27699221_27706255_+_2.R.tl.heart	49.8020871176971	48.6192629245612	47.7221117766396
chr5.18536_chr5_27699221_27706255_+_2.R.tl.kidney	53.2271477542168	47.0758393916687	48.4583403160554
chr5.18536_chr5_27699221_27706255_+_2.R.tl.liver	53.5501136334871	50.1458052677348	48.6863968234821
chr5.18536_chr5_27699221_27706255_+_2.R.tl.stomach	53.2781883439599	50.4582624998473	46.9738171365043
chr5.18536_chr5_27699221_27706255_+_2.R.tl.testicle	53.0323475689557	51.0745750769323	46.9711100562024


diffExp=1.77596993592941,-18.1318605278465,8.4464775564269,1.1828241931359,6.15130836254811,3.40430836575231,2.81992584411255,1.95777249202344
diffExpScore=5.0972281615299
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	52.4159055788884	61.9053436443852	55.0499207314226
cerebhem	55.0653995184675	62.0147797938134	52.8877863653656
cortex	55.3233094813708	60.0217658937306	54.0991749391054
heart	54.4862112439452	59.9020846227713	55.9963446826504
kidney	57.1013386546398	56.3386112628766	56.7733868946915
liver	55.1834835702094	57.7419073331317	54.3419568300181
stomach	55.6418540874156	57.7302687508617	54.5409193033118
testicle	56.9944332881333	54.6742893950089	56.5463476607305
cont.diffExp=-9.48943806549678,-6.94938027534592,-4.69845641235981,-5.41587337882608,0.762727391763214,-2.55842376292232,-2.08841466344606,2.32014389312437
cont.diffExpScore=1.17741258092538

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=-0.149541173420694
cont.tran.correlation=-0.821292251030607

tran.covariance=-0.000586989204430201
cont.tran.covariance=-0.000985250998899037

tran.mean=52.2443034736364
cont.tran.mean=57.0338116324781

weightedLogRatios:
wLogRatio
Lung	0.137515285514732
cerebhem	-1.22164583713648
cortex	0.654792936207742
heart	0.0936494022630151
kidney	0.480570444156478
liver	0.259302000937512
stomach	0.214712581601886
testicle	0.148658972113963

cont.weightedLogRatios:
wLogRatio
Lung	-0.672642252952404
cerebhem	-0.483480316675472
cortex	-0.330449415541093
heart	-0.383350152198803
kidney	0.0543021893560399
liver	-0.182788238484528
stomach	-0.14876025442615
testicle	0.167161662384147

varWeightedLogRatios=0.320228104522521
cont.varWeightedLogRatios=0.0769620742392446

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81463315160917	0.0675842086604973	56.4426694817366	2.20196207968353e-301	***
df.mm.trans1	-0.0750728071974374	0.0580432687280512	-1.29339385673082	0.196198821240434	   
df.mm.trans2	0.0800453999674938	0.0509657305201286	1.57057299386458	0.116624961180916	   
df.mm.exp2	0.374434873923887	0.0648473793318034	5.77409415433773	1.05683070630481e-08	***
df.mm.exp3	-0.108755236332559	0.0648473793318034	-1.67709531908905	0.0938628015049448	.  
df.mm.exp4	-0.085643036205501	0.0648473793318034	-1.3206861570657	0.186933744371229	   
df.mm.exp5	-0.0667008870772986	0.0648473793318034	-1.02858261605317	0.303945606920268	   
df.mm.exp6	-0.0021717674287162	0.0648473793318034	-0.0334904424989013	0.97329073594254	   
df.mm.exp7	0.0347582797636065	0.0648473793318034	0.536001302161487	0.592086930875296	   
df.mm.exp8	0.0423312584227861	0.0648473793318034	0.652782870471766	0.514058994908949	   
df.mm.trans1:exp2	-0.360304460362798	0.0595305277031412	-6.05243182387893	2.07333134284861e-09	***
df.mm.trans2:exp2	-0.027772479902213	0.0421950240555156	-0.658193247281315	0.510578270847547	   
df.mm.trans1:exp3	0.171626440480054	0.0595305277031412	2.88299880921428	0.00403064190883688	** 
df.mm.trans2:exp3	0.0397359848587055	0.0421950240555156	0.94172205723654	0.346581630809613	   
df.mm.trans1:exp4	0.0502708199985771	0.0595305277031412	0.844454466274192	0.398634554367196	   
df.mm.trans2:exp4	0.0612613086903098	0.0421950240555156	1.45186097322065	0.146880448461944	   
df.mm.trans1:exp5	0.097840340046749	0.0595305277031412	1.64353221484355	0.100613797076360	   
df.mm.trans2:exp5	0.0100592560395039	0.0421950240555156	0.238399106640371	0.811624533640716	   
df.mm.trans1:exp6	0.0393605769742023	0.0595305277031412	0.661183068466658	0.508660100042704	   
df.mm.trans2:exp6	0.00870509825855423	0.0421950240555156	0.206306275524361	0.836597223895556	   
df.mm.trans1:exp7	-0.00266036601612739	0.0595305277031411	-0.0446891052166335	0.964364794440512	   
df.mm.trans2:exp7	-0.0220133067265991	0.0421950240555156	-0.521703855355939	0.602001837774674	   
df.mm.trans1:exp8	-0.0148583089106967	0.0595305277031411	-0.249591419461122	0.802958929825833	   
df.mm.trans2:exp8	-0.0174459737094882	0.0421950240555156	-0.413460451794853	0.679365438216685	   
df.mm.trans1:probe2	0.104160815596143	0.0426447172973153	2.44252564438270	0.0147717371596661	*  
df.mm.trans1:probe3	0.268850802452172	0.0426447172973153	6.30443392502211	4.47906535290679e-10	***
df.mm.trans1:probe4	0.082717010161063	0.0426447172973153	1.93967777027028	0.0527235650147016	.  
df.mm.trans1:probe5	0.0624647452158748	0.0426447172973153	1.46477100036508	0.143324295350850	   
df.mm.trans1:probe6	0.574207555151203	0.0426447172973153	13.4649164431758	7.37424984772003e-38	***
df.mm.trans1:probe7	0.347928755577332	0.0426447172973153	8.1587773967782	1.10271565833290e-15	***
df.mm.trans1:probe8	0.233101332367185	0.0426447172973153	5.46612446137286	5.92218366339997e-08	***
df.mm.trans1:probe9	0.00767052289781996	0.0426447172973153	0.179870412654907	0.857293826754021	   
df.mm.trans1:probe10	1.10285789909285	0.0426447172973153	25.8615361758367	2.51617350188186e-111	***
df.mm.trans1:probe11	0.0966748559986377	0.0426447172973153	2.26698316053144	0.0236221210046219	*  
df.mm.trans1:probe12	0.07494219147218	0.0426447172973153	1.75736166685523	0.079188038944465	.  
df.mm.trans1:probe13	0.115588170827394	0.0426447172973153	2.71049213485279	0.00684313824539564	** 
df.mm.trans1:probe14	0.0369658963149339	0.0426447172973153	0.866834127594534	0.386258428058793	   
df.mm.trans1:probe15	0.198181505561395	0.0426447172973153	4.64726977036078	3.8532481579877e-06	***
df.mm.trans1:probe16	0.127362835528254	0.0426447172973153	2.98660288073413	0.00289559091760684	** 
df.mm.trans1:probe17	0.6791987781846	0.0426447172973153	15.9269147793684	1.23231117174396e-50	***
df.mm.trans1:probe18	0.559766922687061	0.0426447172973153	13.1262899173282	3.29284717213810e-36	***
df.mm.trans1:probe19	0.810042965915766	0.0426447172973153	18.9951538491442	3.72309896583183e-68	***
df.mm.trans1:probe20	0.443627167356714	0.0426447172973153	10.4028633667280	4.84364413471397e-24	***
df.mm.trans1:probe21	0.535541038683685	0.0426447172973153	12.5582035155712	1.66391093296212e-33	***
df.mm.trans1:probe22	0.673555005910806	0.0426447172973153	15.7945707838755	6.45269241674531e-50	***
df.mm.trans2:probe2	0.00226723624817819	0.0426447172973153	0.0531657000413723	0.957611401197435	   
df.mm.trans2:probe3	0.0484277743666968	0.0426447172973153	1.13561016313140	0.256414860187999	   
df.mm.trans2:probe4	0.062628431854934	0.0426447172973153	1.46860938057800	0.142279849772068	   
df.mm.trans2:probe5	0.0853940751915637	0.0426447172973153	2.00245377630724	0.0455283904670963	*  
df.mm.trans2:probe6	0.0620191933286903	0.0426447172973153	1.45432300315881	0.146197100376208	   
df.mm.trans3:probe2	0.108593961393473	0.0426447172973153	2.54648097761710	0.0110431086093283	*  
df.mm.trans3:probe3	0.0249715327661407	0.0426447172973153	0.58557153965968	0.558306774206379	   
df.mm.trans3:probe4	0.164098605695956	0.0426447172973153	3.8480406506596	0.000127268149262371	***
df.mm.trans3:probe5	0.0663922942788614	0.0426447172973153	1.55687031094567	0.119844447539649	   
df.mm.trans3:probe6	-0.100534368302167	0.0426447172973153	-2.35748703881068	0.0186072125059066	*  
df.mm.trans3:probe7	-0.0720560826544425	0.0426447172973153	-1.68968367528558	0.091426550595867	.  
df.mm.trans3:probe8	-0.0108908581994582	0.0426447172973153	-0.255385869333546	0.798482042546377	   
df.mm.trans3:probe9	0.189127950392666	0.0426447172973153	4.43496785484781	1.03184285418206e-05	***
df.mm.trans3:probe10	-0.00880330567640485	0.0426447172973153	-0.206433674188269	0.836497745363146	   
df.mm.trans3:probe11	-0.0895020224574279	0.0426447172973153	-2.09878334597524	0.0361074845413954	*  
df.mm.trans3:probe12	-0.0861226351026737	0.0426447172973153	-2.01953818810039	0.0437202088169691	*  
df.mm.trans3:probe13	0.0734250953051464	0.0426447172973153	1.72178642417144	0.0854436220193776	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.1467135983068	0.133320418304509	31.1033647436926	8.27853573995189e-146	***
df.mm.trans1	-0.157941353704347	0.11449942257159	-1.37940742544440	0.168103641093172	   
df.mm.trans2	0.0144539358095671	0.100537871890626	0.143766080759014	0.885716603591313	   
df.mm.exp2	0.0911457041166133	0.127921594553202	0.712512257488363	0.476327891753433	   
df.mm.exp3	0.0405064889546088	0.127921594553202	0.316650907112968	0.751580182561987	   
df.mm.exp4	-0.0112036105232425	0.127921594553202	-0.0875818548257931	0.93022804215807	   
df.mm.exp5	-0.0394361686256663	0.127921594553202	-0.308283904397900	0.757935947628022	   
df.mm.exp6	-0.00522587619298016	0.127921594553202	-0.0408521814571872	0.96742258364012	   
df.mm.exp7	-0.000810085697517116	0.127921594553202	-0.00633267354387302	0.994948661210477	   
df.mm.exp8	-0.0672896100517658	0.127921594553202	-0.526022289565662	0.598999217973786	   
df.mm.trans1:exp2	-0.0418342299778616	0.117433273431370	-0.356238302445945	0.721743609241646	   
df.mm.trans2:exp2	-0.0893794667236599	0.0832362820982203	-1.07380416893429	0.283191407675567	   
df.mm.trans1:exp3	0.013477753522925	0.117433273431369	0.114769461236229	0.908652843899731	   
df.mm.trans2:exp3	-0.0714057306347008	0.0832362820982203	-0.85786785323305	0.391188378946605	   
df.mm.trans1:exp4	0.0499411886336899	0.117433273431370	0.425272899021048	0.670736984209033	   
df.mm.trans2:exp4	-0.0216915862596310	0.0832362820982203	-0.260602536692287	0.794457224107066	   
df.mm.trans1:exp5	0.125053642202639	0.117433273431370	1.06489105300912	0.287204027058205	   
df.mm.trans2:exp5	-0.0547902214655474	0.0832362820982203	-0.658249264436078	0.510542297301063	   
df.mm.trans1:exp6	0.0566794871695585	0.117433273431370	0.482652705774086	0.62945691993427	   
df.mm.trans2:exp6	-0.0643974199986798	0.0832362820982203	-0.773670067611737	0.439324243111431	   
df.mm.trans1:exp7	0.0605356882707909	0.117433273431369	0.515490086429118	0.60633417130972	   
df.mm.trans2:exp7	-0.0690147929481012	0.0832362820982204	-0.82914314777614	0.407238011225799	   
df.mm.trans1:exp8	0.151033124656003	0.117433273431369	1.28611866332986	0.198724530397067	   
df.mm.trans2:exp8	-0.0569233233748434	0.0832362820982203	-0.683876333011521	0.494225096450452	   
df.mm.trans1:probe2	-0.0219178874635573	0.0841233723267472	-0.260544565170605	0.79450192102384	   
df.mm.trans1:probe3	0.0131927935835375	0.0841233723267472	0.156826732198691	0.875415737989847	   
df.mm.trans1:probe4	-0.08200761681497	0.0841233723267472	-0.974849373565775	0.329890674001942	   
df.mm.trans1:probe5	-0.00258903510355874	0.0841233723267472	-0.0307766442541385	0.975454327434629	   
df.mm.trans1:probe6	-0.0960631059806518	0.0841233723267472	-1.14193122937974	0.253779079661305	   
df.mm.trans1:probe7	-0.0709133582611963	0.0841233723267472	-0.842968562717132	0.39946465146237	   
df.mm.trans1:probe8	0.0520235891599598	0.0841233723267472	0.618420157455085	0.536451153330056	   
df.mm.trans1:probe9	-0.126856364200493	0.0841233723267472	-1.50798001425531	0.13190220364952	   
df.mm.trans1:probe10	-0.0823383761618872	0.0841233723267472	-0.97878121007885	0.327944842189852	   
df.mm.trans1:probe11	0.0805168147122156	0.0841233723267472	0.957127757544916	0.338753671216769	   
df.mm.trans1:probe12	-0.0450575652986208	0.0841233723267472	-0.535612922454069	0.59235526770814	   
df.mm.trans1:probe13	-0.0425053654062816	0.0841233723267472	-0.505274149509659	0.613487053831911	   
df.mm.trans1:probe14	-0.0781863354441538	0.0841233723267472	-0.929424644799865	0.352912325696604	   
df.mm.trans1:probe15	-0.0156049604421280	0.0841233723267472	-0.185500890067936	0.85287698177174	   
df.mm.trans1:probe16	-0.115466869458838	0.0841233723267472	-1.37258964144171	0.170213735573920	   
df.mm.trans1:probe17	-0.122243150312077	0.0841233723267472	-1.45314134384992	0.146524770794158	   
df.mm.trans1:probe18	-0.060194231913286	0.0841233723267472	-0.715547061992273	0.474452234397354	   
df.mm.trans1:probe19	-0.104351393952529	0.0841233723267472	-1.24045661825365	0.215122116197361	   
df.mm.trans1:probe20	-0.0290274163985580	0.0841233723267472	-0.345057688436591	0.730129702176152	   
df.mm.trans1:probe21	0.0295809031813975	0.0841233723267472	0.351637153423915	0.725190743211226	   
df.mm.trans1:probe22	-0.114666589380913	0.0841233723267472	-1.36307646982496	0.173191190142411	   
df.mm.trans2:probe2	-0.167640884886746	0.0841233723267472	-1.99279796149403	0.0465779817590185	*  
df.mm.trans2:probe3	-0.0868773456873762	0.0841233723267472	-1.03273731526040	0.301997656813014	   
df.mm.trans2:probe4	-0.180806426981309	0.0841233723267472	-2.14930074699134	0.0318702552288706	*  
df.mm.trans2:probe5	-0.0940157603898283	0.0841233723267472	-1.11759381239090	0.264031638207987	   
df.mm.trans2:probe6	-0.146319172991701	0.0841233723267472	-1.73934031583251	0.0823086533308027	.  
df.mm.trans3:probe2	0.106505378724111	0.0841233723267472	1.26606168747525	0.205810791626768	   
df.mm.trans3:probe3	-0.0271402450423663	0.0841233723267472	-0.322624311076709	0.747052924680309	   
df.mm.trans3:probe4	0.0554320778785185	0.0841233723267472	0.658937894967077	0.510100175481092	   
df.mm.trans3:probe5	-0.0193244594400669	0.0841233723267472	-0.229715700947032	0.818363644593365	   
df.mm.trans3:probe6	0.0354919258349243	0.0841233723267472	0.421903269605842	0.67319397632323	   
df.mm.trans3:probe7	0.00249349473984833	0.0841233723267472	0.0296409270204152	0.976359839243097	   
df.mm.trans3:probe8	0.0513207204874671	0.0841233723267472	0.610064944711561	0.541969114179681	   
df.mm.trans3:probe9	-0.0497142646562495	0.0841233723267472	-0.590968517799693	0.554686354662494	   
df.mm.trans3:probe10	-0.0661482944889862	0.0841233723267472	-0.786324806761868	0.431879269130334	   
df.mm.trans3:probe11	-0.0609174604427173	0.0841233723267472	-0.724144298520334	0.469160850682445	   
df.mm.trans3:probe12	-0.0368221159483603	0.0841233723267472	-0.437715642275228	0.661694945994155	   
df.mm.trans3:probe13	0.074008398985306	0.0841233723267472	0.87976024900484	0.379218441523404