chr11.4355_chr11_88061789_88067026_-_2.R 

fitVsDatCorrelation=0.765691570394549
cont.fitVsDatCorrelation=0.264111855289607

fstatistic=8686.42978633647,51,669
cont.fstatistic=3855.91243670623,51,669

residuals=-0.531579475520998,-0.0867457143735106,-0.00982526779810398,0.0759367389067734,1.17867999313876
cont.residuals=-0.425393567698491,-0.160751426817170,-0.0418386111793988,0.117529403526528,1.31084273051071

predictedValues:
Include	Exclude	Both
chr11.4355_chr11_88061789_88067026_-_2.R.tl.Lung	49.588651748682	41.4278405520022	55.8347539981288
chr11.4355_chr11_88061789_88067026_-_2.R.tl.cerebhem	53.616479662106	46.3954749249259	54.3581216868649
chr11.4355_chr11_88061789_88067026_-_2.R.tl.cortex	49.8340009335024	41.3458164116337	56.7852366119513
chr11.4355_chr11_88061789_88067026_-_2.R.tl.heart	50.5992564988963	45.7077456692922	57.7655524042009
chr11.4355_chr11_88061789_88067026_-_2.R.tl.kidney	47.5949274652037	40.4748757047205	59.8688880831179
chr11.4355_chr11_88061789_88067026_-_2.R.tl.liver	50.3100430830221	43.6254133623434	59.735035806423
chr11.4355_chr11_88061789_88067026_-_2.R.tl.stomach	51.6419166854032	41.3196785090454	63.9636391856906
chr11.4355_chr11_88061789_88067026_-_2.R.tl.testicle	50.0101413367719	45.3722088304894	56.6368847809612


diffExp=8.1608111966798,7.22100473718015,8.48818452186867,4.89151082960409,7.12005176048321,6.6846297206787,10.3222381763578,4.6379325062825
diffExpScore=0.982913682978627
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=0,0,1,0,0,0,1,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	49.6443699316112	54.4859772073114	54.7680992080722
cerebhem	46.9025359137722	53.2513542153453	51.9121031573888
cortex	48.3398547445952	46.9255740223709	49.8089449209519
heart	49.387523359992	48.2684381615624	50.4850737289092
kidney	51.3073769654462	50.56589506186	50.7399181959173
liver	49.8051836519724	52.8889618904354	50.069279400914
stomach	49.8354968486418	51.1084110911832	53.2258811304322
testicle	48.075894437685	47.9617977337751	54.6116056214847
cont.diffExp=-4.84160727570022,-6.34881830157308,1.41428072222423,1.11908519842966,0.74148190358617,-3.08377823846303,-1.27291424254145,0.114096703909908
cont.diffExpScore=1.4391102642833

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=0.619548379973819
cont.tran.correlation=0.114502697606102

tran.covariance=0.00115109681406214
cont.tran.covariance=0.000179204828571510

tran.mean=46.8040294611275
cont.tran.mean=49.9221653273475

weightedLogRatios:
wLogRatio
Lung	0.685765459432907
cerebhem	0.565531171714556
cortex	0.712423115581484
heart	0.393774864541527
kidney	0.612805290148251
liver	0.548435956786408
stomach	0.85470262657794
testicle	0.376025244982656

cont.weightedLogRatios:
wLogRatio
Lung	-0.367712204942537
cerebhem	-0.496576785568724
cortex	0.114718366161824
heart	0.0891181624520572
kidney	0.0572179043567043
liver	-0.236587093743968
stomach	-0.098902292897308
testicle	0.0091992254596581

varWeightedLogRatios=0.0258933261599955
cont.varWeightedLogRatios=0.0520021036426088

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.60251165708474	0.0749488177035275	48.066290669655	3.58585729991183e-219	***
df.mm.trans1	0.312063879510301	0.0632409513679448	4.93452221638269	1.01522464438912e-06	***
df.mm.trans2	0.121574751222149	0.0577821116836252	2.10402056414636	0.0357492610342636	*  
df.mm.exp2	0.218145735977708	0.0750223730736464	2.90774241123463	0.00376106868284038	** 
df.mm.exp3	-0.0139262681761056	0.0750223730736464	-0.18562820136914	0.852792602285119	   
df.mm.exp4	0.0844933995921464	0.0750223730736464	1.12624269441867	0.260466640310007	   
df.mm.exp5	-0.13406799130035	0.0750223730736464	-1.78704012959895	0.0743836402347105	.  
df.mm.exp6	-0.00139277307317278	0.0750223730736464	-0.0185647696295284	0.985193842999392	   
df.mm.exp7	-0.0979608759662644	0.0750223730736464	-1.30575549603183	0.192084543879736	   
df.mm.exp8	0.0851465043064078	0.0750223730736464	1.13494816036841	0.256803505425472	   
df.mm.trans1:exp2	-0.140051270937153	0.0658333546571516	-2.12736038846136	0.0337553042667894	*  
df.mm.trans2:exp2	-0.104896936592258	0.0531411679384516	-1.97392982995312	0.0488007183707353	*  
df.mm.trans1:exp3	0.0188617567441653	0.0658333546571516	0.286507604578165	0.774578073369446	   
df.mm.trans2:exp3	0.0119443775547204	0.0531411679384516	0.224766937161682	0.822229221780707	   
df.mm.trans1:exp4	-0.0643185292681298	0.0658333546571516	-0.976990001543887	0.328927247244198	   
df.mm.trans2:exp4	0.0138212415061582	0.0531411679384516	0.260085392217312	0.794877929975978	   
df.mm.trans1:exp5	0.0930321688383677	0.0658333546571516	1.41314641070416	0.158077764613870	   
df.mm.trans2:exp5	0.110796288023096	0.0531411679384516	2.08494265973645	0.0374530974144107	*  
df.mm.trans1:exp6	0.0158354817633501	0.0658333546571516	0.240538885581913	0.809986213274434	   
df.mm.trans2:exp6	0.0530794969840845	0.0531411679384516	0.998839488164082	0.318233588774869	   
df.mm.trans1:exp7	0.138532545385798	0.0658333546571516	2.10429114705229	0.0357255800447231	*  
df.mm.trans2:exp7	0.0953466077492583	0.0531411679384516	1.794213628494	0.073230487381113	.  
df.mm.trans1:exp8	-0.076682704873133	0.0658333546571516	-1.16480020306550	0.244515050372548	   
df.mm.trans2:exp8	0.00580014108757307	0.0531411679384516	0.109145909143187	0.913119491363016	   
df.mm.trans1:probe2	0.0501510599921047	0.0458615258010895	1.09353230438995	0.274553742112058	   
df.mm.trans1:probe3	-0.0797192447939616	0.0458615258010895	-1.73825975916544	0.082625146446513	.  
df.mm.trans1:probe4	0.0191904616381408	0.0458615258010895	0.418443593032068	0.675757111183831	   
df.mm.trans1:probe5	0.0176814778498025	0.0458615258010895	0.385540549315575	0.69995952738725	   
df.mm.trans1:probe6	0.0360326957967821	0.0458615258010895	0.785684627089448	0.432330443461337	   
df.mm.trans1:probe7	-0.134854588571330	0.0458615258010895	-2.94047322272314	0.00339022749741756	** 
df.mm.trans1:probe8	-0.0765989537713338	0.0458615258010895	-1.67022253257684	0.0953430979181214	.  
df.mm.trans1:probe9	-0.0527927668043977	0.0458615258010895	-1.15113411257555	0.250088315718934	   
df.mm.trans1:probe10	-0.114376243632097	0.0458615258010895	-2.49394763114008	0.0128734505358802	*  
df.mm.trans1:probe11	-0.0836380533596411	0.0458615258010895	-1.82370847673921	0.0686421790680932	.  
df.mm.trans1:probe12	-0.088893899520855	0.0458615258010895	-1.93831099092528	0.0530056312703931	.  
df.mm.trans1:probe13	0.248293468825855	0.0458615258010895	5.4139818614573	8.60053012644203e-08	***
df.mm.trans2:probe2	0.00355655925627278	0.0458615258010895	0.0775499548728117	0.938209240045551	   
df.mm.trans2:probe3	-0.00314339962929053	0.0458615258010895	-0.0685411044308485	0.94537538080692	   
df.mm.trans2:probe4	-0.03889147504095	0.0458615258010895	-0.848019649621559	0.396730324917182	   
df.mm.trans2:probe5	0.0101540716242774	0.0458615258010895	0.221407191472818	0.824842955135279	   
df.mm.trans2:probe6	0.0260585452358289	0.0458615258010895	0.568200572934489	0.570089439636459	   
df.mm.trans3:probe2	-0.179978432748865	0.0458615258010895	-3.92438824494124	9.59446931502832e-05	***
df.mm.trans3:probe3	0.458361740026893	0.0458615258010895	9.99447209878927	5.14709044650729e-22	***
df.mm.trans3:probe4	-0.165272442462382	0.0458615258010895	-3.60372751615813	0.000336972264116633	***
df.mm.trans3:probe5	-0.0355206775905606	0.0458615258010895	-0.774520188111944	0.43889687391584	   
df.mm.trans3:probe6	-0.217506559535485	0.0458615258010895	-4.74268040009951	2.57911557329330e-06	***
df.mm.trans3:probe7	0.191923884165072	0.0458615258010895	4.18485605990267	3.23443216945464e-05	***
df.mm.trans3:probe8	-0.117685609623620	0.0458615258010895	-2.56610759384774	0.0105012725673025	*  
df.mm.trans3:probe9	-0.0178177548669802	0.0458615258010895	-0.388512038266223	0.697760754185238	   
df.mm.trans3:probe10	-0.247680136819921	0.0458615258010895	-5.40060829842773	9.23808502332724e-08	***
df.mm.trans3:probe11	0.179884400641764	0.0458615258010895	3.92233789651827	9.67464467986679e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93305939503573	0.112385960929221	34.996002725934	2.66838310551819e-153	***
df.mm.trans1	-0.0618623036570874	0.0948299827447456	-0.652349624734221	0.514399728813937	   
df.mm.trans2	0.0557155965963526	0.0866444374315746	0.643037201786355	0.520420293733344	   
df.mm.exp2	-0.0261774496201153	0.112496257411615	-0.232696182276838	0.81606850478105	   
df.mm.exp3	-0.0810958645581566	0.112496257411615	-0.720876111117484	0.471237563246507	   
df.mm.exp4	-0.044922466614928	0.112496257411615	-0.39932409885033	0.689781855560006	   
df.mm.exp5	0.0346784734651106	0.112496257411615	0.308263352604031	0.757977906805765	   
df.mm.exp6	0.0631856254499236	0.112496257411615	0.561668689285655	0.574529878076961	   
df.mm.exp7	-0.0315886282785059	0.112496257411615	-0.280797148325793	0.778952806779171	   
df.mm.exp8	-0.156781172834790	0.112496257411615	-1.39365678860889	0.163884051789770	   
df.mm.trans1:exp2	-0.0306357946610464	0.098717298698495	-0.310338664701665	0.756400085915587	   
df.mm.trans2:exp2	0.00325731554664105	0.0796853293575422	0.0408772301363745	0.967405965958224	   
df.mm.trans1:exp3	0.0544672460314512	0.098717298698495	0.551749761688744	0.581304060351788	   
df.mm.trans2:exp3	-0.0682846898955207	0.0796853293575422	-0.856929254682908	0.391790835356161	   
df.mm.trans1:exp4	0.0397353064185879	0.098717298698495	0.402516144003784	0.687432790298779	   
df.mm.trans2:exp4	-0.0762430101464919	0.0796853293575422	-0.956801092010239	0.339013283272976	   
df.mm.trans1:exp5	-0.00172892005841377	0.098717298698495	-0.0175138509785837	0.986031904865257	   
df.mm.trans2:exp5	-0.109344504667882	0.0796853293575422	-1.37220371114062	0.170459938695955	   
df.mm.trans1:exp6	-0.0599515463669605	0.098717298698495	-0.607305377652868	0.543854336718316	   
df.mm.trans2:exp6	-0.0929343378880634	0.0796853293575422	-1.16626659684211	0.243922262384	   
df.mm.trans1:exp7	0.0354311575938906	0.098717298698495	0.358915388295879	0.719771629475041	   
df.mm.trans2:exp7	-0.0324056570605196	0.0796853293575422	-0.406670303326699	0.684380219020074	   
df.mm.trans1:exp8	0.124677080256857	0.098717298698495	1.26297094734783	0.207039625085048	   
df.mm.trans2:exp8	0.0292426167015321	0.0796853293575422	0.366976166595518	0.71375284129316	   
df.mm.trans1:probe2	0.126139377476660	0.0687694856938766	1.83423470750038	0.0670631323268059	.  
df.mm.trans1:probe3	0.0530467331861381	0.0687694856938766	0.771370218213825	0.440759882180647	   
df.mm.trans1:probe4	0.00759844518825627	0.0687694856938766	0.110491522680282	0.91205271013482	   
df.mm.trans1:probe5	0.0539628837555542	0.0687694856938766	0.784692268832238	0.43291178595056	   
df.mm.trans1:probe6	0.082290424613357	0.0687694856938766	1.19661247693007	0.231881518087018	   
df.mm.trans1:probe7	0.0246534144553661	0.0687694856938766	0.358493512153186	0.720087117999269	   
df.mm.trans1:probe8	0.0459594422303791	0.0687694856938766	0.66831155950424	0.5041652450011	   
df.mm.trans1:probe9	0.151238031802329	0.0687694856938766	2.19920260092618	0.0282049677043418	*  
df.mm.trans1:probe10	0.0576809920695855	0.0687694856938766	0.838758520404662	0.401904422512239	   
df.mm.trans1:probe11	0.101848539303504	0.0687694856938766	1.48101353784842	0.139073769718939	   
df.mm.trans1:probe12	0.0712653941848784	0.0687694856938766	1.03629383680594	0.300439504991222	   
df.mm.trans1:probe13	0.0328258629127888	0.0687694856938766	0.477331807582671	0.633281763694296	   
df.mm.trans2:probe2	-0.040768141307469	0.0687694856938766	-0.592823123455453	0.553499863477628	   
df.mm.trans2:probe3	0.0116044385154447	0.0687694856938766	0.168744006129421	0.86604900784097	   
df.mm.trans2:probe4	0.0598885543755503	0.0687694856938766	0.870859419279951	0.384143300701172	   
df.mm.trans2:probe5	0.122052703857101	0.0687694856938766	1.77480902504363	0.0763840799690227	.  
df.mm.trans2:probe6	0.00308487024745336	0.0687694856938766	0.0448581259017332	0.964233779879524	   
df.mm.trans3:probe2	0.0339060363252268	0.0687694856938766	0.493038969000838	0.62214684262388	   
df.mm.trans3:probe3	0.061694263079148	0.0687694856938766	0.897116831057548	0.369979295926752	   
df.mm.trans3:probe4	0.207822646210105	0.0687694856938766	3.02201832852459	0.00260683638546854	** 
df.mm.trans3:probe5	0.0466947875062947	0.0687694856938766	0.679004460119913	0.497369866853459	   
df.mm.trans3:probe6	0.0506769618834376	0.0687694856938766	0.73691058428186	0.461435070844045	   
df.mm.trans3:probe7	0.108634995644433	0.0687694856938766	1.57969766020959	0.114648788213495	   
df.mm.trans3:probe8	0.0305780041429781	0.0687694856938766	0.44464494440302	0.656720172666062	   
df.mm.trans3:probe9	0.149076775794157	0.0687694856938766	2.16777505735267	0.0305277481398887	*  
df.mm.trans3:probe10	0.0546485112008133	0.0687694856938766	0.794662205910308	0.427091834097368	   
df.mm.trans3:probe11	0.094414275801107	0.0687694856938766	1.37290943575450	0.170240511156038